xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

6ecec0d62ed58ef3452eab17794af2c3544799fb

207

py

Python

src/flor-0.0.0-alpha/flor/object_model/__init__.py

ucbrise/flor-camp2018

a0b48bb2c058fb07dca1f6ac7ce2e34941146282

[ "Apache-2.0" ]

1

2019-04-24T05:02:06.000Z

src/flor-0.0.0-alpha/flor/object_model/__init__.py

ucbrise/flor-camp2018

a0b48bb2c058fb07dca1f6ac7ce2e34941146282

[ "Apache-2.0" ]

null

src/flor-0.0.0-alpha/flor/object_model/__init__.py

ucbrise/flor-camp2018

a0b48bb2c058fb07dca1f6ac7ce2e34941146282

[ "Apache-2.0" ]

2

2019-08-09T20:31:00.000Z

2021-08-04T02:34:26.000Z

#!/usr/bin/env python3 from flor.object_model.action import Action from flor.object_model.artifact import Artifact from flor.object_model.literal import Literal __all__ = ["Action", "Artifact", "Literal"]

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py

Python

naked/funcs/__init__.py

MaxHalford/naked

f1990a22903db61e6ac74ce1eccf5d43537ebfc4

[ "MIT" ]

26

2021-02-05T09:46:44.000Z

2021-11-14T19:40:47.000Z

naked/funcs/__init__.py

MaxHalford/naked

f1990a22903db61e6ac74ce1eccf5d43537ebfc4

[ "MIT" ]

null

naked/funcs/__init__.py

MaxHalford/naked

f1990a22903db61e6ac74ce1eccf5d43537ebfc4

[ "MIT" ]

1

2021-08-19T06:21:28.000Z

from . import sklearn

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6e227fc66c0dd353c6e35a224ae2f04a56def698

421

py

Python

allennlp_models/rc/dataset_readers/__init__.py

matt-peters/allennlp-models

cdd505ed539fdc2b82e4cc0a23eae4bfd3368e7e

[ "Apache-2.0" ]

402

2020-03-11T22:58:35.000Z

2022-03-29T09:05:27.000Z

allennlp_models/rc/dataset_readers/__init__.py

matt-peters/allennlp-models

cdd505ed539fdc2b82e4cc0a23eae4bfd3368e7e

[ "Apache-2.0" ]

116

2020-03-11T01:26:57.000Z

2022-03-25T13:03:56.000Z

allennlp_models/rc/dataset_readers/__init__.py

matt-peters/allennlp-models

cdd505ed539fdc2b82e4cc0a23eae4bfd3368e7e

[ "Apache-2.0" ]

140

2020-03-11T00:51:35.000Z

2022-03-29T09:05:36.000Z

from allennlp_models.rc.dataset_readers.drop import DropReader from allennlp_models.rc.dataset_readers.qangaroo import QangarooReader from allennlp_models.rc.dataset_readers.quac import QuACReader from allennlp_models.rc.dataset_readers.squad import SquadReader from allennlp_models.rc.dataset_readers.transformer_squad import TransformerSquadReader from allennlp_models.rc.dataset_readers.triviaqa import TriviaQaReader

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421

6

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true

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null

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null

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1

0

1

0

6

280937a2cd8d8fa7a36d5aa430769732b981d1d2

134

py

Python

app/api/__init__.py

gibran-abdillah/flask-blog-app

17a1b4a9ba4fd56c92104ddd2c4d8a18365d292a

[ "MIT" ]

null

app/api/__init__.py

gibran-abdillah/flask-blog-app

17a1b4a9ba4fd56c92104ddd2c4d8a18365d292a

[ "MIT" ]

null

app/api/__init__.py

gibran-abdillah/flask-blog-app

17a1b4a9ba4fd56c92104ddd2c4d8a18365d292a

[ "MIT" ]

1

2021-12-18T02:36:22.000Z

from flask import Blueprint api_blueprint = Blueprint('api',__name__, url_prefix='/api') from .errors import * from .views import *

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6

281684f285f6bdcd301e6048aa58679446b09a5d

31,076

py

Python

tests/test_dataframes.py

owid/data-utils-py

274b12b107553df6c7bafff5c95622bae88eac8f

[ "MIT" ]

1

2022-03-30T04:35:55.000Z

tests/test_dataframes.py

owid/data-utils-py

274b12b107553df6c7bafff5c95622bae88eac8f

[ "MIT" ]

3

2022-03-24T19:42:02.000Z

2022-03-30T22:17:32.000Z

tests/test_dataframes.py

owid/data-utils-py

274b12b107553df6c7bafff5c95622bae88eac8f

[ "MIT" ]

null

"""Test functions in owid.datautils.dataframes module. """ import numpy as np import pandas as pd from pytest import warns from typing import Any, Dict from owid.datautils import dataframes class TestCompareDataFrames: def test_with_large_absolute_tolerance_all_equal(self): assert dataframes.compare( df1=pd.DataFrame({"col_01": [1, 2]}), df2=pd.DataFrame({"col_01": [2, 3]}), absolute_tolerance=1, relative_tolerance=1e-8, ).equals(pd.DataFrame({"col_01": [True, True]})) def test_with_large_absolute_tolerance_all_unequal(self): assert dataframes.compare( df1=pd.DataFrame({"col_01": [1, 2]}), df2=pd.DataFrame({"col_01": [2, 3]}), absolute_tolerance=0.9, relative_tolerance=1e-8, ).equals(pd.DataFrame({"col_01": [False, False]})) def test_with_large_absolute_tolerance_mixed(self): assert dataframes.compare( df1=pd.DataFrame({"col_01": [1, 2]}), df2=pd.DataFrame({"col_01": [2, 3.1]}), absolute_tolerance=1, relative_tolerance=1e-8, ).equals(pd.DataFrame({"col_01": [True, False]})) def test_with_large_relative_tolerance_all_equal(self): assert dataframes.compare( df1=pd.DataFrame({"col_01": [1, 2]}), df2=pd.DataFrame({"col_01": [2, 3]}), absolute_tolerance=1e-8, relative_tolerance=0.5, ).equals(pd.DataFrame({"col_01": [True, True]})) def test_with_large_relative_tolerance_all_unequal(self): assert dataframes.compare( df1=pd.DataFrame({"col_01": [1, 2]}), df2=pd.DataFrame({"col_01": [2, 3]}), absolute_tolerance=1e-8, relative_tolerance=0.3, ).equals(pd.DataFrame({"col_01": [False, False]})) def test_with_large_relative_tolerance_mixed(self): assert dataframes.compare( df1=pd.DataFrame({"col_01": [1, 2]}), df2=pd.DataFrame({"col_01": [2, 3]}), absolute_tolerance=1e-8, relative_tolerance=0.4, ).equals(pd.DataFrame({"col_01": [False, True]})) def test_with_dataframes_of_equal_values_but_different_indexes(self): # Even if dataframes are not identical, compare_dataframes should return all Trues (since it does not care about # indexes, only values). assert dataframes.compare( df1=pd.DataFrame({"col_01": [1, 2], "col_02": ["a", "b"]}).set_index( "col_02" ), df2=pd.DataFrame({"col_01": [1, 2], "col_02": ["a", "c"]}).set_index( "col_02" ), ).equals(pd.DataFrame({"col_01": [True, True]})) def test_with_two_dataframes_with_object_columns_with_nans(self): assert dataframes.compare( df1=pd.DataFrame({"col_01": [np.nan, "b", "c"]}), df2=pd.DataFrame({"col_01": [np.nan, "b", "c"]}), ).equals(pd.DataFrame({"col_01": [True, True, True]})) class TestAreDataFramesEqual: def test_on_equal_dataframes_with_one_integer_column(self): assert dataframes.are_equal( df1=pd.DataFrame({"col_01": [1, 2, 3]}), df2=pd.DataFrame({"col_01": [1, 2, 3]}), )[0] def test_on_almost_equal_dataframes_but_differing_by_one_element(self): assert not dataframes.are_equal( df1=pd.DataFrame({"col_01": [1, 2, 3]}), df2=pd.DataFrame({"col_01": [1, 2, 0]}), )[0] def test_on_almost_equal_dataframes_but_differing_by_type(self): assert not dataframes.are_equal( df1=pd.DataFrame({"col_01": [1, 2, 3]}), df2=pd.DataFrame({"col_01": [1, 2, 3.0]}), )[0] def test_on_equal_dataframes_containing_nans(self): assert dataframes.are_equal( df1=pd.DataFrame({"col_01": [1, 2, np.nan]}), df2=pd.DataFrame({"col_01": [1, 2, np.nan]}), )[0] def test_on_equal_dataframes_containing_only_nans(self): assert dataframes.are_equal( df1=pd.DataFrame({"col_01": [np.nan, np.nan]}), df2=pd.DataFrame({"col_01": [np.nan, np.nan]}), )[0] def test_on_equal_dataframes_both_empty(self): assert dataframes.are_equal(df1=pd.DataFrame(), df2=pd.DataFrame())[0] def test_on_equal_dataframes_with_various_types_of_columns(self): assert dataframes.are_equal( df1=pd.DataFrame( { "col_01": [1, 2], "col_02": [0.1, 0.2], "col_03": ["1", "2"], "col_04": [True, False], } ), df2=pd.DataFrame( { "col_01": [1, 2], "col_02": [0.1, 0.2], "col_03": ["1", "2"], "col_04": [True, False], } ), )[0] def test_on_almost_equal_dataframes_but_columns_sorted_differently(self): assert not dataframes.are_equal( df1=pd.DataFrame( { "col_01": [1, 2], "col_02": [0.1, 0.2], "col_03": ["1", "2"], "col_04": [True, False], } ), df2=pd.DataFrame( { "col_02": [0.1, 0.2], "col_01": [1, 2], "col_03": ["1", "2"], "col_04": [True, False], } ), )[0] def test_on_unequal_dataframes_with_all_columns_different(self): assert not dataframes.are_equal( df1=pd.DataFrame({"col_01": [1, 2], "col_02": [0.1, 0.2]}), df2=pd.DataFrame({"col_03": [0.1, 0.2], "col_04": [1, 2]}), )[0] def test_on_unequal_dataframes_with_some_common_columns(self): assert not dataframes.are_equal( df1=pd.DataFrame({"col_01": [1, 2], "col_02": [0.1, 0.2]}), df2=pd.DataFrame({"col_01": [1, 2], "col_03": [1, 2]}), )[0] def test_on_equal_dataframes_given_large_absolute_tolerance(self): assert dataframes.are_equal( df1=pd.DataFrame({"col_01": [10, 20]}), df2=pd.DataFrame({"col_01": [11, 21]}), absolute_tolerance=1, relative_tolerance=1e-8, )[0] def test_on_unequal_dataframes_given_large_absolute_tolerance(self): assert not dataframes.are_equal( df1=pd.DataFrame({"col_01": [10, 20]}), df2=pd.DataFrame({"col_01": [11, 21]}), absolute_tolerance=0.9, relative_tolerance=1e-8, )[0] def test_on_equal_dataframes_given_large_relative_tolerance(self): assert dataframes.are_equal( df1=pd.DataFrame({"col_01": [1]}), df2=pd.DataFrame({"col_01": [2]}), absolute_tolerance=1e-8, relative_tolerance=0.5, )[0] def test_on_unequal_dataframes_given_large_relative_tolerance(self): assert not dataframes.are_equal( df1=pd.DataFrame({"col_01": [1]}), df2=pd.DataFrame({"col_01": [2]}), absolute_tolerance=1e-8, relative_tolerance=0.49, )[0] def test_on_equal_dataframes_with_non_numeric_indexes(self): assert dataframes.are_equal( df1=pd.DataFrame({"col_01": [1, 2], "col_02": ["a", "b"]}).set_index( "col_02" ), df2=pd.DataFrame({"col_01": [1, 2], "col_02": ["a", "b"]}).set_index( "col_02" ), )[0] def test_on_dataframes_of_equal_values_but_different_indexes(self): assert not dataframes.are_equal( df1=pd.DataFrame({"col_01": [1, 2], "col_02": ["a", "b"]}).set_index( "col_02" ), df2=pd.DataFrame({"col_01": [1, 2], "col_02": ["a", "c"]}).set_index( "col_02" ), )[0] def test_on_dataframes_with_object_columns_with_nans(self): assert dataframes.are_equal( df1=pd.DataFrame({"col_01": [np.nan, "b", "c"]}), df2=pd.DataFrame({"col_01": [np.nan, "b", "c"]}), )[0] class TestGroupbyAggregate: def test_default_aggregate_single_groupby_column_as_string(self): df_in = pd.DataFrame( { "year": [2001, 2003, 2003, 2003, 2002, 2002], "value_01": [1, 2, 3, 4, 5, 6], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [1, 11, 9], } ).set_index("year") assert dataframes.groupby_agg( df_in, "year", aggregations=None, num_allowed_nans=None, frac_allowed_nans=None, ).equals(df_out) def test_default_aggregate_single_groupby_column_as_list(self): df_in = pd.DataFrame( { "year": [2001, 2003, 2003, 2003, 2002, 2002], "value_01": [1, 2, 3, 4, 5, 6], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [1, 11, 9], } ).set_index("year") assert dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=None, frac_allowed_nans=None, ).equals(df_out) def test_default_aggregate_with_some_nans_ignored(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [np.nan, 2, np.nan, 4, 5, 6], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [0.0, 2.0, 15.0], } ).set_index("year") assert dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=None, frac_allowed_nans=None, ).equals(df_out) def test_default_aggregate_with_some_nans_ignored_different_types(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [np.nan, 2, np.nan, 4, 5, 6], "value_02": ["a", "b", "c", "d", "e", "f"], "value_03": [True, False, False, True, True, False], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [0.0, 2.0, 15.0], "value_02": ["a", "bc", "def"], "value_03": [1, 0, 2], } ).set_index("year") assert dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=None, frac_allowed_nans=None, ).equals(df_out) def test_default_aggregate_with_some_nans_ignored_different_types_and_more_nans( self, ): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [np.nan, 2, np.nan, 4, 5, 6], "value_02": [np.nan, "b", np.nan, "d", "e", "f"], "value_03": [np.nan, False, False, True, True, np.nan], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [0.0, 2.0, 15.0], "value_02": [0, "b", "def"], "value_03": [0, 0, 2], } ).set_index("year") df_out["value_03"] = df_out["value_03"].astype(object) assert dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=None, frac_allowed_nans=None, ).equals(df_out) def test_default_aggregate_with_num_allowed_nans_zero(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [np.nan, 2, np.nan, 4, 5, 6], "value_02": [np.nan, "b", np.nan, "d", "e", "f"], "value_03": [np.nan, False, False, True, True, np.nan], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [np.nan, np.nan, 15.0], "value_02": [np.nan, np.nan, "def"], } ).set_index("year") df_out["value_03"] = pd.Series( [np.nan, 0, np.nan], index=[2001, 2002, 2003], dtype=object ) assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=0, frac_allowed_nans=None, ), df2=df_out, )[0] def test_default_aggregate_with_num_allowed_nans_one(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [np.nan, 2, np.nan, 4, 5, 6], "value_02": [np.nan, "b", np.nan, "d", "e", "f"], "value_03": [np.nan, False, False, True, np.nan, np.nan], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [0.0, 2.0, 15.0], "value_02": [0, "b", "def"], } ).set_index("year") df_out["value_03"] = pd.Series( [0, 0, np.nan], index=[2001, 2002, 2003], dtype=object ) assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=1, frac_allowed_nans=None, ), df2=df_out, )[0] def test_default_aggregate_with_num_allowed_nans_two(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [np.nan, 2, np.nan, 4, 5, 6], "value_02": [np.nan, "b", np.nan, "d", "e", "f"], "value_03": [np.nan, False, False, True, np.nan, np.nan], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [0.0, 2.0, 15.0], "value_02": [0, "b", "def"], } ).set_index("year") df_out["value_03"] = pd.Series( [0, 0, 1], index=[2001, 2002, 2003], dtype=object ) assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=2, frac_allowed_nans=None, ), df2=df_out, )[0] def test_default_aggregate_with_num_allowed_nans_the_length_of_the_dataframe(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2004, 2004, 2004, 2004], "value_01": [np.nan, 2, np.nan, 4, 5, 6, 7], "value_02": [np.nan, "b", np.nan, "d", "e", "f", "g"], "value_03": [np.nan, False, False, True, np.nan, np.nan, np.nan], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2004], "value_01": [0.0, 2.0, 22.0], "value_02": [0, "b", "defg"], } ).set_index("year") df_out["value_03"] = pd.Series( [0, 0, 1], index=[2001, 2002, 2004], dtype=object ) assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=None, frac_allowed_nans=len(df_in), ), df2=df_out, )[0] def test_default_aggregate_with_frac_allowed_nans_zero(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [np.nan, 2, np.nan, 4, 5, 6], "value_02": [np.nan, "b", np.nan, "d", "e", "f"], "value_03": [np.nan, False, False, True, True, np.nan], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [np.nan, np.nan, 15.0], "value_02": [np.nan, np.nan, "def"], } ).set_index("year") df_out["value_03"] = pd.Series( [np.nan, 0, np.nan], index=[2001, 2002, 2003], dtype=object ) assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=None, frac_allowed_nans=0, ), df2=df_out, )[0] def test_default_aggregate_with_frac_allowed_nans_half(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [np.nan, 2, np.nan, 4, 5, 6], "value_02": [np.nan, "b", np.nan, "d", "e", "f"], "value_03": [np.nan, False, False, True, np.nan, np.nan], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [np.nan, 2.0, 15.0], "value_02": [np.nan, "b", "def"], } ).set_index("year") df_out["value_03"] = pd.Series( [np.nan, 0, np.nan], index=[2001, 2002, 2003], dtype=object ) assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=None, frac_allowed_nans=0.5, ), df2=df_out, )[0] def test_default_aggregate_with_frac_allowed_nans_two_thirds(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [np.nan, 2, np.nan, 4, 5, 6], "value_02": [np.nan, "b", np.nan, "d", "e", "f"], "value_03": [np.nan, False, False, True, np.nan, np.nan], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [np.nan, 2.0, 15.0], "value_02": [np.nan, "b", "def"], } ).set_index("year") df_out["value_03"] = pd.Series( [np.nan, 0, 1], index=[2001, 2002, 2003], dtype=object ) assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=None, frac_allowed_nans=0.67, ), df2=df_out, )[0] def test_default_aggregate_with_frac_allowed_nans_one(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003, 2004, 2004, 2004, 2004], "value_01": [np.nan, 2, np.nan, 4, 5, 6, 7, np.nan, np.nan, np.nan], "value_02": [np.nan, "b", np.nan, "d", "e", "f", "g", "h", "i", "j"], "value_03": [ np.nan, False, False, True, np.nan, np.nan, np.nan, np.nan, np.nan, True, ], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003, 2004], "value_01": [0, 2.0, 15.0, 7], "value_02": [0, "b", "def", "ghij"], } ).set_index("year") df_out["value_03"] = pd.Series( [0, 0, 1, 1], index=[2001, 2002, 2003, 2004], dtype=object ) assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=None, frac_allowed_nans=None, ), df2=df_out, )[0] def test_default_aggregate_with_both_num_allowed_nans_and_frac_allowed_nans(self): df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003, 2004, 2004, 2004, 2004], "value_01": [np.nan, 2, np.nan, 4, 5, 6, 7, np.nan, np.nan, np.nan], "value_02": [np.nan, "b", np.nan, "d", "e", "f", "g", "h", "i", "j"], "value_03": [ np.nan, False, False, True, np.nan, np.nan, np.nan, np.nan, np.nan, True, ], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003, 2004], "value_01": [np.nan, 2.0, 15.0, np.nan], "value_02": [np.nan, "b", "def", "ghij"], } ).set_index("year") df_out["value_03"] = pd.Series( [np.nan, 0, np.nan, np.nan], index=[2001, 2002, 2003, 2004], dtype=object ) assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["year"], aggregations=None, num_allowed_nans=2, frac_allowed_nans=0.5, ), df2=df_out, )[0] def test_default_aggregate_with_two_groupby_columns(self): df_in = pd.DataFrame( { "country": [ "country_a", "country_a", "country_a", "country_b", "country_b", "country_c", ], "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [1, 2, 3, 4, 5, 6], } ) df_out = pd.DataFrame( { "country": ["country_a", "country_a", "country_b", "country_c"], "year": [2001, 2002, 2003, 2003], "value_01": [1, 5, 9, 6], } ).set_index(["country", "year"]) assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["country", "year"], aggregations=None, num_allowed_nans=None, frac_allowed_nans=None, ), df2=df_out, )[0] def test_custom_aggregate(self): aggregations = {"value_01": "sum", "value_02": "mean"} df_in = pd.DataFrame( { "year": [2001, 2002, 2002, 2003, 2003, 2003], "value_01": [1, 2, 3, 4, 5, np.nan], "value_02": [1, 2, 3, 4, 5, 6], } ) df_out = pd.DataFrame( { "year": [2001, 2002, 2003], "value_01": [1.0, 5.0, np.nan], "value_02": [1, 2.5, 7.5], } ).set_index("year") assert dataframes.are_equal( df1=dataframes.groupby_agg( df_in, ["year"], aggregations=aggregations, num_allowed_nans=0, frac_allowed_nans=None, ), df2=df_out, ) class TestMultiMerge: df1 = pd.DataFrame({"col_01": ["aa", "ab", "ac"], "col_02": ["ba", "bb", "bc"]}) def test_merge_identical_dataframes(self): df1 = self.df1.copy() df2 = self.df1.copy() df3 = self.df1.copy() assert dataframes.multi_merge( [df1, df2, df3], how="inner", on=["col_01", "col_02"] ).equals(df1) def test_inner_join_with_non_overlapping_dataframes(self): df1 = self.df1.copy() df2 = pd.DataFrame({"col_01": ["ad", "ae"]}) df3 = pd.DataFrame({"col_01": ["af"], "col_03": ["ca"]}) # For some reason the order of columns changes on the second merge. df_out = pd.DataFrame({"col_02": [], "col_01": [], "col_03": []}, dtype=str) assert dataframes.are_equal( df1=dataframes.multi_merge([df1, df2, df3], how="inner", on="col_01"), df2=df_out, ) def test_outer_join_with_non_overlapping_dataframes(self): df1 = self.df1.copy() df2 = pd.DataFrame({"col_01": ["ad"]}) df3 = pd.DataFrame({"col_01": ["ae"]}) df_out = pd.DataFrame( { "col_01": ["aa", "ab", "ac", "ad", "ae"], "col_02": ["ba", "bb", "bc", np.nan, np.nan], } ) assert dataframes.are_equal( df1=dataframes.multi_merge([df1, df2, df3], how="outer", on="col_01"), df2=df_out, )[0] def test_left_join(self): df1 = self.df1.copy() df2 = pd.DataFrame( { "col_01": ["aa", "ab", "ad"], "col_02": ["ba", "bB", "bc"], "col_03": [1, 2, 3], } ) # df_12 = pd.DataFrame({'col_01': ['aa', 'ab', 'ac'], 'col_02': ['ba', 'bb', 'bc'], # 'col_03': [1, np.nan, np.nan]}) df3 = pd.DataFrame({"col_01": [], "col_02": [], "col_04": []}) df_out = pd.DataFrame( { "col_01": ["aa", "ab", "ac"], "col_02": ["ba", "bb", "bc"], "col_03": [1, np.nan, np.nan], "col_04": [np.nan, np.nan, np.nan], } ) assert dataframes.multi_merge( [df1, df2, df3], how="left", on=["col_01", "col_02"] ).equals(df_out) def test_right_join(self): df1 = self.df1.copy() df2 = pd.DataFrame( { "col_01": ["aa", "ab", "ad"], "col_02": ["ba", "bB", "bc"], "col_03": [1, 2, 3], } ) # df12 = pd.DataFrame({'col_01': ['aa', 'ab', 'ad'], 'col_02': ['ba', 'bB', 'bc'], 'col_03': [1, 2, 3]}) df3 = pd.DataFrame( {"col_01": ["aa", "ae"], "col_02": ["ba", "be"], "col_04": [4, 5]} ) df_out = pd.DataFrame( { "col_01": ["aa", "ae"], "col_02": ["ba", "be"], "col_03": [1, np.nan], "col_04": [4, 5], } ) assert dataframes.multi_merge( [df1, df2, df3], how="right", on=["col_01", "col_02"] ).equals(df_out) class TestMapSeries: mapping = { "country_01": "Country 1", "country_02": "Country 2", } def test_all_countries_mapped_and_all_mappings_used(self): series_in = pd.Series(["country_01", "country_02"]) series_out = pd.Series(["Country 1", "Country 2"]) assert dataframes.map_series(series=series_in, mapping=self.mapping).equals( series_out ) def test_one_country_missing_in_mapping(self): series_in = pd.Series(["country_01", "country_02", "country_03"]) series_out = pd.Series(["Country 1", "Country 2", "country_03"]) assert dataframes.map_series( series=series_in, mapping=self.mapping, make_unmapped_values_nan=False ).equals(series_out) def test_one_country_missing_in_mapping_converted_into_nan(self): series_in = pd.Series(["country_01", "country_02", "country_03"]) series_out = pd.Series(["Country 1", "Country 2", np.nan]) assert dataframes.map_series( series=series_in, mapping=self.mapping, make_unmapped_values_nan=True ).equals(series_out) def test_warn_if_one_country_missing_in_mapping(self): series_in = pd.Series(["country_01", "country_02", "country_03"]) with warns(UserWarning, match="missing"): dataframes.map_series( series=series_in, mapping=self.mapping, warn_on_missing_mappings=True ) def test_one_country_unused_in_mapping(self): series_in = pd.Series(["country_01"]) series_out = pd.Series(["Country 1"]) assert dataframes.map_series( series=series_in, mapping=self.mapping, warn_on_unused_mappings=False ).equals(series_out) def test_warn_when_one_country_unused_in_mapping(self): series_in = pd.Series(["country_01"]) with warns(UserWarning, match="unused"): dataframes.map_series( series=series_in, mapping=self.mapping, warn_on_unused_mappings=True ) def test_empty_series(self): series_in = pd.Series([], dtype=object) series_out = pd.Series([], dtype=object) assert dataframes.map_series(series=series_in, mapping=self.mapping).equals( series_out ) def test_empty_mapping(self): mapping = {} # type: Dict[Any, Any] series_in = pd.Series(["country_01", "country_02"]) series_out = pd.Series(["country_01", "country_02"]) assert dataframes.map_series(series=series_in, mapping=mapping).equals( series_out ) def test_mappings_of_mixed_types(self): # Note: A series containing 1 and True are considered identical. Therefore, a mapping # > pd.Series([1, 2, True]).map({1: 10, 2: 20, True: 30}) # would result in # > pd.Series([30, 20, 30]) # since 1 is considered identical to True, and the latest occurrence in the mapping prevails (namely True: 30). mapping = {2: "20", 3: False, "4": 40, True: 50} series_in = pd.Series([2, 3, "4", True]) series_out = pd.Series(["20", False, 40, 50]) assert dataframes.map_series(series=series_in, mapping=mapping).equals( series_out ) class TestConcatenate: def test_concat_categoricals(self): a = pd.DataFrame({"x": ["a"], "d": [1]}).astype("category") b = pd.DataFrame({"x": ["b"], "d": [2]}).astype("category") out = dataframes.concatenate([a, b]) assert list(out.x.cat.categories) == ["a", "b"] assert out.to_dict(orient="records") == [{"x": "a", "d": 1}, {"x": "b", "d": 2}] class TestApplyOnCategoricals: def test_string_func(self): df = pd.DataFrame({"x": ["a", "b"], "y": ["b", "c"]}).astype("category") out = dataframes.apply_on_categoricals( [df.x, df.y], lambda x, y: str(x + "|" + y) ) assert list(out.categories) == ["a|b", "b|c"]

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28498c052f9ba4f89c45789e186699b5dcd525f3

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Python

test_autoastro/unit/galaxy/test_fit_galaxy.py

woodyZootopia/PyAutoAstro

6500b9746b3e73c3f3129fcbaa3a0419bb400915

[ "MIT" ]

null

test_autoastro/unit/galaxy/test_fit_galaxy.py

woodyZootopia/PyAutoAstro

6500b9746b3e73c3f3129fcbaa3a0419bb400915

[ "MIT" ]

null

test_autoastro/unit/galaxy/test_fit_galaxy.py

woodyZootopia/PyAutoAstro

6500b9746b3e73c3f3129fcbaa3a0419bb400915

[ "MIT" ]

null

import autoarray as aa import autoastro as aast import autofit as af import os import numpy as np import pytest from test_autoastro.mock.mock_galaxy import MockGalaxy @pytest.fixture(autouse=True) def reset_config(): """ Use configuration from the default path. You may want to change this to set a specific path. """ af.conf.instance = af.conf.default class TestLikelihood: def test__1x1_image__light_profile_fits_data_perfectly__lh_is_noise(self): image = aa.array.ones(shape_2d=(3, 3), pixel_scales=1.0) noise_map = aa.array.ones(shape_2d=(3, 3), pixel_scales=1.0) galaxy_data = aast.galaxy_data( image=image, noise_map=noise_map, pixel_scales=3.0 ) mask = aa.mask.manual( mask_2d=np.array( [[True, True, True], [True, False, True], [True, True, True]] ), pixel_scales=1.0, sub_size=1, ) g0 = MockGalaxy(value=1.0) galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=galaxy_data, mask=mask, use_image=True ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0) galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=galaxy_data, mask=mask, use_convergence=True ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0) galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=galaxy_data, mask=mask, use_potential=True ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0) galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=galaxy_data, mask=mask, use_deflections_y=True ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0) galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=galaxy_data, mask=mask, use_deflections_x=True ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0) def test__1x2_image__noise_not_1__alls_correct(self): image = aa.array.full(fill_value=5.0, shape_2d=(3, 4), pixel_scales=1.0) image[6] = 4.0 noise_map = aa.array.full(fill_value=2.0, shape_2d=(3, 4), pixel_scales=1.0) galaxy_data = aast.galaxy_data( image=image, noise_map=noise_map, pixel_scales=3.0 ) mask = aa.mask.manual( mask_2d=np.array( [ [True, True, True, True], [True, False, False, True], [True, True, True, True], ] ), pixel_scales=1.0, sub_size=1, ) g0 = MockGalaxy(value=1.0, shape=2) galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=galaxy_data, mask=mask, use_image=True ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.chi_squared == (25.0 / 4.0) assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0 assert fit.likelihood == -0.5 * ( (25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0 ** 2) ) galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=galaxy_data, mask=mask, use_convergence=True ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.chi_squared == (25.0 / 4.0) assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0 assert fit.likelihood == -0.5 * ( (25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0 ** 2) ) galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=galaxy_data, mask=mask, use_potential=True ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.chi_squared == (25.0 / 4.0) assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0 assert fit.likelihood == -0.5 * ( (25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0 ** 2) ) galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=galaxy_data, mask=mask, use_deflections_y=True ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.chi_squared == (25.0 / 4.0) assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0 assert fit.likelihood == -0.5 * ( (25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0 ** 2) ) galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=galaxy_data, mask=mask, use_deflections_x=True ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.chi_squared == (25.0 / 4.0) assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0 assert fit.likelihood == -0.5 * ( (25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0 ** 2) ) class TestCompareToManual: def test__image(self, gal_data_7x7, sub_mask_7x7): galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=gal_data_7x7, mask=sub_mask_7x7, use_image=True ) galaxy = aast.Galaxy( redshift=0.5, light=aast.lp.SphericalSersic(centre=(1.0, 2.0), intensity=1.0), ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[galaxy]) assert fit.model_galaxies == [galaxy] model_data = galaxy.profile_image_from_grid(grid=galaxy_fit_data.grid) residual_map = aa.util.fit.residual_map_from_data_and_model_data( data=galaxy_fit_data.image, model_data=model_data.in_1d_binned ) assert residual_map == pytest.approx(fit.residual_map, 1e-4) chi_squared_map = aa.util.fit.chi_squared_map_from_residual_map_and_noise_map( residual_map=residual_map, noise_map=galaxy_fit_data.noise_map ) assert chi_squared_map == pytest.approx(fit.chi_squared_map, 1e-4) chi_squared = aa.util.fit.chi_squared_from_chi_squared_map( chi_squared_map=chi_squared_map ) noise_normalization = aa.util.fit.noise_normalization_from_noise_map( noise_map=galaxy_fit_data.noise_map ) likelihood = aa.util.fit.likelihood_from_chi_squared_and_noise_normalization( chi_squared=chi_squared, noise_normalization=noise_normalization ) assert likelihood == pytest.approx(fit.likelihood, 1e-4) def test__convergence(self, gal_data_7x7, sub_mask_7x7): galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=gal_data_7x7, mask=sub_mask_7x7, use_convergence=True ) galaxy = aast.Galaxy( redshift=0.5, mass=aast.mp.SphericalIsothermal(centre=(1.0, 2.0), einstein_radius=1.0), ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[galaxy]) assert fit.model_galaxies == [galaxy] model_data = galaxy.convergence_from_grid(grid=galaxy_fit_data.grid) residual_map = aa.util.fit.residual_map_from_data_and_model_data( data=galaxy_fit_data.image, model_data=model_data.in_1d_binned ) assert residual_map == pytest.approx(fit.residual_map, 1e-4) chi_squared_map = aa.util.fit.chi_squared_map_from_residual_map_and_noise_map( residual_map=residual_map, noise_map=galaxy_fit_data.noise_map ) assert chi_squared_map == pytest.approx(fit.chi_squared_map, 1e-4) chi_squared = aa.util.fit.chi_squared_from_chi_squared_map( chi_squared_map=chi_squared_map ) noise_normalization = aa.util.fit.noise_normalization_from_noise_map( noise_map=galaxy_fit_data.noise_map ) likelihood = aa.util.fit.likelihood_from_chi_squared_and_noise_normalization( chi_squared=chi_squared, noise_normalization=noise_normalization ) assert likelihood == pytest.approx(fit.likelihood, 1e-4) def test__potential(self, gal_data_7x7, sub_mask_7x7): galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=gal_data_7x7, mask=sub_mask_7x7, use_potential=True ) galaxy = aast.Galaxy( redshift=0.5, mass=aast.mp.SphericalIsothermal(centre=(1.0, 2.0), einstein_radius=1.0), ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[galaxy]) assert fit.model_galaxies == [galaxy] model_data = galaxy.potential_from_grid(grid=galaxy_fit_data.grid) residual_map = aa.util.fit.residual_map_from_data_and_model_data( data=galaxy_fit_data.image, model_data=model_data.in_1d_binned ) assert residual_map == pytest.approx(fit.residual_map, 1e-4) chi_squared_map = aa.util.fit.chi_squared_map_from_residual_map_and_noise_map( residual_map=residual_map, noise_map=galaxy_fit_data.noise_map ) assert chi_squared_map == pytest.approx(fit.chi_squared_map, 1e-4) chi_squared = aa.util.fit.chi_squared_from_chi_squared_map( chi_squared_map=chi_squared_map ) noise_normalization = aa.util.fit.noise_normalization_from_noise_map( noise_map=galaxy_fit_data.noise_map ) likelihood = aa.util.fit.likelihood_from_chi_squared_and_noise_normalization( chi_squared=chi_squared, noise_normalization=noise_normalization ) assert likelihood == pytest.approx(fit.likelihood, 1e-4) def test__deflections_y(self, gal_data_7x7, sub_mask_7x7): galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=gal_data_7x7, mask=sub_mask_7x7, use_deflections_y=True ) galaxy = aast.Galaxy( redshift=0.5, mass=aast.mp.SphericalIsothermal(centre=(1.0, 2.0), einstein_radius=1.0), ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[galaxy]) assert fit.model_galaxies == [galaxy] model_data = galaxy.deflections_from_grid( grid=galaxy_fit_data.grid ).in_1d_binned[:, 0] residual_map = aa.util.fit.residual_map_from_data_and_model_data( data=galaxy_fit_data.image, model_data=model_data ) assert residual_map == pytest.approx(fit.residual_map, 1e-4) chi_squared_map = aa.util.fit.chi_squared_map_from_residual_map_and_noise_map( residual_map=residual_map, noise_map=galaxy_fit_data.noise_map ) assert chi_squared_map == pytest.approx(fit.chi_squared_map, 1e-4) chi_squared = aa.util.fit.chi_squared_from_chi_squared_map( chi_squared_map=chi_squared_map ) noise_normalization = aa.util.fit.noise_normalization_from_noise_map( noise_map=galaxy_fit_data.noise_map ) likelihood = aa.util.fit.likelihood_from_chi_squared_and_noise_normalization( chi_squared=chi_squared, noise_normalization=noise_normalization ) assert likelihood == pytest.approx(fit.likelihood, 1e-4) def test__deflections_x(self, gal_data_7x7, sub_mask_7x7): galaxy_fit_data = aast.masked.galaxy_data( galaxy_data=gal_data_7x7, mask=sub_mask_7x7, use_deflections_x=True ) galaxy = aast.Galaxy( redshift=0.5, mass=aast.mp.SphericalIsothermal(centre=(1.0, 2.0), einstein_radius=1.0), ) fit = aast.fit_galaxy(galaxy_data=galaxy_fit_data, model_galaxies=[galaxy]) assert fit.model_galaxies == [galaxy] model_data = galaxy.deflections_from_grid( grid=galaxy_fit_data.grid ).in_1d_binned[:, 1] residual_map = aa.util.fit.residual_map_from_data_and_model_data( data=galaxy_fit_data.image, model_data=model_data ) assert residual_map == pytest.approx(fit.residual_map, 1e-4) chi_squared_map = aa.util.fit.chi_squared_map_from_residual_map_and_noise_map( residual_map=residual_map, noise_map=galaxy_fit_data.noise_map ) assert chi_squared_map == pytest.approx(fit.chi_squared_map, 1e-4) chi_squared = aa.util.fit.chi_squared_from_chi_squared_map( chi_squared_map=chi_squared_map ) noise_normalization = aa.util.fit.noise_normalization_from_noise_map( noise_map=galaxy_fit_data.noise_map ) likelihood = aa.util.fit.likelihood_from_chi_squared_and_noise_normalization( chi_squared=chi_squared, noise_normalization=noise_normalization ) assert likelihood == pytest.approx(fit.likelihood, 1e-4)

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null

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6

287b88a81fa427aed75f11ab5cbe85ef1851b258

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py

Python

scattertext/indexstore/__init__.py

shettyprithvi/scattertext

a15613b6feef3ddc56c03aadb8e1e629d28a427d

[ "Apache-2.0" ]

1,823

2016-07-28T00:25:56.000Z

2022-03-30T12:33:57.000Z

scattertext/indexstore/__init__.py

shettyprithvi/scattertext

a15613b6feef3ddc56c03aadb8e1e629d28a427d

[ "Apache-2.0" ]

92

2016-07-28T23:13:20.000Z

2022-01-24T03:53:38.000Z

scattertext/indexstore/__init__.py

shettyprithvi/scattertext

a15613b6feef3ddc56c03aadb8e1e629d28a427d

[ "Apache-2.0" ]

271

2016-12-26T12:56:08.000Z

2022-03-24T19:35:13.000Z

from .IndexStore import IndexStore from .IndexStoreFromDict import IndexStoreFromDict from .IndexStoreFromList import IndexStoreFromList

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953d67869cd5a42555477042a947b6b731270097

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py

Python

vectorhub/encoders/audio/pytorch/__init__.py

NanaAkwasiAbayieBoateng/vectorhub

265933521cf0a3113a47182a30b0037bf163584b

[ "Apache-2.0" ]

1

2020-11-04T16:02:39.000Z

vectorhub/encoders/audio/pytorch/__init__.py

NanaAkwasiAbayieBoateng/vectorhub

265933521cf0a3113a47182a30b0037bf163584b

[ "Apache-2.0" ]

null

vectorhub/encoders/audio/pytorch/__init__.py

NanaAkwasiAbayieBoateng/vectorhub

265933521cf0a3113a47182a30b0037bf163584b

[ "Apache-2.0" ]

null

from .fairseq import *

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6

25213f2925aaee2e522912d6a3401b2cd431fd84

2,974

py

Python

tests/test_url_parsing.py

malexer/pkgimport

6641604ac3d5b3fa630e68a86b294d1cd3646183

[ "MIT" ]

1

2018-06-09T08:40:31.000Z

tests/test_url_parsing.py

malexer/pkgimport

6641604ac3d5b3fa630e68a86b294d1cd3646183

[ "MIT" ]

1

2017-12-14T06:33:11.000Z

tests/test_url_parsing.py

malexer/pkgimport

6641604ac3d5b3fa630e68a86b294d1cd3646183

[ "MIT" ]

1

2018-06-29T07:52:33.000Z

"""Tests for url parsing and choosing correct *Importer for processing.""" from unittest.mock import patch import pytest from importable.importable import importable, HttpZipImporter, \ GitHubHttpZipImporter, HdfsZipImporter @patch.object(HdfsZipImporter, 'add_pkg_to_python_path') @patch.object(GitHubHttpZipImporter, 'add_pkg_to_python_path') @patch.object(HttpZipImporter, 'add_pkg_to_python_path') class TestIsMineChecks(object): def test_github_http(self, http, github, hdfs): importable('http://github.com/malexer/meteocalc/archive/master.zip') assert github.called assert not http.called assert not hdfs.called def test_github_https(self, http, github, hdfs): importable('https://github.com/malexer/meteocalc/archive/master.zip') assert github.called assert not http.called assert not hdfs.called def test_regular_http(self, http, github, hdfs): importable('http://www.somerepository.com/path/to/mymodule.zip') assert not github.called assert http.called assert not hdfs.called def test_regular_https(self, http, github, hdfs): importable('https://www.somerepository.com/path/to/mymodule.zip') assert not github.called assert http.called assert not hdfs.called def test_regular_http_with_port(self, http, github, hdfs): importable('http://localhost:8080/mymodule.zip') assert not github.called assert http.called assert not hdfs.called def test_regular_https_with_port(self, http, github, hdfs): importable('http://localhost:8080/folder1/mymodule.zip') assert not github.called assert http.called assert not hdfs.called def test_webhdfs(self, http, github, hdfs): importable('webhdfs://localhost/dir/mymodule.zip') assert not github.called assert not http.called assert hdfs.called def test_webhdfs_with_port(self, http, github, hdfs): importable('webhdfs://localhost:8080/dir/mymodule.zip') assert not github.called assert not http.called assert hdfs.called def test_no_match_for_ftp(self, http, github, hdfs): with pytest.raises(ValueError): importable('ftp://localhost/dir/mymodule.zip') def test_no_match_for_hdfs(self, http, github, hdfs): with pytest.raises(ValueError): importable('hdfs://localhost/dir/mymodule.zip') def test_not_a_url(self, http, github, hdfs): with pytest.raises(ValueError): importable('Just some text.') def test_empty_str(self, http, github, hdfs): with pytest.raises(ValueError): importable('') def test_with_none(self, http, github, hdfs): with pytest.raises(TypeError): importable(None) def test_with_int(self, http, github, hdfs): with pytest.raises(TypeError): importable(123)

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6

c274fa320e7ff4bd7dce24563ad53c6c0b84aa53

44,424

py

Python

tests/test_simulator.py

khoak20hcmut/Penalty_BF

3cfa69931a117511ff7c40f8bb72f1f6f31b0a95

[ "MIT" ]

1

2021-02-08T07:23:08.000Z

tests/test_simulator.py

khoak20hcmut/Penalty_BF

3cfa69931a117511ff7c40f8bb72f1f6f31b0a95

[ "MIT" ]

null

tests/test_simulator.py

khoak20hcmut/Penalty_BF

3cfa69931a117511ff7c40f8bb72f1f6f31b0a95

[ "MIT" ]

1

2021-02-08T07:29:36.000Z

import subprocess import numpy as np import pytest import batsim_py from batsim_py import simulator from batsim_py import protocol from batsim_py.events import JobEvent from batsim_py.events import SimulatorEvent from batsim_py.events import HostEvent from batsim_py.jobs import Job from batsim_py.protocol import BatsimMessage from batsim_py.protocol import JobCompletedBatsimEvent from batsim_py.protocol import JobSubmittedBatsimEvent from batsim_py.protocol import NotifyBatsimEvent from batsim_py.protocol import RequestedCallBatsimEvent from batsim_py.protocol import ResourcePowerStateChangedBatsimEvent from batsim_py.protocol import SimulationBeginsBatsimEvent from batsim_py.protocol import SimulationEndsBatsimEvent from batsim_py.resources import Host, PowerStateType from batsim_py.simulator import SimulatorHandler from .utils import BatsimEventAPI from .utils import BatsimJobProfileAPI from .utils import BatsimPlatformAPI class TestSimulatorHandler: @pytest.fixture(autouse=True) def setup(self, mocker): mocker.patch("batsim_py.simulator.which", return_value=True) mocker.patch("batsim_py.simulator.subprocess.Popen") mocker.patch.object(protocol.NetworkHandler, 'bind') mocker.patch.object(protocol.NetworkHandler, 'send') watts = [(90, 100), (120, 130)] props = BatsimPlatformAPI.get_resource_properties(watt_on=watts) r = [ BatsimPlatformAPI.get_resource(0, properties=props), BatsimPlatformAPI.get_resource(1, properties=props), ] s = [ BatsimPlatformAPI.get_resource(2, properties={"role": "storage"}) ] e = BatsimEventAPI.get_simulation_begins(resources=r, storages=s) events = [SimulationBeginsBatsimEvent(0, e['data'])] msg = BatsimMessage(0, events) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) def test_current_time_must_truncate(self, mocker): s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_notify_no_more_static_job_to_submit(10) msg = BatsimMessage(10.00199, [NotifyBatsimEvent(10.00199, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.current_time == 10 def test_batsim_not_found_must_raise(self, mocker): mocker.patch("batsim_py.simulator.which", return_value=None) with pytest.raises(ImportError) as excinfo: SimulatorHandler() assert 'Batsim' in str(excinfo.value) def test_start_cmd(self): platform = "p.xml" workload = "w.json" verbosity = "quiet" address = "tcp://localhost:21050" s = SimulatorHandler(address) cmd = ( f'batsim -E --forward-profiles-on-submission ' f'--disable-schedule-tracing --disable-machine-state-tracing ' f'-s {address} -p {platform} -w {workload} ' f'-v {verbosity} -e /tmp/batsim' ) s.start(platform, workload, verbosity) simulator.subprocess.Popen.assert_called_once_with( # type: ignore cmd.split(), stdout=subprocess.PIPE) def test_start_cmd_with_compute_sharing_enable(self): platform = "p.xml" workload = "w.json" verbosity = "quiet" address = "tcp://localhost:21050" s = SimulatorHandler(address) cmd = ( f'batsim -E --forward-profiles-on-submission ' f'--disable-schedule-tracing --disable-machine-state-tracing ' f'-s {address} -p {platform} -w {workload} ' f'-v {verbosity} -e /tmp/batsim --enable-compute-sharing' ) s.start(platform, workload, verbosity, allow_compute_sharing=True) simulator.subprocess.Popen.assert_called_once_with( # type: ignore cmd.split(), stdout=subprocess.PIPE) def test_start_cmd_with_storage_sharing_disable(self): platform = "p.xml" workload = "w.json" verbosity = "quiet" address = "tcp://localhost:21050" s = SimulatorHandler(address) cmd = ( f'batsim -E --forward-profiles-on-submission ' f'--disable-schedule-tracing --disable-machine-state-tracing ' f'-s {address} -p {platform} -w {workload} ' f'-v {verbosity} -e /tmp/batsim --disable-storage-sharing' ) s.start(platform, workload, verbosity, allow_storage_sharing=False) simulator.subprocess.Popen.assert_called_once_with( # type: ignore cmd.split(), stdout=subprocess.PIPE) def test_start_cmd_with_external_events(self): platform = "p.xml" workload = "w.json" verbosity = "quiet" address = "tcp://localhost:21050" events = "events.txt" s = SimulatorHandler(address) cmd = ( f'batsim -E --forward-profiles-on-submission ' f'--disable-schedule-tracing --disable-machine-state-tracing ' f'-s {address} -p {platform} -w {workload} ' f'-v {verbosity} -e /tmp/batsim --events {events}' ) s.start(platform, workload, verbosity, external_events=events) simulator.subprocess.Popen.assert_called_once_with( # type: ignore cmd.split(), stdout=subprocess.PIPE) def test_start_already_running_must_raise(self): s = SimulatorHandler() s.start("p", "w") with pytest.raises(RuntimeError) as excinfo: s.start("p2", "w2") assert "running" in str(excinfo.value) def test_start_verbosity_invalid_value_must_raise(self): s = SimulatorHandler() with pytest.raises(ValueError) as excinfo: s.start("p", "w", verbosity="l") # type: ignore assert "verbosity" in str(excinfo.value) def test_start_with_simulation_time_less_than_zero_must_raise(self): s = SimulatorHandler() with pytest.raises(ValueError) as excinfo: s.start("p2", "w2", simulation_time=-1) assert "simulation_time" in str(excinfo.value) def test_start_with_simulation_time_equal_to_zero_must_raise(self): s = SimulatorHandler() with pytest.raises(ValueError) as excinfo: s.start("p2", "w2", simulation_time=0) assert "simulation_time" in str(excinfo.value) def test_start_with_simulation_time_must_setup_call_request(self, mocker): mocker.patch("batsim_py.simulator.CallMeLaterBatsimRequest") s = SimulatorHandler() s.start("p", "w", simulation_time=100) batsim_py.simulator.CallMeLaterBatsimRequest.assert_called_once_with( # type: ignore 0, 100+0.09) def test_start_must_dispatch_event(self): def foo(h: SimulatorHandler): self.__called = True self.__called = False s = SimulatorHandler() s.subscribe(SimulatorEvent.SIMULATION_BEGINS, foo) s.start("p", "w") assert self.__called def test_start_valid(self): s = SimulatorHandler("tcp://localhost:21050") assert not s.is_running s.start("p", "w") assert s.address == "tcp://localhost:21050" assert s.is_running assert s.platform assert s.current_time == 0 assert not s.jobs assert not s.is_submitter_finished protocol.NetworkHandler.bind.assert_called_once() def test_close_valid(self): s = SimulatorHandler() s.start("p", "w") s.close() assert not s.is_running def test_close_not_running_must_not_raise(self): s = SimulatorHandler() try: s.close() except: raise pytest.fail("Close raised an exception.") # type: ignore def test_close_call_network_close(self, mocker): s = SimulatorHandler() mocker.patch("batsim_py.protocol.NetworkHandler.close") s.start("p", "w") s.close() protocol.NetworkHandler.close.assert_called_once() def test_close_dispatch_event(self, mocker): def foo(h: SimulatorHandler): self.__called = True self.__called = False s = SimulatorHandler() s.start("p", "w") s.subscribe(SimulatorEvent.SIMULATION_ENDS, foo) s.close() assert self.__called def test_proceed_time_with_simulation_time_must_force_close(self, mocker): s = SimulatorHandler() s.start("p2", "w2", simulation_time=10) # setup e = BatsimEventAPI.get_job_submitted(res=1) events = [ JobSubmittedBatsimEvent(5, e['data']), RequestedCallBatsimEvent(10) ] msg = BatsimMessage(10, events) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert not s.is_running def test_proceed_time_not_running_must_raise(self, mocker): s = SimulatorHandler() with pytest.raises(RuntimeError) as excinfo: s.proceed_time() assert "running" in str(excinfo.value) def test_proceed_time_less_than_zero_must_raise(self, mocker): s = SimulatorHandler() s.start("p", "w") with pytest.raises(ValueError) as excinfo: s.proceed_time(-1) assert "time" in str(excinfo.value) def test_proceed_time_without_time_must_go_to_next_event(self, mocker): s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_job_submitted() msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) mocker.patch.object(SimulatorHandler, 'set_callback') s.proceed_time() SimulatorHandler.set_callback.assert_not_called() assert s.current_time == 150 def test_proceed_time_with_time_must_setup_call_request(self, mocker): mocker.patch("batsim_py.simulator.SimulatorHandler.set_callback") s = SimulatorHandler() s.start("p", "w") msg = BatsimMessage(50, [SimulationEndsBatsimEvent(50)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time(50) simulator.SimulatorHandler.set_callback.assert_called_once_with( 50, mocker.ANY) def test_proceed_time_with_submitter_finished_without_external_events_must_not_allow_callback(self, mocker): s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_notify_no_more_static_job_to_submit(10) msg = BatsimMessage(10, [NotifyBatsimEvent(10, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.is_submitter_finished msg = BatsimMessage(50, [SimulationEndsBatsimEvent(50)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) mocker.patch.object(SimulatorHandler, 'set_callback') s.proceed_time(100) assert not s.is_running assert s.current_time == 50 SimulatorHandler.set_callback.assert_not_called() def test_proceed_time_with_submitter_and_external_events_finished_must_not_allow_callback(self, mocker): s = SimulatorHandler() s.start("p", "w", external_events=".txt") e = BatsimEventAPI.get_notify_no_more_static_job_to_submit(10) e2 = BatsimEventAPI.get_notify_no_more_external_event_to_occur(10) msg = BatsimMessage(10, [ NotifyBatsimEvent(10, e['data']), NotifyBatsimEvent(10, e2['data']) ]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.is_submitter_finished msg = BatsimMessage(50, [SimulationEndsBatsimEvent(50)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) mocker.patch.object(SimulatorHandler, 'set_callback') s.proceed_time(100) assert not s.is_running assert s.current_time == 50 SimulatorHandler.set_callback.assert_not_called() def test_proceed_time_with_is_submitter_finished_and_external_events_to_happen_must_allow_callback(self, mocker): s = SimulatorHandler() s.start("p", "w", external_events=".txt") e = BatsimEventAPI.get_notify_no_more_static_job_to_submit(10) msg = BatsimMessage(10, [NotifyBatsimEvent(10, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.is_submitter_finished msg = BatsimMessage(50, [SimulationEndsBatsimEvent(50)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) mocker.patch.object(SimulatorHandler, 'set_callback') s.proceed_time(100) assert not s.is_running assert s.current_time == 50 SimulatorHandler.set_callback.assert_called() def test_proceed_time_with_is_submitter_finished_and_queue_must_allow_callback(self, mocker): s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_notify_no_more_static_job_to_submit(10) e2 = BatsimEventAPI.get_job_submitted() events = [ JobSubmittedBatsimEvent(10, e2['data']), NotifyBatsimEvent(10, e['data']), ] msg = BatsimMessage(10, events) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.is_submitter_finished msg = BatsimMessage(50, [SimulationEndsBatsimEvent(50)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) mocker.patch.object(SimulatorHandler, 'set_callback') s.proceed_time(50) SimulatorHandler.set_callback.assert_called_once() def test_proceed_time_with_is_submitter_finished_and_sim_time_must_allow_callback(self, mocker): s = SimulatorHandler() s.start("p", "w", simulation_time=100) e = BatsimEventAPI.get_notify_no_more_static_job_to_submit(10) msg = BatsimMessage(10, [NotifyBatsimEvent(10, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.is_submitter_finished msg = BatsimMessage(50, [SimulationEndsBatsimEvent(50)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) mocker.patch.object(SimulatorHandler, 'set_callback') s.proceed_time(50) SimulatorHandler.set_callback.assert_called_once() def test_callback_not_running_must_raise(self): def foo(p): pass s = SimulatorHandler() with pytest.raises(RuntimeError) as excinfo: s.set_callback(10, foo) assert "running" in str(excinfo.value) def test_callback_invalid_time_must_raise(self, mocker): def foo(p): pass s = SimulatorHandler() s.start("p", "w") msg = BatsimMessage(50, [RequestedCallBatsimEvent(50)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time(50) with pytest.raises(ValueError) as excinfo: s.set_callback(50, foo) assert "at" in str(excinfo.value) def test_callback_must_setup_call_request(self, mocker): def foo(p): pass mocker.patch("batsim_py.simulator.CallMeLaterBatsimRequest") s = SimulatorHandler() s.start("p", "w") s.set_callback(50, foo) simulator.CallMeLaterBatsimRequest.assert_called_once_with( # type: ignore 0, 50+0.09) def test_queue(self, mocker): s = SimulatorHandler() s.start("p", "w") e = [ JobSubmittedBatsimEvent( 0, BatsimEventAPI.get_job_submitted(job_id="w!0")['data']), JobSubmittedBatsimEvent( 0, BatsimEventAPI.get_job_submitted(job_id="w!1")['data']), ] msg = BatsimMessage(150, e) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.queue and len(s.queue) == 2 s.allocate("w!1", [0]) assert s.queue and len(s.queue) == 1 def test_agenda_without_platform(self, mocker): s = SimulatorHandler() assert not list(s.agenda) def test_agenda_with_job_not_running(self, mocker): s = SimulatorHandler() s.start("p", "w") s.switch_off([h.id for h in s.platform.hosts]) e = BatsimEventAPI.get_job_submitted(res=1, walltime=100) e = JobSubmittedBatsimEvent(0, e['data']) msg = BatsimMessage(0, [e]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() s.allocate(e.job.id, [0]) msg = BatsimMessage(10, [RequestedCallBatsimEvent(10)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() agenda = list(s.agenda) assert s.current_time == 10 assert agenda[0].host.id == 0 and agenda[0].release_time == e.job.walltime def test_agenda_with_job_without_walltime(self, mocker): s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_job_submitted(res=1) e = JobSubmittedBatsimEvent(0, e['data']) msg = BatsimMessage(0, [e]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() s.allocate(e.job.id, [0]) msg = BatsimMessage(10, [RequestedCallBatsimEvent(10)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() agenda = list(s.agenda) assert s.current_time == 10 assert agenda[0].host.id == 0 and agenda[0].release_time == np.inf assert agenda[1].host.id == 1 and agenda[1].release_time == 0 def test_agenda_with_multiple_jobs_in_one_host(self, mocker): s = SimulatorHandler() s.start("p", "w") e1 = BatsimEventAPI.get_job_submitted( job_id="w!0", res=1, walltime=100) e1 = JobSubmittedBatsimEvent(0, e1['data']) e2 = BatsimEventAPI.get_job_submitted( job_id="w!1", res=1, walltime=200) e2 = JobSubmittedBatsimEvent(0, e2['data']) msg = BatsimMessage(0, [e1, e2]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() s.allocate(e1.job.id, [0]) s.allocate(e2.job.id, [0]) msg = BatsimMessage(10, [RequestedCallBatsimEvent(10)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() agenda = list(s.agenda) assert s.current_time == 10 assert agenda[0].host.id == 0 and agenda[0].release_time == e2.job.walltime-10 assert agenda[1].host.id == 1 and agenda[1].release_time == 0 def test_allocate_not_running_must_raise(self): s = SimulatorHandler() with pytest.raises(RuntimeError) as excinfo: s.allocate("1", [1, 2]) assert "running" in str(excinfo.value) def test_allocate_invalid_job_must_raise(self): s = SimulatorHandler() s.start("p", "w") with pytest.raises(LookupError) as excinfo: s.allocate("1", [0]) assert "job" in str(excinfo.value) def test_allocate_invalid_host_must_raise(self, mocker): s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_job_submitted(res=1) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() with pytest.raises(LookupError) as excinfo: s.allocate(e['data']['job_id'], [3]) assert "resources" in str(excinfo.value) def test_allocate_must_start_job_and_host(self, mocker): mocker.patch("batsim_py.simulator.ExecuteJobBatsimRequest") s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_job_submitted(res=1) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.queue job = s.jobs[0] s.allocate(job.id, [0]) assert job.is_running assert s.platform.get_host(0).is_computing simulator.ExecuteJobBatsimRequest.assert_called_once_with( # type: ignore 150, job.id, job.allocation, job.storage_mapping) def test_allocate_with_staging_job_must_allocate_storages(self, mocker): mocker.patch("batsim_py.simulator.ExecuteJobBatsimRequest") s = SimulatorHandler() s.start("p", "w") profile = BatsimJobProfileAPI.get_data_staging("a", "b", 10) e = BatsimEventAPI.get_job_submitted(res=1, profile=profile) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.queue job = s.jobs[0] storage = list(s.platform.storages)[0] s.allocate(job.id, [0], {"a": storage.id, "b": storage.id}) assert job.is_running assert s.platform.get_host(0).is_computing assert storage.jobs and storage.jobs[0] == job.id simulator.ExecuteJobBatsimRequest.assert_called_once_with( # type: ignore 150, job.id, job.allocation, job.storage_mapping) def test_allocate_with_pfs_job_must_allocate_storages(self, mocker): mocker.patch("batsim_py.simulator.ExecuteJobBatsimRequest") s = SimulatorHandler() s.start("p", "w") profile = BatsimJobProfileAPI.get_parallel_homogeneous_pfs("a", 1, 2) e = BatsimEventAPI.get_job_submitted(res=1, profile=profile) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.queue job = s.jobs[0] storage = list(s.platform.storages)[0] s.allocate(job.id, [0], {"a": storage.id}) assert job.is_running assert s.platform.get_host(0).is_computing assert storage.jobs and storage.jobs[0] == job.id simulator.ExecuteJobBatsimRequest.assert_called_once_with( # type: ignore 150, job.id, job.allocation, job.storage_mapping) def test_allocate_start_must_dispatch_events(self, mocker): def foo_j(j: Job): self.__j_called, self.__j_id = True, j.id def foo_h(h: Host): self.__h_called, self.__h_id = True, h.id self.__j_called = self.__h_called = False self.__j_id = self.__h_id = -1 s = SimulatorHandler() s.start("p", "w") s.subscribe(JobEvent.STARTED, foo_j) s.subscribe(HostEvent.STATE_CHANGED, foo_h) e = BatsimEventAPI.get_job_submitted(res=1) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.queue job = s.jobs[0] s.allocate(job.id, [0]) assert self.__j_called and self.__j_id == job.id assert self.__h_called and self.__h_id == 0 def test_allocate_must_init_host(self, mocker): mocker.patch("batsim_py.simulator.SetResourceStateBatsimRequest") mocker.patch("batsim_py.simulator.ExecuteJobBatsimRequest") s = SimulatorHandler() s.start("p", "w") # setup host = s.platform.get_host(0) host._switch_off() host._set_off() e = BatsimEventAPI.get_job_submitted(res=2) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() s.allocate(e['data']['job_id'], [0, 1]) assert s.jobs[0].is_runnable assert host.is_switching_on simulator.ExecuteJobBatsimRequest.assert_not_called() # type: ignore simulator.SetResourceStateBatsimRequest.assert_called_once_with( # type: ignore 150, [0], host.get_default_pstate().id) def test_allocate_must_dispatch_job_event(self, mocker): def foo(j: Job): self.__called = True self.__job_id = j.id self.__called, self.__job_id = False, -1 s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_job_submitted(res=1) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() s.subscribe(JobEvent.ALLOCATED, foo) job = s.jobs[0] s.allocate(job.id, [0]) assert self.__called and self.__job_id == job.id def test_allocate_with_switching_off_host_must_not_start_job(self, mocker): mocker.patch("batsim_py.protocol.ExecuteJobBatsimRequest") s = SimulatorHandler() s.start("p", "w") # setup host = s.platform.get_host(0) host._switch_off() e = BatsimEventAPI.get_job_submitted(res=1) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() s.allocate(e['data']['job_id'], [0]) assert s.jobs[0].is_runnable assert host.is_switching_off protocol.ExecuteJobBatsimRequest.assert_not_called() # type: ignore def test_allocate_with_switching_on_host_must_not_start_job(self, mocker): mocker.patch("batsim_py.protocol.ExecuteJobBatsimRequest") s = SimulatorHandler() s.start("p", "w") # setup host = s.platform.get_host(0) host._switch_off() host._set_off() host._switch_on() e = BatsimEventAPI.get_job_submitted(res=1) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() s.allocate(e['data']['job_id'], [0]) assert s.jobs[0].is_runnable assert host.is_switching_on protocol.ExecuteJobBatsimRequest.assert_not_called() # type: ignore def test_kill_job_sim_not_running_must_raise(self): s = SimulatorHandler() with pytest.raises(RuntimeError) as excinfo: s.kill_job("1") assert "running" in str(excinfo.value) def test_kill_job_not_found_must_raise(self): s = SimulatorHandler() s.start("p", "w") with pytest.raises(LookupError) as excinfo: s.kill_job("1") assert "job" in str(excinfo.value) def test_kill_job_not_running_must_raise(self, mocker): mocker.patch("batsim_py.simulator.KillJobBatsimRequest") s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_job_submitted(res=1) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() with pytest.raises(RuntimeError) as excinfo: s.kill_job(s.jobs[0].id) assert "not running" in str(excinfo.value) def test_kill_job_must_sync_with_batsim(self, mocker): mocker.patch("batsim_py.simulator.KillJobBatsimRequest") s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_job_submitted(res=1) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) mocker.patch.object(batsim_py.jobs.Job, '_terminate') mocker.patch.object(batsim_py.resources.Host, '_release') s.proceed_time() mocker.patch("batsim_py.simulator.BatsimMessage") mocker.patch.object(batsim_py.jobs.Job, 'is_running', return_value=True) mocker.patch.object( protocol.NetworkHandler, 'recv', return_value=BatsimMessage(s.current_time, [])) job_id = s.jobs[0].id s.kill_job(job_id) assert s.jobs batsim_py.jobs.Job._terminate.assert_not_called() batsim_py.resources.Host._release.assert_not_called() simulator.KillJobBatsimRequest.assert_called_once_with( # type: ignore 150, job_id) assert simulator.NetworkHandler.send.call_count == 2 def test_reject_job_not_running_must_raise(self, mocker): s = SimulatorHandler() with pytest.raises(RuntimeError) as excinfo: s.reject_job("1") assert "running" in str(excinfo.value) def test_reject_job_not_found_must_raise(self, mocker): s = SimulatorHandler() s.start("p", "w") with pytest.raises(LookupError) as excinfo: s.reject_job("1") assert "job" in str(excinfo.value) def test_reject_job(self, mocker): mocker.patch("batsim_py.simulator.RejectJobBatsimRequest") s = SimulatorHandler() s.start("p", "w") e = BatsimEventAPI.get_job_submitted(res=1) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) mocker.patch.object(batsim_py.jobs.Job, '_reject') s.proceed_time() job_id = e['data']['job_id'] s.reject_job(job_id) assert not s.jobs batsim_py.jobs.Job._reject.assert_called_once() simulator.RejectJobBatsimRequest.assert_called_once_with( # type: ignore 150, job_id) def test_reject_job_must_dispatch_event(self, mocker): def foo(j: Job): self.__called, self.__job_id = True, j.id self.__called, self.__job_id = False, -1 s = SimulatorHandler() s.start("p", "w") s.subscribe(JobEvent.REJECTED, foo) e = BatsimEventAPI.get_job_submitted(res=1) msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() job_id = e['data']['job_id'] s.reject_job(job_id) assert self.__called and self.__job_id == job_id def test_switch_on_not_running_must_raise(self): s = SimulatorHandler() with pytest.raises(RuntimeError) as excinfo: s.switch_on([0]) assert 'running' in str(excinfo.value) def test_switch_on_not_found_must_raise(self): s = SimulatorHandler() s.start("p", "w") with pytest.raises(LookupError) as excinfo: s.switch_on([30]) assert 'resources' in str(excinfo.value) def test_switch_on(self, mocker): mocker.patch("batsim_py.simulator.SetResourceStateBatsimRequest") mocker.patch.object(batsim_py.resources.Host, '_switch_on') s = SimulatorHandler() s.start("p", "w") s.switch_on([1]) ps = s.platform.get_host(1).get_default_pstate() batsim_py.resources.Host._switch_on.assert_called_once() simulator.SetResourceStateBatsimRequest.assert_called_once_with( # type: ignore 0, [1], ps.id) def test_switch_on_must_dispatch_host_event(self, mocker): def foo(h: Host): self.__nb_called += 1 self.__nb_called = 0 mocker.patch.object(batsim_py.resources.Host, '_switch_on') s = SimulatorHandler() s.start("p", "w") s.subscribe(HostEvent.STATE_CHANGED, foo) s.switch_on([0, 1]) assert self.__nb_called == 2 def test_switch_off_not_running_must_raise(self): s = SimulatorHandler() with pytest.raises(RuntimeError) as excinfo: s.switch_off([0]) assert 'running' in str(excinfo.value) def test_switch_off_not_found_must_raise(self): s = SimulatorHandler() s.start("p", "w") with pytest.raises(LookupError) as excinfo: s.switch_off([10]) assert 'resources' in str(excinfo.value) def test_switch_off(self, mocker): mocker.patch("batsim_py.simulator.SetResourceStateBatsimRequest") mocker.patch.object(batsim_py.resources.Host, '_switch_off') s = SimulatorHandler() s.start("p", "w") s.switch_off([0]) ps = s.platform.get_host(0).get_sleep_pstate() batsim_py.resources.Host._switch_off.assert_called_once() simulator.SetResourceStateBatsimRequest.assert_called_once_with( # type: ignore 0, [0], ps.id) def test_switch_off_must_dispatch_host_event(self, mocker): def foo(h: Host): self.__nb_called += 1 self.__nb_called = 0 mocker.patch.object(batsim_py.resources.Host, '_switch_off') s = SimulatorHandler() s.start("p", "w") s.subscribe(HostEvent.STATE_CHANGED, foo) s.switch_off([0, 1]) assert self.__nb_called == 2 def test_switch_ps_not_running_must_raise(self): s = SimulatorHandler() with pytest.raises(RuntimeError) as excinfo: s.switch_power_state(0, 0) assert 'running' in str(excinfo.value) def test_switch_ps_not_found_must_raise(self): s = SimulatorHandler() s.start("p", "w") with pytest.raises(LookupError) as excinfo: s.switch_power_state(10, 0) assert 'resources' in str(excinfo.value) def test_switch_ps(self, mocker): mocker.patch("batsim_py.simulator.SetResourceStateBatsimRequest") mocker.patch.object(batsim_py.resources.Host, '_set_computation_pstate') s = SimulatorHandler() s.start("p", "w") h = s.platform.get_host(0) ps = h.get_pstate_by_type(PowerStateType.COMPUTATION) assert len(ps) == 2 s.switch_power_state(0, ps[-1].id) batsim_py.resources.Host._set_computation_pstate.assert_called_once() simulator.SetResourceStateBatsimRequest.assert_called_once_with( # type: ignore 0, [0], ps[-1].id) def test_switch_ps_must_dispatch_host_event(self, mocker): def foo(h: Host): self.__called, self.__h_id = True, h.id self.__called, self.__h_id = False, -1 s = SimulatorHandler() s.start("p", "w") s.subscribe(HostEvent.COMPUTATION_POWER_STATE_CHANGED, foo) h = s.platform.get_host(0) ps = h.get_pstate_by_type(PowerStateType.COMPUTATION) s.switch_power_state(0, ps[-1].id) assert self.__called and self.__h_id == 0 def test_on_batsim_job_submitted_must_append_in_queue(self, mocker): s = SimulatorHandler() s.start("p", "w") # Setup Allocate e = BatsimEventAPI.get_job_submitted(res=1) job_id, job_alloc = e['data']['job_id'], [0] msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.queue and s.queue[0].id == job_id def test_on_batsim_job_submitted_must_dispatch_event(self, mocker): def foo(j: Job): self.__called, self.__j_id = True, j.id self.__called, self.__j_id = False, -1 s = SimulatorHandler() s.start("p", "w") s.subscribe(JobEvent.SUBMITTED, foo) # Setup Allocate e = BatsimEventAPI.get_job_submitted(res=1) job_id = e['data']['job_id'] msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert self.__called and self.__j_id == job_id def test_on_batsim_job_completed_must_terminate_job_and_release_resources(self, mocker): s = SimulatorHandler() s.start("p", "w") # Setup Allocate profile = BatsimJobProfileAPI.get_data_staging("a", "b", 10) e = BatsimEventAPI.get_job_submitted(res=1, profile=profile) job_id, job_alloc = e['data']['job_id'], [0] msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) storage = list(s.platform.storages)[0] s.proceed_time() s.allocate(job_id, job_alloc, {"a": storage.id, "b": storage.id}) # Setup Completed mocker.patch.object(batsim_py.jobs.Job, '_terminate') mocker.patch.object(batsim_py.resources.Host, '_release') mocker.patch.object(batsim_py.resources.Storage, '_release') e = BatsimEventAPI.get_job_completted(100, job_id, alloc=job_alloc) msg = BatsimMessage(150, [JobCompletedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() batsim_py.jobs.Job._terminate.assert_called_once() batsim_py.resources.Host._release.assert_called_once_with(job_id) batsim_py.resources.Storage._release.assert_called_once_with(job_id) assert not s.jobs def test_on_batsim_job_completed_must_dispatch_event(self, mocker): def foo_j(j: Job): self.__j_called, self.__j_id = True, j.id def foo_h(h: Host): self.__h_called, self.__h_id = True, h.id self.__j_called = self.__h_called = False self.__j_id = self.__h_id = -1 s = SimulatorHandler() s.start("p", "w") s.subscribe(HostEvent.STATE_CHANGED, foo_h) s.subscribe(JobEvent.COMPLETED, foo_j) # Setup Allocate e = BatsimEventAPI.get_job_submitted(res=1) job_id, job_alloc = e['data']['job_id'], [0] msg = BatsimMessage(150, [JobSubmittedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() s.allocate(job_id, job_alloc) # Setup Completed mocker.patch.object(batsim_py.jobs.Job, '_terminate') e = BatsimEventAPI.get_job_completted(100, job_id, alloc=job_alloc) msg = BatsimMessage(150, [JobCompletedBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert self.__j_called and self.__j_id == job_id assert self.__h_called and self.__h_id == job_alloc[0] def test_on_batsim_host_ps_changed_must_set_off_and_dispatch_event(self, mocker): def foo_h(h: Host): self.__h_called, self.__h_id = True, h.id self.__j_id = self.__h_id = -1 s = SimulatorHandler() s.start("p", "w") s.switch_off([0]) assert s.platform.get_host(0).is_switching_off # Setup p_id = s.platform.get_host(0).get_sleep_pstate().id e = BatsimEventAPI.get_resource_state_changed(150, [0], p_id) e = ResourcePowerStateChangedBatsimEvent(150, e['data']) msg = BatsimMessage(150, [e]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.subscribe(HostEvent.STATE_CHANGED, foo_h) s.proceed_time() assert s.platform.get_host(0).is_sleeping assert self.__h_called and self.__h_id == 0 def test_on_batsim_host_ps_changed_must_set_on_and_dispatch_event(self, mocker): def foo_h(h: Host): self.__h_called, self.__h_id = True, h.id self.__j_id = self.__h_id = -1 s = SimulatorHandler() s.start("p", "w") s.platform.get_host(0)._switch_off() s.platform.get_host(0)._set_off() s.switch_on([0]) assert s.platform.get_host(0).is_switching_on # Setup p_id = s.platform.get_host(0).get_default_pstate().id e = BatsimEventAPI.get_resource_state_changed(150, [0], p_id) e = ResourcePowerStateChangedBatsimEvent(150, e['data']) msg = BatsimMessage(150, [e]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.subscribe(HostEvent.STATE_CHANGED, foo_h) s.proceed_time() assert s.platform.get_host(0).is_idle assert self.__h_called and self.__h_id == 0 def test_on_batsim_host_ps_changed_must_set_comp_ps_and_dispatch_event(self, mocker): def foo_h(h: Host): self.__h_called, self.__h_id = True, h.id self.__j_id = self.__h_id = -1 s = SimulatorHandler() s.start("p", "w") # Setup host = s.platform.get_host(0) new_ps = host.get_pstate_by_type(PowerStateType.COMPUTATION)[-1] assert host.pstate != new_ps e = BatsimEventAPI.get_resource_state_changed( 150, [host.id], new_ps.id) e = ResourcePowerStateChangedBatsimEvent(150, e['data']) msg = BatsimMessage(150, [e]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.subscribe(HostEvent.COMPUTATION_POWER_STATE_CHANGED, foo_h) s.proceed_time() assert host.pstate == new_ps assert self.__h_called and self.__h_id == 0 def test_on_batsim_simulation_ends_must_ack(self, mocker): s = SimulatorHandler() s.start("p", "w") msg = BatsimMessage(100, [SimulationEndsBatsimEvent(100)]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert not s.is_running assert protocol.NetworkHandler.send.call_count == 2 def test_on_batsim_notify_machine_unavailable(self, mocker): s = SimulatorHandler() s.start("p", "w") # Setup e = BatsimEventAPI.get_notify_machine_unavailable(10, [0, 1, 2]) msg = BatsimMessage(150, [NotifyBatsimEvent(150, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert s.platform.get(0).is_unavailable assert s.platform.get(1).is_unavailable assert s.platform.get(2).is_unavailable def test_on_batsim_notify_machine_available(self, mocker): s = SimulatorHandler() s.start("p", "w") # Setup s.platform.get(0)._set_unavailable() s.platform.get(1)._set_unavailable() s.platform.get(2)._set_unavailable() e = BatsimEventAPI.get_notify_machine_available(10, [0, 1, 2]) msg = BatsimMessage(10, [NotifyBatsimEvent(10, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert not s.platform.get(0).is_unavailable assert not s.platform.get(1).is_unavailable assert not s.platform.get(2).is_unavailable def test_on_batsim_notify_machine_unavailable_must_dispatch_host_event(self, mocker): def foo(h: Host): self.nb_called += 1 assert h.is_unavailable self.nb_called = 0 s = SimulatorHandler() s.start("p", "w") s.subscribe(HostEvent.STATE_CHANGED, foo) # Setup e = BatsimEventAPI.get_notify_machine_unavailable(10, [0, 1, 2]) msg = BatsimMessage(10, [NotifyBatsimEvent(10, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert self.nb_called == 2 def test_on_batsim_notify_machine_available_must_dispatch_host_event(self, mocker): def foo(h: Host): self.nb_called += 1 assert not h.is_unavailable self.nb_called = 0 s = SimulatorHandler() s.start("p", "w") s.subscribe(HostEvent.STATE_CHANGED, foo) # Setup s.platform.get(0)._set_unavailable() s.platform.get(1)._set_unavailable() s.platform.get(2)._set_unavailable() e = BatsimEventAPI.get_notify_machine_available(10, [0, 1, 2]) msg = BatsimMessage(10, [NotifyBatsimEvent(10, e['data'])]) mocker.patch.object(protocol.NetworkHandler, 'recv', return_value=msg) s.proceed_time() assert self.nb_called == 2

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6

6c100de8649adc3ca1b4b4c573ca329e94b5884f

85

py

Python

sandbox/__init__.py

Chasesc/codeinterview-sandbox

132bc02fe087b112f9e03267a7bc7a68ea96eb8a

[ "Apache-2.0" ]

24

2020-06-01T18:01:08.000Z

2022-01-21T09:43:12.000Z

sandbox/__init__.py

Chasesc/codeinterview-sandbox

132bc02fe087b112f9e03267a7bc7a68ea96eb8a

[ "Apache-2.0" ]

null

sandbox/__init__.py

Chasesc/codeinterview-sandbox

132bc02fe087b112f9e03267a7bc7a68ea96eb8a

[ "Apache-2.0" ]

7

2020-06-02T12:05:21.000Z

2021-03-18T16:03:44.000Z

from .sandbox import Sandbox, UnsupportedLanguage, TimeoutError, MemoryLimitExceeded

42.5

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6c3eacab5b2968b597ecafa60f4ae3b2b9aa4a45

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py

Python

plugin/src/test/resources/refactoring/move/starImportWithUsages/before/src/a.py

consulo/consulo-python

586c3eaee3f9c2cc87fb088dc81fb12ffa4b3a9d

[ "Apache-2.0" ]

null

plugin/src/test/resources/refactoring/move/starImportWithUsages/before/src/a.py

consulo/consulo-python

586c3eaee3f9c2cc87fb088dc81fb12ffa4b3a9d

[ "Apache-2.0" ]

11

2017-02-27T22:35:32.000Z

2021-12-24T08:07:40.000Z

plugin/src/test/resources/refactoring/move/starImportWithUsages/before/src/a.py

consulo/consulo-python

586c3eaee3f9c2cc87fb088dc81fb12ffa4b3a9d

[ "Apache-2.0" ]

null

from b import * print(f())

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0

1

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6

6c636562e36549988622ed32acc6060beaa0a644

7,278

py

Python

streamm/forcefields/tests/test_dihtype.py

NREL/streamm-tools

663ceff5e9a1145b74ee8c1857988dc94d6535a2

[ "Apache-2.0" ]

4

2017-01-04T02:20:52.000Z

2022-01-23T21:14:32.000Z

streamm/forcefields/tests/test_dihtype.py

NREL/streamm-tools

663ceff5e9a1145b74ee8c1857988dc94d6535a2

[ "Apache-2.0" ]

null

streamm/forcefields/tests/test_dihtype.py

NREL/streamm-tools

663ceff5e9a1145b74ee8c1857988dc94d6535a2

[ "Apache-2.0" ]

4

2017-04-25T06:23:08.000Z

2021-04-14T07:10:24.000Z

# coding: utf-8 # Copyright (c) Alliance for Sustainable Energy, LLC # Distributed under the terms of the Apache License, Version 2.0 from __future__ import division, unicode_literals __author__ = "Travis W. Kemper, Ph.D." __copyright__ = "Copyright 2015, Alliance for Sustainable Energy, LLC" __version__ = "0.3.4" __email__ = "organicelectronics@nrel.gov" __status__ = "Beta" ''' Unit tests for the particles module ''' import logging logger = logging.getLogger(__name__) import unittest import os import streamm.forcefields.dihtype as dihtype from streamm_testutil import * class Testdihtypeharmonic(unittest.TestCase): @setUp_streamm def setUp(self): self.dihtype_i = dihtype.Dihtype("HC","CH","N","HN",type="harmonic") self.dihtype_i.d = 4.0 self.dihtype_i.mult = 3.0 self.dihtype_i.theta_s = 45.0 self.dihtype_i.kb = 80.6 def test_dihstr(self): dih_str = ' dihedral HC - CH - N - HN type harmonic \n harmonic d = 4.000000 mult = 3.000000 K = 80.600000 theta_s = 45.000000 lammps index 0 gromcas index 0 ' self.assertEqual(str(self.dihtype_i),dih_str) def test_save(self): json_data = self.dihtype_i.export_json() del self.dihtype_i self.dihtype_i = dihtype.Dihtype("X","X","X","X",type="X") self.dihtype_i.import_json(json_data) self.assertEqual(self.dihtype_i.fftype1,'HC') self.assertEqual(self.dihtype_i.fftype2,'CH') self.assertEqual(self.dihtype_i.fftype3,'N') self.assertEqual(self.dihtype_i.fftype4,'HN') self.assertEqual(self.dihtype_i.type,'harmonic') self.assertEqual(self.dihtype_i.d,4.0) self.assertEqual(self.dihtype_i.mult,3.0) self.assertEqual(self.dihtype_i.kb,80.6) self.assertEqual(self.dihtype_i.theta_s,45.0) # @tearDown_streamm def tearDown(self): del self.dihtype_i self.dihtype_i = None class Testdihtypemultiharmonic(unittest.TestCase): @setUp_streamm def setUp(self): self.dihtype_i = dihtype.Dihtype("HC","CH","N","HN",type="multiharmonic") self.dihtype_i.d = 4.0 self.dihtype_i.mult = 3.0 self.dihtype_i.theta_s = 45.0 self.dihtype_i.kb = 80.6 def test_dihstr(self): dih_str = ' dihedral HC - CH - N - HN type multiharmonic \n harmonic d = 4.000000 mult = 3.000000 K = 80.600000 theta_s = 45.000000 lammps index 0 gromcas index 0 ' self.assertEqual(str(self.dihtype_i),dih_str) def test_save(self): json_data = self.dihtype_i.export_json() del self.dihtype_i self.dihtype_i = dihtype.Dihtype("X","X","X","X",type="X") self.dihtype_i.import_json(json_data) self.assertEqual(self.dihtype_i.fftype1,'HC') self.assertEqual(self.dihtype_i.fftype2,'CH') self.assertEqual(self.dihtype_i.fftype3,'N') self.assertEqual(self.dihtype_i.fftype4,'HN') self.assertEqual(self.dihtype_i.type,'multiharmonic') self.assertEqual(self.dihtype_i.d,4.0) self.assertEqual(self.dihtype_i.mult,3.0) self.assertEqual(self.dihtype_i.theta_s,45.0) self.assertEqual(self.dihtype_i.kb,80.6) # @tearDown_streamm def tearDown(self): del self.dihtype_i self.dihtype_i = None class Testdihtypeopls(unittest.TestCase): @setUp_streamm def setUp(self): self.dihtype_i = dihtype.Dihtype("HC","CH","CH","HC",type="opls") self.dihtype_i.setopls(14.0,1.0,45.0,100.0) def test_dihstropls(self): dih_str = ' dihedral HC - CH - CH - HC type opls \n k1 = 14.000000 k2 = 1.000000 k3 = 45.000000 k4 = 100.000000 lammps index 0 gromcas index 0 ' self.assertEqual(str(self.dihtype_i),dih_str) def test_dihstrrb(self): self.dihtype_i.type = "rb" dih_str = ' dihedral HC - CH - CH - HC type rb \n C0 = 30.500000 C1 = 60.500000 C2 = 179.000000 C3 = -90.000000 C4 = -400.000000 C5 = 0.000000 lammps index 0 gromcas index 0 ' self.assertEqual(str(self.dihtype_i),dih_str) def test_save(self): json_data = self.dihtype_i.export_json() del self.dihtype_i self.dihtype_i = dihtype.Dihtype("X","X","X","X",type="X") self.dihtype_i.import_json(json_data) self.assertEqual(self.dihtype_i.fftype1,'HC') self.assertEqual(self.dihtype_i.fftype2,'CH') self.assertEqual(self.dihtype_i.fftype3,'CH') self.assertEqual(self.dihtype_i.fftype4,'HC') self.assertEqual(self.dihtype_i.type,'opls') self.assertEqual(self.dihtype_i.k1,14.0) self.assertEqual(self.dihtype_i.k2,1.0) self.assertEqual(self.dihtype_i.k3,45.0) self.assertEqual(self.dihtype_i.k4,100.0) self.assertEqual(self.dihtype_i.C0,30.50) self.assertEqual(self.dihtype_i.C1,60.50) self.assertEqual(self.dihtype_i.C2,179.0) self.assertEqual(self.dihtype_i.C3,-90.0) self.assertEqual(self.dihtype_i.C4,-400.0) self.assertEqual(self.dihtype_i.C5,0.0) # @tearDown_streamm def tearDown(self): del self.dihtype_i self.dihtype_i = None class Testdihtyperb(unittest.TestCase): @setUp_streamm def setUp(self): self.dihtype_i = dihtype.Dihtype("HC","CH","CH","HC",type="rb") self.dihtype_i.setrb(0.1,23.4,73.1,32.5,66.7,55.0) def test_dihstrrb(self): dih_str = ' dihedral HC - CH - CH - HC type rb \n C0 = 0.100000 C1 = 23.400000 C2 = 73.100000 C3 = 32.500000 C4 = 66.700000 C5 = 55.000000 lammps index 0 gromcas index 0 ' self.assertEqual(str(self.dihtype_i),dih_str) def test_dihstropls(self): self.dihtype_i.type = "opls" dih_str = ' dihedral HC - CH - CH - HC type opls \n k1 = -95.550000 k2 = -139.800000 k3 = -16.250000 k4 = -16.675000 lammps index 0 gromcas index 0 ' self.assertEqual(str(self.dihtype_i),dih_str) def test_save(self): json_data = self.dihtype_i.export_json() del self.dihtype_i self.dihtype_i = dihtype.Dihtype("X","X","X","X",type="X") self.dihtype_i.import_json(json_data) self.assertEqual(self.dihtype_i.fftype1,'HC') self.assertEqual(self.dihtype_i.fftype2,'CH') self.assertEqual(self.dihtype_i.fftype3,'CH') self.assertEqual(self.dihtype_i.fftype4,'HC') self.assertEqual(self.dihtype_i.type,'rb') self.assertEqual(self.dihtype_i.k1,-95.550) self.assertEqual(self.dihtype_i.k2,-139.80) self.assertEqual(self.dihtype_i.k3,-16.250) self.assertEqual(self.dihtype_i.k4,-16.6750) self.assertEqual(self.dihtype_i.C0,0.10) self.assertEqual(self.dihtype_i.C1,23.40) self.assertEqual(self.dihtype_i.C2,73.10) self.assertEqual(self.dihtype_i.C3,32.50) self.assertEqual(self.dihtype_i.C4,66.70) self.assertEqual(self.dihtype_i.C5,55.0) # @tearDown_streamm def tearDown(self): del self.dihtype_i self.dihtype_i = None if __name__ == '__main__': unittest.main()

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6c7cc9025e19b49f3aeb48906c89c824805e6c7e

78

py

Python

test/tests/14.py

kevinxucs/pyston

bdb87c1706ac74a0d15d9bc2bae53798678a5f14

[ "Apache-2.0" ]

1

2020-02-06T14:28:45.000Z

test/tests/14.py

kevinxucs/pyston

bdb87c1706ac74a0d15d9bc2bae53798678a5f14

[ "Apache-2.0" ]

null

test/tests/14.py

kevinxucs/pyston

bdb87c1706ac74a0d15d9bc2bae53798678a5f14

[ "Apache-2.0" ]

1

2020-02-06T14:29:00.000Z

# None handling def f1(): pass n = f1() print "got n" print n print None

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6687d3aa384a2fefab2820d65b3d39a8b9910dd6

176

py

Python

src/UQpy/dimension_reduction/grassmann_manifold/projections/__init__.py

SURGroup/UncertaintyQuantification

a94c8db47d07134ea2b3b0a3ca53ca818532c3e6

[ "MIT" ]

null

src/UQpy/dimension_reduction/grassmann_manifold/projections/__init__.py

SURGroup/UncertaintyQuantification

a94c8db47d07134ea2b3b0a3ca53ca818532c3e6

[ "MIT" ]

null

src/UQpy/dimension_reduction/grassmann_manifold/projections/__init__.py

SURGroup/UncertaintyQuantification

a94c8db47d07134ea2b3b0a3ca53ca818532c3e6

[ "MIT" ]

null

from UQpy.dimension_reduction.grassmann_manifold.projections.baseclass import * from UQpy.dimension_reduction.grassmann_manifold.projections.SVDProjection import SVDProjection

58.666667

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0

6

668f8563e979242ac195875354f3681e980c7b23

129

py

Python

pgsyn/__init__.py

Y1fanHE/kdps

c09810afb35d93018b9a7d7edb182e2f8f8a6049

[ "MIT" ]

null

pgsyn/__init__.py

Y1fanHE/kdps

c09810afb35d93018b9a7d7edb182e2f8f8a6049

[ "MIT" ]

null

pgsyn/__init__.py

Y1fanHE/kdps

c09810afb35d93018b9a7d7edb182e2f8f8a6049

[ "MIT" ]

null

''' Author: He,Yifan Date: 2022-02-16 21:46:19 LastEditors: He,Yifan LastEditTime: 2022-02-16 21:52:34 ''' __version__ = "0.0.0"

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8

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null

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6

669847a33bb7f60f3e0fd1432511574628feefdb

108

py

Python

gBlockChain/models/__init__.py

gCloudNative/gBlockChain

af7f4e977462767063aed2c11cfc00223615d4f1

[ "Apache-2.0" ]

1

2018-03-07T16:35:34.000Z

gBlockChain/models/__init__.py

gCloudNative/gBlockChain

af7f4e977462767063aed2c11cfc00223615d4f1

[ "Apache-2.0" ]

null

gBlockChain/models/__init__.py

gCloudNative/gBlockChain

af7f4e977462767063aed2c11cfc00223615d4f1

[ "Apache-2.0" ]

3

2018-03-07T06:04:03.000Z

2021-05-11T09:37:14.000Z

# -*- coding: utf-8 -*- from .user import User from .chain import BlockChain from .chain_host import Host

15.428571

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6

66b94a4f4da0f38a8ab044038a48b3375a219494

34,715

py

Python

mpcpy/units.py

YangyangFu/MPCPy

c9980cbfe7b5ea21b003c2c0bab800099dccf3f1

[ "BSD-3-Clause-LBNL" ]

96

2017-03-31T09:59:44.000Z

2022-03-23T18:39:37.000Z

mpcpy/units.py

kuzha/MPCPy

9f78aa68236f87d39a50de54978c5064f9cc13c6

[ "BSD-3-Clause-LBNL" ]

150

2017-03-03T17:28:34.000Z

2021-02-24T20:03:24.000Z

mpcpy/units.py

kuzha/MPCPy

9f78aa68236f87d39a50de54978c5064f9cc13c6

[ "BSD-3-Clause-LBNL" ]

32

2017-04-24T18:22:40.000Z

2022-03-29T17:51:20.000Z

# -*- coding: utf-8 -*- """ ``units`` classes manage the conversion of units for MPCPy variables. See documentation on ``variables`` for more information. """ from abc import ABCMeta, abstractmethod import numpy as np #%% Display unit abstract interface class _DisplayUnit(object): __metaclass__ = ABCMeta; @abstractmethod def _define_quantity(self): pass; @abstractmethod def _define_display_unit(self): pass; @abstractmethod def _convert_to_base(self): pass; @abstractmethod def _convert_from_base(self): pass; def __init__(self, variable): self._define_quantity(variable); self._define_display_unit(); #%% Display unit quantity implementation class _Boolean(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Boolean'; variable.base_unit = boolean_integer; class _Temperature(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Temperature'; variable.base_unit = K; class _Power(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Power'; variable.base_unit = W; class _Energy(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Energy'; variable.base_unit = J; class _PowerFlux(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'PowerFlux'; variable.base_unit = W_m2; class _EnergyIntensity(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'EnergyIntensity'; variable.base_unit = J_m2; class _Pressure(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Pressure'; variable.base_unit = Pa; class _DimensionlessRatio(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'DimensionlessRatio'; variable.base_unit = unit1; class _Angle(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Angle'; variable.base_unit = rad; class _Time(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Time'; variable.base_unit = s; class _Mass(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Mass'; variable.base_unit = kg; class _Length(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Length'; variable.base_unit = m; class _Area(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Area'; variable.base_unit = m2; class _Volume(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Volume'; variable.base_unit = m3; class _MassFlow(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'MassFlow'; variable.base_unit = kg_s; class _VolumetricFlow(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'VolumetricFlow'; variable.base_unit = m3_s; class _Velocity(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Velocity'; variable.base_unit = m_s; class _Illuminance(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Illuminance'; variable.base_unit = lx; class _Luminance(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Luminance'; variable.base_unit = cd_m2; class _EnergyPrice(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'EnergyPrice'; variable.base_unit = dol_J; class _PowerPrice(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'PowerPrice'; variable.base_unit = dol_W; class _SpecificHeatCapacity(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'SpecificHeatCapacity'; variable.base_unit = J_kgK; class _HeatCapacity(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'HeatCapacity'; variable.base_unit = J_K; class _HeatCapacityCoefficient(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'HeatCapacityCoefficient'; variable.base_unit = J_m2K; class _HeatResistance(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'HeatResistance'; variable.base_unit = K_W; class _HeatResistanceCoefficient(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'HeatResistanceCoefficient'; variable.base_unit = m2K_W; class _HeatTransferCoefficient(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'HeatTransferCoefficient'; variable.base_unit = W_m2K; class _Density(_DisplayUnit): def _define_quantity(self, variable): variable.quantity_name = 'Density'; variable.base_unit = kg_m3; #%% Boolean display unit implementation class boolean_integer(_Boolean): def _define_display_unit(self): self.name = 'boolean_integer'; def _convert_to_base(self, display_data): base_data = int(display_data); return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class boolean(_Boolean): def _define_display_unit(self): self.name = 'boolean'; def _convert_to_base(self, display_data): base_data = int(display_data); return base_data; def _convert_from_base(self, base_data): display_data = bool(base_data); return display_data; #%% Temperature display unit implementation class K(_Temperature): def _define_display_unit(self): self.name = 'K'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class degC(_Temperature): def _define_display_unit(self): self.name = 'degC'; def _convert_to_base(self, display_data): base_data = display_data + 273.15; return base_data; def _convert_from_base(self, base_data): display_data = base_data - 273.15; return display_data; class degF(_Temperature): def _define_display_unit(self): self.name = 'degF'; def _convert_to_base(self, display_data): base_data = (display_data-32)*5/9 + 273.15; return base_data; def _convert_from_base(self, base_data): display_data = (base_data-273.15)*9/5 + 32; return display_data; class degR(_Temperature): def _define_display_unit(self): self.name = 'degR'; def _convert_to_base(self, display_data): base_data = ((display_data - 459.67)-32)*5/9 + 273.15; return base_data; def _convert_from_base(self, base_data): display_data = (base_data-273.15)*9/5 + 32 + 459.67; return display_data; #%% Power display unit implementation class W(_Power): def _define_display_unit(self): self.name = 'W'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class kW(_Power): def _define_display_unit(self): self.name = 'kW'; def _convert_to_base(self, display_data): base_data = display_data*1e3; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1e3; return display_data; class MW(_Power): def _define_display_unit(self): self.name = 'MW'; def _convert_to_base(self, display_data): base_data = display_data*1e6; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1e6; return display_data; class Btuh(_Power): def _define_display_unit(self): self.name = 'Btuh'; def _convert_to_base(self, display_data): base_data = display_data*0.29307107; return base_data; def _convert_from_base(self, base_data): display_data = base_data/0.29307107; return display_data; class kBtuh(_Power): def _define_display_unit(self): self.name = 'kBtuh'; def _convert_to_base(self, display_data): base_data = (display_data*1e3)*0.29307107; return base_data; def _convert_from_base(self, base_data): display_data = base_data/0.29307107/1e3; return display_data; class hp(_Power): def _define_display_unit(self): self.name = 'hp'; def _convert_to_base(self, display_data): base_data = display_data*745.699872; return base_data; def _convert_from_base(self, base_data): display_data = base_data/745.699872; return display_data; #%% Energy display unit implementation class J(_Energy): def _define_display_unit(self): self.name = 'J'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class kJ(_Energy): def _define_display_unit(self): self.name = 'kJ'; def _convert_to_base(self, display_data): base_data = display_data*1e3; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1e3; return display_data; class MJ(_Energy): def _define_display_unit(self): self.name = 'MJ'; def _convert_to_base(self, display_data): base_data = display_data*1e6; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1e6; return display_data; class Btu(_Energy): def _define_display_unit(self): self.name = 'Btu'; def _convert_to_base(self, display_data): base_data = display_data*1055.05585; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1055.05585; return display_data; class kBtu(_Energy): def _define_display_unit(self): self.name = 'kBtu'; def _convert_to_base(self, display_data): base_data = (display_data*1e3)*1055.05585; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1055.05585/1e3; return display_data; class Wh(_Energy): def _define_display_unit(self): self.name = 'Wh'; def _convert_to_base(self, display_data): base_data = display_data*3600; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3600; return display_data; class kWh(_Energy): def _define_display_unit(self): self.name = 'kWh'; def _convert_to_base(self, display_data): base_data = display_data*1e3*3600; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3600/1e3; return display_data; class MWh(_Energy): def _define_display_unit(self): self.name = 'MWh'; def _convert_to_base(self, display_data): base_data = display_data*1e6*3600; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3600/1e6; return display_data; #%% Power Flux display unit implementation class W_m2(_PowerFlux): def _define_display_unit(self): self.name = 'W/m2'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class kW_m2(_PowerFlux): def _define_display_unit(self): self.name = 'kW/m2'; def _convert_to_base(self, display_data): base_data = display_data*1e3; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1e3; return display_data; class W_sf(_PowerFlux): def _define_display_unit(self): self.name = 'W/sf'; def _convert_to_base(self, display_data): base_data = display_data*10.7639; return base_data; def _convert_from_base(self, base_data): display_data = base_data/10.7639; return display_data; class kW_sf(_PowerFlux): def _define_display_unit(self): self.name = 'kW/sf'; def _convert_to_base(self, display_data): base_data = display_data*1e3*10.7639; return base_data; def _convert_from_base(self, base_data): display_data = base_data/10.7639/1e3; return display_data; class Btuh_sf(_PowerFlux): def _define_display_unit(self): self.name = 'Btuh/sf'; def _convert_to_base(self, display_data): base_data = display_data*3.154594; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3.154594; return display_data; class kBtuh_sf(_PowerFlux): def _define_display_unit(self): self.name = 'kBtuh/sf'; def _convert_to_base(self, display_data): base_data = display_data*1e3*3.154594; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3.154594/1e3; return display_data; #%% Energy Intensity display unit implementation class J_m2(_EnergyIntensity): def _define_display_unit(self): self.name = 'J/m2'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class Wh_m2(_EnergyIntensity): def _define_display_unit(self): self.name = 'Wh/m2'; def _convert_to_base(self, display_data): base_data = display_data*3600; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3600; return display_data; class kWh_m2(_EnergyIntensity): def _define_display_unit(self): self.name = 'kWh/m2'; def _convert_to_base(self, display_data): base_data = display_data*1e3*3600; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3600/1e3; return display_data; class Wh_sf(_EnergyIntensity): def _define_display_unit(self): self.name = 'Wh/sf'; def _convert_to_base(self, display_data): base_data = display_data*3600*10.7639; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3600/10.7639; return display_data; class kWh_sf(_EnergyIntensity): def _define_display_unit(self): self.name = 'kWh/sf'; def _convert_to_base(self, display_data): base_data = display_data*1e3*3600*10.7639; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3600/10.7639/1e3; return display_data; class Btu_sf(_EnergyIntensity): def _define_display_unit(self): self.name = 'Btu/sf'; def _convert_to_base(self, display_data): base_data = display_data*1055.05585*10.7639; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1055.05585/10.7639; return display_data; class kBtu_sf(_EnergyIntensity): def _define_display_unit(self): self.name = 'kBtu/sf'; def _convert_to_base(self, display_data): base_data = display_data*1e3*1055.05585*10.7639; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1055.05585/10.7639/1e3; return display_data; #%% Pressure display unit implementation class Pa(_Pressure): def _define_display_unit(self): self.name = 'Pa'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class kPa(_Pressure): def _define_display_unit(self): self.name = 'kPa'; def _convert_to_base(self, display_data): base_data = display_data*1e3; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1e3; return display_data; class MPa(_Pressure): def _define_display_unit(self): self.name = 'MPa'; def _convert_to_base(self, display_data): base_data = display_data*1e6; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1e6; return display_data; class bar(_Pressure): def _define_display_unit(self): self.name = 'bar'; def _convert_to_base(self, display_data): base_data = display_data*1e5; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1e5; return display_data; class inwg(_Pressure): def _define_display_unit(self): self.name = 'inwg'; def _convert_to_base(self, display_data): base_data = display_data*248.84; return base_data; def _convert_from_base(self, base_data): display_data = base_data/248.84; return display_data; class inHg(_Pressure): def _define_display_unit(self): self.name = 'inHg'; def _convert_to_base(self, display_data): base_data = display_data*3386.389; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3386.389; return display_data; class psi(_Pressure): def _define_display_unit(self): self.name = 'psi'; def _convert_to_base(self, display_data): base_data = display_data*6894.757; return base_data; def _convert_from_base(self, base_data): display_data = base_data/6894.757; return display_data; class atm(_Pressure): def _define_display_unit(self): self.name = 'atm'; def _convert_to_base(self, display_data): base_data = display_data*101325; return base_data; def _convert_from_base(self, base_data): display_data = base_data/101325; return display_data; #%% Dimensionless Ratio display unit implementation class unit1(_DimensionlessRatio): def _define_display_unit(self): self.name = '1'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class percent(_DimensionlessRatio): def _define_display_unit(self): self.name = 'percent'; def _convert_to_base(self, display_data): base_data = display_data/100; return base_data; def _convert_from_base(self, base_data): display_data = base_data*100; return display_data; class unit10(_DimensionlessRatio): def _define_display_unit(self): self.name = '10'; def _convert_to_base(self, display_data): base_data = display_data/10; return base_data; def _convert_from_base(self, base_data): display_data = base_data*10; return display_data; #%% Angle display unit implementation class rad(_Angle): def _define_display_unit(self): self.name = 'rad'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class deg(_Angle): def _define_display_unit(self): self.name = 'deg'; def _convert_to_base(self, display_data): base_data = display_data/180*np.pi; return base_data; def _convert_from_base(self, base_data): display_data = base_data*180/np.pi; return display_data; #%% Time display unit implementation class s(_Time): def _define_display_unit(self): self.name = 's'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class minute(_Time): def _define_display_unit(self): self.name = 'min'; def _convert_to_base(self, display_data): base_data = display_data*60; return base_data; def _convert_from_base(self, base_data): display_data = base_data/60; return display_data; class hour(_Time): def _define_display_unit(self): self.name = 'h'; def _convert_to_base(self, display_data): base_data = display_data*3600; return base_data; def _convert_from_base(self, base_data): display_data = base_data/3600; return display_data; class day(_Time): def _define_display_unit(self): self.name = 'd'; def _convert_to_base(self, display_data): base_data = display_data*86400; return base_data; def _convert_from_base(self, base_data): display_data = base_data/86400; return display_data; #%% Mass display unit implementation class kg(_Mass): def _define_display_unit(self): self.name = 'kg'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% Length display unit implementation class m(_Length): def _define_display_unit(self): self.name = 'm'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class cm(_Length): def _define_display_unit(self): self.name = 'cm'; def _convert_to_base(self, display_data): base_data = display_data/1e2; return base_data; def _convert_from_base(self, base_data): display_data = base_data*1e2; return display_data; class mm(_Length): def _define_display_unit(self): self.name = 'mm'; def _convert_to_base(self, display_data): base_data = display_data/1e3; return base_data; def _convert_from_base(self, base_data): display_data = base_data*1e3; return display_data; class km(_Length): def _define_display_unit(self): self.name = 'km'; def _convert_to_base(self, display_data): base_data = display_data*1e3; return base_data; def _convert_from_base(self, base_data): display_data = base_data/1e3; return display_data; class inch(_Length): def _define_display_unit(self): self.name = 'inch'; def _convert_to_base(self, display_data): base_data = display_data*0.0254; return base_data; def _convert_from_base(self, base_data): display_data = base_data/0.0254; return display_data; class ft(_Length): def _define_display_unit(self): self.name = 'ft'; def _convert_to_base(self, display_data): base_data = display_data*12*0.0254; return base_data; def _convert_from_base(self, base_data): display_data = base_data/0.0254/12; return display_data; class yd(_Length): def _define_display_unit(self): self.name = 'yd'; def _convert_to_base(self, display_data): base_data = display_data*12*0.0254*3; return base_data; def _convert_from_base(self, base_data): display_data = base_data/0.0254/12/3; return display_data; #%% Area display unit implementation class m2(_Area): def _define_display_unit(self): self.name = 'm2'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class sf(_Area): def _define_display_unit(self): self.name = 'sf'; def _convert_to_base(self, display_data): base_data = display_data/10.7639; return base_data; def _convert_from_base(self, base_data): display_data = base_data*10.7639; return display_data; #%% Volume display unit implementation class m3(_Volume): def _define_display_unit(self): self.name = 'm3'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class cf(_Volume): def _define_display_unit(self): self.name = 'cf'; def _convert_to_base(self, display_data): base_data = display_data/35.3147; return base_data; def _convert_from_base(self, base_data): display_data = base_data*35.3147; return display_data; #%% Mass Flow display unit implementation class kg_s(_MassFlow): def _define_display_unit(self): self.name = 'kg/s'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% Volumetric Flow display unit implementation class m3_s(_VolumetricFlow): def _define_display_unit(self): self.name = 'm3/s'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class cfm(_VolumetricFlow): def _define_display_unit(self): self.name = 'cfm'; def _convert_to_base(self, display_data): base_data = display_data/2118.88; return base_data; def _convert_from_base(self, base_data): display_data = base_data*2118.88; return display_data; #%% Velocity display unit implementation class m_s(_Velocity): def _define_display_unit(self): self.name = 'm/s'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class mph(_Velocity): def _define_display_unit(self): self.name = 'mph'; def _convert_to_base(self, display_data): base_data = display_data*0.44704; return base_data; def _convert_from_base(self, base_data): display_data = base_data/0.44704; return display_data; class km_h(_Velocity): def _define_display_unit(self): self.name = 'km/h'; def _convert_to_base(self, display_data): base_data = display_data*0.277778; return base_data; def _convert_from_base(self, base_data): display_data = base_data/0.277778; return display_data; #%% Illuminance display unit implementation class lx(_Illuminance): def _define_display_unit(self): self.name = 'lx'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class fc(_Illuminance): def _define_display_unit(self): self.name = 'fc'; def _convert_to_base(self, display_data): base_data = display_data*10.764 ; return base_data; def _convert_from_base(self, base_data): display_data = base_data/10.764 ; return display_data; #%% Luminance display unit implementation class cd_m2(_Luminance): def _define_display_unit(self): self.name = 'cd/m2'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; class nt(_Luminance): def _define_display_unit(self): self.name = 'nt'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% EnergyPrice unit implementation class cents_kWh(_EnergyPrice): def _define_display_unit(self): self.name = 'cents/kWh'; def _convert_to_base(self, display_data): base_data = display_data/3.6e8; return base_data; def _convert_from_base(self, base_data): display_data = base_data*3.6e8; return display_data; class dol_kWh(_EnergyPrice): def _define_display_unit(self): self.name = '$/kWh'; def _convert_to_base(self, display_data): base_data = display_data/3.6e6; return base_data; def _convert_from_base(self, base_data): display_data = base_data*3.6e6; return display_data; class dol_MWh(_EnergyPrice): def _define_display_unit(self): self.name = '$/MWh'; def _convert_to_base(self, display_data): base_data = display_data/3.6e9; return base_data; def _convert_from_base(self, base_data): display_data = base_data*3.6e9; return display_data; class dol_J(_EnergyPrice): def _define_display_unit(self): self.name = '$/J'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% PowerPrice unit implementation class cents_kW(_PowerPrice): def _define_display_unit(self): self.name = 'cents/kW'; def _convert_to_base(self, display_data): base_data = display_data/1e5; return base_data; def _convert_from_base(self, base_data): display_data = base_data*1e5; return display_data; class dol_kW(_PowerPrice): def _define_display_unit(self): self.name = '$/kW'; def _convert_to_base(self, display_data): base_data = display_data/1e3; return base_data; def _convert_from_base(self, base_data): display_data = base_data*1e3; return display_data; class dol_MW(_PowerPrice): def _define_display_unit(self): self.name = '$/MW'; def _convert_to_base(self, display_data): base_data = display_data/1e6; return base_data; def _convert_from_base(self, base_data): display_data = base_data*1e6; return display_data; class dol_W(_PowerPrice): def _define_display_unit(self): self.name = '$/W'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% Specific heat capacity unit implementation class J_kgK(_SpecificHeatCapacity): def _define_display_unit(self): self.name = 'J/(kg.K)'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% Heat capacity unit implementation class J_K(_HeatCapacity): def _define_display_unit(self): self.name = 'J/K'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% Heat capacity coefficient unit implementation class J_m2K(_HeatCapacityCoefficient): def _define_display_unit(self): self.name = 'J/(m2.K)'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% Heat resistance unit implementation class K_W(_HeatResistance): def _define_display_unit(self): self.name = 'K/W'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% Heat resistance coefficient unit implementation class m2K_W(_HeatResistanceCoefficient): def _define_display_unit(self): self.name = '(m2.K)/W'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% Heat transfer coefficient unit implementation class W_m2K(_HeatTransferCoefficient): def _define_display_unit(self): self.name = 'W/(m2.K)'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data; #%% Density unit implementation class kg_m3(_Density): def _define_display_unit(self): self.name = 'kg/m3'; def _convert_to_base(self, display_data): base_data = display_data; return base_data; def _convert_from_base(self, base_data): display_data = base_data; return display_data;

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null

0

1

0

1

0

6

66e457e80b06199c470dd25e81c8f5579ce1d2e5

114

py

Python

inscrawler/secret.py

hdson07/Insta_Printer

de9be224d59eefa0ad6c0f0bbce95c9103abca66

[ "MIT" ]

null

inscrawler/secret.py

hdson07/Insta_Printer

de9be224d59eefa0ad6c0f0bbce95c9103abca66

[ "MIT" ]

null

inscrawler/secret.py

hdson07/Insta_Printer

de9be224d59eefa0ad6c0f0bbce95c9103abca66

[ "MIT" ]

null

import os username = os.environ.get('USERNAME', 'duckeely') password = os.environ.get('PASSWORD', 'heeduck!@07')

22.8

52

0.710526

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114

5.4

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6

dd07d2fcc02648b0c8c069fed9fd0a0dda9b1188

37

py

Python

echobot/plugins/admin/cli/__init__.py

jks15satoshi/echobot

b8f980b330123068f2e9edaa7fd143e70e0ac0fe

[ "MIT" ]

9

2021-01-21T18:08:11.000Z

2021-04-29T13:40:24.000Z

echobot/plugins/admin/cli/__init__.py

jks15satoshi/echobot

b8f980b330123068f2e9edaa7fd143e70e0ac0fe

[ "MIT" ]

16

2021-01-22T11:41:11.000Z

2021-08-23T09:40:56.000Z

echobot/plugins/admin/cli/__init__.py

jks15satoshi/echobot

b8f980b330123068f2e9edaa7fd143e70e0ac0fe

[ "MIT" ]

1

2021-02-22T17:05:06.000Z

"""群组管理 (CLI)""" from . import title

12.333333

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6

06ba802fa4223937e591a95e4671256bbe77e5de

31

py

Python

antelope_core/data_sources/uslci/__init__.py

AntelopeLCA/core

ee40685add52ba41a462e2147fe8c377c6ba2a80

[ "BSD-3-Clause" ]

1

2021-10-06T18:42:49.000Z

antelope_core/data_sources/uslci/__init__.py

AntelopeLCA/core

ee40685add52ba41a462e2147fe8c377c6ba2a80

[ "BSD-3-Clause" ]

6

2021-01-09T08:56:46.000Z

2022-03-29T08:26:21.000Z

antelope_core/data_sources/uslci/__init__.py

AntelopeLCA/core

ee40685add52ba41a462e2147fe8c377c6ba2a80

[ "BSD-3-Clause" ]

null

from .uslci import UsLciConfig

15.5

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0

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6

06f135c7b53ac2e2e0719b61a6f1ae5dc60d5dcc

40

py

Python

src/__init__.py

ikramelk/maneuver_anticipation

94fa67136125fe22402d18b3ed8c83295981235d

[ "BSD-3-Clause" ]

null

src/__init__.py

ikramelk/maneuver_anticipation

94fa67136125fe22402d18b3ed8c83295981235d

[ "BSD-3-Clause" ]

null

src/__init__.py

ikramelk/maneuver_anticipation

94fa67136125fe22402d18b3ed8c83295981235d

[ "BSD-3-Clause" ]

null

from .views import maneuverAnticipation

20

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660ad07477290f9ed344f58c5c2029f00a79bc5b

47

py

Python

scclient/__init__.py

Jnesselr/scclient

7b9ddc1e223384bbc8f462bde68668dbfbb7583d

[ "MIT" ]

null

scclient/__init__.py

Jnesselr/scclient

7b9ddc1e223384bbc8f462bde68668dbfbb7583d

[ "MIT" ]

null

scclient/__init__.py

Jnesselr/scclient

7b9ddc1e223384bbc8f462bde68668dbfbb7583d

[ "MIT" ]

null

from scclient.socket_client import SocketClient

47

0.914894

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47

1

47

0.954545

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null

0

1

0

1

0

1

0

6

66277db8801fb76c6f8b21e54b2d0ef9f77d6c76

95

py

Python

teste.py

JoaoGasparini/ADS2D

9e8559acbbf36304a5a406744d3304be10468fbe

[ "Apache-2.0" ]

null

teste.py

JoaoGasparini/ADS2D

9e8559acbbf36304a5a406744d3304be10468fbe

[ "Apache-2.0" ]

1

2020-04-03T22:29:43.000Z

teste.py

JoaoGasparini/ADS2D

9e8559acbbf36304a5a406744d3304be10468fbe

[ "Apache-2.0" ]

null

import pytest from principal import soma def test_soma(): assert soma(3, 2) == 5

15.833333

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95

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null

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6

b0c45d7e92f7fc61bbaadd63c379c412d973233b

14,562

py

Python

mymoney/transactions/tests/test_models.py

ychab/mymoney-server

40dc9fdd08b3561287a9153342b25c58de8ad8ce

[ "BSD-3-Clause" ]

6

2015-12-11T13:36:27.000Z

2018-10-17T03:08:15.000Z

mymoney/transactions/tests/test_models.py

ychab/mymoney-server

40dc9fdd08b3561287a9153342b25c58de8ad8ce

[ "BSD-3-Clause" ]

2

2016-06-12T12:42:47.000Z

2017-12-12T14:05:14.000Z

mymoney/transactions/tests/test_models.py

ychab/mymoney-server

40dc9fdd08b3561287a9153342b25c58de8ad8ce

[ "BSD-3-Clause" ]

1

2022-02-21T21:20:51.000Z

import datetime from decimal import Decimal from unittest import mock from django.test import TestCase from mymoney.accounts.factories import AccountFactory from mymoney.accounts.models import Account from mymoney.tags.factories import TagFactory from ..factories import TransactionFactory from ..models import Transaction class TransactionModelTestCase(TestCase): def test_status_inactive_create(self): account = AccountFactory(balance=100) TransactionFactory( account=account, amount=Decimal('150'), status=Transaction.STATUS_INACTIVE ) account.refresh_from_db() self.assertEqual(account.balance, Decimal(100)) def test_status_inactive_update(self): account = AccountFactory(balance=100) transaction = TransactionFactory( account=account, amount=Decimal('150'), ) account.refresh_from_db() self.assertEqual(account.balance, Decimal('250')) transaction.status = Transaction.STATUS_INACTIVE transaction.amount = Decimal('180') transaction.save() account.refresh_from_db() self.assertEqual(account.balance, Decimal('250')) def test_force_currency(self): account = AccountFactory(currency='EUR') transaction = TransactionFactory( account=account, currency='USD', ) self.assertEqual(transaction.currency, 'EUR') def test_insert(self): account = AccountFactory(balance=-10) TransactionFactory( account=account, amount='15.59', ) account.refresh_from_db() self.assertEqual(account.balance, Decimal('5.59')) def test_insert_fail(self): account = AccountFactory(balance=0) with mock.patch.object(Transaction, 'save', side_effect=Exception('Bang')): with self.assertRaises(Exception): TransactionFactory( account=account, amount='15.59', ) account.refresh_from_db() self.assertEqual(account.balance, 0) def test_save_account_update_fail(self): account = AccountFactory(balance=0) with mock.patch.object(Account, 'save', side_effect=Exception('Boom')): with self.assertRaises(Exception): TransactionFactory( account=account, amount='15.59', ) account.refresh_from_db() self.assertEqual(account.balance, 0) def test_update(self): account = AccountFactory(balance=-10) transaction = TransactionFactory( account=account, amount='15.59', ) transaction.refresh_from_db() transaction.amount += Decimal('14.41') transaction.save() account.refresh_from_db() self.assertEqual(account.balance, Decimal('20')) def test_update_fail(self): account = AccountFactory(balance=0) transaction = TransactionFactory( account=account, amount='-10', ) account.refresh_from_db() self.assertEqual(account.balance, Decimal(-10)) with mock.patch.object(Transaction, 'save', side_effect=Exception('Bang')): with self.assertRaises(Exception): transaction.amount = -50 transaction.save() account.refresh_from_db() self.assertEqual(account.balance, Decimal(-10)) def test_status_inactive_delete(self): account = AccountFactory(balance=100) transaction = TransactionFactory( account=account, amount=Decimal('150'), status=Transaction.STATUS_INACTIVE, ) account.refresh_from_db() self.assertEqual(account.balance, Decimal('100')) TransactionFactory(account=account, amount=Decimal('50')) account.refresh_from_db() self.assertEqual(account.balance, Decimal('150')) transaction.delete() account.refresh_from_db() self.assertEqual(account.balance, Decimal('150')) def test_delete(self): account = AccountFactory(balance=50) transaction = TransactionFactory( account=account, amount='-25', ) account.refresh_from_db() self.assertEqual(account.balance, Decimal(25)) transaction.delete() account.refresh_from_db() self.assertEqual(account.balance, Decimal(50)) def test_delete_fail(self): account = AccountFactory(balance=50) transaction = TransactionFactory( account=account, amount='-25', ) with mock.patch.object(Transaction, 'delete', side_effect=Exception('Bang')): with self.assertRaises(Exception): transaction.delete() account.refresh_from_db() self.assertEqual(account.balance, Decimal(25)) def test_delete_account_update_fail(self): account = AccountFactory(balance=50) transaction = TransactionFactory( account=account, amount='-25', ) transaction_pk = transaction.pk with mock.patch.object(Account, 'save', side_effect=Exception('Boom')): with self.assertRaises(Exception): transaction.delete() self.assertTrue(Transaction.objects.get(pk=transaction_pk)) account.refresh_from_db() self.assertEqual(account.balance, Decimal(25)) class TransactionManagerTestCase(TestCase): def test_current_balance_none(self): account = AccountFactory(balance=0) self.assertEqual( Transaction.objects.get_current_balance(account), 0, ) def test_current_balance_other_accounts(self): account = AccountFactory(balance=0) TransactionFactory( account=account, amount=-15, date=datetime.date.today() - datetime.timedelta(5), ) TransactionFactory( amount=-15, date=datetime.date.today() - datetime.timedelta(5), ) self.assertEqual( Transaction.objects.get_current_balance(account), Decimal('-15'), ) def test_current_balance_inactive(self): account = AccountFactory(balance=0) TransactionFactory( account=account, amount=-15, date=datetime.date.today() - datetime.timedelta(5), ) TransactionFactory( account=account, amount=-15, date=datetime.date.today() - datetime.timedelta(5), status=Transaction.STATUS_INACTIVE, ) self.assertEqual( Transaction.objects.get_current_balance(account), Decimal('-15'), ) def test_current_balance_future(self): account = AccountFactory(balance=0) TransactionFactory( account=account, amount=-15, date=datetime.date.today() - datetime.timedelta(5), ) TransactionFactory( account=account, amount=-15, date=datetime.date.today() + datetime.timedelta(5), ) self.assertEqual( Transaction.objects.get_current_balance(account), Decimal('-15'), ) def test_current_balance(self): account = AccountFactory(balance=0) TransactionFactory( account=account, amount=-15, date=datetime.date.today() - datetime.timedelta(5), ) TransactionFactory( account=account, amount=-15, date=datetime.date.today() - datetime.timedelta(5), ) TransactionFactory( account=account, amount=40, date=datetime.date.today() - datetime.timedelta(5), ) self.assertEqual( Transaction.objects.get_current_balance(account), Decimal('10'), ) def test_reconciled_balance_none(self): account = AccountFactory(balance=0) self.assertEqual( Transaction.objects.get_reconciled_balance(account), 0, ) def test_reconciled_balance_other_account(self): account = AccountFactory(balance=0) TransactionFactory( account=account, amount=-15, reconciled=True, ) TransactionFactory( amount=-15, reconciled=True, ) self.assertEqual( Transaction.objects.get_reconciled_balance(account), Decimal('-15'), ) def test_reconciled_balance_unreconciled(self): account = AccountFactory(balance=0) TransactionFactory( account=account, amount=-15, reconciled=True, ) TransactionFactory( account=account, amount=-15, reconciled=False, ) self.assertEqual( Transaction.objects.get_reconciled_balance(account), Decimal('-15'), ) def test_reconciled_balance_inactive(self): account = AccountFactory(balance=0) TransactionFactory( account=account, amount=-15, reconciled=True, ) TransactionFactory( account=account, amount=-15, reconciled=True, status=Transaction.STATUS_INACTIVE, ) self.assertEqual( Transaction.objects.get_reconciled_balance(account), Decimal('-15'), ) def test_reconciled_balance(self): account = AccountFactory(balance=0) TransactionFactory( account=account, amount=-15, reconciled=True, ) TransactionFactory( account=account, amount=-15, reconciled=True, ) TransactionFactory( account=account, amount=40, reconciled=True, ) self.assertEqual( Transaction.objects.get_reconciled_balance(account), Decimal('10'), ) def test_total_unscheduled_period_none(self): account = AccountFactory(balance=0) self.assertEqual( Transaction.objects.get_total_unscheduled_period(account), 0, ) @mock.patch( 'mymoney.transactions.models.timezone.now', return_value=datetime.date(2015, 10, 26)) def test_total_unscheduled_period_other_account(self, mock_tz): account = AccountFactory(balance=0) TransactionFactory( account=account, date=datetime.date(2015, 10, 26), amount=-15, scheduled=False, ) TransactionFactory( date=datetime.date(2015, 10, 26), amount=-15, scheduled=False, ) self.assertEqual( Transaction.objects.get_total_unscheduled_period(account), Decimal('-15'), ) @mock.patch( 'mymoney.transactions.models.timezone.now', return_value=datetime.date(2015, 10, 26)) def test_total_unscheduled_period_out_of_ranges(self, mock_tz): account = AccountFactory(balance=0) TransactionFactory( account=account, date=datetime.date(2015, 10, 26), amount=-15, scheduled=False, ) TransactionFactory( account=account, date=datetime.date(2015, 11, 26), amount=-15, scheduled=False, ) self.assertEqual( Transaction.objects.get_total_unscheduled_period(account), Decimal('-15'), ) @mock.patch( 'mymoney.transactions.models.timezone.now', return_value=datetime.date(2015, 10, 26)) def test_total_unscheduled_period_scheduled(self, mock_tz): account = AccountFactory(balance=0) TransactionFactory( account=account, date=datetime.date(2015, 10, 26), amount=-15, scheduled=False, ) TransactionFactory( account=account, date=datetime.date(2015, 10, 26), amount=-15, scheduled=True, ) self.assertEqual( Transaction.objects.get_total_unscheduled_period(account), Decimal('-15'), ) @mock.patch( 'mymoney.transactions.models.timezone.now', return_value=datetime.date(2015, 10, 26)) def test_total_unscheduled_period_inactive(self, mock_tz): account = AccountFactory(balance=0) TransactionFactory( account=account, date=datetime.date(2015, 10, 26), amount=-15, scheduled=False, ) TransactionFactory( account=account, date=datetime.date(2015, 10, 26), amount=-15, scheduled=False, status=Transaction.STATUS_INACTIVE, ) self.assertEqual( Transaction.objects.get_total_unscheduled_period(account), Decimal('-15'), ) @mock.patch( 'mymoney.transactions.models.timezone.now', return_value=datetime.date(2015, 10, 26)) def test_total_unscheduled_period(self, mock_tz): account = AccountFactory(balance=0) TransactionFactory( account=account, date=datetime.date(2015, 10, 26), amount=-15, scheduled=False, ) TransactionFactory( account=account, date=datetime.date(2015, 10, 26), amount=-15, scheduled=False, ) TransactionFactory( account=account, date=datetime.date(2015, 10, 26), amount=40, scheduled=False, ) self.assertEqual( Transaction.objects.get_total_unscheduled_period(account), Decimal('10'), ) class RelationshipTestCase(TestCase): def test_delete_account(self): account = AccountFactory() tag = TagFactory() transaction = TransactionFactory(account=account, tag=tag) account.delete() with self.assertRaises(Transaction.DoesNotExist): transaction.refresh_from_db() # Should not be deleted. tag.refresh_from_db() self.assertTrue(tag)

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0.586389

1,300

14,562

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0.119703

0.15322

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b0f7d802f7d3ad6063da39d829536f4ff91ea471

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py

Python

tests/test_import.py

rexyeah/jira-cli

6a03e904b0aca4905ea8f5c22239f84d7a82b32d

[ "MIT" ]

125

2015-02-05T01:06:07.000Z

2021-12-08T19:20:26.000Z

tests/test_import.py

lewis6991/jira-cli

a56540231fc189ac3823df97bd4d30272430446e

[ "MIT" ]

90

2015-02-12T12:41:15.000Z

2022-02-21T02:07:17.000Z

tests/test_import.py

lewis6991/jira-cli

a56540231fc189ac3823df97bd4d30272430446e

[ "MIT" ]

68

2015-01-30T14:17:29.000Z

2021-05-20T17:22:12.000Z

def test_basic_import(): import jiracli.cli

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9feb0793dd7380188a0dabb08c8692858781a0e3

154

py

Python

churches/selectors.py

mennonitengemeinde/church_site

ae9ef5f0f78811cecd734705339511dc0efb8340

[ "MIT" ]

null

churches/selectors.py

mennonitengemeinde/church_site

ae9ef5f0f78811cecd734705339511dc0efb8340

[ "MIT" ]

44

2020-05-13T20:15:26.000Z

2022-03-04T02:58:58.000Z

churches/selectors.py

mennonitengemeinde/church_site

ae9ef5f0f78811cecd734705339511dc0efb8340

[ "MIT" ]

4

2020-06-05T17:59:52.000Z

2021-02-06T19:09:43.000Z

from accounts.models import User from churches.models import Church def get_member_churches(user: User): return Church.objects.filter(members=user)

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6

b01655e461b6acb4b20292b3bae16ef22c8d0416

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py

Python

scripts/domain_plot.py

zeeshansayyed/multiparser

f77e7c688ec51bc09f52441900fbe27c5c62f6bc

[ "MIT" ]

null

scripts/domain_plot.py

zeeshansayyed/multiparser

f77e7c688ec51bc09f52441900fbe27c5c62f6bc

[ "MIT" ]

null

scripts/domain_plot.py

zeeshansayyed/multiparser

f77e7c688ec51bc09f52441900fbe27c5c62f6bc

[ "MIT" ]

1

2021-09-10T14:58:02.000Z

from pathlib import Path import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from matplotlib.lines import Line2D results_dir = Path('results/domain') result_file = 'de_word.csv' ############# # Common for all plots ############# linestyles = ['-', '-', '-', '--', '--', '--'] color = ['red', 'blue', '#00FF00', 'red', 'blue', '#00FF00'] plt.rcParams['font.size'] = '13' # # ############### # # # German Plots # # ############### # # figure, axes = plt.subplots(1, 3, figsize=(20, 5)) # # full_data = pd.read_csv(results_dir / result_file, index_col=0) # # data = full_data.iloc[0:5].astype('float') # # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # # g = sns.pointplot(ax=axes[0], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # # g.set(xlim=(-0.1, 4.1)) # # g.get_legend().remove() # # g.title.set_text('Word') # # g.set_xlabel('GSD Training Size') # # g.set_ylabel('LAS', fontsize=12) # # data = full_data.iloc[6:11].astype('float') # # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # # data.columns = column_names # # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # # g = sns.pointplot(ax=axes[1], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # # g.set(xlim=(-0.1, 4.1)) # # g.get_legend().remove() # # g.title.set_text('Word + Tag') # # g.set_xlabel('GSD Training Size') # # g.set_ylabel('LAS', fontsize=12) # # data = full_data.iloc[12:17].astype('float') # # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # # data.columns = column_names # # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # # g = sns.pointplot(ax=axes[2], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # # g.set(xlim=(-0.1, 4.1)) # # g.get_legend().remove() # # g.title.set_text('Word + Tag + Bert') # # g.set_xlabel('GSD Training Size') # # g.set_ylabel('LAS', fontsize=12) # # plt.tight_layout() # # plt.savefig(f'results/domain/german.png') # ############## # #Italian plots (Twittiro) # ############## # figure, axes = plt.subplots(1, 3, figsize=(20, 5)) # full_data = pd.read_csv(results_dir / result_file, index_col=0) # data = full_data.iloc[18:23].astype('float') # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # g = sns.pointplot(ax=axes[0], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # g.set(xlim=(-0.1, 4.1)) # g.get_legend().remove() # g.title.set_text('Word') # g.set_xlabel('ISDT Training Size') # g.set_ylabel('LAS', fontsize=12) # data = full_data.iloc[24:29].astype('float') # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # data.columns = column_names # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # g = sns.pointplot(ax=axes[1], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # g.set(xlim=(-0.1, 4.1)) # g.get_legend().remove() # g.title.set_text('Word+POS') # g.set_xlabel('ISDT Training Size') # g.set_ylabel('LAS', fontsize=12) # data = full_data.iloc[30:35].astype('float') # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # data.columns = column_names # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # g = sns.pointplot(ax=axes[2], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # g.set(xlim=(-0.1, 4.1)) # g.get_legend().remove() # g.title.set_text('Word+POS+BERT') # g.set_xlabel('ISDT Training Size') # g.set_ylabel('LAS', fontsize=12) # plt.tight_layout() # plt.savefig(f'results/domain/italian_tw.png') # # ############## # # #Italian plots (Postwita) # # ############## # figure, axes = plt.subplots(1, 3, figsize=(20, 5)) # full_data = pd.read_csv(results_dir / result_file, index_col=0) # data = full_data.iloc[36:41].astype('float') # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # g = sns.pointplot(ax=axes[0], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # g.set(xlim=(-0.1, 4.1)) # g.get_legend().remove() # g.title.set_text('Word') # g.set_xlabel('ISDT Training Size') # g.set_ylabel('LAS', fontsize=12) # data = full_data.iloc[42:47].astype('float') # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # data.columns = column_names # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # g = sns.pointplot(ax=axes[1], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # g.set(xlim=(-0.1, 4.1)) # g.get_legend().remove() # g.title.set_text('Word+POS') # g.set_xlabel('ISDT Training Size') # g.set_ylabel('LAS', fontsize=12) # data = full_data.iloc[48:53].astype('float') # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # data.columns = column_names # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # g = sns.pointplot(ax=axes[2], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # g.set(xlim=(-0.1, 4.1)) # g.get_legend().remove() # g.title.set_text('Word+POS+BERT') # g.set_xlabel('ISDT Training Size') # g.set_ylabel('LAS', fontsize=12) # plt.tight_layout() # plt.savefig(f'results/domain/italian_po.png') # # ############# # # # Legend # # ############# # # from matplotlib.lines import Line2D # # legend_elements = [ # # Line2D([0], [0], color='r', lw=2, linestyle='-', label='Single Task Baseline (ISDT/GSD)'), # # Line2D([0], [0], color='#00FF00', lw=2, linestyle='-', label='Shared MTL (ISDT/GSD)'), # # Line2D([0], [0], color='b', lw=2, linestyle='-', label='Unshared MTL (ISDT/GSD)'), # # Line2D([0], [0], color='r', lw=2, linestyle='--', label='Single Task Baseline (Twitter)'), # # Line2D([0], [0], color='#00FF00', lw=2, linestyle='--', label='Shared MTL (Twitter)'), # # Line2D([0], [0], color='b', lw=2, linestyle='--', label='Unshared MTL (Twitter)'), # # ] # # fig, ax = plt.subplots() # # ax.legend(handles=legend_elements, loc='center', handlelength=3) # # ax.set_axis_off() # # plt.tight_layout() # # plt.savefig('results/domain/legend.png', bbox_inches='tight', pad_inches=0) # ############### # # German Plots with Legend # ############### # figure, axes = plt.subplots(2, 2, figsize=(20, 12)) # full_data = pd.read_csv(results_dir / result_file, index_col=0) # data = full_data.iloc[0:5].astype('float') # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # g = sns.pointplot(ax=axes[0,0], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # g.set(xlim=(-0.1, 4.1)) # g.get_legend().remove() # g.title.set_text('Word') # g.set_xlabel('GSD Training Size') # g.set_ylabel('LAS', fontsize=12) # data = full_data.iloc[6:11].astype('float') # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # data.columns = column_names # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # g = sns.pointplot(ax=axes[0,1], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # g.set(xlim=(-0.1, 4.1)) # g.get_legend().remove() # g.title.set_text('Word+POS') # g.set_xlabel('GSD Training Size') # g.set_ylabel('LAS', fontsize=12) # data = full_data.iloc[12:17].astype('float') # column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] # data.columns = column_names # df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') # g = sns.pointplot(ax=axes[1,0], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) # g.set(xlim=(-0.1, 4.1)) # g.get_legend().remove() # g.title.set_text('Word+POS+BERT') # g.set_xlabel('GSD Training Size') # g.set_ylabel('LAS', fontsize=12) # legend_elements = [ # Line2D([0], [0], color='r', lw=2, linestyle='-', label='Single Task Baseline (ISDT/GSD)'), # Line2D([0], [0], color='#00FF00', lw=2, linestyle='-', label='Shared MTL (ISDT/GSD)'), # Line2D([0], [0], color='b', lw=2, linestyle='-', label='Unshared MTL (ISDT/GSD)'), # Line2D([0], [0], color='r', lw=2, linestyle='--', label='Single Task Baseline (Twitter)'), # Line2D([0], [0], color='#00FF00', lw=2, linestyle='--', label='Shared MTL (Twitter)'), # Line2D([0], [0], color='b', lw=2, linestyle='--', label='Unshared MTL (Twitter)'), # ] # plt.rcParams['font.size'] = '25' # axes[1,1].legend(handles=legend_elements, loc='center', handlelength=3) # axes[1,1].set_axis_off() # plt.tight_layout() # plt.savefig(f'results/domain/german_legend.png') # ############## # #Italian plots (Postwita) # ############## figure, axes = plt.subplots(1, 2, figsize=(15, 5)) full_data = pd.read_csv(results_dir / result_file, index_col=0) plt.rcParams['font.size'] = '13' data = full_data.iloc[54:58].astype('float') column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') g = sns.pointplot(ax=axes[0], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) g.set(xlim=(-0.1, 3.1)) g.get_legend().remove() g.title.set_text('Word+POS') g.set_xlabel('PoSTWITA Training Size') g.set_ylabel('LAS', fontsize=12) data = full_data.iloc[59:64].astype('float') column_names = ['stl', 'mtl-noshare', 'mtl-share', 'tw-stl', 'tw-mtl-noshare', 'tw-mtl-share'] data.columns = column_names df = data.iloc[0:5].reset_index().melt('index', var_name='cols', value_name='vals') g = sns.pointplot(ax=axes[1], x="index", y="vals", hue='cols', data=df, kind='point', linestyles=linestyles, palette=color) g.set(xlim=(-0.1, 4.1)) g.get_legend().remove() g.title.set_text('Word+POS') g.set_xlabel('ISDT Training Size') g.set_ylabel('LAS', fontsize=12) plt.tight_layout() plt.savefig(f'results/domain/domain_diff.png')

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b0248b20a13ddea588f7d4529f299eb934116049

23

py

Python

python-efl-backsupport/usr/lib/python2.7/dist-packages/edje/__init__.py

Deepspeed/bodhi3packages

2d1c09780694ff7355e692137e33594836bc80cc

[ "BSD-3-Clause" ]

null

python-efl-backsupport/usr/lib/python2.7/dist-packages/edje/__init__.py

Deepspeed/bodhi3packages

2d1c09780694ff7355e692137e33594836bc80cc

[ "BSD-3-Clause" ]

null

python-efl-backsupport/usr/lib/python2.7/dist-packages/edje/__init__.py

Deepspeed/bodhi3packages

2d1c09780694ff7355e692137e33594836bc80cc

[ "BSD-3-Clause" ]

null

from efl.edje import *

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c66949cc94854069d19d0a0a3a2e90d0b0e2b5e3

4,934

py

Python

closedexpressions/excess_time_statistics.py

uit-cosmo/fpp-closed-expresions

1cfd487242416aa7c8fd7318f5c51042e0737423

[ "MIT" ]

1

2022-03-07T18:46:46.000Z

closedexpressions/excess_time_statistics.py

uit-cosmo/fpp-closed-expressions

1cfd487242416aa7c8fd7318f5c51042e0737423

[ "MIT" ]

null

closedexpressions/excess_time_statistics.py

uit-cosmo/fpp-closed-expressions

1cfd487242416aa7c8fd7318f5c51042e0737423

[ "MIT" ]

null

""" Excess time statisitics In all cases, the signal z should have been normalized as (z-<z>)/z_rms """ import numpy as np import mpmath as mm import warnings def eT(X, g): """ Returns the fraction of time above threshold for the normalized shot noise process X. Input: X: the values of the shot noise process, 1d numpy array g: Intermittency parameter, float Output: F: The fraction of time above threshold. The total time is T*F. """ F = np.ones(len(X)) assert g > 0 g = mm.mpf(g) for i in range(len(X)): if X[i] > -np.sqrt(g): F[i] = mm.gammainc(g, a=np.sqrt(g) * X[i] + g, regularized=True) return F def eX(X, g, l): """ Returns the rate of upwards level crossings above threshold for the normalized shot noise process X. Input: X: the values of the shot noise process, 1d numpy array g: Intermittency parameter, float l: pulse asymmetry parameter, float. Output: F: The rate of upward crossings above threshold. The total number of crossings is td*F/T. """ assert g > 0 assert l >= 0 assert l <= 1 l = mm.mpf(l) g = mm.mpf(g) F = np.zeros(len(X)) def eXtmp(x, g, l): if (l > 0) & (l < 1): return ( ( l ** (g * l - 1) * (1 - l) ** (g * (1 - l) - 1) * g ** (g / 2 - 1) / (mm.gamma(g * l) * mm.gamma(g * (1 - l))) ) * (x + np.sqrt(g)) ** g * mm.exp(-np.sqrt(g) * x - g) ) else: return ( g ** (g / 2) * (x + np.sqrt(g)) ** g * mm.exp(-np.sqrt(g) * x - g) / mm.gamma(g) ) for i in range(len(X)): if X[i] > -np.sqrt(g): F[i] = eXtmp(X[i], g, l) return F def eX_l0(X, g): """ Returns the rate of upwards level crossings above threshold for the normalized shot noise process X with a one sided pulse shape (l=0). Input: X: the values of the shot noise process, 1d numpy array g: Intermittency parameter, float Output: F: The rate of upward crossings above threshold. The total number of crossings is td*F/T. """ warnings.warn("The functionality of eX_l0 has been added to eX.") assert g > 0 g = mm.mpf(g) F = np.zeros(len(X)) for i in range(len(X)): if X[i] > -np.sqrt(g): F[i] = ( g ** (g / 2) * (X[i] + np.sqrt(g)) ** g * mm.exp(-np.sqrt(g) * X[i] - g) / mm.gamma(g) ) return F def avT(X, g, l): """ Returns the normalized average time above threshold for the normalized shot noise process X. Input: X: the values of the shot noise process, 1d numpy array g: Intermittency parameter, float l: pulse asymmetry parameter, float. Output: F: The normalized average time above threshold. The unnormalized version is F/td. """ assert g > 0 assert l >= 0 assert l <= 1 l = mm.mpf(l) g = mm.mpf(g) F = np.zeros(len(X)) def avTtmp(x, g, l): if (l > 0) & (l < 1): return ( ( mm.gamma(g * l) * mm.gamma(g * (1 - l)) * l ** (1 - g * l) * (1 - l) ** (1 - g * (1 - l)) * g ** (1 - g / 2) ) * mm.gammainc(g, a=np.sqrt(g) * x + g, regularized=True) * (x + np.sqrt(g)) ** (-g) * mm.exp(np.sqrt(g) * x + g) ) else: return ( (mm.gamma(g) * g ** (-g / 2)) * mm.gammainc(g, a=np.sqrt(g) * X[i] + g, regularized=True) * (x + np.sqrt(g)) ** (-g) * mm.exp(np.sqrt(g) * x + g) ) for i in range(len(X)): if X[i] > -np.sqrt(g): F[i] = avTtmp(X[i], g, l) return F def avT_l0(X, g): """ Returns the normalized average time above threshold for the normalized shot noise process X with pulse asymmetry parameter l=0. Input: X: the values of the shot noise process, 1d numpy array g: Intermittency parameter, float Output: F: The normalized average time above threshold. The unnormalized version is F/td. """ warnings.warn("The functionality of avT_l0 has been added to avT.") assert g > 0 g = mm.mpf(g) F = np.zeros(len(X)) for i in range(len(X)): if X[i] > -np.sqrt(g): F[i] = ( (mm.gamma(g) * g ** (-g / 2)) * mm.gammainc(g, a=np.sqrt(g) * X[i] + g, regularized=True) * (X[i] + np.sqrt(g)) ** (-g) * mm.exp(np.sqrt(g) * X[i] + g) ) return F

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c673033ba5aede1672bdf31fdb8e23656fad40b8

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py

Python

tests/charts/listPageOfChartsAfterTest.py

nathanielwarner/seatsio-python

e731ed0c37f2496c620b40e38527a58bf3b9a9b2

[ "MIT" ]

2

2018-03-29T18:21:01.000Z

2022-02-08T10:49:47.000Z

tests/charts/listPageOfChartsAfterTest.py

nathanielwarner/seatsio-python

e731ed0c37f2496c620b40e38527a58bf3b9a9b2

[ "MIT" ]

7

2018-09-03T12:31:52.000Z

2022-02-01T08:25:09.000Z

tests/charts/listPageOfChartsAfterTest.py

nathanielwarner/seatsio-python

e731ed0c37f2496c620b40e38527a58bf3b9a9b2

[ "MIT" ]

2

2020-12-22T09:51:07.000Z

2021-12-13T15:37:14.000Z

from tests.seatsioClientTest import SeatsioClientTest from tests.util.asserts import assert_that class ListChartsAfterTest(SeatsioClientTest): def test_withPreviousPage(self): chart1 = self.client.charts.create() chart2 = self.client.charts.create() chart3 = self.client.charts.create() charts = self.client.charts.list_page_after(chart3.id) assert_that(charts.items).extracting("id").contains_exactly(chart2.id, chart1.id) assert_that(charts.next_page_starts_after).is_none() assert_that(charts.previous_page_ends_before).is_equal_to(chart2.id) def test_withNextAndPreviousPages(self): chart1 = self.client.charts.create() chart2 = self.client.charts.create() chart3 = self.client.charts.create() charts = self.client.charts.list_page_after(chart3.id, page_size=1) assert_that(charts.items).extracting("id").contains_exactly(chart2.id) assert_that(charts.next_page_starts_after).is_equal_to(chart2.id) assert_that(charts.previous_page_ends_before).is_equal_to(chart2.id)

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1,100

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null

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1

0

null

0

6

059f66982d9c058d2b30d13a4d463f7c95dfc9a1

42

py

Python

medusa/core/__init__.py

Basie12/Medusa

80daa51fd23b92e0f58235f025c84654571a401f

[ "MIT" ]

null

medusa/core/__init__.py

Basie12/Medusa

80daa51fd23b92e0f58235f025c84654571a401f

[ "MIT" ]

null

medusa/core/__init__.py

Basie12/Medusa

80daa51fd23b92e0f58235f025c84654571a401f

[ "MIT" ]

null

from medusa.core.ensemble import Ensemble

21

41

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42

6

0.833333

0

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1

42

0.947368

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1

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1

0

null

0

1

0

1

0

1

0

6

05ac5884788794f981d7e18ee670f2867e9055f2

134

py

Python

Modules/carlosma7/models/__init__.py

Carlosma7/Odoo

c234fcc18d15d4d8369e237286bee610fd76ceee

[ "CC0-1.0" ]

null

Modules/carlosma7/models/__init__.py

Carlosma7/Odoo

c234fcc18d15d4d8369e237286bee610fd76ceee

[ "CC0-1.0" ]

null

Modules/carlosma7/models/__init__.py

Carlosma7/Odoo

c234fcc18d15d4d8369e237286bee610fd76ceee

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- # Import models from . import patient from . import doctor from . import sale from . import appointment

19.142857

25

0.671642

17

134

5.294118

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0.444444

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0.009615

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7

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0

null

0

1

0

1

0

1

0

6

05cef072df3be528bce2eb862afc3bea8c59bdb9

1,540

py

Python

tests/zeus/artifacts/test_manager.py

conrad-kronos/zeus

ddb6bc313e51fb22222b30822b82d76f37dbbd35

[ "Apache-2.0" ]

221

2017-07-03T17:29:21.000Z

2021-12-07T19:56:59.000Z

tests/zeus/artifacts/test_manager.py

conrad-kronos/zeus

ddb6bc313e51fb22222b30822b82d76f37dbbd35

[ "Apache-2.0" ]

298

2017-07-04T18:08:14.000Z

2022-03-03T22:24:51.000Z

tests/zeus/artifacts/test_manager.py

conrad-kronos/zeus

ddb6bc313e51fb22222b30822b82d76f37dbbd35

[ "Apache-2.0" ]

24

2017-07-15T13:46:45.000Z

2020-08-16T16:14:45.000Z

from io import BytesIO from zeus import factories from zeus.artifacts.manager import Manager def test_process_behavior_with_filenames(mocker, default_job): handler = mocker.Mock() handler.__name__ = "CoverageHandler" handler.supported_types = frozenset([]) manager = Manager() manager.register(handler, ["coverage.xml"]) artifact = factories.ArtifactFactory(job=default_job, name="junit.xml") artifact.file.save(BytesIO(), artifact.name) manager.process(artifact) assert not handler.called artifact = factories.ArtifactFactory(job=default_job, name="coverage.xml") artifact.file.save(BytesIO(), artifact.name) manager.process(artifact) handler.assert_called_once_with(default_job) handler.return_value.process.assert_called_once() def test_process_behavior_with_types(mocker, default_job): handler = mocker.Mock() handler.__name__ = "CoverageHandler" handler.supported_types = frozenset(["text/xml+coverage"]) manager = Manager() manager.register(handler, []) artifact = factories.ArtifactFactory(job=default_job, name="coverage.xml") artifact.file.save(BytesIO(), artifact.name) manager.process(artifact) assert not handler.called artifact = factories.ArtifactFactory( job=default_job, name="coverage.xml", type="text/xml+coverage" ) artifact.file.save(BytesIO(), artifact.name) manager.process(artifact) handler.assert_called_once_with(default_job) handler.return_value.process.assert_called_once()

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179

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0.061538

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1,540

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0.142857

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null

0

1

0

null

0

6

af1b922c3ebf0e1c5c763e50baea7c4d52887dbc

68

py

Python

ela/__init__.py

DIRECT-Energy-Storage/ela

9d3959da26516c15b87a355f552801dde91f48d0

[ "MIT" ]

2

2017-02-14T00:13:06.000Z

2017-02-27T01:12:01.000Z

ela/__init__.py

DIRECT-Energy-Storage/ela

9d3959da26516c15b87a355f552801dde91f48d0

[ "MIT" ]

null

ela/__init__.py

DIRECT-Energy-Storage/ela

9d3959da26516c15b87a355f552801dde91f48d0

[ "MIT" ]

null

from .ela import * from .ela_widget import * from .mapping import *

17

25

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0.285714

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0.176471

68

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0

1

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1

0

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

af2790c585ff4187644c5dde66c84e912cf350ad

58

py

Python

lotusops/cli/__constants__.py

deep2essence/lotusops

8ed0f7050b664805621d20ba90e234391bca25ad

[ "Apache-2.0", "MIT" ]

1

2022-01-16T03:44:28.000Z

lotusops/cli/__constants__.py

deep2essence/lotusops

8ed0f7050b664805621d20ba90e234391bca25ad

[ "Apache-2.0", "MIT" ]

null

lotusops/cli/__constants__.py

deep2essence/lotusops

8ed0f7050b664805621d20ba90e234391bca25ad

[ "Apache-2.0", "MIT" ]

null

msg_file_or_dir_not_found = "such a file or dir not found"

58

0.810345

13

58

3.230769

0.615385

0.285714

0.428571

0.571429

0.809524

0

0.137931

58

1

58

0.84

0

0.474576

0

1

0

false

0

1

0

null

1

0

1

0

null

0

6

af3b0ed5cb5d0e7045f0b244c7ac68d1c023edac

357

py

Python

tools/__init__.py

xinyufei/Quantum-Control-qutip

bd8a119b9ff8ac0929ffb1f706328759d89fcb5e

[ "BSD-3-Clause" ]

1

2021-08-31T02:28:54.000Z

tools/__init__.py

xinyufei/Quantum-Control-qutip

bd8a119b9ff8ac0929ffb1f706328759d89fcb5e

[ "BSD-3-Clause" ]

null

tools/__init__.py

xinyufei/Quantum-Control-qutip

bd8a119b9ff8ac0929ffb1f706328759d89fcb5e

[ "BSD-3-Clause" ]

null

from tools.auxiliary_energy_origin import * from tools.auxiliary_energy import * from tools.auxiliary_hadamard import * from tools.evolution import * from tools.auxiliary_molecule import * from tools.circuitutil import * from tools.uccsdcircuit import * try: from tools.rounding import * except: print("Warning: No package pycombina for rounding")

27.461538

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0.257143

0

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357

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0.909091

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1

0

1

0

1

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6

afc8388520f99055ebfd1397ecbeda75f6f09727

12,316

py

Python

tests/test_refraction.py

dkirkby/batoid

734dccc289eb7abab77a62cdc14563ed5981753b

[ "BSD-2-Clause" ]

null

tests/test_refraction.py

dkirkby/batoid

734dccc289eb7abab77a62cdc14563ed5981753b

[ "BSD-2-Clause" ]

null

tests/test_refraction.py

dkirkby/batoid

734dccc289eb7abab77a62cdc14563ed5981753b

[ "BSD-2-Clause" ]

null

import os import numpy as np import batoid from test_helpers import timer from batoid.utils import normalized @timer def test_plane_refraction_plane(): import random random.seed(5) wavelength = 500e-9 # arbitrary plane = batoid.Plane() m1 = batoid.ConstMedium(1.1) m2 = batoid.ConstMedium(1.3) for i in range(1000): x = random.gauss(0, 1) y = random.gauss(0, 1) vx = random.gauss(0, 1e-1) vy = random.gauss(0, 1e-1) v = np.array([vx, vy, 1]) v /= np.linalg.norm(v) ray = batoid.Ray([x, y, -10], v/m1.getN(wavelength), 0) rray = plane.refract(ray, m1, m2) np.testing.assert_allclose(np.linalg.norm(rray.v), 1./m2.getN(wavelength), rtol=1e-15) # also check refractInPlace rray2 = batoid.Ray(ray) plane.refractInPlace(rray2, m1, m2) assert rray == rray2 # ray.v, surfaceNormal, and rray.v should all be in the same plane, and # hence (ray.v x surfaceNormal) . rray.v should have zero magnitude. normal = plane.normal(rray.r[0], rray.r[1]) np.testing.assert_allclose( np.dot(np.cross(ray.v, normal), rray.v), 0.0, rtol=0, atol=1e-15) # Test Snell's law np.testing.assert_allclose( m1.getN(wavelength)*np.linalg.norm(np.cross(normalized(ray.v), normal)), m2.getN(wavelength)*np.linalg.norm(np.cross(normalized(rray.v), normal)), rtol=0, atol=1e-15) @timer def test_plane_refraction_reversal(): import random random.seed(57) wavelength = 500e-9 # arbitrary plane = batoid.Plane() m1 = batoid.ConstMedium(1.5) m2 = batoid.ConstMedium(1.2) for i in range(1000): x = random.gauss(0, 1) y = random.gauss(0, 1) vx = random.gauss(0, 1e-1) vy = random.gauss(0, 1e-1) ray = batoid.Ray([x, y, -10], normalized(np.array([vx, vy, 1]))/m1.getN(wavelength), 0) rray = plane.refract(ray, m1, m2) np.testing.assert_allclose(np.linalg.norm(rray.v), 1./m2.getN(wavelength), rtol=1e-15) # Invert the refracted ray, and see that it ends back at the starting # point # Keep going a bit before turning around though turn_around = rray.positionAtTime(rray.t+0.1) return_ray = batoid.Ray(turn_around, -rray.v, -(rray.t+0.1)) riray = plane.intersect(return_ray) np.testing.assert_allclose(rray.r[0], riray.r[0], rtol=0, atol=1e-10) np.testing.assert_allclose(rray.r[1], riray.r[1], rtol=0, atol=1e-10) np.testing.assert_allclose(rray.r[2], riray.r[2], rtol=0, atol=1e-10) # Refract and propagate back to t=0. cray = plane.refract(return_ray, m2, m1) np.testing.assert_allclose(np.linalg.norm(cray.v), 1./m1.getN(wavelength), rtol=1e-15) cpoint = cray.positionAtTime(0) np.testing.assert_allclose(cpoint[0], x, rtol=0, atol=1e-10) np.testing.assert_allclose(cpoint[1], y, rtol=0, atol=1e-10) np.testing.assert_allclose(cpoint[2], -10, rtol=0, atol=1e-10) @timer def test_paraboloid_refraction_plane(): import random random.seed(577) wavelength = 500e-9 # arbitrary para = batoid.Paraboloid(-20.0) m1 = batoid.ConstMedium(1.11) m2 = batoid.ConstMedium(1.32) for i in range(1000): x = random.gauss(0, 1) y = random.gauss(0, 1) vx = random.gauss(0, 1e-1) vy = random.gauss(0, 1e-1) v = normalized(np.array([vx, vy, 1]))/m1.getN(wavelength) ray = batoid.Ray(x, y, -10, v[0], v[1], v[2], 0) rray = para.refract(ray, m1, m2) np.testing.assert_allclose(np.linalg.norm(rray.v), 1./m2.getN(wavelength), rtol=1e-15) # also check refractInPlace rray2 = batoid.Ray(ray) para.refractInPlace(rray2, m1, m2) assert rray == rray2 # ray.v, surfaceNormal, and rray.v should all be in the same plane, and # hence (ray.v x surfaceNormal) . rray.v should have zero magnitude. # magnitude zero. normal = para.normal(rray.r[0], rray.r[1]) np.testing.assert_allclose( np.dot(np.cross(ray.v, normal), rray.v), 0.0, rtol=0, atol=1e-15) # Test Snell's law np.testing.assert_allclose( m1.getN(wavelength)*np.linalg.norm(np.cross(normalized(ray.v), normal)), m2.getN(wavelength)*np.linalg.norm(np.cross(normalized(rray.v), normal)), rtol=0, atol=1e-15) @timer def test_paraboloid_refraction_reversal(): import random random.seed(5772) wavelength = 500e-9 # arbitrary para = batoid.Paraboloid(-20.0) m1 = batoid.ConstMedium(1.43) m2 = batoid.ConstMedium(1.34) for i in range(1000): x = random.gauss(0, 1) y = random.gauss(0, 1) vx = random.gauss(0, 1e-1) vy = random.gauss(0, 1e-1) ray = batoid.Ray([x, y, -10], normalized(np.array([vx, vy, 1]))/m1.getN(wavelength), 0) rray = para.refract(ray, m1, m2) np.testing.assert_allclose(np.linalg.norm(rray.v), 1./m2.getN(wavelength), rtol=1e-15) # Invert the refracted ray, and see that it ends back at the starting # point # Keep going a bit before turning around though turn_around = rray.positionAtTime(rray.t+0.1) return_ray = batoid.Ray(turn_around, -rray.v, -(rray.t+0.1)) riray = para.intersect(return_ray) # First check that we intersected at the same point np.testing.assert_allclose(rray.r[0], riray.r[0], rtol=0, atol=1e-10) np.testing.assert_allclose(rray.r[1], riray.r[1], rtol=0, atol=1e-10) np.testing.assert_allclose(rray.r[2], riray.r[2], rtol=0, atol=1e-10) # Refract and propagate back to t=0. cray = para.refract(return_ray, m2, m1) np.testing.assert_allclose(np.linalg.norm(cray.v), 1./m1.getN(wavelength), rtol=1e-15) cpoint = cray.positionAtTime(0) np.testing.assert_allclose(cpoint[0], x, rtol=0, atol=1e-10) np.testing.assert_allclose(cpoint[1], y, rtol=0, atol=1e-10) np.testing.assert_allclose(cpoint[2], -10, rtol=0, atol=1e-10) @timer def test_asphere_refraction_plane(): import random random.seed(57721) wavelength = 500e-9 # arbitrary asphere = batoid.Asphere(25.0, -0.97, [1e-3, 1e-5]) m1 = batoid.ConstMedium(1.7) m2 = batoid.ConstMedium(1.2) for i in range(1000): x = random.gauss(0, 1) y = random.gauss(0, 1) vx = random.gauss(0, 1e-1) vy = random.gauss(0, 1e-1) v = normalized(np.array([vx, vy, 1]))/m1.getN(wavelength) ray = batoid.Ray(x, y, -0.1, v[0], v[1], v[2], 0) rray = asphere.refract(ray, m1, m2) np.testing.assert_allclose(np.linalg.norm(rray.v), 1./m2.getN(wavelength), rtol=1e-15) # also check refractInPlace rray2 = batoid.Ray(ray) asphere.refractInPlace(rray2, m1, m2) assert rray == rray2 # ray.v, surfaceNormal, and rray.v should all be in the same plane, and # hence (ray.v x surfaceNormal) . rray.v should have zero magnitude. # magnitude zero. normal = asphere.normal(rray.r[0], rray.r[1]) np.testing.assert_allclose( np.dot(np.cross(ray.v, normal), rray.v), 0.0, rtol=0, atol=1e-15) # Test Snell's law np.testing.assert_allclose( m1.getN(wavelength)*np.linalg.norm(np.cross(normalized(ray.v), normal)), m2.getN(wavelength)*np.linalg.norm(np.cross(normalized(rray.v), normal)), rtol=0, atol=1e-15) @timer def test_asphere_refraction_reversal(): import random random.seed(577215) wavelength = 500e-9 # arbitrary asphere = batoid.Asphere(23.0, -0.97, [1e-5, 1e-6]) m1 = batoid.ConstMedium(1.7) m2 = batoid.ConstMedium(1.9) for i in range(1000): x = random.gauss(0, 1) y = random.gauss(0, 1) vx = random.gauss(0, 1e-1) vy = random.gauss(0, 1e-1) ray = batoid.Ray([x, y, -0.1], normalized(np.array([vx, vy, 1]))/m1.getN(wavelength), 0) rray = asphere.refract(ray, m1, m2) np.testing.assert_allclose(np.linalg.norm(rray.v), 1./m2.getN(wavelength), rtol=1e-15) # Invert the refracted ray, and see that it ends back at the starting # point # Keep going a bit before turning around though turn_around = rray.positionAtTime(rray.t+0.1) return_ray = batoid.Ray(turn_around, -rray.v, -(rray.t+0.1)) riray = asphere.intersect(return_ray) # First check that we intersected at the same point np.testing.assert_allclose(rray.r[0], riray.r[0], rtol=0, atol=1e-10) np.testing.assert_allclose(rray.r[1], riray.r[1], rtol=0, atol=1e-10) np.testing.assert_allclose(rray.r[2], riray.r[2], rtol=0, atol=1e-10) # Refract and propagate back to t=0. cray = asphere.refract(return_ray, m2, m1) np.testing.assert_allclose(np.linalg.norm(cray.v), 1./m1.getN(wavelength), rtol=1e-15) cpoint = cray.positionAtTime(0) np.testing.assert_allclose(cpoint[0], x, rtol=0, atol=1e-10) np.testing.assert_allclose(cpoint[1], y, rtol=0, atol=1e-10) np.testing.assert_allclose(cpoint[2], -0.1, rtol=0, atol=1e-10) @timer def test_table_medium_refraction(): import random random.seed(57721566) filename = os.path.join(batoid.datadir, "media", "silica_dispersion.txt") wave, n = np.genfromtxt(filename).T table = batoid.Table(wave, n, batoid.Table.Interpolant.linear) silica = batoid.TableMedium(table) air = batoid.ConstMedium(1.000277) asphere = batoid.Asphere(25.0, -0.97, [1e-3, 1e-5]) for i in range(10000): x = random.gauss(0, 1) y = random.gauss(0, 1) vx = random.gauss(0, 1e-1) vy = random.gauss(0, 1e-1) wavelength = random.uniform(0.3, 1.2) ray = batoid.Ray(x, y, -0.1, vx, vy, 1, 0, wavelength) cm1 = batoid.ConstMedium(silica.getN(wavelength)) cm2 = batoid.ConstMedium(air.getN(wavelength)) rray1 = asphere.refract(ray, silica, air) rray2 = asphere.refract(ray, cm1, cm2) assert rray1 == rray2 @timer def test_refraction_chromatic(): import random random.seed(577215664) wavelength1 = 500e-9 wavelength2 = 600e-9 flux = 1.0 plane = batoid.Plane() filename = os.path.join(batoid.datadir, "media", "silica_dispersion.txt") wave, n = np.genfromtxt(filename).T wave *= 1e-6 # micron -> meters table = batoid.Table(wave, n, batoid.Table.Interpolant.linear) silica = batoid.TableMedium(table) air = batoid.Air() thx, thy = 0.001, 0.0001 dirCos = batoid.utils.gnomicToDirCos(thx, thy) rv1 = batoid.rayGrid(10.0, 1., dirCos[0], dirCos[1], -dirCos[2], 2, wavelength1, flux, silica) rv2 = batoid.rayGrid(10.0, 1., dirCos[0], dirCos[1], -dirCos[2], 2, wavelength2, flux, silica) rays = [] for ray in rv1: rays.append(ray) for ray in rv2: rays.append(ray) rvCombined = batoid.RayVector(rays) rv1r = plane.refract(rv1, silica, air) rv2r = plane.refract(rv2, silica, air) assert rv1r != rv2r rays = [] for ray in rv1r: rays.append(ray) for ray in rv2r: rays.append(ray) rvrCombined1 = batoid.RayVector(rays) rvrCombined2 = plane.refract(rvCombined, silica, air) assert rvrCombined1 == rvrCombined2 # Check in-place plane.refractInPlace(rv1, silica, air) plane.refractInPlace(rv2, silica, air) assert rv1 != rv2 plane.refractInPlace(rvCombined, silica, air) rays = [] for ray in rv1: rays.append(ray) for ray in rv2: rays.append(ray) rvCombined2 = batoid.RayVector(rays) assert rvCombined == rvCombined2 if __name__ == '__main__': test_plane_refraction_plane() test_plane_refraction_reversal() test_paraboloid_refraction_plane() test_paraboloid_refraction_reversal() test_asphere_refraction_plane() test_asphere_refraction_reversal() test_table_medium_refraction() test_refraction_chromatic()

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0.031128

false

0

0.050584

0

0.081712

0

null

0

1

0

null

0

6

bb89383da16d09903769f2b9bd425a5d87c9ab41

89

py

Python

root_solvers/scalar/__init__.py

SeanMatthewNolan/algorithm_sandbox

07e5f4880f4cdbea99f3722ba3c898ea95d8ba13

[ "MIT" ]

null

root_solvers/scalar/__init__.py

SeanMatthewNolan/algorithm_sandbox

07e5f4880f4cdbea99f3722ba3c898ea95d8ba13

[ "MIT" ]

null

root_solvers/scalar/__init__.py

SeanMatthewNolan/algorithm_sandbox

07e5f4880f4cdbea99f3722ba3c898ea95d8ba13

[ "MIT" ]

null

from .newton_raphson import NewtonRaphsonScalar, DampedNewtonScalar, BoundedNewtonScalar

44.5

88

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null

0

1

0

1

0

1

0

6

bb943a587aa2e5d2cc555f8f5d0289c35816799a

43

py

Python

rdflib_endpoint/__init__.py

vemonet/sparql-engine-for-python

c69e0e20a1e0d52b4829e276c02439651c4acabc

[ "MIT" ]

18

2021-08-31T19:04:27.000Z

2022-03-24T10:05:32.000Z

rdflib_endpoint/__init__.py

vemonet/sparql-engine-for-python

c69e0e20a1e0d52b4829e276c02439651c4acabc

[ "MIT" ]

1

2021-12-16T22:53:40.000Z

2022-02-07T18:22:04.000Z

rdflib_endpoint/__init__.py

vemonet/rdflib-endpoint

c69e0e20a1e0d52b4829e276c02439651c4acabc

[ "MIT" ]

1

2021-05-20T08:34:33.000Z

from .sparql_endpoint import SparqlEndpoint

43

0.906977

5

43

7.6

1

0

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1

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0.95

0

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null

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1

0

1

0

1

0

6

bb9a5c24e28c4bc522fb53eb28460ce7fda6cfb4

248

py

Python

spreadflow_delta/test/test_delta_proc.py

znerol/spreadflow-delta

246f6d61072c41b5a8a68053650b731981259aab

[ "MIT" ]

null

spreadflow_delta/test/test_delta_proc.py

znerol/spreadflow-delta

246f6d61072c41b5a8a68053650b731981259aab

[ "MIT" ]

null

spreadflow_delta/test/test_delta_proc.py

znerol/spreadflow-delta

246f6d61072c41b5a8a68053650b731981259aab

[ "MIT" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from testtools import TestCase from spreadflow_delta.proc import Filter, Extractor class SpreadflowDeltaTestCase(TestCase): pass

20.666667

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0

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true

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1

0

null

0

1

0

1

0

6

bba365e934491ce278bd047d45f23705aa4b2f98

2,923

py

Python

portxpress/news/migrations/0001_initial.py

zoeinola/PortXpress

c69d9071e36a87942c3bba63a3ef079d06fe7baf

[ "MIT" ]

null

portxpress/news/migrations/0001_initial.py

zoeinola/PortXpress

c69d9071e36a87942c3bba63a3ef079d06fe7baf

[ "MIT" ]

null

portxpress/news/migrations/0001_initial.py

zoeinola/PortXpress

c69d9071e36a87942c3bba63a3ef079d06fe7baf

[ "MIT" ]

null

# Generated by Django 3.0.10 on 2020-11-19 19:16 import ckeditor_uploader.fields from django.db import migrations, models import django.utils.timezone import model_utils.fields import portxpress.news.models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='News', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')), ('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')), ('title', models.CharField(max_length=500, null=True, verbose_name='Post Title')), ('slug', models.SlugField(blank=True, max_length=600, null=True, unique=True)), ('image', models.ImageField(blank=True, null=True, upload_to=portxpress.news.models.blog_file_path, verbose_name='Upload Info')), ('pub_date', models.DateField(null=True, verbose_name='Post Published Date')), ('draft', models.BooleanField(default=False)), ('content', ckeditor_uploader.fields.RichTextUploadingField()), ], options={ 'verbose_name': 'Post', 'verbose_name_plural': 'Posts', 'ordering': ['title', '-created'], 'managed': True, }, ), migrations.CreateModel( name='Traffic', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')), ('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')), ('title', models.CharField(max_length=500, null=True, verbose_name='Post Title')), ('slug', models.SlugField(blank=True, max_length=600, null=True, unique=True)), ('image', models.FileField(blank=True, null=True, upload_to=portxpress.news.models.blog_file_path, verbose_name='Upload Info')), ('pub_date', models.DateField(null=True, verbose_name='Post Published Date')), ('draft', models.BooleanField(default=False)), ('content', ckeditor_uploader.fields.RichTextUploadingField()), ], options={ 'verbose_name': 'Traffic', 'verbose_name_plural': 'Traffics', 'ordering': ['title', '-created'], 'managed': True, }, ), ]

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1

0

1

0

null

0

6

bbd034fb577341df8a4a0c953b23b9aa074ac720

202

py

Python

readabilipy/__init__.py

Tara-Morovatdar/html_parser

17ab858b7a013b4a4cc411b35347fc6d8f077793

[ "MIT" ]

null

readabilipy/__init__.py

Tara-Morovatdar/html_parser

17ab858b7a013b4a4cc411b35347fc6d8f077793

[ "MIT" ]

3

2020-04-07T03:45:22.000Z

2022-03-26T05:01:25.000Z

readabilipy/__init__.py

Tara-Morovatdar/html_parser

17ab858b7a013b4a4cc411b35347fc6d8f077793

[ "MIT" ]

null

from .simple_json import simple_json_from_html_string from .simple_tree import simple_tree_from_html_string __all__ = [ 'simple_json_from_html_string', 'simple_tree_from_html_string', ]

25.25

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7

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null

1

0

1

0

null

0

1

0

6

bbe0c16bc3ce9eee041aff021293b9fb536392bf

19

py

Python

m2dp/__init__.py

adnan33/M2DP-python

eb897fd9d42764b08ad8bfb58e6c8327cee3ed34

[ "MIT" ]

13

2020-04-28T02:20:58.000Z

2022-03-06T11:05:58.000Z

m2dp/__init__.py

adnan33/M2DP-python

eb897fd9d42764b08ad8bfb58e6c8327cee3ed34

[ "MIT" ]

null

m2dp/__init__.py

adnan33/M2DP-python

eb897fd9d42764b08ad8bfb58e6c8327cee3ed34

[ "MIT" ]

1

2021-09-22T03:34:55.000Z

from .M2DP import *

19

0.736842

3

19

4.666667

1

0

0.0625

0.157895

19

1

19

0.8125

0

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1

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

a549ffd1d78c18fefaf7549d10d75c207613f61a

207

py

Python

fsetools/tests/test_fse_thermal_radiation_2d.py

fsepy/fsetools

6b6c647912551680109a84d8640b9cfbe7970970

[ "Apache-2.0" ]

1

2020-02-25T21:47:56.000Z

fsetools/tests/test_fse_thermal_radiation_2d.py

fsepy/fsetools

6b6c647912551680109a84d8640b9cfbe7970970

[ "Apache-2.0" ]

12

2020-02-24T10:10:57.000Z

2020-09-18T11:18:08.000Z

fsetools/tests/test_fse_thermal_radiation_2d.py

fsepy/fsetools

6b6c647912551680109a84d8640b9cfbe7970970

[ "Apache-2.0" ]

null

from fsetools.lib.fse_thermal_radiation_2d_parallel import _test_main as test_main from fsetools.lib.fse_thermal_radiation_2d_parallel import _test_solve_phi as test_solve_phi test_solve_phi() test_main()

29.571429

92

0.884058

35

207

4.685714

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0.146341

0.219512

0.658537

0

0.010471

0.077295

207

6

93

34.5

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1

0

1

0

null

0

1

0

1

0

6

a5568bfab2bb32c9bc41a8a61f1ae38814dfff19

49

py

Python

lab7/e4_set.py

daem-uni/dagdim-lab-informatica

9a5c3f829e8372ef994efb28e81a2f7d77c88681

[ "MIT" ]

null

lab7/e4_set.py

daem-uni/dagdim-lab-informatica

9a5c3f829e8372ef994efb28e81a2f7d77c88681

[ "MIT" ]

null

lab7/e4_set.py

daem-uni/dagdim-lab-informatica

9a5c3f829e8372ef994efb28e81a2f7d77c88681

[ "MIT" ]

1

2020-12-03T15:17:29.000Z

def sameSet(a, b): return set(a) == set(b)

16.333333

28

0.530612

9

49

2.888889

0.666667

0

0.265306

49

2

29

24.5

0.722222

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

a5641a1f02a9dbc9a63aae57afcbfa243ac147b2

2,196

py

Python

scripts/main_warp.py

RACT-CF/RaCT

ced06c9e3398184c82aa42d5eb0cd5679c905375

[ "Apache-2.0" ]

36

2019-06-12T16:35:24.000Z

2022-02-18T02:17:03.000Z

scripts/main_warp.py

RACT-CF/RaCT

ced06c9e3398184c82aa42d5eb0cd5679c905375

[ "Apache-2.0" ]

1

2019-08-07T06:49:33.000Z

2020-06-20T19:04:50.000Z

scripts/main_warp.py

RACT-CF/RaCT

ced06c9e3398184c82aa42d5eb0cd5679c905375

[ "Apache-2.0" ]

6

2019-11-19T05:33:59.000Z

2021-05-05T15:44:20.000Z

import sys import os UTILS_DIR = os.path.join(os.path.abspath(os.path.dirname(__file__)), '..', 'utils') sys.path.insert(1, UTILS_DIR) from training import train, test if __name__ == '__main__': """ NOTE: This takes roughly 30 minutes per epoch with a good GPU """ train( model_class='warp_encoder', n_epochs_pred_only=0, n_epochs_ac_only=10, n_epochs_pred_and_ac=10, epochs_to_anneal_over=100, # min_kl=0.0001, max_kl=0.0, ac_reg_loss_scaler=0.0, actor_reg_loss_scaler=1e-5, # positive_weights=5, # evaluation_metric='AP', evaluation_metric="NDCG", logging_frequency=25, # logging_frequency=50, # logging_frequency=50, batch_size=500, # batch_size=25, break_early=False, verbose=False, # path_to_save_actor="best_ndcg_trained_150_epochs", # path_to_save_last_actor="last_actor_after_150_trained_epochs", version_tag="WARP_WITH_CRITIC", # path_to_save_actor="BEST_WARP_RUN_15_EPOCHS_TRUTHFUL_LOSS", restore_trained_actor_path="BEST_WARP_RUN_15_EPOCHS_TRUTHFUL_LOSS" ) print("On to testing.") test( # model_class="wmf", # model_class='multi_vae', model_class='warp_encoder', n_epochs_pred_only=0, n_epochs_ac_only=10, n_epochs_pred_and_ac=10, epochs_to_anneal_over=100, # min_kl=0.0001, max_kl=0.0, ac_reg_loss_scaler=0.0, actor_reg_loss_scaler=1e-5, # positive_weights=5, # evaluation_metric='AP', evaluation_metric="NDCG", # logging_frequency=25, # logging_frequency=50, # logging_frequency=50, batch_size=500, # batch_size=25, break_early=False, verbose=False, # path_to_save_actor="best_ndcg_trained_150_epochs", # path_to_save_last_actor="last_actor_after_150_trained_epochs", version_tag="WARP_WITH_CRITIC", # path_to_save_actor="BEST_WARP_RUN_15_EPOCHS_TRUTHFUL_LOSS", restore_trained_actor_path="BEST_WARP_RUN_15_EPOCHS_TRUTHFUL_LOSS" ) exit()

29.28

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0

0.051893

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74

84

29.675676

0.723443

0.295537

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0

1

0

false

0

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0

0.073171

0.02439

0

null

0

1

0

null

0

6

a585b5c4bc18b60627e1f46ca226eb2788def594

3,256

py

Python

export_support/core/tests/test_forms/test_enquiry_details.py

uktrade/export-support

5f4f445ddb1836737484439f9f81f05d3fc1aaa9

[ "MIT" ]

1

2021-08-16T09:19:32.000Z

export_support/core/tests/test_forms/test_enquiry_details.py

uktrade/export-support

5f4f445ddb1836737484439f9f81f05d3fc1aaa9

[ "MIT" ]

1

2021-09-24T10:58:08.000Z

2021-09-24T13:32:30.000Z

export_support/core/tests/test_forms/test_enquiry_details.py

uktrade/export-support

5f4f445ddb1836737484439f9f81f05d3fc1aaa9

[ "MIT" ]

null

from ...forms import EnquiryDetailsForm, HowDidYouHearAboutThisServiceChoices def test_enquiry_details_validation_how_did_you_hear_required(): form = EnquiryDetailsForm( { "nature_of_enquiry": "TEST", "question": "TEST", } ) assert not form.is_valid() assert form.errors == { "how_did_you_hear_about_this_service": [ "Select how you heard about this service" ], } def test_enquiry_details_validation_how_did_you_hear_other_required(): form = EnquiryDetailsForm( { "nature_of_enquiry": "TEST", "question": "TEST", "how_did_you_hear_about_this_service": HowDidYouHearAboutThisServiceChoices.OTHER, } ) assert not form.is_valid() assert form.errors == { "other_how_did_you_hear_about_this_service": [ "Enter how you heard about this service" ], } def test_get_zendesk_data(): form = EnquiryDetailsForm( { "nature_of_enquiry": "NATURE OF ENQUIRY", "question": "QUESTION", "how_did_you_hear_about_this_service": HowDidYouHearAboutThisServiceChoices.SEARCH_ENGINE, } ) assert form.is_valid() assert form.get_zendesk_data() == { "nature_of_enquiry": "NATURE OF ENQUIRY", "question": "QUESTION", "how_did_you_hear_about_this_service": "Search engine", "marketing_consent": False, } form = EnquiryDetailsForm( { "nature_of_enquiry": "NATURE OF ENQUIRY", "question": "QUESTION", "how_did_you_hear_about_this_service": HowDidYouHearAboutThisServiceChoices.SEARCH_ENGINE, "email_consent": True, } ) assert form.is_valid() assert form.get_zendesk_data() == { "nature_of_enquiry": "NATURE OF ENQUIRY", "question": "QUESTION", "how_did_you_hear_about_this_service": "Search engine", "marketing_consent": True, } form = EnquiryDetailsForm( { "nature_of_enquiry": "NATURE OF ENQUIRY", "question": "QUESTION", "marketing_consent": False, "how_did_you_hear_about_this_service": HowDidYouHearAboutThisServiceChoices.OTHER, "other_how_did_you_hear_about_this_service": "HEARD FROM OTHER", } ) assert form.is_valid() assert form.get_zendesk_data() == { "nature_of_enquiry": "NATURE OF ENQUIRY", "question": "QUESTION", "marketing_consent": False, "how_did_you_hear_about_this_service": "HEARD FROM OTHER", } form = EnquiryDetailsForm( { "nature_of_enquiry": "NATURE OF ENQUIRY", "question": "QUESTION", "marketing_consent": False, "how_did_you_hear_about_this_service": HowDidYouHearAboutThisServiceChoices.SEARCH_ENGINE, "other_how_did_you_hear_about_this_service": "Search engine", } ) assert form.is_valid() assert form.get_zendesk_data() == { "nature_of_enquiry": "NATURE OF ENQUIRY", "question": "QUESTION", "marketing_consent": False, "how_did_you_hear_about_this_service": "Search engine", }

30.716981

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0.62715

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3,256

5.801223

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0.14233

0.102794

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0.919346

0.826041

0.654191

0

0.273342

3,256

105

103

31.009524

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0

0.588889

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0.033333

false

0

0.011111

0

0.044444

0

null

0

1

0

null

0

6

a5a264464924921c6850ce444ad74328331dec71

288

py

Python

layers/modules/__init__.py

Ze-Yang/Context-Transformer

493fb6b3eb9f546dc172601de787fe89a1489065

[ "MIT" ]

86

2020-03-25T10:33:56.000Z

2022-03-24T04:11:43.000Z

layers/modules/__init__.py

Ze-Yang/Context-Transformer

493fb6b3eb9f546dc172601de787fe89a1489065

[ "MIT" ]

16

2020-04-03T08:43:40.000Z

2021-12-07T14:15:56.000Z

layers/modules/__init__.py

Ze-Yang/Context-Transformer

493fb6b3eb9f546dc172601de787fe89a1489065

[ "MIT" ]

12

2020-03-29T04:26:20.000Z

2021-12-21T04:33:52.000Z

# from .multibox_loss import MultiBoxLoss # # from .multibox_loss_combined import MultiBoxLoss_combined # from .multibox_loss_combined_tf import MultiBoxLoss_combined # from .multibox_loss_combined_meta1 import MultiBoxLoss_combined # __all__ = ['MultiBoxLoss', 'MultiBoxLoss_combined']

41.142857

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288

6

66

48

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0.954861

0

null

0

null

0

null

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1

null

true

0

null

0

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1

0

1

0

null

0

1

0

6

3c15472fcbacbb8d1bf6cc7319d3365fe6a66667

1,445

py

Python

icbd/type_analyzer/tests/top.py

kmod/icbd

9636564eb3993afa07c6220d589bbd1991923d74

[ "MIT" ]

7

2015-04-06T15:17:13.000Z

2020-10-21T04:57:00.000Z

icbd/type_analyzer/tests/top.py

kmod/icbd

9636564eb3993afa07c6220d589bbd1991923d74

[ "MIT" ]

null

icbd/type_analyzer/tests/top.py

kmod/icbd

9636564eb3993afa07c6220d589bbd1991923d74

[ "MIT" ]

4

2016-05-16T17:53:08.000Z

2020-11-28T17:18:50.000Z

x = getattr(None, '') # 0 <mixed> l = [] # 0 [<mixed>] l.extend(x) # 0 [<mixed>] # e 0 # TODO: test converting to top s = [ # 0 [(str, int)] ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ('', 1), ]

13.256881

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3c324d6ca2692442f749ffd00d836579154a094d

170

py

Python

serverlib/__init__.py

PaulLockett/OSConferenceCall

93c165da54efd3fac67dd4e54c7619f7d312c1a5

[ "MIT" ]

null

serverlib/__init__.py

PaulLockett/OSConferenceCall

93c165da54efd3fac67dd4e54c7619f7d312c1a5

[ "MIT" ]

null

serverlib/__init__.py

PaulLockett/OSConferenceCall

93c165da54efd3fac67dd4e54c7619f7d312c1a5

[ "MIT" ]

null

from serverlib.streaming import StreamingServer , StreamingClient from serverlib.audio import AudioServer, AudioClient from serverlib.chat import ChatServer , ChatClient

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6

3c82788c1e1907302cf2d4100d5320db57abedb0

5,210

py

Python

tests/test_container.py

WinVector/data_algebra

3d6002ddf8231d310e03537a0435df0554b62234

[ "BSD-3-Clause" ]

37

2019-08-28T08:16:48.000Z

2022-03-14T21:18:39.000Z

tests/test_container.py

WinVector/data_algebra

3d6002ddf8231d310e03537a0435df0554b62234

[ "BSD-3-Clause" ]

1

2019-09-02T23:13:29.000Z

2019-09-08T01:43:10.000Z

tests/test_container.py

WinVector/data_algebra

3d6002ddf8231d310e03537a0435df0554b62234

[ "BSD-3-Clause" ]

3

2019-08-28T12:23:11.000Z

2020-02-08T19:22:31.000Z

import pytest import data_algebra from data_algebra.data_ops import * # https://github.com/WinVector/data_algebra from data_algebra.op_container import Pipeline, one import data_algebra.test_util import data_algebra.MySQL def test_container_1(): d = data_algebra.default_data_model.pd.DataFrame( { "subjectID": [1, 1, 2, 2], "surveyCategory": [ "withdrawal behavior", "positive re-framing", "withdrawal behavior", "positive re-framing", ], "assessmentTotal": [5.0, 2.0, 3.0, 4.0], "irrelevantCol1": ["irrel1"] * 4, "irrelevantCol2": ["irrel2"] * 4, } ) scale = 0.237 with Pipeline() as (pipeline, _): ops2 = ( pipeline.start(describe_table(d, "d")) .extend({"probability": (_.assessmentTotal * scale).exp()}) .extend({"total": _.probability.sum()}, partition_by="subjectID") .extend({"probability": _.probability / _.total}) .extend({"ncat": one.sum()}, partition_by=["subjectID"],) .extend( {"row_number": one.cumsum()}, partition_by=["subjectID"], order_by=["probability"], reverse=["probability"], ) .select_rows(_.row_number == 1) .select_columns(["subjectID", "surveyCategory", "probability", "ncat"]) .rename_columns({"diagnosis": "surveyCategory"}) .get_ops() ) db_handle = data_algebra.MySQL.MySQLModel().db_handle(conn=None) sql = db_handle.to_sql(ops2) assert isinstance(sql, str) # print(sql) expect = data_algebra.default_data_model.pd.DataFrame( { "subjectID": [1, 2], "diagnosis": ["withdrawal behavior", "positive re-framing"], "probability": [0.670622, 0.558974], "ncat": [2, 2], } ) data_algebra.test_util.check_transform( ops=ops2, data=d, expect=expect, float_tol=1e-4 ) def test_container_2(): d = data_algebra.default_data_model.pd.DataFrame( { "subjectID": [1, 1, 2, 2], "surveyCategory": [ "withdrawal behavior", "positive re-framing", "withdrawal behavior", "positive re-framing", ], "assessmentTotal": [5.0, 2.0, 3.0, 4.0], "irrelevantCol1": ["irrel1"] * 4, "irrelevantCol2": ["irrel2"] * 4, } ) scale = 0.237 with Pipeline() as (pipeline, _): res = ( pipeline.start(describe_table(d, "d", keep_all=True)) .extend({"probability": (_.assessmentTotal * scale).exp()}) .extend({"total": _.probability.sum()}, partition_by="subjectID") .extend({"probability": _.probability / _.total}) .extend({"ncat": one.sum()}, partition_by=["subjectID"],) .extend( {"row_number": one.cumsum()}, partition_by=["subjectID"], order_by=["probability"], reverse=["probability"], ) .select_rows(_.row_number == 1) .select_columns(["subjectID", "surveyCategory", "probability", "ncat"]) .rename_columns({"diagnosis": "surveyCategory"}) .ex() ) expect = data_algebra.default_data_model.pd.DataFrame( { "subjectID": [1, 2], "diagnosis": ["withdrawal behavior", "positive re-framing"], "probability": [0.670622, 0.558974], "ncat": [2, 2], } ) data_algebra.test_util.equivalent_frames(res, expect) def test_container_2(): d = data_algebra.default_data_model.pd.DataFrame( { "subjectID": [1, 1, 2, 2], "surveyCategory": [ "withdrawal behavior", "positive re-framing", "withdrawal behavior", "positive re-framing", ], "assessmentTotal": [5.0, 2.0, 3.0, 4.0], "irrelevantCol1": ["irrel1"] * 4, "irrelevantCol2": ["irrel2"] * 4, } ) scale = 0.237 # forget to use result with pytest.raises(AssertionError): with Pipeline() as (pipeline, _): res = ( pipeline.start(describe_table(d, "d", keep_all=True)) .extend({"probability": (_.assessmentTotal * scale).exp()}) .extend({"total": _.probability.sum()}, partition_by="subjectID") .extend({"probability": _.probability / _.total}) .extend({"ncat": one.sum()}, partition_by=["subjectID"],) .extend( {"row_number": one.cumsum()}, partition_by=["subjectID"], order_by=["probability"], reverse=["probability"], ) .select_rows(_.row_number == 1) .select_columns(["subjectID", "surveyCategory", "probability", "ncat"]) .rename_columns({"diagnosis": "surveyCategory"}) )

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null

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1

0

null

0

6

3c8fc83ee14aaecd729e129b55c0966855e31188

300

py

Python

emoji_search/test/util/test_cleaning.py

AdamKBeck/EmojiFi

b90aeb656a4474b3011c074b57741282aaf3bb23

[ "MIT" ]

1

2019-02-16T16:34:09.000Z

emoji_search/test/util/test_cleaning.py

AdamKBeck/EmojiFi

b90aeb656a4474b3011c074b57741282aaf3bb23

[ "MIT" ]

29

2019-02-22T21:00:04.000Z

2019-04-10T01:41:34.000Z

emoji_search/test/util/test_cleaning.py

AdamKBeck/EmojiFi

b90aeb656a4474b3011c074b57741282aaf3bb23

[ "MIT" ]

1

2019-02-18T15:57:42.000Z

from emojisearch.util.cleaning import cleaned_of_punctuation from emojisearch.util.cleaning import filter_stop_words def test_cleaned_of_punctuation(): assert cleaned_of_punctuation('t.h,e;"[]') == 'the' def test_filter_stop_words(): assert filter_stop_words("it's not a joke") == "joke"

27.272727

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6

b1b02f2432df8d5011f386816e26240719caeee9

40

py

Python

cerebrum/neuralnet/__init__.py

Maxprofs/Cerebrum

1364c75ef7bc7b7069f2eafa2caecf28aeca6394

[ "MIT" ]

2

2021-02-02T13:31:58.000Z

2021-09-10T19:27:41.000Z

cerebrum/neuralnet/__init__.py

Python3pkg/Cerebrum

b4f88cd467233443c47d699efd30defd8c464166

[ "MIT" ]

null

cerebrum/neuralnet/__init__.py

Python3pkg/Cerebrum

b4f88cd467233443c47d699efd30defd8c464166

[ "MIT" ]

1

2019-02-17T19:57:36.000Z

from cerebrum.neuralnet.weaver import *

20

39

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5

40

6.6

1

0

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40

1

40

0.916667

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null

0

1

0

1

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1

0

6

3ca6c05c4419930eeac383f3ade66b0248c14aaf

228

py

Python

successor/skaters/scalarskaters/allscalarskaters.py

microprediction/successor

80f61a59c93d45cff2851f8048fda5378bd05c4c

[ "MIT" ]

null

successor/skaters/scalarskaters/allscalarskaters.py

microprediction/successor

80f61a59c93d45cff2851f8048fda5378bd05c4c

[ "MIT" ]

null

successor/skaters/scalarskaters/allscalarskaters.py

microprediction/successor

80f61a59c93d45cff2851f8048fda5378bd05c4c

[ "MIT" ]

1

2021-12-19T16:01:49.000Z

from successor.skaters.scalarskaters.scalartsaskaters import SCALAR_TSA_SKATERS from successor.skaters.scalarskaters.scalarsimpleskaters import SCALAR_SIMPLE_SKATERS SCALAR_SKATERS = SCALAR_TSA_SKATERS + SCALAR_SIMPLE_SKATERS

38

85

0.899123

26

228

7.538462

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0.19898

0.204082

0.336735

0

0.065789

228

5

86

45.6

0.920188

0

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false

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0.666667

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0.666667

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1

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null

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1

0

1

0

null

0

1

0

1

0

6

3cbfd59e0894142571689555ffd8da2ebcc6f320

1,023

py

Python

common/appium_common/gestures.py

lineOneTwo/test

5c7da7b3e3142fbaa0142d62a196288b65a37c8e

[ "MIT" ]

null

common/appium_common/gestures.py

lineOneTwo/test

5c7da7b3e3142fbaa0142d62a196288b65a37c8e

[ "MIT" ]

null

common/appium_common/gestures.py

lineOneTwo/test

5c7da7b3e3142fbaa0142d62a196288b65a37c8e

[ "MIT" ]

null

def swipe_up(driver, t=500, n=1): # 向上滑 size = driver.get_window_size() x_start = size['width'] * 0.5 # x坐标 y_start = size['height'] * 0.75 # 起始y坐标 x_end = size['height'] * 0.25 # 终点y坐标 for i in range(n): driver.swipe(x_start, y_start, x_start, x_end, t) def swipe_down(driver, t=500, n=1): # 向下滑 size = driver.get_window_size() x_start = size['width'] * 0.5 # x坐标 y_start = size['height'] * 0.25 # 起始y坐标 y_end = size['height'] * 0.75 # 终点y坐标 for i in range(n): driver.swipe(x_start, y_start, x_start, y_end, t) def swip_left(driver, t=500, n=1): # 向左滑 size = driver.get_window_size() x_start = size['width'] * 0.75 y_start = size['height'] * 0.5 x_end = size['width'] * 0.25 for i in range(n): driver.swipe(x_start, y_start, x_end, y_start, t) def swip_right(driver, t=500, n=1): # 向右滑 size = driver.get_window_size() x_start = size['width'] * 0.25 y_start = size['height'] * 0.5 x_end = size['width'] * 0.75 for i in range(n): driver.swipe(x_start, y_start, x_end, y_start, t)

29.228571

51

0.642229

198

1,023

3.116162

0.181818

0.097245

0.071313

0.800648

0.722853

0

0.057485

0.183773

1,023

34

52

30.088235

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false

0

0.142857

0

null

0

1

0

null

0

6

3cd114f0378d0a11f23adfa24172763f1784247d

205

py

Python

cart/views.py

KalifiaBillal/Vege-Foods-Store

53cec831279874e574829507843fd33d4a3a45b1

[ "MIT" ]

null

cart/views.py

KalifiaBillal/Vege-Foods-Store

53cec831279874e574829507843fd33d4a3a45b1

[ "MIT" ]

9

2021-03-19T13:16:56.000Z

2022-03-12T00:32:49.000Z

cart/views.py

KalifiaBillal/VEGEFOODS

53cec831279874e574829507843fd33d4a3a45b1

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. def cart_page(request): return render(request, 'cart/cart.html') def checkout(request): return render(request, 'cart/checkout.html')

22.777778

48

0.746341

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205

5.428571

0.571429

0.171053

0.25

0.342105

0.394737

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0.141463

205

8

49

25.625

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1

0

null

0

1

0

1

0

6

3ce6fc1ada981243ab5b7dbe4e47e5b7ab8d820f

48

py

Python

rdfsync/githubcon/__init__.py

weso/wikibase-sync

da17aa1e691cde4c1c66bd87bc3ca3d7b899c261

[ "MIT" ]

5

2021-03-30T06:16:33.000Z

2021-04-17T09:11:32.000Z

rdfsync/githubcon/__init__.py

weso/rdfsync

fd58206d8953e8e60e366dbc0d04d6444e6b3a5e

[ "MIT" ]

7

2021-01-30T16:28:15.000Z

2021-02-17T12:01:37.000Z

rdfsync/githubcon/__init__.py

weso/wikibase-sync

da17aa1e691cde4c1c66bd87bc3ca3d7b899c261

[ "MIT" ]

4

2020-09-01T10:47:39.000Z

2021-07-14T11:38:21.000Z

from .github_connection import GithubConnection

24

47

0.895833

5

48

8.4

1

0

0.083333

48

1

48

0.954545

0

1

0

true

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1

0

1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

6

3ced608e1b3f77e707a49fcd0deba7dd8c133768

66

py

Python

lib/backbone/__init__.py

ZhangMingliangAI/Bayesian_Prior_Rescale

7da8b28be2bc259e2e580baade6e85b95c58362d

[ "MIT" ]

1

2020-09-23T09:21:47.000Z

lib/backbone/__init__.py

ZhangMingliangAI/Bayesian_Prior_Modulation

7da8b28be2bc259e2e580baade6e85b95c58362d

[ "MIT" ]

1

2021-02-25T11:56:14.000Z

lib/backbone/__init__.py

ZhangMingliangAI/Bayesian_Prior_Modulation

7da8b28be2bc259e2e580baade6e85b95c58362d

[ "MIT" ]

null

from .resnet import res50 from .resnet_cifar import res32_cifar

22

38

0.818182

10

66

5.2

0.6

0.384615

0

0.071429

0.151515

66

2

39

33

0.857143

0

1

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true

0

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1

0

null

0

1

0

1

0

1

0

6

a71f5fc61cbf5791d1843295a49365d0f83fcdca

41

py

Python

picpay/__init__.py

hudsonbrendon/picpay-python

f40a4d436ab32160b9ae7514f985f0b5bc7c68c8

[ "MIT" ]

17

2020-01-29T11:50:26.000Z

2022-03-22T13:35:02.000Z

picpay/__init__.py

hudsonbrendon/picpay-python

f40a4d436ab32160b9ae7514f985f0b5bc7c68c8

[ "MIT" ]

2

2020-02-08T02:15:11.000Z

2020-03-31T11:02:41.000Z

picpay/__init__.py

hudsonbrendon/picpay-python

f40a4d436ab32160b9ae7514f985f0b5bc7c68c8

[ "MIT" ]

4

2020-01-31T09:28:15.000Z

2020-09-02T13:19:33.000Z

from .picpay import PicPay # noqa: F401

20.5

40

0.731707

6

41

5

0.833333

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0.090909

0.195122

41

1

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true

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1

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1

0

1

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6

59863f6d391585500a82d8aeff1c7b4600081799

131

py

Python

examplePackage/__init__.py

filimor/example-package

59027f0fbc307f76abaea5d3892a43448e26b81e

[ "MIT" ]

null

examplePackage/__init__.py

filimor/example-package

59027f0fbc307f76abaea5d3892a43448e26b81e

[ "MIT" ]

null

examplePackage/__init__.py

filimor/example-package

59027f0fbc307f76abaea5d3892a43448e26b81e

[ "MIT" ]

1

2021-09-16T18:42:06.000Z

from examplePackage.SerializerFactory import SerializerFactory from examplePackage.TargetServiceBuilder import TargetServiceBuilder

65.5

68

0.931298

10

131

12.2

0.5

0.295082

0

0.053435

131

2

68

65.5

0.983871

0

1

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true

0

1

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1

0

1

null

1

0

1

0

null

0

1

0

1

0

6

598a4cb80891f3ec52403c70642bc4b0d1d914cc

202

py

Python

tests/model_indexes/models.py

JBKahn/django

32265361279b3316f5bce8efa71f2049409461e3

[ "PSF-2.0", "BSD-3-Clause" ]

2

2015-02-06T05:25:49.000Z

2019-07-25T03:44:02.000Z

tests/model_indexes/models.py

seanfagan/django

66bbde6819586cc3a75630e12e569dc8ae72f211

[ "PSF-2.0", "BSD-3-Clause" ]

null

tests/model_indexes/models.py

seanfagan/django

66bbde6819586cc3a75630e12e569dc8ae72f211

[ "PSF-2.0", "BSD-3-Clause" ]

1

2020-02-06T10:31:51.000Z

from django.db import models class Book(models.Model): title = models.CharField(max_length=50) author = models.CharField(max_length=50) pages = models.IntegerField(db_column='page_count')

25.25

55

0.747525

28

202

5.25

0.678571

0.204082

0.244898

0.326531

0.353742

0

0.023121

0.143564

202

7

56

28.857143

0.82659

0

0.049505

0

1

0

false

0

0.2

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1

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1

0

null

1

0

1

0

null

0

1

0

6

5990b602170e13984cb772a781095093a4f5fcfa

43

py

Python

microquake/plugin/grid/__init__.py

jeanphilippemercier/microquake

0b9d07be11eddd64619e46939c320487531602a3

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

microquake/plugin/grid/__init__.py

jeanphilippemercier/microquake

0b9d07be11eddd64619e46939c320487531602a3

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

microquake/plugin/grid/__init__.py

jeanphilippemercier/microquake

0b9d07be11eddd64619e46939c320487531602a3

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

from microquake.plugin.grid.core import *

14.333333

41

0.790698

6

43

5.666667

1

0

0.116279

43

2

42

21.5

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

aba69fe840a6128a29bee9238919eaab5c4f4867

35

py

Python

app/__init__.py

osintalex/wetransferchecker

cfc85a5f01877d04d0c8f06adde894cc8ff622eb

[ "CC0-1.0" ]

null

app/__init__.py

osintalex/wetransferchecker

cfc85a5f01877d04d0c8f06adde894cc8ff622eb

[ "CC0-1.0" ]

null

app/__init__.py

osintalex/wetransferchecker

cfc85a5f01877d04d0c8f06adde894cc8ff622eb

[ "CC0-1.0" ]

null

from app.wetransferchecker import *

35

0.857143

4

35

7.5

1

0

0.085714

35

1

35

0.9375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e6461b176c209da37884336056837f74bc29669f

114

py

Python

web/contest/forms.py

haihua-sysu/onlinejudge

41baebbf1630bd647009b7efe3ecd09de628387f

[ "MIT" ]

null

web/contest/forms.py

haihua-sysu/onlinejudge

41baebbf1630bd647009b7efe3ecd09de628387f

[ "MIT" ]

null

web/contest/forms.py

haihua-sysu/onlinejudge

41baebbf1630bd647009b7efe3ecd09de628387f

[ "MIT" ]

null

#/usr/bin/env python # coding: utf-8 from django import forms from django.core.exceptions import ValidationError

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e6648acd7632dca28b90819ba22065404c5ab639

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py

Python

BankUIDemo/CommonFile.py

TecWriterWang/PySide2_Bank_GUI

60b0665184b0f04c69fc3d1aad0f66152f83a872

[ "Apache-2.0" ]

null

BankUIDemo/CommonFile.py

TecWriterWang/PySide2_Bank_GUI

60b0665184b0f04c69fc3d1aad0f66152f83a872

[ "Apache-2.0" ]

null

BankUIDemo/CommonFile.py

TecWriterWang/PySide2_Bank_GUI

60b0665184b0f04c69fc3d1aad0f66152f83a872

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # @Time : 2020/7/18 17:11

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050ac6c41b17f8188ce8da01bd015c9a0e18b17a

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py

Python

Modules/vms/pscandef/pscandef.py

vmssoftware/cpython

b5d2c7f578d33963798a02ca32f0c151c908aa7c

[ "0BSD" ]

2

2021-10-06T15:46:53.000Z

2022-01-26T02:58:54.000Z

Modules/vms/pscandef/pscandef.py

vmssoftware/cpython

b5d2c7f578d33963798a02ca32f0c151c908aa7c

[ "0BSD" ]

null

Modules/vms/pscandef/pscandef.py

vmssoftware/cpython

b5d2c7f578d33963798a02ca32f0c151c908aa7c

[ "0BSD" ]

null

from _pscandef import *

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py

Python

DataView/shell/gen_echarts.py

DYC2016/zhaopin

eb3920d05160a9e5570c958e08e9b950db660f64

[ "Apache-2.0" ]

null

DataView/shell/gen_echarts.py

DYC2016/zhaopin

eb3920d05160a9e5570c958e08e9b950db660f64

[ "Apache-2.0" ]

null

DataView/shell/gen_echarts.py

DYC2016/zhaopin

eb3920d05160a9e5570c958e08e9b950db660f64

[ "Apache-2.0" ]

null

import sys import os,django sys.path.append(os.path.dirname(os.path.abspath('.'))) os.environ.setdefault("DJANGO_SETTINGS_MODULE", "DataView.settings")# project_name 项目名称 django.setup() from django_pandas.io import read_frame import pandas as pd import random from DataView.settings import * from zp.models import * from pyecharts import Page,Line,Bar,Pie,Map,Grid,Overlap,Timeline,WordCloud import math zwlb_list = CategoryModel.objects.all() zwlb_list=[item.category for item in zwlb_list]+[''] def get_echarts_all_by_zwyx_value(x,key): return pd.Series({key: x[key].tolist()[0], 'max_zwyx': x['max_zwyx'].max(), 'min_zwyx': x['min_zwyx'].min(), 'count': x['count'].sum(),'zprs':x['zprs'].sum()}) def get_echarts_all_by_value(x,key): return pd.Series({key:x[key].tolist()[0],'count':x['count'].sum()}) # 地点(地图+柱状) def gen_zwyx_dd(zwlb): qs = ZpZwByAreaModel.objects if zwlb: qs = qs.filter(zwlb=zwlb) path=f'zwyx_dd/{zwlb}.html' else: path = 'zwyx_dd.html' page=Page() df = read_frame(qs.all()) if len(df)>0: df_group=df.groupby(['year','month']) time_line_chart1= Timeline(width=1500, height=450,is_auto_play=False,timeline_bottom=0) time_line_chart2= Timeline(width=1500, height=450,is_auto_play=False,timeline_bottom=0) for name,group in df_group: # 地图平均薪资 month=group['month'].tolist()[0] year=group['year'].tolist()[0] df_new=group.groupby('province').apply(get_echarts_all_by_zwyx_value,'province') data = [(a, (b + c) / 2) for a, b, c in zip(df_new['province'].tolist(), df_new['max_zwyx'].tolist(), df_new['min_zwyx'].tolist())] chart = Map(f'{zwlb}平均职位月薪与地点',width=1500, height=450) attr, value = chart.cast(data) chart.add(f'平均薪资', attr, value, wmaptype='china', is_label_show=True, is_visualmap=True, visual_range=[int(min(value)), int(max(value))],visual_pos='right',visual_top='top') time_line_chart1.add(chart,f'{year}年{month}月') # 本月职位量Top20 chart3=Pie(f'{zwlb}职位量及招聘人数',width=1500) chart3.add('职位量', df_new['province'].tolist(), df_new['count'].tolist(),center=[25,50],is_label_show=True) chart3.add('招聘人数', df_new['province'].tolist(), df_new['zprs'].tolist(),center=[75,50],is_label_show=True) time_line_chart2.add(chart3,f'{year}年{month}月') page.add(time_line_chart1) page.add(time_line_chart2) page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path))) # 学历（柱状图+饼图） def gen_zwyx_xl(zwlb): qs = ZpZwyxByXlModel.objects if zwlb: qs = qs.filter(zwlb=zwlb) path = f'zwyx_xl/{zwlb}.html' else: path = 'zwyx_xl.html' df = read_frame(qs.all()) if len(df) > 0: page = Page() df_group = df.groupby(['year', 'month']) time_line_chart1 = Timeline(width=1500, height=450, is_auto_play=False, timeline_bottom=0) time_line_chart2 = Timeline(width=1500, height=450, is_auto_play=False, timeline_bottom=0) for name, group in df_group: # 地图平均薪资 month = group['month'].tolist()[0] year = group['year'].tolist()[0] df_new=group.groupby('xl').apply(get_echarts_all_by_zwyx_value, 'xl') Overlap_chart = Overlap(width=1500, height=450) bar_chart = Bar(f'{zwlb}职位月薪与学历') bar_chart.add('最低薪资', df_new['xl'].tolist(), df_new['min_zwyx'].tolist(), is_label_show=True, is_stack=True,is_more_utils=True) bar_chart.add('最高薪资', df_new['xl'].tolist(), df_new['max_zwyx'].tolist(),is_more_utils=True) line_chart = Line() line_chart.add("平均薪资", df_new['xl'].tolist(), [(a + b) / 2 for a, b in zip(df_new['min_zwyx'].tolist(), df_new['max_zwyx'].tolist())]) Overlap_chart.add(bar_chart) Overlap_chart.add(line_chart) time_line_chart1.add(Overlap_chart,f'{year}年{month}月') chart3 = Pie(f'{zwlb}职位量及招聘人数', width=1500) chart3.add('职位量', df_new['xl'].tolist(), df_new['count'].tolist(), is_label_show=True, is_stack=True, center=[25, 50]) chart3.add('招聘人数', df_new['xl'].tolist(), df_new['zprs'].tolist(), is_label_show=True, is_stack=True, center=[75, 50]) time_line_chart2.add(chart3,f'{year}年{month}月') page.add(time_line_chart1) page.add(time_line_chart2) page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path))) # 公司规模（折线图+柱状图） def gen_zwyx_gsgm(zwlb): qs = ZpZwyxByGsgmModel.objects if zwlb: qs = qs.filter(zwlb=zwlb) path = f'zwyx_gsgm/{zwlb}.html' else: path = 'zwyx_gsgm.html' # 当月职位月薪与公司规模 df = read_frame(qs.all()) if len(df) > 0: page = Page() Grid_chart1 = Timeline(width=1500, height=450,timeline_bottom=0) Grid_chart2 = Timeline(width=1500, height=450,timeline_bottom=0) df_group=df.groupby(['year','month']) for name,group in df_group: month=group['month'].tolist()[0] year=group['year'].tolist()[0] df_new=group.groupby('gsgm').apply(get_echarts_all_by_zwyx_value, 'gsgm') # 薪资 Overlap_chart = Overlap(width=800, height=450) bar_chart = Bar(f'{zwlb}职位月薪与公司规模') bar_chart.add('最低薪资', df_new['gsgm'].tolist(), df_new['min_zwyx'].tolist(), is_label_show=True,is_more_utils=True) bar_chart.add('最高薪资', df_new['gsgm'].tolist(), df_new['max_zwyx'].tolist(),is_label_show=True,is_more_utils=True) line_chart = Line() line_chart.add("平均薪资", df_new['gsgm'].tolist(), [(a + b) / 2 for a, b in zip(df_new['min_zwyx'].tolist(), df_new['max_zwyx'].tolist())],is_label_show=True) Overlap_chart.add(bar_chart) Overlap_chart.add(line_chart) Grid_chart1.add(Overlap_chart,f'{year}年{month}月') # 职位量 chart3 = Bar(f'{zwlb}职位量及招聘人数', width=1500) chart3.add('职位量', df_new['gsgm'].tolist(), df_new['count'].tolist(), is_label_show=True,is_toolbox_show=True) chart3.add('招聘人数', df_new['gsgm'].tolist(), df_new['zprs'].tolist(), is_label_show=True) Grid_chart2.add(chart3,f'{year}年{month}月') page.add(Grid_chart1) page.add(Grid_chart2) page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path))) # 公司性质（柱状+饼图） def gen_zwyx_gsxz(zwlb): qs = ZpZwyxByGsxzModel.objects if zwlb: qs = qs.filter(zwlb=zwlb) path = f'zwyx_gsxz/{zwlb}.html' else: path = 'zwyx_gsxz.html' # 公司性质 df = read_frame(qs.all()) if len(df) > 0: page = Page() Grid_chart1 = Timeline(width=1500, height=450, timeline_bottom=0) Grid_chart2 = Timeline(width=1500, height=450, timeline_bottom=0) df_group = df.groupby(['year', 'month']) for name, group in df_group: month = group['month'].tolist()[0] year = group['year'].tolist()[0] df_new = group.groupby('gsxz').apply(get_echarts_all_by_zwyx_value, 'gsxz') # 薪资 Overlap_chart = Overlap(width=800, height=450) bar_chart = Bar(f'{zwlb}职位月薪与公司性质') bar_chart.add('最低薪资', df_new['gsxz'].tolist(), df_new['min_zwyx'].tolist(), is_label_show=True,is_more_utils=True) bar_chart.add('最高薪资', df_new['gsxz'].tolist(), df_new['max_zwyx'].tolist(), is_label_show=True,is_more_utils=True) line_chart = Line() line_chart.add("平均薪资", df_new['gsxz'].tolist(), [(a + b) / 2 for a, b in zip(df_new['min_zwyx'].tolist(), df_new['max_zwyx'].tolist())], is_label_show=True,is_more_utils=True) Overlap_chart.add(bar_chart) Overlap_chart.add(line_chart) Grid_chart1.add(Overlap_chart, f'{year}年{month}月') # 职位量 chart3 = Pie(f'{zwlb}职位量及招聘人数', width=1500) chart3.add('职位量'.format(zwlb), df_new['gsxz'].tolist(), df_new['count'].tolist(), is_label_show=True,is_stack=True,center=[25, 50]) chart3.add('招聘人数'.format(zwlb), df_new['gsxz'].tolist(), df_new['zprs'].tolist(), is_label_show=True,is_stack=True,center=[75, 50]) Grid_chart2.add(chart3, f'{year}年{month}月') page.add(Grid_chart1) page.add(Grid_chart2) page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path))) # 公司行业（复合图+折线图） def gen_zwyx_gshy(zwlb): qs = ZpZwyxByGshyModel.objects if zwlb: qs = qs.filter(zwlb=zwlb) path = f'zwyx_gshy/{zwlb}.html' else: path = 'zwyx_gshy.html' df = read_frame(qs.all()) if len(df) > 0: page = Page() Grid_chart1 = Timeline(width=1500, height=450, timeline_bottom=0) Grid_chart2 = Timeline(width=1500, height=450, timeline_bottom=0) df_group = df.groupby(['year', 'month']) for name, group in df_group: month = group['month'].tolist()[0] year = group['year'].tolist()[0] df_new = group.groupby('gshy').apply(get_echarts_all_by_zwyx_value, 'gshy') # 薪资 Overlap_chart = Overlap(width=800, height=450) bar_chart = Bar(f'{zwlb}职位月薪与公司行业') data_len=math.ceil(0.1*len(df_new)) bar_chart.add('最低薪资', df_new['gshy'].tolist(), df_new['min_zwyx'].tolist(), is_label_show=True,datazoom_type="both",datazoom_range=[0,data_len], is_datazoom_show=True,is_more_utils=True) bar_chart.add('最高薪资', df_new['gshy'].tolist(), df_new['max_zwyx'].tolist(), is_label_show=True,datazoom_type="both",datazoom_range=[0,data_len], is_datazoom_show=True,is_more_utils=True) line_chart = Line() line_chart.add("平均薪资", df_new['gshy'].tolist(), [(a + b) / 2 for a, b in zip(df_new['min_zwyx'].tolist(), df_new['max_zwyx'].tolist())], is_label_show=True,datazoom_type="both",datazoom_range=[0,10], is_datazoom_show=True) Overlap_chart.add(bar_chart) Overlap_chart.add(line_chart) Grid_chart1.add(Overlap_chart, f'{year}年{month}月') # 职位量 chart3 = Bar(f'{zwlb}职位量及招聘人数', width=1500) chart3.add('职位量', df_new['gshy'].tolist(), df_new['count'].tolist(), is_label_show=True, is_toolbox_show=True,datazoom_type="both",datazoom_range=[0,data_len], is_datazoom_show=True) chart3.add('招聘人数', df_new['gshy'].tolist(), df_new['zprs'].tolist(), is_label_show=True,datazoom_type="both",datazoom_range=[0,data_len], is_datazoom_show=True) Grid_chart2.add(chart3, f'{year}年{month}月') page.add(Grid_chart1) page.add(Grid_chart2) page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path))) # 职位类型（饼图+柱状） def gen_zwyx_type(zwlb): qs = ZpZwyxByTypeModel.objects if zwlb: qs = qs.filter(zwlb=zwlb) path = f'zwyx_type/{zwlb}.html' else: path = 'zwyx_type.html' # 当月职位月薪与公司性质 df = read_frame(qs.all()) if len(df) > 0: page = Page() Grid_chart1 = Timeline(width=1500, height=450, timeline_bottom=0) Grid_chart2 = Timeline(width=1500, height=450, timeline_bottom=0) df_group = df.groupby(['year', 'month']) for name, group in df_group: month = group['month'].tolist()[0] year = group['year'].tolist()[0] df_new = group.groupby('type').apply(get_echarts_all_by_zwyx_value, 'type') # 薪资 Overlap_chart = Overlap(width=800, height=450) bar_chart = Bar(f'{zwlb}职位月薪与公司性质') bar_chart.add('最低薪资', df_new['type'].tolist(), df_new['min_zwyx'].tolist(), is_label_show=True,is_more_utils=True) bar_chart.add('最高薪资', df_new['type'].tolist(), df_new['max_zwyx'].tolist(), is_label_show=True,is_more_utils=True) line_chart = Line() line_chart.add("平均薪资", df_new['type'].tolist(), [(a + b) / 2 for a, b in zip(df_new['min_zwyx'].tolist(), df_new['max_zwyx'].tolist())], is_label_show=True) Overlap_chart.add(bar_chart) Overlap_chart.add(line_chart) Grid_chart1.add(Overlap_chart, f'{year}年{month}月') # 职位量 chart3 = Pie(f'{zwlb}职位量及招聘人数', width=1500) chart3.add('职位量'.format(zwlb), df_new['type'].tolist(), df_new['count'].tolist(), is_label_show=True, is_stack=True, center=[25, 50]) chart3.add('招聘人数'.format(zwlb), df_new['type'].tolist(), df_new['zprs'].tolist(), is_label_show=True, is_stack=True, center=[75, 50]) Grid_chart2.add(chart3, f'{year}年{month}月') page.add(Grid_chart1) page.add(Grid_chart2) page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path))) # 各就业方向分析（折线+饼图） def gen_zwyx_zw_count(zwlb): # 不同薪资的职位量和招聘人数分布 qs = ZpZwCountByZwyxModel.objects if zwlb: qs = qs.filter(zwlb=zwlb) path = f'zwyx_zw_count/{zwlb}.html' else: path = 'zwyx_zw_count.html' df = read_frame(qs.all()) if len(df) > 0: page = Page() Grid_chart1 = Timeline(width=1500, height=800, timeline_bottom=0) df_group = df.groupby(['year', 'month']) for name, group in df_group: month = group['month'].tolist()[0] year = group['year'].tolist()[0] df_new = group.groupby('zwyx').apply(get_echarts_all_by_zwyx_value, 'zwyx') # 薪资 bar_chart = Bar(f'{zwlb}招聘人数与职位量', width=1500) bar_chart.add('职位量', df_new['zwyx'].tolist(), df_new['count'].tolist(),is_label_show=True,datazoom_type="both",datazoom_range=[0,5], is_datazoom_show=True) bar_chart.add('招聘人数'.format(zwlb), df_new['zwyx'].tolist(), df_new['zprs'].tolist(), is_label_show=True,datazoom_type="both",datazoom_range=[0, 5], is_datazoom_show=True) Grid_chart1.add(bar_chart, f'{year}年{month}月') page.add(Grid_chart1) page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path))) # 职位技能词云 def gen_gwzz_word(zwlb): qs = ZpWordByZwlbModel.objects if zwlb: qs = qs.filter(zwlb=zwlb) path = f'zp_word/{zwlb}.html' else: path = 'zp_word.html' df = read_frame(qs.all()) if len(df) > 0: page = Page() Grid_chart1 = Timeline(width=1500, height=800, timeline_bottom=0) df_group = df.groupby(['year', 'month']) for name, group in df_group: month = group['month'].tolist()[0] year = group['year'].tolist()[0] df_new = group.groupby('word').apply(get_echarts_all_by_value, 'word') chart = WordCloud(f'{zwlb}岗位需求词云', width=1500) shape_list=[None,'circle', 'cardioid', 'diamond', 'triangle-forward', 'triangle', 'pentagon', 'star'] chart.add("", df_new['word'].tolist(), df_new['count'].tolist(), word_size_range=[30, 100], rotate_step=66,shape=shape_list[random.randint(0,len(shape_list)-1)]) Grid_chart1.add(chart, f'{year}年{month}月') page.add(Grid_chart1) page.render(os.path.join(BASE_DIR, 'templates/{}'.format(path))) for zwlb in zwlb_list: print(zwlb) gen_zwyx_zw_count(zwlb) # gen_zwyx_dd(zwlb) # gen_zwyx_xl(zwlb) # gen_zwyx_gsgm(zwlb) # gen_zwyx_gsxz(zwlb) # gen_zwyx_gshy(zwlb) # gen_zwyx_type(zwlb) # gen_gwzz_word(zwlb)

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e9567ea167197a6c046dfe02739922c8ea0e49fd

161

py

Python

cgspan_mining/__init__.py

NaazS03/cgSpan

7f2086b7d053bf0f38022a27c5c0c4abdfbafb61

[ "MIT" ]

2

2021-12-26T01:24:05.000Z

2022-03-24T03:54:14.000Z

cgspan_mining/__init__.py

NaazS03/CloseGraph

7afc33dbddba79be622636ba1f6ce972d36416f5

[ "MIT" ]

1

2022-03-24T03:53:48.000Z

2022-03-25T03:02:31.000Z

cgspan_mining/__init__.py

NaazS03/CloseGraph

7afc33dbddba79be622636ba1f6ce972d36416f5

[ "MIT" ]

2

2021-12-28T09:02:22.000Z

2022-03-24T03:54:20.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function from .cgspan import cgSpan __version__ = '0.2.2'

17.888889

38

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e95f4be73117830cc569f43ca977e89c0ccc3aec

96

py

Python

venv/lib/python3.8/site-packages/cachecontrol/heuristics.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/cachecontrol/heuristics.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/cachecontrol/heuristics.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/f2/40/32/b992d20b2108810afabdb5307e1a6a83da30b3898cd0857a0d66b37af2

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6

e96eef103f515998492e0fbc04b479e4627f6f8e

72

py

Python

py_tdlib/constructors/get_favorite_stickers.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/get_favorite_stickers.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/get_favorite_stickers.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Method class getFavoriteStickers(Method): pass

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6

e9b01a7fc09e7b6a9b102516fa0c5706d1debe50

20,327

py

Python

controller/machine_learning_controller/machine_learning_controller.py

rohandhanraj/Auto-AI-Pipeline

d5f39715c802db45afae0d5978d228bf0bcd2f0a

[ "MIT" ]

null

controller/machine_learning_controller/machine_learning_controller.py

rohandhanraj/Auto-AI-Pipeline

d5f39715c802db45afae0d5978d228bf0bcd2f0a

[ "MIT" ]

null

controller/machine_learning_controller/machine_learning_controller.py

rohandhanraj/Auto-AI-Pipeline

d5f39715c802db45afae0d5978d228bf0bcd2f0a

[ "MIT" ]

null

import os import sys from os import abort from flask import render_template, redirect, url_for, jsonify, session, request, Response, stream_with_context import threading import json import time from data_access_layer.mongo_db.mongo_db_atlas import MongoDBOperation from project_library_layer.initializer.initializer import Initializer from integration_layer.file_management.file_manager import FileManager from cloud_storage_layer.aws.amazon_simple_storage_service import AmazonSimpleStorageService from entity_layer.registration.registration import Register from logging_layer.logger.log_request import LogRequest from logging_layer.logger.log_exception import LogExceptionDetail from entity_layer.project.project import Project from entity_layer.project.project_configuration import ProjectConfiguration from thread_layer.train_model_thread.train_model_thread import TrainModelThread from thread_layer.predict_from_model_thread.predict_from_model_thread import PredictFromModelThread from data_access_layer.mongo_db.mongo_db_atlas import MongoDBOperation import json import uuid from logging_layer.logger.logger import AppLogger global process_value class MachineLearningController: def __init__(self): self.registration_obj = Register() self.project_detail = Project() self.project_config = ProjectConfiguration() self.WRITE = "WRITE" self.READ = "READ" def predict_route_client(self): project_id = None try: log_writer = LogRequest(executed_by=None, execution_id=str(uuid.uuid4())) try: # log_writer = LogRequest(executed_by=None, execution_id=str(uuid.uuid4())) if 'email_address' in session: log_writer.executed_by = session['email_address'] log_writer.log_start(request) requested_project_data = json.loads(request.data) project_id = None if 'project_id' in requested_project_data: project_id = int(requested_project_data['project_id']) if project_id is None: raise Exception('Project id required') result = self.registration_obj.validate_access(session['email_address'], operation_type=self.WRITE) if not result['status']: log_writer.log_stop(result) result.update( {'message_status': 'info', 'project_id': project_id, 'execution_id': log_writer.execution_id}) return jsonify(result) database_name = Initializer().get_training_thread_database_name() collection_name = Initializer().get_thread_status_collection_name() query = {'project_id': project_id, 'is_running': True} result = MongoDBOperation().get_record(database_name=database_name, collection_name=collection_name, query=query) if result is not None: execution_id = result['execution_id'] else: execution_id = None if execution_id is not None: result = {'message': 'Training/prediction is in progress.', 'execution_id': execution_id, 'status': True, 'message_status': 'info'} log_writer.log_stop(result) return jsonify(result) result = {} if project_id == 16: sentiment_project_id = requested_project_data['sentiment_project_id'] sentiment_user_id = requested_project_data['sentiment_user_id'] sentiment_data = requested_project_data['sentiment_data'] record = { 'execution_id': log_writer.execution_id, 'sentiment_user_id': sentiment_user_id, 'sentiment_data': sentiment_data, 'sentiment_project_id': sentiment_project_id } MongoDBOperation().insert_record_in_collection("sentiment_data_prediction", "sentiment_input", record ) predict_from_model_obj = PredictFromModelThread(project_id=project_id, executed_by=log_writer.executed_by, execution_id=log_writer.execution_id, log_writer=log_writer) predict_from_model_obj.start() result.update( {'message': 'Prediction started your execution id {0}'.format(log_writer.execution_id)}) result.update({'message_status': 'info', 'project_id': project_id, 'status': True, 'execution_id': log_writer.execution_id}) return jsonify(result) else: result = {'status': True, 'message': 'Please login to your account', 'execution_id': log_writer.execution_id} log_writer.log_stop(result) return jsonify(result) except Exception as e: result = {'status': False, 'message': str(e), 'message_status': 'info', 'project_id': project_id, 'execution_id': log_writer.execution_id} log_writer.log_stop(result) log_exception = LogExceptionDetail(log_writer.executed_by, log_writer.execution_id) log_exception.log(str(e)) return jsonify(result) except Exception as e: return jsonify({'status': False, 'message': str(e) , 'message_status': 'info', 'project_id': project_id}) def train_route_client(self): project_id = None try: log_writer = LogRequest(executed_by=None, execution_id=str(uuid.uuid4())) try: # log_writer = LogRequest(executed_by=None, execution_id=str(uuid.uuid4())) if 'email_address' in session: log_writer.executed_by = session['email_address'] log_writer.log_start(request) requested_project_data = json.loads(request.data) project_id = None if 'project_id' in requested_project_data: project_id = int(requested_project_data['project_id']) if project_id is None: raise Exception('Project id required') result = self.registration_obj.validate_access(session['email_address'], operation_type=self.WRITE) if not result['status']: log_writer.log_stop(result) result.update( {'message_status': 'info', 'project_id': project_id, 'execution_id': log_writer.execution_id}) return jsonify(result) database_name = Initializer().get_training_thread_database_name() collection_name = Initializer().get_thread_status_collection_name() query = {'project_id': project_id, 'is_running': True} result = MongoDBOperation().get_record(database_name=database_name, collection_name=collection_name, query=query) if result is not None: execution_id = result['execution_id'] else: execution_id = None if execution_id is not None: result = {'message': 'Training/prediction is in progress.', 'execution_id': execution_id, 'status': True, 'message_status': 'info'} log_writer.log_stop(result) return jsonify(result) result = {} if project_id == 16: sentiment_project_id = requested_project_data['sentiment_project_id'] sentiment_user_id = requested_project_data['sentiment_user_id'] sentiment_data = requested_project_data['sentiment_data'] record = { 'execution_id': log_writer.execution_id, 'sentiment_user_id': sentiment_user_id, 'sentiment_data': sentiment_data, 'sentiment_project_id': sentiment_project_id } print(record) MongoDBOperation().insert_record_in_collection("sentiment_data_training", "sentiment_input", record) train_model = TrainModelThread(project_id=project_id, executed_by=log_writer.executed_by, execution_id=log_writer.execution_id, log_writer=log_writer) train_model.start() result.update({'status': True, 'message': 'Training started. keep execution_id[{}] to track'.format( log_writer.execution_id), 'message_status': 'info', 'project_id': project_id, 'execution_id': log_writer.execution_id}) log_writer.log_stop(result) return jsonify(result) else: result = {'status': True, 'message': 'Please login to your account', 'execution_id': log_writer.execution_id} log_writer.log_stop(result) return jsonify(result) except Exception as e: result = {'status': False, 'message': str(e), 'message_status': 'info', 'project_id': project_id, 'execution_id': log_writer.execution_id} log_writer.log_stop(result) log_exception = LogExceptionDetail(log_writer.executed_by, log_writer.execution_id) log_exception.log(str(e)) return render_template('error.html', context=result) except Exception as e: result = {'status': False, 'message': str(e) , 'message_status': 'info', 'project_id': project_id, 'execution_id': None} return render_template('error.html', context=result) def prediction_output_file(self): project_id = None try: log_writer = LogRequest(executed_by=None, execution_id=str(uuid.uuid4())) try: # log_writer = LogRequest(executed_by=None, execution_id=str(uuid.uuid4())) if 'email_address' in session: log_writer.executed_by = session['email_address'] log_writer.log_start(request) project_id = request.args.get('project_id', None) error_message = "" if project_id is None: error_message = error_message + "Project id required" project_id = int(project_id) result = self.project_detail.get_project_detail(project_id=project_id) project_detail = result.get('project_detail', None) project_name = project_detail.get('project_name', None) result = self.registration_obj.validate_access(session['email_address'], operation_type=self.READ) if not result['status']: error_message = error_message + result['message'] context = {'status': True, 'project_name': project_name, 'output_file': None, 'message': error_message} log_writer.log_stop(context) return render_template('prediction_output.html', context=context) prediction_file_path = Initializer().get_prediction_output_file_path(project_id=project_id, ) prediction_file = Initializer().get_prediction_output_file_name() project_config_detail = self.project_config.get_project_configuration_detail(project_id=project_id) project_config_detail = project_config_detail.get('project_config_detail', None) if project_config_detail is None: context = {'status': True, 'project_name': project_name, 'output_file': None, 'message': 'project config missing'} log_writer.log_stop(context) return render_template('prediction_output.html', context=context) cloud_name = project_config_detail['cloud_storage'] file_manager = FileManager(cloud_name) result = file_manager.read_file_content(directory_full_path=prediction_file_path, file_name=prediction_file) file_content = result.get('file_content', None) if file_content is None: context = {'status': True, 'project_name': project_name, 'output_file': None, 'message': 'Output file not found'} log_writer.log_stop(context) return render_template('prediction_output.html', context=context) context = {'status': True, 'project_name': project_name, 'output_file': file_content.to_html(header="true"), 'message': 'Output file retrived', } log_writer.log_stop(context) return render_template('prediction_output.html', context=context) else: result = {'status': True, 'message': 'Please login to your account'} log_writer.log_stop(result) return Response(result) except Exception as e: exc_type, exc_obj, exc_tb = sys.exc_info() file_name = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] exception_type = e.__repr__() exception_detail = {'exception_type': exception_type, 'file_name': file_name, 'line_number': exc_tb.tb_lineno, 'detail': sys.exc_info().__str__()} print(exception_detail) return render_template('error.html', context={'message': None, 'status ': False, 'message_status': 'info', 'error_message': exception_detail.__str__()}) except Exception as e: exc_type, exc_obj, exc_tb = sys.exc_info() file_name = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] exception_type = e.__repr__() exception_detail = {'exception_type': exception_type, 'file_name': file_name, 'line_number': exc_tb.tb_lineno, 'detail': sys.exc_info().__str__()} print(exception_detail) return render_template('error.html', context={'message': None, 'status ': False, 'message_status': 'info', 'error_message': exception_detail.__str__()}) def get_log_detail(self): project_id = None try: log_writer = LogRequest(executed_by=None, execution_id=str(uuid.uuid4())) try: # log_writer = LogRequest(executed_by=None, execution_id=str(uuid.uuid4())) if 'email_address' in session: log_writer.executed_by = session['email_address'] log_writer.log_start(request) project_id = request.args.get('project_id', None) execution_id = request.args.get('execution_id', None) error_message = "" if project_id is None: error_message = error_message + "Project id required" if execution_id is None: error_message = error_message + "Execution id required" result = self.registration_obj.validate_access(session['email_address'], operation_type=self.READ) if not result['status']: error_message = error_message + result['message'] if len(error_message) > 0: log_writer.log_stop({'status': True, 'message': error_message}) return Response(error_message) result = MongoDBOperation().get_record(Initializer().get_training_thread_database_name(), Initializer().get_thread_status_collection_name(), {'execution_id': execution_id} ) if result is None: return Response("We don't have any log yet with execution id {}".format(execution_id)) process_type = result['process_type'] project_id = int(project_id) return Response( stream_with_context(AppLogger().get_log(project_id=project_id, execution_id=execution_id, process_type=process_type))) else: result = {'status': True, 'message': 'Please login to your account'} log_writer.log_stop(result) return Response(result) except Exception as e: exc_type, exc_obj, exc_tb = sys.exc_info() file_name = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] exception_type = e.__repr__() exception_detail = {'exception_type': exception_type, 'file_name': file_name, 'line_number': exc_tb.tb_lineno, 'detail': sys.exc_info().__str__()} result = {'status': False, 'message': f"{exception_detail}", 'message_status': 'info', 'project_id': project_id} log_writer.log_stop(result) log_exception = LogExceptionDetail(log_writer.executed_by, log_writer.execution_id) log_exception.log(f"{exception_detail}") return render_template('error.html', context={'message': None, 'status ': False, 'message_status': 'info', 'error_message': f"{exception_detail}"}) except Exception as e: exc_type, exc_obj, exc_tb = sys.exc_info() file_name = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] exception_type = e.__repr__() exception_detail = {'exception_type': exception_type, 'file_name': file_name, 'line_number': exc_tb.tb_lineno, 'detail': sys.exc_info().__str__()} return render_template('error.html', context={'message': None, 'status ': False, 'message_status': 'info', 'error_message': f"{exception_detail}"})

57.420904

128

0.537954

1,928

20,327

5.319502

0.089212

0.063183

0.026911

0.037051

0.773011

0.746685

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0.703296

0.690913

0

0.001431

0.380971

20,327

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129

57.583569

0.813637

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0.704762

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0.12618

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0.015873

false

0

0.069841

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0

null

0

1

0

1

0

null

0

6

e9dfc905d2603711d96972efda86ce34b6e0a982

36

py

Python

git/19110-branch.py

djangojeng-e/TIL

bdbe1dfb6ebc48b89067fddda195227cca64b8dc

[ "MIT" ]

null

git/19110-branch.py

djangojeng-e/TIL

bdbe1dfb6ebc48b89067fddda195227cca64b8dc

[ "MIT" ]

null

git/19110-branch.py

djangojeng-e/TIL

bdbe1dfb6ebc48b89067fddda195227cca64b8dc

[ "MIT" ]

null

print("fizzbuzz starts from here")

12

34

0.75

5

36

5.4

1

0

0.138889

36

2

35

18

0.870968

0

0.714286

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

7574915c378de2f362a442c6382d533c4ce1ba8f

86

py

Python

d3d/dataset/nuscenes/__init__.py

minghanz/d3d

1d08013238b300489f61be57cdd20a105d16a632

[ "MIT" ]

null

d3d/dataset/nuscenes/__init__.py

minghanz/d3d

1d08013238b300489f61be57cdd20a105d16a632

[ "MIT" ]

null

d3d/dataset/nuscenes/__init__.py

minghanz/d3d

1d08013238b300489f61be57cdd20a105d16a632

[ "MIT" ]

null

from .loader import NuscenesObjectLoader, NuscenesObjectClass, NuscenesDetectionClass

43

85

0.895349

6

86

12.833333

1

0

0.069767

86

1

86

0.9625

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

2f049139a78465c6012690075efabd66c543cacb

78

py

Python

minimal_log_host/__init__.py

mverleg/django_minimal_log

ec5aae411d6e7e30f33a07e6efe097f57d11b9df

[ "BSD-3-Clause" ]

4

2016-02-04T14:11:55.000Z

2019-08-06T18:21:01.000Z

minimal_log_host/__init__.py

mverleg/django_minimal_log

ec5aae411d6e7e30f33a07e6efe097f57d11b9df

[ "BSD-3-Clause" ]

9

2016-02-01T23:25:10.000Z

2016-12-23T20:49:17.000Z

minimal_log_host/__init__.py

mverleg/django_minimal_log

ec5aae411d6e7e30f33a07e6efe097f57d11b9df

[ "BSD-3-Clause" ]

null

""" Minimal log server, as Django app """ from .utils import generate_key

8.666667

33

0.692308

11

78

4.818182

1

0

0.205128

78

8

34

9.75

0.854839

0.423077

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

f93864ed25a15f5e9e0d195ebbbe5d0a406778f9

5,217

py

Python

tests/test_execute.py

choldgraf/MyST-NB

831eab2246d6af7a8c8674eef56a2e03c4c23a95

[ "BSD-3-Clause" ]

null

tests/test_execute.py

choldgraf/MyST-NB

831eab2246d6af7a8c8674eef56a2e03c4c23a95

[ "BSD-3-Clause" ]

null

tests/test_execute.py

choldgraf/MyST-NB

831eab2246d6af7a8c8674eef56a2e03c4c23a95

[ "BSD-3-Clause" ]

null

import pytest @pytest.mark.sphinx_params( "basic_unrun.ipynb", conf={"jupyter_execute_notebooks": "cache"} ) def test_basic_unrun(sphinx_run, file_regression, check_nbs): """The outputs should be populated.""" sphinx_run.build() assert sphinx_run.warnings() == "" assert "test_name" in sphinx_run.app.env.metadata["basic_unrun"] file_regression.check(sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb") file_regression.check(sphinx_run.get_doctree().pformat(), extension=".xml") @pytest.mark.sphinx_params( "basic_unrun.ipynb", conf={"jupyter_execute_notebooks": "cache"} ) def test_rebuild_cache(sphinx_run): """The notebook should only be executed once.""" sphinx_run.build() assert "Executing" in sphinx_run.status(), sphinx_run.status() sphinx_run.invalidate_files() sphinx_run.build() assert "Executing" not in sphinx_run.status(), sphinx_run.status() @pytest.mark.sphinx_params( "basic_unrun.ipynb", conf={"jupyter_execute_notebooks": "force"} ) def test_rebuild_force(sphinx_run): """The notebook should be executed twice.""" sphinx_run.build() assert "Executing" in sphinx_run.status(), sphinx_run.status() sphinx_run.invalidate_files() sphinx_run.build() assert "Executing" in sphinx_run.status(), sphinx_run.status() @pytest.mark.sphinx_params( "basic_unrun.ipynb", conf={ "jupyter_execute_notebooks": "cache", "execution_excludepatterns": ["basic_*"], }, ) def test_exclude_path(sphinx_run, file_regression): """The notebook should not be executed.""" sphinx_run.build() assert len(sphinx_run.app.env.excluded_nb_exec_paths) == 1 assert "Executing" not in sphinx_run.status(), sphinx_run.status() file_regression.check(sphinx_run.get_doctree().pformat(), extension=".xml") @pytest.mark.sphinx_params( "basic_failing.ipynb", conf={"jupyter_execute_notebooks": "cache"} ) def test_basic_failing(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert "Execution Failed" in sphinx_run.warnings() assert ( "Couldn't find cache key for notebook file source/basic_failing.ipynb" in sphinx_run.warnings() ) file_regression.check(sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb") file_regression.check(sphinx_run.get_doctree().pformat(), extension=".xml") sphinx_run.get_report_file() @pytest.mark.sphinx_params( "basic_unrun.ipynb", conf={"jupyter_execute_notebooks": "auto"} ) def test_basic_unrun_nbclient(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" assert "test_name" in sphinx_run.app.env.metadata["basic_unrun"] file_regression.check(sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb") file_regression.check(sphinx_run.get_doctree().pformat(), extension=".xml") @pytest.mark.sphinx_params( "basic_unrun.ipynb", conf={"jupyter_execute_notebooks": "force"} ) def test_outputs_present(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" assert "test_name" in sphinx_run.app.env.metadata["basic_unrun"] file_regression.check(sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb") file_regression.check(sphinx_run.get_doctree().pformat(), extension=".xml") @pytest.mark.sphinx_params( "complex_outputs_unrun.ipynb", conf={"jupyter_execute_notebooks": "cache"} ) def test_complex_outputs_unrun(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" file_regression.check(sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb") file_regression.check(sphinx_run.get_doctree().pformat(), extension=".xml") @pytest.mark.sphinx_params( "complex_outputs_unrun.ipynb", conf={"jupyter_execute_notebooks": "auto"} ) def test_complex_outputs_unrun_nbclient(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" file_regression.check(sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb") file_regression.check(sphinx_run.get_doctree().pformat(), extension=".xml") @pytest.mark.sphinx_params( "basic_unrun.ipynb", conf={"jupyter_execute_notebooks": "off"} ) def test_no_execute(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" file_regression.check(sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb") file_regression.check(sphinx_run.get_doctree().pformat(), extension=".xml") @pytest.mark.sphinx_params( "basic_unrun.ipynb", conf={"jupyter_execute_notebooks": "cache"} ) def test_jupyter_cache_path(sphinx_run, file_regression, check_nbs): sphinx_run.build() assert "Execution Succeeded" in sphinx_run.status() assert sphinx_run.warnings() == "" file_regression.check(sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb") file_regression.check(sphinx_run.get_doctree().pformat(), extension=".xml")

38.080292

86

0.734713

688

5,217

5.236919

0.113372

0.184846

0.131835

0.117957

0.874549

0.843741

0.832362

0.795448

0

0.000219

0.123826

5,217

136

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qrainbowstyle/widgets/__init__.py

desty2k/QDarkStyleSheet

4a8cd42acf5e9e7fce5fabbe37b1f97d89d203b2

[ "CC-BY-4.0" ]

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qrainbowstyle/widgets/__init__.py

desty2k/QDarkStyleSheet

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[ "CC-BY-4.0" ]

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qrainbowstyle/widgets/__init__.py

desty2k/QDarkStyleSheet

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[ "CC-BY-4.0" ]

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from qrainbowstyle.widgets.QtWaitingSpinner.pyqtspinner import WaitingSpinner from qrainbowstyle.widgets.GoogleMapsWidget.MapsWidget import GoogleMapsView, OpenStreetMapsView from qrainbowstyle.widgets.PythonQtWidgets.picker import (StylePickerGrid, StylePickerVertical, StylePickerHorizontal) from qrainbowstyle.widgets.QRoundProgressBar.qroundprogressbar import QRoundProgressBar

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zprev versions/Models_py_backup/Models backup/ln.py

lefthandedroo/Cosmodels

c355d18021467cf92546cf2fc9cb1d1abe59b8d8

[ "MIT" ]

null

zprev versions/Models_py_backup/Models backup/ln.py

lefthandedroo/Cosmodels

c355d18021467cf92546cf2fc9cb1d1abe59b8d8

[ "MIT" ]

null

zprev versions/Models_py_backup/Models backup/ln.py

lefthandedroo/Cosmodels

c355d18021467cf92546cf2fc9cb1d1abe59b8d8

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Feb 15 13:52:36 2018 @author: BallBlueMeercat """ import numpy as np from datasim import magn #def lnlike(theta, data, sigma, firstderivs_key, ndim): # ''' # Finding matter density m, interaction gamma. # ''' # mag = data['mag'] # # params = {} # if ndim == 1: # params = {'m':theta} # elif ndim == 2: # params = {'m':theta[0],'gamma':theta[1]} # # model = magn(params, data, firstderivs_key) # var = sigma**2 # return -0.5*np.sum((mag-model)**2 /var +0.5*np.log(2*np.pi*var)) #def lnlike(theta, data, sigma, firstderivs_key, ndim): # ''' # Finding matter density m, corrected absolute mag M, interaction gamma. # ''' # mag = data['mag'] # # params = {} # if ndim == 2: # params = {'m':theta[0], 'M':theta[1]} # elif ndim == 3: # params = {'m':theta[0],'M':theta[1], 'gamma':theta[2]} # # model = magn(params, data, firstderivs_key) # var = sigma**2 # return -0.5*np.sum((mag-model)**2 /var +0.5*np.log(2*np.pi*var)) def lnlike(theta, data, sigma, firstderivs_key, ndim): ''' Finding matter density m, absolute M, alpha, beta, interaction gamma. ''' mag = data['mag'] params = {} if ndim == 4: params= {'m':theta[0], 'M':theta[1], 'alpha':theta[2], 'beta':theta[3]} elif ndim == 5: params= {'m':theta[0], 'M':theta[1], 'alpha':theta[2], 'beta':theta[3],'gamma':theta[4]} elif ndim == 6: params= {'m':theta[0], 'M':theta[1], 'alpha':theta[2], 'beta':theta[3],'gamma':theta[4], 'zeta':theta[5]} model = magn(params, data, firstderivs_key) var = sigma**2 return -0.5*np.sum((mag-model)**2 /var +0.5*np.log(2*np.pi*var)) #def lnprior(theta, key): # ''' # Finding matter density m, interaction gamma. # ''' # # if key == 'LCDM': # m = theta # if 0 < m < 1 or m == 1: # return 0.0 # elif key == 'late_int' or 'heaviside_late_int' or 'late_intxde': # m, gamma = theta # if (0 < m < 1 or m == 1) and -1.45 < gamma < 0.2: # return 0.0 # elif key == 'rdecay': # m, gamma = theta # if (0 < m < 1 or m == 1) and -10 < gamma < 0: # return 0.0 # elif key == 'interacting': # m, gamma = theta # if (0 < m < 1 or m == 1) and abs(gamma) < 1.45: # return 0.0 # elif key == 'expgamma': # m, gamma = theta # if (0 < m < 1 or m == 1) and abs(gamma) < 25: # return 0.0 # elif key == 'zxxgamma' or 'gammaxxz': # m, gamma = theta # if (0 < m < 1 or m == 1) and 0 < gamma < 10: # return 0.0 # else: # m, gamma = theta # if (0 < m < 1 or m == 1) and abs(gamma) < 10: # return 0.0 # # return -np.inf #def lnprior(theta, key): # ''' # Finding matter density m, corrected absolute mag M, interaction gamma. # ''' # # Mmin = -20 # # Mmax = -18 # # if key == 'LCDM': # m, M = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax: # return 0.0 # elif key == 'late_int' or 'heaviside_late_int' or 'late_intxde': # m, M, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and -1.45 < gamma < 0.2: # return 0.0 # elif key == 'rdecay': # m, M, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and -10 < gamma < 0 : # return 0.0 # elif key == 'interacting': # m, M, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(gamma) < 1.45: # return 0.0 # elif key == 'expgamma': # m, M, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(gamma) < 25 : # return 0.0 # elif key == 'zxxgamma' or 'gammaxxz': # m, M, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and 0 < gamma < 10: # return 0.0 # else: # m, M, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(gamma) < 10: # return 0.0 # # return -np.inf #def lnprior(theta, key): # ''' # Finding matter density m, absolute M, alpha, beta, interaction gamma. # ''' # # Mmin, Mmax = -20, -18 # amax = 5 # bmax = 5 # # print('key ln prior gets is = ',key) # # if key == 'LCDM': # m, M, alpha, beta = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(alpha) < amax and abs(beta) < bmax: # return 0.0 # elif key == 'late_int' or key == 'heaviside_late_int' or key == 'late_intxde': # m, M, alpha, beta, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(alpha) < amax and abs(beta) < bmax and -1.45 < gamma < 0.2: # return 0.0 # elif key == 'rdecay': # m, M, alpha, beta, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(alpha) < amax and abs(beta) < bmax and -10 < gamma < 0 : # return 0.0 # elif key == 'interacting': # m, M, alpha, beta, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(alpha) < amax and abs(beta) < bmax and abs(gamma) < 1.45: # return 0.0 # elif key == 'expgamma': # m, M, alpha, beta, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(alpha) < amax and abs(beta) < bmax and abs(gamma) < 25 : # return 0.0 # elif key == 'zxxgamma' or key == 'gammaxxz': # m, M, alpha, beta, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(alpha) < amax and abs(beta) < bmax and 0 < gamma < 10: # return 0.0 # elif key == 'exotic': # m, M, alpha, beta, gamma, zeta = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(alpha) < amax and abs(beta) < bmax and 0 < gamma < 10 and 0 < zeta < 10: # return 0.0 # else: # m, M, alpha, beta, gamma = theta # if (0 < m < 1 or m == 1) and Mmin < M < Mmax and abs(alpha) < amax and abs(beta) < bmax and abs(gamma) < 10: # return 0.0 # # return -np.inf def lnprior(theta, key): ''' Finding matter density m, absolute M, alpha, beta, interaction gamma. ''' Mmin, Mmax = -20, -18 amax = 5 bmax = 5 if key == 'LCDM': m, M, alpha, beta = theta elif key == 'exotic': m, M, alpha, beta, gamma, zeta = theta else: m, M, alpha, beta, gamma = theta if (0 < m < 1 or m == 1): if Mmin < M < Mmax: if abs(alpha) < amax: if abs(beta) < bmax: if key == 'exotic': if -2 < gamma < 0.1 and -1.5 < abs(zeta) < 3.5: return 0.0 elif key == 'late_intxde': if -2 < gamma < 0.1: return 0.0 elif key == 'heaviside_late_int': if -1.45 < gamma < 0.1: return 0.0 elif key == 'late_int': if -15 < gamma < 0.1: return 0.0 elif key == 'expgamma': if -0.1 < gamma < 1.5: return 0.0 elif key == 'txgamma': if -0.5 < gamma < 0.1: return 0.0 elif key == 'zxgamma': if -10 < gamma < 0.1: return 0.0 elif key == 'zxxgamma': if -0.1 < gamma < 12: return 0.0 elif key == 'gammaxxz': if -1 < gamma < 1: return 0.0 elif key == 'rdecay': if -2 < gamma < 0.1: return 0.0 elif key == 'interacting': if -1.5 < gamma < 0.1: return 0.0 elif key == 'LCDM': return 0.0 else: if abs(gamma) < 10: return 0.0 return -np.inf def lnprob(theta, data, sigma, firstderivs_key, ndim): lp = lnprior(theta, firstderivs_key) if not np.isfinite(lp): return -np.inf return lp + lnlike(theta, data, sigma, firstderivs_key, ndim)

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Python

pkgs/conf-pkg/src/genie/libs/conf/prefix_list/__init__.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

94

2018-04-30T20:29:15.000Z

2022-03-29T13:40:31.000Z

pkgs/conf-pkg/src/genie/libs/conf/prefix_list/__init__.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

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2018-12-06T21:08:09.000Z

2022-03-29T18:00:46.000Z

pkgs/conf-pkg/src/genie/libs/conf/prefix_list/__init__.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

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2018-06-29T18:59:03.000Z

2022-03-10T02:07:59.000Z

from .prefix_list import *

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Python

crypto/Irreducible/secret.py

Enigmatrix/hats-ctf-2019

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2019-10-04T07:20:37.000Z

2021-06-15T21:34:07.000Z

crypto/Irreducible/secret.py

Enigmatrix/hats-ctf-2019

0dc1b9a5a4583c81b5f1b7bce0cbb9bd0fd2b192

[ "MIT" ]

null

crypto/Irreducible/secret.py

Enigmatrix/hats-ctf-2019

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[ "MIT" ]

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flag = 'HATS{copp3r5m17h_1337}'

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lib/python2.7/site-packages/routes/base.py

nishaero/wifi-userseg-ryu

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lib/python2.7/site-packages/routes/base.py

nishaero/wifi-userseg-ryu

1132f2c813b79eff755bdd1a9e73e7ad3980af7c

[ "Apache-2.0" ]

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2015-01-05T21:16:02.000Z

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lib/python2.7/site-packages/routes/base.py

nishaero/wifi-userseg-ryu

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[ "Apache-2.0" ]

48

2015-01-19T00:40:23.000Z

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"""Route and Mapper core classes""" from routes import request_config from routes.mapper import Mapper from routes.route import Route

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Python

tests/functional/tests/__init__.py

mjeffrey/local-kms

914befbc57ff340e9d12cf640aa40d1f5be7cb6e

[ "MIT" ]

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2018-11-14T18:52:11.000Z

2022-03-23T08:35:45.000Z

tests/functional/tests/__init__.py

mjeffrey/local-kms

914befbc57ff340e9d12cf640aa40d1f5be7cb6e

[ "MIT" ]

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2019-03-19T13:51:51.000Z

2022-03-30T14:59:26.000Z

tests/functional/tests/__init__.py

mjeffrey/local-kms

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[ "MIT" ]

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2019-06-09T01:14:51.000Z

2022-03-31T13:04:43.000Z

from .helpers import validate_error_response

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Python

src/test/resources/expressions/enclosure/display/starred_set.py

oxisto/reticulated-python

a38c8bd9c842be4f4c8ddc73c61c70aeceb07248

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src/test/resources/expressions/enclosure/display/starred_set.py

oxisto/reticulated-python

a38c8bd9c842be4f4c8ddc73c61c70aeceb07248

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src/test/resources/expressions/enclosure/display/starred_set.py

oxisto/reticulated-python

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models/model_monosceneflow_ablation.py

NIRVANALAN/self-mono-sf

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2020-03-12T07:43:26.000Z

2022-03-24T05:13:04.000Z

models/model_monosceneflow_ablation.py

NIRVANALAN/self-mono-sf

80ac323099b3ca32802c5d3f91db3e6a5cafca25

[ "Apache-2.0" ]

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2020-04-20T12:30:46.000Z

2022-02-18T09:26:35.000Z

models/model_monosceneflow_ablation.py

NIRVANALAN/self-mono-sf

80ac323099b3ca32802c5d3f91db3e6a5cafca25

[ "Apache-2.0" ]

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2020-04-09T01:37:20.000Z

2022-03-24T05:12:48.000Z

from __future__ import absolute_import, division, print_function import torch import torch.nn as nn import torch.nn.functional as tf import logging from .correlation_package.correlation import Correlation from .modules_sceneflow import get_grid, WarpingLayer_SF, WarpingLayer_Flow from .modules_sceneflow import initialize_msra, upsample_outputs_as from .modules_sceneflow import conv, upconv from .modules_sceneflow import FeatureExtractor, MonoSceneFlowDecoder, ContextNetwork from .modules_camconv import CamConvModule from utils.interpolation import interpolate2d_as from utils.sceneflow_util import flow_horizontal_flip, post_processing class MonoSceneFlow_CamConv(nn.Module): def __init__(self, args): super(MonoSceneFlow_CamConv, self).__init__() self._args = args self.num_chs = [3, 32, 64, 96, 128, 192, 256] self.search_range = 4 self.output_level = 4 self.num_levels = 7 self.leakyRELU = nn.LeakyReLU(0.1, inplace=True) self.feature_pyramid_extractor = FeatureExtractor(self.num_chs) self.warping_layer_sf = WarpingLayer_SF() self.flow_estimators = nn.ModuleList() self.upconv_layers = nn.ModuleList() self.dim_corr = (self.search_range * 2 + 1) ** 2 for l, ch in enumerate(self.num_chs[::-1]): if l > self.output_level: break if l == 0: num_ch_in = self.dim_corr + ch + 6 else: num_ch_in = self.dim_corr + ch + 32 + 3 + 1 + 6 self.upconv_layers.append(upconv(32, 32, 3, 2)) layer_sf = MonoSceneFlowDecoder(num_ch_in) self.flow_estimators.append(layer_sf) self.corr_params = {"pad_size": self.search_range, "kernel_size": 1, "max_disp": self.search_range, "stride1": 1, "stride2": 1, "corr_multiply": 1} self.context_networks = ContextNetwork(32 + 3 + 1) self.sigmoid = torch.nn.Sigmoid() self.camconv = CamConvModule() initialize_msra(self.modules()) def run_pwc(self, input_dict, x1_raw, x2_raw, k1, k2): output_dict = {} # on the bottom level are original images x1_pyramid = self.feature_pyramid_extractor(x1_raw) + [x1_raw] x2_pyramid = self.feature_pyramid_extractor(x2_raw) + [x2_raw] # outputs sceneflows_f = [] sceneflows_b = [] disps_1 = [] disps_2 = [] for l, (x1, x2) in enumerate(zip(x1_pyramid, x2_pyramid)): # warping if l == 0: x2_warp = x2 x1_warp = x1 else: flow_f = interpolate2d_as(flow_f, x1, mode="bilinear") flow_b = interpolate2d_as(flow_b, x1, mode="bilinear") disp_l1 = interpolate2d_as(disp_l1, x1, mode="bilinear") disp_l2 = interpolate2d_as(disp_l2, x1, mode="bilinear") x1_out = self.upconv_layers[l-1](x1_out) x2_out = self.upconv_layers[l-1](x2_out) x2_warp = self.warping_layer_sf(x2, flow_f, disp_l1, k1, input_dict['aug_size']) # becuase K can be changing when doing augmentation x1_warp = self.warping_layer_sf(x1, flow_b, disp_l2, k2, input_dict['aug_size']) # correlation out_corr_f = Correlation.apply(x1, x2_warp, self.corr_params) out_corr_b = Correlation.apply(x2, x1_warp, self.corr_params) out_corr_relu_f = self.leakyRELU(out_corr_f) out_corr_relu_b = self.leakyRELU(out_corr_b) # monosf estimator if l == 0: x1 = self.camconv(x1, x1_raw, k1) x2 = self.camconv(x2, x2_raw, k2) x1_out, flow_f, disp_l1 = self.flow_estimators[l](torch.cat([out_corr_relu_f, x1], dim=1)) x2_out, flow_b, disp_l2 = self.flow_estimators[l](torch.cat([out_corr_relu_b, x2], dim=1)) else: x1 = self.camconv(x1) x2 = self.camconv(x2) x1_out, flow_f_res, disp_l1 = self.flow_estimators[l](torch.cat([out_corr_relu_f, x1, x1_out, flow_f, disp_l1], dim=1)) x2_out, flow_b_res, disp_l2 = self.flow_estimators[l](torch.cat([out_corr_relu_b, x2, x2_out, flow_b, disp_l2], dim=1)) flow_f = flow_f + flow_f_res flow_b = flow_b + flow_b_res # upsampling or post-processing if l != self.output_level: disp_l1 = self.sigmoid(disp_l1) * 0.3 disp_l2 = self.sigmoid(disp_l2) * 0.3 sceneflows_f.append(flow_f) sceneflows_b.append(flow_b) disps_1.append(disp_l1) disps_2.append(disp_l2) else: flow_res_f, disp_l1 = self.context_networks(torch.cat([x1_out, flow_f, disp_l1], dim=1)) flow_res_b, disp_l2 = self.context_networks(torch.cat([x2_out, flow_b, disp_l2], dim=1)) flow_f = flow_f + flow_res_f flow_b = flow_b + flow_res_b sceneflows_f.append(flow_f) sceneflows_b.append(flow_b) disps_1.append(disp_l1) disps_2.append(disp_l2) break x1_rev = x1_pyramid[::-1] output_dict['flow_f'] = upsample_outputs_as(sceneflows_f[::-1], x1_rev) output_dict['flow_b'] = upsample_outputs_as(sceneflows_b[::-1], x1_rev) output_dict['disp_l1'] = upsample_outputs_as(disps_1[::-1], x1_rev) output_dict['disp_l2'] = upsample_outputs_as(disps_2[::-1], x1_rev) return output_dict def forward(self, input_dict): output_dict = {} ## Left output_dict = self.run_pwc(input_dict, input_dict['input_l1_aug'], input_dict['input_l2_aug'], input_dict['input_k_l1_aug'], input_dict['input_k_l2_aug']) ## Right if not self._args.evaluation: input_r1_flip = torch.flip(input_dict['input_r1_aug'], [3]) input_r2_flip = torch.flip(input_dict['input_r2_aug'], [3]) k_r1_flip = input_dict["input_k_r1_flip_aug"] k_r2_flip = input_dict["input_k_r2_flip_aug"] output_dict_r = self.run_pwc(input_dict, input_r1_flip, input_r2_flip, k_r1_flip, k_r2_flip) for ii in range(0, len(output_dict_r['flow_f'])): output_dict_r['flow_f'][ii] = flow_horizontal_flip(output_dict_r['flow_f'][ii]) output_dict_r['flow_b'][ii] = flow_horizontal_flip(output_dict_r['flow_b'][ii]) output_dict_r['disp_l1'][ii] = torch.flip(output_dict_r['disp_l1'][ii], [3]) output_dict_r['disp_l2'][ii] = torch.flip(output_dict_r['disp_l2'][ii], [3]) output_dict['output_dict_r'] = output_dict_r ## Eval if self._args.evaluation: input_l1_flip = torch.flip(input_dict['input_l1_aug'], [3]) input_l2_flip = torch.flip(input_dict['input_l2_aug'], [3]) k_l1_flip = input_dict["input_k_l1_flip_aug"] k_l2_flip = input_dict["input_k_l2_flip_aug"] output_dict_flip = self.run_pwc(input_dict, input_l1_flip, input_l2_flip, k_l1_flip, k_l2_flip) flow_f_pp = [] flow_b_pp = [] disp_l1_pp = [] disp_l2_pp = [] for ii in range(0, len(output_dict_flip['flow_f'])): flow_f_pp.append(post_processing(output_dict['flow_f'][ii], flow_horizontal_flip(output_dict_flip['flow_f'][ii]))) flow_b_pp.append(post_processing(output_dict['flow_b'][ii], flow_horizontal_flip(output_dict_flip['flow_b'][ii]))) disp_l1_pp.append(post_processing(output_dict['disp_l1'][ii], torch.flip(output_dict_flip['disp_l1'][ii], [3]))) disp_l2_pp.append(post_processing(output_dict['disp_l2'][ii], torch.flip(output_dict_flip['disp_l2'][ii], [3]))) output_dict['flow_f_pp'] = flow_f_pp output_dict['flow_b_pp'] = flow_b_pp output_dict['disp_l1_pp'] = disp_l1_pp output_dict['disp_l2_pp'] = disp_l2_pp return output_dict class OpticalFlowDecoder(nn.Module): def __init__(self, ch_in): super(OpticalFlowDecoder, self).__init__() self.convs = nn.Sequential( conv(ch_in, 128), conv(128, 128), conv(128, 96), conv(96, 64), conv(64, 32) ) self.conv_sf = conv(32, 2, isReLU=False) def forward(self, x): x_out = self.convs(x) sf = self.conv_sf(x_out) return x_out, sf class OpticalFlowContextNet(nn.Module): def __init__(self, ch_in): super(OpticalFlowContextNet, self).__init__() self.convs = nn.Sequential( conv(ch_in, 128, 3, 1, 1), conv(128, 128, 3, 1, 2), conv(128, 128, 3, 1, 4), conv(128, 96, 3, 1, 8), conv(96, 64, 3, 1, 16), conv(64, 32, 3, 1, 1) ) self.conv_sf = conv(32, 2, isReLU=False) def forward(self, x): x_out = self.convs(x) sf = self.conv_sf(x_out) return sf class MonoSceneFlow_OpticalFlowOnly(nn.Module): def __init__(self, args): super(MonoSceneFlow_OpticalFlowOnly, self).__init__() self._args = args self.num_chs = [3, 32, 64, 96, 128, 192, 256] self.search_range = 4 self.output_level = 4 self.num_levels = 7 self.leakyRELU = nn.LeakyReLU(0.1, inplace=True) self.feature_pyramid_extractor = FeatureExtractor(self.num_chs) self.warping_layer = WarpingLayer_Flow() self.flow_estimators = nn.ModuleList() self.upconv_layers = nn.ModuleList() self.dim_corr = (self.search_range * 2 + 1) ** 2 for l, ch in enumerate(self.num_chs[::-1]): if l > self.output_level: break if l == 0: num_ch_in = self.dim_corr + ch else: num_ch_in = self.dim_corr + ch + 32 + 2 self.upconv_layers.append(upconv(32, 32, 3, 2)) layer_flow = OpticalFlowDecoder(num_ch_in) self.flow_estimators.append(layer_flow) self.corr_params = {"pad_size": self.search_range, "kernel_size": 1, "max_disp": self.search_range, "stride1": 1, "stride2": 1, "corr_multiply": 1} self.context_networks = OpticalFlowContextNet(32 + 2) initialize_msra(self.modules()) def run_pwc(self, input_dict, x1_raw, x2_raw, k1, k2): output_dict = {} # on the bottom level are original images x1_pyramid = self.feature_pyramid_extractor(x1_raw) + [x1_raw] x2_pyramid = self.feature_pyramid_extractor(x2_raw) + [x2_raw] # outputs flows_f = [] flows_b = [] for l, (x1, x2) in enumerate(zip(x1_pyramid, x2_pyramid)): # warping if l == 0: x2_warp = x2 x1_warp = x1 else: flow_f = interpolate2d_as(flow_f, x1, mode="bilinear") flow_b = interpolate2d_as(flow_b, x1, mode="bilinear") x1_out = self.upconv_layers[l-1](x1_out) x2_out = self.upconv_layers[l-1](x2_out) x2_warp = self.warping_layer(x2, flow_f) x1_warp = self.warping_layer(x1, flow_b) # correlation out_corr_f = Correlation.apply(x1, x2_warp, self.corr_params) out_corr_b = Correlation.apply(x2, x1_warp, self.corr_params) out_corr_relu_f = self.leakyRELU(out_corr_f) out_corr_relu_b = self.leakyRELU(out_corr_b) # flow estimator if l == 0: x1_out, flow_f = self.flow_estimators[l](torch.cat([out_corr_relu_f, x1], dim=1)) x2_out, flow_b = self.flow_estimators[l](torch.cat([out_corr_relu_b, x2], dim=1)) else: x1_out, flow_f_res = self.flow_estimators[l](torch.cat([out_corr_relu_f, x1, x1_out, flow_f], dim=1)) x2_out, flow_b_res = self.flow_estimators[l](torch.cat([out_corr_relu_b, x2, x2_out, flow_b], dim=1)) flow_f = flow_f + flow_f_res flow_b = flow_b + flow_b_res # upsampling or post-processing if l != self.output_level: flows_f.append(flow_f) flows_b.append(flow_b) else: flow_res_f = self.context_networks(torch.cat([x1_out, flow_f], dim=1)) flow_res_b = self.context_networks(torch.cat([x2_out, flow_b], dim=1)) flow_f = flow_f + flow_res_f flow_b = flow_b + flow_res_b flows_f.append(flow_f) flows_b.append(flow_b) break x1_rev = x1_pyramid[::-1] output_dict['flow_f'] = upsample_outputs_as(flows_f[::-1], x1_rev) output_dict['flow_b'] = upsample_outputs_as(flows_b[::-1], x1_rev) return output_dict def forward(self, input_dict): output_dict = {} output_dict = self.run_pwc(input_dict, input_dict['input_l1_aug'], input_dict['input_l2_aug'], input_dict['input_k_l1_aug'], input_dict['input_k_l2_aug']) return output_dict class DisparityDecoder(nn.Module): def __init__(self, ch_in): super(DisparityDecoder, self).__init__() self.convs = nn.Sequential( conv(ch_in, 128), conv(128, 128), conv(128, 96), conv(96, 64), conv(64, 32) ) self.conv_d1 = conv(32, 1, isReLU=False) def forward(self, x): x_out = self.convs(x) disp1 = self.conv_d1(x_out) return x_out, disp1 class DisparityContextNet(nn.Module): def __init__(self, ch_in): super(DisparityContextNet, self).__init__() self.convs = nn.Sequential( conv(ch_in, 128, 3, 1, 1), conv(128, 128, 3, 1, 2), conv(128, 128, 3, 1, 4), conv(128, 96, 3, 1, 8), conv(96, 64, 3, 1, 16), conv(64, 32, 3, 1, 1) ) self.conv_d1 = nn.Sequential( conv(32, 1, isReLU=False), torch.nn.Sigmoid() ) def forward(self, x): x_out = self.convs(x) disp1 = self.conv_d1(x_out) * 0.3 return sf class MonoSceneFlow_DisparityOnly(nn.Module): def __init__(self, args): super(MonoSceneFlow_DisparityOnly, self).__init__() self._args = args self.num_chs = [3, 32, 64, 96, 128, 192, 256] self.search_range = 4 self.output_level = 4 self.num_levels = 7 self.leakyRELU = nn.LeakyReLU(0.1, inplace=True) self.feature_pyramid_extractor = FeatureExtractor(self.num_chs) self.disp_estimators = nn.ModuleList() self.upconv_layers = nn.ModuleList() for l, ch in enumerate(self.num_chs[::-1]): if l > self.output_level: break if l == 0: num_ch_in = ch else: num_ch_in = ch + 32 + 1 self.upconv_layers.append(upconv(32, 32, 3, 2)) layer_disp = DisparityDecoder(num_ch_in) self.disp_estimators.append(layer_disp) self.sigmoid = torch.nn.Sigmoid() self.context_networks = DisparityContextNet(32 + 1) initialize_msra(self.modules()) def run_pwc(self, input_dict, x1_raw, x2_raw, k1, k2): output_dict = {} # on the bottom level are original images x1_pyramid = self.feature_pyramid_extractor(x1_raw) + [x1_raw] x2_pyramid = self.feature_pyramid_extractor(x2_raw) + [x2_raw] # outputs disps_1 = [] disps_2 = [] for l, (x1, x2) in enumerate(zip(x1_pyramid, x2_pyramid)): # warping if l == 0: x2_warp = x2 x1_warp = x1 else: disp_1 = interpolate2d_as(disp_1, x1, mode="bilinear") disp_2 = interpolate2d_as(disp_2, x1, mode="bilinear") x1_out = self.upconv_layers[l-1](x1_out) x2_out = self.upconv_layers[l-1](x2_out) # disparity estimator if l == 0: x1_out, disp_1 = self.disp_estimators[l](x1) x2_out, disp_2 = self.disp_estimators[l](x2) else: x1_out, disp_1 = self.disp_estimators[l](torch.cat([x1, x1_out, disp_1], dim=1)) x2_out, disp_2 = self.disp_estimators[l](torch.cat([x2, x2_out, disp_2], dim=1)) # upsampling or post-processing disp_1 = self.sigmoid(disp_1) * 0.3 disp_2 = self.sigmoid(disp_2) * 0.3 disps_1.append(disp_1) disps_2.append(disp_2) if l == self.output_level: break x1_rev = x1_pyramid[::-1] output_dict['disp_l1'] = upsample_outputs_as(disps_1[::-1], x1_rev) output_dict['disp_l2'] = upsample_outputs_as(disps_2[::-1], x1_rev) return output_dict def forward(self, input_dict): output_dict = {} ## Left output_dict = self.run_pwc(input_dict, input_dict['input_l1_aug'], input_dict['input_l2_aug'], input_dict['input_k_l1_aug'], input_dict['input_k_l2_aug']) ## Right if not self._args.evaluation: input_r1_flip = torch.flip(input_dict['input_r1_aug'], [3]) input_r2_flip = torch.flip(input_dict['input_r2_aug'], [3]) k_r1_flip = input_dict["input_k_r1_flip_aug"] k_r2_flip = input_dict["input_k_r2_flip_aug"] output_dict_r = self.run_pwc(input_dict, input_r1_flip, input_r2_flip, k_r1_flip, k_r2_flip) for ii in range(0, len(output_dict_r['disp_l1'])): output_dict_r['disp_l1'][ii] = torch.flip(output_dict_r['disp_l1'][ii], [3]) output_dict_r['disp_l2'][ii] = torch.flip(output_dict_r['disp_l2'][ii], [3]) output_dict['output_dict_r'] = output_dict_r ## Eval if self._args.evaluation: input_l1_flip = torch.flip(input_dict['input_l1_aug'], [3]) input_l2_flip = torch.flip(input_dict['input_l2_aug'], [3]) k_l1_flip = input_dict["input_k_l1_flip_aug"] k_l2_flip = input_dict["input_k_l2_flip_aug"] output_dict_flip = self.run_pwc(input_dict, input_l1_flip, input_l2_flip, k_l1_flip, k_l2_flip) disp_l1_pp = [] disp_l2_pp = [] for ii in range(0, len(output_dict_flip['disp_l1'])): disp_l1_pp.append(post_processing(output_dict['disp_l1'][ii], torch.flip(output_dict_flip['disp_l1'][ii], [3]))) disp_l2_pp.append(post_processing(output_dict['disp_l2'][ii], torch.flip(output_dict_flip['disp_l2'][ii], [3]))) output_dict['disp_l1_pp'] = disp_l1_pp output_dict['disp_l2_pp'] = disp_l2_pp return output_dict

36.885714

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0.066119

0.065074

0.047591

0.027972

0.819153

0.802156

0.785548

0.776709

0.710761

0.693279

0

0.049394

0.305809

19,365

524

164

36.956107

0.716507

0.02143

0

0.683333

0

0.048334

0

1

0.047222

false

0

0.036111

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0.002778

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null

0

1

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null

0

6

34da6f73c1938ba2e91f95ccfdf87c1374aaa30a

5,310

py

Python

chipseq_h2ax.py

rogerzou/chipseq_cgRNA

bc36158904d18d5a71226ea266efc9687d7fdc4f

[ "MIT" ]

null

chipseq_h2ax.py

rogerzou/chipseq_cgRNA

bc36158904d18d5a71226ea266efc9687d7fdc4f

[ "MIT" ]

null

chipseq_h2ax.py

rogerzou/chipseq_cgRNA

bc36158904d18d5a71226ea266efc9687d7fdc4f

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ gamma H2AX ChIP-seq analysis for ACTB-targeting Cas9/cgRNA """ import src.chipseq as c """ Home directory of BAM files and 'analysis' output directory; MODIFY AS APPROPRIATE. """ base = "/Volumes/Lab-Home/rzou4/NGS_data/4_damage/cgRNA_SRA/" base_a = "/Volumes/Lab-Home/rzou4/NGS_data/4_damage/cgRNA_SRA/analysis/" win = 50000 # window span in base pairs numbins = 50 # number of bins per window for significance testing """ Convert BAM file to WIG file that counts the number of reads in each window span. """ c.to_wiggle_windows(base+"gh2ax_actb_00m_rep1.bam", base_a+"gh2ax_00m_rep1", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_02m_rep1.bam", base_a+"gh2ax_02m_rep1", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_05m_rep1.bam", base_a+"gh2ax_05m_rep1", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_15m_rep1.bam", base_a+"gh2ax_15m_rep1", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_30m_rep1.bam", base_a+"gh2ax_30m_rep1", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_60m_rep1.bam", base_a+"gh2ax_60m_rep1", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_00m_rep2.bam", base_a+"gh2ax_00m_rep2", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_02m_rep2.bam", base_a+"gh2ax_02m_rep2", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_05m_rep2.bam", base_a+"gh2ax_05m_rep2", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_15m_rep2.bam", base_a+"gh2ax_15m_rep2", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_30m_rep2.bam", base_a+"gh2ax_30m_rep2", win, ['chr7']) c.to_wiggle_windows(base+"gh2ax_actb_60m_rep2.bam", base_a+"gh2ax_60m_rep2", win, ['chr7']) """ For each window span, count number of reads in each bin. """ c.to_bins(base+"gh2ax_actb_00m_rep1.bam", base_a+"gh2ax_00m_rep1", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_02m_rep1.bam", base_a+"gh2ax_02m_rep1", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_05m_rep1.bam", base_a+"gh2ax_05m_rep1", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_15m_rep1.bam", base_a+"gh2ax_15m_rep1", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_30m_rep1.bam", base_a+"gh2ax_30m_rep1", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_60m_rep1.bam", base_a+"gh2ax_60m_rep1", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_00m_rep2.bam", base_a+"gh2ax_00m_rep2", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_02m_rep2.bam", base_a+"gh2ax_02m_rep2", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_05m_rep2.bam", base_a+"gh2ax_05m_rep2", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_15m_rep2.bam", base_a+"gh2ax_15m_rep2", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_30m_rep2.bam", base_a+"gh2ax_30m_rep2", win, numbins, ['chr7']) c.to_bins(base+"gh2ax_actb_60m_rep2.bam", base_a+"gh2ax_60m_rep2", win, numbins, ['chr7']) """ Perform T-test on bins by comparing each time point to no-light samples. """ c.ttest_two(base_a+"gh2ax_00m_rep1.csv", base_a+"gh2ax_00m_rep1.csv", base_a+"ttest-00m_rep1", p=0.05) c.ttest_two(base_a+"gh2ax_02m_rep1.csv", base_a+"gh2ax_00m_rep1.csv", base_a+"ttest-02m_rep1", p=0.05) c.ttest_two(base_a+"gh2ax_05m_rep1.csv", base_a+"gh2ax_00m_rep1.csv", base_a+"ttest-05m_rep1", p=0.05) c.ttest_two(base_a+"gh2ax_15m_rep1.csv", base_a+"gh2ax_00m_rep1.csv", base_a+"ttest-15m_rep1", p=0.05) c.ttest_two(base_a+"gh2ax_30m_rep1.csv", base_a+"gh2ax_00m_rep1.csv", base_a+"ttest-30m_rep1", p=0.05) c.ttest_two(base_a+"gh2ax_60m_rep1.csv", base_a+"gh2ax_00m_rep1.csv", base_a+"ttest-60m_rep1", p=0.05) c.ttest_two(base_a+"gh2ax_00m_rep2.csv", base_a+"gh2ax_00m_rep2.csv", base_a+"ttest-00m_rep2", p=0.05) c.ttest_two(base_a+"gh2ax_02m_rep2.csv", base_a+"gh2ax_00m_rep2.csv", base_a+"ttest-02m_rep2", p=0.05) c.ttest_two(base_a+"gh2ax_05m_rep2.csv", base_a+"gh2ax_00m_rep2.csv", base_a+"ttest-05m_rep2", p=0.05) c.ttest_two(base_a+"gh2ax_15m_rep2.csv", base_a+"gh2ax_00m_rep2.csv", base_a+"ttest-15m_rep2", p=0.05) c.ttest_two(base_a+"gh2ax_30m_rep2.csv", base_a+"gh2ax_00m_rep2.csv", base_a+"ttest-30m_rep2", p=0.05) c.ttest_two(base_a+"gh2ax_60m_rep2.csv", base_a+"gh2ax_00m_rep2.csv", base_a+"ttest-60m_rep2", p=0.05) # Converts BAM to WIG format in 40kb window around cut site to visualize sub kilobase-scale features tr = 5529660 # ACTB cleavage site rr = "chr7:5509660-5549660" # 40kb window centered at cut site c.to_wiggle_pairs(base+"gh2ax_actb_00m_rep1.bam", base_a+"40kb_00m_rep1", rr) c.to_wiggle_pairs(base+"gh2ax_actb_02m_rep1.bam", base_a+"40kb_02m_rep1", rr) c.to_wiggle_pairs(base+"gh2ax_actb_05m_rep1.bam", base_a+"40kb_05m_rep1", rr) c.to_wiggle_pairs(base+"gh2ax_actb_15m_rep1.bam", base_a+"40kb_15m_rep1", rr) c.to_wiggle_pairs(base+"gh2ax_actb_30m_rep1.bam", base_a+"40kb_30m_rep1", rr) c.to_wiggle_pairs(base+"gh2ax_actb_60m_rep1.bam", base_a+"40kb_60m_rep1", rr) c.to_wiggle_pairs(base+"gh2ax_actb_00m_rep2.bam", base_a+"40kb_00m_rep2", rr) c.to_wiggle_pairs(base+"gh2ax_actb_02m_rep2.bam", base_a+"40kb_02m_rep2", rr) c.to_wiggle_pairs(base+"gh2ax_actb_05m_rep2.bam", base_a+"40kb_05m_rep2", rr) c.to_wiggle_pairs(base+"gh2ax_actb_15m_rep2.bam", base_a+"40kb_15m_rep2", rr) c.to_wiggle_pairs(base+"gh2ax_actb_30m_rep2.bam", base_a+"40kb_30m_rep2", rr) c.to_wiggle_pairs(base+"gh2ax_actb_60m_rep2.bam", base_a+"40kb_60m_rep2", rr)

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1

0

null

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6

34e443013be2e1e361765ead005618dd588aecf2

188

py

Python

app/views/home/routes.py

sagarkaurav/worktable

2ebae2e41481bb03a16a08437760b94908692f48

[ "MIT" ]

3

2021-03-01T08:41:51.000Z

2021-03-03T05:56:46.000Z

app/views/home/routes.py

sagarkaurav/worktable

2ebae2e41481bb03a16a08437760b94908692f48

[ "MIT" ]

null

app/views/home/routes.py

sagarkaurav/worktable

2ebae2e41481bb03a16a08437760b94908692f48

[ "MIT" ]

null

from flask import Blueprint, render_template home = Blueprint("home", __name__, template_folder="templates") @home.route("/") def index(): return render_template("home/index.html")

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0

1

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1

0

6

550f1f719d70a8b5940e842a2865410ac9796aad

129

py

Python

data_mine/nlp/hotpot_qa/constants.py

SebiSebi/DataMine

d2dd9ed7e2608918dd2908fa29238f600c768eb3

[ "Apache-2.0" ]

9

2020-07-01T21:53:36.000Z

2020-12-15T08:49:08.000Z

data_mine/nlp/hotpot_qa/constants.py

ChewKokWah/DataMine

d2dd9ed7e2608918dd2908fa29238f600c768eb3

[ "Apache-2.0" ]

7

2020-04-04T19:30:16.000Z

2020-06-26T12:18:10.000Z

data_mine/nlp/hotpot_qa/constants.py

ChewKokWah/DataMine

d2dd9ed7e2608918dd2908fa29238f600c768eb3

[ "Apache-2.0" ]

2

2020-03-21T13:55:27.000Z

2020-07-01T21:53:38.000Z

import os from data_mine.utils import datamine_cache_dir HOTPOT_QA_CACHE_DIR = os.path.join(datamine_cache_dir(), "HOTPOT_QA")

21.5

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null

0

1

0

6

9b38f02ab4cc1062d1327f0667f203ec0ba75f06

23

py

Python

Adafruit_LSM9DS0/__init__.py

jckw/Adafruit_LSM9DS0

98ff135fbf1702160a9277df1fd637022f91e234

[ "MIT" ]

6

2017-11-14T07:21:58.000Z

2018-08-24T03:47:58.000Z

Adafruit_LSM9DS0/__init__.py

jckw/Adafruit_LSM9DS0

98ff135fbf1702160a9277df1fd637022f91e234

[ "MIT" ]

null

Adafruit_LSM9DS0/__init__.py

jckw/Adafruit_LSM9DS0

98ff135fbf1702160a9277df1fd637022f91e234

[ "MIT" ]

2

2017-09-26T16:57:16.000Z

2018-12-06T12:33:11.000Z

from .LSM9DS0 import *

11.5

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23

5.666667

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23

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0

1

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1

0

6

9b45474374c590ea2e31bb764b908d4d331bda90

25

py

Python

nnpy/utils/__init__.py

AlexBacho/nnpy

e88fe6965a0b69ca3e6d4e31cc76a58349321c08

[ "MIT" ]

null

nnpy/utils/__init__.py

AlexBacho/nnpy

e88fe6965a0b69ca3e6d4e31cc76a58349321c08

[ "MIT" ]

null

nnpy/utils/__init__.py

AlexBacho/nnpy

e88fe6965a0b69ca3e6d4e31cc76a58349321c08

[ "MIT" ]

null

from .math_utils import *

25

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25

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25

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1

0

1

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1

0

null

0

1

0

1

0

1

0

6

9b7b3fd0769935ceb7b8606042c44fac31025628

6,974

py

Python

tests/unit/test_command_containers.py

gtmanfred/teststack

c7f671b45b81a036abcb21df6f1ef26c8a138e93

[ "Apache-2.0" ]

1

2021-11-09T18:44:40.000Z

tests/unit/test_command_containers.py

gtmanfred/teststack

c7f671b45b81a036abcb21df6f1ef26c8a138e93

[ "Apache-2.0" ]

2

2021-11-11T17:43:42.000Z

2022-03-08T19:26:31.000Z

tests/unit/test_command_containers.py

gtmanfred/teststack

c7f671b45b81a036abcb21df6f1ef26c8a138e93

[ "Apache-2.0" ]

null

import pathlib import tempfile from unittest import mock from docker.errors import ImageNotFound from docker.errors import NotFound from teststack import cli def test_render(runner, tag): with tempfile.NamedTemporaryFile() as tmpfile: result = runner.invoke(cli, ['render', f'--dockerfile={tmpfile.name}']) assert result.exit_code == 0 with open(tmpfile.name, 'r') as fh_: assert fh_.readline() == 'FROM python:slim\n' assert fh_.readline() == 'ENV PYTHON=True\n' assert fh_.readline() == 'WORKDIR /srv\n' assert fh_.readline() == '\n' assert 'docker-metadata' in fh_.readline() assert tag['commit'] in fh_.readline() def test_render_isolated(runner): with open('Dockerfile.j2') as fh_, runner.isolated_filesystem() as th_: with open('Dockerfile.j2', 'w') as wh_: wh_.write(fh_.read()) result = runner.invoke(cli, [f'--path={th_}', 'render']) assert result.exit_code == 0 with open('Dockerfile', 'r') as fh_: assert fh_.readline() == 'FROM python:slim\n' assert fh_.readline() == 'ENV PYTHON=True\n' assert fh_.readline() == 'WORKDIR /srv\n' assert not fh_.readline() def test_container_start_no_tests(runner, attrs): client = mock.MagicMock() client.containers.get.return_value.attrs = attrs with mock.patch('docker.from_env', return_value=client): result = runner.invoke(cli, ['start', '-n']) assert client.containers.get.call_count == 4 assert client.containers.run.called is False assert result.exit_code == 0 def test_container_start_no_tests_not_started(runner, attrs): client = mock.MagicMock() client.containers.get.return_value.attrs = attrs client.containers.get.side_effect = NotFound('container not found') with mock.patch('docker.from_env', return_value=client): result = runner.invoke(cli, ['start', '-n']) assert client.containers.get.call_count == 2 assert client.containers.run.call_count == 2 assert result.exit_code == 0 def test_container_start_with_tests(runner, attrs): client = mock.MagicMock() client.containers.get.return_value.attrs = attrs client.images.get.return_value.id = client.containers.get.return_value.image.id with mock.patch('docker.from_env', return_value=client): result = runner.invoke(cli, ['start']) assert client.containers.get.call_count == 11 assert client.containers.run.called is False assert result.exit_code == 0 def test_container_start_with_tests_old_image(runner, attrs): client = mock.MagicMock() client.containers.get.return_value.attrs = attrs with mock.patch('docker.from_env', return_value=client): result = runner.invoke(cli, ['start']) assert client.containers.get.call_count == 11 assert client.containers.run.called is True assert client.containers.get.return_value.stop.called is True assert client.containers.get.return_value.wait.called is True client.containers.get.return_value.remove.assert_called_with(v=True) assert result.exit_code == 0 def test_container_start_with_tests_not_started(runner, attrs): client = mock.MagicMock() client.containers.get.return_value.attrs = attrs client.containers.get.side_effect = NotFound('container not found') with mock.patch('docker.from_env', return_value=client): result = runner.invoke(cli, ['start']) assert client.containers.get.call_count == 6 assert client.containers.run.call_count == 3 assert result.exit_code == 0 def test_container_stop(runner, attrs): client = mock.MagicMock() client.containers.get.return_value.attrs = attrs with mock.patch('docker.from_env', return_value=client), mock.patch( 'teststack.containers.docker.Client.end_container' ) as end_container: result = runner.invoke(cli, ['stop']) assert client.containers.get.call_count == 3 assert end_container.call_count == 3 assert result.exit_code == 0 def test_container_stop_without_containers(runner, attrs): client = mock.MagicMock() client.containers.get.return_value.attrs = attrs client.containers.get.side_effect = NotFound('container not found') with mock.patch('docker.from_env', return_value=client), mock.patch( 'teststack.containers.docker.Client.end_container' ) as end_container: result = runner.invoke(cli, ['stop']) assert client.containers.get.call_count == 3 assert end_container.called is False assert result.exit_code == 0 def test_container_build(runner, build_output): client = mock.MagicMock() client.api.build.return_value = build_output with mock.patch('docker.from_env', return_value=client): result = runner.invoke(cli, ['build', '--tag=blah']) client.api.build.assert_called_with(path='.', dockerfile='Dockerfile', tag='blah', nocache=False, rm=True) assert result.exit_code == 0 def test_container_start_with_tests_without_image(runner, attrs): client = mock.MagicMock() client.containers.get.return_value.attrs = attrs image = mock.MagicMock() client.images.get.side_effect = [ImageNotFound('image not found'), image, image, image] with mock.patch('docker.from_env', return_value=client): result = runner.invoke(cli, ['start']) assert client.containers.get.call_count == 11 assert client.containers.run.called is True assert client.images.get.call_count == 4 assert result.exit_code == 0 def test_container_run(runner, attrs): client = mock.MagicMock() client.containers.get.return_value.attrs = attrs client.images.get.return_value.id = client.containers.get.return_value.image.id client.containers.get.return_value.exec_run.return_value.output = [ 'foo', 'bar', 'baz', ] with mock.patch('docker.from_env', return_value=client): result = runner.invoke(cli, ['run']) assert client.containers.get.call_count == 14 assert client.containers.run.called is False assert result.exit_code == 0 assert 'foobarbaz' in result.output assert 'Run Command: env' in result.output def test_container_run_step(runner, attrs): client = mock.MagicMock() client.containers.get.return_value.attrs = attrs client.images.get.return_value.id = client.containers.get.return_value.image.id client.containers.get.return_value.exec_run.return_value.output = [ 'foo', 'bar', 'baz', ] with mock.patch('docker.from_env', return_value=client): result = runner.invoke(cli, ['run', '--step=install']) assert client.containers.get.call_count == 13 assert client.containers.run.called is False assert result.exit_code == 0 assert 'foobarbaz' in result.output assert 'Run Command: env' not in result.output assert 'Run Command: python -m pip install' in result.output

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176

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0

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0

0.130137

0

null

0

1

0

null

0

6

32e54545de74c129a8174fccb97452f976ed7e6d

124

py

Python

addons14/contract/wizards/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

1

2021-06-10T14:59:13.000Z

addons14/contract/wizards/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

null

addons14/contract/wizards/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

1

2021-04-09T09:44:44.000Z

from . import contract_line_wizard from . import contract_manually_create_invoice from . import contract_contract_terminate

31

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16

124

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0

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124

3

47

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0

1

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true

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1

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0

1

0

null

0

1

0

1

0

6

fd5febbd5c265e2cb5f1ac526523ef47bb980383

74

py

Python

amocrm_asterisk_ng/infrastructure/get_version/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

amocrm_asterisk_ng/infrastructure/get_version/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

amocrm_asterisk_ng/infrastructure/get_version/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

from .get_app_version import get_app_version from .Version import Version

24.666667

44

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74

5

0.416667

0.2

0.433333

0

0.108108

74

2

45

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0

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true

0

1

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null

0

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1

0

null

0

1

0

1

0

1

0

6

b5cb14903de6a66cf3e87ae842e9eab5e85182d0

46

py

Python

hkjournalist/__init__.py

li-xin-yi/HK-journalist

ec95050b13fed27f77ac3ab7ecbc7b1f27f50921

[ "MIT" ]

66

2019-12-18T09:50:59.000Z

2022-03-22T13:38:47.000Z

hkjournalist/__init__.py

li-xin-yi/HK-journalist

ec95050b13fed27f77ac3ab7ecbc7b1f27f50921

[ "MIT" ]

2

2020-05-26T12:33:57.000Z

2020-05-27T07:34:28.000Z

hkjournalist/__init__.py

li-xin-yi/HK-journalist

ec95050b13fed27f77ac3ab7ecbc7b1f27f50921

[ "MIT" ]

4

2020-10-29T11:30:51.000Z

2022-02-10T03:44:17.000Z

from hkjournalist.journalist import Journalist

46

0.913043

5

46

8.4

0.8

0

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1

46

0.976744

0

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1

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1

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1

0

6

bd12c88adee19f51daac4830a17525a0655b0512

62

py

Python

y.py

CyberFork/Version1

862893df224e246ea670a3c924cd4eca7424d4b6

[ "MIT" ]

null

y.py

CyberFork/Version1

862893df224e246ea670a3c924cd4eca7424d4b6

[ "MIT" ]

null

y.py

CyberFork/Version1

862893df224e246ea670a3c924cd4eca7424d4b6

[ "MIT" ]

null

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6

bd45078ac351e6c9e38077e5a55697aac92d4f83

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py

Python

anuncio/tests.py

ESEGroup/Peru

71a2745a837d963159954bb9277c8bd8472aee2a

[ "Apache-2.0" ]

3

2016-11-08T16:54:04.000Z

2016-11-22T10:26:32.000Z

anuncio/tests.py

ESEGroup/Peru

71a2745a837d963159954bb9277c8bd8472aee2a

[ "Apache-2.0" ]

39

2016-11-10T09:31:51.000Z

2016-12-20T13:08:43.000Z

anuncio/tests.py

ESEGroup/Peru

71a2745a837d963159954bb9277c8bd8472aee2a

[ "Apache-2.0" ]

null

import datetime from django.test import TestCase from django.utils import timezone from django.db import models from django.urls import reverse from .models import Anuncio, Localidade, Usuario class TestesUnitariosAnuncio(TestCase): #################################################### #Cenário 1: # #Título: Choppada Engenharia Eletrônica (válido) #Data Inicio: data atual (válido) #Data fim: data atual + 10 dias (válido) #################################################### def teste_cenario_1(self): inicio = timezone.now() fim = inicio + datetime.timedelta(days=10) c_user = Usuario(nome="Test User") c_user.save() c_local = Localidade(nome="Web") c_local.save() anunciante = Usuario.objects.get(nome="Test User") localidade = Localidade.objects.get(nome = "Web") anuncio = Anuncio(anunciante=anunciante, titulo="Choppada Engenharia Eletrônica", descricao="", data_inicio=inicio, data_fim=fim, localidade=localidade) self.assertIs(anuncio.publicar(), None) #################################################### #Cenário 2: # #Título: Choppada de Engenharia Eletrônica, de Engenharia de Controle e Automação, de Engenharia de Computação e Informação, de Engenharia de Produção, de Engenharia Metalúrgica, de Psicologia e de Ciências Sociais (inválido) #Data Inicio: data atual (válido) #Data fim: data atual + 10 dias (válido) #################################################### def teste_cenario_2(self): c_user = Usuario(nome="Test User") c_user.save() c_local = Localidade(nome="Web") c_local.save() anunciante = Usuario.objects.get(nome="Test User") localidade = Localidade.objects.get(nome = "Web") inicio = timezone.now() fim = inicio + datetime.timedelta(days=10) titulo = "Choppada de Engenharia Eletrônica, de Engenharia de Controle e Automação, de Engenharia de Computação e Informação, de Engenharia de Produção, de Engenharia Metalúrgica, de Psicologia e de Ciências Sociais" anuncio = Anuncio(anunciante=anunciante, titulo=titulo, data_inicio=inicio, data_fim=fim, localidade=localidade) self.assertIsNot(anuncio.publicar(), None) #################################################### #Cenário 3: # #Título: Choppada Engenharia Eletrônica (válido) #Data Inicio: em branco (inválido) #Data fim: data atual + 10 dias (válido) #################################################### def teste_cenario_3(self): c_user = Usuario(nome="Test User") c_user.save() c_local = Localidade(nome="Web") c_local.save() anunciante = Usuario.objects.get(nome="Test User") localidade = Localidade.objects.get(nome = "Web") fim = timezone.now() + datetime.timedelta(days=10) titulo = "Choppada de Engenharia Eletrônica, de Engenharia de Controle e Automação, de Engenharia de Computação e Informação, de Engenharia de Produção, de Engenharia Metalúrgica, de Psicologia e de Ciências Sociais" anuncio = Anuncio(anunciante=anunciante, titulo=titulo, data_fim=fim, localidade=localidade) self.assertIsNot(anuncio.publicar(), None) #################################################### #Cenário 4: # #Título: Choppada Engenharia Eletrônica (válido) #Data Inicio: em branco (inválido) #Data fim: data atual + 10 dias (válido) #################################################### def teste_cenario_4(self): c_user = Usuario(nome="Test User") c_user.save() c_local = Localidade(nome="Web") c_local.save() anunciante = Usuario.objects.get(nome="Test User") localidade = Localidade.objects.get(nome = "Web") inicio = timezone.now() titulo = "Choppada de Engenharia Eletrônica, de Engenharia de Controle e Automação, de Engenharia de Computação e Informação, de Engenharia de Produção, de Engenharia Metalúrgica, de Psicologia e de Ciências Sociais" anuncio = Anuncio(anunciante=anunciante, titulo=titulo, data_inicio=inicio, localidade=localidade) self.assertIsNot(anuncio.publicar(), None)

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bd4854667e358dd33f4cd942033ce08e76347b60

21,712

py

Python

handcam/scratch/compile_results.py

luketaverne/handcam

e294ebf2be8b5512c8607d3c8ba3f6946f3b8e30

[ "MIT" ]

1

2022-02-10T13:19:20.000Z

handcam/scratch/compile_results.py

luketaverne/handcam

e294ebf2be8b5512c8607d3c8ba3f6946f3b8e30

[ "MIT" ]

null

handcam/scratch/compile_results.py

luketaverne/handcam

e294ebf2be8b5512c8607d3c8ba3f6946f3b8e30

[ "MIT" ]

null

import subprocess from handcam.ltt.util.TFTools import ( _DatasetInitializerHook, shuffle_dataset, per_sequence_standardization, per_sequence_standardization_rgbd, ) import tensorflow as tf import glob import sys import numpy as np import six import os import pickle import datetime # from handcam.ltt.network.model.Wide_ResNet import wide_resnet_tf_depth as resnet_model_rgbd from handcam.ltt.network.model.Wide_ResNet import wide_resnet_tf as resnet_model from handcam.ltt.network.model.RNNModel import LSTMModel as lstm_model from handcam.ltt.util.Utils import AttrDict, handcam_gesture_spotting_acc models_to_eval = { "/media/luke/hdd-3tb/models/handcam/split0/sequence_resnet-18/rgbd-imu/train/2018-09-05/23:33": None, "/media/luke/hdd-3tb/models/handcam/split1/sequence_resnet-18/rgbd-imu/train/2018-09-06/02:41": None, "/media/luke/hdd-3tb/models/handcam/split2/sequence_resnet-18/rgbd-imu/train/2018-09-06/05:56": None, "/media/luke/hdd-3tb/models/handcam/split3/sequence_resnet-18/rgbd-imu/train/2018-09-06/08:51": None, "/media/luke/hdd-3tb/models/handcam/split4/sequence_resnet-18/rgbd-imu/train/2018-09-06/11:48": None, "/media/luke/hdd-3tb/models/handcam/split5/sequence_resnet-18/rgbd-imu/train/2018-09-06/16:19": None, "/media/luke/hdd-3tb/models/handcam/split6/sequence_resnet-18/rgbd-imu/train/2018-09-06/18:59": None, "/media/luke/hdd-3tb/models/handcam/split7/sequence_resnet-18/rgbd-imu/train/2018-09-06/21:20": None, "/media/luke/hdd-3tb/models/handcam/split8/sequence_resnet-18/rgbd-imu/train/2018-09-07/00:59": None, "/media/luke/hdd-3tb/models/handcam/split9/sequence_resnet-18/rgbd-imu/train/2018-09-07/03:36": None # '/media/luke/hdd-3tb/models/handcam/split0/sequence_resnet-18/depth/frozen_train/2018-08-20/19:40': None, # '/media/luke/hdd-3tb/models/handcam/split0/sequence_resnet-18/depth/train/2018-08-20/22:25': None, # '/media/luke/hdd-3tb/models/handcam/split0/sequence_resnet-18/rgbd/frozen_train/2018-08-20/21:32': None, # '/media/luke/hdd-3tb/models/handcam/split0/sequence_resnet-18/rgbd/train/2018-08-21/00:01': None, # '/media/luke/hdd-3tb/models/handcam/split0/sequence_resnet-18/rgb/frozen_train/2018-08-20/20:22': None, # '/media/luke/hdd-3tb/models/handcam/split0/sequence_resnet-18/rgb/train/2018-08-20/23:09': None, # '/media/luke/hdd-3tb/models/handcam/split0/single_frames_resnet-18/depth/train/2018-08-20/18:22': None, # '/media/luke/hdd-3tb/models/handcam/split0/single_frames_resnet-18/rgbd/train/2018-08-20/19:08': None, # '/media/luke/hdd-3tb/models/handcam/split0/single_frames_resnet-18/rgb/train/2018-08-20/18:46': None, # '/media/luke/hdd-3tb/models/handcam/split1/sequence_resnet-18/depth/frozen_train/2018-08-21/21:19': None, # '/media/luke/hdd-3tb/models/handcam/split1/sequence_resnet-18/depth/train/2018-08-21/23:38': None, # '/media/luke/hdd-3tb/models/handcam/split1/sequence_resnet-18/rgbd/frozen_train/2018-08-21/22:40': None, # '/media/luke/hdd-3tb/models/handcam/split1/sequence_resnet-18/rgbd/train/2018-08-22/01:17': None, # '/media/luke/hdd-3tb/models/handcam/split1/sequence_resnet-18/rgb/frozen_train/2018-08-21/21:57': None, # '/media/luke/hdd-3tb/models/handcam/split1/sequence_resnet-18/rgb/train/2018-08-22/00:17': None, # '/media/luke/hdd-3tb/models/handcam/split1/sequence_resnet-50/depth/frozen_train/2018-08-23/19:12': None, # '/media/luke/hdd-3tb/models/handcam/split1/sequence_resnet-50/rgbd/frozen_train/2018-08-23/23:04': None, # '/media/luke/hdd-3tb/models/handcam/split1/sequence_resnet-50/rgb/frozen_train/2018-08-23/20:16': None, # '/media/luke/hdd-3tb/models/handcam/split1/single_frames_resnet-18/depth/train/2018-08-21/17:55': None, # '/media/luke/hdd-3tb/models/handcam/split1/single_frames_resnet-18/rgbd/train/2018-08-21/18:44': None, # '/media/luke/hdd-3tb/models/handcam/split1/single_frames_resnet-18/rgb/train/2018-08-21/18:14': None, # '/media/luke/hdd-3tb/models/handcam/split1/single_frames_resnet-50/depth/train/2018-08-23/11:51': None, # '/media/luke/hdd-3tb/models/handcam/split1/single_frames_resnet-50/rgbd/train/2018-08-23/13:36': None, # '/media/luke/hdd-3tb/models/handcam/split1/single_frames_resnet-50/rgb/train/2018-08-23/12:32': None, # '/media/luke/hdd-3tb/models/handcam/split2/sequence_resnet-18/depth/frozen_train/2018-08-22/04:11': None, # '/media/luke/hdd-3tb/models/handcam/split2/sequence_resnet-18/depth/train/2018-08-22/08:31': None, # '/media/luke/hdd-3tb/models/handcam/split2/sequence_resnet-18/rgbd/frozen_train/2018-08-22/06:55': None, # '/media/luke/hdd-3tb/models/handcam/split2/sequence_resnet-18/rgbd/train/2018-08-22/09:59': None, # '/media/luke/hdd-3tb/models/handcam/split2/sequence_resnet-18/rgb/frozen_train/2018-08-22/04:55': None, # '/media/luke/hdd-3tb/models/handcam/split2/sequence_resnet-18/rgb/train/2018-08-22/08:55': None, # '/media/luke/hdd-3tb/models/handcam/split2/sequence_resnet-50/depth/frozen_train/2018-08-24/04:35': None, # '/media/luke/hdd-3tb/models/handcam/split2/sequence_resnet-50/rgbd/frozen_train/2018-08-24/07:02': None, # '/media/luke/hdd-3tb/models/handcam/split2/sequence_resnet-50/rgb/frozen_train/2018-08-24/05:26': None, # '/media/luke/hdd-3tb/models/handcam/split2/single_frames_resnet-18/depth/train/2018-08-22/02:17': None, # '/media/luke/hdd-3tb/models/handcam/split2/single_frames_resnet-18/rgbd/train/2018-08-22/03:35': None, # '/media/luke/hdd-3tb/models/handcam/split2/single_frames_resnet-18/rgb/train/2018-08-22/02:57': None, # '/media/luke/hdd-3tb/models/handcam/split2/single_frames_resnet-50/depth/train/2018-08-24/02:08': None, # '/media/luke/hdd-3tb/models/handcam/split2/single_frames_resnet-50/rgbd/train/2018-08-24/03:37': None, # '/media/luke/hdd-3tb/models/handcam/split2/single_frames_resnet-50/rgb/train/2018-08-24/02:42': None, # '/media/luke/hdd-3tb/models/handcam/split3/sequence_resnet-18/depth/frozen_train/2018-08-22/12:23': None, # '/media/luke/hdd-3tb/models/handcam/split3/sequence_resnet-18/depth/train/2018-08-22/15:58': None, # '/media/luke/hdd-3tb/models/handcam/split3/sequence_resnet-18/rgbd/frozen_train/2018-08-22/15:08': None, # '/media/luke/hdd-3tb/models/handcam/split3/sequence_resnet-18/rgbd/train/2018-08-22/18:08': None, # '/media/luke/hdd-3tb/models/handcam/split3/sequence_resnet-18/rgb/frozen_train/2018-08-22/13:59': None, # '/media/luke/hdd-3tb/models/handcam/split3/sequence_resnet-18/rgb/train/2018-08-22/17:17': None, # '/media/luke/hdd-3tb/models/handcam/split3/sequence_resnet-50/depth/frozen_train/2018-08-24/12:50': None, # '/media/luke/hdd-3tb/models/handcam/split3/sequence_resnet-50/rgbd/frozen_train/2018-08-24/16:53': None, # '/media/luke/hdd-3tb/models/handcam/split3/sequence_resnet-50/rgb/frozen_train/2018-08-24/14:32': None, # '/media/luke/hdd-3tb/models/handcam/split3/single_frames_resnet-18/depth/train/2018-08-22/11:00': None, # '/media/luke/hdd-3tb/models/handcam/split3/single_frames_resnet-18/rgbd/train/2018-08-22/11:48': None, # '/media/luke/hdd-3tb/models/handcam/split3/single_frames_resnet-18/rgb/train/2018-08-22/11:16': None, # '/media/luke/hdd-3tb/models/handcam/split3/single_frames_resnet-50/depth/train/2018-08-24/09:24': None, # '/media/luke/hdd-3tb/models/handcam/split3/single_frames_resnet-50/rgbd/train/2018-08-24/11:23': None, # '/media/luke/hdd-3tb/models/handcam/split3/single_frames_resnet-50/rgb/train/2018-08-24/10:10': None, # '/media/luke/hdd-3tb/models/handcam/split4/sequence_resnet-18/depth/frozen_train/2018-08-22/20:36': None, # '/media/luke/hdd-3tb/models/handcam/split4/sequence_resnet-18/depth/train/2018-08-22/23:04': None, # '/media/luke/hdd-3tb/models/handcam/split4/sequence_resnet-18/rgbd/frozen_train/2018-08-22/22:14': None, # '/media/luke/hdd-3tb/models/handcam/split4/sequence_resnet-18/rgbd/train/2018-08-23/01:16': None, # '/media/luke/hdd-3tb/models/handcam/split4/sequence_resnet-18/rgb/frozen_train/2018-08-22/21:36': None, # '/media/luke/hdd-3tb/models/handcam/split4/sequence_resnet-18/rgb/train/2018-08-23/00:10': None, # '/media/luke/hdd-3tb/models/handcam/split4/sequence_resnet-50/depth/frozen_train/2018-08-24/23:00': None, # '/media/luke/hdd-3tb/models/handcam/split4/sequence_resnet-50/rgbd/frozen_train/2018-08-25/04:29': None, # '/media/luke/hdd-3tb/models/handcam/split4/sequence_resnet-50/rgb/frozen_train/2018-08-25/01:49': None, # '/media/luke/hdd-3tb/models/handcam/split4/single_frames_resnet-18/depth/train/2018-08-22/19:11': None, # '/media/luke/hdd-3tb/models/handcam/split4/single_frames_resnet-18/rgbd/train/2018-08-22/19:55': None, # '/media/luke/hdd-3tb/models/handcam/split4/single_frames_resnet-18/rgb/train/2018-08-22/19:30': None, # '/media/luke/hdd-3tb/models/handcam/split4/single_frames_resnet-50/depth/train/2018-08-24/19:55': None, # '/media/luke/hdd-3tb/models/handcam/split4/single_frames_resnet-50/rgbd/train/2018-08-24/21:43': None, # '/media/luke/hdd-3tb/models/handcam/split4/single_frames_resnet-50/rgb/train/2018-08-24/20:50': None, # '/media/luke/hdd-3tb/models/handcam/split5/sequence_resnet-18/depth/frozen_train/2018-08-23/04:06': None, # '/media/luke/hdd-3tb/models/handcam/split5/sequence_resnet-18/depth/train/2018-08-23/06:34': None, # '/media/luke/hdd-3tb/models/handcam/split5/sequence_resnet-18/rgbd/frozen_train/2018-08-23/05:55': None, # '/media/luke/hdd-3tb/models/handcam/split5/sequence_resnet-18/rgbd/train/2018-08-23/08:19': None, # '/media/luke/hdd-3tb/models/handcam/split5/sequence_resnet-18/rgb/frozen_train/2018-08-23/05:13': None, # '/media/luke/hdd-3tb/models/handcam/split5/sequence_resnet-18/rgb/train/2018-08-23/07:38': None, # '/media/luke/hdd-3tb/models/handcam/split5/sequence_resnet-50/depth/frozen_train/2018-08-25/11:27': None, # '/media/luke/hdd-3tb/models/handcam/split5/sequence_resnet-50/rgbd/frozen_train/2018-08-25/15:06': None, # '/media/luke/hdd-3tb/models/handcam/split5/sequence_resnet-50/rgb/frozen_train/2018-08-25/13:11': None, # '/media/luke/hdd-3tb/models/handcam/split5/single_frames_resnet-18/depth/train/2018-08-23/02:54': None, # '/media/luke/hdd-3tb/models/handcam/split5/single_frames_resnet-18/rgbd/train/2018-08-23/03:39': None, # '/media/luke/hdd-3tb/models/handcam/split5/single_frames_resnet-18/rgb/train/2018-08-23/03:22': None, # '/media/luke/hdd-3tb/models/handcam/split5/single_frames_resnet-50/depth/train/2018-08-25/07:09': None, # '/media/luke/hdd-3tb/models/handcam/split5/single_frames_resnet-50/rgbd/train/2018-08-25/09:37': None, # '/media/luke/hdd-3tb/models/handcam/split5/single_frames_resnet-50/rgb/train/2018-08-25/08:35': None, # '/media/luke/hdd-3tb/models/handcam/split6/sequence_resnet-18/depth/frozen_train/2018-08-23/12:15': None, # '/media/luke/hdd-3tb/models/handcam/split6/sequence_resnet-18/depth/train/2018-08-23/15:13': None, # '/media/luke/hdd-3tb/models/handcam/split6/sequence_resnet-18/rgbd/frozen_train/2018-08-23/13:58': None, # '/media/luke/hdd-3tb/models/handcam/split6/sequence_resnet-18/rgbd/train/2018-08-23/17:53': None, # '/media/luke/hdd-3tb/models/handcam/split6/sequence_resnet-18/rgb/frozen_train/2018-08-23/12:50': None, # '/media/luke/hdd-3tb/models/handcam/split6/sequence_resnet-18/rgb/train/2018-08-23/16:47': None, # '/media/luke/hdd-3tb/models/handcam/split6/sequence_resnet-50/depth/frozen_train/2018-08-26/00:18': None, # '/media/luke/hdd-3tb/models/handcam/split6/sequence_resnet-50/rgbd/frozen_train/2018-08-26/03:24': None, # '/media/luke/hdd-3tb/models/handcam/split6/sequence_resnet-50/rgb/frozen_train/2018-08-26/01:13': None, # '/media/luke/hdd-3tb/models/handcam/split6/single_frames_resnet-18/depth/train/2018-08-23/10:39': None, # '/media/luke/hdd-3tb/models/handcam/split6/single_frames_resnet-18/rgbd/train/2018-08-23/11:38': None, # '/media/luke/hdd-3tb/models/handcam/split6/single_frames_resnet-18/rgb/train/2018-08-23/11:06': None, # '/media/luke/hdd-3tb/models/handcam/split6/single_frames_resnet-50/depth/train/2018-08-25/19:42': None, # '/media/luke/hdd-3tb/models/handcam/split6/single_frames_resnet-50/rgbd/train/2018-08-25/21:27': None, # '/media/luke/hdd-3tb/models/handcam/split6/single_frames_resnet-50/rgb/train/2018-08-25/20:25': None, # '/media/luke/hdd-3tb/models/handcam/split7/sequence_resnet-18/depth/frozen_train/2018-08-22/16:30': None, # '/media/luke/hdd-3tb/models/handcam/split7/sequence_resnet-18/depth/train/2018-08-22/21:24': None, # '/media/luke/hdd-3tb/models/handcam/split7/sequence_resnet-18/rgb/frozen_train/2018-08-22/18:23': None, # '/media/luke/hdd-3tb/models/handcam/split7/sequence_resnet-18/rgb/train/2018-08-22/22:36': None, # '/media/luke/hdd-3tb/models/handcam/split7/sequence_resnet-18/rgbd/frozen_train/2018-08-28/10:04': None, # '/media/luke/hdd-3tb/models/handcam/split7/sequence_resnet-18/rgbd/train/2018-08-28/10:38': None, # '/media/luke/hdd-3tb/models/handcam/split7/sequence_resnet-50/depth/frozen_train/2018-08-26/11:24': None, # '/media/luke/hdd-3tb/models/handcam/split7/sequence_resnet-50/rgbd/frozen_train/2018-08-26/14:05': None, # '/media/luke/hdd-3tb/models/handcam/split7/sequence_resnet-50/rgb/frozen_train/2018-08-26/12:13': None, # '/media/luke/hdd-3tb/models/handcam/split7/single_frames_resnet-18/depth/train/2018-08-22/14:30': None, # '/media/luke/hdd-3tb/models/handcam/split7/single_frames_resnet-18/rgb/train/2018-08-22/14:57': None, # '/media/luke/hdd-3tb/models/handcam/split7/single_frames_resnet-18/rgbd/train/2018-08-28/09:21': None, # '/media/luke/hdd-3tb/models/handcam/split7/single_frames_resnet-50/depth/train/2018-08-26/07:39': None, # '/media/luke/hdd-3tb/models/handcam/split7/single_frames_resnet-50/rgbd/train/2018-08-26/09:30': None, # '/media/luke/hdd-3tb/models/handcam/split7/single_frames_resnet-50/rgb/train/2018-08-26/08:36': None, # '/media/luke/hdd-3tb/models/handcam/split8/sequence_resnet-18/depth/frozen_train/2018-08-23/00:54': None, # '/media/luke/hdd-3tb/models/handcam/split8/sequence_resnet-18/depth/train/2018-08-23/03:20': None, # '/media/luke/hdd-3tb/models/handcam/split8/sequence_resnet-18/rgbd/frozen_train/2018-08-23/02:27': None, # '/media/luke/hdd-3tb/models/handcam/split8/sequence_resnet-18/rgbd/train/2018-08-23/04:51': None, # '/media/luke/hdd-3tb/models/handcam/split8/sequence_resnet-18/rgb/frozen_train/2018-08-23/01:27': None, # '/media/luke/hdd-3tb/models/handcam/split8/sequence_resnet-18/rgb/train/2018-08-23/04:08': None, # '/media/luke/hdd-3tb/models/handcam/split8/sequence_resnet-50/depth/frozen_train/2018-08-26/22:18': None, # '/media/luke/hdd-3tb/models/handcam/split8/sequence_resnet-50/rgbd/frozen_train/2018-08-27/02:58': None, # '/media/luke/hdd-3tb/models/handcam/split8/sequence_resnet-50/rgb/frozen_train/2018-08-26/23:59': None, # '/media/luke/hdd-3tb/models/handcam/split8/single_frames_resnet-18/depth/train/2018-08-22/23:20': None, # '/media/luke/hdd-3tb/models/handcam/split8/single_frames_resnet-18/rgbd/train/2018-08-23/00:30': None, # '/media/luke/hdd-3tb/models/handcam/split8/single_frames_resnet-18/rgb/train/2018-08-22/23:55': None, # '/media/luke/hdd-3tb/models/handcam/split8/single_frames_resnet-50/depth/train/2018-08-26/19:11': None, # '/media/luke/hdd-3tb/models/handcam/split8/single_frames_resnet-50/rgbd/train/2018-08-26/21:03': None, # '/media/luke/hdd-3tb/models/handcam/split8/single_frames_resnet-50/rgb/train/2018-08-26/20:06': None, # '/media/luke/hdd-3tb/models/handcam/split9/sequence_resnet-18/depth/frozen_train/2018-08-23/07:21': None, # '/media/luke/hdd-3tb/models/handcam/split9/sequence_resnet-18/depth/train/2018-08-23/09:15': None, # '/media/luke/hdd-3tb/models/handcam/split9/sequence_resnet-18/rgbd/frozen_train/2018-08-23/08:35': None, # '/media/luke/hdd-3tb/models/handcam/split9/sequence_resnet-18/rgbd/train/2018-08-23/10:38': None, # '/media/luke/hdd-3tb/models/handcam/split9/sequence_resnet-18/rgb/frozen_train/2018-08-23/07:58': None, # '/media/luke/hdd-3tb/models/handcam/split9/sequence_resnet-18/rgb/train/2018-08-23/09:49': None, # '/media/luke/hdd-3tb/models/handcam/split9/sequence_resnet-50/depth/frozen_train/2018-08-27/07:32': None, # '/media/luke/hdd-3tb/models/handcam/split9/sequence_resnet-50/rgbd/frozen_train/2018-08-27/12:23': None, # '/media/luke/hdd-3tb/models/handcam/split9/sequence_resnet-50/rgb/frozen_train/2018-08-27/09:29': None, # '/media/luke/hdd-3tb/models/handcam/split9/single_frames_resnet-18/depth/train/2018-08-23/05:40': None, # '/media/luke/hdd-3tb/models/handcam/split9/single_frames_resnet-18/rgbd/train/2018-08-23/06:58': None, # '/media/luke/hdd-3tb/models/handcam/split9/single_frames_resnet-18/rgb/train/2018-08-23/06:25': None, # '/media/luke/hdd-3tb/models/handcam/split9/single_frames_resnet-50/depth/train/2018-08-27/04:49': None, # '/media/luke/hdd-3tb/models/handcam/split9/single_frames_resnet-50/rgbd/train/2018-08-27/06:13': None, # '/media/luke/hdd-3tb/models/handcam/split9/single_frames_resnet-50/rgb/train/2018-08-27/05:31': None } # dict[resnet_size][seq_or_single][modality][gesture_or_accuracy] compiled_results_dict = { "resnet-50": {"single_frames": {}, "sequence_frozen": {}}, "resnet-18": {"single_frames": {}, "sequence_frozen": {}, "sequence_end2end": {}}, } for resnet_type in compiled_results_dict.keys(): for model_type in compiled_results_dict[resnet_type].keys(): for split_id in range(0, 10): compiled_results_dict[resnet_type][model_type]["split%d" % split_id] = {} if split_id == 0 and resnet_type == "resnet-50": if model_type == "single_frames": compiled_results_dict[resnet_type][model_type][ "split%d" % split_id ]["rgb"] = {"accuracy": 0.9818, "gesture_spotting": 0.9945} compiled_results_dict[resnet_type][model_type][ "split%d" % split_id ]["depth"] = {"accuracy": 0.8604, "gesture_spotting": 0.9394} compiled_results_dict[resnet_type][model_type][ "split%d" % split_id ]["rgbd"] = {"accuracy": 0.9813, "gesture_spotting": 0.9924} else: compiled_results_dict[resnet_type][model_type][ "split%d" % split_id ]["rgb"] = {"accuracy": 0.9540, "gesture_spotting": 0.9922} compiled_results_dict[resnet_type][model_type][ "split%d" % split_id ]["depth"] = {"accuracy": 0.9461, "gesture_spotting": 0.9864} compiled_results_dict[resnet_type][model_type][ "split%d" % split_id ]["rgbd"] = {"accuracy": 0.9602, "gesture_spotting": 0.9919} # need to put the numbers from the paper in here, models are gone. pass else: for modality in ["depth", "rgb", "rgbd"]: compiled_results_dict[resnet_type][model_type][ "split%d" % split_id ][modality] = {"accuracy": None, "gesture_spotting": None} # run for each model. Load the FLAGS.pckl file to set everything up the same way as for training for model_path in models_to_eval.keys(): checkpoint_id = models_to_eval[model_path] if ( "/tmp/luke/handcam" not in model_path and "/media/luke/hdd-3tb" not in model_path ): model_path = os.path.join("/tmp/luke/handcam/", model_path) with open(os.path.join(model_path, "FLAGS.pckl"), "rb") as f: flags_dict = pickle.load(f) FLAGS = AttrDict(flags_dict) # allow attribute access to FLAGS. # need to modify FLAGS a bit FLAGS.batch_size = 1 # Sanity check FLAGS if FLAGS.input_modality not in ["rgb", "rgbd", "depth"]: raise (ValueError("input_modality must be one of: rgb, rgbd, depth.")) else: print(FLAGS.input_modality) if FLAGS.model_type not in ["single_frames", "sequence"]: raise (ValueError("model_type must be one of: single_frames, sequence.")) if FLAGS.mode not in ["train", "eval", "frozen_train"]: raise (ValueError("mode must be one of: train, eval, frozen_train")) if FLAGS.resnet_size not in [18, 50]: raise (ValueError("resnet size must be one of: 18, 50")) with open(os.path.join(model_path, "results.pckl"), "rb") as f: out_dict = pickle.load(f) print( "per frame: %.2f\tgesture spotting: %.2f" % (100 * out_dict["val_accuracy"], 100 * out_dict["gesture_spotting_accuracy"]) ) dict_resnet_type = "resnet-%d" % FLAGS.resnet_size dict_model_type = FLAGS.model_type if FLAGS.model_type != "single_frames": dict_model_type = ( "sequence_frozen" if FLAGS.mode == "frozen_train" else "sequence_end2end" ) dict_split_name = "split%d" % FLAGS.validation_split_num dict_modality = FLAGS.input_modality compiled_results_dict[dict_resnet_type][dict_model_type][dict_split_name][ dict_modality ] = { "accuracy": out_dict["val_accuracy"], "gesture_spotting": out_dict["gesture_spotting_accuracy"], } with open("/home/luke/github/master-thesis/python/all_validations_imu.pckl", "wb") as f: pickle.dump(compiled_results_dict, f)

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6

bd53f661283495e1b47b6b132ef614f421415436

3,954

py

Python

tests/test_rfc822.py

abravalheri/python-pep621

00c905f3bf4f3321363c07337375bf8fc8ae5600

[ "MIT" ]

4

2021-09-27T14:11:26.000Z

2022-02-23T19:29:40.000Z

tests/test_rfc822.py

abravalheri/python-pep621

00c905f3bf4f3321363c07337375bf8fc8ae5600

[ "MIT" ]

9

2021-09-20T22:20:11.000Z

2022-03-25T21:03:32.000Z

tests/test_rfc822.py

abravalheri/python-pep621

00c905f3bf4f3321363c07337375bf8fc8ae5600

[ "MIT" ]

2

2021-10-11T17:52:26.000Z

2021-11-21T21:23:57.000Z

# SPDX-License-Identifier: MIT import textwrap import pytest import pep621 @pytest.mark.parametrize( ('items', 'data'), [ # empty ([], ''), # simple ( [ ('Foo', 'Bar'), ], 'Foo: Bar\n', ), ( [ ('Foo', 'Bar'), ('Foo2', 'Bar2'), ], '''\ Foo: Bar Foo2: Bar2 ''', ), # None ( [ ('Item', None), ], '', ), # order ( [ ('ItemA', 'ValueA'), ('ItemB', 'ValueB'), ('ItemC', 'ValueC'), ], '''\ ItemA: ValueA ItemB: ValueB ItemC: ValueC ''', ), ( [ ('ItemB', 'ValueB'), ('ItemC', 'ValueC'), ('ItemA', 'ValueA'), ], '''\ ItemB: ValueB ItemC: ValueC ItemA: ValueA ''', ), # multiple keys ( [ ('ItemA', 'ValueA1'), ('ItemB', 'ValueB'), ('ItemC', 'ValueC'), ('ItemA', 'ValueA2'), ], '''\ ItemA: ValueA1 ItemA: ValueA2 ItemB: ValueB ItemC: ValueC ''', ), ], ) def test_headers(items, data): message = pep621.RFC822Message() for name, value in items: message[name] = value data = textwrap.dedent(data) assert str(message) == data assert bytes(message) == data.encode() def test_body(): message = pep621.RFC822Message() message['ItemA'] = 'ValueA' message['ItemB'] = 'ValueB' message['ItemC'] = 'ValueC' message.body = textwrap.dedent(''' Lorem ipsum dolor sit amet, consectetur adipiscing elit. Mauris congue semper fermentum. Nunc vitae tempor ante. Aenean aliquet posuere lacus non faucibus. In porttitor congue luctus. Vivamus eu dignissim orci. Donec egestas mi ac ipsum volutpat, vel elementum sapien consectetur. Praesent dictum finibus fringilla. Sed vel feugiat leo. Nulla a pharetra augue, at tristique metus. Aliquam fermentum elit at risus sagittis, vel pretium augue congue. Donec leo risus, faucibus vel posuere efficitur, feugiat ut leo. Aliquam vestibulum vel dolor id elementum. Ut bibendum nunc interdum neque interdum, vel tincidunt lacus blandit. Ut volutpat sollicitudin dapibus. Integer vitae lacinia ex, eget finibus nulla. Donec sit amet ante in neque pulvinar faucibus sed nec justo. Fusce hendrerit massa libero, sit amet pulvinar magna tempor quis. ''') assert str(message) == textwrap.dedent('''\ ItemA: ValueA ItemB: ValueB ItemC: ValueC Lorem ipsum dolor sit amet, consectetur adipiscing elit. Mauris congue semper fermentum. Nunc vitae tempor ante. Aenean aliquet posuere lacus non faucibus. In porttitor congue luctus. Vivamus eu dignissim orci. Donec egestas mi ac ipsum volutpat, vel elementum sapien consectetur. Praesent dictum finibus fringilla. Sed vel feugiat leo. Nulla a pharetra augue, at tristique metus. Aliquam fermentum elit at risus sagittis, vel pretium augue congue. Donec leo risus, faucibus vel posuere efficitur, feugiat ut leo. Aliquam vestibulum vel dolor id elementum. Ut bibendum nunc interdum neque interdum, vel tincidunt lacus blandit. Ut volutpat sollicitudin dapibus. Integer vitae lacinia ex, eget finibus nulla. Donec sit amet ante in neque pulvinar faucibus sed nec justo. Fusce hendrerit massa libero, sit amet pulvinar magna tempor quis. ''')

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6

1f16dabba6679763413d60a695faab69aa49b057

253

py

Python

quorum/structs/node.py

LSaldyt/quorum

a9def89f8183e5307366f8ba1785e5ef55aeb1af

[ "MIT" ]

null

quorum/structs/node.py

LSaldyt/quorum

a9def89f8183e5307366f8ba1785e5ef55aeb1af

[ "MIT" ]

null

quorum/structs/node.py

LSaldyt/quorum

a9def89f8183e5307366f8ba1785e5ef55aeb1af

[ "MIT" ]

null

class Node(object): def __init__(self, item): self.item = item def __str__(self): return str(self.item) def __repr__(self): return str(self) def __getattr__(self, attr): return getattr(self.item, attr)

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1f31695efddf6b34149b066e22b128b0341ce7ec

273

py

Python

imutils/face_utils/__init__.py

wpf535236337/imutils

4635e73e75965c6fef09347bead510f81142cf2e

[ "MIT" ]

1

2019-04-04T03:19:48.000Z

imutils/face_utils/__init__.py

wpf535236337/imutils

4635e73e75965c6fef09347bead510f81142cf2e

[ "MIT" ]

null

imutils/face_utils/__init__.py

wpf535236337/imutils

4635e73e75965c6fef09347bead510f81142cf2e

[ "MIT" ]

null

# import the necessary packages from .helpers import FACIAL_LANDMARKS_68_IDXS from .helpers import FACIAL_LANDMARKS_5_IDXS from .helpers import rect_to_bb from .helpers import shape_to_np from .helpers import visualize_facial_landmarks from .facealigner import FaceAligner

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6

1f81fd5204932e8c08a20d3e45951232ee92004b

6,700

py

Python

pycqed/tests/test_rb_decomposition.py

sergimasot/PycQED_py3

54ad1b14929ffe5cc87cf59423a970e4b9baa3e1

[ "MIT" ]

7

2017-02-27T09:49:23.000Z

2022-03-07T16:09:50.000Z

pycqed/tests/test_rb_decomposition.py

sergimasot/PycQED_py3

54ad1b14929ffe5cc87cf59423a970e4b9baa3e1

[ "MIT" ]

109

2019-10-01T16:09:24.000Z

2022-01-23T19:48:20.000Z

pycqed/tests/test_rb_decomposition.py

sergimasot/PycQED_py3

54ad1b14929ffe5cc87cf59423a970e4b9baa3e1

[ "MIT" ]

3

2019-11-07T08:31:00.000Z

2021-04-20T08:10:55.000Z

import unittest import pycqed as pq import numpy as np import qutip as qtp import os from pycqed.measurement.randomized_benchmarking import \ two_qubit_clifford_group as tqc from pycqed.measurement.randomized_benchmarking import \ randomized_benchmarking as rb class Test_rb_decomposition(unittest.TestCase): @classmethod def setUpClass(self): self.standard_pulses = { 'I': qtp.qeye(2), 'Z0': qtp.qeye(2), 'X180': qtp.sigmax(), 'mX180': qtp.sigmax(), 'Y180': qtp.sigmay(), 'mY180': qtp.sigmay(), 'X90': qtp.rotation(qtp.sigmax(), np.pi/2), 'mX90': qtp.rotation(qtp.sigmax(), -np.pi/2), 'Y90': qtp.rotation(qtp.sigmay(), np.pi/2), 'mY90': qtp.rotation(qtp.sigmay(), -np.pi/2), 'Z90': qtp.rotation(qtp.sigmaz(), np.pi/2), 'mZ90': qtp.rotation(qtp.sigmaz(), -np.pi/2), 'Z180': qtp.sigmaz(), 'mZ180': qtp.sigmaz(), 'CZ': qtp.gates.cphase(np.pi) } def test_file_generation(self): filedir = os.path.join( pq.__path__[0], 'measurement', 'randomized_benchmarking', 'clifford_hash_tables') if 'single_qubit_hash_lut.txt' in os.listdir(filedir): os.remove(os.path.join(filedir, 'single_qubit_hash_lut.txt')) if 'two_qubit_hash_lut.txt' in os.listdir(filedir): os.remove(os.path.join(filedir, 'two_qubit_hash_lut.txt')) tqc.CLut.create_lut_files() self.assertIn('single_qubit_hash_lut.txt', os.listdir(filedir)) self.assertIn('two_qubit_hash_lut.txt', os.listdir(filedir)) def test_recovery_single_qubit_rb(self): cliffords = [0, 1, 50, 100] nr_seeds = 100 for cl in cliffords: for _ in range(nr_seeds): cl_seq = rb.randomized_benchmarking_sequence( cl, desired_net_cl=0, interleaved_gate=None) for decomp in ['HZ', 'XY']: pulse_keys = rb.decompose_clifford_seq( cl_seq, gate_decomp=decomp) gproduct = qtp.tensor(qtp.identity(2)) for pk in pulse_keys: gproduct = self.standard_pulses[pk]*gproduct x = gproduct.full()/gproduct.full()[0][0] self.assertTrue(np.all(( np.allclose(np.real(x), np.eye(2)), np.allclose(np.imag(x), np.zeros(2))))) def test_recovery_Y180_irb(self): cliffords = [0, 1, 50, 100] nr_seeds = 100 for cl in cliffords: for _ in range(nr_seeds): cl_seq = rb.randomized_benchmarking_sequence( cl, desired_net_cl=0, interleaved_gate='Y180') for decomp in ['HZ', 'XY']: pulse_keys = rb.decompose_clifford_seq( cl_seq, gate_decomp=decomp) gproduct = qtp.tensor(qtp.identity(2)) for pk in pulse_keys: gproduct = self.standard_pulses[pk]*gproduct x = gproduct.full()/gproduct.full()[0][0] self.assertTrue(np.all(( np.allclose(np.real(x), np.eye(2)), np.allclose(np.imag(x), np.zeros(2))))) def test_recovery_two_qubit_rb(self): cliffords = [0, 1, 50] nr_seeds = 50 for cl in cliffords: for _ in range(nr_seeds): cl_seq = rb.randomized_benchmarking_sequence_new( cl, number_of_qubits=2, max_clifford_idx=11520, interleaving_cl=None, desired_net_cl=0) for decomp in ['HZ', 'XY']: tqc.gate_decomposition = \ rb.get_clifford_decomposition(decomp) pulse_tuples_list_all = [] for i, idx in enumerate(cl_seq): pulse_tuples_list = \ tqc.TwoQubitClifford(idx).gate_decomposition pulse_tuples_list_all += pulse_tuples_list gproduct = qtp.tensor(qtp.identity(2), qtp.identity(2)) for i, cl_tup in enumerate(pulse_tuples_list_all): if cl_tup[0] == 'CZ': gproduct = self.standard_pulses[cl_tup[0]]*gproduct else: eye_2qb = [qtp.identity(2), qtp.identity(2)] eye_2qb[int(cl_tup[1][-1])] = self.standard_pulses[ cl_tup[0]] gproduct = qtp.tensor(eye_2qb)*gproduct x = gproduct.full()/gproduct.full()[0][0] self.assertTrue(np.all(( np.allclose(np.real(x), np.eye(4)), np.allclose(np.imag(x), np.zeros(4))))) def test_recovery_cz_irb(self): cliffords = [0, 1, 50] nr_seeds = 50 for cl in cliffords: for _ in range(nr_seeds): cl_seq = rb.randomized_benchmarking_sequence_new( cl, number_of_qubits=2, max_clifford_idx=11520, interleaving_cl=4368, desired_net_cl=0) for decomp in ['HZ', 'XY']: tqc.gate_decomposition = \ rb.get_clifford_decomposition(decomp) pulse_tuples_list_all = [] for i, idx in enumerate(cl_seq): pulse_tuples_list = \ tqc.TwoQubitClifford(idx).gate_decomposition pulse_tuples_list_all += pulse_tuples_list gproduct = qtp.tensor(qtp.identity(2), qtp.identity(2)) for i, cl_tup in enumerate(pulse_tuples_list_all): if cl_tup[0] == 'CZ': gproduct = self.standard_pulses[cl_tup[0]]*gproduct else: eye_2qb = [qtp.identity(2), qtp.identity(2)] eye_2qb[int(cl_tup[1][-1])] = self.standard_pulses[ cl_tup[0]] gproduct = qtp.tensor(eye_2qb)*gproduct x = gproduct.full()/gproduct.full()[0][0] self.assertTrue(np.all(( np.allclose(np.real(x), np.eye(4)), np.allclose(np.imag(x), np.zeros(4)))))

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