xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

8c2ee052a268689f4ba7670fc7f13f62f8ca6b0f

709

py

Python

d4rl/gym_minigrid/__init__.py

chappers/d4rl

b838da60b51c98c1d673a81657f58a44ccf5d3fe

[ "Apache-2.0" ]

552

2020-04-20T01:07:02.000Z

2022-03-31T16:47:39.000Z

d4rl/gym_minigrid/__init__.py

chappers/d4rl

b838da60b51c98c1d673a81657f58a44ccf5d3fe

[ "Apache-2.0" ]

103

2020-04-20T14:18:32.000Z

2022-03-30T14:33:45.000Z

d4rl/gym_minigrid/__init__.py

chappers/d4rl

b838da60b51c98c1d673a81657f58a44ccf5d3fe

[ "Apache-2.0" ]

135

2020-04-21T16:57:52.000Z

2022-03-30T14:29:55.000Z

from gym.envs.registration import register register( id='minigrid-fourrooms-v0', entry_point='d4rl.gym_minigrid.envs.fourrooms:FourRoomsEnv', max_episode_steps=50, kwargs={ 'ref_min_score': 0.01442, 'ref_max_score': 2.89685, 'dataset_url':'http://rail.eecs.berkeley.edu/datasets/offline_rl/minigrid/minigrid4rooms.hdf5' } ) register( id='minigrid-fourrooms-random-v0', entry_point='d4rl.gym_minigrid.envs.fourrooms:FourRoomsEnv', max_episode_steps=50, kwargs={ 'ref_min_score': 0.01442, 'ref_max_score': 2.89685, 'dataset_url':'http://rail.eecs.berkeley.edu/datasets/offline_rl/minigrid/minigrid4rooms_random.hdf5' } )

29.541667

109

0.696756

90

709

5.255556

0.433333

0.042283

0.07611

0.114165

0.765328

0

0.060914

0.166432

709

23

110

30.826087

0.739425

0

0.571429

0

0.530324

0.196051

0

1

0

true

0

0.047619

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0.047619

0

null

0

1

0

null

0

1

0

6

8b158fb18872802ded4f2a57a300f8a4d1376e4d

315

py

Python

rrr.py

jhfwb/utils_xhr

ad58553c5aef3d3c4e942b95a2676e99618cf7c0

[ "MIT" ]

null

rrr.py

jhfwb/utils_xhr

ad58553c5aef3d3c4e942b95a2676e99618cf7c0

[ "MIT" ]

null

rrr.py

jhfwb/utils_xhr

ad58553c5aef3d3c4e942b95a2676e99618cf7c0

[ "MIT" ]

null

if __name__ == '__main__': arr=[592.3, 592.3, 593.3, 593.3, 593.3, 594.1, 594.1, 594.1, 594.1, 594.3, 594.3, 594.3, 594.3, 594.3, 594.3, 594.3, 594.3, 594.3, 594.3, 594.3, 594.7, 594.7, 595.0, 595.1, 595.1, 595.1, 595.1, 595.1, 596.3, 720.0, 720.0, 720.0, 720.0, 720.0, 720.0, 720.0] t=sum(arr) print(t)

78.75

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0.565079

81

315

2.098765

0.209877

0.282353

0.452941

0.470588

0.729412

0.652941

0.558824

0.441176

0

0.549618

0.168254

315

4

261

78.75

0.099237

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false

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1

0

1

0

null

0

6

8c800110fc2537e5808c26e48ae75be6eff09e44

4,354

py

Python

blockfrost/api/nutlink.py

AstroWa3l/blockfrost-python

dd3661ee3b882cc2081292327adbbf2d61fda0f9

[ "Apache-2.0" ]

1

2021-12-13T22:28:45.000Z

blockfrost/api/nutlink.py

AstroWa3l/blockfrost-python

dd3661ee3b882cc2081292327adbbf2d61fda0f9

[ "Apache-2.0" ]

null

blockfrost/api/nutlink.py

AstroWa3l/blockfrost-python

dd3661ee3b882cc2081292327adbbf2d61fda0f9

[ "Apache-2.0" ]

null

import requests from dataclasses import dataclass from ..utils import request_wrapper, list_request_wrapper @request_wrapper def nutlink_address(self, address: str, **kwargs): """ List metadata about specific address https://docs.blockfrost.io/#tag/Nut.link :param address: Address of a metadata oracle. :type address: str :param return_type: Optional. "object", "json" or "pandas". Default: "object". :type return_type: str :returns NutlinkAddressResponse object. :rtype NutlinkAddressResponse :raises ApiError: If API fails :raises Exception: If the API response is somehow malformed. """ return requests.get( url=f"{self.url}/nutlink/{address}", headers=self.default_headers ) @list_request_wrapper def nutlink_address_tickers(self, address: str, **kwargs): """ List tickers for a specific metadata oracle https://docs.blockfrost.io/#tag/Nut.link/paths/~1nutlink~1{address}~1tickers/get :param address: Address of a metadata oracle. :type address: str :param return_type: Optional. "object", "json" or "pandas". Default: "object". :type return_type: str :param gather_pages: Optional. Default: 100. Will collect all pages into one return :type gather_pages: bool :param count: Optional. Default: 1. The number of results displayed on one page. :type count: int :param page: Optional. The page number for listing the results. :type page: int :param order: Optional. "asc" or "desc". Default: "asc". :type order: str :returns A list of NutlinkAddressTickersResponse objects. :rtype [NutlinkAddressTickersResponse] :raises ApiError: If API fails :raises Exception: If the API response is somehow malformed. """ return requests.get( url=f"{self.url}/nutlink/{address}/tickers", params=self.query_parameters(kwargs), headers=self.default_headers ) @list_request_wrapper def nutlink_address_ticker(self, address: str, ticker: str, **kwargs): """ List of records of a specific ticker https://docs.blockfrost.io/#tag/Nut.link/paths/~1nutlink~1{address}~1tickers~1{ticker}/get :param address: Address of a metadata oracle. :type address: str :param ticker: Ticker for the pool record. :type ticker: str :param return_type: Optional. "object", "json" or "pandas". Default: "object". :type return_type: str :param gather_pages: Optional. Default: 100. Will collect all pages into one return :type gather_pages: bool :param count: Optional. Default: 1. The number of results displayed on one page. :type count: int :param page: Optional. The page number for listing the results. :type page: int :param order: Optional. "asc" or "desc". Default: "asc". :type order: str :returns A list of NutlinkAddressTickerResponse objects. :rtype [NutlinkAddressTickerResponse] :raises ApiError: If API fails :raises Exception: If the API response is somehow malformed. """ return requests.get( url=f"{self.url}/nutlink/{address}/tickers/{ticker}", params=self.query_parameters(kwargs), headers=self.default_headers ) @list_request_wrapper def nutlink_ticker(self, ticker: str, **kwargs): """ List of records of a specific ticker https://docs.blockfrost.io/#tag/Nut.link/paths/~1nutlink~1tickers~1{ticker}/get :param ticker: Ticker for the pool record. :type ticker: str :param return_type: Optional. "object", "json" or "pandas". Default: "object". :type return_type: str :param gather_pages: Optional. Default: 100. Will collect all pages into one return :type gather_pages: bool :param count: Optional. Default: 1. The number of results displayed on one page. :type count: int :param page: Optional. The page number for listing the results. :type page: int :param order: Optional. "asc" or "desc". Default: "asc". :type order: str :returns A list of NutlinkTickerResponse objects. :rtype [NutlinkTickerResponse] :raises ApiError: If API fails :raises Exception: If the API response is somehow malformed. """ return requests.get( url=f"{self.url}/nutlink/tickers/{ticker}", params=self.query_parameters(kwargs), headers=self.default_headers )

35.983471

94

0.695223

564

4,354

5.301418

0.163121

0.036789

0.02408

0.032107

0.851839

0.817057

0.806689

0

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0.201654

4,354

120

95

36.283333

0.853855

0.68305

0

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0.133333

false

0

0.1

0

0.366667

0

null

0

1

0

null

0

6

8cd1f287117098c4b87df3b3791eb82a878696c3

37

py

Python

algorithms/PALE/__init__.py

thanhtrunghuynh93/holisticEmbeddingsNA

d1bb58e879a9fb868729ea13c198e46c9c5f45c9

[ "MIT" ]

null

algorithms/PALE/__init__.py

thanhtrunghuynh93/holisticEmbeddingsNA

d1bb58e879a9fb868729ea13c198e46c9c5f45c9

[ "MIT" ]

null

algorithms/PALE/__init__.py

thanhtrunghuynh93/holisticEmbeddingsNA

d1bb58e879a9fb868729ea13c198e46c9c5f45c9

[ "MIT" ]

null

from algorithms.PALE.pale import PALE

37

0.864865

6

37

5.333333

0.666667

0

0.081081

37

1

37

0.941176

0

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true

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1

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null

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null

0

1

0

1

0

1

0

6

5072a8ffa0817033e4e59d877bdb848f0b2be2be

6,682

py

Python

lyricsgenius/api/public_methods/artist.py

jack-debug/LyricsGenius

7da13630f75e4ff49aeeb080b6d85ce7b97a3b33

[ "MIT" ]

692

2018-02-21T15:32:21.000Z

2022-03-26T09:50:13.000Z

lyricsgenius/api/public_methods/artist.py

jack-debug/LyricsGenius

7da13630f75e4ff49aeeb080b6d85ce7b97a3b33

[ "MIT" ]

171

2018-02-21T05:39:07.000Z

2022-03-30T22:00:05.000Z

lyricsgenius/api/public_methods/artist.py

jack-debug/LyricsGenius

7da13630f75e4ff49aeeb080b6d85ce7b97a3b33

[ "MIT" ]

170

2018-02-22T19:56:44.000Z

2022-03-18T11:12:37.000Z

class ArtistMethods(object): """Artist methods of the public API.""" def artist(self, artist_id, text_format=None): """Gets data for a specific artist. Args: artist_id (:obj:`int`): Genius artist ID text_format (:obj:`str`, optional): Text format of the results ('dom', 'html', 'markdown' or 'plain'). Returns: :obj:`dict` """ endpoint = 'artists/{}'.format(artist_id) params = {'text_format': text_format or self.response_format} return self._make_request(path=endpoint, params_=params, public_api=True) def artist_activity(self, artist_id, per_page=None, page=None, text_format=None): """Gets activities on artist's songs. Args: artist_id (:obj:`int`): Genius artist ID per_page (:obj:`int`, optional): Number of results to return per request. It can't be more than 50. page (:obj:`int`, optional): Paginated offset (number of the page). text_format (:obj:`str`, optional): Text format of the results ('dom', 'html', 'markdown' or 'plain'). Returns: :obj:`dict` """ endpoint = 'artists/{}/activity_stream/line_items'.format(artist_id) params = {'per_page': per_page, 'page': page, 'text_format': text_format or self.response_format} return self._make_request(path=endpoint, params_=params, public_api=True) def artist_albums(self, artist_id, per_page=None, page=None): """Gets artist's albums. Args: artist_id (:obj:`int`): Genius artist ID per_page (:obj:`int`, optional): Number of results to return per request. It can't be more than 50. page (:obj:`int`, optional): Paginated offset (number of the page). Returns: :obj:`dict` """ endpoint = 'artists/{}/albums'.format(artist_id) params = {'per_page': per_page, 'page': page} return self._make_request(path=endpoint, params_=params, public_api=True) def artist_contribution_opportunities(self, artist_id, per_page=None, next_curosr=None, text_format=None): """Gets contribution opportunities related to the artist. Args: artist_id (:obj:`int`): Genius artist ID per_page (:obj:`int`, optional): Number of results to return per request. It can't be more than 50. next_cursor (:obj:`int`, optional): Paginated offset (address of the next cursor). text_format (:obj:`str`, optional): Text format of the results ('dom', 'html', 'markdown' or 'plain'). Returns: :obj:`dict` Warning: This method requires a logged in user and will raise ``NotImplementedError``. """ raise NotImplementedError('This action requires a logged in user.') endpoint = 'artists/{}/contribution_opportunities'.format(artist_id) params = {'per_page': per_page, 'next_curosr': next_curosr, 'text_format': text_format or self.response_format} return self._make_request(path=endpoint, params_=params, public_api=True) def artist_followers(self, artist_id, per_page=None, page=None): """Gets artist's followers. Args: artist_id (:obj:`int`): Genius artist ID per_page (:obj:`int`, optional): Number of results to return per request. It can't be more than 50. page (:obj:`int`, optional): Paginated offset (number of the page). Returns: :obj:`dict` """ endpoint = 'artists/{}/followers'.format(artist_id) params = {'per_page': per_page, 'page': page} return self._make_request(path=endpoint, params_=params, public_api=True) def artist_leaderboard(self, artist_id, per_page=None, page=None): """Gets artist's top scholars. Args: artist_id (:obj:`int`): Genius artist ID per_page (:obj:`int`, optional): Number of results to return per request. It can't be more than 50. page (:obj:`int`, optional): Paginated offset (number of the page). Returns: :obj:`dict` """ endpoint = 'artists/{}/leaderboard'.format(artist_id) params = {'per_page': per_page, 'page': page} return self._make_request(path=endpoint, params_=params, public_api=True) def artist_songs(self, artist_id, per_page=None, page=None, sort='popularity'): """Gets artist's songs. Args: artist_id (:obj:`int`): Genius artist ID per_page (:obj:`int`, optional): Number of results to return per request. It can't be more than 50. page (:obj:`int`, optional): Paginated offset (number of the page). sort (:obj:`str`, optional): Sorting preference. ('title' or 'popularity') Returns: :obj:`dict` """ endpoint = 'artists/{}/songs'.format(artist_id) params = {'per_page': per_page, 'page': page, 'sort': sort} return self._make_request(path=endpoint, params_=params, public_api=True) def search_artist_songs(self, artist_id, search_term, per_page=None, page=None, sort='popularity'): """Searches artist's songs. Args: artist_id (:obj:`int`): Genius artist ID search_term (:obj:`str`): A term to search on Genius. per_page (:obj:`int`, optional): Number of results to return per request. It can't be more than 50. page (:obj:`int`, optional): Paginated offset (number of the page). sort (:obj:`str`, optional): Sorting preference. ('title' or 'popularity') Returns: :obj:`dict` """ endpoint = 'artists/{}/songs/search'.format(artist_id) params = {'q': search_term, 'per_page': per_page, 'page': page, 'sort': sort} return self._make_request(path=endpoint, params_=params, public_api=True)

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0.1278

0.072194

0.055274

0.06599

0.832205

0.793006

0.78652

0.773266

0.746193

0.733785

0

0.003164

0.337773

6,682

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38.402299

0.798192

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0.038056

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0.145455

false

0

0.309091

0

null

0

1

0

null

0

6

5090285d04736c8f961cdbc679728bcef84cd73c

153

py

Python

DeepAlignmentNetwork/menpofit/clm/expert/__init__.py

chiawei-liu/DeepAlignmentNetwork

52621cd2f697abe372b88c9ea0ee08f0d93b43d8

[ "MIT" ]

220

2019-09-01T01:52:04.000Z

2022-03-28T12:52:07.000Z

DeepAlignmentNetwork/menpofit/clm/expert/__init__.py

chiawei-liu/DeepAlignmentNetwork

52621cd2f697abe372b88c9ea0ee08f0d93b43d8

[ "MIT" ]

16

2019-10-24T07:55:11.000Z

2022-02-10T01:28:13.000Z

DeepAlignmentNetwork/menpofit/clm/expert/__init__.py

chiawei-liu/DeepAlignmentNetwork

52621cd2f697abe372b88c9ea0ee08f0d93b43d8

[ "MIT" ]

33

2019-09-23T15:08:50.000Z

2022-02-08T07:54:52.000Z

from .ensemble import ExpertEnsemble, CorrelationFilterExpertEnsemble, FcnFilterExpertEnsemble from .base import IncrementalCorrelationFilterThinWrapper

51

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6

50e6552111e4d496bdd83971a3b18a59ab88530b

4,376

py

Python

tests/mutate/test_merge_partial_tuplets.py

gilbertohasnofb/auxjad

553b7fe97221b6f378a93ade6262f024e3cbc678

[ "MIT" ]

6

2020-05-18T09:28:29.000Z

2021-12-22T00:40:54.000Z

tests/mutate/test_merge_partial_tuplets.py

gilbertohasnofb/auxjad

553b7fe97221b6f378a93ade6262f024e3cbc678

[ "MIT" ]

1

2021-04-21T20:29:38.000Z

2021-04-22T19:44:54.000Z

tests/mutate/test_merge_partial_tuplets.py

gilbertohasnofb/auxjad

553b7fe97221b6f378a93ade6262f024e3cbc678

[ "MIT" ]

1

2021-04-21T18:54:46.000Z

import abjad import auxjad def test_merge_partial_tuplets_01(): staff = abjad.Staff(r"\times 2/3 {c'1} \times 2/3 {d'2}") auxjad.mutate.merge_partial_tuplets(staff[:]) assert abjad.lilypond(staff) == abjad.String.normalize( r""" \new Staff { \times 2/3 { c'1 d'2 } } """ ) def test_merge_partial_tuplets_02(): staff = abjad.Staff( r"\times 2/3 {r4} \times 2/3 {c'2} " r"\times 4/5 {d'2~} \times 4/5{d'8}" ) auxjad.mutate.merge_partial_tuplets(staff[:]) assert abjad.lilypond(staff) == abjad.String.normalize( r""" \new Staff { \times 2/3 { r4 c'2 } \times 4/5 { d'2 ~ d'8 } } """ ) def test_merge_partial_tuplets_03(): staff = abjad.Staff(r"\times 2/3 {c'2} \times 2/3 {d'2} \times 2/3 {e'2}") auxjad.mutate.merge_partial_tuplets(staff[:]) assert abjad.lilypond(staff) == abjad.String.normalize( r""" \new Staff { \times 2/3 { c'2 d'2 e'2 } } """ ) def test_merge_partial_tuplets_04(): staff = abjad.Staff(r"\times 2/3 {c'2\p\< d'2} \times 2/3 {e'2\ff}") auxjad.mutate.merge_partial_tuplets(staff[:]) assert abjad.lilypond(staff) == abjad.String.normalize( r""" \new Staff { \times 2/3 { c'2 \p \< d'2 e'2 \ff } } """ ) def test_merge_partial_tuplets_05(): staff = abjad.Staff( r"\times 2/3 {c'4 d'4 e'4} \times 2/3 {f'4} \times 2/3 {g'4 a'4}" ) auxjad.mutate.merge_partial_tuplets(staff[:]) assert abjad.lilypond(staff) == abjad.String.normalize( r""" \new Staff { \times 2/3 { c'4 d'4 e'4 } \times 2/3 { f'4 g'4 a'4 } } """ ) def test_merge_partial_tuplets_06(): staff = abjad.Staff( r"\time 3/4 c'2. " r"\times 2/3 {d'4} r4 \times 2/3 {e'2} " r"\times 2/3 {f'4} r4 \times 2/3 {g'2}" ) auxjad.mutate.merge_partial_tuplets(staff[:]) assert abjad.lilypond(staff) == abjad.String.normalize( r""" \new Staff { \time 3/4 c'2. \tweak edge-height #'(0.7 . 0) \times 2/3 { d'4 } r4 \tweak edge-height #'(0.7 . 0) \times 2/3 { e'2 } \tweak edge-height #'(0.7 . 0) \times 2/3 { f'4 } r4 \tweak edge-height #'(0.7 . 0) \times 2/3 { g'2 } } """ ) staff = abjad.Staff( r"\time 3/4 c'2. " r"\times 2/3 {d'4} r4 \times 2/3 {e'2} " r"\times 2/3 {f'4} r4 \times 2/3 {g'2}" ) auxjad.mutate.merge_partial_tuplets(staff[:], merge_across_barlines=True) assert abjad.lilypond(staff) == abjad.String.normalize( r""" \new Staff { \time 3/4 c'2. \tweak edge-height #'(0.7 . 0) \times 2/3 { d'4 } r4 \times 2/3 { e'2 f'4 } r4 \tweak edge-height #'(0.7 . 0) \times 2/3 { g'2 } } """ ) def test_merge_partial_tuplets_07(): staff = abjad.Staff(r"\times 2/3 {c'1} \times 2/3 {d'2}") abjad.mutate.merge_partial_tuplets(staff[:]) assert abjad.lilypond(staff) == abjad.String.normalize( r""" \new Staff { \times 2/3 { c'1 d'2 } } """ )

21.771144

78

0.383684

510

4,376

3.201961

0.094118

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0.053889

0.939988

0.849357

0.80098

0.77526

0.761788

0.73117

0

0.082888

0.487203

4,376

200

79

21.88

0.644831

0

0.419355

0

0.112903

0.216015

0

0.129032

1

0.112903

false

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0

0.145161

0

null

0

1

0

null

0

6

0feea268b32852e7d758135266de3e1e2ccfd403

37

py

Python

tests/import_clr.py

michaelosthege/pythonnet-docker

c3dd8429cd0d86d0cef0f1d0d7bf90562473931a

[ "MIT" ]

14

2018-01-29T02:42:57.000Z

2021-09-20T21:05:57.000Z

tests/import_clr.py

michaelosthege/pythonnet-docker

c3dd8429cd0d86d0cef0f1d0d7bf90562473931a

[ "MIT" ]

4

2020-06-10T08:04:46.000Z

2021-08-07T21:38:28.000Z

tests/import_clr.py

michaelosthege/pythonnet-docker

c3dd8429cd0d86d0cef0f1d0d7bf90562473931a

[ "MIT" ]

3

2018-02-03T20:46:47.000Z

2021-09-14T05:11:56.000Z

import clr print("Hello pythonnet")

9.25

24

0.756757

5

37

5.6

1

0

0.135135

37

3

25

12.333333

0.875

0

0.405405

0

1

0

true

0

0.5

0

0.5

1

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null

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1

0

null

0

1

0

1

0

1

0

6

ba2d1d72750940b83e927c82815c105bfd9ff3fa

9,203

py

Python

tests/test_redis_throttled_queue.py

ionelmc/python-redis-throttled-queue

e616ceb1ef4265c928e9cd25704575f970dd34ff

[ "BSD-2-Clause" ]

1

2022-03-31T17:56:50.000Z

tests/test_redis_throttled_queue.py

ionelmc/python-redis-throttled-queue

e616ceb1ef4265c928e9cd25704575f970dd34ff

[ "BSD-2-Clause" ]

null

tests/test_redis_throttled_queue.py

ionelmc/python-redis-throttled-queue

e616ceb1ef4265c928e9cd25704575f970dd34ff

[ "BSD-2-Clause" ]

null

import platform from functools import partial from time import sleep from types import SimpleNamespace from unittest.mock import call import freezegun import pytest from redis.client import StrictRedis from redis_throttled_queue import Resolution from redis_throttled_queue import ThrottledQueue pytest_plugins = ('pytester',) skipifpypy = partial(pytest.mark.skipif(platform.python_implementation() == 'PyPy')) def get_ttl(redis_conn): return {':'.join(key.split(':')[:2]): redis_conn.ttl(key) for key in redis_conn.keys('*:usage:*')} def test_simple(redis_conn: StrictRedis, redis_monitor): queue = ThrottledQueue(redis_conn, 'test', limit=5, resolution=Resolution.SECOND) for item in range(10): queue.push('aaaaaa', f'a{item}', priority=10 - item) for item in range(10): queue.push('bbbbbb', f'b{item}', priority=10 - item) assert len(queue) == 20 items = ','.join(queue.pop() for _ in range(10)) assert items == 'a0,b0,a1,b1,a2,b2,a3,b3,a4,b4' assert len(queue) == 10 assert get_ttl(redis_conn) == {'test:usage': 1} assert queue.pop() is None assert len(queue) == 10 sleep(1) items = ','.join(queue.pop() for _ in range(10)) assert items == 'a5,b5,a6,b6,a7,b7,a8,b8,a9,b9' assert queue.pop() is None assert len(queue) == 0 def test_usage_expiry(redis_conn: StrictRedis, redis_monitor): queue = ThrottledQueue(redis_conn, 'test', limit=5, resolution=Resolution.SECOND) queue.push('name', 'foo') assert len(queue) == 1 assert queue.pop() == 'foo' assert len(queue) == 0 assert get_ttl(redis_conn) == {'test:usage': 1} assert queue.pop() is None assert get_ttl(redis_conn) == {'test:usage': 1} sleep(1) assert get_ttl(redis_conn) == {} assert queue.pop() is None assert get_ttl(redis_conn) == {'test:usage': 1} sleep(1) assert get_ttl(redis_conn) == {} def test_dupes(redis_conn: StrictRedis, redis_monitor): queue = ThrottledQueue(redis_conn, 'test', limit=5, resolution=Resolution.SECOND) for _ in range(3): for item in range(10): queue.push('aaaaaa', f'a{item}', priority=10 - item) for item in range(10): queue.push('bbbbbb', f'b{item}', priority=10 - item) assert len(queue) == 20 items = ','.join(queue.pop() for _ in range(10)) assert items == 'a0,b0,a1,b1,a2,b2,a3,b3,a4,b4' assert len(queue) == 10 assert get_ttl(redis_conn) == {'test:usage': 1} assert queue.pop() is None assert len(queue) == 10 sleep(1) items = ','.join(queue.pop() for _ in range(10)) assert items == 'a5,b5,a6,b6,a7,b7,a8,b8,a9,b9' assert queue.pop() is None assert len(queue) == 0 def test_cleanup(redis_conn: StrictRedis, redis_monitor): queue = ThrottledQueue(redis_conn, 'test', limit=5, resolution=Resolution.SECOND) for item in range(10): queue.push('aaaaaa', f'a{item}', priority=10 - item) for item in range(10): queue.push('bbbbbb', f'b{item}', priority=10 - item) assert len(queue) == 20 items = ','.join(queue.pop() for _ in range(10)) assert items == 'a0,b0,a1,b1,a2,b2,a3,b3,a4,b4' assert len(queue) == 10 assert queue.pop() is None assert len(queue) == 10 assert get_ttl(redis_conn) == {'test:usage': 1} sleep(1) queue.cleanup() assert len(queue) == 0 assert queue.pop() is None assert get_ttl(redis_conn) == {} def test_cleanup_directly(redis_conn: StrictRedis, redis_monitor): queue = ThrottledQueue(redis_conn, 'test', limit=5, resolution=Resolution.SECOND) for item in range(10): queue.push('aaaaaa', f'a{item}', priority=10 - item) for item in range(10): queue.push('bbbbbb', f'b{item}', priority=10 - item) assert len(queue) == 20 queue.cleanup() assert len(queue) == 0 assert queue.pop() is None def test_cleanup_nothing(redis_conn: StrictRedis, redis_monitor): queue = ThrottledQueue(redis_conn, 'test', limit=5, resolution=Resolution.SECOND) assert len(queue) == 0 queue.cleanup() assert len(queue) == 0 queue.cleanup() assert queue.pop() is None def test_priority(redis_conn: StrictRedis, redis_monitor): queue = ThrottledQueue(redis_conn, 'test', limit=5, resolution=Resolution.SECOND) for item in range(10): queue.push('aaaaaa', f'a{item}', priority=10 - item) for item in range(10): queue.push('bbbbbb', f'b{item}', priority=item) assert len(queue) == 20 items = ','.join(queue.pop() for _ in range(10)) assert items == 'a0,b9,a1,b8,a2,b7,a3,b6,a4,b5' assert len(queue) == 10 assert queue.pop() is None assert len(queue) == 10 assert get_ttl(redis_conn) == {'test:usage': 1} sleep(1) assert queue.idle_seconds == pytest.approx(1, 0.01) items = ','.join(queue.pop() for _ in range(10)) assert items == 'a5,b4,a6,b3,a7,b2,a8,b1,a9,b0' assert queue.pop() is None assert len(queue) == 0 assert get_ttl(redis_conn) == {'test:usage': 1} def test_window(redis_conn: StrictRedis, redis_monitor): queue = ThrottledQueue(redis_conn, 'test', limit=1, resolution=Resolution.SECOND) for item in range(10): queue.push('A', f'a{item}', priority=item) assert len(queue) == 10 assert queue.pop('X') == 'a9' assert get_ttl(redis_conn) == {'test:usage': 1} assert len(queue) == 9 assert queue.pop('X') is None assert len(queue) == 9 assert queue.pop('Y') == 'a8' assert len(queue) == 8 assert queue.pop('Y') is None assert len(queue) == 8 sleep(1) assert queue.pop('X') == 'a7' assert len(queue) == 7 assert queue.pop('X') is None assert len(queue) == 7 assert queue.pop('Y') == 'a6' assert len(queue) == 6 assert queue.pop('Y') is None assert len(queue) == 6 def test_extras(redis_conn: StrictRedis, redis_monitor): queue = ThrottledQueue(redis_conn, 'test', limit=5, resolution=Resolution.SECOND) for item in range(10): queue.push('aaaaaa', f'a{item}', priority=10 - item) for item in range(10): queue.push('bbbbbb', f'b{item}', priority=10 - item) assert len(queue) == 20 assert queue.pop() == 'a0' assert get_ttl(redis_conn) == {'test:usage': 1} assert len(queue) == 19 queue.push('cccccc', 'c0', priority=11) queue.push('cccccc', 'c1', priority=-1) queue.push('aaaaaa', 'aX', priority=11) queue.push('aaaaaa', 'aY', priority=-1) assert len(queue) == 23 items = ','.join(str(queue.pop()) for _ in range(13)) assert len(queue) == 12 assert items in ['b0,aX,b1,a1,b2,a2,b3,a3,b4,c0,c1,None,None'] sleep(1) assert queue.idle_seconds == pytest.approx(1, 0.01) items = ','.join(str(queue.pop()) for _ in range(12)) assert len(queue) == 2 assert items in ['a4,b5,a5,b6,a6,b7,a7,b8,a8,b9,None,None'] sleep(1) assert queue.idle_seconds == pytest.approx(1, 0.01) items = ','.join(str(queue.pop()) for _ in range(4)) assert len(queue) == 0 assert items in ['a9,aY,None,None'] def test_mocked_resolution(mocker): script = mocker.patch.object(ThrottledQueue, '_pop_item_script') conn = SimpleNamespace(info=lambda: {'redis_version': '10'}, register_script=lambda _: None) with freezegun.freeze_time('2022-02-22') as ft: queue = ThrottledQueue( conn, 'foobar', limit='?', resolution=10, ) queue.pop() assert script.mock_calls == [call(client=conn, keys=(), args=('foobar', 0, '?', 10))] ft.tick(9) queue.pop() assert script.mock_calls == [ call(client=conn, keys=(), args=('foobar', 0, '?', 10)), call(client=conn, keys=(), args=('foobar', 0, '?', 10)), ] ft.tick(1) queue.pop() assert script.mock_calls == [ call(client=conn, keys=(), args=('foobar', 0, '?', 10)), call(client=conn, keys=(), args=('foobar', 0, '?', 10)), call(client=conn, keys=(), args=('foobar', 1, '?', 10)), ] def test_mocked_window(mocker): script = mocker.patch.object(ThrottledQueue, '_pop_item_script') conn = SimpleNamespace(info=lambda: {'redis_version': '10'}, register_script=lambda _: script) queue = ThrottledQueue( conn, 'foobar', limit='?', resolution=10, ) queue.pop(window='foobar1') assert script.mock_calls == [call(client=conn, keys=(), args=('foobar', 'foobar1', '?', 10))] queue.pop(window='foobar2') assert script.mock_calls == [ call(client=conn, keys=(), args=('foobar', 'foobar1', '?', 10)), call(client=conn, keys=(), args=('foobar', 'foobar2', '?', 10)), ] def test_validation(): old_conn = SimpleNamespace(info=lambda: {'redis_version': '6.1'}) pytest.raises(RuntimeError, ThrottledQueue, old_conn, 'foo') conn = SimpleNamespace(info=lambda: {'redis_version': '10'}, register_script=lambda _: None) pytest.raises(TypeError, ThrottledQueue, conn, b'caca') pytest.raises(TypeError, ThrottledQueue, conn, 123) pytest.raises(ValueError, ThrottledQueue(conn, 'foo').push, ':', None)

33.344203

102

0.62534

1,297

9,203

4.346184

0.11488

0.059074

0.091893

0.037254

0.797055

0.769203

0.759092

0.722547

0.717225

0.671102

0

0.044194

0.208302

9,203

275

103

33.465455

0.729481

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0.03086

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0.059091

false

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0.045455

0.004545

0.109091

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null

0

1

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null

0

1

0

6

e8624c036b9a9c2ad7f6d20228479a3d6dd90754

41

py

Python

aiortc_pyav_stub/logging.py

ulope/aiortc-pyav-stub

b2af75ae74b96f59f3818b2c7f0081dbbcd3bb75

[ "MIT" ]

null

aiortc_pyav_stub/logging.py

ulope/aiortc-pyav-stub

b2af75ae74b96f59f3818b2c7f0081dbbcd3bb75

[ "MIT" ]

null

aiortc_pyav_stub/logging.py

ulope/aiortc-pyav-stub

b2af75ae74b96f59f3818b2c7f0081dbbcd3bb75

[ "MIT" ]

null

def restore_default_callback(): pass

13.666667

31

0.756098

5

41

5.8

1

0

0.170732

41

2

32

20.5

0.852941

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

e869d9ae499c8ef4e66a4c9d51f6eaa45fc5fc2c

144

py

Python

stv/tools/__init__.py

wp777/stv-compute

313b574c43ef87b629e70c25c38dbb7b24d1f130

[ "MIT" ]

null

stv/tools/__init__.py

wp777/stv-compute

313b574c43ef87b629e70c25c38dbb7b24d1f130

[ "MIT" ]

null

stv/tools/__init__.py

wp777/stv-compute

313b574c43ef87b629e70c25c38dbb7b24d1f130

[ "MIT" ]

null

from .disjoint_set import DisjointSet from .file_tools import FileTools from .list_tools import ListTools from .string_tools import StringTools

28.8

37

0.861111

20

144

6

0.6

0.275

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0.111111

144

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true

0

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1

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null

1

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1

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null

0

1

0

1

0

1

0

6

e87581639ec401ed06e527176764725b6dd71ad8

30,154

py

Python

modelsNIPS.py

jfilter/universal-style-transfer-pytorch

daf144aa1dc43ed869c35685bc9ed2b0cba3988a

[ "MIT" ]

null

modelsNIPS.py

jfilter/universal-style-transfer-pytorch

daf144aa1dc43ed869c35685bc9ed2b0cba3988a

[ "MIT" ]

null

modelsNIPS.py

jfilter/universal-style-transfer-pytorch

daf144aa1dc43ed869c35685bc9ed2b0cba3988a

[ "MIT" ]

null

import torch import torch.nn as nn class encoder1(nn.Module): def __init__(self,vgg1): super(encoder1,self).__init__() # dissemble vgg2 and decoder2 layer by layer # then resemble a new encoder-decoder network # 224 x 224 self.conv1 = nn.Conv2d(3,3,1,1,0) self.conv1.weight = torch.nn.Parameter(vgg1.get(0).weight.float()) self.conv1.bias = torch.nn.Parameter(vgg1.get(0).bias.float()) # 224 x 224 self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1)) # 226 x 226 self.conv2 = nn.Conv2d(3,64,3,1,0) self.conv2.weight = torch.nn.Parameter(vgg1.get(2).weight.float()) self.conv2.bias = torch.nn.Parameter(vgg1.get(2).bias.float()) self.relu = nn.ReLU(inplace=True) # 224 x 224 def forward(self,x): out = self.conv1(x) out = self.reflecPad1(out) out = self.conv2(out) out = self.relu(out) return out class decoder1(nn.Module): def __init__(self,d1): super(decoder1,self).__init__() self.reflecPad2 = nn.ReflectionPad2d((1,1,1,1)) # 226 x 226 self.conv3 = nn.Conv2d(64,3,3,1,0) self.conv3.weight = torch.nn.Parameter(d1.get(1).weight.float()) self.conv3.bias = torch.nn.Parameter(d1.get(1).bias.float()) # 224 x 224 def forward(self,x): out = self.reflecPad2(x) out = self.conv3(out) return out class encoder2(nn.Module): def __init__(self,vgg): super(encoder2,self).__init__() # vgg # 224 x 224 self.conv1 = nn.Conv2d(3,3,1,1,0) self.conv1.weight = torch.nn.Parameter(vgg.get(0).weight.float()) self.conv1.bias = torch.nn.Parameter(vgg.get(0).bias.float()) self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1)) # 226 x 226 self.conv2 = nn.Conv2d(3,64,3,1,0) self.conv2.weight = torch.nn.Parameter(vgg.get(2).weight.float()) self.conv2.bias = torch.nn.Parameter(vgg.get(2).bias.float()) self.relu2 = nn.ReLU(inplace=True) # 224 x 224 self.reflecPad3 = nn.ReflectionPad2d((1,1,1,1)) self.conv3 = nn.Conv2d(64,64,3,1,0) self.conv3.weight = torch.nn.Parameter(vgg.get(5).weight.float()) self.conv3.bias = torch.nn.Parameter(vgg.get(5).bias.float()) self.relu3 = nn.ReLU(inplace=True) # 224 x 224 self.maxPool = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True) # 112 x 112 self.reflecPad4 = nn.ReflectionPad2d((1,1,1,1)) self.conv4 = nn.Conv2d(64,128,3,1,0) self.conv4.weight = torch.nn.Parameter(vgg.get(9).weight.float()) self.conv4.bias = torch.nn.Parameter(vgg.get(9).bias.float()) self.relu4 = nn.ReLU(inplace=True) # 112 x 112 def forward(self,x): out = self.conv1(x) out = self.reflecPad1(out) out = self.conv2(out) out = self.relu2(out) out = self.reflecPad3(out) out = self.conv3(out) pool = self.relu3(out) out,pool_idx = self.maxPool(pool) out = self.reflecPad4(out) out = self.conv4(out) out = self.relu4(out) return out class decoder2(nn.Module): def __init__(self,d): super(decoder2,self).__init__() # decoder self.reflecPad5 = nn.ReflectionPad2d((1,1,1,1)) self.conv5 = nn.Conv2d(128,64,3,1,0) self.conv5.weight = torch.nn.Parameter(d.get(1).weight.float()) self.conv5.bias = torch.nn.Parameter(d.get(1).bias.float()) self.relu5 = nn.ReLU(inplace=True) # 112 x 112 self.unpool = nn.UpsamplingNearest2d(scale_factor=2) # 224 x 224 self.reflecPad6 = nn.ReflectionPad2d((1,1,1,1)) self.conv6 = nn.Conv2d(64,64,3,1,0) self.conv6.weight = torch.nn.Parameter(d.get(5).weight.float()) self.conv6.bias = torch.nn.Parameter(d.get(5).bias.float()) self.relu6 = nn.ReLU(inplace=True) # 224 x 224 self.reflecPad7 = nn.ReflectionPad2d((1,1,1,1)) self.conv7 = nn.Conv2d(64,3,3,1,0) self.conv7.weight = torch.nn.Parameter(d.get(8).weight.float()) self.conv7.bias = torch.nn.Parameter(d.get(8).bias.float()) def forward(self,x): out = self.reflecPad5(x) out = self.conv5(out) out = self.relu5(out) out = self.unpool(out) out = self.reflecPad6(out) out = self.conv6(out) out = self.relu6(out) out = self.reflecPad7(out) out = self.conv7(out) return out class encoder3(nn.Module): def __init__(self,vgg): super(encoder3,self).__init__() # vgg # 224 x 224 self.conv1 = nn.Conv2d(3,3,1,1,0) self.conv1.weight = torch.nn.Parameter(vgg.get(0).weight.float()) self.conv1.bias = torch.nn.Parameter(vgg.get(0).bias.float()) self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1)) # 226 x 226 self.conv2 = nn.Conv2d(3,64,3,1,0) self.conv2.weight = torch.nn.Parameter(vgg.get(2).weight.float()) self.conv2.bias = torch.nn.Parameter(vgg.get(2).bias.float()) self.relu2 = nn.ReLU(inplace=True) # 224 x 224 self.reflecPad3 = nn.ReflectionPad2d((1,1,1,1)) self.conv3 = nn.Conv2d(64,64,3,1,0) self.conv3.weight = torch.nn.Parameter(vgg.get(5).weight.float()) self.conv3.bias = torch.nn.Parameter(vgg.get(5).bias.float()) self.relu3 = nn.ReLU(inplace=True) # 224 x 224 self.maxPool = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True) # 112 x 112 self.reflecPad4 = nn.ReflectionPad2d((1,1,1,1)) self.conv4 = nn.Conv2d(64,128,3,1,0) self.conv4.weight = torch.nn.Parameter(vgg.get(9).weight.float()) self.conv4.bias = torch.nn.Parameter(vgg.get(9).bias.float()) self.relu4 = nn.ReLU(inplace=True) # 112 x 112 self.reflecPad5 = nn.ReflectionPad2d((1,1,1,1)) self.conv5 = nn.Conv2d(128,128,3,1,0) self.conv5.weight = torch.nn.Parameter(vgg.get(12).weight.float()) self.conv5.bias = torch.nn.Parameter(vgg.get(12).bias.float()) self.relu5 = nn.ReLU(inplace=True) # 112 x 112 self.maxPool2 = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True) # 56 x 56 self.reflecPad6 = nn.ReflectionPad2d((1,1,1,1)) self.conv6 = nn.Conv2d(128,256,3,1,0) self.conv6.weight = torch.nn.Parameter(vgg.get(16).weight.float()) self.conv6.bias = torch.nn.Parameter(vgg.get(16).bias.float()) self.relu6 = nn.ReLU(inplace=True) # 56 x 56 def forward(self,x): out = self.conv1(x) out = self.reflecPad1(out) out = self.conv2(out) out = self.relu2(out) out = self.reflecPad3(out) out = self.conv3(out) pool1 = self.relu3(out) out,pool_idx = self.maxPool(pool1) out = self.reflecPad4(out) out = self.conv4(out) out = self.relu4(out) out = self.reflecPad5(out) out = self.conv5(out) pool2 = self.relu5(out) out,pool_idx2 = self.maxPool2(pool2) out = self.reflecPad6(out) out = self.conv6(out) out = self.relu6(out) return out class decoder3(nn.Module): def __init__(self,d): super(decoder3,self).__init__() # decoder self.reflecPad7 = nn.ReflectionPad2d((1,1,1,1)) self.conv7 = nn.Conv2d(256,128,3,1,0) self.conv7.weight = torch.nn.Parameter(d.get(1).weight.float()) self.conv7.bias = torch.nn.Parameter(d.get(1).bias.float()) self.relu7 = nn.ReLU(inplace=True) # 56 x 56 self.unpool = nn.UpsamplingNearest2d(scale_factor=2) # 112 x 112 self.reflecPad8 = nn.ReflectionPad2d((1,1,1,1)) self.conv8 = nn.Conv2d(128,128,3,1,0) self.conv8.weight = torch.nn.Parameter(d.get(5).weight.float()) self.conv8.bias = torch.nn.Parameter(d.get(5).bias.float()) self.relu8 = nn.ReLU(inplace=True) # 112 x 112 self.reflecPad9 = nn.ReflectionPad2d((1,1,1,1)) self.conv9 = nn.Conv2d(128,64,3,1,0) self.conv9.weight = torch.nn.Parameter(d.get(8).weight.float()) self.conv9.bias = torch.nn.Parameter(d.get(8).bias.float()) self.relu9 = nn.ReLU(inplace=True) self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2) # 224 x 224 self.reflecPad10 = nn.ReflectionPad2d((1,1,1,1)) self.conv10 = nn.Conv2d(64,64,3,1,0) self.conv10.weight = torch.nn.Parameter(d.get(12).weight.float()) self.conv10.bias = torch.nn.Parameter(d.get(12).bias.float()) self.relu10 = nn.ReLU(inplace=True) self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1)) self.conv11 = nn.Conv2d(64,3,3,1,0) self.conv11.weight = torch.nn.Parameter(d.get(15).weight.float()) self.conv11.bias = torch.nn.Parameter(d.get(15).bias.float()) def forward(self,x): out = self.reflecPad7(x) out = self.conv7(out) out = self.relu7(out) out = self.unpool(out) out = self.reflecPad8(out) out = self.conv8(out) out = self.relu8(out) out = self.reflecPad9(out) out = self.conv9(out) out = self.relu9(out) out = self.unpool2(out) out = self.reflecPad10(out) out = self.conv10(out) out = self.relu10(out) out = self.reflecPad11(out) out = self.conv11(out) return out class encoder4(nn.Module): def __init__(self,vgg): super(encoder4,self).__init__() # vgg # 224 x 224 self.conv1 = nn.Conv2d(3,3,1,1,0) self.conv1.weight = torch.nn.Parameter(vgg.get(0).weight.float()) self.conv1.bias = torch.nn.Parameter(vgg.get(0).bias.float()) self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1)) # 226 x 226 self.conv2 = nn.Conv2d(3,64,3,1,0) self.conv2.weight = torch.nn.Parameter(vgg.get(2).weight.float()) self.conv2.bias = torch.nn.Parameter(vgg.get(2).bias.float()) self.relu2 = nn.ReLU(inplace=True) # 224 x 224 self.reflecPad3 = nn.ReflectionPad2d((1,1,1,1)) self.conv3 = nn.Conv2d(64,64,3,1,0) self.conv3.weight = torch.nn.Parameter(vgg.get(5).weight.float()) self.conv3.bias = torch.nn.Parameter(vgg.get(5).bias.float()) self.relu3 = nn.ReLU(inplace=True) # 224 x 224 self.maxPool = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True) # 112 x 112 self.reflecPad4 = nn.ReflectionPad2d((1,1,1,1)) self.conv4 = nn.Conv2d(64,128,3,1,0) self.conv4.weight = torch.nn.Parameter(vgg.get(9).weight.float()) self.conv4.bias = torch.nn.Parameter(vgg.get(9).bias.float()) self.relu4 = nn.ReLU(inplace=True) # 112 x 112 self.reflecPad5 = nn.ReflectionPad2d((1,1,1,1)) self.conv5 = nn.Conv2d(128,128,3,1,0) self.conv5.weight = torch.nn.Parameter(vgg.get(12).weight.float()) self.conv5.bias = torch.nn.Parameter(vgg.get(12).bias.float()) self.relu5 = nn.ReLU(inplace=True) # 112 x 112 self.maxPool2 = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True) # 56 x 56 self.reflecPad6 = nn.ReflectionPad2d((1,1,1,1)) self.conv6 = nn.Conv2d(128,256,3,1,0) self.conv6.weight = torch.nn.Parameter(vgg.get(16).weight.float()) self.conv6.bias = torch.nn.Parameter(vgg.get(16).bias.float()) self.relu6 = nn.ReLU(inplace=True) # 56 x 56 self.reflecPad7 = nn.ReflectionPad2d((1,1,1,1)) self.conv7 = nn.Conv2d(256,256,3,1,0) self.conv7.weight = torch.nn.Parameter(vgg.get(19).weight.float()) self.conv7.bias = torch.nn.Parameter(vgg.get(19).bias.float()) self.relu7 = nn.ReLU(inplace=True) # 56 x 56 self.reflecPad8 = nn.ReflectionPad2d((1,1,1,1)) self.conv8 = nn.Conv2d(256,256,3,1,0) self.conv8.weight = torch.nn.Parameter(vgg.get(22).weight.float()) self.conv8.bias = torch.nn.Parameter(vgg.get(22).bias.float()) self.relu8 = nn.ReLU(inplace=True) # 56 x 56 self.reflecPad9 = nn.ReflectionPad2d((1,1,1,1)) self.conv9 = nn.Conv2d(256,256,3,1,0) self.conv9.weight = torch.nn.Parameter(vgg.get(25).weight.float()) self.conv9.bias = torch.nn.Parameter(vgg.get(25).bias.float()) self.relu9 = nn.ReLU(inplace=True) # 56 x 56 self.maxPool3 = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True) # 28 x 28 self.reflecPad10 = nn.ReflectionPad2d((1,1,1,1)) self.conv10 = nn.Conv2d(256,512,3,1,0) self.conv10.weight = torch.nn.Parameter(vgg.get(29).weight.float()) self.conv10.bias = torch.nn.Parameter(vgg.get(29).bias.float()) self.relu10 = nn.ReLU(inplace=True) # 28 x 28 def forward(self,x): out = self.conv1(x) out = self.reflecPad1(out) out = self.conv2(out) out = self.relu2(out) out = self.reflecPad3(out) out = self.conv3(out) pool1 = self.relu3(out) out,pool_idx = self.maxPool(pool1) out = self.reflecPad4(out) out = self.conv4(out) out = self.relu4(out) out = self.reflecPad5(out) out = self.conv5(out) pool2 = self.relu5(out) out,pool_idx2 = self.maxPool2(pool2) out = self.reflecPad6(out) out = self.conv6(out) out = self.relu6(out) out = self.reflecPad7(out) out = self.conv7(out) out = self.relu7(out) out = self.reflecPad8(out) out = self.conv8(out) out = self.relu8(out) out = self.reflecPad9(out) out = self.conv9(out) pool3 = self.relu9(out) out,pool_idx3 = self.maxPool3(pool3) out = self.reflecPad10(out) out = self.conv10(out) out = self.relu10(out) return out class decoder4(nn.Module): def __init__(self,d): super(decoder4,self).__init__() # decoder self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1)) self.conv11 = nn.Conv2d(512,256,3,1,0) self.conv11.weight = torch.nn.Parameter(d.get(1).weight.float()) self.conv11.bias = torch.nn.Parameter(d.get(1).bias.float()) self.relu11 = nn.ReLU(inplace=True) # 28 x 28 self.unpool = nn.UpsamplingNearest2d(scale_factor=2) # 56 x 56 self.reflecPad12 = nn.ReflectionPad2d((1,1,1,1)) self.conv12 = nn.Conv2d(256,256,3,1,0) self.conv12.weight = torch.nn.Parameter(d.get(5).weight.float()) self.conv12.bias = torch.nn.Parameter(d.get(5).bias.float()) self.relu12 = nn.ReLU(inplace=True) # 56 x 56 self.reflecPad13 = nn.ReflectionPad2d((1,1,1,1)) self.conv13 = nn.Conv2d(256,256,3,1,0) self.conv13.weight = torch.nn.Parameter(d.get(8).weight.float()) self.conv13.bias = torch.nn.Parameter(d.get(8).bias.float()) self.relu13 = nn.ReLU(inplace=True) # 56 x 56 self.reflecPad14 = nn.ReflectionPad2d((1,1,1,1)) self.conv14 = nn.Conv2d(256,256,3,1,0) self.conv14.weight = torch.nn.Parameter(d.get(11).weight.float()) self.conv14.bias = torch.nn.Parameter(d.get(11).bias.float()) self.relu14 = nn.ReLU(inplace=True) # 56 x 56 self.reflecPad15 = nn.ReflectionPad2d((1,1,1,1)) self.conv15 = nn.Conv2d(256,128,3,1,0) self.conv15.weight = torch.nn.Parameter(d.get(14).weight.float()) self.conv15.bias = torch.nn.Parameter(d.get(14).bias.float()) self.relu15 = nn.ReLU(inplace=True) # 56 x 56 self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2) # 112 x 112 self.reflecPad16 = nn.ReflectionPad2d((1,1,1,1)) self.conv16 = nn.Conv2d(128,128,3,1,0) self.conv16.weight = torch.nn.Parameter(d.get(18).weight.float()) self.conv16.bias = torch.nn.Parameter(d.get(18).bias.float()) self.relu16 = nn.ReLU(inplace=True) # 112 x 112 self.reflecPad17 = nn.ReflectionPad2d((1,1,1,1)) self.conv17 = nn.Conv2d(128,64,3,1,0) self.conv17.weight = torch.nn.Parameter(d.get(21).weight.float()) self.conv17.bias = torch.nn.Parameter(d.get(21).bias.float()) self.relu17 = nn.ReLU(inplace=True) # 112 x 112 self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2) # 224 x 224 self.reflecPad18 = nn.ReflectionPad2d((1,1,1,1)) self.conv18 = nn.Conv2d(64,64,3,1,0) self.conv18.weight = torch.nn.Parameter(d.get(25).weight.float()) self.conv18.bias = torch.nn.Parameter(d.get(25).bias.float()) self.relu18 = nn.ReLU(inplace=True) # 224 x 224 self.reflecPad19 = nn.ReflectionPad2d((1,1,1,1)) self.conv19 = nn.Conv2d(64,3,3,1,0) self.conv19.weight = torch.nn.Parameter(d.get(28).weight.float()) self.conv19.bias = torch.nn.Parameter(d.get(28).bias.float()) def forward(self,x): # decoder out = self.reflecPad11(x) out = self.conv11(out) out = self.relu11(out) out = self.unpool(out) out = self.reflecPad12(out) out = self.conv12(out) out = self.relu12(out) out = self.reflecPad13(out) out = self.conv13(out) out = self.relu13(out) out = self.reflecPad14(out) out = self.conv14(out) out = self.relu14(out) out = self.reflecPad15(out) out = self.conv15(out) out = self.relu15(out) out = self.unpool2(out) out = self.reflecPad16(out) out = self.conv16(out) out = self.relu16(out) out = self.reflecPad17(out) out = self.conv17(out) out = self.relu17(out) out = self.unpool3(out) out = self.reflecPad18(out) out = self.conv18(out) out = self.relu18(out) out = self.reflecPad19(out) out = self.conv19(out) return out class encoder5(nn.Module): def __init__(self,vgg): super(encoder5,self).__init__() # vgg # 224 x 224 self.conv1 = nn.Conv2d(3,3,1,1,0) self.conv1.weight = torch.nn.Parameter(vgg.get(0).weight.float()) self.conv1.bias = torch.nn.Parameter(vgg.get(0).bias.float()) self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1)) # 226 x 226 self.conv2 = nn.Conv2d(3,64,3,1,0) self.conv2.weight = torch.nn.Parameter(vgg.get(2).weight.float()) self.conv2.bias = torch.nn.Parameter(vgg.get(2).bias.float()) self.relu2 = nn.ReLU(inplace=True) # 224 x 224 self.reflecPad3 = nn.ReflectionPad2d((1,1,1,1)) self.conv3 = nn.Conv2d(64,64,3,1,0) self.conv3.weight = torch.nn.Parameter(vgg.get(5).weight.float()) self.conv3.bias = torch.nn.Parameter(vgg.get(5).bias.float()) self.relu3 = nn.ReLU(inplace=True) # 224 x 224 self.maxPool = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True) # 112 x 112 self.reflecPad4 = nn.ReflectionPad2d((1,1,1,1)) self.conv4 = nn.Conv2d(64,128,3,1,0) self.conv4.weight = torch.nn.Parameter(vgg.get(9).weight.float()) self.conv4.bias = torch.nn.Parameter(vgg.get(9).bias.float()) self.relu4 = nn.ReLU(inplace=True) # 112 x 112 self.reflecPad5 = nn.ReflectionPad2d((1,1,1,1)) self.conv5 = nn.Conv2d(128,128,3,1,0) self.conv5.weight = torch.nn.Parameter(vgg.get(12).weight.float()) self.conv5.bias = torch.nn.Parameter(vgg.get(12).bias.float()) self.relu5 = nn.ReLU(inplace=True) # 112 x 112 self.maxPool2 = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True) # 56 x 56 self.reflecPad6 = nn.ReflectionPad2d((1,1,1,1)) self.conv6 = nn.Conv2d(128,256,3,1,0) self.conv6.weight = torch.nn.Parameter(vgg.get(16).weight.float()) self.conv6.bias = torch.nn.Parameter(vgg.get(16).bias.float()) self.relu6 = nn.ReLU(inplace=True) # 56 x 56 self.reflecPad7 = nn.ReflectionPad2d((1,1,1,1)) self.conv7 = nn.Conv2d(256,256,3,1,0) self.conv7.weight = torch.nn.Parameter(vgg.get(19).weight.float()) self.conv7.bias = torch.nn.Parameter(vgg.get(19).bias.float()) self.relu7 = nn.ReLU(inplace=True) # 56 x 56 self.reflecPad8 = nn.ReflectionPad2d((1,1,1,1)) self.conv8 = nn.Conv2d(256,256,3,1,0) self.conv8.weight = torch.nn.Parameter(vgg.get(22).weight.float()) self.conv8.bias = torch.nn.Parameter(vgg.get(22).bias.float()) self.relu8 = nn.ReLU(inplace=True) # 56 x 56 self.reflecPad9 = nn.ReflectionPad2d((1,1,1,1)) self.conv9 = nn.Conv2d(256,256,3,1,0) self.conv9.weight = torch.nn.Parameter(vgg.get(25).weight.float()) self.conv9.bias = torch.nn.Parameter(vgg.get(25).bias.float()) self.relu9 = nn.ReLU(inplace=True) # 56 x 56 self.maxPool3 = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True) # 28 x 28 self.reflecPad10 = nn.ReflectionPad2d((1,1,1,1)) self.conv10 = nn.Conv2d(256,512,3,1,0) self.conv10.weight = torch.nn.Parameter(vgg.get(29).weight.float()) self.conv10.bias = torch.nn.Parameter(vgg.get(29).bias.float()) self.relu10 = nn.ReLU(inplace=True) # 28 x 28 self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1)) self.conv11 = nn.Conv2d(512,512,3,1,0) self.conv11.weight = torch.nn.Parameter(vgg.get(32).weight.float()) self.conv11.bias = torch.nn.Parameter(vgg.get(32).bias.float()) self.relu11 = nn.ReLU(inplace=True) # 28 x 28 self.reflecPad12 = nn.ReflectionPad2d((1,1,1,1)) self.conv12 = nn.Conv2d(512,512,3,1,0) self.conv12.weight = torch.nn.Parameter(vgg.get(35).weight.float()) self.conv12.bias = torch.nn.Parameter(vgg.get(35).bias.float()) self.relu12 = nn.ReLU(inplace=True) # 28 x 28 self.reflecPad13 = nn.ReflectionPad2d((1,1,1,1)) self.conv13 = nn.Conv2d(512,512,3,1,0) self.conv13.weight = torch.nn.Parameter(vgg.get(38).weight.float()) self.conv13.bias = torch.nn.Parameter(vgg.get(38).bias.float()) self.relu13 = nn.ReLU(inplace=True) # 28 x 28 self.maxPool4 = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True) # 14 x 14 self.reflecPad14 = nn.ReflectionPad2d((1,1,1,1)) self.conv14 = nn.Conv2d(512,512,3,1,0) self.conv14.weight = torch.nn.Parameter(vgg.get(42).weight.float()) self.conv14.bias = torch.nn.Parameter(vgg.get(42).bias.float()) self.relu14 = nn.ReLU(inplace=True) # 14 x 14 def forward(self,x): out = self.conv1(x) out = self.reflecPad1(out) out = self.conv2(out) out = self.relu2(out) out = self.reflecPad3(out) out = self.conv3(out) out = self.relu3(out) out,pool_idx = self.maxPool(out) out = self.reflecPad4(out) out = self.conv4(out) out = self.relu4(out) out = self.reflecPad5(out) out = self.conv5(out) out = self.relu5(out) out,pool_idx2 = self.maxPool2(out) out = self.reflecPad6(out) out = self.conv6(out) out = self.relu6(out) out = self.reflecPad7(out) out = self.conv7(out) out = self.relu7(out) out = self.reflecPad8(out) out = self.conv8(out) out = self.relu8(out) out = self.reflecPad9(out) out = self.conv9(out) out = self.relu9(out) out,pool_idx3 = self.maxPool3(out) out = self.reflecPad10(out) out = self.conv10(out) out = self.relu10(out) out = self.reflecPad11(out) out = self.conv11(out) out = self.relu11(out) out = self.reflecPad12(out) out = self.conv12(out) out = self.relu12(out) out = self.reflecPad13(out) out = self.conv13(out) out = self.relu13(out) out,pool_idx4 = self.maxPool4(out) out = self.reflecPad14(out) out = self.conv14(out) out = self.relu14(out) return out class decoder5(nn.Module): def __init__(self,d): super(decoder5,self).__init__() # decoder self.reflecPad15 = nn.ReflectionPad2d((1,1,1,1)) self.conv15 = nn.Conv2d(512,512,3,1,0) self.conv15.weight = torch.nn.Parameter(d.get(1).weight.float()) self.conv15.bias = torch.nn.Parameter(d.get(1).bias.float()) self.relu15 = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) # 28 x 28 self.reflecPad16 = nn.ReflectionPad2d((1,1,1,1)) self.conv16 = nn.Conv2d(512,512,3,1,0) self.conv16.weight = torch.nn.Parameter(d.get(5).weight.float()) self.conv16.bias = torch.nn.Parameter(d.get(5).bias.float()) self.relu16 = nn.ReLU(inplace=True) # 28 x 28 self.reflecPad17 = nn.ReflectionPad2d((1,1,1,1)) self.conv17 = nn.Conv2d(512,512,3,1,0) self.conv17.weight = torch.nn.Parameter(d.get(8).weight.float()) self.conv17.bias = torch.nn.Parameter(d.get(8).bias.float()) self.relu17 = nn.ReLU(inplace=True) # 28 x 28 self.reflecPad18 = nn.ReflectionPad2d((1,1,1,1)) self.conv18 = nn.Conv2d(512,512,3,1,0) self.conv18.weight = torch.nn.Parameter(d.get(11).weight.float()) self.conv18.bias = torch.nn.Parameter(d.get(11).bias.float()) self.relu18 = nn.ReLU(inplace=True) # 28 x 28 self.reflecPad19 = nn.ReflectionPad2d((1,1,1,1)) self.conv19 = nn.Conv2d(512,256,3,1,0) self.conv19.weight = torch.nn.Parameter(d.get(14).weight.float()) self.conv19.bias = torch.nn.Parameter(d.get(14).bias.float()) self.relu19 = nn.ReLU(inplace=True) # 28 x 28 self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2) # 56 x 56 self.reflecPad20 = nn.ReflectionPad2d((1,1,1,1)) self.conv20 = nn.Conv2d(256,256,3,1,0) self.conv20.weight = torch.nn.Parameter(d.get(18).weight.float()) self.conv20.bias = torch.nn.Parameter(d.get(18).bias.float()) self.relu20 = nn.ReLU(inplace=True) # 56 x 56 self.reflecPad21 = nn.ReflectionPad2d((1,1,1,1)) self.conv21 = nn.Conv2d(256,256,3,1,0) self.conv21.weight = torch.nn.Parameter(d.get(21).weight.float()) self.conv21.bias = torch.nn.Parameter(d.get(21).bias.float()) self.relu21 = nn.ReLU(inplace=True) self.reflecPad22 = nn.ReflectionPad2d((1,1,1,1)) self.conv22 = nn.Conv2d(256,256,3,1,0) self.conv22.weight = torch.nn.Parameter(d.get(24).weight.float()) self.conv22.bias = torch.nn.Parameter(d.get(24).bias.float()) self.relu22 = nn.ReLU(inplace=True) self.reflecPad23 = nn.ReflectionPad2d((1,1,1,1)) self.conv23 = nn.Conv2d(256,128,3,1,0) self.conv23.weight = torch.nn.Parameter(d.get(27).weight.float()) self.conv23.bias = torch.nn.Parameter(d.get(27).bias.float()) self.relu23 = nn.ReLU(inplace=True) self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2) # 112 X 112 self.reflecPad24 = nn.ReflectionPad2d((1,1,1,1)) self.conv24 = nn.Conv2d(128,128,3,1,0) self.conv24.weight = torch.nn.Parameter(d.get(31).weight.float()) self.conv24.bias = torch.nn.Parameter(d.get(31).bias.float()) self.relu24 = nn.ReLU(inplace=True) self.reflecPad25 = nn.ReflectionPad2d((1,1,1,1)) self.conv25 = nn.Conv2d(128,64,3,1,0) self.conv25.weight = torch.nn.Parameter(d.get(34).weight.float()) self.conv25.bias = torch.nn.Parameter(d.get(34).bias.float()) self.relu25 = nn.ReLU(inplace=True) self.unpool4 = nn.UpsamplingNearest2d(scale_factor=2) self.reflecPad26 = nn.ReflectionPad2d((1,1,1,1)) self.conv26 = nn.Conv2d(64,64,3,1,0) self.conv26.weight = torch.nn.Parameter(d.get(38).weight.float()) self.conv26.bias = torch.nn.Parameter(d.get(38).bias.float()) self.relu26 = nn.ReLU(inplace=True) self.reflecPad27 = nn.ReflectionPad2d((1,1,1,1)) self.conv27 = nn.Conv2d(64,3,3,1,0) self.conv27.weight = torch.nn.Parameter(d.get(41).weight.float()) self.conv27.bias = torch.nn.Parameter(d.get(41).bias.float()) def forward(self,x): # decoder out = self.reflecPad15(x) out = self.conv15(out) out = self.relu15(out) out = self.unpool(out) out = self.reflecPad16(out) out = self.conv16(out) out = self.relu16(out) out = self.reflecPad17(out) out = self.conv17(out) out = self.relu17(out) out = self.reflecPad18(out) out = self.conv18(out) out = self.relu18(out) out = self.reflecPad19(out) out = self.conv19(out) out = self.relu19(out) out = self.unpool2(out) out = self.reflecPad20(out) out = self.conv20(out) out = self.relu20(out) out = self.reflecPad21(out) out = self.conv21(out) out = self.relu21(out) out = self.reflecPad22(out) out = self.conv22(out) out = self.relu22(out) out = self.reflecPad23(out) out = self.conv23(out) out = self.relu23(out) out = self.unpool3(out) out = self.reflecPad24(out) out = self.conv24(out) out = self.relu24(out) out = self.reflecPad25(out) out = self.conv25(out) out = self.relu25(out) out = self.unpool4(out) out = self.reflecPad26(out) out = self.conv26(out) out = self.relu26(out) out = self.reflecPad27(out) out = self.conv27(out) return out

38.16962

82

0.593387

4,400

30,154

4.039318

0.042955

0.021493

0.092275

0.072695

0.914646

0.899792

0.874585

0.8388

0.760254

0.715636

0

0.1053

0.250448

30,154

789

83

38.217997

0.681046

0.029184

0

0.675585

0

1

0.033445

false

0

0.003344

0

0.070234

0

null

0

1

0

null

0

6

2ce7bb4cd496f103286f260627cf5b7f97206249

1,512

py

Python

runtime/bindings/python/src/openvino/__init__.py

monroid/openvino

8272b3857ef5be0aaa8abbf7bd0d5d5615dc40b6

[ "Apache-2.0" ]

null

runtime/bindings/python/src/openvino/__init__.py

monroid/openvino

8272b3857ef5be0aaa8abbf7bd0d5d5615dc40b6

[ "Apache-2.0" ]

33

2020-12-02T13:52:11.000Z

2022-02-21T13:14:42.000Z

runtime/bindings/python/src/openvino/__init__.py

monroid/openvino

8272b3857ef5be0aaa8abbf7bd0d5d5615dc40b6

[ "Apache-2.0" ]

null

# Copyright (C) 2021 Intel Corporation # SPDX-License-Identifier: Apache-2.0 from openvino.pyopenvino import Core from openvino.pyopenvino import IENetwork from openvino.pyopenvino import ExecutableNetwork from openvino.pyopenvino import Version from openvino.pyopenvino import Parameter from openvino.pyopenvino import InputInfoPtr from openvino.pyopenvino import InputInfoCPtr from openvino.pyopenvino import DataPtr from openvino.pyopenvino import TensorDesc from openvino.pyopenvino import get_version from openvino.pyopenvino import StatusCode from openvino.pyopenvino import InferQueue from openvino.pyopenvino import InferRequest # TODO: move to ie_api? from openvino.pyopenvino import Blob from openvino.pyopenvino import PreProcessInfo from openvino.pyopenvino import MeanVariant from openvino.pyopenvino import ResizeAlgorithm from openvino.pyopenvino import ColorFormat from openvino.pyopenvino import PreProcessChannel from openvino.pyopenvino import Tensor from openvino.ie_api import BlobWrapper from openvino.ie_api import infer from openvino.ie_api import async_infer from openvino.ie_api import get_result from openvino.ie_api import blob_from_file # Patching for Blob class # flake8: noqa: F811 # this class will be removed Blob = BlobWrapper # Patching ExecutableNetwork ExecutableNetwork.infer = infer # Patching InferRequest InferRequest.infer = infer InferRequest.async_infer = async_infer InferRequest.get_result = get_result # Patching InferQueue InferQueue.async_infer = async_infer

35.162791

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192

1,512

6.661458

0.286458

0.234558

0.344019

0.437842

0.152463

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0

0.007396

0.10582

1,512

42

70

36

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null

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1

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1

0

1

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6

fa3509d3a8b6aebbe546706779457af4a556636b

2,097

py

Python

bench/pyscripts/testferaggdset.py

josborne-noaa/PyFerret

8496508e9902c0184898522e9f89f6caea6d4539

[ "Unlicense" ]

44

2016-03-18T22:05:31.000Z

2021-12-23T01:50:09.000Z

bench/pyscripts/testferaggdset.py

josborne-noaa/PyFerret

8496508e9902c0184898522e9f89f6caea6d4539

[ "Unlicense" ]

88

2016-08-19T08:05:37.000Z

2022-03-28T23:29:21.000Z

bench/pyscripts/testferaggdset.py

josborne-noaa/PyFerret

8496508e9902c0184898522e9f89f6caea6d4539

[ "Unlicense" ]

24

2016-02-07T18:12:06.000Z

2022-02-19T09:06:17.000Z

# To be run in python after importing and starting pyferret # such as from running "pyferret -python" from __future__ import print_function import sys ; sys.ps1 = '' ; sys.ps2 = '' print() print(">>> dsetnames = ['./data/ens1.nc', './data/ens2.nc', './data/ens3.nc', './data/ens4.nc']") dsetnames = ['./data/ens1.nc', './data/ens2.nc', './data/ens3.nc', './data/ens4.nc'] print(">>> onefileagg = pyferret.FerAggDSet(name='onefileagg', dsets=dsetnames[-1:], along='E')") onefileagg = pyferret.FerAggDSet(name='onefileagg', dsets=dsetnames[-1:], along='E') print(">>> pyferret.showdata()") pyferret.showdata() print(">>> print str(onefileagg)") print(str(onefileagg)) print(">>> onefileagg.close()") onefileagg.close() print(">>> pyferret.showdata()") pyferret.showdata() print(">>> print str(onefileagg)") print(str(onefileagg)) print(">>> twofileagg = pyferret.FerAggDSet(name='twofileagg', dsets=dsetnames[-2:], along='E', hide=True)") twofileagg = pyferret.FerAggDSet(name='twofileagg', dsets=dsetnames[-2:], along='E', hide=True) print(">>> pyferret.showdata()") pyferret.showdata() print(">>> print str(twofileagg)") print(str(twofileagg)) print(">>> twofileagg.close()") twofileagg.close() print(">>> pyferret.showdata()") pyferret.showdata() print(">>> print str(twofileagg)") print(str(twofileagg)) print(">>> fourfiles = pyferret.FerAggDSet(name='fourfiles', dsets=dsetnames, along='E')") fourfiles = pyferret.FerAggDSet(name='fourfiles', dsets=dsetnames, along='E') print(">>> pyferret.showdata()") pyferret.showdata() print(">>> fourfiles.show()") fourfiles.show() print(">>> print str(fourfiles)") print(str(fourfiles)) print(">>> fourfiles.getdsetnames()") fourfiles.getdsetnames() print(">>> fourfiles.getdsets()") fourfiles.getdsets() print(">>> fourfiles.SST.showgrid()") fourfiles.SST.showgrid() print(">>> fourfiles.close()") fourfiles.close() print(">>> print str(fourfiles)") print(str(fourfiles)) print(">>> fourfiles.getdsetnames()") fourfiles.getdsetnames() print(">>> fourfiles.getdsets()") fourfiles.getdsets() print(">>> pyferret.showdata()") pyferret.showdata()

32.261538

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239

2,097

6.087866

0.200837

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0.090722

0.119588

0.798625

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109

32.765625

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null

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0

null

0

1

0

6

d7270e3ceaa1559179f4d1863d6d31ff224b74d2

244

py

Python

common/dialogflow_framework/extensions/providers.py

oserikov/dream

109ba2df799025dcdada1fddbb7380e1c03100eb

[ "Apache-2.0" ]

34

2021-08-18T14:51:44.000Z

2022-03-10T14:14:48.000Z

common/dialogflow_framework/extensions/providers.py

oserikov/dream

109ba2df799025dcdada1fddbb7380e1c03100eb

[ "Apache-2.0" ]

27

2021-08-30T14:42:09.000Z

2022-03-17T22:11:45.000Z

common/dialogflow_framework/extensions/providers.py

oserikov/dream

109ba2df799025dcdada1fddbb7380e1c03100eb

[ "Apache-2.0" ]

40

2021-08-22T07:13:32.000Z

2022-03-29T11:45:32.000Z

from common.dialogflow_framework.extensions.facts_utils import provide_facts_response def fact_provider(page_source, wiki_page): def response(vars): return provide_facts_response(vars, page_source, wiki_page) return response

27.111111

85

0.803279

32

244

5.78125

0.5625

0.12973

0.216216

0.194595

0

0.139344

244

8

86

30.5

0.880952

0

1

0.4

false

0

0.2

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null

0

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0

1

0

null

0

1

0

1

0

6

d7765a2d7e7157c492a3d4200d6b982e883cdad2

181

py

Python

gpu_pairwise/util.py

stestagg/gpu-pairwise-jaccard

4e24703cdc53ae5fecfca02d54131c0ee2113ed3

[ "MIT" ]

2

2021-05-17T07:03:26.000Z

2022-02-24T21:48:12.000Z

gpu_pairwise/util.py

stestagg/gpu-pairwise-jaccard

4e24703cdc53ae5fecfca02d54131c0ee2113ed3

[ "MIT" ]

null

gpu_pairwise/util.py

stestagg/gpu-pairwise-jaccard

4e24703cdc53ae5fecfca02d54131c0ee2113ed3

[ "MIT" ]

null

from numba import cuda def check_cuda(): if not cuda.is_available(): raise ImportError('gpu_pairwise requires a CUDA compatible device, and correct CUDA installation')

30.166667

106

0.751381

25

181

5.32

0.84

0

0.18232

181

5

107

36.2

0.898649

0

0.425414

0

1

0.25

true

0

0.5

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d7926c5bc57c08ae62b34da9ad3a5ea051670fe4

234

py

Python

tests/conftest.py

prowincial/task-for-allegro

3dcaeb4716b9d1683869bedace5a467e38a2a0e2

[ "Apache-2.0" ]

null

tests/conftest.py

prowincial/task-for-allegro

3dcaeb4716b9d1683869bedace5a467e38a2a0e2

[ "Apache-2.0" ]

null

tests/conftest.py

prowincial/task-for-allegro

3dcaeb4716b9d1683869bedace5a467e38a2a0e2

[ "Apache-2.0" ]

null

import pytest from mserver import api def test_user_request(): assert api.user_exists("allegro") == True assert api.user_exists("samara228") == True assert api.user_exists("pochemutakslozhnohmmmm") == True

19.5

61

0.692308

28

234

5.607143

0.535714

0.171975

0.248408

0.363057

0.292994

0

0.016129

0.205128

234

11

62

21.272727

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true

0

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null

0

1

0

1

0

1

0

6

ad0f293ce2a288057ea07297e21504c7669edfd0

1,071

py

Python

sqliteschema/_logger/_null_logger.py

thombashi/sqlitestructure

c599ca889658f3080fbf900110a3edb0541a6d35

[ "MIT" ]

510

2016-05-24T15:11:07.000Z

2022-03-30T00:38:43.000Z

sqliteschema/_logger/_null_logger.py

thombashi/sqlitestructure

c599ca889658f3080fbf900110a3edb0541a6d35

[ "MIT" ]

52

2016-06-14T03:57:04.000Z

2022-03-21T14:38:03.000Z

sqliteschema/_logger/_null_logger.py

thombashi/sqlitestructure

c599ca889658f3080fbf900110a3edb0541a6d35

[ "MIT" ]

39

2016-05-26T15:40:10.000Z

2022-02-25T11:12:16.000Z

class NullLogger: level_name = None def remove(self, handler_id=None): # pragma: no cover pass def add(self, sink, **kwargs): # pragma: no cover pass def disable(self, name): # pragma: no cover pass def enable(self, name): # pragma: no cover pass def critical(self, __message, *args, **kwargs): # pragma: no cover pass def debug(self, __message, *args, **kwargs): # pragma: no cover pass def error(self, __message, *args, **kwargs): # pragma: no cover pass def exception(self, __message, *args, **kwargs): # pragma: no cover pass def info(self, __message, *args, **kwargs): # pragma: no cover pass def log(self, __level, __message, *args, **kwargs): # pragma: no cover pass def success(self, __message, *args, **kwargs): # pragma: no cover pass def trace(self, __message, *args, **kwargs): # pragma: no cover pass def warning(self, __message, *args, **kwargs): # pragma: no cover pass

25.5

75

0.5845

131

1,071

4.610687

0.229008

0.172185

0.279801

0.365894

0.768212

0.735099

0.692053

0.599338

0.475166

0

0.294118

1,071

41

76

26.121951

0.798942

0.205416

0

0.464286

0

1

0.464286

false

0.464286

0

0.535714

0

null

0

1

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1

0

null

0

1

0

1

0

1

0

6

ad619e87dcd8d17ca86d453e13be533d4cca4be0

135

py

Python

utils/smoothing.py

QingquanBao/VAD

f774fccd56e412d327900742bf93b30acd2a6d78

[ "Apache-2.0" ]

2

2021-05-17T00:43:30.000Z

2021-05-17T01:03:06.000Z

utils/smoothing.py

QingquanBao/VAD

f774fccd56e412d327900742bf93b30acd2a6d78

[ "Apache-2.0" ]

null

utils/smoothing.py

QingquanBao/VAD

f774fccd56e412d327900742bf93b30acd2a6d78

[ "Apache-2.0" ]

null

import numpy as np def averageSmooth(data, windowLen=40): return np.convolve(data, np.ones((windowLen,))/windowLen, mode='same')

22.5

74

0.725926

19

135

5.157895

0.736842

0

0.016949

0.125926

135

5

75

27

0.813559

0

0.029851

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

d129deb639179b32930aaefc402bc0f421e3af8f

45

py

Python

labuda/03.py

mallimuondu/python-homworks

352721a8e77d0b3bdb7a8a54197b6a04e1aec3c0

[ "MIT" ]

null

labuda/03.py

mallimuondu/python-homworks

352721a8e77d0b3bdb7a8a54197b6a04e1aec3c0

[ "MIT" ]

null

labuda/03.py

mallimuondu/python-homworks

352721a8e77d0b3bdb7a8a54197b6a04e1aec3c0

[ "MIT" ]

null

x = lambda a,b,c: a + b + c print(x(4,6,2))

15

27

0.488889

13

45

1.692308

0.692308

0.181818

0.272727

0

0.088235

0.244444

45

3

28

15

0.558824

0

1

0

false

0

0.5

1

null

0

1

0

1

0

null

0

1

0

6

d16d3c261dfda3b867dc6f84d33cc353b0fbeb68

92

py

Python

lib/layers/__init__.py

jwyang/C3Net.pytorch

70026fc80c5427484268c428a9dcd4cde2e8197f

[ "MIT" ]

43

2019-12-13T06:13:40.000Z

2021-07-25T06:29:17.000Z

lib/layers/__init__.py

jwyang/C3Net.pytorch

70026fc80c5427484268c428a9dcd4cde2e8197f

[ "MIT" ]

2

2020-12-05T14:24:17.000Z

2020-12-24T09:47:10.000Z

lib/layers/__init__.py

jwyang/C3Net.pytorch

70026fc80c5427484268c428a9dcd4cde2e8197f

[ "MIT" ]

4

2019-12-16T20:25:20.000Z

2020-06-23T08:45:17.000Z

from .cross_neuron_distributed import * from .selayer import * from .data_parallel import *

23

39

0.804348

12

92

5.916667

0.666667

0.28169

0

0.130435

92

3

40

30.666667

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0

1

0

true

0

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1

0

null

0

1

0

1

0

1

0

6

d174c466677718f977c4bf5548d3c82cf04c58fe

42

py

Python

src/aceinna/devices/__init__.py

LukaszChl/ros_openimu

1bcf547fa42ee7c7dcc856c1d4eb5702d301b059

[ "Apache-2.0" ]

41

2018-07-20T17:30:33.000Z

2022-02-24T08:17:39.000Z

src/aceinna/devices/__init__.py

LukaszChl/ros_openimu

1bcf547fa42ee7c7dcc856c1d4eb5702d301b059

[ "Apache-2.0" ]

52

2018-06-25T22:15:14.000Z

2022-03-10T07:30:56.000Z

src/aceinna/devices/__init__.py

LukaszChl/ros_openimu

1bcf547fa42ee7c7dcc856c1d4eb5702d301b059

[ "Apache-2.0" ]

31

2018-12-19T00:10:08.000Z

2022-03-19T02:14:03.000Z

from .device_manager import DeviceManager

21

41

0.880952

5

42

7.2

1

0

0.095238

42

1

42

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

0f0834a380e85cf649ad2a33de351c7d03c8435a

161

py

Python

winter/pagination/sort/__init__.py

EvgenySmekalin/winter

24b6a02f958478547a4a120324823743a1f7e1a1

[ "MIT" ]

1

2020-03-28T14:54:28.000Z

winter/pagination/sort/__init__.py

EvgenySmekalin/winter

24b6a02f958478547a4a120324823743a1f7e1a1

[ "MIT" ]

null

winter/pagination/sort/__init__.py

EvgenySmekalin/winter

24b6a02f958478547a4a120324823743a1f7e1a1

[ "MIT" ]

null

from .order_by import order_by from .order_by_annotation import OrderByAnnotation from .sort import Order from .sort import Sort from .sort import SortDirection

26.833333

50

0.84472

24

161

5.5

0.333333

0.159091

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0

0.124224

161

5

51

32.2

0.93617

0

1

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true

0

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1

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null

0

1

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0

null

0

1

0

1

0

1

0

6

0f0a27d752be14f249538a906df4d553109d2060

10,078

py

Python

lcr.py

EroSkulled/PHY224-324

060a020d75e938d240d926e05e41f8e5c4bf435a

[ "MIT" ]

null

lcr.py

EroSkulled/PHY224-324

060a020d75e938d240d926e05e41f8e5c4bf435a

[ "MIT" ]

null

lcr.py

EroSkulled/PHY224-324

060a020d75e938d240d926e05e41f8e5c4bf435a

[ "MIT" ]

null

import numpy as np from scipy.optimize import curve_fit import matplotlib.pyplot as plt import pandas as pd def rcs(pred, target, uncertainty, n_params): return np.square((pred - target) / uncertainty).sum() / (pred.size - n_params) def rc(x, r, c): return np.exp(-x / (r * c)) def rc_(x, tao): return np.exp(-x / tao) # noinspection PyShadowingNames def lr(x, R, L): return np.exp(-x * R / L) # RC circuit on battery data = pd.read_csv(r"C:\Users\Walter\IdeaProjects\PHY224\LCR\exp1RC.csv", delimiter=',', skiprows=1) data.plot(x='second') plt.title('RC circuit on Battery') plt.ylabel('Voltage (V)') plt.xlabel('Time (s)') # choose a segment that contains one decay data_seg = data[:1880] xdata = data_seg['second'] - data_seg['second'].iloc[0] # set the beginning of timeline to 0 ydata = data_seg['Volt'] / data_seg['Volt'].iloc[0] # set it as a ratio over the initial value sigma_y = np.ones_like(ydata) * 0.000001 # error sigma_x = np.ones_like(xdata) * 0.000001 p_opt, p_cov = curve_fit(rc, xdata, ydata, (1e5, 2.2e-8), sigma_y, True) fig, ax = plt.subplots() ax.plot(xdata, rc(xdata, *p_opt), 'r-', label='fit: R=%.3e, C=%.3e' % tuple(p_opt)) ax.errorbar(xdata, ydata, yerr=sigma_y, xerr=sigma_x, label="error", linestyle='None', marker=".") ax.set_title('RC circuit') ax.set_xlabel('Time (s)') ax.set_ylabel('Voltage (ratio)') ax.legend(loc="best") time_constant = p_opt[0] * p_opt[1] time_constant_sd = time_constant * np.sqrt((p_cov[0, 0] / p_opt[0]) ** 2 + (p_cov[1, 1] / p_opt[1]) ** 2) print( "[RC] The time constant estimated is {:.3e} with standard deviation {:.3e}".format(time_constant, time_constant_sd)) # extreme high reduced chi square value due to small uncertainties print(rcs(rc(xdata, *p_opt), ydata, sigma_y, 1)) # LR circuit on wave gen data = pd.read_csv(r"C:\Users\Walter\IdeaProjects\PHY224\LCR\exp1LR.csv", delimiter=',', skiprows=1) data.plot(x='second') plt.title('LR circuit on Wavegen') plt.ylabel('Voltage (V)') plt.xlabel('Time (s)') # choose a segment that contains one decay data_seg = data[1000:1800] y_corr = abs(min(data_seg['Volt'])) + data_seg['Volt'] xdata = data_seg['second'] - data_seg['second'].iloc[0] # set the beginning of timeline to 0 ydata = y_corr / y_corr.iloc[1] # set it as a ratio over the initial value sigma_y = np.ones_like(ydata) * 0.000001 # error sigma_x = np.ones_like(xdata) * 0.000001 p_opt, p_cov = curve_fit(lr, xdata, ydata, (1, 0.0426), sigma_y, True) # noinspection PyRedeclaration fig, ax = plt.subplots() ax.plot(xdata, lr(xdata, *p_opt), 'r-', label='fit: R=%.3e, L=%.3e' % tuple(p_opt)) ax.errorbar(xdata, ydata, yerr=sigma_y, xerr=sigma_x, label="error", linestyle='None', marker=".") ax.set_title('LR circuit') ax.set_xlabel('Time (s)') ax.set_ylabel('Voltage (ratio)') ax.legend() # extreme high reduced chi square value due to small uncertainties print(rcs(lr(xdata, *p_opt), ydata, sigma_y, 1)) # LR circuit on wave gen time_constant = p_opt[1] / p_opt[0] time_constant_sd = time_constant * np.sqrt((p_cov[0, 0] / p_opt[0]) ** 2 + (p_cov[1, 1] / p_opt[1]) ** 2) print("[LR] The time constant estimated is {:.3e} with standard deviation {:.3e}".format(time_constant, time_constant_sd)) # LC circuit on wave gen, VL measured # Theoretical frequency is: # 1/(2*np.pi*np.sqrt(22e-9*42.6e-3)) # =5198.8105 data = pd.read_csv(r"C:\Users\Walter\IdeaProjects\PHY224\LCR\exp1LC.csv", delimiter=',', skiprows=1) data.plot(x='second') plt.title("LC circuit") plt.ylabel("Voltage (V)") plt.xlabel('Time (s)') plt.show() ################# experiment 2 ############# data = pd.read_csv(r"C:\Users\Walter\IdeaProjects\PHY224\LCR\scope_5.csv", delimiter=',', skiprows=1) data.plot(x='second') plt.title("RC circuit") plt.ylabel("Voltage (V)") plt.xlabel('Time (s)') plt.show() # impedance RC R = 10000 C = 2.2e-8 data = pd.read_csv(r"C:\Users\Walter\IdeaProjects\PHY224\LCR\exp2RC.csv", delimiter=',', skiprows=1) plt.semilogx(data["freq"], data["phase"]) plt.title("RC circuit semi log plot") plt.ylabel("Phases (degree)") plt.xlabel('log Frequency (log(Hz))') data['angular_freq'] = 2 * np.pi * data['freq'] # TODO: Check formula Z_RC data['Z_RC'] = data.V1 / data.V2 * R + R # noinspection PyRedeclaration fig, ax = plt.subplots() ax.plot(data['angular_freq'], data['Z_RC'], label="Observed") freq = np.linspace(100, 10000, 100) angular_freq = 2 * np.pi * freq Z_RC = np.sqrt(R ** 2 + (1 / (angular_freq * C)) ** 2) ax.plot(angular_freq, Z_RC, label="Theoretical") ax.legend() ax.set_xlabel('Angular Frequency (rad/s)') ax.set_ylabel('Z') plt.show() sub_data = data[:] # noinspection PyShadowingNames def impedance_rc(angular_freq, C): R = 10000 Z_RC = np.sqrt(R ** 2 + (1 / (angular_freq * C)) ** 2) return Z_RC xdata = sub_data['angular_freq'] ydata = sub_data['Z_RC'] reading_error = 0.03 #error sigma_x = np.ones_like(xdata) * 0.000001 sigma_y = ydata * np.sqrt( (sub_data['V1'] * reading_error / sub_data['V1']) ** 2 + (sub_data['V2'] * reading_error / sub_data['V2']) ** 2) p_opt, p_cov = curve_fit(impedance_rc, xdata, ydata, 2.2e-8, sigma_y, True) # noinspection PyRedeclaration fig, ax = plt.subplots() ax.plot(xdata, impedance_rc(xdata, *p_opt), 'r-', label='fit: C=%.3e (F)' % tuple(p_opt)) ax.errorbar(xdata, ydata, yerr=sigma_y, label="error", linestyle='None', marker=".") ax.set_title('RC circuit fit') ax.set_xlabel('Frequency (Hz)') ax.set_ylabel('Z') ax.legend() plt.show() print("The capacitance estimated is {:.3e} (F) with standard deviation {:.3e}".format(p_opt[0], p_cov[0, 0])) # extreme high reduced chi square value due to small uncertainties print(rcs(impedance_rc(xdata, *p_opt), ydata, sigma_y, 1)) # LR circuit on wave gen # impedance RL # theoretical curve R = 512.4 L = 4.26e-2 freq = np.linspace(1, 25000, 100) angular_freq = 2 * np.pi * freq Z_RL = np.sqrt(R ** 2 + (angular_freq * L) ** 2) # actual curve data = pd.read_csv(r"C:\Users\Walter\IdeaProjects\PHY224\LCR\exp2LR(V_Vr).csv", delimiter=',', skiprows=1) plt.semilogx(data["freq"], data["phase"]) plt.title("RL circuit semi log plot") plt.ylabel("Phases (degree)") plt.xlabel('log Frequency (log(Hz))') data['angular_freq'] = 2 * np.pi * data['freq'] # TODO: Check formula Z_RL data['Z_RL'] = data.V1 / data.V2 * R # noinspection PyRedeclaration fig, ax = plt.subplots() ax.plot(data['angular_freq'], data['Z_RL'], label="observed") ax.plot(angular_freq, Z_RL, label="theoretical") ax.legend() ax.set_xlabel('Angular Frequency (rad/s)') ax.set_ylabel('Z') plt.show() sub_data = data[0:6] # noinspection PyShadowingNames def impedance_rl(angular_freq, L): R = 512.4 Z_RL = np.sqrt(R ** 2 + (angular_freq * L) ** 2) return Z_RL xdata = sub_data['angular_freq'] ydata = sub_data['Z_RL'] reading_error = 0.03 # the error # noinspection PyRedeclaration sigma_x = np.ones_like(xdata) * 0.000001 sigma_y = ydata * np.sqrt( (sub_data['V1'] * reading_error / sub_data['V1']) ** 2 + (sub_data['V2'] * reading_error / sub_data['V2']) ** 2) p_opt, p_cov = curve_fit(impedance_rl, xdata, ydata, 0.04, sigma_y, True) # noinspection PyRedeclaration fig, ax = plt.subplots() ax.plot(xdata, impedance_rl(xdata, *p_opt), 'r-', label='fit: L=%.3e (H)' % tuple(p_opt)) ax.errorbar(xdata, ydata, yerr=sigma_y, label="error", linestyle='None', marker=".") ax.set_title('RL circuit fit') ax.set_xlabel('Frequency (Hz)') ax.set_ylabel('Z') ax.legend() plt.show() print("The Inductance estimated is {:.3e} (H) with standard deviation {:.3e}".format(p_opt[0], p_cov[0, 0])) # extreme high reduced chi square value due to small uncertainties print(rcs(impedance_rl(xdata, *p_opt), ydata, sigma_y, 1)) # LR circuit on wave gen # impedance RCL # theoretical curve R = 512.4 C = 2.2e-8 L = 4.26e-2 freq = np.linspace(100, 25000, 1000) angular_freq = 2 * np.pi * freq Z_RCL = np.sqrt(R ** 2 + (angular_freq * L - 1 / (angular_freq * C)) ** 2) # actual curve data = pd.read_csv(r"C:\Users\Walter\IdeaProjects\PHY224\LCR\exp2LR(V_Vr).csv", delimiter=',', skiprows=1) plt.semilogx(data["freq"], data["phase"]) plt.title("RCL circuit semi log plot") plt.ylabel("Phases (degree)") plt.xlabel('log Frequency (log(Hz))') data['angular_freq'] = 2 * np.pi * data['freq'] # TODO: Check formula Z_RCL data['Z_RCL'] = data.V1 / data.V2 * R + R # noinspection PyRedeclaration fig, ax = plt.subplots() ax.plot(data['angular_freq'], data['Z_RCL'], label="observed") ax.plot(angular_freq, Z_RCL, label="theoretical") ax.legend() ax.set_xlabel('Angular Frequency (rad/s)') ax.set_ylabel('Z') plt.show() sub_data = data[0:6] # noinspection PyShadowingNames def impedance_rcl(angular_freq, L, C): R = 512.4 Z_RCL = np.sqrt(R ** 2 + ((angular_freq * L) + (1 / (angular_freq * C))) ** 2) return Z_RCL # problematic xdata = sub_data['angular_freq'] ydata = sub_data['Z_RCL'] reading_error = 0.03 sigma_y = ydata * np.sqrt( (sub_data['V1'] * reading_error / sub_data['V1']) ** 2 + (sub_data['V2'] * reading_error / sub_data['V2']) ** 2) p0 = [0.04, 0.04] sigmas = np.array(sigma_x, sigma_y) p_opt, p_cov = curve_fit(impedance_rcl, xdata, ydata, p0, sigmas, True) # noinspection PyRedeclaration fig, ax = plt.subplots() ax.plot(xdata, impedance_rcl(xdata, *p_opt), 'r-', label='fit: L=%.3e (H), C=%.3e (F)' % tuple(p_opt)) ax.errorbar(xdata, ydata, yerr=sigma_y, label="error", linestyle='None', marker=".") ax.set_title('RCL circuit fit') ax.set_xlabel('Frequency (Hz)') ax.set_ylabel('Z') ax.legend() plt.show() print("The Inductance estimated is {:.3e} (H) with standard deviation {:.3e}".format(p_opt[0], p_cov[0, 0])) print("The Capacitance estimated is {:.3e} (F) with standard deviation {:.3e}".format(p_opt[1], p_cov[1, 1])) # extreme high reduced chi square value due to small uncertainties print(rcs(impedance_rcl(xdata, *p_opt), ydata, sigma_y, 1)) # LR circuit on wave gen

32.614887

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0.6667

1,668

10,078

3.886091

0.121703

0.019747

0.013885

0.019747

0.846961

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0.75054

0

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0.152907

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0.034314

false

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0.019608

0.088235

0.053922

0

null

0

1

0

null

0

6

0f2b7512dc0706e5b95f5cd69f5503abd3f9a817

143

py

Python

mshow/__init__.py

nishantsinghdev/mshow

f2f1c810a1ba30d2823843ef78812bea4d59ea4f

[ "MIT" ]

null

mshow/__init__.py

nishantsinghdev/mshow

f2f1c810a1ba30d2823843ef78812bea4d59ea4f

[ "MIT" ]

null

mshow/__init__.py

nishantsinghdev/mshow

f2f1c810a1ba30d2823843ef78812bea4d59ea4f

[ "MIT" ]

null

def info(): print("Library By : Nishant Singh\n") def show(): print("Hello Guys !!\n Its a sample fun() for Suprath Technologies.\n")

23.833333

75

0.643357

22

143

4.181818

0.818182

0

0.195804

143

5

76

28.6

0.8

0

0.629371

0

1

0.5

true

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

0f3c9259213d7098389f8c5e114f5b665c13da30

1,020

py

Python

plot_solution.py

zhang-liu-official/project3-pinn-test

fcf586a4b15176ee4595bcb5c9b0bc9f3b18f5a8

[ "Apache-2.0" ]

null

plot_solution.py

zhang-liu-official/project3-pinn-test

fcf586a4b15176ee4595bcb5c9b0bc9f3b18f5a8

[ "Apache-2.0" ]

null

plot_solution.py

zhang-liu-official/project3-pinn-test

fcf586a4b15176ee4595bcb5c9b0bc9f3b18f5a8

[ "Apache-2.0" ]

null

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits import mplot3d data = np.genfromtxt("test.dat", delimiter=' ') theta = data[:, 0] x = np.cos(theta) y = np.sin(theta) true = data[:, 1] pred = data[:, 2] fig, ax = plt.subplots(nrows=1) ax.tricontour(x, y, true, levels=14, linewidths=0.5, colors='k') cntr = ax.tricontourf(x, y, true, levels=14, cmap="RdBu_r") fig.colorbar(cntr, ax=ax) ax.plot(x, y, 'k.', ms=3) plt.show() fig, ax = plt.subplots(nrows=1) ax.tricontour(x, y, pred, levels=14, linewidths=0.5, colors='k') cntr = ax.tricontourf(x, y, pred, levels=14, cmap="RdBu_r") fig.colorbar(cntr, ax=ax) ax.plot(x, y, 'k.', ms=3) plt.show() # import numpy as np # import matplotlib.pyplot as plt # from mpl_toolkits import mplot3d # data = np.genfromtxt("test.dat", delimiter=' ') # theta = data[:, 0] # x = np.cos(theta) # y = np.sin(theta) # true = data[:, 1] # pred = data[:, 2] # fig, ax = plt.subplots(nrows=1) # plt.plot(theta, true) # plt.show() # plt.plot(theta, pred) # plt.show()

23.181818

64

0.652941

178

1,020

3.719101

0.264045

0.018127

0.036254

0.072508

0.930514

0.906344

0

0.028736

0.147059

1,020

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0.142857

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null

0

1

0

null

0

6

0f623dd0fee1d6322211f8a46e5280758ead581b

67

py

Python

chap10/pack/sub/abc.py

ytianjin/GitTest

a657f46098728ad90f7140fadad356e8561c9a7a

[ "MIT" ]

null

chap10/pack/sub/abc.py

ytianjin/GitTest

a657f46098728ad90f7140fadad356e8561c9a7a

[ "MIT" ]

null

chap10/pack/sub/abc.py

ytianjin/GitTest

a657f46098728ad90f7140fadad356e8561c9a7a

[ "MIT" ]

null

# pack.sub.abc模块 def func(): print('pack.sub.abc.func()')

13.4

33

0.567164

10

67

3.8

0.7

0.368421

0

0.208955

67

4

34

16.75

0.716981

0.208955

0

0.404255

0

1

0.5

true

0

0.5

1

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null

1

0

1

0

null

0

1

0

1

0

6

7e25bff9d2845fb6e4e774224f2aaa3a3dffb01c

9,711

py

Python

sec/fscModule.py

srccircumflex/SefiTrash

ba950f67dcd70eb34312ffeafb257df06d276913

[ "MIT" ]

null

sec/fscModule.py

srccircumflex/SefiTrash

ba950f67dcd70eb34312ffeafb257df06d276913

[ "MIT" ]

null

sec/fscModule.py

srccircumflex/SefiTrash

ba950f67dcd70eb34312ffeafb257df06d276913

[ "MIT" ]

null

from sec import fscFunction, fscIO from sys import stdout, stderr from time import time coding_a = 'ISO-8859-1' bench_out = stderr class FStreamCipher: def __init__(self, ib:bytes, seed:str, ilv:int=0, pp:tuple=(0, 1), hps:int=3, v:str=None): global coding_a self.enc = coding_a self.ib = ib self.sd = seed self.ilv = 256 + ilv self.pp = pp self.hps = hps self.v = v if v: self.vp0 = (True if 'p' in v else False) self.vp1 = (True if 'v' in v else False) self.vp2 = (True if 'P' in v else False) self.vb1 = (True if 'b' in v else False) self.tst = (True if 't' in v else False) def enc_vlv2(self): if self.vb1: t = time() print(" [+] involve InitializationVector", end="", file=stdout) self.ib = fscFunction.init_vec(self.ib, self.ilv) print(" _DONE_", file=stdout) print(" [+] Transposition", end=(" " if self.vp0 else '\n'), file=stdout) srm, bas = fscFunction.transposition(self.sd, self.ib, part_prio=self.pp, hps=self.hps, ilv=self.ilv, vp0=self.vp0, vp2=self.vp2, vb1=self.vb1) if self.tst: fscIO.to_stderr(bas, srm) print(" [+] StreamCiphering", end="", file=stdout) o = fscFunction.x_stream(srm, bas) print(" _DONE_", file=stdout) if self.vb1: print(f"—————————————————————————————————\n" f"-$$ {time() - t} s", file=bench_out) return o def enc_vlv1(self): if self.vb1: t = time() self.ib = fscFunction.init_vec(self.ib, self.ilv) srm, bas = fscFunction.transposition(self.sd, self.ib, part_prio=self.pp, hps=self.hps, ilv=self.ilv, vp0=self.vp0, vp2=self.vp2, vb1=self.vb1) if self.tst: fscIO.to_stderr(bas, srm) o = fscFunction.x_stream(srm, bas) if self.vb1: print(f"—————————————————————————————————\n" f"-$$ {time() - t} s", file=bench_out) return o def enc_vlv0(self): return fscFunction.x_stream( *fscFunction.transposition( self.sd, fscFunction.init_vec( self.ib, self.ilv ), part_prio=self.pp, hps=self.hps, ilv=self.ilv, vp0=False, vp2=False, vb1=False ) ) def dec_vlv2(self): if self.vb1: t = time() print(" [+] StreamGenerate ", end="", file=stdout) l0 = len(self.ib) - 128 pr = l0 // 10 srm = "" gst = fscFunction.GSTool(fscFunction.gen_stream(self.sd, l0), self.pp) for n in range(l0): srm += gst.srm(n) (print("|", end="", file=stdout, flush=True) if self.vp0 and not n % pr else None) for n in range(128): srm += gst.srm(n) print(" _DONE_", end=("" if self.vb1 else "\n"), file=stdout) if self.vb1: print(f" -$ {time() - t} s", file=bench_out) print(" [+] StreamCiphering", end="", file=stdout) o = fscFunction.x_stream(bytearray(srm, self.enc), self.ib) print(" _DONE_\n [+] Transposition", end=(" " if self.vp0 else '\n'), file=stdout) bas = fscFunction.transposition(self.sd, o, part_prio=self.pp, hps=self.hps, de_=True, vp0=self.vp0, vp2=self.vp2, vb1=self.vb1)[1] print(" [+] remove InitializationVector", end="", file=stdout) bas = fscFunction.init_vec(bas, ilv=self.ilv, rm=True) print(" _DONE_", file=stdout) if self.vb1: print(f"—————————————————————————————————\n" f"-$$ {time() - t} s", file=bench_out) return bas def dec_vlv1(self): if self.vb1: t = time() l0 = len(self.ib) - 128 * self.hps gst = fscFunction.GSTool(fscFunction.gen_stream(self.sd, l0), self.pp) pr = l0 // 10 srm = "" for n in range(l0): srm += gst.srm(n) (print("|", end="", file=stdout, flush=True) if self.vp0 and not n % pr else None) for n in range(128 * self.hps): srm += gst.srm(n) if self.vb1: print(f" -$ {time() - t} s", file=bench_out) o = fscFunction.x_stream(bytearray(srm, self.enc), self.ib) bas = fscFunction.transposition(self.sd, o, part_prio=self.pp, hps=self.hps, de_=True, vp0=self.vp0, vp2=self.vp2, vb1=self.vb1)[1] bas = fscFunction.init_vec(bas, ilv=self.ilv, rm=True) if self.vb1: print(f"—————————————————————————————————\n" f"-$$ {time() - t} s", file=bench_out) return bas def dec_vlv0(self): l0 = len(self.ib) - 128 * self.hps gst = fscFunction.GSTool(fscFunction.gen_stream(self.sd, l0), self.pp) srm = "" return fscFunction.init_vec( fscFunction.transposition( self.sd, fscFunction.x_stream( bytearray(srm.join( [gst.srm(n) for n in range(l0)] + [gst.srm(n) for n in range(128 * self.hps)] ), self.enc), self.ib ), part_prio=self.pp, hps=self.hps, de_=True, vp0=False, vp2=False, vb1=False )[1], ilv=self.ilv, rm=True ) def Dec_vlv2(self): if self.vb1: t = time() l0 = len(self.ib) - 128 * self.hps pr = l0 // 10 srm = "" pos = [] gen_pos = fscFunction.basis_and_pos(l0, self.hps) gen_srm = fscFunction.gen_stream(self.sd, l0) gst = fscFunction.GSTool(gen_srm, self.pp) pst = fscFunction.PSTool(gen_pos, gen_srm, self.pp) next(gen_pos) for n in range(l0): p, s = pst.pos_srm(n) srm += s pos.append(p) (print("|", end="", file=stdout, flush=True) if self.vp0 and not n % pr else None) for n in range(128 * self.hps): srm += gst.srm(n) print(" _DONE_", end=("" if self.vb1 else "\n"), file=stdout) if self.vb1: print(f" -$ {time() - t} s", file=bench_out) print(" [+] StreamCiphering", end="", file=stdout) o = fscFunction.x_stream(bytearray(srm, self.enc), self.ib) print(" _DONE_\n [+] Transposition", end=(" " if self.vp0 else '\n'), file=stdout) bas = fscFunction.transposition(self.sd, o, part_prio=self.pp, hps=self.hps, de_=pos, vp0=self.vp0, vp2=self.vp2, vb1=self.vb1)[1] print(" [+] remove InitializationVector", end="", file=stdout) bas = fscFunction.init_vec(bas, ilv=self.ilv, rm=True) print(" _DONE_", file=stdout) if self.vb1: print(f"—————————————————————————————————\n" f"-$$ {time() - t} s", file=bench_out) return bas def Dec_vlv1(self): if self.vb1: t = time() l0 = len(self.ib) - 128 * self.hps pr = l0 // 10 srm = "" pos = [] gen_pos = fscFunction.basis_and_pos(l0, self.hps) gen_srm = fscFunction.gen_stream(self.sd, l0) gst = fscFunction.GSTool(gen_srm, self.pp) pst = fscFunction.PSTool(gen_pos, gen_srm, self.pp) next(gen_pos) for n in range(l0): p, s = pst.pos_srm(n) srm += s pos.append(p) (print("|", end="", file=stdout, flush=True) if self.vp0 and not n % pr else None) for n in range(128 * self.hps): srm += gst.srm(n) if self.vb1: print(f" -$ {time() - t} s", file=bench_out) bas = fscFunction.transposition( self.sd, fscFunction.x_stream( bytearray(srm, self.enc), self.ib ), part_prio=self.pp, hps=self.hps, de_=pos, vp0=self.vp0, vp2=self.vp2, vb1=self.vb1)[1] bas = fscFunction.init_vec(bas, ilv=self.ilv, rm=True) if self.vb1: print(f"—————————————————————————————————\n" f"-$$ {time() - t} s", file=bench_out) return bas def Dec_vlv0(self): l0 = len(self.ib) - 128 * self.hps srm = "" pos = [] gen_pos = fscFunction.basis_and_pos(l0, self.hps) gen_srm = fscFunction.gen_stream(self.sd, l0) gst = fscFunction.GSTool(gen_srm, self.pp) pst = fscFunction.PSTool(gen_pos, gen_srm, self.pp) next(gen_pos) for n in range(l0): p, s = pst.pos_srm(n) pos.append(p) srm += s for n in range(128 * self.hps): srm += gst.srm(n) return fscFunction.init_vec( fscFunction.transposition( self.sd, fscFunction.x_stream( bytearray(srm, self.enc), self.ib ), part_prio=self.pp, hps=self.hps, de_=pos, vp0=False, vp2=False, vb1=False )[1], ilv=self.ilv, rm=True ) def encrypt(self): if self.v: if self.vp1: return self.enc_vlv2() else: return self.enc_vlv1() else: return self.enc_vlv0() def decrypt(self, pos_cache:bool=False): if pos_cache: if self.v: if self.vp1: return self.Dec_vlv2() else: return self.Dec_vlv1() else: return self.Dec_vlv0() else: if self.v: if self.vp1: return self.dec_vlv2() else: return self.dec_vlv1() else: return self.dec_vlv0()

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0.026543

0.882365

0.866077

0.85562

0.835713

0.8148

0.774382

0

0.028349

0.338894

9,711

235

110

41.323404

0.715421

0

0.694064

0

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false

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null

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6

7e2bf670f8d025f4b7e81b525530bb1c670e74d4

21,576

py

Python

tests/unit/topotools/GraphToolsTest.py

edgargmartinez/OpenPNM

c68745993b3e9895f53938164a9cf6305500748e

[ "MIT" ]

3

2019-07-05T22:07:21.000Z

2019-07-05T22:07:30.000Z

tests/unit/topotools/GraphToolsTest.py

ChahatAggarwal/OpenPNM

b3873d35270b0acaad019264368d0055c677d159

[ "MIT" ]

null

tests/unit/topotools/GraphToolsTest.py

ChahatAggarwal/OpenPNM

b3873d35270b0acaad019264368d0055c677d159

[ "MIT" ]

null

import openpnm as op import numpy as np import pytest from openpnm import topotools class GraphToolsTest: def setup_class(self): r""" Make a nice and simple network to test stuff The Network: :: 3 ― 4 ― 5 | \ | 0 ― 1 ― 2 The Adjacency Matrix: The Enumreated Adjacency Matrix: | 0 1 2 3 4 5 | 0 1 2 3 4 5 ――|―――――――――――――――――― ――|―――――――――――――――――― 0 | - 1 1 0 | - 0 1 1 | 1 - 1 1 1 1 | 0 - 3 2 5 2 | 1 - 2 | 3 - 3 | 1 1 - 1 3 | 1 2 - 4 4 | 1 1 - 1 4 | 5 4 - 6 5 | 1 - 5 | 6 - The Incidence Matrix: The Enumerated Incidence Matrix | 0 1 2 3 4 5 | 0 1 2 3 4 5 ――|―――――――――――――――――― ――|―――――――――――――――――― 0 | 1 1 0 | 1 0 1 | 1 1 1 | 3 0 2 | 1 1 2 | 3 1 3 | 1 1 3 | 2 1 4 | 1 1 4 | 4 3 5 | 1 1 5 | 4 1 6 | 1 1 6 | 5 4 """ self.ws = op.Workspace() self.net = op.network.GenericNetwork(Np=6, Nt=7) coords = np.array([[0, 0, 0], [1, 0, 0], [2, 0, 0], [0, 1, 0], [1, 1, 0], [2, 1, 0]]) conns = np.array([[0, 1], [0, 3], [1, 3], [1, 2], [3, 4], [1, 4], [4, 5]]) self.net['pore.coords'] = coords self.net['throat.conns'] = conns def teardown_class(self): self.ws.clear() def test_find_connected_sites(self): am = self.net.create_adjacency_matrix(weights=self.net.Ts, fmt='coo') Ps = topotools.find_connected_sites(bonds=[0], am=am, flatten=True) assert np.all(Ps == [0, 1]) Ps = topotools.find_connected_sites(bonds=[1], am=am, flatten=True) assert np.all(Ps == [0, 3]) Ps = topotools.find_connected_sites(bonds=[2], am=am, flatten=True) assert np.all(Ps == [1, 3]) Ps = topotools.find_connected_sites(bonds=[3], am=am, flatten=True) assert np.all(Ps == [1, 2]) Ps = topotools.find_connected_sites(bonds=[4], am=am, flatten=True) assert np.all(Ps == [3, 4]) Ps = topotools.find_connected_sites(bonds=[5], am=am, flatten=True) assert np.all(Ps == [1, 4]) Ps = topotools.find_connected_sites(bonds=[6], am=am, flatten=True) assert np.all(Ps == [4, 5]) def test_find_connected_sites_single(self): am = self.net.create_adjacency_matrix(fmt='coo') Ps = topotools.find_connected_sites(bonds=0, am=am, flatten=True) assert np.all(Ps == [0, 1]) def test_find_connected_sites_fmt_not_coo(self): am = self.net.create_adjacency_matrix(fmt='csr') with pytest.raises(Exception): topotools.find_connected_sites(bonds=[0], am=am, flatten=True) def test_find_connected_sites_with_logic_flattened(self): am = self.net.get_adjacency_matrix(fmt='coo') a = topotools.find_connected_sites(bonds=[0, 2], am=am, logic='or', flatten=True) assert np.all(a == [0, 1, 3]) a = topotools.find_connected_sites(bonds=[0, 2], am=am, logic='and', flatten=True) assert np.all(a == []) a = topotools.find_connected_sites(bonds=[0, 2], am=am, logic='xor', flatten=True) assert np.all(a == [0, 3]) a = topotools.find_connected_sites(bonds=[0, 2], am=am, logic='xnor', flatten=True) assert np.all(a == [1]) with pytest.raises(Exception): topotools.find_neighbor_bonds(bonds=[0], am=am, logic='nor') with pytest.raises(Exception): topotools.find_neighbor_bonds(bonds=[0], am=am, logic='nand') def test_find_connecting_bonds(self): am = self.net.create_adjacency_matrix(weights=self.net.Ts, fmt='dok') T = topotools.find_connecting_bonds(sites=[0, 1], am=am) assert np.all(T == [0]) T = topotools.find_connecting_bonds(sites=[0, 3], am=am) assert np.all(T == [1]) T = topotools.find_connecting_bonds(sites=[1, 3], am=am) assert np.all(T == [2]) T = topotools.find_connecting_bonds(sites=[1, 2], am=am) assert np.all(T == [3]) T = topotools.find_connecting_bonds(sites=[3, 4], am=am) assert np.all(T == [4]) T = topotools.find_connecting_bonds(sites=[1, 4], am=am) assert np.all(T == [5]) T = topotools.find_connecting_bonds(sites=[4, 5], am=am) assert np.all(T == [6]) def test_find_connecting_bonds_fmt_not_dok(self): am = self.net.create_adjacency_matrix(weights=self.net.Ts, fmt='csr') T = topotools.find_connecting_bonds(sites=[0, 1], am=am) assert np.all(T == [0]) T = topotools.find_connecting_bonds(sites=[0, 3], am=am) assert np.all(T == [1]) T = topotools.find_connecting_bonds(sites=[1, 3], am=am) assert np.all(T == [2]) T = topotools.find_connecting_bonds(sites=[1, 2], am=am) assert np.all(T == [3]) T = topotools.find_connecting_bonds(sites=[3, 4], am=am) assert np.all(T == [4]) T = topotools.find_connecting_bonds(sites=[1, 4], am=am) assert np.all(T == [5]) T = topotools.find_connecting_bonds(sites=[4, 5], am=am) assert np.all(T == [6]) def test_find_connecting_bonds_multiple_sites(self): am = self.net.create_adjacency_matrix(weights=self.net.Ts, fmt='dok') T = topotools.find_connecting_bonds(sites=[[0, 1], [0, 3]], am=am) assert np.all(T == [0, 1]) T = topotools.find_connecting_bonds(sites=[[0, 1], [0, 3], [4, 5]], am=am) assert np.all(T == [0, 1, 6]) def test_find_connecting_bonds_no_sites(self): am = self.net.create_adjacency_matrix(weights=self.net.Ts, fmt='dok') T = topotools.find_connecting_bonds(sites=[], am=am) assert T == [] T = topotools.find_connecting_bonds(sites=[[], []], am=am) assert T == [] def test_find_connecting_bonds_nonexistant_connections(self): am = self.net.create_adjacency_matrix(weights=self.net.Ts, fmt='dok') T = topotools.find_connecting_bonds(sites=[0, 5], am=am) assert np.all(T == [None]) T = topotools.find_connecting_bonds(sites=[[0, 1], [0, 5]], am=am) assert np.all(T == [0, None]) def test_find_connecting_bonds_multiple_sites_fmt_not_dok(self): am = self.net.create_adjacency_matrix(weights=self.net.Ts, fmt='csr') T = topotools.find_connecting_bonds(sites=[[0, 1], [0, 3]], am=am) assert np.all(T == [0, 1]) T = topotools.find_connecting_bonds(sites=[[0, 1], [0, 3], [4, 5]], am=am) assert np.all(T == [0, 1, 6]) def test_find_neighbor_bonds(self): im = self.net.create_incidence_matrix(fmt='lil') Ts = topotools.find_neighbor_bonds(sites=[0], im=im) assert np.all(Ts == [0, 1]) Ts = topotools.find_neighbor_bonds(sites=[1], im=im) assert np.all(Ts == [0, 2, 3, 5]) Ts = topotools.find_neighbor_bonds(sites=[2], im=im) assert np.all(Ts == [3]) Ts = topotools.find_neighbor_bonds(sites=[3], im=im) assert np.all(Ts == [1, 2, 4]) Ts = topotools.find_neighbor_bonds(sites=[4], im=im) assert np.all(Ts == [4, 5, 6]) Ts = topotools.find_neighbor_bonds(sites=[5], im=im) assert np.all(Ts == [6]) def test_find_neighbor_bonds_single(self): im = self.net.create_incidence_matrix(fmt='lil') Ts = topotools.find_neighbor_bonds(sites=0, im=im) assert np.all(Ts == [0, 1]) def test_find_neighbor_bonds_fmt_not_lil(self): # Make sure results are correct even if converting to lil internally im = self.net.create_incidence_matrix(fmt='coo') Ts = topotools.find_neighbor_bonds(sites=[0], im=im) assert np.all(Ts == [0, 1]) Ts = topotools.find_neighbor_bonds(sites=[1], im=im) assert np.all(Ts == [0, 2, 3, 5]) Ts = topotools.find_neighbor_bonds(sites=[2], im=im) assert np.all(Ts == [3]) Ts = topotools.find_neighbor_bonds(sites=[3], im=im) assert np.all(Ts == [1, 2, 4]) Ts = topotools.find_neighbor_bonds(sites=[4], im=im) assert np.all(Ts == [4, 5, 6]) Ts = topotools.find_neighbor_bonds(sites=[5], im=im) assert np.all(Ts == [6]) def test_find_neighbor_bonds_with_logic(self): im = self.net.get_incidence_matrix(fmt='lil') a = topotools.find_neighbor_bonds(sites=[0, 2], im=im, logic='or') assert np.all(a == [0, 1, 3]) a = topotools.find_neighbor_bonds(sites=[0, 2], im=im, logic='and') assert np.all(a == []) a = topotools.find_neighbor_bonds(sites=[0], im=im, logic='xor') assert np.all(a == [0, 1]) a = topotools.find_neighbor_bonds(sites=[0], im=im, logic='xnor') assert np.all(a == []) with pytest.raises(Exception): topotools.find_neighbor_bonds(sites=[0], im=im, logic='nor') with pytest.raises(Exception): topotools.find_neighbor_bonds(sites=[0], im=im, logic='nand') def test_find_neighbor_bonds_with_am_and_logic(self): am = self.net.get_adjacency_matrix(fmt='coo') im = self.net.get_incidence_matrix(fmt='coo') Ts1 = topotools.find_neighbor_bonds(sites=[1], am=am, flatten=True, logic='or') Ts2 = topotools.find_neighbor_bonds(sites=[1], im=im, flatten=True, logic='or') assert np.all(Ts1 == Ts2) Ts1 = topotools.find_neighbor_bonds(sites=[1], am=am, flatten=True, logic='xor') Ts2 = topotools.find_neighbor_bonds(sites=[1], im=im, flatten=True, logic='xor') assert np.all(Ts1 == Ts2) Ts1 = topotools.find_neighbor_bonds(sites=[1], am=am, flatten=True, logic='xnor') Ts2 = topotools.find_neighbor_bonds(sites=[1], im=im, flatten=True, logic='xnor') assert np.all(Ts1 == Ts2) def test_find_neighbor_bonds_with_am_exceptions(self): am = self.net.get_adjacency_matrix(fmt='coo') with pytest.raises(Exception): topotools.find_neighbor_bonds(sites=[1], am=am, flatten=True, logic='intersection') with pytest.raises(Exception): topotools.find_neighbor_bonds(sites=[1], am=am, flatten=False, logic='or') def test_find_neighbor_sites(self): am = self.net.create_adjacency_matrix(fmt='lil') Ps = topotools.find_neighbor_sites(sites=[0], am=am) assert np.all(Ps == [1, 3]) Ps = topotools.find_neighbor_sites(sites=[1], am=am) assert np.all(Ps == [0, 2, 3, 4]) Ps = topotools.find_neighbor_sites(sites=[2], am=am) assert np.all(Ps == [1]) Ps = topotools.find_neighbor_sites(sites=[3], am=am) assert np.all(Ps == [0, 1, 4]) Ps = topotools.find_neighbor_sites(sites=[4], am=am) assert np.all(Ps == [1, 3, 5]) Ps = topotools.find_neighbor_sites(sites=[5], am=am) assert np.all(Ps == [4]) def test_find_neighbor_sites_single(self): am = self.net.create_adjacency_matrix(fmt='lil') Ps = topotools.find_neighbor_sites(sites=0, am=am) assert np.all(Ps == [1, 3]) def test_find_neighbor_sites_unflattened_or(self): am = self.net.create_adjacency_matrix(fmt='lil') Ps = topotools.find_neighbor_sites(sites=[0, 1, 2], am=am, logic='or', flatten=False, include_input=True) assert len(Ps) == 3 assert np.all(Ps[0] == [1, 3]) assert np.all(Ps[1] == [0, 2, 3, 4]) assert np.all(Ps[2] == [1]) def test_find_neighbor_sites_unflattened_xor(self): am = self.net.create_adjacency_matrix(fmt='lil') Ps = topotools.find_neighbor_sites(sites=[0, 1, 2], am=am, logic='xor', flatten=False, include_input=True) assert len(Ps) == 3 assert np.all(Ps[0] == []) assert np.all(Ps[1] == [0, 2, 4]) assert np.all(Ps[2] == []) def test_find_neighbor_sites_unflattened_and(self): am = self.net.create_adjacency_matrix(fmt='lil') Ps = topotools.find_neighbor_sites(sites=[0, 2], am=am, logic='and', flatten=False, include_input=True) assert len(Ps) == 2 assert np.all(Ps[0] == [1]) assert np.all(Ps[1] == [1]) def test_find_neighbor_sites_unflattened_xnor(self): am = self.net.create_adjacency_matrix(fmt='lil') Ps = topotools.find_neighbor_sites(sites=[0, 1, 2], am=am, logic='xnor', flatten=False, include_input=True) assert len(Ps) == 3 assert np.all(Ps[0] == [1, 3]) assert np.all(Ps[1] == [3]) assert np.all(Ps[2] == [1]) def test_find_neighbor_sites_unflattened_xor_empty_set(self): am = self.net.create_adjacency_matrix(fmt='lil') Ps = topotools.find_neighbor_sites(sites=[0, 1, 2], am=am, logic='or', flatten=False, include_input=True) assert len(Ps) == 3 assert np.all(Ps[0] == [1, 3]) assert np.all(Ps[1] == [0, 2, 3, 4]) assert np.all(Ps[2] == [1]) def test_find_neighbor_sites_unflattened_and_empty_set(self): am = self.net.create_adjacency_matrix(fmt='lil') Ps = topotools.find_neighbor_sites(sites=[0, 1, 2], am=am, logic='and', flatten=False, include_input=False) assert len(Ps) == 3 assert np.all(Ps[0] == []) assert np.all(Ps[1] == []) assert np.all(Ps[2] == []) def test_find_neighbor_sites_fmt_not_lil(self): # Make sure results are correct even if converting to lil internally am = self.net.create_adjacency_matrix(fmt='csr') Ps = topotools.find_neighbor_sites(sites=[0], am=am) assert np.all(Ps == [1, 3]) Ps = topotools.find_neighbor_sites(sites=[1], am=am) assert np.all(Ps == [0, 2, 3, 4]) Ps = topotools.find_neighbor_sites(sites=[2], am=am) assert np.all(Ps == [1]) Ps = topotools.find_neighbor_sites(sites=[3], am=am) assert np.all(Ps == [0, 1, 4]) Ps = topotools.find_neighbor_sites(sites=[4], am=am) assert np.all(Ps == [1, 3, 5]) Ps = topotools.find_neighbor_sites(sites=[5], am=am) assert np.all(Ps == [4]) def test_find_neighbor_sites_flattened_with_logic(self): am = self.net.create_adjacency_matrix(fmt='lil') Ps = topotools.find_neighbor_sites(sites=[0, 1], am=am, flatten=True, logic='or') assert np.all(Ps == [2, 3, 4]) Ps = topotools.find_neighbor_sites(sites=[0, 1], am=am, flatten=True, logic='and') assert np.all(Ps == [3]) Ps = topotools.find_neighbor_sites(sites=[0, 1], am=am, flatten=True, logic='xor') assert np.all(Ps == [2, 4]) Ps = topotools.find_neighbor_sites(sites=[0, 2], am=am, flatten=True, logic='xnor') assert np.all(Ps == [1]) def test_find_neighbor_sites_with_bad_logic(self): with pytest.raises(Exception): am = self.net.create_adjacency_matrix(fmt='lil') topotools.find_neighbor_sites(sites=[0, 1], am=am, flatten=True, logic='foobar') def test_istriu(self): net = op.network.Cubic(shape=[5, 5, 5]) am = net.create_adjacency_matrix(triu=False) assert not topotools.istriu(am) am = net.create_adjacency_matrix(triu=True) assert topotools.istriu(am) am = am.T assert not topotools.istriu(am) # Now test non-coo AM's am = net.create_adjacency_matrix(triu=False, fmt='lil') assert not topotools.istriu(am) am = net.create_adjacency_matrix(triu=True, fmt='csr') assert topotools.istriu(am) am = am.T assert not topotools.istriu(am) # Now test non-triangular AM im = net.create_incidence_matrix() assert not topotools.istriu(im) def test_istril(self): net = op.network.Cubic(shape=[5, 5, 5]) am = net.create_adjacency_matrix(triu=False) assert not topotools.istril(am) am = net.create_adjacency_matrix(triu=True) assert not topotools.istril(am) am = am.T assert topotools.istril(am) # Now test non-coo AM's am = net.create_adjacency_matrix(triu=False, fmt='lil') assert not topotools.istril(am) am = net.create_adjacency_matrix(triu=True, fmt='csr') assert not topotools.istril(am) am = am.T assert topotools.istril(am) # Now test non-triangular AM im = net.create_incidence_matrix() assert not topotools.istril(im) def test_istriangular(self): net = op.network.Cubic(shape=[5, 5, 5]) am = net.create_adjacency_matrix(triu=False) assert not topotools.istriangular(am) am = net.create_adjacency_matrix(triu=True) assert topotools.istriangular(am) am = am.T assert topotools.istriangular(am) # Now test non-coo AM's am = net.create_adjacency_matrix(triu=False, fmt='lil') assert not topotools.istriangular(am) am = net.create_adjacency_matrix(triu=True, fmt='csr') assert topotools.istriangular(am) am = am.T assert topotools.istriangular(am) # Now test non-triangular AM im = net.create_incidence_matrix() assert not topotools.istriangular(im) def test_issymmetric(self): net = op.network.Cubic(shape=[5, 5, 5]) am = net.create_adjacency_matrix(triu=False) assert topotools.issymmetric(am) am = net.create_adjacency_matrix(triu=True) assert not topotools.issymmetric(am) am = am.T assert not topotools.issymmetric(am) # Now test non-coo AM's am = net.create_adjacency_matrix(triu=False) assert topotools.issymmetric(am) am = net.create_adjacency_matrix(triu=True) assert not topotools.issymmetric(am) am = am.T assert not topotools.issymmetric(am) # Now test non-triangular AM im = net.create_incidence_matrix() assert not topotools.issymmetric(im) def test_find_clusters_sites(self): net = op.network.Cubic(shape=[10, 10, 10]) net['pore.seed'] = np.random.rand(net.Np) net['throat.seed'] = np.random.rand(net.Nt) clusters = topotools.find_clusters(network=net, mask=net['pore.seed'] < 0.5) assert len(clusters[0]) == net.Np assert len(clusters[1]) == net.Nt def test_find_clusters_bonds(self): net = op.network.Cubic(shape=[10, 10, 10]) net['pore.seed'] = np.random.rand(net.Np) net['throat.seed'] = np.random.rand(net.Nt) clusters = topotools.find_clusters(network=net, mask=net['throat.seed'] < 0.5) assert len(clusters[0]) == net.Np assert len(clusters[1]) == net.Nt def test_find_complement(self): am = self.net.create_adjacency_matrix(weights=self.net.Ts, fmt='coo') a = [0, 1, 2] b = topotools.find_complement(sites=a, am=am) assert set(a).isdisjoint(b) a = [0, 1, 2] b = topotools.find_complement(bonds=a, am=am) assert set(a).isdisjoint(b) with pytest.raises(Exception): topotools.find_complement(am=am) with pytest.raises(Exception): topotools.find_complement(am=am, sites=a, bonds=a) def test_find_complement_asmask(self): am = self.net.create_adjacency_matrix(weights=self.net.Ts, fmt='coo') a = [0, 1, 2] b = topotools.find_complement(sites=a, am=am, asmask=True) assert len(b) == self.net.Np if __name__ == '__main__': t = GraphToolsTest() self = t t.setup_class() for item in t.__dir__(): if item.startswith('test'): print('running test: '+item) t.__getattribute__(item)()

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7e61c26afe4af7e4cfd772a3f9f2754a5b12b5f4

123

py

Python

deuces_numpy/__init__.py

grabantot/deuces-numpy

ff0d7649bad9e2db04aa6f3a51f408176dc80025

[ "MIT" ]

1

2020-02-18T03:08:44.000Z

deuces_numpy/__init__.py

grabantot/deuces-numpy

ff0d7649bad9e2db04aa6f3a51f408176dc80025

[ "MIT" ]

null

deuces_numpy/__init__.py

grabantot/deuces-numpy

ff0d7649bad9e2db04aa6f3a51f408176dc80025

[ "MIT" ]

null

from .card import Card from .deck import Deck from .evaluator import Evaluator from .evaluator_numpy import EvaluatorNumpy

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6

7e72703e8186644e7d088906ee4e591c9c8933f4

126

py

Python

main/views.py

sfawcett123/Django-DCC

b19287ff2b0610f4dbb6cfcf94ca29029e449b7f

[ "MIT" ]

null

main/views.py

sfawcett123/Django-DCC

b19287ff2b0610f4dbb6cfcf94ca29029e449b7f

[ "MIT" ]

null

main/views.py

sfawcett123/Django-DCC

b19287ff2b0610f4dbb6cfcf94ca29029e449b7f

[ "MIT" ]

null

from django.shortcuts import render def homepage(request): return render(request = request, template_name="main/home.html" )

31.5

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126

5.823529

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1

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7e7e5a5b174edec8a01206e1a4ec02ea683ae133

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py

Python

Scripts/kim.py

kenanchristian/hacktoberfest

b55750bf4facb77abd532b66ed37101e2895c4d7

[ "MIT" ]

8

2020-10-26T06:51:06.000Z

2021-04-02T13:01:27.000Z

Scripts/kim.py

kenanchristian/hacktoberfest

b55750bf4facb77abd532b66ed37101e2895c4d7

[ "MIT" ]

71

2020-10-25T22:46:02.000Z

2021-10-14T06:47:39.000Z

Scripts/kim.py

kenanchristian/hacktoberfest

b55750bf4facb77abd532b66ed37101e2895c4d7

[ "MIT" ]

77

2020-10-24T01:53:46.000Z

2021-10-01T06:25:27.000Z

## print("Hello Kimbo!")

3.75

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null

0

1

0

1

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6

0e32b6d69e388285892159e269f26e3ecb7eaca9

41

py

Python

app/provider/__init__.py

sh-hka/HCP-Project

17e7e03560479cca4c2dcdfd325668beac4ebaf0

[ "MIT" ]

null

app/provider/__init__.py

sh-hka/HCP-Project

17e7e03560479cca4c2dcdfd325668beac4ebaf0

[ "MIT" ]

28

2019-11-15T22:34:57.000Z

2019-12-23T20:28:30.000Z

app/provider/__init__.py

sh-hka/HCP-Project

17e7e03560479cca4c2dcdfd325668beac4ebaf0

[ "MIT" ]

8

2019-10-16T19:22:16.000Z

2019-12-22T01:29:31.000Z

from app.provider.models import Provider

20.5

40

0.853659

6

41

5.833333

0.833333

0

0.097561

41

1

41

0.945946

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null

0

1

0

1

0

1

0

6

0e382e138352e58e2b8c5e3b04281c046cd99f12

120

py

Python

extorch/vision/__init__.py

A-LinCui/Extorch

70e2100636c68c803a1e763fbf11d8ede73c0d37

[ "MIT" ]

2

2021-09-11T16:09:05.000Z

2022-03-05T08:02:05.000Z

extorch/vision/__init__.py

A-LinCui/Extorch

70e2100636c68c803a1e763fbf11d8ede73c0d37

[ "MIT" ]

null

extorch/vision/__init__.py

A-LinCui/Extorch

70e2100636c68c803a1e763fbf11d8ede73c0d37

[ "MIT" ]

null

from .functional import get_image_size from .transforms import * from .paired_transform import * from .dataset import *

24

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120

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39

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true

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1

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1

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0

1

0

null

0

1

0

1

0

1

0

6

0e486124eebf80e443659aff3379202e05ad2a9a

162

py

Python

farmer/ncc/validations/__init__.py

tamahassam/farmer

512c6fcd5dc5aa223a0fad02527d8000a4cc9ab4

[ "Apache-2.0" ]

10

2019-04-04T07:32:47.000Z

2021-01-07T00:40:50.000Z

farmer/ncc/validations/__init__.py

tamahassam/farmer

512c6fcd5dc5aa223a0fad02527d8000a4cc9ab4

[ "Apache-2.0" ]

59

2019-04-18T05:44:31.000Z

2021-05-02T10:33:02.000Z

farmer/ncc/validations/__init__.py

tamahassam/farmer

512c6fcd5dc5aa223a0fad02527d8000a4cc9ab4

[ "Apache-2.0" ]

4

2020-01-23T14:01:43.000Z

2021-02-11T04:16:14.000Z

from .confidence_plot import confidence_plot from .confusion_visualize import confusion_visualize from .pca import pca from .classify_movie import classify_movie

32.4

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6.181818

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true

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null

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1

0

1

0

6

0e9e12434ceb6ff1fb3ae8b7f9b685af2b318c07

178

py

Python

src/pycellfinder/tests/test_pycellfinder.py

linuxluigi/PyCellFinder

07235ef4fd50d9497b10c645dd7f477bfb08c2ba

[ "MIT" ]

null

src/pycellfinder/tests/test_pycellfinder.py

linuxluigi/PyCellFinder

07235ef4fd50d9497b10c645dd7f477bfb08c2ba

[ "MIT" ]

null

src/pycellfinder/tests/test_pycellfinder.py

linuxluigi/PyCellFinder

07235ef4fd50d9497b10c645dd7f477bfb08c2ba

[ "MIT" ]

null

import pytest import pycellfinder def test_project_defines_author_and_version(): assert hasattr(pycellfinder, '__author__') assert hasattr(pycellfinder, '__version__')

22.25

47

0.803371

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178

6.842105

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0.123596

178

7

48

25.428571

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0

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true

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0

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null

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0

null

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1

0

1

0

1

0

6

0eac1952e3aec169b7f76cba2341e2a2642f2efe

48

py

Python

quantum-black-box/gym-stirap/gym_stirap/envs/__init__.py

Hegemege/quantum-jazz

4e92db5bf7103109f1f8d8a769b1f0342b4dd6ed

[ "MIT" ]

1

2020-03-16T18:33:32.000Z

quantum-black-box/gym-stirap/gym_stirap/envs/__init__.py

Hegemege/quantum-jazz

4e92db5bf7103109f1f8d8a769b1f0342b4dd6ed

[ "MIT" ]

null

quantum-black-box/gym-stirap/gym_stirap/envs/__init__.py

Hegemege/quantum-jazz

4e92db5bf7103109f1f8d8a769b1f0342b4dd6ed

[ "MIT" ]

null

from gym_stirap.envs.stirap_env import StirapEnv

48

0.895833

8

48

5.125

0.875

0

0.0625

48

1

48

0.911111

0

1

0

true

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1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

adfd9eec42643c936eff0422179ad5e3bbd362ef

20

py

Python

elliot/evaluation/metrics/accuracy/mar/__init__.py

gategill/elliot

113763ba6d595976e14ead2e3d460d9705cd882e

[ "Apache-2.0" ]

175

2021-03-04T15:46:25.000Z

2022-03-31T05:56:58.000Z

elliot/evaluation/metrics/accuracy/mar/__init__.py

gategill/elliot

113763ba6d595976e14ead2e3d460d9705cd882e

[ "Apache-2.0" ]

15

2021-03-06T17:53:56.000Z

2022-03-24T17:02:07.000Z

elliot/evaluation/metrics/accuracy/mar/__init__.py

gategill/elliot

113763ba6d595976e14ead2e3d460d9705cd882e

[ "Apache-2.0" ]

39

2021-03-04T15:46:26.000Z

2022-03-09T15:37:12.000Z

from .mar import MAR

20

0.8

4

20

4

0.75

0

0.15

20

1

20

0.941176

0

1

0

true

0

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1

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null

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null

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1

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1

0

1

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6

70b1ee207931ebc821a5a200614ddb0575317cd4

520

py

Python

creten/clients/BinanceSimulationDataListener.py

nardew/Creten

15ddb0b52e6f2afec2c79b3c731fccb34a2c63d6

[ "MIT" ]

9

2019-12-17T10:42:40.000Z

2021-12-02T23:07:05.000Z

creten/clients/BinanceSimulationDataListener.py

nardew/Creten

15ddb0b52e6f2afec2c79b3c731fccb34a2c63d6

[ "MIT" ]

null

creten/clients/BinanceSimulationDataListener.py

nardew/Creten

15ddb0b52e6f2afec2c79b3c731fccb34a2c63d6

[ "MIT" ]

6

2019-03-04T15:01:10.000Z

2022-01-12T23:22:55.000Z

from clients.BinanceDataListener import BinanceDataListener from common.ListOperations import makeList class BinanceSimulationDataListener(BinanceDataListener): def __init__(self, exchangeClient, marketDataManager, marketRulesManager, portfolioManager, orderManager): super(BinanceSimulationDataListener, self).__init__(exchangeClient, marketDataManager, marketRulesManager, portfolioManager, orderManager) def parseOrderUpdate(self, msg): return msg def parsePortfolioUpdate(self, msg): return makeList(msg)

43.333333

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0.851923

43

520

10.116279

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0.225287

0.298851

0.354023

0

0.084615

520

12

141

43.333333

0.913866

0

1

0.333333

false

0

0.222222

0.888889

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1

null

0

1

0

null

0

1

0

1

0

6

70d9c9e8c9ee72708d36d75fa35869c31bff605b

2,303

py

Python

epytope/Data/pssms/smmpmbec/mat/A_11_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/smmpmbec/mat/A_11_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/smmpmbec/mat/A_11_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

A_11_01_9 = {0: {'A': -0.4, 'C': 0.103, 'E': 0.501, 'D': 0.707, 'G': -0.17, 'F': 0.166, 'I': -0.159, 'H': -0.028, 'K': -0.342, 'M': -0.353, 'L': -0.003, 'N': 0.328, 'Q': 0.033, 'P': 0.692, 'S': -0.533, 'R': -0.338, 'T': -0.389, 'W': 0.385, 'V': -0.227, 'Y': 0.029}, 1: {'A': -0.433, 'C': 0.738, 'E': 0.428, 'D': 0.355, 'G': 0.184, 'F': 0.071, 'I': -0.705, 'H': 0.552, 'K': 1.041, 'M': -0.587, 'L': -0.405, 'N': 0.292, 'Q': -0.289, 'P': -0.046, 'S': -0.73, 'R': 0.872, 'T': -0.94, 'W': 0.328, 'V': -0.917, 'Y': 0.191}, 2: {'A': -0.079, 'C': -0.085, 'E': 0.426, 'D': 0.22, 'G': 0.159, 'F': -0.537, 'I': -0.181, 'H': 0.072, 'K': 0.331, 'M': -0.62, 'L': -0.081, 'N': -0.176, 'Q': 0.012, 'P': 0.393, 'S': -0.123, 'R': 0.573, 'T': 0.172, 'W': -0.145, 'V': 0.147, 'Y': -0.479}, 3: {'A': -0.153, 'C': 0.032, 'E': 0.092, 'D': -0.114, 'G': 0.033, 'F': -0.14, 'I': 0.113, 'H': 0.055, 'K': 0.122, 'M': -0.081, 'L': 0.097, 'N': -0.083, 'Q': 0.144, 'P': -0.104, 'S': -0.046, 'R': 0.02, 'T': -0.049, 'W': -0.065, 'V': 0.065, 'Y': 0.061}, 4: {'A': 0.045, 'C': 0.053, 'E': 0.204, 'D': 0.207, 'G': 0.085, 'F': -0.38, 'I': -0.075, 'H': -0.036, 'K': 0.102, 'M': -0.024, 'L': -0.061, 'N': 0.098, 'Q': 0.131, 'P': -0.04, 'S': -0.061, 'R': 0.066, 'T': 0.017, 'W': -0.042, 'V': -0.132, 'Y': -0.157}, 5: {'A': 0.052, 'C': 0.083, 'E': 0.333, 'D': 0.343, 'G': 0.07, 'F': -0.327, 'I': -0.078, 'H': -0.029, 'K': 0.195, 'M': -0.196, 'L': -0.099, 'N': -0.004, 'Q': -0.015, 'P': 0.138, 'S': -0.017, 'R': -0.01, 'T': -0.139, 'W': -0.032, 'V': -0.121, 'Y': -0.145}, 6: {'A': 0.069, 'C': 0.048, 'E': 0.207, 'D': 0.195, 'G': 0.156, 'F': -0.476, 'I': -0.312, 'H': -0.124, 'K': 0.519, 'M': -0.38, 'L': -0.298, 'N': 0.092, 'Q': 0.258, 'P': 0.098, 'S': 0.011, 'R': 0.433, 'T': 0.053, 'W': -0.174, 'V': -0.064, 'Y': -0.313}, 7: {'A': 0.035, 'C': 0.157, 'E': 0.058, 'D': 0.323, 'G': 0.099, 'F': -0.328, 'I': 0.134, 'H': 0.194, 'K': 0.08, 'M': -0.038, 'L': -0.252, 'N': -0.007, 'Q': -0.021, 'P': -0.267, 'S': 0.005, 'R': 0.056, 'T': 0.126, 'W': 0.055, 'V': 0.026, 'Y': -0.435}, 8: {'A': 0.272, 'C': 0.284, 'E': 0.36, 'D': 0.257, 'G': 0.206, 'F': 0.167, 'I': 0.273, 'H': -0.505, 'K': -1.986, 'M': 0.162, 'L': 0.333, 'N': 0.27, 'Q': 0.461, 'P': 0.322, 'S': 0.187, 'R': -1.129, 'T': 0.364, 'W': 0.241, 'V': 0.327, 'Y': -0.867}, -1: {'con': 4.98403}}

2,303

0.395571

557

2,303

1.630162

0.332136

0.019824

0

0.37513

0.161963

2,303

1

2,303

0.095337

0

0.079427

0

1

0

false

0

1

null

0

1

0

1

0

null

0

6

cb0234a20791b01e73fbee9bc5a71221a5d69913

323

py

Python

tests/test_error.py

cardforcoin/sockfilter

431249e1351055e35e6411a695f9bab3bdaba0f6

[ "MIT" ]

1

2015-07-10T06:43:47.000Z

tests/test_error.py

thesis/sockfilter

431249e1351055e35e6411a695f9bab3bdaba0f6

[ "MIT" ]

1

2015-03-19T18:56:51.000Z

2015-03-19T19:00:51.000Z

tests/test_error.py

thesis/sockfilter

431249e1351055e35e6411a695f9bab3bdaba0f6

[ "MIT" ]

2

2017-06-16T17:05:23.000Z

2017-06-16T17:05:42.000Z

from sockfilter import SockFilterError def test_error_equality(): assert SockFilterError(address=('google.com', 80)) \ == SockFilterError(address=('google.com', 80)) def test_error_inequality(): assert SockFilterError(address=('google.com', 80)) \ != SockFilterError(address=('google.com', 81))

26.916667

56

0.696594

34

323

6.5

0.441176

0.39819

0.506787

0.561086

0.642534

0.633484

0

0.029304

0.154799

323

11

57

29.363636

0.78022

0

0.285714

0

0.123839

0

0.285714

1

0.285714

true

0

0.142857

0

0.428571

0

null

1

0

1

0

null

0

1

0

6

cb0b8a07db40b50705f7db25ece61a27879444a2

12,060

py

Python

cayley_adam/grassmann_optimizer.py

taralloc/deeprkm

d7975855be31c096515a976539768caac296c30d

[ "MIT" ]

3

2021-05-03T07:42:13.000Z

2022-01-10T10:58:23.000Z

cayley_adam/grassmann_optimizer.py

taralloc/deeprkm

d7975855be31c096515a976539768caac296c30d

[ "MIT" ]

null

cayley_adam/grassmann_optimizer.py

taralloc/deeprkm

d7975855be31c096515a976539768caac296c30d

[ "MIT" ]

null

#from .optimizer import Optimizer, required import torch from torch.optim.optimizer import Optimizer, required import numpy as np from gutils import unit from gutils import gproj from gutils import clip_by_norm from gutils import xTy from gutils import gexp from gutils import gpt from gutils import gpt2 import pdb class SGDG(Optimizer): r"""This optimizer updates variables with two different routines based on the boolean variable 'grassmann'. If grassmann is True, the variables will be updated by SGD-G proposed in 'Riemannian approach to batch normalization'. If grassmann is False, the variables will be updated by SGD. This routine was taken from https://github.com/pytorch/pytorch/blob/master/torch/optim/sgd.py. References: - Minhyung Cho and Jaehyung Lee, Riemannian approach to batch normalization (https://arxiv.org/abs/1709.09603) Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups -- common parameters lr (float): learning rate momentum (float, optional): momentum factor (default: 0) grassmann (bool, optional): whether to use SGD-G (default: False) -- parameters in case grassmann is False weight_decay (float, optional): weight decay (L2 penalty) (default: 0) dampening (float, optional): dampening for momentum (default: 0) nesterov (bool, optional): enables Nesterov momentum (default: False) -- parameters in case grassmann is True omega (float, optional): orthogonality regularization factor (default: 0) grad_clip (float, optional): threshold for gradient norm clipping (default: None) """ def __init__(self, params, lr=required, momentum=0, dampening=0, weight_decay=0, nesterov=False, grassmann=False, omega=0, grad_clip=None): defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov, grassmann=grassmann, omega=0, grad_clip=grad_clip) if nesterov and (momentum <= 0 or dampening != 0): raise ValueError("Nesterov momentum requires a momentum and zero dampening") super(SGDG, self).__init__(params, defaults) def __setstate__(self, state): super(SGDG, self).__setstate__(state) for group in self.param_groups: group.setdefault('nesterov', False) def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: momentum = group['momentum'] grassmann = group['grassmann'] if grassmann: grad_clip = group['grad_clip'] omega = group['omega'] for p in group['params']: if p.grad is None: continue unity,_ = unit(p.data.view(p.size()[0],-1)) g = p.grad.data.view(p.size()[0],-1) if omega != 0: # L=|Y'Y-I|^2/2=|YY'-I|^2/2+c # dL/dY=2(YY'Y-Y) g.add_(2*omega, torch.mm(torch.mm(unity, unity.t()), unity) - unity) h = gproj(unity, g) if grad_clip is not None: h_hat = clip_by_norm(h, grad_clip) else: h_hat = h param_state = self.state[p] if 'momentum_buffer' not in param_state: param_state['momentum_buffer'] = torch.zeros(h_hat.size()) if p.is_cuda: param_state['momentum_buffer'] = param_state['momentum_buffer'].cuda() mom = param_state['momentum_buffer'] mom_new = momentum*mom - group['lr']*h_hat p.data.copy_(gexp(unity, mom_new).view(p.size())) mom.copy_(gpt(unity, mom_new)) else: # This routine is from https://github.com/pytorch/pytorch/blob/master/torch/optim/sgd.py weight_decay = group['weight_decay'] dampening = group['dampening'] nesterov = group['nesterov'] for p in group['params']: if p.grad is None: continue d_p = p.grad.data if weight_decay != 0: d_p.add_(weight_decay, p.data) if momentum != 0: param_state = self.state[p] if 'momentum_buffer' not in param_state: buf = param_state['momentum_buffer'] = d_p.clone() else: buf = param_state['momentum_buffer'] buf.mul_(momentum).add_(1 - dampening, d_p) if nesterov: d_p = d_p.add(momentum, buf) else: d_p = buf p.data.add_(-group['lr'], d_p) return loss class AdamG(Optimizer): r"""This optimizer updates variables with two different routines based on the boolean variable 'grassmann'. If grassmann is True, the variables will be updated by Adam-G proposed in 'Riemannian approach to batch normalization'. If grassmann is False, the variables will be updated by SGD. This routine was taken from https://github.com/pytorch/pytorch/blob/master/torch/optim/sgd.py. References: - Minhyung Cho and Jaehyung Lee, Riemannian approach to batch normalization (https://arxiv.org/abs/1709.09603) Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups -- common parameters lr (float): learning rate momentum (float, optional): momentum factor (default: 0) grassmann (bool, optional): whether to use Adam-G (default: False) -- parameters in case grassmann is False weight_decay (float, optional): weight decay (L2 penalty) (default: 0) dampening (float, optional): dampening for momentum (default: 0) nesterov (bool, optional): enables Nesterov momentum (default: False) -- parameters in case grassmann is True beta2 (float, optional): the exponential decay rate for the second moment estimates (defulat: 0.99) epsilon (float, optional): a small constant for numerical stability (default: 1e-8) omega (float, optional): orthogonality regularization factor (default: 0) grad_clip (float, optional): threshold for gradient norm clipping (default: None) """ def __init__(self, params, lr=required, momentum=0, dampening=0, weight_decay=0, nesterov=False, grassmann=False, beta2=0.99, epsilon=1e-8, omega=0, grad_clip=None): defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov, grassmann=grassmann, beta2=beta2, epsilon=epsilon, omega=0, grad_clip=grad_clip) if nesterov and (momentum <= 0 or dampening != 0): raise ValueError("Nesterov momentum requires a momentum and zero dampening") super(AdamG, self).__init__(params, defaults) def __setstate__(self, state): super(AdamG, self).__setstate__(state) for group in self.param_groups: group.setdefault('nesterov', False) def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: grassmann = group['grassmann'] if grassmann: beta1 = group['momentum'] beta2 = group['beta2'] epsilon = group['epsilon'] grad_clip = group['grad_clip'] omega = group['omega'] for p in group['params']: if p.grad is None: continue unity,_ = unit(p.data.view(p.size()[0],-1)) g = p.grad.data.view(p.size()[0],-1) if omega != 0: # L=|Y'Y-I|^2/2=|YY'-I|^2/2+c # dL/dY=2(YY'Y-Y) g.add_(2*omega, torch.mm(torch.mm(unity, unity.t()), unity) - unity) h = gproj(unity, g) if grad_clip is not None: h_hat = clip_by_norm(h, grad_clip) else: h_hat = h param_state = self.state[p] if 'm_buffer' not in param_state: size=p.size() param_state['m_buffer'] = torch.zeros([size[0], int(np.prod(size[1:]))]) param_state['v_buffer'] = torch.zeros([size[0], 1]) if p.is_cuda: param_state['m_buffer'] = param_state['m_buffer'].cuda() param_state['v_buffer'] = param_state['v_buffer'].cuda() param_state['beta1_power'] = beta1 param_state['beta2_power'] = beta2 m = param_state['m_buffer'] v = param_state['v_buffer'] beta1_power = param_state['beta1_power'] beta2_power = param_state['beta2_power'] mnew = beta1*m + (1.0-beta1)*h_hat vnew = beta2*v + (1.0-beta2)*xTy(h_hat,h_hat) alpha = np.sqrt(1.-beta2_power) / (1.-beta1_power) deltas = mnew / vnew.add(epsilon).sqrt() deltas.mul_(-alpha*group['lr']) p.data.copy_(gexp(unity, deltas).view(p.size())) m.copy_(gpt2(unity, mnew, deltas)) v.copy_(vnew) param_state['beta1_power']*=beta1 param_state['beta2_power']*=beta2 else: momentum = group['momentum'] weight_decay = group['weight_decay'] dampening = group['dampening'] nesterov = group['nesterov'] for p in group['params']: if p.grad is None: continue d_p = p.grad.data if weight_decay != 0: d_p.add_(weight_decay, p.data) if momentum != 0: param_state = self.state[p] if 'momentum_buffer' not in param_state: buf = param_state['momentum_buffer'] = d_p.clone() else: buf = param_state['momentum_buffer'] buf.mul_(momentum).add_(1 - dampening, d_p) if nesterov: d_p = d_p.add(momentum, buf) else: d_p = buf p.data.add_(-group['lr'], d_p) return loss

42.020906

108

0.51932

1,328

12,060

4.579066

0.152108

0.049334

0.02368

0.031574

0.819602

0.779642

0.773393

0.772899

0.757441

0

0.015862

0.388391

12,060

286

109

42.167832

0.808568

0.277197

0

0.676647

0

0.074033

0

1

0.035928

false

0

0.065868

0

0.125749

0

null

0

1

0

null

0

6

cb0dccc89b5cdc57217f392f3c607527c0309853

166

py

Python

hello_python.py

mikvikpik/Project_Training

4d55b092f6cee696cbd93ec3d018de5ba2135167

[ "MIT" ]

1

2020-01-17T01:06:17.000Z

hello_python.py

mikvikpik/Project_Training

4d55b092f6cee696cbd93ec3d018de5ba2135167

[ "MIT" ]

null

hello_python.py

mikvikpik/Project_Training

4d55b092f6cee696cbd93ec3d018de5ba2135167

[ "MIT" ]

null

message = "Hello Python world!" print(message) message = "Hello Python Crash Course world!" print(message) mesage = "Hello Python Crash Course reader!" print(mesage)

23.714286

44

0.759036

22

166

5.727273

0.409091

0.261905

0.285714

0.349206

0

0.13253

166

6

45

27.666667

0.875

0

0.333333

0

0.506024

0

1

0

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

6

cb22022b4c711ae02c5f87fd337ee5385b6dbc00

44

py

Python

static/templates/gen.py

nilox94/acmx

a680bca10259a53a14bd7e2a42eed4d1cc5a9f13

[ "MIT" ]

127

2019-01-09T03:08:59.000Z

2022-03-22T09:37:19.000Z

static/templates/gen.py

nilox94/acmx

a680bca10259a53a14bd7e2a42eed4d1cc5a9f13

[ "MIT" ]

102

2019-04-14T05:40:54.000Z

2022-02-07T13:21:36.000Z

baekjoon/python/make_1_2_gen.py

yskang/AlgorithmPractice

31b76e38b4c2f1e3e29fb029587662a745437912

[ "MIT" ]

23

2019-04-14T11:01:51.000Z

2021-09-13T18:11:18.000Z

import random print(random.randint(1, 100))

14.666667

29

0.772727

7

44

4.857143

0.857143

0

0.1

0.090909

44

3

29

14.666667

0.75

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

cb3c8cf4a8a488bd5d661fc96b5269a3368bdc3f

46

py

Python

docker/download.py

Pbatch/pictionary

d6c19c7d099b4b59834349420b7491ea5c46a57d

[ "MIT" ]

3

2021-06-16T15:12:55.000Z

2021-06-20T09:30:40.000Z

docker/download.py

Pbatch/pictionary

d6c19c7d099b4b59834349420b7491ea5c46a57d

[ "MIT" ]

null

docker/download.py

Pbatch/pictionary

d6c19c7d099b4b59834349420b7491ea5c46a57d

[ "MIT" ]

null

from utils import download_vit download_vit()

15.333333

30

0.847826

7

46

5.285714

0.714286

0.594595

0

0.108696

46

3

31

15.333333

0.902439

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

cb545b1b0c3dd8dfb691bb3d60a69feab99ebb9b

159

py

Python

src/credo_cf/image/__init__.py

dzwiedziu-nkg/credo-classify-framework

45417b505b4f4b20a7248f3487ca57a3fd49ccee

[ "MIT" ]

null

src/credo_cf/image/__init__.py

dzwiedziu-nkg/credo-classify-framework

45417b505b4f4b20a7248f3487ca57a3fd49ccee

[ "MIT" ]

null

src/credo_cf/image/__init__.py

dzwiedziu-nkg/credo-classify-framework

45417b505b4f4b20a7248f3487ca57a3fd49ccee

[ "MIT" ]

3

2020-06-19T15:41:19.000Z

2020-06-29T12:47:05.000Z

from credo_cf.image.image_utils import load_image, image_basic_metrics from credo_cf.image.gray import convert_to_gray, gray_avg, gray_max, gray_min, gray_rgb

53

87

0.861635

29

159

4.310345

0.551724

0.144

0.176

0.256

0

0.081761

159

2

88

79.5

0.856164

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

cb94be1a0206e01f862cc451306e79934b57ba52

35

py

Python

bank_ddd_es_cqrs/accounts/api/__init__.py

Hyaxia/Bank-DDD-CQRS-ES

116e3eb3e93d549c1da53e6d506ab47667d77445

[ "MIT" ]

8

2020-10-27T09:46:20.000Z

2022-01-27T12:16:48.000Z

bank_ddd_es_cqrs/accounts/api/__init__.py

Hyaxia/Bank-DDD-CQRS-ES

116e3eb3e93d549c1da53e6d506ab47667d77445

[ "MIT" ]

null

bank_ddd_es_cqrs/accounts/api/__init__.py

Hyaxia/Bank-DDD-CQRS-ES

116e3eb3e93d549c1da53e6d506ab47667d77445

[ "MIT" ]

2

2021-05-29T08:11:48.000Z

2021-07-26T04:44:53.000Z

from .api import account_blueprint

17.5

34

0.857143

5

35

5.8

1

0

0.114286

35

1

35

0.935484

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

cbd53e60dc0d252fdf8db730502a5b3a5150f3f5

84

py

Python

haravan/resources/note_attribute.py

vivekpatel111/HaravanAPI

bec18d43b485c966e1396ce990c7b0fd40c96de0

[ "MIT" ]

null

haravan/resources/note_attribute.py

vivekpatel111/HaravanAPI

bec18d43b485c966e1396ce990c7b0fd40c96de0

[ "MIT" ]

null

haravan/resources/note_attribute.py

vivekpatel111/HaravanAPI

bec18d43b485c966e1396ce990c7b0fd40c96de0

[ "MIT" ]

1

2019-03-08T06:28:48.000Z

from ..base import HaravanResource class NoteAttribute(HaravanResource): pass

14

37

0.785714

8

84

8.25

0.875

0

0.154762

84

5

38

16.8

0.929577

0

1

0

true

0.333333

0

0.666667

0

1

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null

0

1

0

null

0

1

0

1

0

6

1ddb58f9f9de5d558b75cd3883b23cbf63054199

99

py

Python

raspberry_cereal/constants.py

hfaran/raspberry-cereal

d6cc1ca98800422d961f71024b6a5eb7cec2dccf

[ "MIT" ]

7

2016-02-04T22:31:52.000Z

2019-04-11T15:02:58.000Z

raspberry_cereal/constants.py

hfaran/raspberry-cereal

d6cc1ca98800422d961f71024b6a5eb7cec2dccf

[ "MIT" ]

2

2016-10-25T17:06:00.000Z

2016-11-05T09:59:24.000Z

raspberry_cereal/constants.py

hfaran/raspberry-cereal

d6cc1ca98800422d961f71024b6a5eb7cec2dccf

[ "MIT" ]

null

CONFIG_PATH = '/etc/raspberry-cereal/raspberry_cereal.cfg' CONFIG_DIR = '/etc/raspberry-cereal/'

33

59

0.767677

13

99

5.615385

0.538462

0.616438

0.493151

0

0.080808

99

2

60

49.5

0.802198

0

0.659794

0

1

0

false

0

1

0

null

1

0

1

0

1

null

0

6

1de7118db8a2a26073cd017bd04128eea22ee993

48

py

Python

archive/utils/__init__.py

pydget/xbrief

9e91927a98754b0fca1fa55eae9a785b15e963f9

[ "MIT" ]

null

archive/utils/__init__.py

pydget/xbrief

9e91927a98754b0fca1fa55eae9a785b15e963f9

[ "MIT" ]

null

archive/utils/__init__.py

pydget/xbrief

9e91927a98754b0fca1fa55eae9a785b15e963f9

[ "MIT" ]

null

from .preci import Preci from .curt import Curt

16

24

0.791667

8

48

4.75

0.5

0

0.166667

48

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1

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1

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6

381388a86af49ca14fbd6e6739686b31e1b70797

425,329

py

Python

src/resources.py

1MLightyears/AuraProII

c4d98f98f556d69bdd90b4063f57b5df40ba3d54

[ "Apache-2.0" ]

1

2020-10-09T10:41:35.000Z

src/resources.py

1MLightyears/AuraProII

c4d98f98f556d69bdd90b4063f57b5df40ba3d54

[ "Apache-2.0" ]

null

src/resources.py

1MLightyears/AuraProII

c4d98f98f556d69bdd90b4063f57b5df40ba3d54

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Resource object code # # Created by: The Resource Compiler for PyQt5 (Qt v5.15.0) # # WARNING! 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QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data) qInitResources()

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1

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1

0

1

0

null

1

0

6

3843ef8669ec42e94478d18d7dee86428d703385

88

py

Python

pvinspect/preproc/__init__.py

KuZero/pvinspect

7d6666c509573fdb36e5312fed1e68d5ec47f2ce

[ "Apache-2.0" ]

null

pvinspect/preproc/__init__.py

KuZero/pvinspect

7d6666c509573fdb36e5312fed1e68d5ec47f2ce

[ "Apache-2.0" ]

null

pvinspect/preproc/__init__.py

KuZero/pvinspect

7d6666c509573fdb36e5312fed1e68d5ec47f2ce

[ "Apache-2.0" ]

null

"""Methods for preprocessing data""" from . import calibration from . import detection

17.6

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0.8

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null

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0

1

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1

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6

699bb9331140aeb26f14a0a6670a13bcc212584b

123

py

Python

isobus/__init__.py

jboomer/python-isobus

da09e6ecaf27a540a87d19319e4bdeb36685aea3

[ "MIT" ]

14

2017-01-25T06:52:56.000Z

2022-01-10T09:57:59.000Z

isobus/__init__.py

jboomer/python-isobus

da09e6ecaf27a540a87d19319e4bdeb36685aea3

[ "MIT" ]

1

2020-01-05T15:42:29.000Z

2020-01-28T20:47:45.000Z

isobus/__init__.py

jboomer/python-isobus

da09e6ecaf27a540a87d19319e4bdeb36685aea3

[ "MIT" ]

9

2020-01-19T05:03:32.000Z

2022-02-09T18:11:56.000Z

""" ISOBUS description """ # isobus imports: from isobus.vt.client import VTClient from isobus.common import IBSException

20.5

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123

6.4

0.666667

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5

39

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true

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null

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1

0

1

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1

0

6

0e0316fe1f5abd1f403cb488bbeea233f77945e9

127

py

Python

magpy/__init__.py

owlas/magpy

45c1332209154ef6be863e4797054a0b642493ec

[ "BSD-3-Clause" ]

5

2017-11-13T21:55:45.000Z

2020-12-29T10:42:41.000Z

magpy/__init__.py

owlas/magpy

45c1332209154ef6be863e4797054a0b642493ec

[ "BSD-3-Clause" ]

9

2017-10-06T09:30:08.000Z

2018-02-17T17:17:38.000Z

magpy/__init__.py

owlas/magpy

45c1332209154ef6be863e4797054a0b642493ec

[ "BSD-3-Clause" ]

2

2017-11-24T16:18:46.000Z

2020-06-08T11:54:40.000Z

from . import geometry from . import initial_conditions from . import results from .model import Model, EnsembleModel, DOModel

25.4

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0.811024

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127

6.375

0.5625

0.294118

0

0.141732

127

4

49

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0

true

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1

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1

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6

3853a4e1ec1eee5dd1cad8dd671e7e75c18b67cc

166

py

Python

core/eval/__init__.py

youngzhou1999/DI-drive

73f0009503f78cfa1347fcc57b80d274a0b3824f

[ "Apache-2.0" ]

21

2022-02-15T10:11:54.000Z

2022-03-24T17:44:29.000Z

core/eval/__init__.py

youngzhou1999/DI-drive

73f0009503f78cfa1347fcc57b80d274a0b3824f

[ "Apache-2.0" ]

null

core/eval/__init__.py

youngzhou1999/DI-drive

73f0009503f78cfa1347fcc57b80d274a0b3824f

[ "Apache-2.0" ]

3

2022-02-22T11:11:43.000Z

2022-03-17T17:58:44.000Z

from .single_carla_evaluator import SingleCarlaEvaluator from .carla_benchmark_evaluator import CarlaBenchmarkEvaluator from .serial_evaluator import SerialEvaluator

41.5

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0.909639

17

166

8.588235

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0.072289

166

3

63

55.333333

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1

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true

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1

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6

388cbc82a2c77ea27d3776e057f0a7a85d4ea7d8

397

py

Python

oemof/solph/__init__.py

jnnr/oemof

83cfc0b6187906d85ebd0c4ea2788e0091dc82f7

[ "MIT" ]

null

oemof/solph/__init__.py

jnnr/oemof

83cfc0b6187906d85ebd0c4ea2788e0091dc82f7

[ "MIT" ]

null

oemof/solph/__init__.py

jnnr/oemof

83cfc0b6187906d85ebd0c4ea2788e0091dc82f7

[ "MIT" ]

null

from oemof.solph.network import (Sink, Source, Transformer, Bus, Flow, EnergySystem) from oemof.solph.models import Model from oemof.solph.groupings import GROUPINGS from oemof.solph.options import Investment, NonConvex from oemof.solph.plumbing import sequence from oemof.solph import components from oemof.solph import custom from oemof.solph import constraints

39.7

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0.768262

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397

5.980392

0.431373

0.236066

0.367213

0.196721

0

0.178841

397

9

71

44.111111

0.935583

0

1

0

true

0

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0

0.888889

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null

1

0

1

0

null

0

1

0

1

0

6

389e8282870a762495571733fed36779b4b7f124

122

py

Python

pdprpr/utils.py

yubessy/pandas-preprocessing

44cefa51186e10faa5473d08b2f29ab187f0c48f

[ "MIT" ]

6

2017-09-29T04:53:50.000Z

2018-01-18T04:27:28.000Z

pdprpr/utils.py

yubessy/pandas-preprocessing

44cefa51186e10faa5473d08b2f29ab187f0c48f

[ "MIT" ]

2

2017-10-03T06:34:13.000Z

2017-10-03T06:43:31.000Z

pdprpr/utils.py

yubessy/pandas-preprocessing

44cefa51186e10faa5473d08b2f29ab187f0c48f

[ "MIT" ]

1

2020-04-14T16:27:26.000Z

from numbers import Number import numpy def is_nan(value): return isinstance(value, Number) and numpy.isnan(value)

15.25

59

0.762295

18

122

5.111111

0.722222

0

0.163934

122

7

60

17.428571

0.901961

0

1

0.25

false

0

0.5

0.25

1

0

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0

null

0

1

0

null

0

1

0

1

0

6

38e469bb4160e91e9680cd5845e6d7834cdfddec

100

py

Python

src/torch_utils/__init__.py

gvashkevich/pytorch-utils-api

744ab960460baaf840a6c782734a37fa24e24cc1

[ "MIT" ]

null

src/torch_utils/__init__.py

gvashkevich/pytorch-utils-api

744ab960460baaf840a6c782734a37fa24e24cc1

[ "MIT" ]

null

src/torch_utils/__init__.py

gvashkevich/pytorch-utils-api

744ab960460baaf840a6c782734a37fa24e24cc1

[ "MIT" ]

null

from . import eval from . import model from . import opt from . import summary from . import train

14.285714

21

0.74

15

100

4.933333

0.466667

0.675676

0

0.21

100

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22

16.666667

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true

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null

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1

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1

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1

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6

2a4fc1df890f36d67d435f37062233e493f46493

4,903

py

Python

BMCodeHelper/CaseMigrate.py

yyc12345/gist

000a03cb4b9fc8ca0b083c3b0bd939c41a590c94

[ "MIT" ]

1

2020-09-07T06:24:10.000Z

BMCodeHelper/CaseMigrate.py

yyc12345/gist

000a03cb4b9fc8ca0b083c3b0bd939c41a590c94

[ "MIT" ]

null

BMCodeHelper/CaseMigrate.py

yyc12345/gist

000a03cb4b9fc8ca0b083c3b0bd939c41a590c94

[ "MIT" ]

null

fr=open('migrating.txt', 'r') caseDict = {} oldList = [ "Ball_LightningSphere1.bmp", "Ball_LightningSphere2.bmp", "Ball_LightningSphere3.bmp", "Ball_Paper.bmp", "Ball_Stone.bmp", "Ball_Wood.bmp", "Brick.bmp", "Button01_deselect.tga", "Button01_select.tga", "Button01_special.tga", "Column_beige.bmp", "Column_beige_fade.tga", "Column_blue.bmp", "Dome.bmp", "DomeEnvironment.bmp", "DomeShadow.tga", "ExtraBall.bmp", "ExtraParticle.bmp", "E_Holzbeschlag.bmp", "FloorGlow.bmp", "Floor_Side.bmp", "Floor_Top_Border.bmp", "Floor_Top_Borderless.bmp", "Floor_Top_Checkpoint.bmp", "Floor_Top_Flat.bmp", "Floor_Top_Profil.bmp", "Floor_Top_ProfilFlat.bmp", "Font_1.tga", "Gravitylogo_intro.bmp", "HardShadow.bmp", "Laterne_Glas.bmp", "Laterne_Schatten.tga", "Laterne_Verlauf.tga", "Metal_stained.bmp", "Misc_Ufo.bmp", "Misc_UFO_Flash.bmp", "Modul03_Floor.bmp", "Modul03_Wall.bmp", "Modul11_13_Wood.bmp", "Modul11_Wood.bmp", "Modul15.bmp", "Modul16.bmp", "Modul18.bmp", "Modul18_Gitter.tga", "Modul30_d_Seiten.bmp", "Particle_Flames.bmp", "Particle_Smoke.bmp", "PE_Bal_balloons.bmp", "PE_Bal_platform.bmp", "PE_Ufo_env.bmp", "P_Extra_Life_Oil.bmp", "P_Extra_Life_Particle.bmp", "P_Extra_Life_Shadow.bmp", "Rail_Environment.bmp", "sandsack.bmp", "SkyLayer.bmp", "Sky_Vortex.bmp", "Stick_Bottom.tga", "Stick_Stripes.bmp", "Target.bmp", "Tower_Roof.bmp", "Trafo_Environment.bmp", "Trafo_FlashField.bmp", "Trafo_Shadow_Big.tga", "Wood_Metal.bmp", "Wood_MetalStripes.bmp", "Wood_Misc.bmp", "Wood_Nailed.bmp", "Wood_Old.bmp", "Wood_Panel.bmp", "Wood_Plain.bmp", "Wood_Plain2.bmp", "Wood_Raft.bmp" ] caseMode = False caseIndex = 0 while True: cache=fr.readline() if cache=='': break cache=cache.strip() cache=cache.replace('\t', '') if cache=='': continue if not caseMode: if cache.startswith('//case'): # ignored item # skip this pass elif cache.startswith('case'): codeline = "" caseMode = True caseIndex=int(cache.split(' ')[1].replace(':', '')) else: if cache=='break;': codeline+=cache+"\n" caseMode = False caseDict[caseIndex] = codeline else: codeline+=cache+"\n" fr.close() fw=open('migrated.txt', 'w') newList = [ "atari.avi", "atari.bmp", "Ball_LightningSphere1.bmp", "Ball_LightningSphere2.bmp", "Ball_LightningSphere3.bmp", "Ball_Paper.bmp", "Ball_Stone.bmp", "Ball_Wood.bmp", "Brick.bmp", "Button01_deselect.tga", "Button01_select.tga", "Button01_special.tga", "Column_beige.bmp", "Column_beige_fade.tga", "Column_blue.bmp", "Cursor.tga", "Dome.bmp", "DomeEnvironment.bmp", "DomeShadow.tga", "ExtraBall.bmp", "ExtraParticle.bmp", "E_Holzbeschlag.bmp", "FloorGlow.bmp", "Floor_Side.bmp", "Floor_Top_Border.bmp", "Floor_Top_Borderless.bmp", "Floor_Top_Checkpoint.bmp", "Floor_Top_Flat.bmp", "Floor_Top_Profil.bmp", "Floor_Top_ProfilFlat.bmp", "Font_1.tga", "Gravitylogo_intro.bmp", "HardShadow.bmp", "Laterne_Glas.bmp", "Laterne_Schatten.tga", "Laterne_Verlauf.tga", "Logo.bmp", "Metal_stained.bmp", "Misc_Ufo.bmp", "Misc_UFO_Flash.bmp", "Modul03_Floor.bmp", "Modul03_Wall.bmp", "Modul11_13_Wood.bmp", "Modul11_Wood.bmp", "Modul15.bmp", "Modul16.bmp", "Modul18.bmp", "Modul18_Gitter.tga", "Modul30_d_Seiten.bmp", "Particle_Flames.bmp", "Particle_Smoke.bmp", "PE_Bal_balloons.bmp", "PE_Bal_platform.bmp", "PE_Ufo_env.bmp", "Pfeil.tga", "P_Extra_Life_Oil.bmp", "P_Extra_Life_Particle.bmp", "P_Extra_Life_Shadow.bmp", "Rail_Environment.bmp", "sandsack.bmp", "SkyLayer.bmp", "Sky_Vortex.bmp", "Stick_Bottom.tga", "Stick_Stripes.bmp", "Target.bmp", "Tower_Roof.bmp", "Trafo_Environment.bmp", "Trafo_FlashField.bmp", "Trafo_Shadow_Big.tga", "Tut_Pfeil01.tga", "Tut_Pfeil_Hoch.tga", "Wolken_intro.tga", "Wood_Metal.bmp", "Wood_MetalStripes.bmp", "Wood_Misc.bmp", "Wood_Nailed.bmp", "Wood_Old.bmp", "Wood_Panel.bmp", "Wood_Plain.bmp", "Wood_Plain2.bmp", "Wood_Raft.bmp" ] counter = 0 for item in newList: if item in oldList: existedCode = caseDict.get(oldList.index(item), '') else: existedCode = '' if existedCode == '': fw.write('//') fw.write('case {}: //{}'.format(counter, item)) fw.write('\n') fw.write(existedCode) counter+=1 fw.close()

23.45933

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null

0

1

0

1

0

null

0

6

2a795453b4723f2fb9d0af3a9b085623ee4bb5ff

125

py

Python

discord/ext/ui/combine/abc.py

Lapis256/discord-ext-ui

593de0a1107d2a0c26023587a2937f00ecec3ed1

[ "MIT" ]

null

discord/ext/ui/combine/abc.py

Lapis256/discord-ext-ui

593de0a1107d2a0c26023587a2937f00ecec3ed1

[ "MIT" ]

null

discord/ext/ui/combine/abc.py

Lapis256/discord-ext-ui

593de0a1107d2a0c26023587a2937f00ecec3ed1

[ "MIT" ]

null

from typing import Any class ValueAble: def __init__(self, value: Any = None) -> None: self.value: Any = value

17.857143

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125

4.529412

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0.311688

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0.256

125

6

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20.833333

0.827957

0

1

0.25

false

0

0.25

0

0.75

0

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0

null

1

0

1

0

null

0

1

0

1

0

6

2a826ff1896b448a9c143341638c02f8ecbb46b1

41

py

Python

tests/fixtures/__init__.py

pphelan69/rest_python

e4258979877a1040a86b63da2010dd44f4b5ddca

[ "MIT" ]

null

tests/fixtures/__init__.py

pphelan69/rest_python

e4258979877a1040a86b63da2010dd44f4b5ddca

[ "MIT" ]

null

tests/fixtures/__init__.py

pphelan69/rest_python

e4258979877a1040a86b63da2010dd44f4b5ddca

[ "MIT" ]

null

import tests.fixtures.jobconfiguration

10.25

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41

3

39

13.666667

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true

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null

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1

0

1

0

1

0

6

aa529ed1be459be80c49abb141d38dee0ed979c9

229

py

Python

segmentation_models/backbones/__init__.py

marcojoao/UNetPlusPlus

d761e64f565b19da3892fb5ca3cd9b69f2c2a3a7

[ "MIT" ]

1,621

2019-02-14T02:56:51.000Z

2022-03-31T02:53:17.000Z

segmentation_models/backbones/__init__.py

marcojoao/UNetPlusPlus

d761e64f565b19da3892fb5ca3cd9b69f2c2a3a7

[ "MIT" ]

72

2019-02-21T04:45:38.000Z

2022-02-09T23:49:43.000Z

segmentation_models/backbones/__init__.py

marcojoao/UNetPlusPlus

d761e64f565b19da3892fb5ca3cd9b69f2c2a3a7

[ "MIT" ]

424

2019-02-15T04:05:55.000Z

2022-03-30T02:06:34.000Z

from .classification_models.classification_models import * from .inception_resnet_v2 import InceptionResNetV2 from .inception_v3 import InceptionV3 from .backbones import get_backbone from .preprocessing import get_preprocessing

38.166667

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0

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229

6

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0.913876

0

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true

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null

0

1

0

1

0

1

0

6

aa93abf95cddecc192ec8958e3b94e0ca78f37e5

61

py

Python

src/models/__init__.py

guritian/fl-noniid

0af633e4df54426ee499b6e27b42589cc23a1eee

[ "MIT" ]

3

2021-07-21T06:07:01.000Z

2021-12-27T06:54:54.000Z

src/models/__init__.py

guritian/fl-noniid

0af633e4df54426ee499b6e27b42589cc23a1eee

[ "MIT" ]

null

src/models/__init__.py

guritian/fl-noniid

0af633e4df54426ee499b6e27b42589cc23a1eee

[ "MIT" ]

2

2021-10-03T15:18:28.000Z

2022-02-17T05:31:42.000Z

from .resnet import * from .vgg import * from .lenet import *

20.333333

21

0.721311

9

61

4.888889

0.555556

0.454545

0

0.180328

61

3

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20.333333

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1

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1

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6

aadbdcfa62c7ceca195310cd0a86ffb904749aef

463

py

Python

devito/types/__init__.py

CavalcanteLucas/devito

f52cfd7d55b91f83245f33af4424adbdb03075d8

[ "MIT" ]

1

2020-01-30T17:49:12.000Z

devito/types/__init__.py

CavalcanteLucas/devito

f52cfd7d55b91f83245f33af4424adbdb03075d8

[ "MIT" ]

1

2019-11-06T18:01:25.000Z

devito/types/__init__.py

CavalcanteLucas/devito

f52cfd7d55b91f83245f33af4424adbdb03075d8

[ "MIT" ]

2

2018-11-15T12:03:48.000Z

2018-11-15T13:16:19.000Z

# Need only from within Devito from devito.types.basic import * # noqa # Needed both within and outside Devito from devito.types.dimension import * # noqa from devito.types.utils import * # noqa from devito.types.caching import _SymbolCache, CacheManager # noqa # Needed only outside Devito from devito.types.constant import * # noqa from devito.types.grid import * # noqa from devito.types.dense import * # noqa from devito.types.sparse import * # noqa

33.071429

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5.4

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0.34188

0.2849

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13

68

35.615385

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0

1

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true

0

1

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null

1

0

null

0

1

0

1

0

1

0

6

aae0617565d203501d774d4111e48a633e00a187

300

py

Python

sage_utils/amqp/__init__.py

OpenMatchmaking/sage-utils-python

348394bf9cd3adb96fe3915d1d4d99daa46ab437

[ "BSD-3-Clause" ]

null

sage_utils/amqp/__init__.py

OpenMatchmaking/sage-utils-python

348394bf9cd3adb96fe3915d1d4d99daa46ab437

[ "BSD-3-Clause" ]

2

2018-04-07T23:24:19.000Z

2018-05-25T08:31:31.000Z

sage_utils/amqp/__init__.py

OpenMatchmaking/sage-utils-python

348394bf9cd3adb96fe3915d1d4d99daa46ab437

[ "BSD-3-Clause" ]

null

from sage_utils.amqp.base import AmqpWorker # NOQA from sage_utils.amqp.clients import RpcAmqpClient # NOQA from sage_utils.amqp.extension import AmqpExtension # NOQA from sage_utils.amqp.mixins import SanicAmqpExtensionMixin # NOQA from sage_utils.amqp.workers import BaseRegisterWorker # NOQA

50

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null

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6

2abc5da83c3cdde7ab5885c24f1e9bd405364ee6

139

py

Python

openspoor/transformers/__init__.py

ProRail-DataLab/openspoor

548c7ffc12a5a97459bafe5327aa9b0d0546537c

[ "MIT" ]

7

2022-01-28T09:54:10.000Z

2022-03-25T10:02:08.000Z

openspoor/transformers/__init__.py

ProRail-DataLab/openspoor

548c7ffc12a5a97459bafe5327aa9b0d0546537c

[ "MIT" ]

11

2022-03-17T12:48:30.000Z

2022-03-25T11:22:39.000Z

openspoor/transformers/__init__.py

ProRail-DataLab/openspoor

548c7ffc12a5a97459bafe5327aa9b0d0546537c

[ "MIT" ]

2

2022-03-16T14:11:48.000Z

2022-03-25T09:10:30.000Z

from .TransformerCoordinatesToSpoor import * from .TransformerGeocodeToCoordinates import * from .TransformerSpoortakToCoordinates import *

46.333333

47

0.877698

9

139

13.555556

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139

3

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null

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1

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1

0

6

2ac643f5e8dbac46d8cc5c82221da186b248397c

5,558

py

Python

backend/api/tests/schema/conference/test_ranking.py

patrick91/pycon

9d7e15f540adcf0eaceb61fdbf67206d6aef73ec

[ "MIT" ]

2

2017-07-18T21:51:25.000Z

2017-12-23T11:08:39.000Z

backend/api/tests/schema/conference/test_ranking.py

patrick91/pycon

9d7e15f540adcf0eaceb61fdbf67206d6aef73ec

[ "MIT" ]

23

2017-07-18T20:22:38.000Z

2018-01-05T05:45:15.000Z

backend/api/tests/schema/conference/test_ranking.py

patrick91/pycon

9d7e15f540adcf0eaceb61fdbf67206d6aef73ec

[ "MIT" ]

2

2017-07-18T21:27:33.000Z

2017-07-18T22:07:03.000Z

import pytest import respx from django.conf import settings pytestmark = [pytest.mark.django_db] @pytest.fixture def mock_users(mocker): with respx.mock as mock: mock.post(f"{settings.USERS_SERVICE}/internal-api").respond( json={ "data": { "usersByIds": [ { "id": 10, "fullname": "Marco Acierno", "name": "Marco", "username": "marco", } ] } } ) def test_conference_ranking_does_not_exists( conference_factory, graphql_client, mock_users ): conference = conference_factory( topics=[ "Sushi", ] ) query = """ query($code: String!, $topic: ID!) { conference(code: $code) { ranking(topic: $topic) { isPublic rankedSubmissions { rank score } } } } """ resp = graphql_client.query( query, variables={"code": conference.code, "topic": conference.topics.first().id}, ) assert "errors" not in resp assert resp["data"]["conference"]["ranking"] is None def test_conference_ranking_is_not_public( conference_factory, rank_request_factory, graphql_client, mock_users ): conference = conference_factory( topics=[ "Sushi", ] ) rank_request_factory(conference=conference, is_public=False) query = """ query($code: String!, $topic: ID!) { conference(code: $code) { ranking(topic: $topic) { isPublic } } } """ resp = graphql_client.query( query, variables={"code": conference.code, "topic": conference.topics.first().id}, ) assert "errors" not in resp assert resp["data"]["conference"]["ranking"] is None def test_conference_ranking_is_public_anyone_can_see( conference, rank_request_factory, rank_submission_factory, graphql_client, mock_users, ): rank_request = rank_request_factory(conference=conference, is_public=True) rank_submission = rank_submission_factory(rank_request=rank_request) query = """ query($code: String!, $topic: ID!) { conference(code: $code) { ranking(topic: $topic) { isPublic rankedSubmissions { rank score } } } } """ resp = graphql_client.query( query, variables={ "code": conference.code, "topic": str(rank_submission.submission.topic.id), }, ) assert "errors" not in resp assert resp["data"]["conference"]["ranking"]["isPublic"] is True rank_submission_data = resp["data"]["conference"]["ranking"]["rankedSubmissions"][0] assert rank_submission_data["rank"] == rank_submission.rank assert float(rank_submission_data["score"]) == float(rank_submission.score) def test_conference_ranking_is_not_public_admin_can_see( conference, rank_request_factory, rank_submission_factory, graphql_client, admin_user, mock_users, ): graphql_client.force_login(admin_user) rank_request = rank_request_factory(conference=conference, is_public=False) rank_submission = rank_submission_factory(rank_request=rank_request) query = """ query($code: String!, $topic: ID!) { conference(code: $code) { ranking(topic: $topic) { isPublic rankedSubmissions { rank score } } } } """ resp = graphql_client.query( query, variables={ "code": conference.code, "topic": rank_submission.submission.topic.id, }, ) assert "errors" not in resp assert resp["data"]["conference"]["ranking"]["isPublic"] is False rank_submission_data = resp["data"]["conference"]["ranking"]["rankedSubmissions"][0] assert rank_submission_data["rank"] == rank_submission.rank assert float(rank_submission_data["score"]) == float(rank_submission.score) def test_conference_ranking_is_not_public_users_cannot_see( conference, rank_request_factory, rank_submission_factory, graphql_client, user, mock_users, ): graphql_client.force_login(user) rank_request = rank_request_factory(conference=conference, is_public=False) rank_submission = rank_submission_factory( rank_request=rank_request, submission__speaker_id=10 ) query = """ query($code: String!, $topic: ID!) { conference(code: $code) { ranking(topic: $topic) { isPublic rankedSubmissions { rank score } } } } """ resp = graphql_client.query( query, variables={ "code": conference.code, "topic": rank_submission.submission.topic.id, }, ) assert "errors" not in resp assert resp["data"]["conference"]["ranking"] is None

26.721154

88

0.538143

497

5,558

5.762575

0.15493

0.107542

0.050279

0.061103

0.863827

0.860684

0.832402

0.814595

0.794693

0

0.00168

0.357503

5,558

207

89

26.850242

0.800336

0

0.59887

0

0.338251

0.006657

0

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1

0.033898

false

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0.016949

0

0.050847

0

null

0

1

0

null

0

6

2ad642dee260f6f0844aa84c53d6063fefd7158c

5,769

py

Python

tests/test_scheduling.py

doctorlard/nikola

d6dd2fc4af6fa0d92dfda500393bbd235b60df2a

[ "MIT" ]

null

tests/test_scheduling.py

doctorlard/nikola

d6dd2fc4af6fa0d92dfda500393bbd235b60df2a

[ "MIT" ]

null

tests/test_scheduling.py

doctorlard/nikola

d6dd2fc4af6fa0d92dfda500393bbd235b60df2a

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from base import BaseTestCase import datetime from nose.plugins.skip import SkipTest try: from freezegun import freeze_time _freeze_time = True except ImportError: _freeze_time = False freeze_time = lambda x: lambda y: y FMT = '%Y/%m/%d %H:%M:%S' NOW = '2013/08/22 10:00:00' # Thursday TODAY = datetime.datetime.strptime(NOW, FMT) RULE_TH = 'RRULE:FREQ=WEEKLY;BYDAY=TH' RULE_FR = 'RRULE:FREQ=WEEKLY;BYDAY=FR' class TestScheduling(BaseTestCase): @classmethod def setUp(self): if not _freeze_time: raise SkipTest('freezegun not installed') @freeze_time(NOW) def test_get_date(self): from nikola.plugins.command.new_post import get_date #### NOW does not match rule ######################################### ## No last date expected = TODAY.replace(day=23).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_FR)) self.assertEqual(expected, get_date(True, RULE_FR, force_today=True)) ## Last date in the past; doesn't match rule date = TODAY.replace(hour=7) expected = TODAY.replace(day=23, hour=7).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_FR, date)) self.assertEqual(expected, get_date(True, RULE_FR, date, True)) ## Last date in the future; doesn't match rule date = TODAY.replace(day=24, hour=7) expected = TODAY.replace(day=30, hour=7).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_FR, date)) self.assertEqual(expected, get_date(True, RULE_FR, date, True)) ## Last date in the past; matches rule date = TODAY.replace(day=16, hour=8) expected = TODAY.replace(day=23, hour=8).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_FR, date)) self.assertEqual(expected, get_date(True, RULE_FR, date, True)) ## Last date in the future; matches rule date = TODAY.replace(day=23, hour=18) expected = TODAY.replace(day=30, hour=18).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_FR, date)) self.assertEqual(expected, get_date(True, RULE_FR, date, True)) #### NOW matches rule ################################################ ## Not scheduling should return NOW self.assertEqual(NOW, get_date(False, RULE_TH)) ## No last date self.assertEqual(NOW, get_date(True, RULE_TH)) self.assertEqual(NOW, get_date(True, RULE_TH, force_today=True)) ## Last date in the past; doesn't match rule ### Corresponding time has already passed, today date = TODAY.replace(day=21, hour=7) expected = TODAY.replace(day=29, hour=7).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH, date)) expected = TODAY.replace(day=22, hour=7).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH, date, True)) ### Corresponding time has not passed today date = TODAY.replace(day=21, hour=18) expected = TODAY.replace(day=22, hour=18).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH, date)) self.assertEqual(expected, get_date(True, RULE_TH, date, True)) ## Last date in the future; doesn't match rule ### Corresponding time has already passed, today date = TODAY.replace(day=24, hour=7) expected = TODAY.replace(day=29, hour=7).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH, date)) self.assertEqual(expected, get_date(True, RULE_TH, date, True)) ### Corresponding time has not passed today date = TODAY.replace(day=24, hour=18) expected = TODAY.replace(day=29, hour=18).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH, date)) self.assertEqual(expected, get_date(True, RULE_TH, date, True)) ## Last date in the past; matches rule ### Corresponding time has already passed, today date = TODAY.replace(day=15, hour=7) expected = TODAY.replace(day=29, hour=7).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH, date)) expected = TODAY.replace(day=22, hour=7).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH, date, True)) ### Corresponding time has already passed, today; rule specifies HOUR date = TODAY.replace(day=15, hour=7) expected = TODAY.replace(day=29, hour=9).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH + ';BYHOUR=9', date)) expected = TODAY.replace(day=22, hour=9).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH + ';BYHOUR=9', date, True)) ### Corresponding time has not passed today date = TODAY.replace(day=15, hour=18) expected = TODAY.replace(day=22, hour=18).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH, date)) self.assertEqual(expected, get_date(True, RULE_TH, date, True)) ## Last date in the future; matches rule ### Corresponding time has already passed, today date = TODAY.replace(day=29, hour=7) expected = TODAY.replace(day=5, month=9, hour=7).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH, date)) ### Corresponding time has not passed today date = TODAY.replace(day=22, hour=18) expected = TODAY.replace(day=29, hour=18).strftime(FMT) self.assertEqual(expected, get_date(True, RULE_TH, date)) self.assertEqual(expected, get_date(True, RULE_TH, date, True)) if __name__ == '__main__': import unittest unittest.main()

45.425197

79

0.647253

785

5,769

4.642038

0.133758

0.090011

0.119374

0.81449

0.799122

0.749726

0.738474

0.706092

0.69786

0

0.0253

0.218929

5,769

126

80

45.785714

0.7834

0.148726

0

0.413793

0

0.028769

0.01092

0

0.344828

1

0.022989

false

0

0.091954

0

0.126437

0

null

0

1

0

null

0

6

632ebe6d66f808b73ae3be3237f970090dffac0c

36,672

py

Python

src/PythonUnitTests/UFUNCTests/UFUNC_FLOAT_Tests.py

thild/numpy.net

1a607cfb42263f92314a1e8dbec6f5436a7feb73

[ "BSD-3-Clause" ]

59

2019-01-20T19:43:05.000Z

2022-03-26T06:08:51.000Z

src/PythonUnitTests/UFUNCTests/UFUNC_FLOAT_Tests.py

thild/numpy.net

1a607cfb42263f92314a1e8dbec6f5436a7feb73

[ "BSD-3-Clause" ]

21

2019-06-06T17:45:01.000Z

2022-03-30T10:37:24.000Z

src/PythonUnitTests/UFUNCTests/UFUNC_FLOAT_Tests.py

thild/numpy.net

1a607cfb42263f92314a1e8dbec6f5436a7feb73

[ "BSD-3-Clause" ]

7

2019-05-12T21:06:18.000Z

2022-02-13T12:23:23.000Z

import unittest import numpy as np class Test_UFUNC_FLOAT_Tests(unittest.TestCase): #region UFUNC FLOAT Tests #region OUTER Tests def test_AddOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.add.outer(a1,a2) print(b) def test_SubtractOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.subtract.outer(a1,a2) print(b) def test_MultiplyOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.multiply.outer(a1,a2) print(b) def test_DivideOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.divide.outer(a1,a2) print(b) def test_RemainderOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.remainder.outer(a1,a2) print(b) def test_FModOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.fmod.outer(a1,a2) print(b) def test_SquareOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.square.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_ReciprocalOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.reciprocal.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_OnesLikeOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.ones_like.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_SqrtOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.sqrt.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_NegativeOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.negative.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_AbsoluteOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.absolute.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_InvertOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.invert.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_LeftShiftOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.left_shift.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_RightShiftOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.right_shift.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseAndOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.bitwise_and.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseOrOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.bitwise_or.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseXorOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.bitwise_xor.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_LessOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.less.outer(a1,a2) print(b) def test_LessEqualOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.less_equal.outer(a1,a2) print(b) def test_EqualOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.equal.outer(a1,a2) print(b) def test_NotEqualOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.not_equal.outer(a1,a2) print(b) def test_GreaterOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.greater.outer(a1,a2) print(b) def test_GreaterEqualOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.greater_equal.outer(a1,a2) print(b) def test_FloorDivideOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.floor_divide.outer(a1,a2) print(b) def test_TrueDivideOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.true_divide.outer(a1,a2) print(b) def test_LogicalAndOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.logical_and.outer(a1,a2) print(b) def test_LogicalOrOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.logical_or.outer(a1,a2) print(b) def test_FloorOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.floor.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_CeilOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.ceil.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_MaximumOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.maximum.outer(a1,a2) print(b) def test_MinimumOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.minimum.outer(a1,a2) print(b) def test_RintOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.rint.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_ConjugateOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.conjugate.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_IsNANOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); try : b = np.isnan.outer(a1,a2) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_FMaxOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.fmax.outer(a1,a2) print(b) def test_FMinOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.fmin.outer(a1,a2) print(b) def test_HeavisideOuter_FLOAT(self): a1 = np.arange(0, 5, dtype=np.float32); a2 = np.arange(3, 8, dtype=np.float32); b = np.heaviside.outer(a1,a2) print(b) #endregion #region REDUCE Tests def test_AddReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.add.reduce(a1) print(b) def test_SubtractReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.subtract.reduce(a1) print(b) def test_MultiplyReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.multiply.reduce(a1) print(b) def test_DivideReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.divide.reduce(a1) print(b) def test_RemainderReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.remainder.reduce(a1) print(b) def test_FModReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.fmod.reduce(a1) print(b) def test_SquareReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.square.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_ReciprocalReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.reciprocal.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_OnesLikeReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.ones_like.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_SqrtReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.sqrt.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_NegativeReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.negative.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_AbsoluteReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.absolute.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_InvertReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.invert.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_LeftShiftReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.left_shift.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_RightShiftReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.right_shift.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseAndReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.bitwise_and.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseOrReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.bitwise_or.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseXorReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.bitwise_xor.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_LessReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.less.reduce(a1) print(b) def test_LessEqualReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.less_equal.reduce(a1) print(b) def test_EqualReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.equal.reduce(a1) print(b) def test_NotEqualReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.not_equal.reduce(a1) print(b) def test_GreaterReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.greater.reduce(a1) print(b) def test_GreaterEqualReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.greater_equal.reduce(a1) print(b) def test_FloorDivideReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.floor_divide.reduce(a1) print(b) def test_TrueDivideReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.true_divide.reduce(a1) print(b) def test_LogicalAndReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.logical_and.reduce(a1) print(b) def test_LogicalOrReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.logical_or.reduce(a1) print(b) def test_FloorReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.floor.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_CeilReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.ceil.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_MaximumReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.maximum.reduce(a1) print(b) def test_MinimumReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.minimum.reduce(a1) print(b) def test_RintReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.rint.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_ConjugateReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.conjugate.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_IsNANReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); try : b = np.isnan.reduce(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_FMaxReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.fmax.reduce(a1) print(b) def test_FMinReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.fmin.reduce(a1) print(b) def test_HeavisideReduce_FLOAT(self): a1 = np.arange(0, 100, dtype=np.float32).reshape((10,10)); b = np.heaviside.reduce(a1) print(b) #endregion #region ACCUMULATE Tests def test_AddAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.add.accumulate(a1) print(b) def test_SubtractAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.subtract.accumulate(a1) print(b) def test_MultiplyAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.multiply.accumulate(a1) print(b) def test_DivideAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.divide.accumulate(a1) print(b) def test_RemainderAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.remainder.accumulate(a1) print(b) def test_FModAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.fmod.accumulate(a1) print(b) def test_SquareAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.square.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_ReciprocalAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.reciprocal.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_OnesLikeAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.ones_like.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_SqrtAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.sqrt.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_NegativeAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.negative.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_AbsoluteAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.absolute.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_InvertAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.invert.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_LeftShiftAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.left_shift.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_RightShiftAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.right_shift.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseAndAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.bitwise_and.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseOrAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.bitwise_or.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseXorAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.bitwise_xor.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_LessAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.less.accumulate(a1) print(b) def test_LessEqualAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.less_equal.accumulate(a1) print(b) def test_EqualAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.equal.accumulate(a1) print(b) def test_NotEqualAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.not_equal.accumulate(a1) print(b) def test_GreaterAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.greater.accumulate(a1) print(b) def test_GreaterEqualAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.greater_equal.accumulate(a1) print(b) def test_FloorDivideAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.floor_divide.accumulate(a1) print(b) def test_TrueDivideAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.true_divide.accumulate(a1) print(b) def test_LogicalAndAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.logical_and.accumulate(a1) print(b) def test_LogicalOrAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.logical_or.accumulate(a1) print(b) def test_FloorAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.floor.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_CeilAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.ceil.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_MaximumAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.maximum.accumulate(a1) print(b) def test_MinimumAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.minimum.accumulate(a1) print(b) def test_RintAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.rint.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_ConjugateAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.conjugate.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_IsNANAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.isnan.accumulate(a1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_FMaxAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.fmax.accumulate(a1) print(b) def test_FMinAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.fmin.accumulate(a1) print(b) def test_HeavisideAccumulate_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.heaviside.accumulate(a1) print(b) #endregion #region REDUCEAT FLOAT Tests def test_AddReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.add.reduceat(a1, [0, 2], axis = 1) print(b) def test_SubtractReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.subtract.reduceat(a1, [0, 2], axis = 1) print(b) def test_MultiplyReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.multiply.reduceat(a1, [0, 2], axis = 1) print(b) def test_DivideReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.divide.reduceat(a1, [0, 2], axis = 1) print(b) def test_RemainderReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.remainder.reduceat(a1, [0, 2], axis = 1) print(b) def test_FModReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.fmod.reduceat(a1, [0, 2], axis = 1) print(b) def test_SquareReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.square.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_ReciprocalReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.reciprocal.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_OnesLikeReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.ones_like.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_SqrtReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.sqrt.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_NegativeReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.negative.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_AbsoluteReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.absolute.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_InvertReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.invert.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_LeftShiftReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.left_shift.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_RightShiftReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.right_shift.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseAndReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.bitwise_and.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseOrReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.bitwise_or.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_BitwiseXorReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.bitwise_xor.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_LessReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.less.reduceat(a1, [0, 2], axis = 1) print(b) def test_LessEqualReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.less_equal.reduceat(a1, [0, 2], axis = 1) print(b) def test_EqualReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.equal.reduceat(a1, [0, 2], axis = 1) print(b) def test_NotEqualReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.not_equal.reduceat(a1, [0, 2], axis = 1) print(b) def test_GreaterReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.greater.reduceat(a1, [0, 2], axis = 1) print(b) def test_GreaterEqualReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.greater_equal.reduceat(a1, [0, 2], axis = 1) print(b) def test_FloorDivideReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.floor_divide.reduceat(a1, [0, 2], axis = 1) print(b) def test_TrueDivideReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.true_divide.reduceat(a1, [0, 2], axis = 1) print(b) def test_LogicalAndReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.logical_and.reduceat(a1, [0, 2], axis = 1) print(b) def test_LogicalOrReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.logical_or.reduceat(a1, [0, 2], axis = 1) print(b) def test_FloorReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.floor.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_CeilReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.ceil.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_MaximumReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.maximum.reduceat(a1, [0, 2], axis = 1) print(b) def test_MinimumReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.minimum.reduceat(a1, [0, 2], axis = 1) print(b) def test_RintReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.rint.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_ConjugateReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.conjugate.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_IsNANReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); try : b = np.isnan.reduceat(a1, [0, 2], axis = 1) print(b) self.fail("should have thrown exception") except: print("Exception occured") def test_FMaxReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.fmax.reduceat(a1, [0, 2], axis = 1) print(b) def test_FMinReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.fmin.reduceat(a1, [0, 2], axis = 1) print(b) def test_HeavisideReduceAt_FLOAT(self): a1 = np.arange(0, 9, dtype=np.float32).reshape((3,3)); b = np.heaviside.reduceat(a1, [0, 2], axis = 1) print(b) #endregion #endregion if __name__ == '__main__': unittest.main()

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Python

Udemy/duce/__init__.py

nagarajpandith/slack-udemy-bot

88d4d8755f3afd726455e35b6ce7c8c287aa9492

[ "MIT" ]

3

2021-07-30T08:07:29.000Z

2021-11-15T12:04:47.000Z

Udemy/duce/__init__.py

nagarajpandith/slack-udemy-bot

88d4d8755f3afd726455e35b6ce7c8c287aa9492

[ "MIT" ]

null

Udemy/duce/__init__.py

nagarajpandith/slack-udemy-bot

88d4d8755f3afd726455e35b6ce7c8c287aa9492

[ "MIT" ]

null

from udemy.duce.duce import Scrapper

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py

Python

examples/examples.py

QAR-Lab/uqoclient

21efbcd718598ef1c3307de4384e8440de1db53b

[ "MIT" ]

2

2021-08-19T12:23:25.000Z

2022-02-02T08:54:38.000Z

examples/examples.py

QAR-Lab/uqoclient

21efbcd718598ef1c3307de4384e8440de1db53b

[ "MIT" ]

2

2021-02-04T10:44:38.000Z

2022-03-25T09:55:15.000Z

examples/examples.py

QAR-Lab/uqoclient

21efbcd718598ef1c3307de4384e8440de1db53b

[ "MIT" ]

5

2021-02-04T20:58:12.000Z

2021-11-28T13:33:41.000Z

from ..client.connection import Connection from . import graph_coloring_example as graphcoloring from .. import Problem from matplotlib import pyplot as plt import numpy as np # Example QUBO with some reward and penalty values example_qubo = {(0, 0): -2, (1, 1): -2, (2, 2): -2, (3, 3): -2, (4, 4): -2, (5, 5): -2, (0, 1): 5, (0, 2): 5, (0, 4): 5, (1, 3): 5, (1, 5): 5, (2, 3): 5, (2, 4): 5, (3, 5): 5, (4, 5): 5} # Ising problem with some example values for external magnetic field (example_ising_h) and interaction (example_ising_J) example_ising_h, example_ising_J = {1: 1, 2: 2, 3: 3}, {(1, 2): 4, (1, 3): 5, (2, 3): 6} def ping(config): """Send a ping message to the server. Parameters ---------- config The config object that contains the users configuration data """ con = config.create_connection() print(con.ping()) def qbsolv_example_qubo(config): """Solve the QUBO example with QBSolv. Parameters ---------- config The config object that contains the users configuration data """ test_dict = {"test_parameter": {(0, 1): 5, (1, 2): 3}} answer = Problem.Qubo(config, example_qubo).with_platform("qbsolv").with_params(**test_dict).solve(100) # ----------- # These calls return arrays containing the raw information in lists # ----------- # answer.solutions # answer.energies # answer.num_occurrences # ----------- # These functions will print the received information in different ways # ----------- # answer.print_solutions() # answer.print_energies() # answer.print_num_occurrences() answer.print_solutions_nice() def qbsolv_example_ising(config): """Solve the Ising example with QBSolv. Parameters ---------- config The config object that contains the users configuration data """ # Create the Ising representation of a problem we want to solve answer = Problem.Ising(config, example_ising_h, example_ising_J).with_platform("qbsolv").solve(48) # ----------- # These calls return arrays containing the raw information in lists # ----------- # answer.solutions # answer.energies # answer.num_occurrences # ----------- # These functions will print the received information in different ways # ----------- # answer.print_solutions() # answer.print_energies() # answer.print_num_occurrences() answer.print_solutions_nice() def dwave_example_qubo(config): """Solve the QUBO example with the quantum solver 'DW_2000Q_6' from DWave. Parameters ---------- config The config object that contains the users configuration data """ answer = Problem.Qubo(config, example_qubo).with_platform("dwave").with_solver("DW_2000Q_6").solve(1) # ----------- # These calls return arrays containing the raw information in lists # ----------- # answer.solutions # answer.energies # answer.num_occurrences # ----------- # These functions will print the received information in different ways # ----------- # answer.print_solutions() # answer.print_energies() # answer.print_num_occurrences() answer.print_solutions_nice() def dwave_example_ising(config): """Solve the Ising example with the quantum solver 'DW_2000Q_6' from DWave. Parameters ---------- config The config object that contains the users configuration data """ answer = Problem.Ising(config, example_ising_h, example_ising_J).with_platform("dwave") \ .with_solver("DW_2000Q_6").solve(1) # ----------- # These calls return arrays containing the raw information in lists # ----------- # answer.solutions # answer.energies # answer.num_occurrences # ----------- # These functions will print the received information in different ways # ----------- # answer.print_solutions() # answer.print_energies() # answer.print_num_occurrences() answer.print_solutions_nice() def get_dwave_solvers(config): """Show all available solvers from DWave. """ connection = config.create_connection() print(connection.get_available_dwave_solvers()) def show_quota(config): """Print the users remaining quota. """ connection = config.create_connection() connection.show_quota() def graph_coloring_example(config): """Process an example of the graph coloring problem as a QUBO by using QBSolv. """ QUBO = graphcoloring.create_qubo() answer = Problem.Qubo(config, QUBO).with_platform("qbsolv").solve(2) for index, solution in enumerate(answer.solutions): graphcoloring.show_solution(solution, "graph_coloring_solution_" + str(index)) def find_chimera_embedding_example(config): """Find a possible Chimera embedding by calling the DW_2000Q_6 solver from DWaveand save it to embedding.pdf. """ problem = Problem.Qubo(config, example_qubo).with_platform("dwave").with_solver("DW_2000Q_6") # Note: If you generate an embedding via problem.find_chimera_embedding(), the embedding will be saved in the # variable problem.embedding - you can directly access, view and save it. problem.find_chimera_embedding() problem.draw_chimera_embedding("embedding.pdf") def find_pegasus_embedding_example(config): """Find a possible Pegasus embedding by calling the Advantage_system4.1 solver from DWave and save it to embedding.pdf. """ problem = Problem.Qubo(config, example_qubo).with_platform("dwave").with_solver("Advantage_system4.1") # Note: If you generate an embedding via problem.find_chimera_embedding(), the embedding will be saved in the # variable problem.embedding - you can directly access, view and save it. problem.find_pegasus_embedding() problem.draw_pegasus_embedding("embedding_pegasus.pdf") def dwave_example_qubo_with_custom_embedding(config): """Solve an example QUBO problem with a given embedding. """ problem = Problem.Qubo(config, example_qubo).with_platform("dwave").with_solver("Advantage_system4.1") # Note: If you generate an embedding via problem.find_chimera_embedding(), the embedding will be saved in the # variable problem.embedding - you can directly access, view and save it. problem.find_chimera_embedding() # This will automatically use the embedding generated with problem.find_chimera_embedding() # In general: if you have an embedding already - just assign this embedding to the problem.embedding variable # If you call problem.solve after, UQ will automatically use this embedding to solve the problem on the D-Wave answer = problem.solve(2) # ----------- # These calls return arrays containing the raw information in lists # ----------- # answer.solutions # answer.energies # answer.num_occurrences # ----------- # These functions will print the received information in different ways # ----------- # answer.print_solutions() # answer.print_energies() # answer.print_num_occurrences() answer.print_solutions_nice() def dwave_example_qubo_reverse_annealing(config): """ Solve a QUBO with reverse annealing (by optimizing a given solution). 1. `initial_state` specifies the classical state at which the reverse anneal should start. An initial state could look like this: {0: 1, 1: 0, 2: 0, 3: 1, 4: 0, 5: 0} 2 possibilities: - define a known solution as an initial state - compute an initial state by using forward annealing 2. `reinitialize_state` specifies whether or not the initial state should be used for every anneal in the request. If False, then after the first, each subsequent anneal starts where the previous finished. 3. `anneal_schedule` defines the annealing schedule that should be followed. A schedule starts at s=1.0, reverses to s_target, pauses for hold_time μs and then anneals forward to s=1.0. """ problem = Problem.Qubo(config, example_qubo).with_platform("dwave").with_solver("Advantage_system4.1") # 1. initial state: # define a known solution as the initial state: # initial = {0: 1, 2: 0, 1: 0, 3: 1, 4: 0, 5: 0} # or calculate an initial state initial = problem.find_initial_state(1) # 2. reinitialize_state: reinitialize_state = False # 3. data for the anneal schedule: s_target = 0.45 hold_time = 80 reverse_anneal_params = {'initial_state': initial, 'reinitialize_state': reinitialize_state, 's_target': s_target, 'hold_time': hold_time} answer = problem.with_params(**reverse_anneal_params).solve(10) # ----------- # These calls return arrays containing the raw information in lists # ----------- # answer.solutions # answer.energies # answer.num_occurrences # ----------- # These functions will print the received information in different ways # ----------- # answer.print_solutions() # answer.print_energies() # answer.print_num_occurrences() answer.print_solutions_nice() def dwave_example_ising_reverse_annealing(config): """ Solve an Ising with reverse annealing (by optimizing a given solution). 1. `initial_state` specifies the classical state at which the reverse anneal should start. An initial state could look like this: {0: 1, 1: 0, 2: 0, 3: 1, 4: 0, 5: 0} 2 possibilities: - define a known solution as an initial state - compute an initial state by using forward annealing 2. `reinitialize_state` specifies whether or not the initial state should be used for every anneal in the request. If False, then after the first, each subsequent anneal starts where the previous finished. 3. `anneal_schedule` defines the annealing schedule that should be followed. A schedule starts at s=1.0, reverses to s_target, pauses for hold_time μs and then anneals forward to s=1.0. """ problem = Problem.Ising(config, example_ising_h, example_ising_J).with_platform("dwave").with_solver("Advantage_system4.1") # 1. initial state: # define a known solution as the initial state: # initial = {0: 1, 2: 0, 1: 0, 3: 1, 4: 0, 5: 0} # or calculate an initial state initial = problem.find_initial_state(1) # 2. reinitialize_state: reinitialize_state = False # 3. data for the anneal schedule: s_target = 0.45 hold_time = 80 reverse_anneal_params = {'initial_state': initial, 'reinitialize_state': reinitialize_state, 's_target': s_target, 'hold_time': hold_time} answer = problem.with_params(**reverse_anneal_params).solve(10) # ----------- # These calls return arrays containing the raw information in lists # ----------- # answer.solutions # answer.energies # answer.num_occurrences # ----------- # These functions will print the received information in different ways # ----------- # answer.print_solutions() # answer.print_energies() # answer.print_num_occurrences() answer.print_solutions_nice() def fujistu_example_qubo(config): """ Solve the QUBO example with Fujitsu DAU. Parameters ---------- config The config object that contains the users configuration data """ solver = "CPU" # CPU or DAU number_runs = 16 parameters = { # default parameters: "number_iterations": 500, # total number of iterations per run "temperature_start": 1000.0, # start temperature as float value "temperature_end": 1.0, # end temperature as float value or None "temperature_mode": 0, # 0, 1, or 2 to define the cooling curve # 0: reduce temperature by factor (1-temperature_decay) every temperature_interval steps # 1: reduce temperature by factor (1-temperature_decay*temperature) every temperature_interval steps # 2: reduce temperature by factor (1-temperature_decay*temperature^2) every temperature_interval steps "temperature_decay": 0.001, # decay per step if temperature_end is None "temperature_interval": 100, # number of iterations keeping temperature constant "offset_increase_rate": 0.0, # increase of dynamic offset when no bit selected, set to 0.0 to switch off dynamic energy feature "solution_mode": "COMPLETE", # COMPLETE returns all runs best configuration, QUICK returns overall best configuration only "optimization_method": "annealing", # annealing or parallel tempering are supported methods "number_replicas": 26, # number of replicas for parallel tempering mode "annealer_version": 2, # Digital Annealer version "guidance_config": {}, # list of variable values that to be set for DA as a starting values of variables for annealing process for each run "auto_tuning": 0, # EXPERIMENTAL! options of automatic tuning the QUBO "bit_precision": 16, # bit precision (DAU version 2) "connection_mode": "CMODE_ASYNC" # Mode can be CMODE_ASYNC (default) or CMODE_SYNC } answer = Problem.Qubo(config, example_qubo).with_platform("fujitsu").with_solver(solver).with_params(**parameters).solve(number_runs) # ----------- # These calls return arrays containing the raw information in lists # ----------- # answer.solutions # answer.energies # answer.num_occurrences # ----------- # These functions will print the received information in different ways # ----------- # answer.print_solutions() # answer.print_energies() # answer.print_num_occurrences() answer.print_solutions_nice() def fujistu_example_ising(config): """ Fujitsu DAU can only solve QUBOs. Translate Ising to QUBO and send the QUBO to the Fujitsu DAU. There may be an offset, that have to be applied to the energies to obtain the correct Ising energies. Parameters ---------- config The config object that contains the users configuration data """ solver = "CPU" # CPU or DAU number_runs = 16 # number of stochastically independent runs parameters = { # default parameters: "number_iterations": 500, # total number of iterations per run "temperature_start": 1000.0, # start temperature as float value "temperature_end": 1.0, # end temperature as float value or None "temperature_mode": 0, # 0, 1, or 2 to define the cooling curve # 0: reduce temperature by factor (1-temperature_decay) every temperature_interval steps # 1: reduce temperature by factor (1-temperature_decay*temperature) every temperature_interval steps # 2: reduce temperature by factor (1-temperature_decay*temperature^2) every temperature_interval steps "temperature_decay": 0.001, # decay per step if temperature_end is None "temperature_interval": 100, # number of iterations keeping temperature constant "offset_increase_rate": 0.0, # increase of dynamic offset when no bit selected, set to 0.0 to switch off dynamic energy feature "solution_mode": "COMPLETE", # COMPLETE returns all runs best configuration, QUICK returns overall best configuration only "optimization_method": "annealing", # annealing or parallel tempering are supported methods "number_replicas": 26, # number of replicas for parallel tempering mode "annealer_version": 2, # Digital Annealer version "guidance_config": {}, # list of variable values that to be set for DA as a starting values of variables for annealing process for each run "auto_tuning": 0, # EXPERIMENTAL! options of automatic tuning the QUBO "bit_precision": 16, # bit precision (DAU version 2) "connection_mode": "CMODE_ASYNC" # Mode can be CMODE_ASYNC (default) or CMODE_SYNC } answer = Problem.Ising(config, example_ising_h, example_ising_J).with_platform("fujitsu").with_solver(solver).with_params(**parameters).solve(number_runs) # ----------- # These calls return arrays containing the raw information in lists # ----------- # answer.solutions # answer.energies # answer.num_occurrences # answer.apply_energy_offset(offset) # answer.print_energies() # ----------- # These functions will print the received information in different ways # ----------- # answer.print_solutions() # answer.print_energies() # answer.print_num_occurrences() answer.print_solutions_nice()

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py

Python

tests/test_module.py

trickeydan/pepper2

3aba1c74568cd0a04c9178caba26e9238c90e9ba

[ "MIT" ]

null

tests/test_module.py

trickeydan/pepper2

3aba1c74568cd0a04c9178caba26e9238c90e9ba

[ "MIT" ]

22

2019-12-22T20:11:24.000Z

2020-01-18T19:09:11.000Z

tests/test_module.py

j5api/pepper2

3aba1c74568cd0a04c9178caba26e9238c90e9ba

[ "MIT" ]

null

"""Test that the pepper2 imports as expected.""" import pepper2 def test_module() -> None: """Test that the module behaves as expected.""" assert pepper2.__version__ is not None

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6

2df4b4503fa27203126c335d385521aa2f193de7

166

py

Python

app/views/login.py

davimiku/squawker

968de5b82ddc1d610af248a70840533c9d7b5cb9

[ "MIT" ]

null

app/views/login.py

davimiku/squawker

968de5b82ddc1d610af248a70840533c9d7b5cb9

[ "MIT" ]

null

app/views/login.py

davimiku/squawker

968de5b82ddc1d610af248a70840533c9d7b5cb9

[ "MIT" ]

null

from app import app from flask import request, redirect, url_for, session, render_template @app.route('/login') def login(): return render_template('login.html')

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6

2df4cbc63f9c36553e3ae1c174977f9fa68a4e09

131

py

Python

examples/demo/orders/forms.py

bartels/satchless

4d333014333dc4fd5815f9e0bbea565959919a30

[ "BSD-4-Clause" ]

1

2015-11-05T10:26:46.000Z

examples/demo/orders/forms.py

bartels/satchless

4d333014333dc4fd5815f9e0bbea565959919a30

[ "BSD-4-Clause" ]

null

examples/demo/orders/forms.py

bartels/satchless

4d333014333dc4fd5815f9e0bbea565959919a30

[ "BSD-4-Clause" ]

null

import django.forms from django.forms.models import modelformset_factory import satchless.order.forms from .app import order_app

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45

py

Python

smithers/io/openfoam/__init__.py

LauraMeneghetti/Smithers

908fb3201eabfd01fe1712352c605e1f4b15d896

[ "MIT" ]

6

2021-04-09T10:00:12.000Z

2022-02-02T05:13:42.000Z

smithers/io/openfoam/__init__.py

LauraMeneghetti/Smithers

908fb3201eabfd01fe1712352c605e1f4b15d896

[ "MIT" ]

5

2021-05-06T14:43:22.000Z

2021-09-30T15:29:59.000Z

smithers/io/openfoam/__init__.py

LauraMeneghetti/Smithers

908fb3201eabfd01fe1712352c605e1f4b15d896

[ "MIT" ]

6

2021-05-06T12:06:57.000Z

2022-03-29T07:08:46.000Z

from .openfoamhandler import OpenFoamHandler

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py

Python

mabooia/reactive/__init__.py

Mabooia/mabooia-py

58da8d74a9a544a208ea4bd35ae017896917aa71

[ "MIT" ]

null

mabooia/reactive/__init__.py

Mabooia/mabooia-py

58da8d74a9a544a208ea4bd35ae017896917aa71

[ "MIT" ]

null

mabooia/reactive/__init__.py

Mabooia/mabooia-py

58da8d74a9a544a208ea4bd35ae017896917aa71

[ "MIT" ]

null

from .observables import *

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1

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6

fab06d28ea8076169bf98f140077fa49211d608d

257

py

Python

examples/nosec.py

magnologan/bandit

e2594a9bab6064947913383fec12096d8f9475b4

[ "Apache-2.0" ]

2

2021-12-19T14:09:27.000Z

2021-12-22T06:47:04.000Z

examples/nosec.py

magnologan/bandit

e2594a9bab6064947913383fec12096d8f9475b4

[ "Apache-2.0" ]

null

examples/nosec.py

magnologan/bandit

e2594a9bab6064947913383fec12096d8f9475b4

[ "Apache-2.0" ]

2

2015-04-03T02:59:00.000Z

2018-03-14T09:25:10.000Z

subprocess.Popen('/bin/ls *', shell=True) #nosec (on the line) subprocess.Popen('/bin/ls *', #nosec (at the start of function call) shell=True) subprocess.Popen('/bin/ls *', shell=True) #nosec (on the specific kwarg line)

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1

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1

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null

0

1

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6

fab7574076ce93d349fe124e947790e62ffd8ea0

214

py

Python

clockify_api_client/factories/tag_factory.py

jeroenpeters1986/clockify-api-aclient

212a3e200ee0f434ef3b2390a7f438493c5baeec

[ "MIT" ]

null

clockify_api_client/factories/tag_factory.py

jeroenpeters1986/clockify-api-aclient

212a3e200ee0f434ef3b2390a7f438493c5baeec

[ "MIT" ]

null

clockify_api_client/factories/tag_factory.py

jeroenpeters1986/clockify-api-aclient

212a3e200ee0f434ef3b2390a7f438493c5baeec

[ "MIT" ]

null

from clockify_api_client.factories.abstract_factory import AbstractFactory from clockify_api_client.models.tag import Tag class TagFactory(AbstractFactory): class Meta: model = Tag api_key = None

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1

0

6

fac2e2f9538503e946f0be2f0ec1756e81081769

210

py

Python

mmfewshot/detection/__init__.py

BIGWangYuDong/mmfewshot

dac097afc92df176bc2de76b7c90968584865197

[ "Apache-2.0" ]

376

2021-11-23T13:29:57.000Z

2022-03-30T07:22:14.000Z

mmfewshot/detection/__init__.py

BIGWangYuDong/mmfewshot

dac097afc92df176bc2de76b7c90968584865197

[ "Apache-2.0" ]

51

2021-11-23T14:45:08.000Z

2022-03-30T03:37:15.000Z

mmfewshot/detection/__init__.py

BIGWangYuDong/mmfewshot

dac097afc92df176bc2de76b7c90968584865197

[ "Apache-2.0" ]

56

2021-11-23T14:02:27.000Z

2022-03-31T09:01:50.000Z

# Copyright (c) OpenMMLab. All rights reserved. from .apis import * # noqa: F401,F403 from .core import * # noqa: F401,F403 from .datasets import * # noqa: F401,F403 from .models import * # noqa: F401,F403

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faca512026fcc3f3d9af2cb6819688c986e2e25a

1,546

py

Python

wineapp/migrations/0006_auto_20190217_1123.py

umangrules/Wine-Connoisseur

3a5fa6f7a269d1a724150f81157201d7519e8749

[ "MIT" ]

6

2019-02-20T16:34:11.000Z

2020-10-03T14:11:32.000Z

wineapp/migrations/0006_auto_20190217_1123.py

umangrules/Wine-Connoisseur

3a5fa6f7a269d1a724150f81157201d7519e8749

[ "MIT" ]

1

2020-10-03T16:09:53.000Z

2020-10-05T02:32:24.000Z

wineapp/migrations/0006_auto_20190217_1123.py

umangrules/Wine-Connoisseur

3a5fa6f7a269d1a724150f81157201d7519e8749

[ "MIT" ]

5

2020-10-03T12:22:15.000Z

2020-11-16T11:07:54.000Z

# Generated by Django 2.1.7 on 2019-02-17 11:23 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('wineapp', '0005_auto_20190217_1113'), ] operations = [ migrations.AlterField( model_name='contact', name='country', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='contact', name='description', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='contact', name='designaion', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='contact', name='province', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='contact', name='region_1', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='contact', name='region_2', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='contact', name='variety', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='contact', name='winery', field=models.TextField(blank=True, null=True), ), ]

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1,546

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false

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0

1

0

1

0

null

0

6

facf83f40ee27569111e35f91e21cfbed55f4287

126

py

Python

Statistics/StandardDev.py

rn44/statsCalculator

b827edfc49d9251c7490ede3e578b58b49d9bb13

[ "MIT" ]

null

Statistics/StandardDev.py

rn44/statsCalculator

b827edfc49d9251c7490ede3e578b58b49d9bb13

[ "MIT" ]

null

Statistics/StandardDev.py

rn44/statsCalculator

b827edfc49d9251c7490ede3e578b58b49d9bb13

[ "MIT" ]

2

2020-11-07T03:34:17.000Z

2020-12-10T16:34:55.000Z

from Calculator.Sqroot import sqroot from Statistics.Variance import variance def stddev(a): return sqroot(variance(a))

18

40

0.785714

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126

5.823529

0.588235

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false

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null

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0

null

0

1

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1

0

6

878e959215382f5f686fc82e10e19b64b11db04d

83

py

Python

minibuild/__init__.py

minibuild/minibuild

478e242f4e7b8df47116ed0b08cc66a7845a6d5e

[ "MIT" ]

null

minibuild/__init__.py

minibuild/minibuild

478e242f4e7b8df47116ed0b08cc66a7845a6d5e

[ "MIT" ]

null

minibuild/__init__.py

minibuild/minibuild

478e242f4e7b8df47116ed0b08cc66a7845a6d5e

[ "MIT" ]

3

2018-05-13T13:59:14.000Z

2018-07-08T10:19:09.000Z

from .error_utils import BuildSystemException from .script_main import script_main

27.666667

45

0.879518

11

83

6.363636

0.636364

0.285714

0

0.096386

83

2

46

41.5

0.933333

0

1

0

true

0

1

0

1

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1

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null

1

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1

0

null

0

1

0

1

0

1

0

6

87913572eb5d97900eb8389a225e39d6d669bbcc

1,361

py

Python

backend/api/migrations/0002_auto_20210203_2219.py

oaguy1/wishlist.vote

3440cc05cfe039e905064fd2dfcb0ae06402e12c

[ "MIT" ]

null

backend/api/migrations/0002_auto_20210203_2219.py

oaguy1/wishlist.vote

3440cc05cfe039e905064fd2dfcb0ae06402e12c

[ "MIT" ]

null

backend/api/migrations/0002_auto_20210203_2219.py

oaguy1/wishlist.vote

3440cc05cfe039e905064fd2dfcb0ae06402e12c

[ "MIT" ]

null

# Generated by Django 3.1.6 on 2021-02-03 22:19 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('api', '0001_initial'), ] operations = [ migrations.AlterField( model_name='listitem', name='created_at', field=models.DateTimeField(auto_now_add=True), ), migrations.AlterField( model_name='listitem', name='updated_at', field=models.DateTimeField(auto_now=True), ), migrations.AlterField( model_name='upvote', name='created_at', field=models.DateTimeField(auto_now_add=True), ), migrations.AlterField( model_name='upvote', name='updated_at', field=models.DateTimeField(auto_now=True), ), migrations.AlterField( model_name='wishlist', name='created_at', field=models.DateTimeField(auto_now_add=True), ), migrations.AlterField( model_name='wishlist', name='description', field=models.TextField(blank=True), ), migrations.AlterField( model_name='wishlist', name='updated_at', field=models.DateTimeField(auto_now=True), ), ]

27.77551

58

0.555474

126

1,361

5.81746

0.333333

0.190996

0.238745

0.276944

0.763984

0.702592

0.603001

0.537517

0

0.020925

0.332844

1,361

48

59

28.354167

0.786344

0.033064

0

0.785714

1

0

0.105023

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1

0

false

0

0.02381

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0.095238

0

null

0

1

0

1

0

1

0

null

0

6

87b5bbf9233c5d7db33a004f58740eb7eaa49178

44

py

Python

src/spaceone/identity/error/__init__.py

choonho/identity

e56263e06e04b4d3ea7b230026c9b2db4e0be685

[ "Apache-2.0" ]

1

2020-07-31T09:42:02.000Z

src/spaceone/identity/error/__init__.py

choonho/identity

e56263e06e04b4d3ea7b230026c9b2db4e0be685

[ "Apache-2.0" ]

null

src/spaceone/identity/error/__init__.py

choonho/identity

e56263e06e04b4d3ea7b230026c9b2db4e0be685

[ "Apache-2.0" ]

null

from spaceone.identity.error.custom import *

44

0.840909

6

44

6.166667

1

0

0.068182

44

1

44

0.902439

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e215b2aba20a92256b583e9de6bd12d6d1ab1129

154

py

Python

project/context_processors.py

sgleisner/django-project-template

1ec8ef798fa0d203fe9d875481a3063c678ed7d6

[ "MIT" ]

13

2015-03-05T07:56:49.000Z

2022-01-13T01:12:46.000Z

project/context_processors.py

sgleisner/django-project-template

1ec8ef798fa0d203fe9d875481a3063c678ed7d6

[ "MIT" ]

36

2020-02-14T21:12:04.000Z

2022-02-21T13:49:37.000Z

project/context_processors.py

sgleisner/django-project-template

1ec8ef798fa0d203fe9d875481a3063c678ed7d6

[ "MIT" ]

12

2016-06-20T23:18:15.000Z

2022-01-04T20:11:53.000Z

from django.conf import settings def google_analytics_code(request): return { 'google_analytics_code': settings.GOOGLE_ANALYTICS_CODE }

19.25

63

0.746753

18

154

6.055556

0.611111

0.412844

0.522936

0

0.188312

154

7

64

22

0.872

0

0.136364

0

1

0.2

false

0

0.2

0.6

0

1

0

null

1

0

1

0

null

0

1

0

6

3567cc0428c89fabd8c71dede881bd88d55a8656

156

py

Python

src/sage/tensor/all.py

bopopescu/sage-5

9d85b34956ca2edd55af307f99c5d3859acd30bf

[ "BSL-1.0" ]

5

2015-01-04T07:15:06.000Z

2022-03-04T15:15:18.000Z

src/sage/tensor/all.py

bopopescu/sage-5

9d85b34956ca2edd55af307f99c5d3859acd30bf

[ "BSL-1.0" ]

null

src/sage/tensor/all.py

bopopescu/sage-5

9d85b34956ca2edd55af307f99c5d3859acd30bf

[ "BSL-1.0" ]

10

2016-09-28T13:12:40.000Z

2022-02-12T09:28:34.000Z

from coordinate_patch import CoordinatePatch from differential_forms import DifferentialForms from differential_form_element import DifferentialForm, wedge

39

61

0.910256

17

156

8.117647

0.705882

0.231884

0

0.083333

156

3

62

52

0.965035

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

35bc0ea344c9a971b6bcf5ddcbd0b49a0a3ab2e0

2,264

py

Python

tests/terraform/checks/resource/aws/test_MQBrokerNotPubliclyExposed.py

antonblr/checkov

9415c6593c537945c08f7a19f28bdd8b96966f67

[ "Apache-2.0" ]

4,013

2019-12-09T13:16:54.000Z

2022-03-31T14:31:01.000Z

tests/terraform/checks/resource/aws/test_MQBrokerNotPubliclyExposed.py

antonblr/checkov

9415c6593c537945c08f7a19f28bdd8b96966f67

[ "Apache-2.0" ]

1,258

2019-12-17T09:55:51.000Z

2022-03-31T19:17:17.000Z

tests/terraform/checks/resource/aws/test_MQBrokerNotPubliclyExposed.py

antonblr/checkov

9415c6593c537945c08f7a19f28bdd8b96966f67

[ "Apache-2.0" ]

638

2019-12-19T08:57:38.000Z

2022-03-30T21:38:37.000Z

import hcl2 import unittest from checkov.terraform.checks.resource.aws.MQBrokerNotPubliclyExposed import check from checkov.common.models.enums import CheckResult class TestMQBrokerNotPubliclyExposed(unittest.TestCase): def test_failure_mqbroker_logging(self): hcl_res = hcl2.loads(""" resource "aws_mq_broker" "example" { broker_name = "example" engine_type = "ActiveMQ" engine_version = "5.15.0" host_instance_type = "mq.t2.micro" publicly_accessible = true user { username = "ExampleUser" password = "MindTheGap" } } """) resource_conf = hcl_res['resource'][0]['aws_mq_broker']['example'] scan_result = check.scan_resource_conf(conf=resource_conf) self.assertEqual(CheckResult.FAILED, scan_result) def test_success_mqbroker_logging(self): hcl_res = hcl2.loads(""" resource "aws_mq_broker" "example" { broker_name = "example" engine_type = "ActiveMQ" engine_version = "5.15.0" host_instance_type = "mq.t2.micro" publicly_accessible = false user { username = "ExampleUser" password = "MindTheGap" } } """) resource_conf = hcl_res['resource'][0]['aws_mq_broker']['example'] scan_result = check.scan_resource_conf(conf=resource_conf) self.assertEqual(CheckResult.PASSED, scan_result) def test_success_missing_mqbroker_logging(self): hcl_res = hcl2.loads(""" resource "aws_mq_broker" "example" { broker_name = "example" engine_type = "ActiveMQ" engine_version = "5.15.0" host_instance_type = "mq.t2.micro" user { username = "ExampleUser" password = "MindTheGap" } } """) resource_conf = hcl_res['resource'][0]['aws_mq_broker']['example'] scan_result = check.scan_resource_conf(conf=resource_conf) self.assertEqual(CheckResult.PASSED, scan_result) if __name__ == '__main__': unittest.main()

31.444444

82

0.581714

222

2,264

5.612613

0.274775

0.086677

0.05297

0.086677

0.797753

0.767255

0

0.014258

0.318463

2,264

71

83

31.887324

0.79326

0

0.666667

0

0.541961

0

0.052632

1

0.052632

false

0.087719

0.070175

0

0.140351

0

null

0

1

0

null

0

1

0

6

ea1dc7d4ce3fcc0f3fc2a614d7290a134d98cc88

6,015

py

Python

skidl/libs/Lattice_sklib.py

arjenroodselaar/skidl

0bf801bd3b74e6ef94bd9aa1b68eef756b568276

[ "MIT" ]

700

2016-08-16T21:12:50.000Z

2021-10-10T02:15:18.000Z

skidl/libs/Lattice_sklib.py

0dvictor/skidl

458709a10b28a864d25ae2c2b44c6103d4ddb291

[ "MIT" ]

118

2016-08-16T20:51:05.000Z

2021-10-10T08:07:18.000Z

skidl/libs/Lattice_sklib.py

0dvictor/skidl

458709a10b28a864d25ae2c2b44c6103d4ddb291

[ "MIT" ]

94

2016-08-25T14:02:28.000Z

2021-09-12T05:17:08.000Z

from skidl import SKIDL, TEMPLATE, Part, Pin, SchLib SKIDL_lib_version = '0.0.1' Lattice = SchLib(tool=SKIDL).add_parts(*[ Part(name='GAL16V8',dest=TEMPLATE,tool=SKIDL,keywords='GAL PLD 16V8',description='Programmable Logic Array, DIP-20/SOIC-20/PLCC-20',ref_prefix='U',num_units=1,fplist=['DIP*', 'PDIP*', 'SOIC*', 'SO*', 'PLCC*'],do_erc=True,pins=[ Pin(num='10',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='20',name='VCC',func=Pin.PWRIN,do_erc=True), Pin(num='1',name='I1/CLK',do_erc=True), Pin(num='2',name='I2',do_erc=True), Pin(num='3',name='I3',do_erc=True), Pin(num='4',name='I4',do_erc=True), Pin(num='5',name='I5',do_erc=True), Pin(num='6',name='I6',do_erc=True), Pin(num='7',name='I7',do_erc=True), Pin(num='8',name='I8',do_erc=True), Pin(num='9',name='I9',do_erc=True), Pin(num='11',name='I10/~OE~',do_erc=True), Pin(num='12',name='IO8',func=Pin.TRISTATE,do_erc=True), Pin(num='13',name='IO7',func=Pin.TRISTATE,do_erc=True), Pin(num='14',name='IO6',func=Pin.TRISTATE,do_erc=True), Pin(num='15',name='IO5',func=Pin.TRISTATE,do_erc=True), Pin(num='16',name='IO4',func=Pin.TRISTATE,do_erc=True), Pin(num='17',name='I03',func=Pin.TRISTATE,do_erc=True), Pin(num='18',name='IO2',func=Pin.TRISTATE,do_erc=True), Pin(num='19',name='IO1',func=Pin.TRISTATE,do_erc=True)]), Part(name='PAL16L8',dest=TEMPLATE,tool=SKIDL,keywords='PAL PLD 16L8',description='Programmable Logic Array, DIP-20',ref_prefix='U',num_units=1,fplist=['DIP*', 'PDIP*'],do_erc=True,pins=[ Pin(num='10',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='20',name='VCC',func=Pin.PWRIN,do_erc=True), Pin(num='1',name='I1',do_erc=True), Pin(num='2',name='I2',do_erc=True), Pin(num='3',name='I3',do_erc=True), Pin(num='4',name='I4',do_erc=True), Pin(num='5',name='I5',do_erc=True), Pin(num='6',name='I6',do_erc=True), Pin(num='7',name='I7',do_erc=True), Pin(num='8',name='I8',do_erc=True), Pin(num='9',name='I9',do_erc=True), Pin(num='11',name='I10',do_erc=True), Pin(num='12',name='IO8',func=Pin.TRISTATE,do_erc=True), Pin(num='13',name='IO7',func=Pin.TRISTATE,do_erc=True), Pin(num='14',name='IO6',func=Pin.TRISTATE,do_erc=True), Pin(num='15',name='IO5',func=Pin.TRISTATE,do_erc=True), Pin(num='16',name='IO4',func=Pin.TRISTATE,do_erc=True), Pin(num='17',name='I03',func=Pin.TRISTATE,do_erc=True), Pin(num='18',name='IO2',func=Pin.TRISTATE,do_erc=True), Pin(num='19',name='IO1',func=Pin.TRISTATE,do_erc=True)]), Part(name='PAL20L8',dest=TEMPLATE,tool=SKIDL,keywords='PAL PLD 20L8',description='Programmable Logic Array, DIP-24',ref_prefix='U',num_units=1,fplist=['DIP*', 'PDIP*'],do_erc=True,pins=[ Pin(num='12',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='24',name='VCC',func=Pin.PWRIN,do_erc=True), Pin(num='1',name='I1',do_erc=True), Pin(num='2',name='I2',do_erc=True), Pin(num='3',name='I3',do_erc=True), Pin(num='4',name='I4',do_erc=True), Pin(num='5',name='I5',do_erc=True), Pin(num='6',name='I6',do_erc=True), Pin(num='7',name='I7',do_erc=True), Pin(num='8',name='I8',do_erc=True), Pin(num='9',name='I9',do_erc=True), Pin(num='10',name='I10',do_erc=True), Pin(num='20',name='I020',func=Pin.TRISTATE,do_erc=True), Pin(num='11',name='I11',do_erc=True), Pin(num='21',name='IO21',func=Pin.TRISTATE,do_erc=True), Pin(num='22',name='IO22',func=Pin.TRISTATE,do_erc=True), Pin(num='13',name='I13',do_erc=True), Pin(num='23',name='I23',do_erc=True), Pin(num='14',name='I14',do_erc=True), Pin(num='15',name='IO15',func=Pin.TRISTATE,do_erc=True), Pin(num='16',name='IO16',func=Pin.TRISTATE,do_erc=True), Pin(num='17',name='IO17',func=Pin.TRISTATE,do_erc=True), Pin(num='18',name='IO18',func=Pin.TRISTATE,do_erc=True), Pin(num='19',name='IO19',func=Pin.TRISTATE,do_erc=True)]), Part(name='PAL20RS10',dest=TEMPLATE,tool=SKIDL,keywords='PAL PLD 20RS10',description='Programmable Logic Array, DIP-24 (Narrow)',ref_prefix='U',num_units=1,fplist=['DIP*', 'PDIP*'],do_erc=True,pins=[ Pin(num='1',name='CLK',do_erc=True), Pin(num='2',name='I0',do_erc=True), Pin(num='3',name='I1',do_erc=True), Pin(num='4',name='I2',do_erc=True), Pin(num='5',name='I3',do_erc=True), Pin(num='6',name='I4',do_erc=True), Pin(num='7',name='I5',do_erc=True), Pin(num='8',name='I6',do_erc=True), Pin(num='9',name='I7',do_erc=True), Pin(num='10',name='I8',do_erc=True), Pin(num='20',name='O3',func=Pin.OUTPUT,do_erc=True), Pin(num='11',name='I9',do_erc=True), Pin(num='21',name='O2',func=Pin.OUTPUT,do_erc=True), Pin(num='12',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='22',name='O1',func=Pin.OUTPUT,do_erc=True), Pin(num='13',name='~OE~',do_erc=True), Pin(num='23',name='O0',func=Pin.OUTPUT,do_erc=True), Pin(num='14',name='O9',func=Pin.OUTPUT,do_erc=True), Pin(num='24',name='VCC',func=Pin.PWRIN,do_erc=True), Pin(num='15',name='O8',func=Pin.OUTPUT,do_erc=True), Pin(num='16',name='O7',func=Pin.OUTPUT,do_erc=True), Pin(num='17',name='O6',func=Pin.OUTPUT,do_erc=True), Pin(num='18',name='O5',func=Pin.OUTPUT,do_erc=True), Pin(num='19',name='O4',func=Pin.OUTPUT,do_erc=True)])])

61.377551

235

0.565087

980

6,015

3.363265

0.123469

0.139563

0.251214

0.305825

0.924757

0.915959

0.862561

0.70267

0.596784

0

0.058652

0.203491

6,015

97

236

62.010309

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0

0.526316

0

0.110723

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0

1

0

false

0

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0.010526

0

null

0

1

0

null

0

6

ea81f4be017a020feb5caccb6e3882fc3ec76049

39

py

Python

examples/example_home.py

juancarlospaco/thatlib

37403983c228521b992ad592231957a1c7af01f2

[ "MIT" ]

31

2021-05-12T16:54:34.000Z

2022-02-17T12:36:52.000Z

examples/example_home.py

juancarlospaco/thatlib

37403983c228521b992ad592231957a1c7af01f2

[ "MIT" ]

1

2021-07-23T02:58:07.000Z

2021-09-03T21:53:29.000Z

examples/example_home.py

juancarlospaco/thatlib

37403983c228521b992ad592231957a1c7af01f2

[ "MIT" ]

1

2021-05-12T22:12:20.000Z

from thatlib import home print(home())

13

24

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6

39

5

0.833333

0

0.128205

39

2

25

19.5

0.882353

0

1

0

true

0

0.5

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0.5

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null

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null

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1

0

1

0

1

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6

577d349586dfe22e12a3719a07e457f994c5c924

32,690

py

Python

pyspedas/mms/__init__.py

jibarnum/pyspedas

a956911ed8f2a11e30c527c92d2bda1342bea8e3

[ "MIT" ]

null

pyspedas/mms/__init__.py

jibarnum/pyspedas

a956911ed8f2a11e30c527c92d2bda1342bea8e3

[ "MIT" ]

null

pyspedas/mms/__init__.py

jibarnum/pyspedas

a956911ed8f2a11e30c527c92d2bda1342bea8e3

[ "MIT" ]

null

""" This module contains routines for loading MMS data """ from .mms_load_data import mms_load_data from .fgm.mms_fgm_remove_flags import mms_fgm_remove_flags from .fgm.mms_fgm_set_metadata import mms_fgm_set_metadata from .fpi.mms_fpi_set_metadata import mms_fpi_set_metadata from .hpca.mms_hpca_set_metadata import mms_hpca_set_metadata import re from pytplot import del_data def mms_load_fgm(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', instrument='fgm', datatype='', varformat=None, prefix='', suffix='', keep_flagged=False, get_support_data=True, time_clip=False, no_update=False, available=False, notplot=False): """ This function loads FGM data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for FGM include 'brst' 'fast' 'slow' 'srvy'. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str no datatype for FGM instrument (all science data are loaded) get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ if (varformat is not None) and (not keep_flagged) and (not available) and (not notplot): varformat_fetch = varformat+'|*_flag_*' else: varformat_fetch = varformat tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument=instrument, datatype=datatype, varformat=varformat_fetch, prefix=prefix, suffix=suffix, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, available=available) if tvars == None or available or notplot: return tvars # the probes will need to be strings beyond this point if isinstance(probe, list): probe = [str(p) for p in probe] # remove flagged data if not keep_flagged: mms_fgm_remove_flags(probe, data_rate, level, instrument, suffix=suffix) # Delete the flags variable if it was not originally requested if varformat is not None: regex = re.compile(varformat.replace("*", ".*")) tvars_to_delete = [tvar for tvar in tvars if not re.match(regex, tvar)] for tvar in tvars_to_delete: del_data(tvar) tvars.remove(tvar) mms_fgm_set_metadata(probe, data_rate, level, instrument, suffix=suffix) return tvars def mms_load_hpca(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='moments', get_support_data=None, time_clip=False, no_update=False, varformat=None, prefix='', suffix='', center_measurement=False, available=False, notplot=False): """ This function loads HPCA data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for HPCA include 'brst', 'srvy'. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for HPCA are 'moments' and 'ion'; the default is 'moments' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. center_measurement: bool If True, the CDF epoch variables are time-shifted to the middle of the accumulation interval by their DELTA_PLUS_VAR and DELTA_MINUS_VAR variable attributes notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ if get_support_data is None: get_support_data = True tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='hpca', datatype=datatype, varformat=varformat, prefix=prefix, suffix=suffix, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, center_measurement=center_measurement, available=available) if tvars == None or available or notplot: return tvars mms_hpca_set_metadata(probe=probe, suffix=suffix) return tvars def mms_load_fpi(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='fast', level='l2', datatype=['des-moms', 'dis-moms'], varformat=None, prefix='', suffix='', get_support_data=False, time_clip=False, no_update=False, center_measurement=False, available=False, notplot=False): """ This function loads FPI data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for FPI include 'brst', 'fast'. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for FPI are: 'des-moms', 'dis-moms' (default) 'des-dist', 'dis-dist' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. center_measurement: bool If True, the CDF epoch variables are time-shifted to the middle of the accumulation interval by their DELTA_PLUS_VAR and DELTA_MINUS_VAR variable attributes notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, probe=probe, data_rate=data_rate, level=level, instrument='fpi', datatype=datatype, varformat=varformat, prefix=prefix, suffix=suffix, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, center_measurement=center_measurement, available=available, notplot=notplot) if tvars == None or available or notplot: return tvars mms_fpi_set_metadata(probe, data_rate, datatype, level, suffix=suffix) return tvars def mms_load_scm(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='', varformat=None, prefix='', suffix='', get_support_data=False, time_clip=False, no_update=False, available=False, notplot=False): """ This function loads SCM data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for SCM include ['brst' 'fast' 'slow' 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for SCM are: ['scsrvy', 'cal', 'scb', 'scf', 'schb', 'scm', 'scs'] If no value is given the default is scsrvy for srvy data, and scb for brst data. get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='scm', datatype=datatype, varformat=varformat, prefix=prefix, suffix=suffix, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, available=available) return tvars def mms_load_mec(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='ephts04d', varformat=None, prefix='', suffix='', get_support_data=False, time_clip=False, no_update=False, available=False, notplot=False): """ This function loads MEC data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for MEC include ['brst', 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for MEC are: ['ephts04d', 'epht89q', 'epht89d']; default is 'ephts04d' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, probe=probe, data_rate=data_rate, level=level, instrument='mec', datatype=datatype, get_support_data=get_support_data, varformat=varformat, prefix=prefix, suffix=suffix, time_clip=time_clip, no_update=no_update, available=available, notplot=notplot) return tvars def mms_load_feeps(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='electron', varformat=None, get_support_data=False, prefix='', suffix='', time_clip=False, no_update=False, available=False, notplot=False): """ This function loads FEEPS data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for FEEPS include ['brst', 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for FEEPS are: L2, L1b: ['electron', 'ion'] L1a: ['electron-bottom', 'electron-top', 'ion-bottom', 'ion-top'] get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='feeps', datatype=datatype, varformat=varformat, get_support_data=get_support_data, prefix=prefix, suffix=suffix, time_clip=time_clip, no_update=no_update, available=available) return tvars def mms_load_eis(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='phxtof', varformat=None, get_support_data=False, prefix='', suffix='', time_clip=False, no_update=False, available=False, notplot=False): """ This function loads EIS data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for EIS include ['brst', 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for EIS are: ['extof', 'phxtof', and 'electronenergy']; default is 'extof' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ from .eis.mms_eis_omni import mms_eis_omni tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='epd-eis', datatype=datatype, varformat=varformat, get_support_data=get_support_data, prefix='', suffix='', time_clip=time_clip, no_update=no_update, available=available) if tvars == [] or available or notplot: return tvars if not isinstance(probe, list): probe = [probe] if not isinstance(data_rate, list): data_rate = [data_rate] if not isinstance(datatype, list): datatype = [datatype] for probe_id in probe: for datatype_id in datatype: for data_rate_id in data_rate: omni_spectra = mms_eis_omni(probe_id, data_rate=data_rate_id, datatype=datatype_id) return tvars def mms_load_edi(trange=['2016-10-16', '2016-10-17'], probe='1', data_rate='srvy', level='l2', datatype='efield', varformat=None, get_support_data=False, prefix='', suffix='', time_clip=False, no_update=False, available=False, notplot=False): """ This function loads EDI data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for EDI include ['brst', 'fast', 'slow', 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for EDI are: ['efield', 'amb']; default is 'efield' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='edi', datatype=datatype, varformat=varformat, get_support_data=get_support_data, prefix=prefix, suffix=suffix, time_clip=time_clip, no_update=no_update, available=available) return tvars def mms_load_edp(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='fast', level='l2', datatype='dce', varformat=None, get_support_data=False, prefix='', suffix='', time_clip=False, no_update=False, available=False, notplot=False): """ This function loads EDP data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for EDP include ['brst', 'fast', 'slow', 'srvy']. The default is 'fast'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for EDP are: ['dce', 'dcv', 'ace', 'hmfe']; default is 'dce' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='edp', datatype=datatype, varformat=varformat, get_support_data=get_support_data, prefix=prefix, suffix=suffix, time_clip=time_clip, no_update=no_update, available=available) return tvars def mms_load_dsp(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='bpsd', varformat=None, prefix='', suffix='', get_support_data=False, time_clip=False, no_update=False, available=False, notplot=False): """ This function loads DSP data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for DSP include ['fast', 'slow', 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for DSP are: ['epsd', 'bpsd', 'swd']; default is 'bpsd' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='dsp', datatype=datatype, varformat=varformat, prefix=prefix, suffix=suffix, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, available=available) return tvars def mms_load_aspoc(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='', varformat=None, get_support_data=False, prefix='', suffix='', time_clip=False, no_update=False, available=False, notplot=False): """ This function loads ASPOC data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for ASPOC include 'srvy', 'sitl'. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for ASPOC are: ['asp1', 'asp2', 'aspoc']; default is 'aspoc' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='aspoc', datatype=datatype, varformat=varformat, get_support_data=get_support_data, prefix=prefix, suffix=suffix, time_clip=time_clip, no_update=no_update, available=available) return tvars def mms_load_fsm(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='brst', level='l3', datatype='8khz', get_support_data=False, time_clip=False, no_update=False, available=False, varformat=None, notplot=False): """ This function loads FSM data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str the current instrument data rate for FSM is 'brst' level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatype for FSM is: 8khz get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. prefix: str The tplot variable names will be given this prefix. By default, no prefix is added. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, varformat=varformat, probe=probe, data_rate=data_rate, level=level, instrument='fsm', datatype=datatype, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, available=available) return tvars

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579fb106d6dd5ce03af8da91c738e3ffcb9f258d

247

py

Python

cryptofolio/resolvers/binance/__init__.py

JerryBeGood/Cryptofolio

7f09bdbb823e4aa70d8c83532264857e550d1c84

[ "MIT" ]

null

cryptofolio/resolvers/binance/__init__.py

JerryBeGood/Cryptofolio

7f09bdbb823e4aa70d8c83532264857e550d1c84

[ "MIT" ]

2

2022-01-20T11:54:33.000Z

2022-01-21T07:13:30.000Z

cryptofolio/resolvers/binance/__init__.py

JerryBeGood/Cryptofolio

7f09bdbb823e4aa70d8c83532264857e550d1c84

[ "MIT" ]

null

from .orders import binance_spot_stop_loss_limit_order, binance_spot_limit_order, binance_spot_market_order from .utility import validate_binance_credentials, binance_exchange_info, binance_account_info, binance_open_orders, binance_closed_orders

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0.161765

0.166667

0.205882

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6

57f07b5939466b0b0c919e0f5d2761772624393b

38

py

Python

src/core/stompest/sync/__init__.py

scwicker/stompest

715f358b8503320ea42bd4de6682916339943edc

[ "Apache-2.0" ]

66

2015-01-06T02:20:04.000Z

2022-03-08T01:32:49.000Z

src/core/stompest/sync/__init__.py

scwicker/stompest

715f358b8503320ea42bd4de6682916339943edc

[ "Apache-2.0" ]

34

2015-05-06T14:34:39.000Z

2022-03-16T23:26:04.000Z

src/core/stompest/sync/__init__.py

scwicker/stompest

715f358b8503320ea42bd4de6682916339943edc

[ "Apache-2.0" ]

38

2015-05-16T18:36:08.000Z

2022-03-16T19:23:11.000Z

from stompest.sync.client import Stomp

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6

17c0ca4317034699db46f4678319c2560846b871

29

py

Python

pkg/pkg/plot/__init__.py

kellymarchisio/goat

143192d1c13eba5242ec5cbba5641f9e86b1397c

[ "MIT" ]

3

2021-03-02T16:02:43.000Z

2021-11-06T12:21:19.000Z

pkg/pkg/plot/__init__.py

kellymarchisio/goat

143192d1c13eba5242ec5cbba5641f9e86b1397c

[ "MIT" ]

1

2021-12-14T20:19:07.000Z

pkg/pkg/plot/__init__.py

kellymarchisio/goat

143192d1c13eba5242ec5cbba5641f9e86b1397c

[ "MIT" ]

1

2021-12-14T20:31:05.000Z

from .theme import set_theme

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1

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6

17d54f0749d53dbd7bce079e455e981500375e7d

29

py

Python

genemethods/cgecore/blaster/__init__.py

adamkoziol/genemethods

eaa493f3090595da5cf594da67178c1ebe4aad12

[ "MIT" ]

1

2020-01-15T18:41:21.000Z

genemethods/cgecore/blaster/__init__.py

adamkoziol/genemethods

eaa493f3090595da5cf594da67178c1ebe4aad12

[ "MIT" ]

null

genemethods/cgecore/blaster/__init__.py

adamkoziol/genemethods

eaa493f3090595da5cf594da67178c1ebe4aad12

[ "MIT" ]

null

from .blaster import Blaster

14.5

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0

6

a4ec31cfe9e542e8feb9494a208bdab4d9d23e20

24

py

Python

api/python/ryml/__init__.py

fargies/rapidyaml

6fd373c860b1eef3b190a521acabebc2fd2dcb04

[ "MIT" ]

314

2018-01-08T07:42:23.000Z

2022-03-21T10:42:29.000Z

api/python/ryml/__init__.py

fargies/rapidyaml

6fd373c860b1eef3b190a521acabebc2fd2dcb04

[ "MIT" ]

209

2018-02-23T04:30:46.000Z

2022-03-23T15:05:48.000Z

api/python/ryml/__init__.py

fargies/rapidyaml

6fd373c860b1eef3b190a521acabebc2fd2dcb04

[ "MIT" ]

53

2018-02-23T00:32:50.000Z

2022-03-07T18:11:45.000Z

from ryml.ryml import *

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1

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1

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6

104457c6fc3ba12dc190d785ad2218edf3a93943

8,718

py

Python

Raifhack_DS_24_26_Sept_2021.py

Anna-Alx-Romanova/Raifhack_DS_Sept_2021

56d0e1106a22329feae0e20c57619a5488b55965

[ "MIT" ]

null

Raifhack_DS_24_26_Sept_2021.py

Anna-Alx-Romanova/Raifhack_DS_Sept_2021

56d0e1106a22329feae0e20c57619a5488b55965

[ "MIT" ]

null

Raifhack_DS_24_26_Sept_2021.py

Anna-Alx-Romanova/Raifhack_DS_Sept_2021

56d0e1106a22329feae0e20c57619a5488b55965

[ "MIT" ]

null

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np from scipy.stats import norm from sklearn.preprocessing import StandardScaler from scipy import stats from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.model_selection import cross_val_score from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline from sklearn.linear_model import LinearRegression from sklearn import metrics from sklearn.model_selection import cross_val_score df_train0 = pd.read_csv('INPUT/train.csv') df_test_sub0 = pd.read_csv('INPUT/test.csv') result = pd.DataFrame(df_test_sub0['id']) pred_res = [] def df_train_region(df, region, city, realty_type): df_train = df.loc[df['region'] == region] if realty_type in df_train['realty_type']: df_train = df_train.loc[df_train['realty_type'] == realty_type] else: df_train = df_train.loc[df_train['realty_type'] == 10] if city in df_train['city']: df_train = df_train.loc[df_train['city'] == city] else: df_train = df_train return df_train i = 0 print(i) while i < 2974: df_test_sub = df_test_sub0.iloc[i:i+1] #id = city = df_test_sub['id'][i] city = df_test_sub['city'][i] region = df_test_sub['region'][i] realty_type = df_test_sub['realty_type'][i] print(city) print(region) print(realty_type) df_train = df_train_region(df_train0, region, city, realty_type) df_train1 = df_train.drop(['lat', 'lng', 'osm_amenity_points_in_0.001', 'osm_amenity_points_in_0.0075', 'osm_amenity_points_in_0.01', 'osm_building_points_in_0.001', 'osm_building_points_in_0.005', 'osm_building_points_in_0.0075', 'osm_building_points_in_0.01', 'osm_catering_points_in_0.001', 'osm_catering_points_in_0.005', 'osm_catering_points_in_0.0075', 'osm_catering_points_in_0.01', 'osm_city_closest_dist', 'osm_city_nearest_name', 'osm_city_nearest_population', 'osm_crossing_closest_dist', 'osm_crossing_points_in_0.001', 'osm_crossing_points_in_0.005', 'osm_crossing_points_in_0.0075', 'osm_crossing_points_in_0.01', 'osm_culture_points_in_0.001', 'osm_culture_points_in_0.005', 'osm_culture_points_in_0.0075', 'osm_culture_points_in_0.01', 'osm_finance_points_in_0.001', 'osm_finance_points_in_0.005', 'osm_finance_points_in_0.0075', 'osm_finance_points_in_0.01', 'osm_healthcare_points_in_0.005', 'osm_healthcare_points_in_0.0075', 'osm_healthcare_points_in_0.01', 'osm_historic_points_in_0.005', 'osm_historic_points_in_0.0075', 'osm_historic_points_in_0.01', 'osm_hotels_points_in_0.005', 'osm_hotels_points_in_0.0075', 'osm_hotels_points_in_0.01', 'osm_leisure_points_in_0.005', 'osm_leisure_points_in_0.0075', 'osm_leisure_points_in_0.01', 'osm_offices_points_in_0.001', 'osm_offices_points_in_0.005', 'osm_offices_points_in_0.0075', 'osm_offices_points_in_0.01', 'osm_shops_points_in_0.001', 'osm_shops_points_in_0.005', 'osm_shops_points_in_0.0075', 'osm_shops_points_in_0.01', 'osm_subway_closest_dist', 'osm_train_stop_closest_dist', 'osm_train_stop_points_in_0.005', 'osm_train_stop_points_in_0.0075', 'osm_train_stop_points_in_0.01', 'osm_transport_stop_closest_dist', 'osm_transport_stop_points_in_0.005', 'osm_transport_stop_points_in_0.0075', 'osm_transport_stop_points_in_0.01', 'reform_count_of_houses_1000', 'reform_count_of_houses_500', 'reform_house_population_1000', 'reform_house_population_500', 'reform_mean_floor_count_1000', 'reform_mean_floor_count_500', 'reform_mean_year_building_1000', 'reform_mean_year_building_500', 'street', 'date', 'id', 'realty_type', 'region', 'city', 'price_type', 'date_n', 'oil', 'osm_amenity_points_in_0.005', 'floor'], axis=1) df_test_sub1 = df_test_sub.drop(['lat', 'lng', 'osm_amenity_points_in_0.001', 'osm_amenity_points_in_0.0075', 'osm_amenity_points_in_0.01', 'osm_building_points_in_0.001', 'osm_building_points_in_0.005', 'osm_building_points_in_0.0075', 'osm_building_points_in_0.01', 'osm_catering_points_in_0.001', 'osm_catering_points_in_0.005', 'osm_catering_points_in_0.0075', 'osm_catering_points_in_0.01', 'osm_city_closest_dist', 'osm_city_nearest_name', 'osm_city_nearest_population', 'osm_crossing_closest_dist', 'osm_crossing_points_in_0.001', 'osm_crossing_points_in_0.005', 'osm_crossing_points_in_0.0075', 'osm_crossing_points_in_0.01', 'osm_culture_points_in_0.001', 'osm_culture_points_in_0.005', 'osm_culture_points_in_0.0075', 'osm_culture_points_in_0.01', 'osm_finance_points_in_0.001', 'osm_finance_points_in_0.005', 'osm_finance_points_in_0.0075', 'osm_finance_points_in_0.01', 'osm_healthcare_points_in_0.005', 'osm_healthcare_points_in_0.0075', 'osm_healthcare_points_in_0.01', 'osm_historic_points_in_0.005', 'osm_historic_points_in_0.0075', 'osm_historic_points_in_0.01', 'osm_hotels_points_in_0.005', 'osm_hotels_points_in_0.0075', 'osm_hotels_points_in_0.01', 'osm_leisure_points_in_0.005', 'osm_leisure_points_in_0.0075', 'osm_leisure_points_in_0.01', 'osm_offices_points_in_0.001', 'osm_offices_points_in_0.005', 'osm_offices_points_in_0.0075', 'osm_offices_points_in_0.01', 'osm_shops_points_in_0.001', 'osm_shops_points_in_0.005', 'osm_shops_points_in_0.0075', 'osm_shops_points_in_0.01', 'osm_subway_closest_dist', 'osm_train_stop_closest_dist', 'osm_train_stop_points_in_0.005', 'osm_train_stop_points_in_0.0075', 'osm_train_stop_points_in_0.01', 'osm_transport_stop_closest_dist', 'osm_transport_stop_points_in_0.005', 'osm_transport_stop_points_in_0.0075', 'osm_transport_stop_points_in_0.01', 'reform_count_of_houses_1000', 'reform_count_of_houses_500', 'reform_house_population_1000', 'reform_house_population_500', 'reform_mean_floor_count_1000', 'reform_mean_floor_count_500', 'reform_mean_year_building_1000', 'reform_mean_year_building_500', 'street', 'date', 'id', 'realty_type', 'region', 'city', 'date_n', 'oil', 'osm_amenity_points_in_0.005', 'floor', 'price_type'], axis=1) x = df_train1.drop('per_square_meter_price', axis=1) y = df_train1['per_square_meter_price'] x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42) def cross_val(model): pred = cross_val_score(model, x, y, cv=10) return pred.mean() def print_evaluate(true, predicted): mae = metrics.mean_absolute_error(true, predicted) mse = metrics.mean_squared_error(true, predicted) rmse = np.sqrt(metrics.mean_squared_error(true, predicted)) r2_square = metrics.r2_score(true, predicted) print('MAE:', mae) print('MSE:', mse) print('RMSE:', rmse) print('R2 Square', r2_square) print('__________________________________') def evaluate(true, predicted): mae = metrics.mean_absolute_error(true, predicted) mse = metrics.mean_squared_error(true, predicted) rmse = np.sqrt(metrics.mean_squared_error(true, predicted)) r2_square = metrics.r2_score(true, predicted) return mae, mse, rmse, r2_square pipeline = Pipeline([ ('std_scalar', StandardScaler()) ]) x_train = pipeline.fit_transform(x_train) x_test = pipeline.transform(x_test) #df_test_sub1 = pipeline.transform(df_test_sub1) lin_reg = LinearRegression(normalize=True) lin_reg.fit(x_train,y_train) #print(lin_reg.intercept_) df_test_sub1 = pipeline.transform(df_test_sub1) pred = lin_reg.predict(df_test_sub1) i += 1 res = pred[0] pred_res.append(res) with open('result.txt', 'a') as file: file.write(str(abs(res)) + '\n')

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108245a577530d151ddcea4a18a5ff4ee9218924

75

py

Python

amocrm_asterisk_ng/crm/amocrm/kernel/redirect_to_responsible/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

amocrm_asterisk_ng/crm/amocrm/kernel/redirect_to_responsible/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

amocrm_asterisk_ng/crm/amocrm/kernel/redirect_to_responsible/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

from .GetResponsibleUserByPhoneQuery import GetResponsibleUserByPhoneQuery

37.5

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6

1093fccb58d87513667e77856af943db477ce307

1,749

py

Python

capstone/capdb/migrations/0037_auto_20180427_1535.py

rachelaus/capstone

2affa02706f9b1a99d032c66f258a7421c40a35e

[ "MIT" ]

134

2017-07-12T17:03:06.000Z

2022-03-27T06:38:29.000Z

capstone/capdb/migrations/0037_auto_20180427_1535.py

rachelaus/capstone

2affa02706f9b1a99d032c66f258a7421c40a35e

[ "MIT" ]

1,362

2017-06-22T17:42:49.000Z

2022-03-31T15:28:00.000Z

capstone/capdb/migrations/0037_auto_20180427_1535.py

rachelaus/capstone

2affa02706f9b1a99d032c66f258a7421c40a35e

[ "MIT" ]

38

2017-06-22T14:46:23.000Z

2022-03-16T05:32:54.000Z

# Generated by Django 2.0.2 on 2018-04-27 15:35 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('capdb', '0036_auto_20180419_1933'), ] operations = [ migrations.AddField( model_name='volumemetadata', name='xml_end_year', field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name='volumemetadata', name='xml_publication_city', field=models.CharField(blank=True, max_length=1024, null=True), ), migrations.AddField( model_name='volumemetadata', name='xml_publication_year', field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name='volumemetadata', name='xml_publisher', field=models.CharField(blank=True, max_length=255, null=True), ), migrations.AddField( model_name='volumemetadata', name='xml_reporter_full_name', field=models.CharField(blank=True, max_length=255, null=True), ), migrations.AddField( model_name='volumemetadata', name='xml_reporter_short_name', field=models.CharField(blank=True, max_length=255, null=True), ), migrations.AddField( model_name='volumemetadata', name='xml_start_year', field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name='volumemetadata', name='xml_volume_number', field=models.CharField(blank=True, max_length=64, null=True), ), ]

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null

0

6

10aa026762d0fc7b072a06adf9d7ae84a54c77e6

145

py

Python

tools/package_parser/__init__.py

fingerco/sims-4-mac-modding-tools

47d4fb8b6065b455f27cc1487cb217f2fc201641

[ "Apache-2.0" ]

3

2020-06-13T16:16:43.000Z

2021-06-19T22:23:13.000Z

tools/package_parser/__init__.py

fingerco/sims-4-mac-modding-tools

47d4fb8b6065b455f27cc1487cb217f2fc201641

[ "Apache-2.0" ]

2

2021-03-31T19:27:34.000Z

2021-12-13T20:21:58.000Z

tools/package_parser/__init__.py

fingerco/sims-4-mac-modding-tools

47d4fb8b6065b455f27cc1487cb217f2fc201641

[ "Apache-2.0" ]

1

2020-05-13T19:59:40.000Z

from .package_file_reader import PackageFileReader from .package_file_writer import PackageFileWriter from .helpers import resource_type_by_name

36.25

50

0.896552

19

145

6.473684

0.684211

0.178862

0.243902

0

0.082759

145

3

51

48.333333

0.924812

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

10ae742212651c6db0116ecc2bb79d733162fa38

112

py

Python

python/varspark/__init__.py

jamesrcounts/VariantSpark

ba8935a342648a15fc7aaa045d51eedcd7938519

[ "BSD-3-Clause" ]

6

2018-06-22T21:27:55.000Z

2019-08-24T00:56:04.000Z

python/varspark/__init__.py

jamesrcounts/VariantSpark

ba8935a342648a15fc7aaa045d51eedcd7938519

[ "BSD-3-Clause" ]

null

python/varspark/__init__.py

jamesrcounts/VariantSpark

ba8935a342648a15fc7aaa045d51eedcd7938519

[ "BSD-3-Clause" ]

1

2019-07-31T14:16:00.000Z

try: from varspark.core import VariantsContext except Exception: pass from varspark.etc import find_jar

18.666667

45

0.785714

15

112

5.8

0.8

0.275862

0

0.178571

112

5

46

22.4

0.945652

0

1

0

true

0.2

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

6