xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

fe2d44da087293544dbbbb7189d1daf2f2f5e328

160

py

Python

fixed_vhd_writer/version.py

fujiawei-dev/fixed-vhd-writer

c3fcc53ae79313b7be44200a0172fbd55c619620

[ "MIT" ]

null

fixed_vhd_writer/version.py

fujiawei-dev/fixed-vhd-writer

c3fcc53ae79313b7be44200a0172fbd55c619620

[ "MIT" ]

null

fixed_vhd_writer/version.py

fujiawei-dev/fixed-vhd-writer

c3fcc53ae79313b7be44200a0172fbd55c619620

[ "MIT" ]

null

# Generated by [Toolkit-Py](https://github.com/fujiawei-dev/toolkit-py) Generator # Created at 2022-02-06 10:58:35.566935, Version 0.2.9 __version__ = '0.0.5'

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1

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1

0

1

0

null

0

4

fe36d18027ab189e35f0662c279b9adcb738a118

1,088

py

Python

src/components/game-server/juego/fabrica_conecta_cuatro.py

adp1002/practica-dms-2019-2020

9ae0c71140d537bb43c0f8ec8a81b8fff38dec21

[ "MIT" ]

null

src/components/game-server/juego/fabrica_conecta_cuatro.py

adp1002/practica-dms-2019-2020

9ae0c71140d537bb43c0f8ec8a81b8fff38dec21

[ "MIT" ]

null

src/components/game-server/juego/fabrica_conecta_cuatro.py

adp1002/practica-dms-2019-2020

9ae0c71140d537bb43c0f8ec8a81b8fff38dec21

[ "MIT" ]

null

from juego.fabrica_abstracta_juegos import FabricaJuegoMesa from juego.datos.pieza_conecta_cuatro import PiezaConectaCuatro from juego.datos.tablero_conecta_cuatro import TableroConectaCuatro from juego.logica.arbitro_conecta_cuatro import ArbitroConectaCuatro class FabricaConectaCuatro(FabricaJuegoMesa): """ Fabrica del conecta 4. --- La clase se encarga de crear tableros y piezas del conecta 4. """ def crear_pieza(self, tipo): """ Método que crea una pieza de un tipo. --- Parametros: - tipo: Un String que representa el tipo de pieza. Returns: Una pieza del juego. """ return PiezaConectaCuatro(tipo) def crear_tablero(self): """ Método que crea un tablero. --- Returns: Un tablero del juego. """ return TableroConectaCuatro() def crear_arbitro(self, tablero): """ Método que crea un arbitro. --- Returns: Un arbitro del juego. """ return ArbitroConectaCuatro(tablero)

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0.002591

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1,088

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4

fe4221571984b39e3a58aa366db32aa7736eee95

174

py

Python

cache_toolbox/app_settings.py

lamby/django-cache-toolbox

544966d30c740088670c31ddf1712fb1cdf7735c

[ "BSD-3-Clause" ]

1

2019-12-28T16:58:46.000Z

cache_toolbox/app_settings.py

lamby/django-cache-toolbox

544966d30c740088670c31ddf1712fb1cdf7735c

[ "BSD-3-Clause" ]

30

2018-03-08T15:47:03.000Z

2021-02-26T09:27:59.000Z

cache_toolbox/app_settings.py

lamby/django-cache-toolbox

544966d30c740088670c31ddf1712fb1cdf7735c

[ "BSD-3-Clause" ]

4

2018-03-07T19:03:19.000Z

2021-02-04T09:09:06.000Z

from django.conf import settings # Default cache timeout CACHE_TOOLBOX_DEFAULT_TIMEOUT = getattr( settings, "CACHE_TOOLBOX_DEFAULT_TIMEOUT", 60 * 60 * 24 * 7, )

19.333333

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0.729885

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4

fe670e941cb7510f437ac9f310aeac8195744f58

3,153

py

Python

mole/molecule.py

nkcxliu2/ctrlq

d412641c6cef62ba0f1cb069c5580b834fa03142

[ "Apache-2.0" ]

6

2020-09-25T14:50:36.000Z

2021-12-09T17:36:53.000Z

mole/molecule.py

nkcxliu2/ctrlq

d412641c6cef62ba0f1cb069c5580b834fa03142

[ "Apache-2.0" ]

1

2021-09-21T18:54:38.000Z

mole/molecule.py

nkcxliu2/ctrlq

d412641c6cef62ba0f1cb069c5580b834fa03142

[ "Apache-2.0" ]

3

2021-07-07T05:08:08.000Z

2021-11-09T01:48:31.000Z

from qiskit.chemistry.drivers import PySCFDriver, UnitsType from qiskit.chemistry import FermionicOperator from qiskit.aqua.operators import Z2Symmetries from qiskit.aqua.operators import op_converter import numpy,time def h2(dist=0.75): mol = PySCFDriver(atom= 'H 0.0 0.0 0.0;'\ 'H 0.0 0.0 {}'.format(dist), unit=UnitsType.ANGSTROM, charge=0, spin=0, basis='sto-3g') mol = mol.run() h1 = mol.one_body_integrals h2 = mol.two_body_integrals nuclear_repulsion_energy = mol.nuclear_repulsion_energy num_particles = mol.num_alpha + mol.num_beta + 0 ferOp = FermionicOperator(h1=h1, h2=h2) qubitOp = ferOp.mapping(map_type='parity',threshold=0.00000001) qubitOp = Z2Symmetries.two_qubit_reduction(qubitOp,num_particles) cHam = op_converter.to_matrix_operator(qubitOp) cHam = cHam.dense_matrix + nuclear_repulsion_energy*numpy.identity(4) return cHam def hehp(dist=1.0): mol = PySCFDriver(atom= 'He 0.0 0.0 0.0;'\ 'H 0.0 0.0 {}'.format(dist), unit=UnitsType.ANGSTROM, charge=1, spin=0, basis='sto-3g') mol = mol.run() h1 = mol.one_body_integrals h2 = mol.two_body_integrals nuclear_repulsion_energy = mol.nuclear_repulsion_energy num_particles = mol.num_alpha + mol.num_beta + 0 ferOp = FermionicOperator(h1=h1, h2=h2) qubitOp = ferOp.mapping(map_type='parity',threshold=0.00000001) qubitOp = Z2Symmetries.two_qubit_reduction(qubitOp,num_particles) cHam = op_converter.to_matrix_operator(qubitOp) cHam = cHam.dense_matrix + nuclear_repulsion_energy*numpy.identity(4) return cHam def lih(dist=1.5): mol = PySCFDriver(atom= 'H 0.0 0.0 0.0;'\ 'Li 0.0 0.0 {}'.format(dist), unit=UnitsType.ANGSTROM, charge=0, spin=0, basis='sto-3g') mol = mol.run() freeze_list = [0] remove_list = [-3, -2] repulsion_energy = mol.nuclear_repulsion_energy num_particles = mol.num_alpha + mol.num_beta num_spin_orbitals = mol.num_orbitals * 2 remove_list = [x % mol.num_orbitals for x in remove_list] freeze_list = [x % mol.num_orbitals for x in freeze_list] remove_list = [x - len(freeze_list) for x in remove_list] remove_list += [x + mol.num_orbitals - len(freeze_list) for x in remove_list] freeze_list += [x + mol.num_orbitals for x in freeze_list] ferOp = FermionicOperator(h1=mol.one_body_integrals, h2=mol.two_body_integrals) ferOp, energy_shift = ferOp.fermion_mode_freezing(freeze_list) num_spin_orbitals -= len(freeze_list) num_particles -= len(freeze_list) ferOp = ferOp.fermion_mode_elimination(remove_list) num_spin_orbitals -= len(remove_list) qubitOp = ferOp.mapping(map_type='parity', threshold=0.00000001) qubitOp = Z2Symmetries.two_qubit_reduction(qubitOp, num_particles) shift = energy_shift + repulsion_energy cHam = op_converter.to_matrix_operator(qubitOp) cHam = cHam.dense_matrix + shift*numpy.identity(16) return cHam

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4

fe6a523346bf5aee5fe3c7b91adf0204eca2b8c5

10,503

py

Python

2020/03.py

SpangleLabs/advent-of-code

9692f533c30f22e59f7055ca12253128ca8dac0a

[ "MIT" ]

1

2021-12-04T11:30:53.000Z

2020/03.py

SpangleLabs/advent-of-code

9692f533c30f22e59f7055ca12253128ca8dac0a

[ "MIT" ]

null

2020/03.py

SpangleLabs/advent-of-code

9692f533c30f22e59f7055ca12253128ca8dac0a

[ "MIT" ]

null

tree_map = """.......#................#...... ...#.#.....#.##.....#..#....... ..#..#.#......#.#.#............ ....#...#...##.....#..#.....#.. ....#.......#.##......#...#..#. ...............#.#.#.....#..#.. ...##...#...#..##.###...##..... ##..#.#...##.....#.#..........# .#....#..#..#......#....#....#. ...........................#... ..........#.......#..#.....#.#. ..#.......###..#.#.......#.#... ....#..#....#....#..........#.. ..##..#.......#.#...#.......... .....#.......#.....#....#...... ..........##..#................ ....##.#..###...#..##.....#.#.. ..#..#.#.#...#......#...#.....# ....#.#....#...####.##......... ..#.........##...##.#..#..#.... .#......#...#..#..##.#......... .#....#.......#..##..##..#.#.#. ...........#....#......#....... ..#....#....#...............#.. ..#.....#....###.##.....#.#..#. #..........#.#......#.#....#... ....###...#.#.....#....#.####.# ........#......#...#...#..##..# ...##..............##.#.......# #..........#...........#.#....# 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...#........#..#....#.......... #...#.#...#..##....##.......... .........#........#.##....#..#. ..#...#.#.......##..........##. ###...........##.#......#.#..#. ...#....#...#..#..#......#..... .....##.......###.#....###..##. ...#...#..........#.#......#... ....#.....##...##..#.#........# .....#...#..#.....##...##....#. ................##.#.##....##.# .#..#..#....#.....#....#..#...# .....###.....#................. #...#..##..#.........#......... .....#..#................#..... .#..#...#......#..#............ ...#...#.#....#....##...#...##. ..........#....#.#..#.#.....#.. ....#...###.##...#..#..#......# #...#.......#..........#..#.... .#............#..##.......#...# ....#..#...#............#..#.#. .#....#.......#..#.#......#.... ...#...#............#...#.....# ....#.#.#..##.#.....#...#.#.... ......#.#.#......#..#...#.....# ......##.....#.............#... ..#...#..#.#....#.............. .#.#..#....#.#..##....###.##... ..#...........#....#.###.#....# .....#.........#.#............. ...#.#.....#......###......##.. ...#...#.....#................. ...#..#...##.....##.........#.. ..#...#..#..##..#...#........#. ##..#.#.##.#....#...........#.. .......#....##....#...##..#..#. #.......##.#...##...##..#.....# ....#.#...............#......#. ....#.#...#.....#....#......#.. .#.........#.#....###........#. .#.#.....#.....#.#.#....#.#.... ............#...........#.#..## #...#......#..#......#.#....... ...#.#.#.....#..#...#..##...... ...#.#..#...#....#.........#.#. ........#..#......##.....#...#. ...#..#..............#..#...... .........#.......#...#......#.. .#......#.....#.....#......#... ......#.......#....#...#.#..... .#.....#.##..#........#...#.... #.....##..##....#.#.......#..#. .#..#...#..#.......#........... ..#..#...#.....##....#.....#... #.#..............#....#..#..... .........##...#......#.##...##. .###...#.#...#.....#.........#. .....#..........##...#..#....## .#..#......#....##.#...#....... .............###.#.#..#.#.#...# .......#...##..#..#.....###.... ##.......#...........#....#.#.. ##......#...#.#................ .#.####..##.#...............#.. ..#...#.#.#..#...#........#...# .##..##.##.....#.......#..#.#.. ...................#......#.#.. #.##..#..........#............. ##..#......#....#.#............ .#........#.....##...#......... .##....#..#..##..........#...#. #..........##........#..#..#.#. ####.###.#.....#....#..#.#....# ..#...#...#.#.......#....#...#. ......##.###..##.#.###......#.#""" position = 0 trees = 0 for line in tree_map.split("\n"): if line[position % len(line)] == "#": trees += 1 position += 3 print(trees)

31.635542

45

0.008759

24

10,503

3.75

0.625

0.155556

0

0.000395

0.034752

10,503

331

46

31.731118

0.008483

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0.98429

0.953347

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1

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false

0

0.00303

1

0

null

0

1

0

1

null

0

4

feb341ebd0d49632013119645c45418908e3f290

144

py

Python

utils/__init__.py

HuangLuGuang/PDFRecognition

ece93c3e2b51f0e6dcc68964d3b25e470086589d

[ "MIT" ]

6

2020-07-30T03:27:59.000Z

2020-07-30T05:02:33.000Z

utils/__init__.py

HuangLuGuang/PDFRecognition

ece93c3e2b51f0e6dcc68964d3b25e470086589d

[ "MIT" ]

null

utils/__init__.py

HuangLuGuang/PDFRecognition

ece93c3e2b51f0e6dcc68964d3b25e470086589d

[ "MIT" ]

null

# -*- coding: utf-8 -*- # @createTime : 2020/5/7 17:17 # @author : Huanglg # @fileName: __init__.py.py # @email: luguang.huang@mabotech.com

24

36

0.631944

20

144

4.35

0.9

0

0.091667

0.166667

144

5

37

28.8

0.633333

0.923611

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

22d6c64365f246a0beddf9970a8cf1794090587f

68

py

Python

tests/__init__.py

andrew000/machine2human

db72e4219d7c9f5a6e1c17802a8a44c8084eeb63

[ "MIT" ]

6

2020-08-27T17:50:52.000Z

2021-06-10T20:14:46.000Z

tests/__init__.py

andrew000/machine2human

db72e4219d7c9f5a6e1c17802a8a44c8084eeb63

[ "MIT" ]

null

tests/__init__.py

andrew000/machine2human

db72e4219d7c9f5a6e1c17802a8a44c8084eeb63

[ "MIT" ]

1

2020-12-13T12:21:20.000Z

from m2h import Sec2Hum, Hum2Sec __all__ = ['Sec2Hum', 'Hum2Sec']

13.6

32

0.705882

8

68

5.5

0.75

0.636364

0

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null

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4

22de219db22750fcc1c4c520f17fac73593c18dc

105

py

Python

control/video/colors.py

oholsen/hagedag

4e2881fa1f636228e5cbe76e61fb4b224f0b1e4a

[ "Apache-2.0" ]

null

control/video/colors.py

oholsen/hagedag

4e2881fa1f636228e5cbe76e61fb4b224f0b1e4a

[ "Apache-2.0" ]

null

control/video/colors.py

oholsen/hagedag

4e2881fa1f636228e5cbe76e61fb4b224f0b1e4a

[ "Apache-2.0" ]

null

# BGR Blue = (255, 0, 0) Green = (0, 255, 0) Red = (0, 0, 255) Black = (0, 0, 0) White = (255, 255, 255)

15

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105

2.47619

0.380952

0.153846

0

0.346154

0.257143

105

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0

1

0

1

0

null

0

4

22eeadf37f799e6b5c6b8f75e8ea4a8fdadc517d

96

py

Python

python/699.falling-squares.py

stavanmehta/leetcode

1224e43ce29430c840e65daae3b343182e24709c

[ "Apache-2.0" ]

null

python/699.falling-squares.py

stavanmehta/leetcode

1224e43ce29430c840e65daae3b343182e24709c

[ "Apache-2.0" ]

null

python/699.falling-squares.py

stavanmehta/leetcode

1224e43ce29430c840e65daae3b343182e24709c

[ "Apache-2.0" ]

null

class Solution: def fallingSquares(self, positions: List[List[int]]) -> List[int]:

24

70

0.635417

11

96

5.545455

0.727273

0.229508

0

0.21875

96

3

71

32

0.813333

0

null

0

null

0

1

0

null

1

0

1

0

null

0

1

0

4

22f8e1a56306bbd61876d5573ba7303f49c13eef

1,465

py

Python

radiomicsfeatureextractionpipeline/backend/src/dal/study_repository.py

Maastro-CDS-Imaging-Group/SQLite4Radiomics

e3a7afc181eec0fe04c18da00edc3772064e6758

[ "Apache-2.0" ]

null

radiomicsfeatureextractionpipeline/backend/src/dal/study_repository.py

Maastro-CDS-Imaging-Group/SQLite4Radiomics

e3a7afc181eec0fe04c18da00edc3772064e6758

[ "Apache-2.0" ]

6

2021-06-09T19:39:27.000Z

2021-09-30T16:41:40.000Z

radiomicsfeatureextractionpipeline/backend/src/dal/study_repository.py

Maastro-CDS-Imaging-Group/SQLite4Radiomics

e3a7afc181eec0fe04c18da00edc3772064e6758

[ "Apache-2.0" ]

null

""" This module is used as a template for all StudyRepositories. All StudyRepositories in the data access layer should inherit from this class. Inherits the Repository class. """ from abc import abstractmethod from typing import List, Optional from dal.database_connector import DatabaseConnector from dal.repository import Repository from logic.entities.study import Study class StudyRepository(Repository): """ This class is a template for the repository that handles all the loading and saving of studies. """ def __init__(self, database_connector: DatabaseConnector, queries_file_directory: str) -> None: """ Constructor for the StudyRepository class. :param database_connector: The database connector that will communicate with the database. :param queries_file_directory: The directory of the SQL-files. """ super().__init__(database_connector, queries_file_directory) @abstractmethod def get_all_studies(self) -> List[Study]: """ Gets a list of all studies in the database. :return: A list of all studies in the database. """ pass @abstractmethod def get_study_by_id(self, study_id: str) -> Optional[Study]: """ Gets the Study object corresponding to it's identifier. :param study_id: The identifier of the Study :return: The Study object that corresponds to the identifier. """ pass

34.069767

99

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183

1,465

5.502732

0.387978

0.084409

0.059583

0.019861

0.059583

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42

100

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null

0

null

0

1

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1

0

1

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4

a3c197bb932cfc9cf3447b7a3b52ce76db262fc9

91

py

Python

glide_text2im/__init__.py

dbanys/glide-text2im

5177545ec62f1fddc3075a8a69b63df3eb2256a5

[ "MIT" ]

2,139

2021-12-21T02:04:22.000Z

2022-03-31T08:33:23.000Z

glide_text2im/__init__.py

Binxly/glide-text2im

9cc8e563851bd38f5ddb3e305127192cb0f02f5c

[ "MIT" ]

28

2021-12-21T02:22:26.000Z

2022-03-31T09:39:26.000Z

glide_text2im/__init__.py

Binxly/glide-text2im

9cc8e563851bd38f5ddb3e305127192cb0f02f5c

[ "MIT" ]

291

2021-12-21T02:08:48.000Z

2022-03-28T21:07:24.000Z

""" A codebase for performing model inference with a text-conditional diffusion model. """

22.75

82

0.769231

12

91

5.833333

0.833333

0

0.142857

91

3

83

30.333333

0.897436

0.901099

0

null

0

null

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null

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true

0

null

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0

1

0

1

0

null

0

1

0

4

a3d41b0cd0fc117e603ebdd8dd82331d56942f7d

87

py

Python

itaigisite/sutian/apps.py

g0v/ita

8854cd96791a4450ed7ed58b64b5db5d11eaf970

[ "MIT" ]

158

2015-12-26T02:23:46.000Z

2022-03-17T21:07:55.000Z

itaigisite/sutian/apps.py

g0v/ita

8854cd96791a4450ed7ed58b64b5db5d11eaf970

[ "MIT" ]

388

2015-10-19T11:40:12.000Z

2021-12-10T03:01:42.000Z

itaigisite/sutian/apps.py

g0v/ita

8854cd96791a4450ed7ed58b64b5db5d11eaf970

[ "MIT" ]

47

2015-10-23T16:03:06.000Z

2021-07-27T12:28:43.000Z

from django.apps import AppConfig class SutianConfig(AppConfig): name = 'sutian'

14.5

33

0.747126

10

87

6.5

0.9

0

0.172414

87

5

34

17.4

0.902778

0

0.068966

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false

0

0.333333

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null

0

1

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null

0

1

0

1

0

4

a3ea176475af6d8ad806b28fe19e22a45887b3bf

290

py

Python

keepercommander/importer/lastpass/__init__.py

Mkn-yskz/Commandy

e360306f41112534ae71102658f560fd974a1f45

[ "MIT" ]

151

2015-11-02T02:04:46.000Z

2022-01-20T00:07:01.000Z

keepercommander/importer/lastpass/__init__.py

Mkn-yskz/Commandy

e360306f41112534ae71102658f560fd974a1f45

[ "MIT" ]

145

2015-12-31T00:11:35.000Z

2022-03-31T19:13:54.000Z

keepercommander/importer/lastpass/__init__.py

Mkn-yskz/Commandy

e360306f41112534ae71102658f560fd974a1f45

[ "MIT" ]

73

2015-10-30T00:53:10.000Z

2022-03-30T03:50:53.000Z

# _ __ # | |/ /___ ___ _ __ ___ _ _ ® # | ' </ -_) -_) '_ \/ -_) '_| # |_|\_\___\___| .__/\___|_| # |_| # # Keeper Commander # Copyright 2018 Keeper Security Inc. # Contact: ops@keepersecurity.com # from .lastpass import LastPassImporter as Importer __all__ = ['Importer']

20.714286

50

0.57931

20

290

6.4

0.9

0

0.0181

0.237931

290

14

51

20.714286

0.556561

0.665517

0

0.093023

0

1

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0.5

1

0

1

0

1

0

null

0

null

0

1

0

1

0

4

a3fa6b72a782e73277a51d11d56e8634d4cdd435

8,436

py

Python

src/comtypes/shelllink.py

KamiGim/PythonLib

604cacc5b05d319cd46f028bd663ec7aa86065c2

[ "Zlib" ]

10

2019-01-24T11:01:39.000Z

2021-11-08T12:11:00.000Z

src/comtypes/shelllink.py

KamiGim/PythonLib

604cacc5b05d319cd46f028bd663ec7aa86065c2

[ "Zlib" ]

3

2018-06-10T06:28:06.000Z

2021-09-24T13:54:19.000Z

Lib/comtypes/shelllink.py

masamitsu-murase/single_binary_stackless_python

1a601683882bc0a024b1bef3e35719554e44cce1

[ "CNRI-Python-GPL-Compatible" ]

3

2019-04-17T14:16:34.000Z

2019-10-31T05:48:26.000Z

from ctypes import * from ctypes.wintypes import DWORD, WIN32_FIND_DATAA, WIN32_FIND_DATAW, MAX_PATH from comtypes import IUnknown, GUID, COMMETHOD, HRESULT, CoClass # for GetPath SLGP_SHORTPATH = 0x1 SLGP_UNCPRIORITY = 0x2 SLGP_RAWPATH = 0x4 # for SetShowCmd, GetShowCmd ##SW_SHOWNORMAL ##SW_SHOWMAXIMIZED ##SW_SHOWMINNOACTIVE # for Resolve ##SLR_INVOKE_MSI ##SLR_NOLINKINFO ##SLR_NO_UI ##SLR_NOUPDATE ##SLR_NOSEARCH ##SLR_NOTRACK ##SLR_UPDATE # fake these... ITEMIDLIST = c_int LPITEMIDLIST = LPCITEMIDLIST = POINTER(ITEMIDLIST) class IShellLinkA(IUnknown): _iid_ = GUID('{000214EE-0000-0000-C000-000000000046}') _methods_ = [ COMMETHOD([], HRESULT, 'GetPath', ( ['in', 'out'], c_char_p, 'pszFile' ), ( ['in'], c_int, 'cchMaxPath' ), ( ['in', 'out'], POINTER(WIN32_FIND_DATAA), 'pfd' ), ( ['in'], DWORD, 'fFlags' )), COMMETHOD([], HRESULT, 'GetIDList', ( ['retval', 'out'], POINTER(LPITEMIDLIST), 'ppidl' )), COMMETHOD([], HRESULT, 'SetIDList', ( ['in'], LPCITEMIDLIST, 'pidl' )), COMMETHOD([], HRESULT, 'GetDescription', ( ['in', 'out'], c_char_p, 'pszName' ), ( ['in'], c_int, 'cchMaxName' )), COMMETHOD([], HRESULT, 'SetDescription', ( ['in'], c_char_p, 'pszName' )), COMMETHOD([], HRESULT, 'GetWorkingDirectory', ( ['in', 'out'], c_char_p, 'pszDir' ), ( ['in'], c_int, 'cchMaxPath' )), COMMETHOD([], HRESULT, 'SetWorkingDirectory', ( ['in'], c_char_p, 'pszDir' )), COMMETHOD([], HRESULT, 'GetArguments', ( ['in', 'out'], c_char_p, 'pszArgs' ), ( ['in'], c_int, 'cchMaxPath' )), COMMETHOD([], HRESULT, 'SetArguments', ( ['in'], c_char_p, 'pszArgs' )), COMMETHOD(['propget'], HRESULT, 'Hotkey', ( ['retval', 'out'], POINTER(c_short), 'pwHotkey' )), COMMETHOD(['propput'], HRESULT, 'Hotkey', ( ['in'], c_short, 'pwHotkey' )), COMMETHOD(['propget'], HRESULT, 'ShowCmd', ( ['retval', 'out'], POINTER(c_int), 'piShowCmd' )), COMMETHOD(['propput'], HRESULT, 'ShowCmd', ( ['in'], c_int, 'piShowCmd' )), COMMETHOD([], HRESULT, 'GetIconLocation', ( ['in', 'out'], c_char_p, 'pszIconPath' ), ( ['in'], c_int, 'cchIconPath' ), ( ['in', 'out'], POINTER(c_int), 'piIcon' )), COMMETHOD([], HRESULT, 'SetIconLocation', ( ['in'], c_char_p, 'pszIconPath' ), ( ['in'], c_int, 'iIcon' )), COMMETHOD([], HRESULT, 'SetRelativePath', ( ['in'], c_char_p, 'pszPathRel' ), ( ['in'], DWORD, 'dwReserved' )), COMMETHOD([], HRESULT, 'Resolve', ( ['in'], c_int, 'hwnd' ), ( ['in'], DWORD, 'fFlags' )), COMMETHOD([], HRESULT, 'SetPath', ( ['in'], c_char_p, 'pszFile' )), ] def GetPath(self, flags=SLGP_SHORTPATH): buf = create_string_buffer(MAX_PATH) # We're not interested in WIN32_FIND_DATA self.__com_GetPath(buf, MAX_PATH, None, flags) return buf.value def GetDescription(self): buf = create_string_buffer(1024) self.__com_GetDescription(buf, 1024) return buf.value def GetWorkingDirectory(self): buf = create_string_buffer(MAX_PATH) self.__com_GetWorkingDirectory(buf, MAX_PATH) return buf.value def GetArguments(self): buf = create_string_buffer(1024) self.__com_GetArguments(buf, 1024) return buf.value def GetIconLocation(self): iIcon = c_int() buf = create_string_buffer(MAX_PATH) self.__com_GetIconLocation(buf, MAX_PATH, byref(iIcon)) return buf.value, iIcon.value class IShellLinkW(IUnknown): _iid_ = GUID('{000214F9-0000-0000-C000-000000000046}') _methods_ = [ COMMETHOD([], HRESULT, 'GetPath', ( ['in', 'out'], c_wchar_p, 'pszFile' ), ( ['in'], c_int, 'cchMaxPath' ), ( ['in', 'out'], POINTER(WIN32_FIND_DATAW), 'pfd' ), ( ['in'], DWORD, 'fFlags' )), COMMETHOD([], HRESULT, 'GetIDList', ( ['retval', 'out'], POINTER(LPITEMIDLIST), 'ppidl' )), COMMETHOD([], HRESULT, 'SetIDList', ( ['in'], LPCITEMIDLIST, 'pidl' )), COMMETHOD([], HRESULT, 'GetDescription', ( ['in', 'out'], c_wchar_p, 'pszName' ), ( ['in'], c_int, 'cchMaxName' )), COMMETHOD([], HRESULT, 'SetDescription', ( ['in'], c_wchar_p, 'pszName' )), COMMETHOD([], HRESULT, 'GetWorkingDirectory', ( ['in', 'out'], c_wchar_p, 'pszDir' ), ( ['in'], c_int, 'cchMaxPath' )), COMMETHOD([], HRESULT, 'SetWorkingDirectory', ( ['in'], c_wchar_p, 'pszDir' )), COMMETHOD([], HRESULT, 'GetArguments', ( ['in', 'out'], c_wchar_p, 'pszArgs' ), ( ['in'], c_int, 'cchMaxPath' )), COMMETHOD([], HRESULT, 'SetArguments', ( ['in'], c_wchar_p, 'pszArgs' )), COMMETHOD(['propget'], HRESULT, 'Hotkey', ( ['retval', 'out'], POINTER(c_short), 'pwHotkey' )), COMMETHOD(['propput'], HRESULT, 'Hotkey', ( ['in'], c_short, 'pwHotkey' )), COMMETHOD(['propget'], HRESULT, 'ShowCmd', ( ['retval', 'out'], POINTER(c_int), 'piShowCmd' )), COMMETHOD(['propput'], HRESULT, 'ShowCmd', ( ['in'], c_int, 'piShowCmd' )), COMMETHOD([], HRESULT, 'GetIconLocation', ( ['in', 'out'], c_wchar_p, 'pszIconPath' ), ( ['in'], c_int, 'cchIconPath' ), ( ['in', 'out'], POINTER(c_int), 'piIcon' )), COMMETHOD([], HRESULT, 'SetIconLocation', ( ['in'], c_wchar_p, 'pszIconPath' ), ( ['in'], c_int, 'iIcon' )), COMMETHOD([], HRESULT, 'SetRelativePath', ( ['in'], c_wchar_p, 'pszPathRel' ), ( ['in'], DWORD, 'dwReserved' )), COMMETHOD([], HRESULT, 'Resolve', ( ['in'], c_int, 'hwnd' ), ( ['in'], DWORD, 'fFlags' )), COMMETHOD([], HRESULT, 'SetPath', ( ['in'], c_wchar_p, 'pszFile' )), ] def GetPath(self, flags=SLGP_SHORTPATH): buf = create_unicode_buffer(MAX_PATH) # We're not interested in WIN32_FIND_DATA self.__com_GetPath(buf, MAX_PATH, None, flags) return buf.value def GetDescription(self): buf = create_unicode_buffer(1024) self.__com_GetDescription(buf, 1024) return buf.value def GetWorkingDirectory(self): buf = create_unicode_buffer(MAX_PATH) self.__com_GetWorkingDirectory(buf, MAX_PATH) return buf.value def GetArguments(self): buf = create_unicode_buffer(1024) self.__com_GetArguments(buf, 1024) return buf.value def GetIconLocation(self): iIcon = c_int() buf = create_unicode_buffer(MAX_PATH) self.__com_GetIconLocation(buf, MAX_PATH, byref(iIcon)) return buf.value, iIcon.value class ShellLink(CoClass): 'ShellLink class' _reg_clsid_ = GUID('{00021401-0000-0000-C000-000000000046}') _idlflags_ = [] _com_interfaces_ = [IShellLinkW, IShellLinkA] if __name__ == "__main__": import sys import comtypes from comtypes.client import CreateObject from comtypes.persist import IPersistFile shortcut = CreateObject(ShellLink) print(shortcut) ##help(shortcut) shortcut.SetPath(sys.executable) shortcut.SetDescription("Python %s" % sys.version) shortcut.SetIconLocation(sys.executable, 1) print(shortcut.GetPath(2)) print(shortcut.GetIconLocation()) pf = shortcut.QueryInterface(IPersistFile) pf.Save("foo.lnk", True) print(pf.GetCurFile())

38.697248

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0

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false

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0

null

0

1

0

1

0

null

0

4

4302f019559f0c9083b044614686db2a436e9fe1

329

py

Python

gcm/conf.py

jualjiman/django-gcm

dfcfc7900efd9ca1e8aaf799532c1bc0b21c52b5

[ "BSD-2-Clause" ]

null

gcm/conf.py

jualjiman/django-gcm

dfcfc7900efd9ca1e8aaf799532c1bc0b21c52b5

[ "BSD-2-Clause" ]

null

gcm/conf.py

jualjiman/django-gcm

dfcfc7900efd9ca1e8aaf799532c1bc0b21c52b5

[ "BSD-2-Clause" ]

1

2020-02-19T19:27:03.000Z

from django.conf import settings GCM_DEVICE_MODEL = getattr(settings, 'GCM_DEVICE_MODEL', 'gcm.models.Device') GCM_APIKEY = getattr(settings, 'GCM_APIKEY', None) GCM_MAX_RECIPIENTS = getattr(settings, 'GCM_MAX_RECIPIENTS', 1000) GCM_API_ENDPOINT = getattr(settings, 'GCM_API_ENDPOINT', "https://fcm.googleapis.com/fcm/send")

32.9

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329

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0

null

1

0

1

0

null

0

4

4aa83af5086856cc6b5d98b4d9bbfa6d2e8c21c7

210

py

Python

infobip_channels/email/models/response/get_sent_bulks.py

infobip-community/infobip-api-python-sdk

5ffc5ab877ee1748aa29391f991c8c5324387487

[ "MIT" ]

null

infobip_channels/email/models/response/get_sent_bulks.py

infobip-community/infobip-api-python-sdk

5ffc5ab877ee1748aa29391f991c8c5324387487

[ "MIT" ]

null

infobip_channels/email/models/response/get_sent_bulks.py

infobip-community/infobip-api-python-sdk

5ffc5ab877ee1748aa29391f991c8c5324387487

[ "MIT" ]

null

from typing import List from infobip_channels.email.models.response.core import BulkBase, BulksBase class Bulk(BulkBase): send_at: str class GetSentEmailBulksResponse(BulksBase): bulks: List[Bulk]

17.5

75

0.785714

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210

6.269231

0.730769

0

0.142857

210

11

76

19.090909

0.905556

0

1

0

true

0

0.333333

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null

0

null

0

1

0

1

0

1

0

4

4ab44b1ffe680d6b44d71a23b3045b247b9e0a94

2,239

py

Python

livinglots/models.py

596acres/django-livinglots

39937df584b955cbf58a84f2cd56cf023fb5b6ef

[ "BSD-3-Clause" ]

19

2015-03-02T15:11:12.000Z

2020-09-25T21:25:38.000Z

livinglots/models.py

596acres/django-livinglots

39937df584b955cbf58a84f2cd56cf023fb5b6ef

[ "BSD-3-Clause" ]

null

livinglots/models.py

596acres/django-livinglots

39937df584b955cbf58a84f2cd56cf023fb5b6ef

[ "BSD-3-Clause" ]

5

2015-03-02T15:11:17.000Z

2020-09-25T21:25:46.000Z

from django.apps import apps from django.conf import settings from django.core.exceptions import ImproperlyConfigured def get_model_name(name, optional=True): try: return settings.LIVING_LOTS['MODELS'][name] except KeyError: if not optional: raise ImproperlyConfigured(('Could not find a %s model. Did you ' 'set LIVING_LOTS.MODELS.%s in your ' 'settings.py?') % (name, name)) return None def get_model(name, optional=True): try: model_name = get_model_name(name, optional=optional) return apps.get_model(*model_name.split('.')) except Exception: if not optional: raise ImproperlyConfigured(('Could not find a %s model. Did you ' 'set LIVING_LOTS.MODELS.%s in your ' 'settings.py?') % (name, name)) return None def get_lot_model(): return get_model('lot') def get_lot_model_name(): return get_model_name('lot') def get_lotgroup_model(): return get_model('lotgroup') def get_lotlayer_model(): return get_model('lotlayer', optional=True) def get_organizer_model(): return get_model('organizer') def get_organizer_model_name(): return get_model_name('organizer') def get_owner_model(): return get_model('owner') def get_owner_model_name(): return get_model_name('owner') def get_owner_contact_model(): return get_model('owner_contact') def get_owner_contact_model_name(): return get_model_name('owner_contact') def get_owner_group_model(): return get_model('owner_group') def get_owner_group_model_name(): return get_model_name('owner_group') def get_parcel_model(): return get_model('parcel') def get_parcel_model_name(): return get_model_name('parcel') def get_pathway_model(): return get_model('pathway') def get_stewardproject_model(): return get_model('stewardproject') def get_stewardproject_model_name(): return get_model_name('stewardproject') def get_watcher_model(): return get_model('watcher') def get_watcher_model_name(): return get_model_name('watcher')

21.737864

77

0.665029

286

2,239

4.884615

0.171329

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0.190408

0.149606

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0.341446

0.24481

0.176092

0

0.237159

2,239

102

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21.95098

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false

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0.783333

0

null

0

1

0

null

0

1

0

1

0

4

436afa6fd6af3ec2d0852d14551b4e6042c90d45

1,933

py

Python

jogo_de _escolha_rapido.py

GabrielJ0shua/Jogo-em-python-de-escolha

e1b1b43d6f9abd1fe1206fe934825151c4284311

[ "MIT" ]

null

jogo_de _escolha_rapido.py

GabrielJ0shua/Jogo-em-python-de-escolha

e1b1b43d6f9abd1fe1206fe934825151c4284311

[ "MIT" ]

null

jogo_de _escolha_rapido.py

GabrielJ0shua/Jogo-em-python-de-escolha

e1b1b43d6f9abd1fe1206fe934825151c4284311

[ "MIT" ]

null

# coding : utf-8 print("\n \n \n \n \n \n Belo Horizonte, 05/06/2020, 17:34.\n Você está andando na sua casa normalmente indo tomar banho, acabado o banho quando você sai do box você tropeça e cai e bate a cabeça na pia. \n Você acorda no hospital e você é uma enfermeira nossa frente.") x = input("\n Você pode fazer uma pergunta para ela: \n 1 - Onde estou? \n 2 - Em que ano estamos? \n 3 - Olá estranho? \n 4 - Onde está meus parentes? \n Escolha: ") if x == '1': print("\n Enfermeira: Você está no hospital, teve uma batida forte, teve um coma, e agora está de volta. Parabéns \n \n ") if x == '2': print("\n Enfermeira: 09/08/2030, 03:50, pós pandemia, e outras coisas que não precisa se preocupar. \n \n ") if x == '3': print("\n Enfermeira: Olá, bem vindo ao mundo novamente, espero que se recupere bem. \n \n ") if x == '4': print("\n Enfermeira: Bem, seus vizinhos que lhe socorreram, tentamos contato mas... , nossa tenho que pegar sua bolsa de soro! \n \n ") print("Você: Quando reberei alta? Posso ter meu celular de volta? \n Enfermeira: Acalma-se, vou pegar para você") print("\n \n Ela te entrega o seu smartphone. \n Quando você vê as notícias se assusta: \n Covid-19 se modifica e está com nova taxa de mortalidade de 20%. \n Países estão a beira do colapso. \n 'unica saida sera a reforma agraria' diz especialista. \n Todos os Presidentes da America morrem. \n No Brasil kkk(ku klus kla) é perseguida pelo PCC(primeiro comando da capital). \n 1° presidente preto no Brasil é eleito via blockchain sendo o novo sistema eleitoral brasileiro. \n Cura é encontrada mas a um preço muito elevado. \n Ditadores foram executados pela IA que desenvolveram em parceria. \n Os mais ricos do mundo estão entrendo em crise por conta das criptomoedas. \n Cidades estão adotando sistemas de anarquia para se manter.") print("\n isso tudo em menos de 4 anos... Você desmaia. FIM DO CAP 1.")

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null

0

1

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4

43b8f09c3113985ed2935959888a69d02a4919ea

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py

Python

tests/functional/test_index.py

infinite-skx/pip

7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4

[ "MIT" ]

1

2022-03-06T13:06:33.000Z

tests/functional/test_index.py

infinite-skx/pip

7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4

[ "MIT" ]

5

2018-01-26T16:41:06.000Z

2021-10-16T08:45:28.000Z

tests/functional/test_index.py

infinite-skx/pip

7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4

[ "MIT" ]

6

2017-07-22T14:09:49.000Z

2019-10-09T09:29:34.000Z

import pytest from pip._internal.cli.status_codes import ERROR, SUCCESS from pip._internal.commands import create_command from tests.lib import PipTestEnvironment @pytest.mark.network def test_list_all_versions_basic_search(script: PipTestEnvironment) -> None: """ End to end test of index versions command. """ output = script.pip("index", "versions", "pip", allow_stderr_warning=True) assert "Available versions:" in output.stdout assert ( "20.2.3, 20.2.2, 20.2.1, 20.2, 20.1.1, 20.1, 20.0.2" ", 20.0.1, 19.3.1, 19.3, 19.2.3, 19.2.2, 19.2.1, 19.2, 19.1.1" ", 19.1, 19.0.3, 19.0.2, 19.0.1, 19.0, 18.1, 18.0, 10.0.1, 10.0.0, " "9.0.3, 9.0.2, 9.0.1, 9.0.0, 8.1.2, 8.1.1, " "8.1.0, 8.0.3, 8.0.2, 8.0.1, 8.0.0, 7.1.2, 7.1.1, 7.1.0, 7.0.3, " "7.0.2, 7.0.1, 7.0.0, 6.1.1, 6.1.0, 6.0.8, 6.0.7, 6.0.6, 6.0.5, " "6.0.4, 6.0.3, 6.0.2, 6.0.1, 6.0, 1.5.6, 1.5.5, 1.5.4, 1.5.3, " "1.5.2, 1.5.1, 1.5, 1.4.1, 1.4, 1.3.1, 1.3, 1.2.1, 1.2, 1.1, 1.0.2," " 1.0.1, 1.0, 0.8.3, 0.8.2, 0.8.1, 0.8, 0.7.2, 0.7.1, 0.7, 0.6.3, " "0.6.2, 0.6.1, 0.6, 0.5.1, 0.5, 0.4, 0.3.1, " "0.3, 0.2.1, 0.2" in output.stdout ) @pytest.mark.network def test_list_all_versions_search_with_pre(script: PipTestEnvironment) -> None: """ See that adding the --pre flag adds pre-releases """ output = script.pip("index", "versions", "pip", "--pre", allow_stderr_warning=True) assert "Available versions:" in output.stdout assert ( "20.2.3, 20.2.2, 20.2.1, 20.2, 20.2b1, 20.1.1, 20.1, 20.1b1, 20.0.2" ", 20.0.1, 19.3.1, 19.3, 19.2.3, 19.2.2, 19.2.1, 19.2, 19.1.1" ", 19.1, 19.0.3, 19.0.2, 19.0.1, 19.0, 18.1, 18.0, 10.0.1, 10.0.0, " "10.0.0b2, 10.0.0b1, 9.0.3, 9.0.2, 9.0.1, 9.0.0, 8.1.2, 8.1.1, " "8.1.0, 8.0.3, 8.0.2, 8.0.1, 8.0.0, 7.1.2, 7.1.1, 7.1.0, 7.0.3, " "7.0.2, 7.0.1, 7.0.0, 6.1.1, 6.1.0, 6.0.8, 6.0.7, 6.0.6, 6.0.5, " "6.0.4, 6.0.3, 6.0.2, 6.0.1, 6.0, 1.5.6, 1.5.5, 1.5.4, 1.5.3, " "1.5.2, 1.5.1, 1.5, 1.4.1, 1.4, 1.3.1, 1.3, 1.2.1, 1.2, 1.1, 1.0.2," " 1.0.1, 1.0, 0.8.3, 0.8.2, 0.8.1, 0.8, 0.7.2, 0.7.1, 0.7, 0.6.3, " "0.6.2, 0.6.1, 0.6, 0.5.1, 0.5, 0.4, 0.3.1, " "0.3, 0.2.1, 0.2" in output.stdout ) @pytest.mark.network def test_list_all_versions_returns_no_matches_found_when_name_not_exact() -> None: """ Test that non exact name do not match """ command = create_command("index") cmdline = "versions pand" with command.main_context(): options, args = command.parse_args(cmdline.split()) status = command.run(options, args) assert status == ERROR @pytest.mark.network def test_list_all_versions_returns_matches_found_when_name_is_exact() -> None: """ Test that exact name matches """ command = create_command("index") cmdline = "versions pandas" with command.main_context(): options, args = command.parse_args(cmdline.split()) status = command.run(options, args) assert status == SUCCESS

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43ca4ab72e3e4216d88eec3408a1de7527d2f6e1

83

py

Python

sparnn/models/__init__.py

QUVA-Lab/VideoLSTM

fcacf9be37cff634652ff4e08c694de91627900f

[ "BSD-2-Clause-FreeBSD" ]

null

sparnn/models/__init__.py

QUVA-Lab/VideoLSTM

fcacf9be37cff634652ff4e08c694de91627900f

[ "BSD-2-Clause-FreeBSD" ]

null

sparnn/models/__init__.py

QUVA-Lab/VideoLSTM

fcacf9be37cff634652ff4e08c694de91627900f

[ "BSD-2-Clause-FreeBSD" ]

null

__author__ = 'zhenyang' from model import Model from video_model import VideoModel

20.75

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0.831325

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0.636364

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0.13253

83

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null

0

1

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4

43dcf91200c3022d02f49e4d78c8d114eb2234dd

181

py

Python

qa/common/rest/base_rest_helper.py

isabella232/aurora

a2021e6829fb6024eac45f4afe54e2fbc70b847c

[ "Apache-2.0" ]

23

2015-01-22T22:23:35.000Z

2021-10-21T23:08:29.000Z

qa/common/rest/base_rest_helper.py

sysbot/aurora

9e319d4eb8c760cf84cb80ed2959cd52976af11c

[ "Apache-2.0" ]

2

2015-10-13T00:47:45.000Z

2019-05-06T14:52:33.000Z

qa/common/rest/base_rest_helper.py

sysbot/aurora

9e319d4eb8c760cf84cb80ed2959cd52976af11c

[ "Apache-2.0" ]

27

2015-03-18T19:39:30.000Z

2022-03-11T00:58:09.000Z

# these imports will be used by classes-successors import json from nose.tools import ok_ class BaseRESTHelper(object): def __init__(self, utils): self.utils = utils

18.1

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0

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0

1

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4

78d8b2d89bb3adee2956f0a1f3f66aa4f0dac833

212

py

Python

backend/hi_hungry/views.py

mattzmyname/hi-im-hungry

d7dcb1fc6b1dd63a00ebce411955f19385bae962

[ "MIT" ]

null

backend/hi_hungry/views.py

mattzmyname/hi-im-hungry

d7dcb1fc6b1dd63a00ebce411955f19385bae962

[ "MIT" ]

null

backend/hi_hungry/views.py

mattzmyname/hi-im-hungry

d7dcb1fc6b1dd63a00ebce411955f19385bae962

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. from django.http import HttpResponse, JsonResponse from hi_hungry.src import hi_hungry def search(request): return JsonResponse(hi_hungry.main())

21.2

50

0.811321

30

212

5.633333

0.666667

0.142012

0

0.122642

212

9

51

23.555556

0.908602

0.108491

0

1

0.2

false

0

0.6

0.2

1

0

null

0

1

0

null

0

1

0

4

78e0a1d18183e5a58cf26302f999bb3cf45215bc

493

py

Python

INBa/2015/Semyenov_A_N/task_3_24.py

YukkaSarasti/pythonintask

eadf4245abb65f4400a3bae30a4256b4658e009c

[ "Apache-2.0" ]

null

INBa/2015/Semyenov_A_N/task_3_24.py

YukkaSarasti/pythonintask

eadf4245abb65f4400a3bae30a4256b4658e009c

[ "Apache-2.0" ]

null

INBa/2015/Semyenov_A_N/task_3_24.py

YukkaSarasti/pythonintask

eadf4245abb65f4400a3bae30a4256b4658e009c

[ "Apache-2.0" ]

null

#Задача 3. Вариант 24 #Напишите программу, которая выводит имя "Максимилиан Гольдман", и запрашивает его псевдоним. Программа должна сцеплять две эти строки и выводить полученную строку, разделяя имя и псевдоним с помощью тире. #Semyenov A.N.Ы #08.03.2016 print("Сегодня речь пойдёт про Максимилиана Гольдмана") print("Под каким именем мы знаем этого человека?"); print("Ваш ответ: Макс Рейнхардт") print("Всё верно: Максимилиан Гольдман - Макс Рейнхардт") input('\nНажмите Enter для выхода')

54.777778

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0.787018

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78e9c98a5d51605ae135cc3bf678304903636b62

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py

Python

tests/data_handling/__init__.py

IMS-Bio2Core-Facility/BIC086

a08c2e594b58a235fb223922d1eeadd0272f8946

[ "MIT" ]

2

2021-06-03T14:26:32.000Z

2021-10-02T21:32:12.000Z

tests/data_handling/__init__.py

IMS-Bio2Core-Facility/GTExQuery

95211f56ba567811c9d7a5def6ef86608be0cecd

[ "MIT" ]

4

2021-07-12T13:34:28.000Z

2021-07-21T12:21:22.000Z

tests/data_handling/__init__.py

IMS-Bio2Core-Facility/GTExQuery

95211f56ba567811c9d7a5def6ef86608be0cecd

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Tests for the data_handling submodule."""

23

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1

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null

0

1

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4

601c75f649ba2e7834d0099d36e458eda199c8f7

178

py

Python

fabfile/data.py

isabella232/lookatthis

fe9ccc896f50ede13f9c469d38d90c8a732f9a71

[ "FSFAP" ]

15

2015-02-21T13:56:25.000Z

2019-08-14T21:19:09.000Z

fabfile/data.py

nprapps/lookatthis

fe9ccc896f50ede13f9c469d38d90c8a732f9a71

[ "FSFAP" ]

444

2015-01-06T16:54:13.000Z

2021-09-22T11:46:33.000Z

fabfile/data.py

isabella232/lookatthis

fe9ccc896f50ede13f9c469d38d90c8a732f9a71

[ "FSFAP" ]

13

2015-01-05T14:33:15.000Z

2021-02-23T10:45:32.000Z

#!/usr/bin/env python """ Commands that update or process the application data. """ from fabric.api import task from fabric.state import env import app_config import copytext

14.833333

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0.769663

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5.037037

0.777778

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1

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1

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1

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4

6038031d297ac66d74c05a5d553968024644b565

211

py

Python

config/__init__.py

shirishgoyal/scholars

c89a87b0f8338e75cb5da0d0d20a4a340deb694e

[ "MIT" ]

null

config/__init__.py

shirishgoyal/scholars

c89a87b0f8338e75cb5da0d0d20a4a340deb694e

[ "MIT" ]

14

2017-05-16T21:05:40.000Z

2021-06-10T20:38:00.000Z

config/__init__.py

shirishgoyal/scholars

c89a87b0f8338e75cb5da0d0d20a4a340deb694e

[ "MIT" ]

3

2016-10-04T21:33:11.000Z

2019-02-03T01:04:39.000Z

# from __future__ import absolute_import # # from configurations import importer # importer.install() # # from .local import Local # from .production import Production # # from .celery import app as celery_app

21.1

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9

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23.444444

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1

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null

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4

60519e68d78e107bd2776ea0e455ec007229936f

67

py

Python

tmplr/__init__.py

joshleejosh/tmplr

19209bb43ce40a307eecf2faf6dae8ccc8b0c9db

[ "MIT" ]

null

tmplr/__init__.py

joshleejosh/tmplr

19209bb43ce40a307eecf2faf6dae8ccc8b0c9db

[ "MIT" ]

null

tmplr/__init__.py

joshleejosh/tmplr

19209bb43ce40a307eecf2faf6dae8ccc8b0c9db

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Yet another static site generator. """

13.4

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6068f1aa6641841518a93aff34dda944408c3fb1

458

py

Python

python_tricks/dict_tricks/sort_dict_demo.py

ftconan/python3

eb63ba33960072f792ecce6db809866b38c402f8

[ "MIT" ]

1

2018-12-19T22:07:56.000Z

python_tricks/dict_tricks/sort_dict_demo.py

ftconan/python3

eb63ba33960072f792ecce6db809866b38c402f8

[ "MIT" ]

12

2020-03-14T05:32:26.000Z

2022-03-12T00:08:49.000Z

python_tricks/dict_tricks/sort_dict_demo.py

ftconan/python3

eb63ba33960072f792ecce6db809866b38c402f8

[ "MIT" ]

1

2018-12-19T22:08:00.000Z

""" @author: magician @date: 2019/12/6 @file: sort_dict_demo.py """ import operator if __name__ == '__main__': xs = {'a': 4, 'c': 2, 'b': 3, 'd': 1} print(sorted(xs.items())) print(sorted(xs.items(), key=lambda x: x[1])) print(sorted(xs.items(), key=operator.itemgetter(1))) print(sorted(xs.items(), key=lambda x: abs(x[1]))) print(sorted(xs.items(), key=lambda x: x[1], reverse=True))

25.444444

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0

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null

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1

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4

606cafc4b5123c351ad00e4553b54e2746a814f5

509

py

Python

blog_haystack/persons_database/persons/search_indexes.py

ghjan/blog-projects

aa6925724e457bec276d98cf7b55b5cdaf2ab5f4

[ "MIT" ]

66

2017-11-18T06:41:39.000Z

2021-09-02T15:47:08.000Z

blog_haystack/persons_database/persons/search_indexes.py

ghjan/blog-projects

aa6925724e457bec276d98cf7b55b5cdaf2ab5f4

[ "MIT" ]

2

2018-05-28T14:06:05.000Z

2020-03-21T14:05:07.000Z

blog_haystack/persons_database/persons/search_indexes.py

ghjan/blog-projects

aa6925724e457bec276d98cf7b55b5cdaf2ab5f4

[ "MIT" ]

35

2017-11-05T23:48:15.000Z

2021-09-15T12:15:39.000Z

from haystack import indexes from .models import Person class PersonIndex(indexes.SearchIndex, indexes.Indexable): text = indexes.CharField(document=True, use_template=True) first_name = indexes.CharField(model_attr='first_name') last_name = indexes.CharField(model_attr='last_name') gender = indexes.CharField(model_attr='gender') email = indexes.CharField(model_attr='email') ip_address = indexes.CharField(model_attr='ip_address') def get_model(self): return Person

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null

1

0

null

0

1

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4

6075d2e6e1cc1408947f83ac1a36f54a163c5725

64

py

Python

src/conftest.py

LibertasConstruction/Libertas

fa17a7b8bf447f9a4cb88ee5bab1833cab1a60e8

[ "MIT" ]

null

src/conftest.py

LibertasConstruction/Libertas

fa17a7b8bf447f9a4cb88ee5bab1833cab1a60e8

[ "MIT" ]

null

src/conftest.py

LibertasConstruction/Libertas

fa17a7b8bf447f9a4cb88ee5bab1833cab1a60e8

[ "MIT" ]

null

# This file notifies pytest of the existence of the submodules

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1

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null

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0

4

60b43d65e61dff2ab0b612cae36106e07d903357

806

py

Python

setup.py

CD3/svgprestools

12f0bdb3faee66e743538e9ca026d38ac46b776a

[ "MIT" ]

null

setup.py

CD3/svgprestools

12f0bdb3faee66e743538e9ca026d38ac46b776a

[ "MIT" ]

null

setup.py

CD3/svgprestools

12f0bdb3faee66e743538e9ca026d38ac46b776a

[ "MIT" ]

null

#! /usr/bin/env python from setuptools import setup, find_packages setup( name='svgprestools', license="MIT", version="0.1", description='A collection of tools for generating svg images for presentations.', url='https://github.com/CD3/svgprestools', author='C.D. Clark III', packages=find_packages(), install_requires=['click','lxml'], entry_points=''' [console_scripts] txt2svg=svgprestools.scripts:txt2svg svgmontage=svgprestools.scripts:svgmontage write2sozi=svgprestools.scripts:write2sozi extract-write-ink=svgprestools.scripts:extractWriteInk update-sozi-presentation=svgprestools.scripts:updateSoziPresentation write-cat=svgprestools.scripts:writeCat write-change-background=svgprestools.scripts:writeChangeBackground ''', )

32.24

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806

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0.709302

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0.010101

0.140199

806

24

86

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0.848485

0.026055

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null

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1

null

0

1

0

4

60d45af86a161b9b5f2fc33df855bbe19d5ba58c

156

py

Python

project/views/home.py

aldencolerain/bmanners

a5664f2b97f74b3b4aa9360c2b01f26eb3706a04

[ "MIT" ]

null

project/views/home.py

aldencolerain/bmanners

a5664f2b97f74b3b4aa9360c2b01f26eb3706a04

[ "MIT" ]

null

project/views/home.py

aldencolerain/bmanners

a5664f2b97f74b3b4aa9360c2b01f26eb3706a04

[ "MIT" ]

null

from django.shortcuts import render import logging logger = logging.getLogger(__name__) def index(request): return render(request, 'home/index.html', {})

22.285714

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156

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null

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null

0

1

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4

60dedfb2b071bb0d5b179b1b97f0a7afccfae80f

206

py

Python

python/testData/testRunner/env/createConfigurationTest/tests_folder/test_lonely.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/testRunner/env/createConfigurationTest/tests_folder/test_lonely.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/testRunner/env/createConfigurationTest/tests_folder/test_lonely.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from unittest import TestCase from logic import smart_func from tests_package.test_tools import ANSWER class TestLonely(TestCase): def test_test(self): self.assertEqual(ANSWER, smart_func())

20.6

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9

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null

0

1

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1

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4

60eadf8cff97ac03889c2a6d4038dba1f02f9c63

118

py

Python

blog/context_processors.py

niloy-biswas/Safe-Saying

e4abed471cbb93293fcaccdc6d72ea52e88a7c3d

[ "MIT" ]

3

2020-08-23T17:26:59.000Z

2020-08-23T17:34:12.000Z

blog/context_processors.py

niloy-biswas/Safe-Saying

e4abed471cbb93293fcaccdc6d72ea52e88a7c3d

[ "MIT" ]

null

blog/context_processors.py

niloy-biswas/Safe-Saying

e4abed471cbb93293fcaccdc6d72ea52e88a7c3d

[ "MIT" ]

1

2020-08-23T17:34:14.000Z

from .models import Post def posts(request): return { 'all_posts': Post.objects.order_by('posted'), }

19.666667

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118

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0.866667

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118

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54

19.666667

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0

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0

null

0

1

0

null

0

1

0

4

60f79efaac34d7bcb69234830f823a8815d11b6a

5,980

py

Python

never_give_up_20190403/cursor_controller.py

JaneHQ1/Predicting-Stroke-Severity-from-Computed-Tomography-Images

6988ec693d44da52c1d09946bda0246205de3703

[ "MIT" ]

null

never_give_up_20190403/cursor_controller.py

JaneHQ1/Predicting-Stroke-Severity-from-Computed-Tomography-Images

6988ec693d44da52c1d09946bda0246205de3703

[ "MIT" ]

null

never_give_up_20190403/cursor_controller.py

JaneHQ1/Predicting-Stroke-Severity-from-Computed-Tomography-Images

6988ec693d44da52c1d09946bda0246205de3703

[ "MIT" ]

null

class ControlSys(): def __init__(self, fig, imdis, vol_tran, vol_fron, vol_sagi, ax_tran, ax_fron, ax_sagi): self.fig =fig self.imdis = imdis self.vol_tran = vol_tran self.vol_fron = vol_fron self.vol_sagi = vol_sagi self.ax_tran = ax_tran self.ax_fron = ax_fron self.ax_sagi = ax_sagi self.scroll_tran = None self.scroll_fron = None self.scroll_sagi = None self.lx_tran = ax_tran.axhline(color='b', linewidth=0.8) self.ly_tran = ax_tran.axvline(color='b', linewidth=0.8) self.lx_fron = ax_fron.axhline(color='b', linewidth=0.8) self.ly_fron = ax_fron.axvline(color='b', linewidth=0.8) self.lx_sagi = ax_sagi.axhline(color='b', linewidth=0.8) self.ly_sagi = ax_sagi.axvline(color='b', linewidth=0.8) self.txt_tran = ax_tran.text(0, -10, '', color='b') self.txt_fron = ax_fron.text(0, -10, '', color='b') self.txt_sagi = ax_sagi.text(0, -10, '', color='b') self.fig.canvas.mpl_connect('button_press_event', self.button_press_events) def button_press_events(self, event): if self.ax_tran.in_axes(event): self.fig.canvas.mpl_connect('button_press_event', self.add_cursor(event, self.ax_tran)) self.fig.canvas.mpl_connect('button_press_event', self.frontal_view(int(event.ydata))) self.fig.canvas.mpl_connect('button_press_event', self.sagittal_view(int(event.xdata))) # self.scroll_tran = self.fig.canvas.mpl_connect('scroll_event', self.transverse_scroll) # self.fig.canvas.mpl_disconnect(self.scroll_fron) # self.fig.canvas.mpl_disconnect(self.scroll_sagi) elif self.ax_fron.in_axes(event): self.fig.canvas.mpl_connect('button_press_event', self.add_cursor(event, self.ax_fron)) self.fig.canvas.mpl_connect('button_press_event', self.transverse_view(int(event.ydata))) self.fig.canvas.mpl_connect('button_press_event', self.sagittal_view(int(event.xdata))) # self.scroll_fron = self.fig.canvas.mpl_connect('scroll_event', self.frontal_scroll) # self.fig.canvas.mpl_disconnect(self.scroll_tran) # self.fig.canvas.mpl_disconnect(self.scroll_sagi) elif self.ax_sagi.in_axes(event): self.fig.canvas.mpl_connect('button_press_event', self.add_cursor(event, self.ax_sagi)) self.fig.canvas.mpl_connect('button_press_event', self.transverse_view(int(event.ydata))) self.fig.canvas.mpl_connect('button_press_event', self.frontal_view(int(event.xdata))) # self.scroll_sagi = self.fig.canvas.mpl_connect('scroll_event', self.sagittal_scroll) # self.fig.canvas.mpl_disconnect(self.scroll_tran) # self.fig.canvas.mpl_disconnect(self.scroll_fron) def transverse_view(self, index): self.ax_tran.index = index self.imdis.update_transverse_display(self.ax_tran.index) self.fig.canvas.draw_idle() def frontal_view(self, index): self.ax_fron.index = index self.imdis.update_frontal_display(self.ax_fron.index) self.fig.canvas.draw_idle() def sagittal_view(self, index): self.ax_sagi.index = index self.imdis.update_sagittal_display(self.ax_sagi.index) self.fig.canvas.draw_idle() def add_cursor(self, event, ax): if ax is self.ax_tran: x, y, z = event.xdata, event.ydata, self.ax_tran.index coord_tran = [x, y, z] coord_fron = [x, z, y] coord_sagi = [y, z, x] self.plot_cursor(coord_tran, self.lx_tran, self.ly_tran, self.txt_tran) self.plot_cursor(coord_fron, self.lx_fron, self.ly_fron, self.txt_fron) self.plot_cursor(coord_sagi, self.lx_sagi, self.ly_sagi, self.txt_sagi) if ax is self.ax_fron: x, y, z = event.xdata, event.ydata, self.ax_fron.index coord_fron = [x, y, z] coord_tran = [x, z, y] coord_sagi = [z, y, x] self.plot_cursor(coord_tran, self.lx_tran, self.ly_tran, self.txt_tran) self.plot_cursor(coord_fron, self.lx_fron, self.ly_fron, self.txt_fron) self.plot_cursor(coord_sagi, self.lx_sagi, self.ly_sagi, self.txt_sagi) if ax is self.ax_sagi: x, y, z = event.xdata, event.ydata, self.ax_sagi.index coord_sagi = [x, y, z] coord_tran = [z, x, y] coord_fron = [z, y, x] self.plot_cursor(coord_tran, self.lx_tran, self.ly_tran, self.txt_tran) self.plot_cursor(coord_fron, self.lx_fron, self.ly_fron, self.txt_fron) self.plot_cursor(coord_sagi, self.lx_sagi, self.ly_sagi, self.txt_sagi) def plot_cursor(self, coord, lx, ly, txt): x, y, z = coord[0], coord[1], coord[2] lx.set_ydata(y) ly.set_xdata(x) txt.set_text('x=%1d y=%1d z=%1d' % (x, y, z)) self.fig.canvas.draw_idle() def transverse_scroll(self, event): if event.button == 'down': self.ax_tran.index -= 1 if event.button == 'up': self.ax_tran.index += 1 self.imdis.update_transverse_display(self.ax_tran.index) self.fig.canvas.draw_idle() def frontal_scroll(self, event): if event.button == 'down': self.ax_fron.index -= 1 if event.button == 'up': self.ax_fron.index += 1 self.imdis.update_frontal_display(self.ax_fron.index) self.fig.canvas.draw_idle() def sagittal_scroll(self, event): if event.button == 'down': self.ax_sagi.index -= 1 if event.button == 'up': self.ax_sagi.index += 1 self.imdis.update_sagittal_display(self.ax_sagi.index) self.fig.canvas.draw_idle()

45.30303

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0.594672

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5,980

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0

0.11

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null

0

1

0

null

0

4

71472a0bae798760e9897265228347ffe4263a8e

22

py

Python

cookbook/control_plane/run_workflow.py

jinserk/flytesnacks

b4742d1dedecdf8d4d6fc20f854128bc24657e8f

[ "Apache-2.0" ]

1

2021-08-20T17:28:42.000Z

cookbook/control_plane/run_workflow.py

jeevb/flytesnacks

f32f32482088d717b399864c5470ae546ebcba7d

[ "Apache-2.0" ]

null

cookbook/control_plane/run_workflow.py

jeevb/flytesnacks

f32f32482088d717b399864c5470ae546ebcba7d

[ "Apache-2.0" ]

null

""" Run a Workflow """

7.333333

14

0.545455

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4

1

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0.181818

22

3

15

7.333333

0.666667

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null

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true

0

null

1

0

null

0

1

0

null

0

1

0

4

7168ff0ea1aeeeae5d4ac368d9ca770da09eedf4

106

py

Python

app/games/enums/game_types.py

ExiledNarwal28/cardbot

1e0fa1e9c08b512056c2af0b408eded224c9adbb

[ "MIT" ]

null

app/games/enums/game_types.py

ExiledNarwal28/cardbot

1e0fa1e9c08b512056c2af0b408eded224c9adbb

[ "MIT" ]

41

2020-05-10T20:18:19.000Z

2020-12-08T10:42:31.000Z

app/games/enums/game_types.py

ExiledNarwal28/cardbot

1e0fa1e9c08b512056c2af0b408eded224c9adbb

[ "MIT" ]

null

from enum import Enum class GameType(Enum): fuck_you = 'Fuck you' ride_the_bus = 'Ride the bus'

15.142857

33

0.679245

17

106

4.058824

0.588235

0.202899

0.289855

0

0.235849

106

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34

17.666667

0.851852

0

0.188679

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null

1

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1

0

null

0

4

717e54de9cee2e3508de6756b0cd91b2450a1c06

1,923

py

Python

src/py/util/test_abundant.py

cgnik/euler

fe70459a0e0d0272980300a4782872f2e545fea5

[ "MIT" ]

null

src/py/util/test_abundant.py

cgnik/euler

fe70459a0e0d0272980300a4782872f2e545fea5

[ "MIT" ]

null

src/py/util/test_abundant.py

cgnik/euler

fe70459a0e0d0272980300a4782872f2e545fea5

[ "MIT" ]

null

from unittest import TestCase from util.abundant import abundant_numbers, contains_sum_for class Test_abundants(TestCase): test_data1 = [] test_data2 = [[1, 2], [3, 4]] test_data3 = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] # def test_abundants_wierdos(self): # # [1, 2, 4, 11, 22, 44, 121, 242, 253, 484, 506, 1012, 2783, 5566] # # [1, 2, 4, 11, 22, 23, 44, 46, 92, 121, 242, 253, 484, 506, 1012, 2783, 5566] # abundants = abundant_numbers(11493) # self.assertTrue(11132 in abundants) # self.assertTrue(11492 in abundants) # def test_abundants(self): self.assertListEqual(abundant_numbers(270), [12, 18, 20, 24, 30, 36, 40, 42, 48, 54, 56, 60, 66, 70, 72, 78, 80, 84, 88, 90, 96, 100, 102, 104, 108, 112, 114, 120, 126, 132, 138, 140, 144, 150, 156, 160, 162, 168, 174, 176, 180, 186, 192, 196, 198, 200, 204, 208, 210, 216, 220, 222, 224, 228, 234, 240, 246, 252, 258, 260, 264, 270]) def test_contains_sum_for(self): self.assertTrue(contains_sum_for(3, [1, 2])) self.assertFalse(contains_sum_for(3, [2, 2])) self.assertTrue(contains_sum_for(15, [1, 2, 3, 4, 5, 10, 20, 30, 40])) self.assertTrue(contains_sum_for(15, [1, 2, 3, 4, 5, 13, 20, 30, 40])) self.assertFalse(contains_sum_for(15, [1, 2, 3, 4, 5, 20, 30, 40])) self.assertTrue(contains_sum_for(24, [12, 17, 20])) self.assertTrue(contains_sum_for(7141, [1, 444, 7723, 7140, 8002])) self.assertTrue(contains_sum_for(34, [12, 17, 20])) self.assertFalse(contains_sum_for(19, [12, 13, 14, 15, 17, 20])) self.assertFalse(contains_sum_for(11132, [])) self.assertFalse(contains_sum_for(11492, []))

51.972973

116

0.542382

276

1,923

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0.180915

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1

0

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null

0

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0

4

71834bcc92dd59f42cce64d7064f5afdf713e69a

2,938

py

Python

tensorflow_checkpoint_reader/pb/tensorflow/compiler/xla/service/hlo_execution_profile_data_pb2.py

shawwn/tensorflow-checkpoint-reader

f0e65548411e3bd66a07e36bb1850907a05952d0

[ "MIT" ]

1

2021-12-02T15:06:09.000Z

tensorflow_checkpoint_reader/pb/tensorflow/compiler/xla/service/hlo_execution_profile_data_pb2.py

shawwn/tensorflow-checkpoint-reader

f0e65548411e3bd66a07e36bb1850907a05952d0

[ "MIT" ]

null

tensorflow_checkpoint_reader/pb/tensorflow/compiler/xla/service/hlo_execution_profile_data_pb2.py

shawwn/tensorflow-checkpoint-reader

f0e65548411e3bd66a07e36bb1850907a05952d0

[ "MIT" ]

null

'Generated protocol buffer code.' from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() from .....tensorflow.compiler.xla.service import hlo_profile_printer_data_pb2 as tensorflow_dot_compiler_dot_xla_dot_service_dot_hlo__profile__printer__data__pb2 DESCRIPTOR = _descriptor.FileDescriptor(name='tensorflow/compiler/xla/service/hlo_execution_profile_data.proto', package='xla', syntax='proto3', serialized_options=b'\xf8\x01\x01', create_key=_descriptor._internal_create_key, serialized_pb=b'\n@tensorflow/compiler/xla/service/hlo_execution_profile_data.proto\x12\x03xla\x1a>tensorflow/compiler/xla/service/hlo_profile_printer_data.proto"e\n\x17HloExecutionProfileData\x120\n\x0cprinter_data\x18\x01 \x01(\x0b2\x1a.xla.HloProfilePrinterData\x12\x18\n\x10profile_counters\x18\x02 \x03(\x03B\x03\xf8\x01\x01b\x06proto3', dependencies=[tensorflow_dot_compiler_dot_xla_dot_service_dot_hlo__profile__printer__data__pb2.DESCRIPTOR]) _HLOEXECUTIONPROFILEDATA = _descriptor.Descriptor(name='HloExecutionProfileData', full_name='xla.HloExecutionProfileData', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='printer_data', full_name='xla.HloExecutionProfileData.printer_data', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='profile_counters', full_name='xla.HloExecutionProfileData.profile_counters', index=1, number=2, type=3, cpp_type=2, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=137, serialized_end=238) _HLOEXECUTIONPROFILEDATA.fields_by_name['printer_data'].message_type = tensorflow_dot_compiler_dot_xla_dot_service_dot_hlo__profile__printer__data__pb2._HLOPROFILEPRINTERDATA DESCRIPTOR.message_types_by_name['HloExecutionProfileData'] = _HLOEXECUTIONPROFILEDATA _sym_db.RegisterFileDescriptor(DESCRIPTOR) HloExecutionProfileData = _reflection.GeneratedProtocolMessageType('HloExecutionProfileData', (_message.Message,), {'DESCRIPTOR': _HLOEXECUTIONPROFILEDATA, '__module__': 'tensorflow.compiler.xla.service.hlo_execution_profile_data_pb2'}) _sym_db.RegisterMessage(HloExecutionProfileData) DESCRIPTOR._options = None

172.823529

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0.059322

0.366949

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null

0

1

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1

0

null

0

1

0

4

71869c687334a4095547763af341ed2c1cf591c3

435,726

py

Python

RegisterUI/source2.py

facundopadilla/QtDesigner-PySide2-PyQT5

5198e68eacc964be1b716e1fc3f9a1ec6f22a6dd

[ "MIT" ]

12

2020-06-27T19:24:37.000Z

2021-09-19T15:35:20.000Z

RegisterUI/source2.py

facundokimbo/QtDesigner-PySide2-PyQT5

5198e68eacc964be1b716e1fc3f9a1ec6f22a6dd

[ "MIT" ]

null

RegisterUI/source2.py

facundokimbo/QtDesigner-PySide2-PyQT5

5198e68eacc964be1b716e1fc3f9a1ec6f22a6dd

[ "MIT" ]

3

2020-10-30T17:22:20.000Z

2021-05-07T00:40:39.000Z

# Resource object code (Python 3) # Created by: object code # Created by: The Resource Compiler for Qt version 5.14.2 # WARNING! 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718b7e87fe3e73f9ac99ebe2d831888c4cdbed14

52

py

Python

ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/util/psi/data/incorrect/keyword/in_2.py

JetBrains-Research/ast-transformations

0ab408af3275b520cc87a473f418c4b4dfcb0284

[ "MIT" ]

8

2021-01-19T21:15:54.000Z

2022-02-23T19:16:25.000Z

ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/util/psi/data/incorrect/keyword/in_2.py

JetBrains-Research/ast-transformations

0ab408af3275b520cc87a473f418c4b4dfcb0284

[ "MIT" ]

4

2020-11-17T14:28:25.000Z

2022-02-24T07:54:28.000Z

ast-transformations-core/src/test/resources/org/jetbrains/research/ml/ast/util/psi/data/incorrect/keyword/in_2.py

nbirillo/ast-transformations

717706765a2da29087a0de768fc851698886dd65

[ "MIT" ]

1

2022-02-23T19:16:30.000Z

# Missing the keyword <in> for i range(10): pass

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718c6727c204a28eb7fb2d79b29ef16665c9e55d

238

py

Python

frappe/workflow/doctype/workflow_state/workflow_state.py

erpnext-tm/frappe

7b470f28e1cf00b0659c01e06a2d0a4693b28d98

[ "MIT" ]

null

frappe/workflow/doctype/workflow_state/workflow_state.py

erpnext-tm/frappe

7b470f28e1cf00b0659c01e06a2d0a4693b28d98

[ "MIT" ]

null

frappe/workflow/doctype/workflow_state/workflow_state.py

erpnext-tm/frappe

7b470f28e1cf00b0659c01e06a2d0a4693b28d98

[ "MIT" ]

null

# Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and Contributors # MIT License. See license.txt from __future__ import unicode_literals import frappe from frappe.model.document import Document class WorkflowState(Document): pass

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71a7a0fbd65732a51ab8ddf7a91d1ee45640a827

225

py

Python

volume_provider/credentials/k8s.py

bento-dbaas/volume-provider

eb945c36dd68696f71849adbfa406727e7331688

[ "BSD-3-Clause" ]

1

2021-02-19T21:59:29.000Z

volume_provider/credentials/k8s.py

bento-dbaas/volume-provider

eb945c36dd68696f71849adbfa406727e7331688

[ "BSD-3-Clause" ]

16

2021-02-19T21:59:33.000Z

2022-03-29T19:36:11.000Z

volume_provider/credentials/k8s.py

bento-dbaas/volume-provider

eb945c36dd68696f71849adbfa406727e7331688

[ "BSD-3-Clause" ]

null

from volume_provider.credentials.base import CredentialBase, CredentialAdd class CredentialK8s(CredentialBase): pass class CredentialAddK8s(CredentialAdd): @property def valid_fields(self): return []

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4

71acd7be30261a0d03fae10206852dc9a314d9e5

301

py

Python

syntax/oop/mroExample.py

Dev-Learn/LearnPython

a601f5eeeb05236a3e179bf8c34425a95cb0c919

[ "Apache-2.0" ]

null

syntax/oop/mroExample.py

Dev-Learn/LearnPython

a601f5eeeb05236a3e179bf8c34425a95cb0c919

[ "Apache-2.0" ]

null

syntax/oop/mroExample.py

Dev-Learn/LearnPython

a601f5eeeb05236a3e179bf8c34425a95cb0c919

[ "Apache-2.0" ]

null

class X: pass class Y: pass class Z: pass class A(X, Y): pass class B(Y, Z): pass class M(B, A, Z): pass # Output: # [<class '__main__.M'>, <class '__main__.B'>, # <class '__main__.A'>, <class '__main__.X'>, # <class '__main__.Y'>, <class '__main__.Z'>, # <class 'object'>] print(M.mro())

12.04

46

0.58804

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301

3.122449

0.265306

0.352941

0.130719

0

0.182724

301

25

47

12.04

0.621951

0.524917

0

1

0

true

0.857143

0

0.857143

0.142857

0

null

1

0

null

0

1

0

1

0

4

71c663cf7beca3b3fc5ae3d88c83f29ac3c87e72

148

py

Python

app/app/old_calc.py

dyehuty/recipe-app-api

d146b957fdae9f8868cbcd80234d79d3453d01d6

[ "MIT" ]

null

app/app/old_calc.py

dyehuty/recipe-app-api

d146b957fdae9f8868cbcd80234d79d3453d01d6

[ "MIT" ]

null

app/app/old_calc.py

dyehuty/recipe-app-api

d146b957fdae9f8868cbcd80234d79d3453d01d6

[ "MIT" ]

null

def add(x,y): """Add two numer together""" return x + y def substract(x,y): """Substract x from y and return value """ return y - x

21.142857

46

0.581081

25

148

3.44

0.48

0.069767

0

0.27027

148

7

47

21.142857

0.796296

0.391892

0

1

0.5

false

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

71d3a8dacb6857924feb0454e6c4681d7a8a4fff

73

py

Python

common/utils/create_coco_tf_record.py

OrganisationXxxx/Trial

81f98680f1b18e11a3741c405c84f371096c39a5

[ "MIT" ]

7

2018-01-21T16:01:22.000Z

2020-06-09T12:29:24.000Z

common/utils/create_coco_tf_record.py

OrganisationXxxx/Trial

81f98680f1b18e11a3741c405c84f371096c39a5

[ "MIT" ]

1

2018-03-18T08:26:48.000Z

common/utils/create_coco_tf_record.py

OrganisationXxxx/Trial

81f98680f1b18e11a3741c405c84f371096c39a5

[ "MIT" ]

4

2018-04-09T01:00:45.000Z

2019-06-15T11:22:15.000Z

""" Convert Microsoft COCO dataset to TFRecord for object_detection. """

18.25

64

0.767123

9

73

6.111111

1

0

0.136986

73

3

65

24.333333

0.873016

0.876712

0

null

0

null

0

null

0

null

1

null

true

0

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null

0

1

0

1

0

null

0

1

0

4

71d66f340f4ca058cd6a9b8f4588d51f5e8444d4

234

py

Python

src/python/packages/amwg/derivations/__init__.py

susburrows/uvcmetrics

5a3c1266f3e5e97398a7671b01fa2816fb307c38

[ "X11", "MIT" ]

null

src/python/packages/amwg/derivations/__init__.py

susburrows/uvcmetrics

5a3c1266f3e5e97398a7671b01fa2816fb307c38

[ "X11", "MIT" ]

null

src/python/packages/amwg/derivations/__init__.py

susburrows/uvcmetrics

5a3c1266f3e5e97398a7671b01fa2816fb307c38

[ "X11", "MIT" ]

null

from oaht import * from ncl_isms import * from package_version import * from atmconst import * from is_numeric_float import * from press2alt import * from vertical import * from masks import * from set13 import * from clouds import *

21.272727

30

0.786325

34

234

5.294118

0.470588

0.5

0

0.015464

0.17094

234

10

31

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0

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true

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null

0

1

0

1

0

1

0

4

e0bcf47bf0a64d6e58b6c12a3e6c07fcaffac719

117

py

Python

src/parrocchie_valmalenco_be/utils/config.py

AleNegrini/parrocchie-valmalenco-be

f52ad63a5e8d81f272ed9a50e43f07e318eaf4b5

[ "MIT" ]

null

src/parrocchie_valmalenco_be/utils/config.py

AleNegrini/parrocchie-valmalenco-be

f52ad63a5e8d81f272ed9a50e43f07e318eaf4b5

[ "MIT" ]

3

2021-01-03T11:13:10.000Z

2021-01-05T10:44:49.000Z

src/parrocchie_valmalenco_be/utils/config.py

AleNegrini/parrocchie-valmalenco-be

f52ad63a5e8d81f272ed9a50e43f07e318eaf4b5

[ "MIT" ]

null

from src.parrocchie_valmalenco_be.utils.utils import get_env_var MIC_CONFIG_PATH_INI = get_env_var('CONFIGINIPATH')

29.25

64

0.863248

19

117

4.842105

0.789474

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0.195652

0

0.068376

117

3

65

39

0.844037

0

0.111111

0

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false

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

4

e0d4991c3fac25175c9eb83d1306e95adf631e4e

44

py

Python

reconframe/variables.py

hackberry-xyz/reconframe

77ba381470b155f8026ebd4639193df3e5cb00dc

[ "MIT" ]

2

2020-08-18T21:25:46.000Z

2022-02-17T03:43:22.000Z

reconframe/variables.py

hackberry-xyz/reconframe

77ba381470b155f8026ebd4639193df3e5cb00dc

[ "MIT" ]

null

reconframe/variables.py

hackberry-xyz/reconframe

77ba381470b155f8026ebd4639193df3e5cb00dc

[ "MIT" ]

null

__version__ = "1.0.0" __info_types__ = set()

22

0.704545

7

44

3.142857

0.857143

0

0.076923

0.113636

44

2

22

0.487179

0

0.111111

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

e0e326420f46057840d7a5c6551965efcebc05cd

25,165

py

Python

code/models.py

tarmeens/active-deep-parsing

77a511e2b3f5f74cb911061797b020bd5c846570

[ "MIT" ]

2

2018-06-20T16:10:52.000Z

2018-07-16T21:03:47.000Z

code/models.py

tarmeens/active-deep-parsing

77a511e2b3f5f74cb911061797b020bd5c846570

[ "MIT" ]

null

code/models.py

tarmeens/active-deep-parsing

77a511e2b3f5f74cb911061797b020bd5c846570

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ DHLAB - IC - EPFL This file contains the BiLSTM and CNN-CNN-LSTM models written with Keras. author: Danny Rodrigues Alves and Mattia Martinelli. version: 2.0 changes: The CNN-CNN-LSTM model has been added. The two models can return either best results or the whole model object. date: 08/06/2018 """ import os import random import numpy as np import tensorflow # default seeds random.seed(42) np.random.seed(42) tensorflow.set_random_seed(42) # Keras function from keras.callbacks import EarlyStopping from keras.layers import Embedding, LSTM, Dense, Bidirectional, Dropout, Input, TimeDistributed, Flatten, Convolution1D, MaxPooling1D, concatenate from keras.models import Sequential, Model from keras.optimizers import Adam, RMSprop from keras_contrib.layers import CRF from keras_contrib.utils import save_load_utils from sklearn.metrics import confusion_matrix from sklearn_crfsuite import metrics # Utils script from utils import * def BiLSTM_model(filename, train, output, X_train, X_test, word2ind, maxWords, y_train, y_test, ind2label, validation=False, X_valid=None, y_valid=None, word_embeddings=True, pretrained_embedding="", word_embedding_size=100, maxChar=0, char_embedding_type="", char2ind="", char_embedding_size=50, lstm_hidden=32, nbr_epochs=5, batch_size=32, dropout=0, optimizer='rmsprop', early_stopping_patience=-1, folder_path="BiLSTM_model", print_to_file = True, gen_confusion_matrix=False, return_model = False ): """ Build, train and test a BiLSTM Keras model. Works for multi-tasking learning. The model architecture looks like: - Words representations: - Word embeddings - Character-level representation [Optional] - Dropout - Bidirectional LSTM - Dropout - Softmax/CRF for predictions :param filename: File to redirect the printing. :param train: Boolean if the model must be trained or not. If False, the model's wieght are expected to be stored in "folder_path/filename/filename.h5" :param output: "crf" or "softmax". Type of prediction layer to use :param X_train: Data to train the model :param X_test: Data to test the model :param word2ind: Dictionary containing all words in the training data and a unique integer per word :param maxWords: Maximum number of words in a sequence :param y_train: Labels to train the model for the prediction task :param y_test: Labels to test the model for the prediction task :param ind2label: Dictionary where all labels are mapped into a unique integer :param validation: Boolean. If true, the validation score will be computed from 'X_valid' and 'y_valid' :param X_valid: Optional. Validation dataset :param y_valid: Optional. Validation dataset labels :param word_embeddings: Boolean value. Add word embeddings into the model. :param pretrained_embedding: Use the pretrained word embeddings. Pretrained vectors must be located here: "dataset/pretrained_vectors" Three values: - "": Do not use pre-trained word embeddings (Default) - False: Use the pre-trained embedding vectors as the weights in the Embedding layer - True: Use the pre-trained embedding vectors as weight initialiers. The Embedding layer will still be trained. :param word_embedding_size: Size of the pre-trained word embedding to use (100 or 300) :param maxChar: The maximum numbers of characters in a word. If set to 0, the model will not use character-level representations of the words :param char_embedding_type: Type of model to use in order to compute the character-level representation of words: Two values: "CNN" or "BILSTM" :param char2ind: A dictionary where each character is maped into a unique integer :param char_embedding_size: size of the character-level word representations :param lstm_hidden: Dimentionality of the LSTM output space :param nbr_epochs: Number of epochs to train the model :param batch_size: Size of batches while training the model :param dropout: Rate to apply for each Dropout layer in the model :param optimizer: Optimizer to use while compiling the model :param early_stopping_patience: Number of continuous tolerated epochs without improvement during training. :param folder_path: Path to the directory storing all to-be-generated files :param print_to_file: if True redirects the printings to a file (given in filename), if False std_out is kept :param gen_confusion_matrix: Boolean value. Generated confusion matrices or not. :param return_model: if True returns the Keras model object, otherwise return best results :return: The classification scores for both tasks (default for compatibility). If returnModel = True, returns model object for further computation """ print("====== {0} start ======".format(filename)) end_string = "====== {0} end ======".format(filename) # Create directory to store results os.makedirs(folder_path+"/"+filename, exist_ok=True) filepath = folder_path+"/"+filename+"/"+filename # Set print outputs file if print_to_file: file, stdout_original = setPrintToFile("{0}.txt".format(filepath)) # Model params nbr_words = len(word2ind)+1 out_size = len(ind2label)+1 best_results = "" embeddings_list = [] inputs = [] # Input - Word Embeddings if word_embeddings: word_input = Input((maxWords,)) inputs.append(word_input) if pretrained_embedding=="": word_embedding = Embedding(nbr_words, word_embedding_size)(word_input) else: # Retrieve embeddings embedding_matrix = word2VecEmbeddings(word2ind, word_embedding_size) word_embedding = Embedding(nbr_words, word_embedding_size, weights=[embedding_matrix], trainable=pretrained_embedding, mask_zero=False)(word_input) embeddings_list.append(word_embedding) # Input - Characters Embeddings if maxChar!=0: character_input = Input((maxWords,maxChar,)) char_embedding = character_embedding_layer(char_embedding_type, character_input, maxChar, len(char2ind)+1, char_embedding_size) embeddings_list.append(char_embedding) inputs.append(character_input) # Model - Inner Layers - BiLSTM with Dropout embeddings = concatenate(embeddings_list) if len(embeddings_list)==2 else embeddings_list[0] model = Dropout(dropout)(embeddings) model = Bidirectional(LSTM(lstm_hidden, return_sequences=True, dropout=dropout))(model) model = Dropout(dropout)(model) if output == "crf": # Output - CRF crfs = [[CRF(out_size),out_size] for out_size in [len(x)+1 for x in ind2label]] outputs = [x[0](Dense(x[1])(model)) for x in crfs] model_loss = [x[0].loss_function for x in crfs] model_metrics = [x[0].viterbi_acc for x in crfs] if output == "softmax": outputs = [Dense(out_size, activation='softmax')(model) for out_size in [len(x)+1 for x in ind2label]] model_loss = ['categorical_crossentropy' for x in outputs] model_metrics = None # Model model = Model(inputs=inputs, outputs=outputs) model.compile(loss=model_loss, metrics=model_metrics, optimizer=get_optimizer(optimizer)) print(model.summary(line_length=150),"\n\n\n\n") # Training Callbacks: callbacks = [] value_to_monitor = 'val_f1' best_model_weights_path = "{0}.h5".format(filepath) # 1) Classifition scores classification_scores = Classification_Scores([X_train, y_train], ind2label, best_model_weights_path) callbacks.append(classification_scores) # 2) EarlyStopping if early_stopping_patience != -1: early_stopping = EarlyStopping(monitor=value_to_monitor, patience=early_stopping_patience, mode='max') callbacks.append(early_stopping) else: early_stopping = EarlyStopping(monitor=value_to_monitor, patience=nbr_epochs, mode='max') callbacks.append(early_stopping) # Train if train: # Train the model. Keras's method argument 'validation_data' is referred as 'testing data' in this code. hist = model.fit(X_train, y_train, validation_data=[X_test, y_test], epochs=nbr_epochs, batch_size=batch_size, callbacks=callbacks, verbose=2) print() print('-------------------------------------------') print("Best F1 score:", early_stopping.best, " (epoch number {0})".format(1+np.argmax(hist.history[value_to_monitor]))) # Save Training scores save_model_training_scores("{0}".format(filepath), hist, classification_scores) # Print best testing classification report best_epoch = np.argmax(hist.history[value_to_monitor]) print(classification_scores.test_report[best_epoch]) # Best epoch results best_results = model_best_scores(classification_scores, best_epoch) # Load weigths from best training epoch into model import h5py with h5py.File(best_model_weights_path, 'a') as f: if 'optimizer_weights' in f.keys(): del f['optimizer_weights'] save_load_utils.load_all_weights(model, best_model_weights_path) # Create confusion matrices if gen_confusion_matrix: for i, y_target in enumerate(y_test): # Compute predictions, flatten predictions, target = compute_predictions(model, X_test, y_target, ind2label[i]) # Generate confusion matrices save_confusion_matrix(target, predictions, list(ind2label[i].values()), "{0}_task_{1}_confusion_matrix_test".format(filepath,str(i+1))) # Validation dataset if validation: print() print("Validation dataset") print("======================") # Compute classification report for i, y_target in enumerate(y_valid): # Compute predictions, flatten predictions, target = compute_predictions(model, X_valid, y_target, ind2label[i], nbrTask=i) # Only for multi-task if len(y_train) > 1: print("For task "+str(i+1)+"\n") print("====================================================================================") print("") print("With padding into account") print(metrics.flat_classification_report([target], [predictions], digits=4)) print("") print('----------------------------------------------') print("") print("Without the padding:") print(metrics.flat_classification_report([target], [predictions], digits=4, labels=list(ind2label[i].values()))) # Generate confusion matrices save_confusion_matrix(target, predictions, list(ind2label[i].values()), "{0}_task_{1}_confusion_matrix_validation".format(filepath,str(i+1))) # Close file if print_to_file: closePrintToFile(file, stdout_original) print(end_string) # Returns model itself for further computation, otherwise best results if return_model: return model else: return best_results def CNN_model(filename, train, X_train, X_test, word2ind, maxWords, y_train, y_test, ind2label, maxChar, char2ind, validation=False, X_valid=None, y_valid=None, pretrained_embedding="", word_embedding_size=100, char_embedding_size=50, lstm_hidden=32, nbr_epochs=25, batch_size=128, dropout=0.5, optimizer='rmsprop', early_stopping_patience=-1, folder_path="CNN_results", print_to_file = True, gen_confusion_matrix=False, return_model = False ): """ Build, train and test the CNN-CNN-LSTM Keras model. Works for multi-tasking learning. The model architecture looks like: - CNN character-level representation - CNN word-level + character representation - LSTM - Softmax for prediction :param filename: File to redirect the printing :param train: Boolean if the model must be trained or not. If False, the model's wieght are expected to be stored in "folder_path/filename/filename.h5" :param X_train: Data to train the model. It must be a list of word and character indices. :param X_test: Data to test the model. It must be a list of word and character indices. :param word2ind: Dictionary containing all words in the training data and a unique integer per word :param maxWords: Maximum number of words in a sequence :param y_train: Labels to train the model for the prediction task :param y_test: Labels to test the model for the prediction task :param ind2label: Dictionary where all labels are mapped into a unique integer :param maxChar: The maximum numbers of characters in a word. If set to 0, the model will not use character-level representations of the words :param char2ind: A dictionary where each character is mapped into a unique integer :param validation: Boolean. If true, the validation score will be computed from 'X_valid' and 'y_valid' :param X_valid: Optional. Validation dataset :param y_valid: Optional. Validation dataset labels :param pretrained_embedding: Use the pretrained word embeddings. Pretrained vectors must be located here: "dataset/pretrained_vectors" Three values: - "": Do not use pre-trained word embeddings (Default) - False: Use the pre-trained embedding vectors as the weights in the Embedding layer - True: Use the pre-trained embedding vectors as weight initialiers. The Embedding layer will still be trained. :param word_embedding_size: Size of the pre-trained word embedding to use (100 or 300) :param char_embedding_size: size of the character-level word representations :param lstm_hidden: Dimentionality of the LSTM output space :param nbr_epochs: Number of epochs to train the model :param batch_size: Size of batches while training the model :param dropout: Rate to apply for each Dropout layer in the model :param optimizer: Optimizer to use while compiling the model :param early_stopping_patience: Number of continuous tolerated epochs without improvement during training. :param folder_path: Path to the directory storing all to-be-generated files :param print_to_file: if True redirects the printings to a file (given in filename), if False std_out is kept :param gen_confusion_matrix: Boolean value. Generated confusion matrices or not. :parm return_model: if True returns the Keras model object, otherwise return best results :return: The classification scores for both tasks (default for compatibility). If returnModel = True, returns model object for further computation """ print("====== {0} start ======".format(filename)) end_string = "====== {0} end ======".format(filename) os.makedirs(folder_path+"/"+filename, exist_ok=True) filepath = folder_path+"/"+filename+"/"+filename # Set print outputs file if print_to_file: file, stdout_original = setPrintToFile("{0}.txt".format(filepath)) nbr_words = len(word2ind)+1 out_size = len(ind2label)+1 nbr_chars = len(char2ind)+1 best_results = None # Embedding - Characters character_input = Input((maxWords,maxChar,)) embed_char_out = TimeDistributed(Embedding(nbr_chars, char_embedding_size))(character_input) conv1d_out = TimeDistributed(Convolution1D(filters = 50, kernel_size = 3, strides=1, activation="relu", padding='same'))(embed_char_out) pool_out = TimeDistributed(MaxPooling1D(pool_size=2, strides=1, padding='same'))(conv1d_out) char_enc = TimeDistributed(Flatten())(pool_out) # Embedding - Words word_input = Input((maxWords,)) if pretrained_embedding=="": word_emb = Embedding(nbr_words, word_embedding_size)(word_input) else: # Retrieve embeddings from word2vec embedding_matrix = word2VecEmbeddings(word2ind, word_embedding_size) word_emb = Embedding(nbr_words, word_embedding_size, weights=[embedding_matrix], trainable=pretrained_embedding, mask_zero=False)(word_input) # Model inputs inputs = [word_input, character_input] # Full word representation word_full = concatenate([char_enc, word_emb]) # encode words w_full = w_char + w_embed conv1d_w1_out = Convolution1D(filters = 800, kernel_size = 5, strides=1, activation="relu", padding='same')(word_full) drop_w1_layer = Dropout(dropout, noise_shape=None, seed=None)(conv1d_w1_out) conv1d_w2_out = Convolution1D(filters = 800, kernel_size = 5, strides=1, activation="relu", padding='same')(drop_w1_layer) # pool size out? word_enc = MaxPooling1D(pool_size=2, strides=1, padding='same')(conv1d_w2_out) # LSTM layer model = LSTM(lstm_hidden, return_sequences=True, dropout=dropout)(word_enc) # Output - Softmax outputs = [Dense(out_size, activation='softmax')(model) for out_size in [len(x)+1 for x in ind2label]] model_loss = ['categorical_crossentropy' for x in outputs] model_metrics = None # Model model = Model(inputs=inputs, outputs=outputs) model.compile(loss=model_loss, metrics=model_metrics, optimizer=get_optimizer(optimizer)) print(model.summary(line_length=150),"\n\n\n\n") # Training Callbacks: callbacks = [] value_to_monitor = 'val_f1' best_model_weights_path = "{0}.h5".format(filepath) # 1) Classifition scores classification_scores = Classification_Scores([X_train, y_train], ind2label, best_model_weights_path) callbacks.append(classification_scores) # 2) EarlyStopping if early_stopping_patience != -1: early_stopping = EarlyStopping(monitor=value_to_monitor, patience=early_stopping_patience, mode='max') callbacks.append(early_stopping) else: early_stopping = EarlyStopping(monitor=value_to_monitor, patience=nbr_epochs, mode='max') callbacks.append(early_stopping) # Train if train: # Train the model. Keras's method argument 'validation_data' is referred as 'testing data' in this code. hist = model.fit(X_train, y_train, validation_data=[X_test, y_test], epochs=nbr_epochs, batch_size=batch_size, callbacks=callbacks, verbose=2) print() print('-------------------------------------------') print("Best F1 score:", early_stopping.best, " (epoch number {0})".format(1+np.argmax(hist.history[value_to_monitor]))) # Save Training scores save_model_training_scores("{0}".format(filepath), hist, classification_scores) # Print best testing classification report best_epoch = np.argmax(hist.history[value_to_monitor]) print(classification_scores.test_report[best_epoch]) # Best epoch results best_results = model_best_scores(classification_scores, best_epoch) # HACK: optmizer weight length issue # https://github.com/keras-team/keras/issues/4044 import h5py with h5py.File(best_model_weights_path, 'a') as f: if 'optimizer_weights' in f.keys(): del f['optimizer_weights'] # Load weigths from best training epoch into model save_load_utils.load_all_weights(model, best_model_weights_path) # Create confusion matrices if gen_confusion_matrix: for i, y_target in enumerate(y_test): # Compute predictions, flatten predictions, target = compute_predictions(model, X_test, y_target, ind2label[i]) # Generate confusion matrices save_confusion_matrix(target, predictions, list(ind2label[i].values()), "{0}_task_{1}_confusion_matrix_test".format(filepath,str(i+1))) # Validation dataset if validation: print() print("Validation dataset") print("======================") # Compute classification report for i, y_target in enumerate(y_valid): # Compute predictions, flatten predictions, target = compute_predictions(model, X_valid, y_target, ind2label[i], nbrTask=i) # Only for multi-task if len(y_train) > 1: print("For task "+str(i+1)+"\n") print("====================================================================================") print("") print("With padding into account") print(metrics.flat_classification_report([target], [predictions], digits=4)) print("") print('----------------------------------------------') print("") print("Without the padding:") print(metrics.flat_classification_report([target], [predictions], digits=4, labels=list(ind2label[i].values()))) # Generate confusion matrices save_confusion_matrix(target, predictions, list(ind2label[i].values()), "{0}_task_{1}_confusion_matrix_validation".format(filepath,str(i+1))) # Close file if print_to_file: closePrintToFile(file, stdout_original) print(end_string) # Returns model itself for further computation, otherwise best results if return_model: return model else: return best_results def character_embedding_layer(layer_type, character_input, maxChar, nbr_chars, char_embedding_size, cnn_kernel_size=2, cnn_filters=30, lstm_units=50, dropout=0.5): """ Return layer for computing the character-level representations of words. There is two type of architectures: Architecture CNN: - Character Embeddings - Dropout - Flatten - Convolution - MaxPool Architecture BILSTM: - Character Embeddings - Dropout - Flatten - Bidirectional LSTM :param layer_type: Model architecture to use "CNN" or "BILSTM" :param character_input: Keras Input layer, size of the input :param maxChar: The maximum numbers of characters in a word. If set to 0, the model will not use character-level representations of the words :param nbr_chars: Numbers of unique characters present in the data :param char_embedding_size: size of the character-level word representations :param cnn_kernel_size: For the CNN architecture, size of the kernel in the Convolution layer :param cnn_filters: For the CNN architecture, number of filters in the Convolution layer :param lstm_units: For the BILSTM architecture, dimensionality of the output LSTM space (half of the Bidirectinal LSTM output space) :param dropout: Rate to apply for each Dropout layer in the model :return: Character-level representation layers """ embed_char_out = TimeDistributed(Embedding(nbr_chars, char_embedding_size), name='char_embedding')(character_input) dropout = Dropout(dropout)(embed_char_out) dropout = TimeDistributed(Flatten())(dropout) if layer_type == "CNN": conv1d_out = TimeDistributed(Convolution1D(kernel_size=cnn_kernel_size, filters=cnn_filters, padding='same'))(dropout) char_emb = TimeDistributed(MaxPooling1D(maxChar))(conv1d_out) if layer_type == "BILSTM": char_emb = Bidirectional(LSTM(lstm_units,return_sequences=True))(dropout) return char_emb def get_optimizer(type, learning_rate=0.001, decay=0.0): """ Return the optimizer needeed to compile Keras models. :param type: Type of optimizer. Two types supported: 'ADAM' and 'RMSprop' :param learning_rate: float >= 0. Learning rate. :pram decay:float >= 0. Learning rate decay over each update :return: The optimizer to use directly into keras model compiling function. """ if type == "adam": return Adam(lr=learning_rate, decay=decay) if type == "rmsprop": return RMSprop(lr=learning_rate, decay=decay)

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e0e981a13ed4ca893e93c8f4d7f11c4f2a3cbded

75

py

Python

run.py

AnsGoo/PyPicGo

653609685566a659187f3fe5b87076c1bfa16da9

[ "MIT" ]

4

2021-08-11T13:51:02.000Z

2022-02-26T03:33:10.000Z

run.py

AnsGoo/PyPicGo

653609685566a659187f3fe5b87076c1bfa16da9

[ "MIT" ]

3

2021-08-11T16:31:25.000Z

2022-01-14T03:39:34.000Z

run.py

AnsGoo/PyPicGo

653609685566a659187f3fe5b87076c1bfa16da9

[ "MIT" ]

2

2021-08-11T13:51:05.000Z

2021-08-11T15:02:47.000Z

from pypicgo.upload import action if __name__ == '__main__': action()

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460da9c334a6988ed101e2b4eb6852abaf379ccc

184

py

Python

weeks/week1_programming_challenges/1_sum_of_two_digits/sum.py

veldakarimi/Data_Structures_Algorithms_py

3c4a5cd260769ea670aad616dc8a14446b66fb2d

[ "MIT" ]

1

2021-06-04T07:45:08.000Z

weeks/week1_programming_challenges/1_sum_of_two_digits/sum.py

veldakarimi/Data_Structures_Algorithms_py

3c4a5cd260769ea670aad616dc8a14446b66fb2d

[ "MIT" ]

null

weeks/week1_programming_challenges/1_sum_of_two_digits/sum.py

veldakarimi/Data_Structures_Algorithms_py

3c4a5cd260769ea670aad616dc8a14446b66fb2d

[ "MIT" ]

null

def sum_of_two_numbers(number_1, number_2): return number_1 + number_2 #print(sum(5,6)) if __name__ == '____': a, b= map(int, input().split()) print(sum(a, b))

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1ca916360a723e3e52fd71bef394beb1d33b8357

837

py

Python

backend/core/db/manager.py

pahumadad/fittrack

6d27d8f5ba92a550cb3c327f0cc603a73e37fa89

[ "MIT" ]

null

backend/core/db/manager.py

pahumadad/fittrack

6d27d8f5ba92a550cb3c327f0cc603a73e37fa89

[ "MIT" ]

null

backend/core/db/manager.py

pahumadad/fittrack

6d27d8f5ba92a550cb3c327f0cc603a73e37fa89

[ "MIT" ]

null

from flask_script import Manager from flask_migrate import Migrate from flask_migrate import MigrateCommand from backend.core.app import app from backend.core.db.db import db from backend.core.db.schema.users import Users from backend.core.db.schema.measurements import Measurements from backend.core.db.schema.user_measurements import UserMeasurements from backend.core.db.schema.measurements_sessions import MeasurementsSessions from backend.core.db.schema.measurements_tracker import MeasurementsTracker from backend.core.db.schema.bfp import BFP from backend.core.db.schema.bmi import BMI from backend.core.db.schema.muscle import Muscle from backend.core.db.schema.visceral import Visceral migrate = Migrate(app, db) manager = Manager(app) manager.add_command("db", MigrateCommand) if __name__ == "__main__": manager.run()

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1cfdf8d15f3c535ce82a3585856209488ea66132

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py

Python

tests/test_mesh_manager.py

TForce1/pcg_gazebo

9ff88016b7b6903236484958ca7c6ed9f8ffb346

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

40

2020-02-04T18:16:49.000Z

2022-02-22T11:36:34.000Z

tests/test_mesh_manager.py

awesomebytes/pcg_gazebo

4f335dd460ef7c771f1df78b46a92fad4a62cedc

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

75

2020-01-23T13:40:50.000Z

2022-02-09T07:26:01.000Z

tests/test_mesh_manager.py

GimpelZhang/gazebo_world_generator

eb7215499d0ddc972d804c988fadab1969579b1b

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

18

2020-09-10T06:35:41.000Z

2022-02-20T19:08:17.000Z

#!/usr/bin/env python # Copyright (c) 2020 - The Procedural Generation for Gazebo authors # For information on the respective copyright owner see the NOTICE file # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest from pcg_gazebo.collection_managers import MeshManager from pcg_gazebo.utils import generate_random_string from pcg_gazebo import random CWD = os.path.dirname(os.path.abspath(__file__)) class TestMeshManager(unittest.TestCase): def test_add_box_primitive(self): mesh_manager = MeshManager.get_instance() mesh_manager.reset() self.assertEqual(len(mesh_manager.tags), 0) tag = mesh_manager.add(tag='box_1', type='box', size=[1, 1, 1]) self.assertEqual(tag, 'box_1') tag = mesh_manager.add(tag='box_2', type='box', size=[2, 2, 2]) self.assertEqual(tag, 'box_2') # Add box with same size of box_1 tag = mesh_manager.add(type='box', size=[1, 1, 1]) self.assertEqual(tag, 'box_1') tag = mesh_manager.find_by_parameters( type='box', size=[2, 2, 2]) self.assertEqual(tag, 'box_2') # Let the manager generate the tag self.assertIsNotNone(mesh_manager.add( type='box', size=random.rand(3))) def test_add_wrong_box_input(self): mesh_manager = MeshManager.get_instance() mesh_manager.reset() self.assertEqual(len(mesh_manager.tags), 0) # Not specifying the type of the geometry self.assertIsNone(mesh_manager.add(size=[1, 1, 1])) wrong_sizes = [ [-1, 1, 1], [2, 3], None, dict(), 'a' ] # Entering wrong size vectors for size in wrong_sizes: with self.assertRaises(AssertionError): mesh_manager.add(type='box', size=size) def test_add_cylinder_primitive(self): mesh_manager = MeshManager.get_instance() mesh_manager.reset() self.assertEqual(len(mesh_manager.tags), 0) tag = mesh_manager.add( tag='cyl_1', type='cylinder', radius=1, height=1) self.assertEqual(tag, 'cyl_1') tag = mesh_manager.add( tag='cyl_2', type='cylinder', radius=2, height=2) self.assertEqual(tag, 'cyl_2') # Add cylinder with same size of cyl_1 tag = mesh_manager.add(type='cylinder', radius=1, height=1) self.assertEqual(tag, 'cyl_1') tag = mesh_manager.find_by_parameters( type='cylinder', radius=2, height=2) self.assertEqual(tag, 'cyl_2') # Let the manager generate the tag self.assertIsNotNone(mesh_manager.add( type='cylinder', radius=random.rand(), height=random.rand())) def test_add_wrong_cylinder_input(self): mesh_manager = MeshManager.get_instance() mesh_manager.reset() self.assertEqual(len(mesh_manager.tags), 0) # Not specifying the type of the geometry self.assertIsNone(mesh_manager.add(radius=1, height=1)) # Missing parameters self.assertIsNone(mesh_manager.add(height=1)) self.assertIsNone(mesh_manager.add(radius=1)) wrong_inputs = [ -10, [1, 2], None, dict(), 'a' ] for radius in wrong_inputs: with self.assertRaises(AssertionError): mesh_manager.add(type='cylinder', radius=radius, height=1) for height in wrong_inputs: with self.assertRaises(AssertionError): mesh_manager.add(type='cylinder', radius=1, height=height) def test_add_sphere_primitive(self): mesh_manager = MeshManager.get_instance() mesh_manager.reset() self.assertEqual(len(mesh_manager.tags), 0) tag = mesh_manager.add( tag='sphere_1', type='sphere', radius=1) self.assertEqual(tag, 'sphere_1') tag = mesh_manager.add( tag='sphere_2', type='sphere', radius=2) self.assertEqual(tag, 'sphere_2') # Add sphere with same size of sphere_1 tag = mesh_manager.add(type='sphere', radius=1) self.assertEqual(tag, 'sphere_1') tag = mesh_manager.find_by_parameters(type='sphere', radius=2) self.assertEqual(tag, 'sphere_2') # Let the manager generate the tag self.assertIsNotNone(mesh_manager.add( type='sphere', radius=random.rand())) def test_add_wrong_sphere_input(self): mesh_manager = MeshManager.get_instance() mesh_manager.reset() self.assertEqual(len(mesh_manager.tags), 0) # Not specifying the type of the geometry self.assertIsNone(mesh_manager.add(radius=1)) wrong_inputs = [ -10, [1, 2], None, dict(), 'a' ] for radius in wrong_inputs: with self.assertRaises(AssertionError): mesh_manager.add(type='sphere', radius=radius) def test_add_capsule_primitive(self): mesh_manager = MeshManager.get_instance() mesh_manager.reset() self.assertEqual(len(mesh_manager.tags), 0) tag = mesh_manager.add( tag='capsule_1', type='capsule', radius=1, height=1) self.assertEqual(tag, 'capsule_1') tag = mesh_manager.add( tag='capsule_2', type='capsule', radius=2, height=2) self.assertEqual(tag, 'capsule_2') # Add cylinder with same size of cyl_1 tag = mesh_manager.add(type='capsule', radius=1, height=1) self.assertEqual(tag, 'capsule_1') tag = mesh_manager.find_by_parameters( type='capsule', radius=2, height=2) self.assertEqual(tag, 'capsule_2') # Let the manager generate the tag self.assertIsNotNone(mesh_manager.add( type='capsule', radius=random.rand(), height=random.rand())) def test_add_wrong_capsule_input(self): mesh_manager = MeshManager.get_instance() mesh_manager.reset() self.assertEqual(len(mesh_manager.tags), 0) # Not specifying the type of the geometry self.assertIsNone(mesh_manager.add(radius=1, height=1)) # Missing parameters self.assertIsNone(mesh_manager.add(height=1)) self.assertIsNone(mesh_manager.add(radius=1)) wrong_inputs = [ -10, [1, 2], None, dict(), 'a' ] for radius in wrong_inputs: with self.assertRaises(AssertionError): mesh_manager.add(type='capsule', radius=radius, height=1) for height in wrong_inputs: with self.assertRaises(AssertionError): mesh_manager.add(type='capsule', radius=1, height=height) def test_add_mesh_file(self): mesh_manager = MeshManager.get_instance() mesh_manager.reset() self.assertEqual(len(mesh_manager.tags), 0) tag_1 = generate_random_string(5) tag = mesh_manager.add( tag=tag_1, filename=os.path.join(CWD, 'meshes', 'monkey.stl')) self.assertEqual(tag, tag_1) tag_2 = generate_random_string(5) tag = mesh_manager.add( tag=tag_2, filename=os.path.join(CWD, 'meshes', 'cube.stl')) self.assertEqual(tag, tag_2) self.assertEqual( mesh_manager.add(filename=os.path.join(CWD, 'meshes', 'cube.stl')), tag_2) if __name__ == '__main__': unittest.main()

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e8109bce2ed845eb94f6a8d1af73c12d6d97297b

188

py

Python

Views/urls/Path converters/int/views.py

looking-for-a-job/django-examples

dfafa450668cac5c0351f6c7238b8886511229bf

[ "Unlicense" ]

null

Views/urls/Path converters/int/views.py

looking-for-a-job/django-examples

dfafa450668cac5c0351f6c7238b8886511229bf

[ "Unlicense" ]

null

Views/urls/Path converters/int/views.py

looking-for-a-job/django-examples

dfafa450668cac5c0351f6c7238b8886511229bf

[ "Unlicense" ]

null

def special_case_2003(request): pass def year_archive(request,year): pass def month_archive(request,year,month): pass def article_detail(request,year,month,title): pass

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e836e7060b4a71e3e131d54523aa9b2eb8fc5d48

150

py

Python

celery_worker.py

gurkslask/hamcwebc

8abbe47cdd58972072d6088b63e1c368a63bb4af

[ "BSD-3-Clause" ]

null

celery_worker.py

gurkslask/hamcwebc

8abbe47cdd58972072d6088b63e1c368a63bb4af

[ "BSD-3-Clause" ]

null

celery_worker.py

gurkslask/hamcwebc

8abbe47cdd58972072d6088b63e1c368a63bb4af

[ "BSD-3-Clause" ]

null

# import os from hamcwebc.app import celery, create_app from hamcwebc.settings import DevConfig app = create_app(DevConfig) app.app_context().push()

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1c0f2b047cbfd512deaea0073807a8f69734a6a4

1,293

py

Python

setup.py

ErikWe/github-labels-copy

d7e0f464627cc1accf2049eec48369325373a824

[ "MIT" ]

7

2016-04-29T15:07:02.000Z

2019-04-14T17:07:29.000Z

setup.py

ErikWe/github-labels-copy

d7e0f464627cc1accf2049eec48369325373a824

[ "MIT" ]

4

2017-07-07T23:04:36.000Z

2021-08-16T12:51:49.000Z

setup.py

ErikWe/github-labels-copy

d7e0f464627cc1accf2049eec48369325373a824

[ "MIT" ]

3

2018-04-29T21:46:08.000Z

2021-12-09T21:55:47.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- from setuptools import setup setup(name='githublabelscopy', version='1.1.1', description='A tool to copy labels between repositories using Github API', url='http://github.com/fpietka/github-labels-copy', author='François Pietka', author_email='francois@pietka.fr', license='MIT', packages=['githublabelscopy'], long_description=open('README.rst').read(), install_requires=[ 'PyGithub==1.34', 'docopt==0.6.2', 'PyYAML==3.12' ], entry_points={ 'console_scripts': [ 'github-labels-copy = githublabelscopy.githublabelscopy:main' ], }, classifiers=[ 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Environment :: Console', 'Programming Language :: Python', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6' ], zip_safe=True)

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1c1ca2a67e401a902753f4513ac5c528b0192b50

157

py

Python

src/__tests__/generated/test.py

gao03/Paw-cURLImporter

0393a01dfba2cff8a08373d28312d783b895805c

[ "MIT" ]

24

2015-11-26T17:39:36.000Z

2021-12-09T16:40:51.000Z

src/__tests__/generated/test.py

gao03/Paw-cURLImporter

0393a01dfba2cff8a08373d28312d783b895805c

[ "MIT" ]

17

2015-11-26T17:31:25.000Z

2020-02-17T10:27:40.000Z

src/__tests__/generated/test.py

gao03/Paw-cURLImporter

0393a01dfba2cff8a08373d28312d783b895805c

[ "MIT" ]

6

2017-04-01T04:08:21.000Z

2022-01-25T05:05:25.000Z

#! /Library/Frameworks/Python.framework/Versions/2.7/bin/python import sys import json sys.stdout.write(json.dumps({ 'args':sys.argv }).encode('utf-8'))

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1c6265edaa54036cf5d2ccc1b4778c534f2c1d15

102,237

py

Python

pyuvdata/uvflag/tests/test_uvflag.py

e-koch/pyuvdata

ac36067f195c75127b28f02479eda1eb7a3400ed

[ "BSD-2-Clause" ]

null

pyuvdata/uvflag/tests/test_uvflag.py

e-koch/pyuvdata

ac36067f195c75127b28f02479eda1eb7a3400ed

[ "BSD-2-Clause" ]

null

pyuvdata/uvflag/tests/test_uvflag.py

e-koch/pyuvdata

ac36067f195c75127b28f02479eda1eb7a3400ed

[ "BSD-2-Clause" ]

null

# -*- mode: python; coding: utf-8 -*- # Copyright (c) 2019 Radio Astronomy Software Group # Licensed under the 2-clause BSD License import pytest from _pytest.outcomes import Skipped import os import numpy as np import pyuvdata.tests as uvtest from pyuvdata import UVData, UVCal, utils as uvutils from pyuvdata.data import DATA_PATH from pyuvdata import UVFlag from ..uvflag import lst_from_uv, flags2waterfall, and_rows_cols from pyuvdata import __version__ import shutil import copy import warnings import h5py import pathlib test_d_file = os.path.join(DATA_PATH, "zen.2457698.40355.xx.HH.uvcAA.uvh5") test_c_file = os.path.join(DATA_PATH, "zen.2457555.42443.HH.uvcA.omni.calfits") test_f_file = test_d_file.rstrip(".uvh5") + ".testuvflag.h5" pyuvdata_version_str = " Read/written with pyuvdata version: " + __version__ + "." pytestmark = pytest.mark.filterwarnings( "ignore:telescope_location is not set. Using known values for HERA.", "ignore:antenna_positions is not set. Using known values for HERA.", ) @pytest.fixture(scope="session") def uvdata_obj_main(): uvdata_object = UVData() uvdata_object.read(test_d_file) yield uvdata_object # cleanup del uvdata_object return @pytest.fixture(scope="function") def uvdata_obj(uvdata_obj_main): uvdata_object = uvdata_obj_main.copy() yield uvdata_object # cleanup del uvdata_object return # The following three fixtures are used regularly # to initizize UVFlag objects from standard files # We need to define these here in order to set up # some skips for developers who do not have `pytest-cases` installed @pytest.fixture(scope="function") def uvf_from_data(uvdata_obj): uvf = UVFlag() uvf.from_uvdata(uvdata_obj) # yield the object for the test yield uvf # do some cleanup del (uvf, uvdata_obj) @pytest.fixture(scope="function") def uvf_from_uvcal(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag() uvf.from_uvcal(uvc) # the antenna type test file is large, so downselect to speed up if uvf.type == "antenna": uvf.select(antenna_nums=uvf.ant_array[:5]) # yield the object for the test yield uvf # do some cleanup del (uvf, uvc) @pytest.fixture(scope="function") def uvf_from_waterfall(uvdata_obj): uvf = UVFlag() uvf.from_uvdata(uvdata_obj, waterfall=True) # yield the object for the test yield uvf # do some cleanup del uvf # Try to import `pytest-cases` and define decorators used to # iterate over the three main types of UVFlag objects # otherwise make the decorators skip the tests that use these iterators try: pytest_cases = pytest.importorskip("pytest_cases", minversion="1.12.1") cases_decorator = pytest_cases.parametrize( "input_uvf", [ pytest_cases.fixture_ref(uvf_from_data), pytest_cases.fixture_ref(uvf_from_uvcal), pytest_cases.fixture_ref(uvf_from_waterfall), ], ) cases_decorator_no_waterfall = pytest_cases.parametrize( "input_uvf", [ pytest_cases.fixture_ref(uvf_from_data), pytest_cases.fixture_ref(uvf_from_uvcal), ], ) # This warning is raised by pytest_cases # It is due to a feature the developer does # not know how to handle yet. ignore for now. warnings.filterwarnings( "ignore", message="WARNING the new order is not" + " taken into account !!", append=True, ) except Skipped: cases_decorator = pytest.mark.skipif( True, reason="pytest-cases not installed or not required version" ) cases_decorator_no_waterfall = pytest.mark.skipif( True, reason="pytest-cases not installed or not required version" ) @pytest.fixture() def test_outfile(tmp_path): yield str(tmp_path / "outtest_uvflag.h5") @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_check_flag_array(uvdata_obj): uvf = UVFlag() uvf.from_uvdata(uvdata_obj, mode="flag") uvf.flag_array = np.ones((uvf.flag_array.shape), dtype=int) with pytest.raises( ValueError, match="UVParameter _flag_array is not the appropriate type.", ): uvf.check() @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_init_bad_mode(uvdata_obj): uv = uvdata_obj with pytest.raises(ValueError) as cm: UVFlag(uv, mode="bad_mode", history="I made a UVFlag object", label="test") assert str(cm.value).startswith("Input mode must be within acceptable") uv = UVCal() uv.read_calfits(test_c_file) with pytest.raises(ValueError) as cm: UVFlag(uv, mode="bad_mode", history="I made a UVFlag object", label="test") assert str(cm.value).startswith("Input mode must be within acceptable") @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_init_uvdata(uvdata_obj): uv = uvdata_obj uvf = UVFlag(uv, history="I made a UVFlag object", label="test") assert uvf.metric_array.shape == uv.flag_array.shape assert np.all(uvf.metric_array == 0) assert uvf.weights_array.shape == uv.flag_array.shape assert np.all(uvf.weights_array == 1) assert uvf.type == "baseline" assert uvf.mode == "metric" assert np.all(uvf.time_array == uv.time_array) assert np.all(uvf.lst_array == uv.lst_array) assert np.all(uvf.freq_array == uv.freq_array[0]) assert np.all(uvf.polarization_array == uv.polarization_array) assert np.all(uvf.baseline_array == uv.baseline_array) assert np.all(uvf.ant_1_array == uv.ant_1_array) assert np.all(uvf.ant_2_array == uv.ant_2_array) assert "I made a UVFlag object" in uvf.history assert 'Flag object with type "baseline"' in uvf.history assert pyuvdata_version_str in uvf.history assert uvf.label == "test" assert uvf.filename == uv.filename def test_add_extra_keywords(uvdata_obj): uv = uvdata_obj uvf = UVFlag(uv, history="I made a UVFlag object", label="test") uvf.extra_keywords = {"keyword1": 1, "keyword2": 2} assert "keyword1" in uvf.extra_keywords assert "keyword2" in uvf.extra_keywords uvf.extra_keywords["keyword3"] = 3 assert "keyword3" in uvf.extra_keywords assert uvf.extra_keywords.get("keyword1") == 1 assert uvf.extra_keywords.get("keyword2") == 2 assert uvf.extra_keywords.get("keyword3") == 3 def test_read_extra_keywords(uvdata_obj): uv = uvdata_obj uv.extra_keywords = {"keyword1": 1, "keyword2": 2} assert "keyword1" in uv.extra_keywords assert "keyword2" in uv.extra_keywords uvf = UVFlag(uv, history="I made a UVFlag object", label="test") assert "keyword1" in uvf.extra_keywords assert "keyword2" in uvf.extra_keywords @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_init_uvdata_x_orientation(uvdata_obj): uv = uvdata_obj uv.x_orientation = "east" uvf = UVFlag(uv, history="I made a UVFlag object", label="test") assert uvf.x_orientation == uv.x_orientation @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @pytest.mark.parametrize("future_shapes", [True, False]) def test_init_uvdata_copy_flags(uvdata_obj, future_shapes): uv = uvdata_obj if future_shapes: uv.use_future_array_shapes() with uvtest.check_warnings(UserWarning, 'Copying flags to type=="baseline"'): uvf = UVFlag(uv, copy_flags=True, mode="metric") # with copy flags uvf.metric_array should be none assert hasattr(uvf, "metric_array") assert uvf.metric_array is None if future_shapes: assert np.array_equal(uvf.flag_array[:, 0, :, :], uv.flag_array) else: assert np.array_equal(uvf.flag_array, uv.flag_array) assert uvf.weights_array is None assert uvf.type == "baseline" assert uvf.mode == "flag" assert np.all(uvf.time_array == uv.time_array) assert np.all(uvf.lst_array == uv.lst_array) if future_shapes: assert np.all(uvf.freq_array == uv.freq_array) else: assert np.all(uvf.freq_array == uv.freq_array[0]) assert np.all(uvf.polarization_array == uv.polarization_array) assert np.all(uvf.baseline_array == uv.baseline_array) assert np.all(uvf.ant_1_array == uv.ant_1_array) assert np.all(uvf.ant_2_array == uv.ant_2_array) assert 'Flag object with type "baseline"' in uvf.history assert pyuvdata_version_str in uvf.history @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_init_uvdata_mode_flag(uvdata_obj): uv = uvdata_obj uvf = UVFlag() uvf.from_uvdata(uv, copy_flags=False, mode="flag") # with copy flags uvf.metric_array should be none assert hasattr(uvf, "metric_array") assert uvf.metric_array is None assert np.array_equal(uvf.flag_array, uv.flag_array) assert uvf.weights_array is None assert uvf.type == "baseline" assert uvf.mode == "flag" assert np.all(uvf.time_array == uv.time_array) assert np.all(uvf.lst_array == uv.lst_array) assert np.all(uvf.freq_array == uv.freq_array[0]) assert np.all(uvf.polarization_array == uv.polarization_array) assert np.all(uvf.baseline_array == uv.baseline_array) assert np.all(uvf.ant_1_array == uv.ant_1_array) assert np.all(uvf.ant_2_array == uv.ant_2_array) assert 'Flag object with type "baseline"' in uvf.history assert pyuvdata_version_str in uvf.history def test_init_uvcal(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) assert uvf.metric_array.shape == uvc.flag_array.shape assert np.all(uvf.metric_array == 0) assert uvf.weights_array.shape == uvc.flag_array.shape assert np.all(uvf.weights_array == 1) assert uvf.type == "antenna" assert uvf.mode == "metric" assert np.all(uvf.time_array == uvc.time_array) assert uvf.x_orientation == uvc.x_orientation lst = lst_from_uv(uvc) assert np.all(uvf.lst_array == lst) assert np.all(uvf.freq_array == uvc.freq_array[0]) assert np.all(uvf.polarization_array == uvc.jones_array) assert np.all(uvf.ant_array == uvc.ant_array) assert 'Flag object with type "antenna"' in uvf.history assert pyuvdata_version_str in uvf.history assert uvf.filename == uvc.filename def test_init_uvcal_mode_flag(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc, copy_flags=False, mode="flag") assert hasattr(uvf, "metric_array") assert uvf.metric_array is None assert np.array_equal(uvf.flag_array, uvc.flag_array) assert uvf.weights_array is None assert uvf.type == "antenna" assert uvf.mode == "flag" assert np.all(uvf.time_array == uvc.time_array) lst = lst_from_uv(uvc) assert np.all(uvf.lst_array == lst) assert np.all(uvf.freq_array == uvc.freq_array[0]) assert np.all(uvf.polarization_array == uvc.jones_array) assert np.all(uvf.ant_array == uvc.ant_array) assert 'Flag object with type "antenna"' in uvf.history assert pyuvdata_version_str in uvf.history def test_init_cal_copy_flags(): uv = UVCal() uv.read_calfits(test_c_file) with uvtest.check_warnings(UserWarning, 'Copying flags to type=="antenna"'): uvf = UVFlag(uv, copy_flags=True, mode="metric") # with copy flags uvf.metric_array should be none assert hasattr(uvf, "metric_array") assert uvf.metric_array is None assert np.array_equal(uvf.flag_array, uv.flag_array) assert uvf.type == "antenna" assert uvf.mode == "flag" assert np.all(uvf.time_array == np.unique(uv.time_array)) assert np.all(uvf.freq_array == uv.freq_array[0]) assert np.all(uvf.polarization_array == uv.jones_array) assert pyuvdata_version_str in uvf.history @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @pytest.mark.parametrize("future_shapes", [True, False]) def test_init_waterfall_uvd(uvdata_obj, future_shapes): uv = uvdata_obj if future_shapes: uv.use_future_array_shapes() uvf = UVFlag(uv, waterfall=True) assert uvf.metric_array.shape == (uv.Ntimes, uv.Nfreqs, uv.Npols) assert np.all(uvf.metric_array == 0) assert uvf.weights_array.shape == (uv.Ntimes, uv.Nfreqs, uv.Npols) assert np.all(uvf.weights_array == 1) assert uvf.type == "waterfall" assert uvf.mode == "metric" assert np.all(uvf.time_array == np.unique(uv.time_array)) assert np.all(uvf.lst_array == np.unique(uv.lst_array)) if future_shapes: assert np.all(uvf.freq_array == uv.freq_array) else: assert np.all(uvf.freq_array == uv.freq_array[0]) assert np.all(uvf.polarization_array == uv.polarization_array) assert 'Flag object with type "waterfall"' in uvf.history assert pyuvdata_version_str in uvf.history def test_init_waterfall_uvc(): uv = UVCal() uv.read_calfits(test_c_file) uvf = UVFlag(uv, waterfall=True, history="input history check") assert uvf.metric_array.shape == (uv.Ntimes, uv.Nfreqs, uv.Njones) assert np.all(uvf.metric_array == 0) assert uvf.weights_array.shape == (uv.Ntimes, uv.Nfreqs, uv.Njones) assert np.all(uvf.weights_array == 1) assert uvf.type == "waterfall" assert uvf.mode == "metric" assert np.all(uvf.time_array == np.unique(uv.time_array)) assert np.all(uvf.freq_array == uv.freq_array[0]) assert np.all(uvf.polarization_array == uv.jones_array) assert 'Flag object with type "waterfall"' in uvf.history assert "input history check" in uvf.history assert pyuvdata_version_str in uvf.history def test_init_waterfall_flag_uvcal(): uv = UVCal() uv.read_calfits(test_c_file) uvf = UVFlag(uv, waterfall=True, mode="flag") assert uvf.flag_array.shape == (uv.Ntimes, uv.Nfreqs, uv.Njones) assert not np.any(uvf.flag_array) assert uvf.weights_array is None assert uvf.type == "waterfall" assert uvf.mode == "flag" assert np.all(uvf.time_array == np.unique(uv.time_array)) assert np.all(uvf.freq_array == uv.freq_array[0]) assert np.all(uvf.polarization_array == uv.jones_array) assert 'Flag object with type "waterfall"' in uvf.history assert pyuvdata_version_str in uvf.history @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_init_waterfall_flag_uvdata(uvdata_obj): uv = uvdata_obj uvf = UVFlag(uv, waterfall=True, mode="flag") assert uvf.flag_array.shape == (uv.Ntimes, uv.Nfreqs, uv.Npols) assert not np.any(uvf.flag_array) assert uvf.weights_array is None assert uvf.type == "waterfall" assert uvf.mode == "flag" assert np.all(uvf.time_array == np.unique(uv.time_array)) assert np.all(uvf.freq_array == uv.freq_array[0]) assert np.all(uvf.polarization_array == uv.polarization_array) assert 'Flag object with type "waterfall"' in uvf.history assert pyuvdata_version_str in uvf.history @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_init_waterfall_copy_flags(uvdata_obj): uv = UVCal() uv.read_calfits(test_c_file) with pytest.raises(NotImplementedError) as cm: UVFlag(uv, copy_flags=True, mode="flag", waterfall=True) assert str(cm.value).startswith("Cannot copy flags when initializing") uv = uvdata_obj with pytest.raises(NotImplementedError) as cm: UVFlag(uv, copy_flags=True, mode="flag", waterfall=True) assert str(cm.value).startswith("Cannot copy flags when initializing") def test_init_invalid_input(): # input is not UVData, UVCal, path, or list/tuple with pytest.raises(ValueError) as cm: UVFlag(14) assert str(cm.value).startswith("input to UVFlag.__init__ must be one of:") @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_from_uvcal_error(uvdata_obj): uv = uvdata_obj uvf = UVFlag() with pytest.raises(ValueError) as cm: uvf.from_uvcal(uv) assert str(cm.value).startswith("from_uvcal can only initialize a UVFlag object") def test_from_udata_error(): uv = UVCal() uv.read_calfits(test_c_file) uvf = UVFlag() with pytest.raises(ValueError) as cm: uvf.from_uvdata(uv) assert str(cm.value).startswith("from_uvdata can only initialize a UVFlag object") def test_init_list_files_weights(tmpdir): # Test that weights are preserved when reading list of files tmp_path = tmpdir.strpath # Create two files to read uvf = UVFlag(test_f_file) np.random.seed(0) wts1 = np.random.rand(*uvf.weights_array.shape) uvf.weights_array = wts1.copy() uvf.write(os.path.join(tmp_path, "test1.h5")) wts2 = np.random.rand(*uvf.weights_array.shape) uvf.weights_array = wts2.copy() uvf.write(os.path.join(tmp_path, "test2.h5")) uvf2 = UVFlag( [os.path.join(tmp_path, "test1.h5"), os.path.join(tmp_path, "test2.h5")] ) assert np.all(uvf2.weights_array == np.concatenate([wts1, wts2], axis=0)) def test_init_posix(): # Test that weights are preserved when reading list of files testfile_posix = pathlib.Path(test_f_file) uvf1 = UVFlag(test_f_file) uvf2 = UVFlag(testfile_posix) assert uvf1 == uvf2 @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_data_like_property_mode_tamper(uvdata_obj): uv = uvdata_obj uvf = UVFlag(uv, label="test") uvf.mode = "test" with pytest.raises(ValueError) as cm: list(uvf.data_like_parameters) assert str(cm.value).startswith("Invalid mode. Mode must be one of") def test_read_write_loop(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, label="test") uvf.write(test_outfile, clobber=True) uvf2 = UVFlag(test_outfile) assert uvf.__eq__(uvf2, check_history=True) assert uvf2.filename == [os.path.basename(test_outfile)] def test_read_write_loop_with_optional_x_orientation(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, label="test") uvf.x_orientation = "east" uvf.write(test_outfile, clobber=True) uvf2 = UVFlag(test_outfile) assert uvf.__eq__(uvf2, check_history=True) def test_read_write_loop_waterfal(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, label="test") uvf.to_waterfall() uvf.write(test_outfile, clobber=True) uvf2 = UVFlag(test_outfile) assert uvf.__eq__(uvf2, check_history=True) def test_read_write_loop_ret_wt_sq(test_outfile): uvf = UVFlag(test_f_file) uvf.weights_array = 2 * np.ones_like(uvf.weights_array) uvf.to_waterfall(return_weights_square=True) uvf.write(test_outfile, clobber=True) uvf2 = UVFlag(test_outfile) assert uvf.__eq__(uvf2, check_history=True) def test_bad_mode_savefile(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, label="test") # create the file so the clobber gets tested with h5py.File(test_outfile, "w") as h5file: h5file.create_dataset("Test", list(range(10))) uvf.write(test_outfile, clobber=True) # manually re-read and tamper with parameters with h5py.File(test_outfile, "a") as h5: mode = h5["Header/mode"] mode[...] = np.string_("test") with pytest.raises(ValueError) as cm: uvf = UVFlag(test_outfile) assert str(cm.value).startswith("File cannot be read. Received mode") def test_bad_type_savefile(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, label="test") uvf.write(test_outfile, clobber=True) # manually re-read and tamper with parameters with h5py.File(test_outfile, "a") as h5: mode = h5["Header/type"] mode[...] = np.string_("test") with pytest.raises(ValueError) as cm: uvf = UVFlag(test_outfile) assert str(cm.value).startswith("File cannot be read. Received type") def test_write_add_version_str(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, label="test") uvf.history = uvf.history.replace(pyuvdata_version_str, "") assert pyuvdata_version_str not in uvf.history uvf.write(test_outfile, clobber=True) with h5py.File(test_outfile, "r") as h5: assert h5["Header/history"].dtype.type is np.string_ hist = h5["Header/history"][()].decode("utf8") assert pyuvdata_version_str in hist def test_read_add_version_str(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, label="test") assert pyuvdata_version_str in uvf.history uvf.write(test_outfile, clobber=True) with h5py.File(test_outfile, "r") as h5: hist = h5["Header/history"] del hist uvf2 = UVFlag(test_outfile) assert pyuvdata_version_str in uvf2.history assert uvf == uvf2 def test_read_write_ant(test_outfile): uv = UVCal() uv.read_calfits(test_c_file) uvf = UVFlag(uv, mode="flag", label="test") uvf.write(test_outfile, clobber=True) uvf2 = UVFlag(test_outfile) assert uvf.__eq__(uvf2, check_history=True) def test_read_missing_nants_data(test_outfile): uv = UVCal() uv.read_calfits(test_c_file) uvf = UVFlag(uv, mode="flag", label="test") uvf.write(test_outfile, clobber=True) with h5py.File(test_outfile, "a") as h5: del h5["Header/Nants_data"] with uvtest.check_warnings(UserWarning, "Nants_data not available in file,"): uvf2 = UVFlag(test_outfile) # make sure this was set to None assert uvf2.Nants_data == len(uvf2.ant_array) uvf2.Nants_data = uvf.Nants_data # verify no other elements were changed assert uvf.__eq__(uvf2, check_history=True) def test_read_missing_nspws(test_outfile): uv = UVCal() uv.read_calfits(test_c_file) uvf = UVFlag(uv, mode="flag", label="test") uvf.write(test_outfile, clobber=True) with h5py.File(test_outfile, "a") as h5: del h5["Header/Nspws"] uvf2 = UVFlag(test_outfile) # make sure Nspws was calculated assert uvf2.Nspws == 1 # verify no other elements were changed assert uvf.__eq__(uvf2, check_history=True) def test_read_write_nocompress(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, label="test") uvf.write(test_outfile, clobber=True, data_compression=None) uvf2 = UVFlag(test_outfile) assert uvf.__eq__(uvf2, check_history=True) def test_read_write_nocompress_flag(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, mode="flag", label="test") uvf.write(test_outfile, clobber=True, data_compression=None) uvf2 = UVFlag(test_outfile) assert uvf.__eq__(uvf2, check_history=True) def test_read_write_extra_keywords(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, label="test") uvf.extra_keywords = {"keyword1": 1, "keyword2": "string"} uvf.write(test_outfile, clobber=True, data_compression=None) uvf2 = UVFlag(test_outfile) assert uvf2.extra_keywords["keyword1"] == 1 assert uvf2.extra_keywords["keyword2"] == "string" @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_init_list(uvdata_obj): uv = uvdata_obj uv.time_array -= 1 uvf = UVFlag([uv, test_f_file]) uvf1 = UVFlag(uv) uvf2 = UVFlag(test_f_file) assert np.array_equal( np.concatenate((uvf1.metric_array, uvf2.metric_array), axis=0), uvf.metric_array ) assert np.array_equal( np.concatenate((uvf1.weights_array, uvf2.weights_array), axis=0), uvf.weights_array, ) assert np.array_equal( np.concatenate((uvf1.time_array, uvf2.time_array)), uvf.time_array ) assert np.array_equal( np.concatenate((uvf1.baseline_array, uvf2.baseline_array)), uvf.baseline_array ) assert np.array_equal( np.concatenate((uvf1.ant_1_array, uvf2.ant_1_array)), uvf.ant_1_array ) assert np.array_equal( np.concatenate((uvf1.ant_2_array, uvf2.ant_2_array)), uvf.ant_2_array ) assert uvf.mode == "metric" assert np.all(uvf.freq_array == uv.freq_array[0]) assert np.all(uvf.polarization_array == uv.polarization_array) def test_read_list(uvdata_obj, test_outfile): uv = uvdata_obj uv.time_array -= 1 uvf = UVFlag(uv) uvf.write(test_outfile, clobber=True) uvf.read([test_outfile, test_f_file]) assert uvf.filename == sorted( os.path.basename(file) for file in [test_outfile, test_f_file] ) uvf1 = UVFlag(uv) uvf2 = UVFlag(test_f_file) assert np.array_equal( np.concatenate((uvf1.metric_array, uvf2.metric_array), axis=0), uvf.metric_array ) assert np.array_equal( np.concatenate((uvf1.weights_array, uvf2.weights_array), axis=0), uvf.weights_array, ) assert np.array_equal( np.concatenate((uvf1.time_array, uvf2.time_array)), uvf.time_array ) assert np.array_equal( np.concatenate((uvf1.baseline_array, uvf2.baseline_array)), uvf.baseline_array ) assert np.array_equal( np.concatenate((uvf1.ant_1_array, uvf2.ant_1_array)), uvf.ant_1_array ) assert np.array_equal( np.concatenate((uvf1.ant_2_array, uvf2.ant_2_array)), uvf.ant_2_array ) assert uvf.mode == "metric" assert np.all(uvf.freq_array == uv.freq_array[0]) assert np.all(uvf.polarization_array == uv.polarization_array) def test_read_error(): with pytest.raises(IOError) as cm: UVFlag("foo") assert str(cm.value).startswith("foo not found") def test_read_change_type(test_outfile): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) uvf.write(test_outfile, clobber=True) assert hasattr(uvf, "ant_array") uvf.read(test_f_file) # clear sets these to None now assert hasattr(uvf, "ant_array") assert uvf.ant_array is None assert hasattr(uvf, "baseline_array") assert hasattr(uvf, "ant_1_array") assert hasattr(uvf, "ant_2_array") uvf.read(test_outfile) assert hasattr(uvf, "ant_array") assert hasattr(uvf, "baseline_array") assert uvf.baseline_array is None assert hasattr(uvf, "ant_1_array") assert uvf.ant_1_array is None assert hasattr(uvf, "ant_2_array") assert uvf.ant_2_array is None def test_read_change_mode(uvdata_obj, test_outfile): uv = uvdata_obj uvf = UVFlag(uv, mode="flag") assert hasattr(uvf, "flag_array") assert hasattr(uvf, "metric_array") assert uvf.metric_array is None uvf.write(test_outfile, clobber=True) uvf.read(test_f_file) assert hasattr(uvf, "metric_array") assert hasattr(uvf, "flag_array") assert uvf.flag_array is None uvf.read(test_outfile) assert hasattr(uvf, "flag_array") assert hasattr(uvf, "metric_array") assert uvf.metric_array is None def test_write_no_clobber(): uvf = UVFlag(test_f_file) with pytest.raises(ValueError) as cm: uvf.write(test_f_file) assert str(cm.value).startswith("File " + test_f_file + " exists;") @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_lst_from_uv(uvdata_obj): uv = uvdata_obj lst_array = lst_from_uv(uv) assert np.allclose(uv.lst_array, lst_array) def test_lst_from_uv_error(): with pytest.raises(ValueError) as cm: lst_from_uv(4) assert str(cm.value).startswith("Function lst_from_uv can only operate on") def test_add(): uv1 = UVFlag(test_f_file) uv2 = copy.deepcopy(uv1) uv2.time_array += 1 # Add a day uv3 = uv1 + uv2 assert np.array_equal( np.concatenate((uv1.time_array, uv2.time_array)), uv3.time_array ) assert np.array_equal( np.concatenate((uv1.baseline_array, uv2.baseline_array)), uv3.baseline_array ) assert np.array_equal( np.concatenate((uv1.ant_1_array, uv2.ant_1_array)), uv3.ant_1_array ) assert np.array_equal( np.concatenate((uv1.ant_2_array, uv2.ant_2_array)), uv3.ant_2_array ) assert np.array_equal(np.concatenate((uv1.lst_array, uv2.lst_array)), uv3.lst_array) assert np.array_equal( np.concatenate((uv1.metric_array, uv2.metric_array), axis=0), uv3.metric_array ) assert np.array_equal( np.concatenate((uv1.weights_array, uv2.weights_array), axis=0), uv3.weights_array, ) assert np.array_equal(uv1.freq_array, uv3.freq_array) assert uv3.type == "baseline" assert uv3.mode == "metric" assert np.array_equal(uv1.polarization_array, uv3.polarization_array) assert "Data combined along time axis. " in uv3.history def test_add_collapsed_pols(): uvf = UVFlag(test_f_file) uvf.weights_array = np.ones_like(uvf.weights_array) uvf2 = uvf.copy() uvf2.polarization_array[0] = -4 uvf.__add__(uvf2, inplace=True, axis="pol") # Concatenate to form multi-pol object uvf.collapse_pol() uvf3 = uvf.copy() uvf3.time_array += 1 # increment the time array uvf4 = uvf + uvf3 assert uvf4.Ntimes == 2 * uvf.Ntimes assert uvf4.check() def test_add_add_version_str(): uv1 = UVFlag(test_f_file) uv1.history = uv1.history.replace(pyuvdata_version_str, "") assert pyuvdata_version_str not in uv1.history uv2 = copy.deepcopy(uv1) uv2.time_array += 1 # Add a day uv3 = uv1 + uv2 assert pyuvdata_version_str in uv3.history def test_add_baseline(): uv1 = UVFlag(test_f_file) uv2 = copy.deepcopy(uv1) uv2.baseline_array += 100 # Arbitrary uv3 = uv1.__add__(uv2, axis="baseline") assert np.array_equal( np.concatenate((uv1.time_array, uv2.time_array)), uv3.time_array ) assert np.array_equal( np.concatenate((uv1.baseline_array, uv2.baseline_array)), uv3.baseline_array ) assert np.array_equal( np.concatenate((uv1.ant_1_array, uv2.ant_1_array)), uv3.ant_1_array ) assert np.array_equal( np.concatenate((uv1.ant_2_array, uv2.ant_2_array)), uv3.ant_2_array ) assert np.array_equal(np.concatenate((uv1.lst_array, uv2.lst_array)), uv3.lst_array) assert np.array_equal( np.concatenate((uv1.metric_array, uv2.metric_array), axis=0), uv3.metric_array ) assert np.array_equal( np.concatenate((uv1.weights_array, uv2.weights_array), axis=0), uv3.weights_array, ) assert np.array_equal(uv1.freq_array, uv3.freq_array) assert uv3.type == "baseline" assert uv3.mode == "metric" assert np.array_equal(uv1.polarization_array, uv3.polarization_array) assert "Data combined along baseline axis. " in uv3.history def test_add_antenna(): uvc = UVCal() uvc.read_calfits(test_c_file) uv1 = UVFlag(uvc) uv2 = copy.deepcopy(uv1) uv2.ant_array += 100 # Arbitrary uv3 = uv1.__add__(uv2, axis="antenna") assert np.array_equal(np.concatenate((uv1.ant_array, uv2.ant_array)), uv3.ant_array) assert np.array_equal( np.concatenate((uv1.metric_array, uv2.metric_array), axis=0), uv3.metric_array ) assert np.array_equal( np.concatenate((uv1.weights_array, uv2.weights_array), axis=0), uv3.weights_array, ) assert np.array_equal(uv1.freq_array, uv3.freq_array) assert np.array_equal(uv1.time_array, uv3.time_array) assert np.array_equal(uv1.lst_array, uv3.lst_array) assert uv3.type == "antenna" assert uv3.mode == "metric" assert np.array_equal(uv1.polarization_array, uv3.polarization_array) assert "Data combined along antenna axis. " in uv3.history def test_add_frequency(): uv1 = UVFlag(test_f_file) uv2 = copy.deepcopy(uv1) uv2.freq_array += 1e4 # Arbitrary uv3 = uv1.__add__(uv2, axis="frequency") assert np.array_equal( np.concatenate((uv1.freq_array, uv2.freq_array), axis=-1), uv3.freq_array ) assert np.array_equal(uv1.time_array, uv3.time_array) assert np.array_equal(uv1.baseline_array, uv3.baseline_array) assert np.array_equal(uv1.ant_1_array, uv3.ant_1_array) assert np.array_equal(uv1.ant_2_array, uv3.ant_2_array) assert np.array_equal(uv1.lst_array, uv3.lst_array) assert np.array_equal( np.concatenate((uv1.metric_array, uv2.metric_array), axis=2), uv3.metric_array ) assert np.array_equal( np.concatenate((uv1.weights_array, uv2.weights_array), axis=2), uv3.weights_array, ) assert uv3.type == "baseline" assert uv3.mode == "metric" assert np.array_equal(uv1.polarization_array, uv3.polarization_array) assert "Data combined along frequency axis. " in uv3.history def test_add_frequency_with_weights_square(): # Same test as above, just checking an optional parameter (also in waterfall mode) uvf1 = UVFlag(test_f_file) uvf1.weights_array = 2 * np.ones_like(uvf1.weights_array) uvf1.to_waterfall(return_weights_square=True) uvf2 = copy.deepcopy(uvf1) uvf2.freq_array += 1e4 uvf3 = uvf1.__add__(uvf2, axis="frequency") assert np.array_equal( np.concatenate((uvf1.weights_square_array, uvf2.weights_square_array), axis=1), uvf3.weights_square_array, ) def test_add_frequency_mix_weights_square(): # Same test as above, checking some error handling uvf1 = UVFlag(test_f_file) uvf1.weights_array = 2 * np.ones_like(uvf1.weights_array) uvf2 = copy.deepcopy(uvf1) uvf1.to_waterfall(return_weights_square=True) uvf2.to_waterfall(return_weights_square=False) uvf2.freq_array += 1e4 with pytest.raises( ValueError, match="weights_square_array optional parameter is missing from second UVFlag", ): uvf1.__add__(uvf2, axis="frequency", inplace=True) def test_add_pol(): uv1 = UVFlag(test_f_file) uv2 = copy.deepcopy(uv1) uv2.polarization_array += 1 # Arbitrary uv3 = uv1.__add__(uv2, axis="polarization") assert np.array_equal(uv1.freq_array, uv3.freq_array) assert np.array_equal(uv1.time_array, uv3.time_array) assert np.array_equal(uv1.baseline_array, uv3.baseline_array) assert np.array_equal(uv1.ant_1_array, uv3.ant_1_array) assert np.array_equal(uv1.ant_2_array, uv3.ant_2_array) assert np.array_equal(uv1.lst_array, uv3.lst_array) assert np.array_equal( np.concatenate((uv1.metric_array, uv2.metric_array), axis=3), uv3.metric_array ) assert np.array_equal( np.concatenate((uv1.weights_array, uv2.weights_array), axis=3), uv3.weights_array, ) assert uv3.type == "baseline" assert uv3.mode == "metric" assert np.array_equal( np.concatenate((uv1.polarization_array, uv2.polarization_array)), uv3.polarization_array, ) assert "Data combined along polarization axis. " in uv3.history @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_add_flag(uvdata_obj): uv = uvdata_obj uv1 = UVFlag(uv, mode="flag") uv2 = copy.deepcopy(uv1) uv2.time_array += 1 # Add a day uv3 = uv1 + uv2 assert np.array_equal( np.concatenate((uv1.time_array, uv2.time_array)), uv3.time_array ) assert np.array_equal( np.concatenate((uv1.baseline_array, uv2.baseline_array)), uv3.baseline_array ) assert np.array_equal( np.concatenate((uv1.ant_1_array, uv2.ant_1_array)), uv3.ant_1_array ) assert np.array_equal( np.concatenate((uv1.ant_2_array, uv2.ant_2_array)), uv3.ant_2_array ) assert np.array_equal(np.concatenate((uv1.lst_array, uv2.lst_array)), uv3.lst_array) assert np.array_equal( np.concatenate((uv1.flag_array, uv2.flag_array), axis=0), uv3.flag_array ) assert np.array_equal(uv1.freq_array, uv3.freq_array) assert uv3.type == "baseline" assert uv3.mode == "flag" assert np.array_equal(uv1.polarization_array, uv3.polarization_array) assert "Data combined along time axis. " in uv3.history @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_add_errors(uvdata_obj): uv = uvdata_obj uvc = UVCal() uvc.read_calfits(test_c_file) uv1 = UVFlag(uv) # Mismatched classes with pytest.raises(ValueError) as cm: uv1.__add__(3) assert str(cm.value).startswith( "Only UVFlag objects can be added to a UVFlag object" ) # Mismatched types uv2 = UVFlag(uvc) with pytest.raises(ValueError) as cm: uv1.__add__(uv2) assert str(cm.value).startswith("UVFlag object of type ") # Mismatched modes uv3 = UVFlag(uv, mode="flag") with pytest.raises(ValueError) as cm: uv1.__add__(uv3) assert str(cm.value).startswith("UVFlag object of mode ") # Invalid axes with pytest.raises(ValueError) as cm: uv1.__add__(uv1, axis="antenna") assert str(cm.value).endswith("concatenated along antenna axis.") with pytest.raises(ValueError) as cm: uv2.__add__(uv2, axis="baseline") assert str(cm.value).endswith("concatenated along baseline axis.") def test_inplace_add(): uv1a = UVFlag(test_f_file) uv1b = copy.deepcopy(uv1a) uv2 = copy.deepcopy(uv1a) uv2.time_array += 1 uv1a += uv2 assert uv1a.__eq__(uv1b + uv2) def test_clear_unused_attributes(): uv = UVFlag(test_f_file) assert hasattr(uv, "baseline_array") assert hasattr(uv, "ant_1_array") assert hasattr(uv, "ant_2_array") assert hasattr(uv, "Nants_telescope") uv._set_type_antenna() uv.clear_unused_attributes() # clear_unused_attributes now sets these to None assert hasattr(uv, "baseline_array") assert uv.baseline_array is None assert hasattr(uv, "ant_1_array") assert uv.ant_1_array is None assert hasattr(uv, "ant_2_array") assert uv.ant_2_array is None assert hasattr(uv, "Nants_telescope") assert uv.Nants_telescope is None uv._set_mode_flag() assert hasattr(uv, "metric_array") uv.clear_unused_attributes() assert hasattr(uv, "metric_array") assert uv.metric_array is None # Start over uv = UVFlag(test_f_file) uv.ant_array = np.array([4]) uv.flag_array = np.array([5]) uv.clear_unused_attributes() assert hasattr(uv, "ant_array") assert uv.ant_array is None assert hasattr(uv, "flag_array") assert uv.flag_array is None def test_not_equal(): uvf1 = UVFlag(test_f_file) # different class assert not uvf1.__eq__(5) # different mode uvf2 = uvf1.copy() uvf2.mode = "flag" assert not uvf1.__eq__(uvf2) # different type uvf2 = uvf1.copy() uvf2.type = "antenna" assert not uvf1.__eq__(uvf2) # array different uvf2 = uvf1.copy() uvf2.freq_array += 1 assert not uvf1.__eq__(uvf2) # history different uvf2 = uvf1.copy() uvf2.history += "hello" assert not uvf1.__eq__(uvf2, check_history=True) def test_to_waterfall_bl(): uvf = UVFlag(test_f_file) uvf.weights_array = np.ones_like(uvf.weights_array) uvf.to_waterfall() assert uvf.type == "waterfall" assert uvf.metric_array.shape == ( len(uvf.time_array), len(uvf.freq_array), len(uvf.polarization_array), ) assert uvf.weights_array.shape == uvf.metric_array.shape def test_to_waterfall_add_version_str(): uvf = UVFlag(test_f_file) uvf.weights_array = np.ones_like(uvf.weights_array) uvf.history = uvf.history.replace(pyuvdata_version_str, "") assert pyuvdata_version_str not in uvf.history uvf.to_waterfall() assert pyuvdata_version_str in uvf.history def test_to_waterfall_bl_multi_pol(): uvf = UVFlag(test_f_file) uvf.weights_array = np.ones_like(uvf.weights_array) uvf2 = uvf.copy() uvf2.polarization_array[0] = -4 uvf.__add__(uvf2, inplace=True, axis="pol") # Concatenate to form multi-pol object uvf2 = uvf.copy() # Keep a copy to run with keep_pol=False uvf.to_waterfall() assert uvf.type == "waterfall" assert uvf.metric_array.shape == ( len(uvf.time_array), len(uvf.freq_array), len(uvf.polarization_array), ) assert uvf.weights_array.shape == uvf.metric_array.shape assert len(uvf.polarization_array) == 2 # Repeat with keep_pol=False uvf2.to_waterfall(keep_pol=False) assert uvf2.type == "waterfall" assert uvf2.metric_array.shape == (len(uvf2.time_array), len(uvf.freq_array), 1) assert uvf2.weights_array.shape == uvf2.metric_array.shape assert len(uvf2.polarization_array) == 1 assert uvf2.polarization_array[0] == np.str_( ",".join(map(str, uvf.polarization_array)) ) def test_to_waterfall_bl_ret_wt_sq(): uvf = UVFlag(test_f_file) Nbls = uvf.Nbls uvf.weights_array = 2 * np.ones_like(uvf.weights_array) uvf.to_waterfall(return_weights_square=True) assert np.all(uvf.weights_square_array == 4 * Nbls) # Switch to flag and check that it is now set to None uvf.to_flag() assert uvf.weights_square_array is None def test_collapse_pol(test_outfile): uvf = UVFlag(test_f_file) uvf.weights_array = np.ones_like(uvf.weights_array) uvf2 = uvf.copy() uvf2.polarization_array[0] = -4 uvf.__add__(uvf2, inplace=True, axis="pol") # Concatenate to form multi-pol object uvf2 = uvf.copy() uvf2.collapse_pol() assert len(uvf2.polarization_array) == 1 assert uvf2.polarization_array[0] == np.str_( ",".join(map(str, uvf.polarization_array)) ) assert uvf2.mode == "metric" assert hasattr(uvf2, "metric_array") assert hasattr(uvf2, "flag_array") assert uvf2.flag_array is None # test check passes just to be sure assert uvf2.check() # test writing it out and reading in to make sure polarization_array has # correct type uvf2.write(test_outfile, clobber=True) with h5py.File(test_outfile, "r") as h5: assert h5["Header/polarization_array"].dtype.type is np.string_ uvf = UVFlag(test_outfile) assert uvf._polarization_array.expected_type == str assert uvf._polarization_array.acceptable_vals is None assert uvf == uvf2 os.remove(test_outfile) def test_collapse_pol_add_pol_axis(): uvf = UVFlag(test_f_file) uvf.weights_array = np.ones_like(uvf.weights_array) uvf2 = uvf.copy() uvf2.polarization_array[0] = -4 uvf.__add__(uvf2, inplace=True, axis="pol") # Concatenate to form multi-pol object uvf2 = uvf.copy() uvf2.collapse_pol() with pytest.raises(NotImplementedError) as cm: uvf2.__add__(uvf2, axis="pol") assert str(cm.value).startswith("Two UVFlag objects with their") def test_collapse_pol_or(): uvf = UVFlag(test_f_file) uvf.to_flag() assert uvf.weights_array is None uvf2 = uvf.copy() uvf2.polarization_array[0] = -4 uvf.__add__(uvf2, inplace=True, axis="pol") # Concatenate to form multi-pol object uvf2 = uvf.copy() uvf2.collapse_pol(method="or") assert len(uvf2.polarization_array) == 1 assert uvf2.polarization_array[0] == np.str_( ",".join(map(str, uvf.polarization_array)) ) assert uvf2.mode == "flag" assert hasattr(uvf2, "flag_array") assert hasattr(uvf2, "metric_array") assert uvf2.metric_array is None def test_collapse_pol_add_version_str(): uvf = UVFlag(test_f_file) uvf.to_flag() uvf2 = uvf.copy() uvf2.polarization_array[0] = -4 uvf.__add__(uvf2, inplace=True, axis="pol") # Concatenate to form multi-pol object uvf.history = uvf.history.replace(pyuvdata_version_str, "") assert pyuvdata_version_str not in uvf.history uvf2 = uvf.copy() uvf2.collapse_pol(method="or") assert pyuvdata_version_str in uvf2.history def test_collapse_single_pol(): uvf = UVFlag(test_f_file) uvf.weights_array = np.ones_like(uvf.weights_array) uvf2 = uvf.copy() with uvtest.check_warnings(UserWarning, "Cannot collapse polarization"): uvf.collapse_pol() assert uvf == uvf2 def test_collapse_pol_flag(): uvf = UVFlag(test_f_file) uvf.to_flag() assert uvf.weights_array is None uvf2 = uvf.copy() uvf2.polarization_array[0] = -4 uvf.__add__(uvf2, inplace=True, axis="pol") # Concatenate to form multi-pol object uvf2 = uvf.copy() uvf2.collapse_pol() assert len(uvf2.polarization_array) == 1 assert uvf2.polarization_array[0] == np.str_( ",".join(map(str, uvf.polarization_array)) ) assert uvf2.mode == "metric" assert hasattr(uvf2, "metric_array") assert hasattr(uvf2, "flag_array") assert uvf2.flag_array is None def test_to_waterfall_bl_flags(): uvf = UVFlag(test_f_file) uvf.to_flag() uvf.to_waterfall() assert uvf.type == "waterfall" assert uvf.mode == "metric" assert uvf.metric_array.shape == ( len(uvf.time_array), len(uvf.freq_array), len(uvf.polarization_array), ) assert uvf.weights_array.shape == uvf.metric_array.shape assert len(uvf.lst_array) == len(uvf.time_array) def test_to_waterfall_bl_flags_or(): uvf = UVFlag(test_f_file) uvf.to_flag() assert uvf.weights_array is None uvf.to_waterfall(method="or") assert uvf.type == "waterfall" assert uvf.mode == "flag" assert uvf.flag_array.shape == ( len(uvf.time_array), len(uvf.freq_array), len(uvf.polarization_array), ) assert len(uvf.lst_array) == len(uvf.time_array) uvf = UVFlag(test_f_file) uvf.to_flag() uvf.to_waterfall(method="or") assert uvf.type == "waterfall" assert uvf.mode == "flag" assert uvf.flag_array.shape == ( len(uvf.time_array), len(uvf.freq_array), len(uvf.polarization_array), ) assert len(uvf.lst_array) == len(uvf.time_array) def test_to_waterfall_ant(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) uvf.weights_array = np.ones_like(uvf.weights_array) uvf.to_waterfall() assert uvf.type == "waterfall" assert uvf.metric_array.shape == ( len(uvf.time_array), len(uvf.freq_array), len(uvf.polarization_array), ) assert uvf.weights_array.shape == uvf.metric_array.shape assert len(uvf.lst_array) == len(uvf.time_array) def test_to_waterfall_waterfall(): uvf = UVFlag(test_f_file) uvf.weights_array = np.ones_like(uvf.weights_array) uvf.to_waterfall() with uvtest.check_warnings(UserWarning, "This object is already a waterfall"): uvf.to_waterfall() @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_to_baseline_flags(uvdata_obj): uv = uvdata_obj uvf = UVFlag(uv) uvf.to_waterfall() uvf.to_flag() uvf.flag_array[0, 10, 0] = True # Flag time0, chan10 uvf.flag_array[1, 15, 0] = True # Flag time1, chan15 uvf.to_baseline(uv) assert uvf.type == "baseline" assert np.all(uvf.baseline_array == uv.baseline_array) assert np.all(uvf.time_array == uv.time_array) times = np.unique(uvf.time_array) ntrue = 0.0 ind = np.where(uvf.time_array == times[0])[0] ntrue += len(ind) assert np.all(uvf.flag_array[ind, 0, 10, 0]) ind = np.where(uvf.time_array == times[1])[0] ntrue += len(ind) assert np.all(uvf.flag_array[ind, 0, 15, 0]) assert uvf.flag_array.mean() == ntrue / uvf.flag_array.size @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @pytest.mark.parametrize("future_shapes", [True, False]) def test_to_baseline_metric(uvdata_obj, future_shapes): uv = uvdata_obj if future_shapes: uv.use_future_array_shapes() uvf = UVFlag(uv) uvf.to_waterfall() uvf.metric_array[0, 10, 0] = 3.2 # Fill in time0, chan10 uvf.metric_array[1, 15, 0] = 2.1 # Fill in time1, chan15 uvf.to_baseline(uv) assert np.all(uvf.baseline_array == uv.baseline_array) assert np.all(uvf.time_array == uv.time_array) times = np.unique(uvf.time_array) ind = np.where(uvf.time_array == times[0])[0] nt0 = len(ind) assert np.all(uvf.metric_array[ind, 0, 10, 0] == 3.2) ind = np.where(uvf.time_array == times[1])[0] nt1 = len(ind) assert np.all(uvf.metric_array[ind, 0, 15, 0] == 2.1) assert np.isclose( uvf.metric_array.mean(), (3.2 * nt0 + 2.1 * nt1) / uvf.metric_array.size ) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_to_baseline_add_version_str(uvdata_obj): uv = uvdata_obj uvf = UVFlag(uv) uvf.to_waterfall() uvf.metric_array[0, 10, 0] = 3.2 # Fill in time0, chan10 uvf.metric_array[1, 15, 0] = 2.1 # Fill in time1, chan15 uvf.history = uvf.history.replace(pyuvdata_version_str, "") assert pyuvdata_version_str not in uvf.history uvf.to_baseline(uv) assert pyuvdata_version_str in uvf.history @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_baseline_to_baseline(uvdata_obj): uv = uvdata_obj uvf = UVFlag(uv) uvf2 = uvf.copy() uvf.to_baseline(uv) assert uvf == uvf2 def test_to_baseline_metric_error(uvdata_obj, uvf_from_uvcal): uvf = uvf_from_uvcal uvf.select(polarizations=uvf.polarization_array[0]) uv = uvdata_obj with pytest.raises(NotImplementedError) as cm: uvf.to_baseline(uv, force_pol=True) assert str(cm.value).startswith( "Cannot currently convert from " "antenna type, metric mode" ) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_to_baseline_from_antenna(uvdata_obj, uvf_from_uvcal): uvf = uvf_from_uvcal uvf.select(polarizations=uvf.polarization_array[0]) uvf.to_flag() uv = uvdata_obj ants_data = np.unique(uv.ant_1_array.tolist() + uv.ant_2_array.tolist()) new_ants = np.setdiff1d(ants_data, uvf.ant_array) old_baseline = (uvf.ant_array[0], uvf.ant_array[1]) old_times = np.unique(uvf.time_array) or_flags = np.logical_or(uvf.flag_array[0], uvf.flag_array[1]) or_flags = np.transpose(or_flags, [2, 0, 1, 3]) uv2 = copy.deepcopy(uv) uvf2 = uvf.copy() # hack in the exact times so we can compare some values later uv2.select(bls=old_baseline) uv2.time_array[: uvf2.time_array.size] = uvf.time_array uvf.to_baseline(uv, force_pol=True) uvf2.to_baseline(uv2, force_pol=True) assert uvf.check() uvf2.select(bls=old_baseline, times=old_times) assert np.allclose(or_flags, uvf2.flag_array) # all new antenna should be completely flagged # checks auto correlations uvf_new = uvf.select(antenna_nums=new_ants, inplace=False) for bl in np.unique(uvf_new.baseline_array): uvf2 = uvf_new.select(bls=uv.baseline_to_antnums(bl), inplace=False) assert np.all(uvf2.flag_array) # check for baselines with one new antenna bls = [ uvf.baseline_to_antnums(bl) for bl in uvf.baseline_array if np.intersect1d(new_ants, uvf.baseline_to_antnums(bl)).size > 0 ] uvf_new = uvf.select(bls=bls, inplace=False) for bl in np.unique(uvf_new.baseline_array): uvf2 = uvf_new.select(bls=uv.baseline_to_antnums(bl), inplace=False) assert np.all(uvf2.flag_array) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_to_baseline_errors(uvdata_obj): uvc = UVCal() uvc.read_calfits(test_c_file) uv = uvdata_obj uvf = UVFlag(test_f_file) uvf.to_waterfall() with pytest.raises(ValueError) as cm: uvf.to_baseline(7.3) # invalid matching object assert str(cm.value).startswith("Must pass in UVData object or UVFlag object") uvf = UVFlag(test_f_file) uvf.to_waterfall() uvf2 = uvf.copy() uvf.polarization_array[0] = -4 with pytest.raises(ValueError) as cm: uvf.to_baseline(uv) # Mismatched pols assert str(cm.value).startswith("Polarizations do not match.") uvf.__iadd__(uvf2, axis="polarization") with pytest.raises(ValueError) as cm: uvf.to_baseline(uv) # Mismatched pols, can't be forced assert str(cm.value).startswith("Polarizations could not be made to match.") @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_to_baseline_force_pol(uvdata_obj): uv = uvdata_obj uvf = UVFlag(uv) uvf.to_waterfall() uvf.to_flag() uvf.flag_array[0, 10, 0] = True # Flag time0, chan10 uvf.flag_array[1, 15, 0] = True # Flag time1, chan15 uvf.polarization_array[0] = -4 # Change pol, but force pol anyway uvf.to_baseline(uv, force_pol=True) assert np.all(uvf.baseline_array == uv.baseline_array) assert np.all(uvf.time_array == uv.time_array) assert np.array_equal(uvf.polarization_array, uv.polarization_array) times = np.unique(uvf.time_array) ntrue = 0.0 ind = np.where(uvf.time_array == times[0])[0] ntrue += len(ind) assert np.all(uvf.flag_array[ind, 0, 10, 0]) ind = np.where(uvf.time_array == times[1])[0] ntrue += len(ind) assert np.all(uvf.flag_array[ind, 0, 15, 0]) assert uvf.flag_array.mean() == ntrue / uvf.flag_array.size @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_to_baseline_force_pol_npol_gt_1(uvdata_obj): uv = uvdata_obj uvf = UVFlag(uv) uvf.to_waterfall() uvf.to_flag() uvf.flag_array[0, 10, 0] = True # Flag time0, chan10 uvf.flag_array[1, 15, 0] = True # Flag time1, chan15 uv2 = copy.deepcopy(uv) uv2.polarization_array[0] = -6 uv += uv2 uvf.to_baseline(uv, force_pol=True) assert np.all(uvf.baseline_array == uv.baseline_array) assert np.all(uvf.time_array == uv.time_array) assert np.array_equal(uvf.polarization_array, uv.polarization_array) assert uvf.Npols == len(uvf.polarization_array) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_to_baseline_metric_force_pol(uvdata_obj): uv = uvdata_obj uvf = UVFlag(uv) uvf.to_waterfall() uvf.metric_array[0, 10, 0] = 3.2 # Fill in time0, chan10 uvf.metric_array[1, 15, 0] = 2.1 # Fill in time1, chan15 uvf.polarization_array[0] = -4 uvf.to_baseline(uv, force_pol=True) assert np.all(uvf.baseline_array == uv.baseline_array) assert np.all(uvf.time_array == uv.time_array) assert np.array_equal(uvf.polarization_array, uv.polarization_array) times = np.unique(uvf.time_array) ind = np.where(uvf.time_array == times[0])[0] nt0 = len(ind) assert np.all(uvf.metric_array[ind, 0, 10, 0] == 3.2) ind = np.where(uvf.time_array == times[1])[0] nt1 = len(ind) assert np.all(uvf.metric_array[ind, 0, 15, 0] == 2.1) assert np.isclose( uvf.metric_array.mean(), (3.2 * nt0 + 2.1 * nt1) / uvf.metric_array.size ) def test_to_antenna_flags(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) uvf.to_waterfall() uvf.to_flag() uvf.flag_array[0, 10, 0] = True # Flag time0, chan10 uvf.flag_array[1, 15, 0] = True # Flag time1, chan15 uvf.to_antenna(uvc) assert uvf.type == "antenna" assert np.all(uvf.ant_array == uvc.ant_array) assert np.all(uvf.time_array == uvc.time_array) assert np.all(uvf.flag_array[:, 0, 10, 0, 0]) assert np.all(uvf.flag_array[:, 0, 15, 1, 0]) assert uvf.flag_array.mean() == 2.0 * uvc.Nants_data / uvf.flag_array.size def test_to_antenna_add_version_str(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) uvf.to_waterfall() uvf.to_flag() uvf.flag_array[0, 10, 0] = True # Flag time0, chan10 uvf.flag_array[1, 15, 0] = True # Flag time1, chan15 uvf.history = uvf.history.replace(pyuvdata_version_str, "") assert pyuvdata_version_str not in uvf.history uvf.to_antenna(uvc) assert pyuvdata_version_str in uvf.history def test_to_antenna_metric(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) uvf.to_waterfall() uvf.metric_array[0, 10, 0] = 3.2 # Fill in time0, chan10 uvf.metric_array[1, 15, 0] = 2.1 # Fill in time1, chan15 uvf.to_antenna(uvc) assert np.all(uvf.ant_array == uvc.ant_array) assert np.all(uvf.time_array == uvc.time_array) assert np.all(uvf.metric_array[:, 0, 10, 0, 0] == 3.2) assert np.all(uvf.metric_array[:, 0, 15, 1, 0] == 2.1) assert np.isclose( uvf.metric_array.mean(), (3.2 + 2.1) * uvc.Nants_data / uvf.metric_array.size ) def test_to_antenna_flags_match_uvflag(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) uvf2 = uvf.copy() uvf.to_waterfall() uvf.to_flag() uvf.flag_array[0, 10, 0] = True # Flag time0, chan10 uvf.flag_array[1, 15, 0] = True # Flag time1, chan15 uvf.to_antenna(uvf2) assert np.all(uvf.ant_array == uvc.ant_array) assert np.all(uvf.time_array == uvc.time_array) assert np.all(uvf.flag_array[:, 0, 10, 0, 0]) assert np.all(uvf.flag_array[:, 0, 15, 1, 0]) assert uvf.flag_array.mean() == 2.0 * uvc.Nants_data / uvf.flag_array.size def test_antenna_to_antenna(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) uvf2 = uvf.copy() uvf.to_antenna(uvc) assert uvf == uvf2 @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_to_antenna_errors(uvdata_obj): uvc = UVCal() uvc.read_calfits(test_c_file) uv = uvdata_obj uvf = UVFlag(test_f_file) uvf.to_waterfall() with pytest.raises(ValueError) as cm: uvf.to_antenna(7.3) # invalid matching object assert str(cm.value).startswith("Must pass in UVCal object or UVFlag object ") uvf = UVFlag(uv) with pytest.raises(ValueError) as cm: uvf.to_antenna(uvc) # Cannot pass in baseline type assert str(cm.value).startswith('Cannot convert from type "baseline" to "antenna".') uvf = UVFlag(test_f_file) uvf.to_waterfall() uvf2 = uvf.copy() uvf.polarization_array[0] = -4 with pytest.raises(ValueError) as cm: uvf.to_antenna(uvc) # Mismatched pols assert str(cm.value).startswith("Polarizations do not match. ") uvf.__iadd__(uvf2, axis="polarization") with pytest.raises(ValueError) as cm: uvf.to_antenna(uvc) # Mismatched pols, can't be forced assert str(cm.value).startswith("Polarizations could not be made to match.") def test_to_antenna_force_pol(): uvc = UVCal() uvc.read_calfits(test_c_file) uvc.select(jones=-5) uvf = UVFlag(uvc) uvf.to_waterfall() uvf.to_flag() uvf.flag_array[0, 10, 0] = True # Flag time0, chan10 uvf.flag_array[1, 15, 0] = True # Flag time1, chan15 uvf.polarization_array[0] = -4 # Change pol, but force pol anyway uvf.to_antenna(uvc, force_pol=True) assert np.all(uvf.ant_array == uvc.ant_array) assert np.all(uvf.time_array == uvc.time_array) assert np.array_equal(uvf.polarization_array, uvc.jones_array) assert np.all(uvf.flag_array[:, 0, 10, 0, 0]) assert np.all(uvf.flag_array[:, 0, 15, 1, 0]) assert uvf.flag_array.mean() == 2 * uvc.Nants_data / uvf.flag_array.size def test_to_antenna_metric_force_pol(): uvc = UVCal() uvc.read_calfits(test_c_file) uvc.select(jones=-5) uvf = UVFlag(uvc) uvf.to_waterfall() uvf.metric_array[0, 10, 0] = 3.2 # Fill in time0, chan10 uvf.metric_array[1, 15, 0] = 2.1 # Fill in time1, chan15 uvf.polarization_array[0] = -4 uvf.to_antenna(uvc, force_pol=True) assert np.all(uvf.ant_array == uvc.ant_array) assert np.all(uvf.time_array == uvc.time_array) assert np.array_equal(uvf.polarization_array, uvc.jones_array) assert np.all(uvf.metric_array[:, 0, 10, 0, 0] == 3.2) assert np.all(uvf.metric_array[:, 0, 15, 1, 0] == 2.1) assert np.isclose( uvf.metric_array.mean(), (3.2 + 2.1) * uvc.Nants_data / uvf.metric_array.size ) def test_copy(): uvf = UVFlag(test_f_file) uvf2 = uvf.copy() assert uvf == uvf2 # Make sure it's a copy and not just pointing to same object uvf.to_waterfall() assert uvf != uvf2 def test_or(): uvf = UVFlag(test_f_file) uvf.to_flag() uvf2 = uvf.copy() uvf2.flag_array = np.ones_like(uvf2.flag_array) uvf.flag_array[0] = True uvf2.flag_array[0] = False uvf2.flag_array[1] = False uvf3 = uvf | uvf2 assert np.all(uvf3.flag_array[0]) assert not np.any(uvf3.flag_array[1]) assert np.all(uvf3.flag_array[2:]) def test_or_add_version_str(): uvf = UVFlag(test_f_file) uvf.to_flag() uvf.history = uvf.history.replace(pyuvdata_version_str, "") assert pyuvdata_version_str not in uvf.history uvf2 = uvf.copy() uvf2.flag_array = np.ones_like(uvf2.flag_array) uvf.flag_array[0] = True uvf2.flag_array[0] = False uvf2.flag_array[1] = False uvf3 = uvf | uvf2 assert pyuvdata_version_str in uvf3.history def test_or_error(): uvf = UVFlag(test_f_file) uvf2 = uvf.copy() uvf.to_flag() with pytest.raises(ValueError) as cm: uvf.__or__(uvf2) assert str(cm.value).startswith('UVFlag object must be in "flag" mode') def test_or_add_history(): uvf = UVFlag(test_f_file) uvf.to_flag() uvf2 = uvf.copy() uvf2.history = "Different history" uvf3 = uvf | uvf2 assert uvf.history in uvf3.history assert uvf2.history in uvf3.history assert "Flags OR'd with:" in uvf3.history def test_ior(): uvf = UVFlag(test_f_file) uvf.to_flag() uvf2 = uvf.copy() uvf2.flag_array = np.ones_like(uvf2.flag_array) uvf.flag_array[0] = True uvf2.flag_array[0] = False uvf2.flag_array[1] = False uvf |= uvf2 assert np.all(uvf.flag_array[0]) assert not np.any(uvf.flag_array[1]) assert np.all(uvf.flag_array[2:]) def test_to_flag(): uvf = UVFlag(test_f_file) uvf.to_flag() assert hasattr(uvf, "flag_array") assert hasattr(uvf, "metric_array") assert uvf.metric_array is None assert uvf.mode == "flag" assert 'Converted to mode "flag"' in uvf.history def test_to_flag_add_version_str(): uvf = UVFlag(test_f_file) uvf.history = uvf.history.replace(pyuvdata_version_str, "") assert pyuvdata_version_str not in uvf.history uvf.to_flag() assert pyuvdata_version_str in uvf.history def test_to_flag_threshold(): uvf = UVFlag(test_f_file) uvf.metric_array = np.zeros_like(uvf.metric_array) uvf.metric_array[0, 0, 4, 0] = 2.0 uvf.to_flag(threshold=1.0) assert hasattr(uvf, "flag_array") assert hasattr(uvf, "metric_array") assert uvf.metric_array is None assert uvf.mode == "flag" assert uvf.flag_array[0, 0, 4, 0] assert np.sum(uvf.flag_array) == 1.0 assert 'Converted to mode "flag"' in uvf.history def test_flag_to_flag(): uvf = UVFlag(test_f_file) uvf.to_flag() uvf2 = uvf.copy() uvf2.to_flag() assert uvf == uvf2 def test_to_flag_unknown_mode(): uvf = UVFlag(test_f_file) uvf.mode = "foo" with pytest.raises(ValueError) as cm: uvf.to_flag() assert str(cm.value).startswith("Unknown UVFlag mode: foo") def test_to_metric_baseline(): uvf = UVFlag(test_f_file) uvf.to_flag() uvf.flag_array[:, :, 10] = True uvf.flag_array[1, :, :] = True assert hasattr(uvf, "flag_array") assert hasattr(uvf, "metric_array") assert uvf.metric_array is None assert uvf.mode == "flag" uvf.to_metric(convert_wgts=True) assert hasattr(uvf, "metric_array") assert hasattr(uvf, "flag_array") assert uvf.flag_array is None assert uvf.mode == "metric" assert 'Converted to mode "metric"' in uvf.history assert np.isclose(uvf.weights_array[1], 0.0).all() assert np.isclose(uvf.weights_array[:, :, 10], 0.0).all() def test_to_metric_add_version_str(): uvf = UVFlag(test_f_file) uvf.to_flag() uvf.flag_array[:, :, 10] = True uvf.flag_array[1, :, :] = True assert hasattr(uvf, "flag_array") assert hasattr(uvf, "metric_array") assert uvf.metric_array is None assert uvf.mode == "flag" uvf.history = uvf.history.replace(pyuvdata_version_str, "") assert pyuvdata_version_str not in uvf.history uvf.to_metric(convert_wgts=True) assert pyuvdata_version_str in uvf.history def test_to_metric_waterfall(): uvf = UVFlag(test_f_file) uvf.to_waterfall() uvf.to_flag() uvf.flag_array[:, 10] = True uvf.flag_array[1, :, :] = True uvf.to_metric(convert_wgts=True) assert np.isclose(uvf.weights_array[1], 0.0).all() assert np.isclose(uvf.weights_array[:, 10], 0.0).all() def test_to_metric_antenna(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc, mode="flag") uvf.flag_array[10, :, :, 1, :] = True uvf.flag_array[15, :, 3, :, :] = True uvf.to_metric(convert_wgts=True) assert np.isclose(uvf.weights_array[10, :, :, 1, :], 0.0).all() assert np.isclose(uvf.weights_array[15, :, 3, :, :], 0.0).all() def test_metric_to_metric(): uvf = UVFlag(test_f_file) uvf2 = uvf.copy() uvf.to_metric() assert uvf == uvf2 def test_to_metric_unknown_mode(): uvf = UVFlag(test_f_file) uvf.mode = "foo" with pytest.raises(ValueError) as cm: uvf.to_metric() assert str(cm.value).startswith("Unknown UVFlag mode: foo") def test_antpair2ind(): uvf = UVFlag(test_f_file) ind = uvf.antpair2ind(uvf.ant_1_array[0], uvf.ant_2_array[0]) assert np.all(uvf.ant_1_array[ind] == uvf.ant_1_array[0]) assert np.all(uvf.ant_2_array[ind] == uvf.ant_2_array[0]) def test_antpair2ind_nonbaseline(): uvf = UVFlag(test_f_file) uvf.to_waterfall() with pytest.raises(ValueError) as cm: uvf.antpair2ind(0, 3) assert str(cm.value).startswith( "UVFlag object of type " + uvf.type + " does not contain antenna " + "pairs to index." ) def test_baseline_to_antnums(): uvf = UVFlag(test_f_file) a1, a2 = uvf.baseline_to_antnums(uvf.baseline_array[0]) assert a1 == uvf.ant_1_array[0] assert a2 == uvf.ant_2_array[0] def test_get_baseline_nums(): uvf = UVFlag(test_f_file) bls = uvf.get_baseline_nums() assert np.array_equal(bls, np.unique(uvf.baseline_array)) def test_get_antpairs(): uvf = UVFlag(test_f_file) antpairs = uvf.get_antpairs() for a1, a2 in antpairs: ind = np.where((uvf.ant_1_array == a1) & (uvf.ant_2_array == a2))[0] assert len(ind) > 0 for a1, a2 in zip(uvf.ant_1_array, uvf.ant_2_array): assert (a1, a2) in antpairs def test_missing_nants_telescope(tmp_path): testfile = str(tmp_path / "test_missing_Nants.h5") shutil.copyfile(test_f_file, testfile) with h5py.File(testfile, "r+") as f: del f["/Header/Nants_telescope"] with uvtest.check_warnings( UserWarning, match="Nants_telescope not available in file", ): uvf = UVFlag(testfile) uvf2 = UVFlag(test_f_file) uvf2.Nants_telescope = 2047 assert uvf == uvf2 os.remove(testfile) def test_combine_metrics_inplace(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) np.random.seed(44) uvf.metric_array = np.random.normal(size=uvf.metric_array.shape) uvf2 = uvf.copy() uvf2.metric_array *= 2 uvf3 = uvf.copy() uvf3.metric_array *= 3 uvf.combine_metrics([uvf2, uvf3]) factor = np.sqrt((1 + 4 + 9) / 3.0) / 2.0 assert np.allclose(uvf.metric_array, np.abs(uvf2.metric_array) * factor) def test_combine_metrics_not_inplace(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) np.random.seed(44) uvf.metric_array = np.random.normal(size=uvf.metric_array.shape) uvf2 = uvf.copy() uvf2.metric_array *= 2 uvf3 = uvf.copy() uvf3.metric_array *= 3 uvf4 = uvf.combine_metrics([uvf2, uvf3], inplace=False) factor = np.sqrt((1 + 4 + 9) / 3.0) assert np.allclose(uvf4.metric_array, np.abs(uvf.metric_array) * factor) def test_combine_metrics_not_uvflag(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) with pytest.raises(ValueError) as cm: uvf.combine_metrics("bubblegum") assert str(cm.value).startswith('"others" must be UVFlag or list of UVFlag objects') def test_combine_metrics_not_metric(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) np.random.seed(44) uvf.metric_array = np.random.normal(size=uvf.metric_array.shape) uvf2 = uvf.copy() uvf2.to_flag() with pytest.raises(ValueError) as cm: uvf.combine_metrics(uvf2) assert str(cm.value).startswith('UVFlag object and "others" must be in "metric"') def test_combine_metrics_wrong_shape(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) np.random.seed(44) uvf.metric_array = np.random.normal(size=uvf.metric_array.shape) uvf2 = uvf.copy() uvf2.to_waterfall() with pytest.raises(ValueError) as cm: uvf.combine_metrics(uvf2) assert str(cm.value).startswith("UVFlag metric array shapes do not match.") def test_combine_metrics_add_version_str(): uvc = UVCal() uvc.read_calfits(test_c_file) uvf = UVFlag(uvc) uvf.history = uvf.history.replace(pyuvdata_version_str, "") assert pyuvdata_version_str not in uvf.history np.random.seed(44) uvf.metric_array = np.random.normal(size=uvf.metric_array.shape) uvf2 = uvf.copy() uvf2.metric_array *= 2 uvf3 = uvf.copy() uvf3.metric_array *= 3 uvf4 = uvf.combine_metrics([uvf2, uvf3], inplace=False) assert pyuvdata_version_str in uvf4.history @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_super(uvdata_obj): class TestClass(UVFlag): def __init__( self, indata, mode="metric", copy_flags=False, waterfall=False, history="", label="", test_property="prop", ): super(TestClass, self).__init__( indata, mode=mode, copy_flags=copy_flags, waterfall=waterfall, history=history, label=label, ) self.test_property = test_property uv = uvdata_obj tc = TestClass(uv, test_property="test_property") # UVFlag.__init__ is tested, so just see if it has a metric array assert hasattr(tc, "metric_array") # Check that it has the property assert tc.test_property == "test_property" @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_flags2waterfall(uvdata_obj): uv = uvdata_obj np.random.seed(0) uv.flag_array = np.random.randint(0, 2, size=uv.flag_array.shape, dtype=bool) wf = flags2waterfall(uv) assert np.allclose(np.mean(wf), np.mean(uv.flag_array)) assert wf.shape == (uv.Ntimes, uv.Nfreqs) wf = flags2waterfall(uv, keep_pol=True) assert wf.shape == (uv.Ntimes, uv.Nfreqs, uv.Npols) # Test external flag_array uv.flag_array = np.zeros_like(uv.flag_array) f = np.random.randint(0, 2, size=uv.flag_array.shape, dtype=bool) wf = flags2waterfall(uv, flag_array=f) assert np.allclose(np.mean(wf), np.mean(f)) assert wf.shape == (uv.Ntimes, uv.Nfreqs) # UVCal version uvc = UVCal() uvc.read_calfits(test_c_file) uvc.flag_array = np.random.randint(0, 2, size=uvc.flag_array.shape, dtype=bool) wf = flags2waterfall(uvc) assert np.allclose(np.mean(wf), np.mean(uvc.flag_array)) assert wf.shape == (uvc.Ntimes, uvc.Nfreqs) wf = flags2waterfall(uvc, keep_pol=True) assert wf.shape == (uvc.Ntimes, uvc.Nfreqs, uvc.Njones) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_flags2waterfall_errors(uvdata_obj): # First argument must be UVData or UVCal object with pytest.raises(ValueError) as cm: flags2waterfall(5) assert str(cm.value).startswith( "flags2waterfall() requires a UVData or " + "UVCal object" ) uv = uvdata_obj # Flag array must have same shape as uv.flag_array with pytest.raises(ValueError) as cm: flags2waterfall(uv, np.array([4, 5])) assert str(cm.value).startswith("Flag array must align with UVData or UVCal") def test_and_rows_cols(): d = np.zeros((10, 20), np.bool_) d[1, :] = True d[:, 2] = True d[5, 10:20] = True d[5:8, 5] = True o = and_rows_cols(d) assert o[1, :].all() assert o[:, 2].all() assert not o[5, :].all() assert not o[:, 5].all() def test_select_waterfall_errors(uvf_from_waterfall): uvf = uvf_from_waterfall with pytest.raises(ValueError) as cm: uvf.select(antenna_nums=[0, 1, 2]) assert str(cm.value).startswith("Cannot select on antenna_nums with waterfall") with pytest.raises(ValueError) as cm: uvf.select(bls=[(0, 1), (0, 2)]) assert str(cm.value).startswith("Cannot select on bls with waterfall") @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @cases_decorator @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) @pytest.mark.parametrize("dimension", list(range(1, 4))) def test_select_blt_inds(input_uvf, uvf_mode, dimension): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() np.random.seed(0) if uvf.type == "baseline": n_select = uvf.Nblts else: n_select = uvf.Ntimes blt_inds = np.random.choice(n_select, size=n_select // 2, replace=False) new_nblts = n_select // 2 if dimension == 1: blt_inds = np.atleast_1d(blt_inds) elif dimension == 2: blt_inds = np.atleast_2d(blt_inds) elif dimension == 3: blt_inds = np.atleast_3d(blt_inds) uvf1 = uvf.select(blt_inds=blt_inds, inplace=False) # test the data was extracted correctly for each case for param_name, new_param in zip(uvf._data_params, uvf1.data_like_parameters): old_param = getattr(uvf, param_name) if uvf.type == "baseline": assert np.allclose(old_param[blt_inds.squeeze()], new_param) if uvf.type == "antenna": assert np.allclose(old_param[:, :, :, blt_inds.squeeze()], new_param) if uvf.type == "waterfall": assert np.allclose(old_param[blt_inds.squeeze()], new_param) if uvf.type == "baseline": assert uvf1.Nblts == new_nblts else: assert uvf1.Ntimes == new_nblts # verify that histories are different assert not uvutils._check_histories(uvf.history, uvf1.history) if uvf.type == "baseline": addition_str = "baseline-times" else: addition_str = "times" assert uvutils._check_histories( uvf.history + f" Downselected to specific {addition_str} using pyuvdata.", uvf1.history, ) @cases_decorator @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) @pytest.mark.parametrize( "select_kwargs,err_msg", [ ({"blt_inds": []}, "No baseline-times were found"), ({"blt_inds": [int(1e9)]}, "blt_inds contains indices that are too large"), ({"blt_inds": [-1]}, "blt_inds contains indices that are negative"), ], ) def test_select_blt_inds_errors(input_uvf, uvf_mode, select_kwargs, err_msg): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() with pytest.raises(ValueError, match=err_msg): uvf.select(**select_kwargs) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @cases_decorator_no_waterfall @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) @pytest.mark.parametrize("dimension", list(range(1, 4))) def test_select_antenna_nums(input_uvf, uvf_mode, dimension): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() old_history = copy.deepcopy(uvf.history) np.random.seed(0) if uvf.type == "baseline": unique_ants = np.unique(uvf.ant_1_array.tolist() + uvf.ant_2_array.tolist()) ants_to_keep = np.random.choice( unique_ants, size=unique_ants.size // 2, replace=False ) blts_select = [ (a1 in ants_to_keep) & (a2 in ants_to_keep) for (a1, a2) in zip(uvf.ant_1_array, uvf.ant_2_array) ] Nblts_selected = np.sum(blts_select) else: unique_ants = np.unique(uvf.ant_array) ants_to_keep = np.random.choice( unique_ants, size=unique_ants.size // 2, replace=False ) if dimension == 1: ants_to_keep = np.atleast_1d(ants_to_keep) elif dimension == 2: ants_to_keep = np.atleast_2d(ants_to_keep) elif dimension == 3: ants_to_keep = np.atleast_3d(ants_to_keep) uvf2 = copy.deepcopy(uvf) uvf2.select(antenna_nums=ants_to_keep) # make 1-D for the remaining iterators in tests ants_to_keep = ants_to_keep.squeeze() assert ants_to_keep.size == uvf2.Nants_data if uvf2.type == "baseline": assert Nblts_selected == uvf2.Nblts for ant in ants_to_keep: assert ant in uvf2.ant_1_array or ant in uvf2.ant_2_array for ant in np.unique(uvf2.ant_1_array.tolist() + uvf2.ant_2_array.tolist()): assert ant in ants_to_keep else: for ant in ants_to_keep: assert ant in uvf2.ant_array for ant in np.unique(uvf2.ant_array): assert ant in ants_to_keep assert uvutils._check_histories( old_history + " Downselected to " "specific antennas using pyuvdata.", uvf2.history, ) @cases_decorator_no_waterfall @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) def test_select_antenna_nums_error(input_uvf, uvf_mode): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() # also test for error if antenna numbers not present in data with pytest.raises(ValueError) as cm: uvf.select(antenna_nums=[708, 709, 710]) assert str(cm.value).startswith("Antenna number 708 is not present") def sort_bl(p): """Sort a tuple that starts with a pair of antennas, and may have stuff after.""" if p[1] >= p[0]: return p return (p[1], p[0]) + p[2:] @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @cases_decorator_no_waterfall @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) def test_select_bls(input_uvf, uvf_mode): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() np.random.seed(0) if uvf.type != "baseline": with pytest.raises(ValueError) as cm: uvf.select(bls=[(0, 1)]) assert str(cm.value).startswith( 'Only "baseline" mode UVFlag ' "objects may select along the " "baseline axis" ) else: old_history = copy.deepcopy(uvf.history) bls_select = np.random.choice( uvf.baseline_array, size=uvf.Nbls // 2, replace=False ) first_ants, second_ants = uvf.baseline_to_antnums(bls_select) # give the conjugate bls for a few baselines first_ants[5:8], second_ants[5:8] = ( copy.copy(second_ants[5:8]), copy.copy(first_ants[5:8]), ) new_unique_ants = np.unique(first_ants.tolist() + second_ants.tolist()) ant_pairs_to_keep = list(zip(first_ants, second_ants)) sorted_pairs_to_keep = [sort_bl(p) for p in ant_pairs_to_keep] blts_select = [ sort_bl((a1, a2)) in sorted_pairs_to_keep for (a1, a2) in zip(uvf.ant_1_array, uvf.ant_2_array) ] Nblts_selected = np.sum(blts_select) uvf2 = copy.deepcopy(uvf) uvf2.select(bls=ant_pairs_to_keep) sorted_pairs_object2 = [ sort_bl(p) for p in zip(uvf2.ant_1_array, uvf2.ant_2_array) ] assert len(new_unique_ants) == uvf2.Nants_data assert Nblts_selected == uvf2.Nblts for ant in new_unique_ants: assert ant in uvf2.ant_1_array or ant in uvf2.ant_2_array for ant in np.unique(uvf2.ant_1_array.tolist() + uvf2.ant_2_array.tolist()): assert ant in new_unique_ants for pair in sorted_pairs_to_keep: assert pair in sorted_pairs_object2 for pair in sorted_pairs_object2: assert pair in sorted_pairs_to_keep assert uvutils._check_histories( old_history + " Downselected to " "specific baselines using pyuvdata.", uvf2.history, ) # Check with polarization too first_ants, second_ants = uvf.baseline_to_antnums(bls_select) # conjugate a few bls first_ants[5:8], second_ants[5:8] = ( copy.copy(second_ants[5:8]), copy.copy(first_ants[5:8]), ) pols = ["xx"] * len(first_ants) new_unique_ants = np.unique(first_ants.tolist() + second_ants.tolist()) ant_pairs_to_keep = list(zip(first_ants, second_ants, pols)) sorted_pairs_to_keep = [sort_bl(p) for p in ant_pairs_to_keep] blts_select = [ sort_bl((a1, a2, "xx")) in sorted_pairs_to_keep for (a1, a2) in zip(uvf.ant_1_array, uvf.ant_2_array) ] Nblts_selected = np.sum(blts_select) uvf2 = copy.deepcopy(uvf) uvf2.select(bls=ant_pairs_to_keep) sorted_pairs_object2 = [ sort_bl(p) + ("xx",) for p in zip(uvf2.ant_1_array, uvf2.ant_2_array) ] assert len(new_unique_ants) == uvf2.Nants_data assert Nblts_selected == uvf2.Nblts for ant in new_unique_ants: assert ant in uvf2.ant_1_array or ant in uvf2.ant_2_array for ant in np.unique(uvf2.ant_1_array.tolist() + uvf2.ant_2_array.tolist()): assert ant in new_unique_ants for pair in sorted_pairs_to_keep: assert pair in sorted_pairs_object2 for pair in sorted_pairs_object2: assert pair in sorted_pairs_to_keep assert uvutils._check_histories( old_history + " Downselected to " "specific baselines, polarizations using pyuvdata.", uvf2.history, ) # check that you can specify a single pair without errors assert isinstance(ant_pairs_to_keep[0], tuple) uvf2.select(bls=ant_pairs_to_keep[0]) sorted_pairs_object2 = [ sort_bl(p) + ("xx",) for p in zip(uvf2.ant_1_array, uvf2.ant_2_array) ] assert list(set(sorted_pairs_object2)) == [ant_pairs_to_keep[0]] @cases_decorator_no_waterfall @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) @pytest.mark.parametrize( "select_kwargs,err_msg", [ ({"bls": [3]}, "bls must be a list of tuples"), ({"bls": [(np.pi, 2 * np.pi)]}, "bls must be a list of tuples of integer"), ( {"bls": (0, 1, "xx"), "polarizations": [-5]}, "Cannot provide length-3 tuples and also specify polarizations.", ), ( {"bls": (0, 1, 5)}, "The third element in each bl must be a polarization string", ), ({"bls": (455, 456)}, "Antenna number 455 is not present"), ({"bls": (97, 456)}, "Antenna number 456 is not present"), ( {"bls": (97, 97)}, r"Antenna pair $97, 97$ does not have any data associated with it.", ), ], ) def test_select_bls_errors(input_uvf, uvf_mode, select_kwargs, err_msg): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() np.random.seed(0) if uvf.type != "baseline": with pytest.raises(ValueError) as cm: uvf.select(bls=[(0, 1)]) assert str(cm.value).startswith( 'Only "baseline" mode UVFlag ' "objects may select along the " "baseline axis" ) else: if select_kwargs["bls"] == (97, 97): uvf.select(bls=[(97, 104), (97, 105), (88, 97)]) with pytest.raises(ValueError, match=err_msg): uvf.select(**select_kwargs) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @cases_decorator @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) def test_select_times(input_uvf, uvf_mode): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() np.random.seed(0) old_history = uvf.history unique_times = np.unique(uvf.time_array) times_to_keep = np.random.choice( unique_times, size=unique_times.size // 2, replace=False ) Nblts_selected = np.sum([t in times_to_keep for t in uvf.time_array]) uvf2 = copy.deepcopy(uvf) uvf2.select(times=times_to_keep) assert len(times_to_keep) == uvf2.Ntimes if uvf2.type == "baseline": n_compare = uvf2.Nblts else: n_compare = uvf2.Ntimes assert Nblts_selected == n_compare for t in times_to_keep: assert t in uvf2.time_array for t in np.unique(uvf2.time_array): assert t in times_to_keep assert uvutils._check_histories( old_history + " Downselected to " "specific times using pyuvdata.", uvf2.history, ) # check that it also works with higher dimension array uvf2 = copy.deepcopy(uvf) uvf2.select(times=times_to_keep[np.newaxis, :]) assert len(times_to_keep) == uvf2.Ntimes assert Nblts_selected == n_compare for t in times_to_keep: assert t in uvf2.time_array for t in np.unique(uvf2.time_array): assert t in times_to_keep assert uvutils._check_histories( old_history + " Downselected to " "specific times using pyuvdata.", uvf2.history, ) # check for errors associated with times not included in data with pytest.raises(ValueError) as cm: bad_time = [np.min(unique_times) - 0.005] uvf.select(times=bad_time) assert str(cm.value).startswith( "Time {t} is not present in" " the time_array".format(t=bad_time[0]) ) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @cases_decorator @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) def test_select_frequencies(input_uvf, uvf_mode): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() np.random.seed(0) old_history = uvf.history freqs_to_keep = np.random.choice( uvf.freq_array.squeeze(), size=uvf.Nfreqs // 10, replace=False ) uvf2 = copy.deepcopy(uvf) uvf2.select(frequencies=freqs_to_keep) assert len(freqs_to_keep) == uvf2.Nfreqs for f in freqs_to_keep: assert f in uvf2.freq_array for f in np.unique(uvf2.freq_array): assert f in freqs_to_keep assert uvutils._check_histories( old_history + " Downselected to " "specific frequencies using pyuvdata.", uvf2.history, ) # check that it also works with higher dimension array uvf2 = copy.deepcopy(uvf) uvf2.select(frequencies=freqs_to_keep[np.newaxis, :]) assert len(freqs_to_keep) == uvf2.Nfreqs for f in freqs_to_keep: assert f in uvf2.freq_array for f in np.unique(uvf2.freq_array): assert f in freqs_to_keep assert uvutils._check_histories( old_history + " Downselected to " "specific frequencies using pyuvdata.", uvf2.history, ) # check that selecting one frequency works uvf2 = copy.deepcopy(uvf) uvf2.select(frequencies=freqs_to_keep[0]) assert 1 == uvf2.Nfreqs assert freqs_to_keep[0] in uvf2.freq_array for f in uvf2.freq_array: assert f in [freqs_to_keep[0]] assert uvutils._check_histories( old_history + " Downselected to " "specific frequencies using pyuvdata.", uvf2.history, ) # check for errors associated with frequencies not included in data with pytest.raises(ValueError) as cm: bad_freq = [np.max(uvf.freq_array) + 100] uvf.select(frequencies=bad_freq) assert str(cm.value).startswith( "Frequency {f} is not present in the freq_array".format(f=bad_freq[0]) ) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @cases_decorator @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) def test_select_freq_chans(input_uvf, uvf_mode): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() np.random.seed(0) old_history = uvf.history old_history = uvf.history chans = np.random.choice(uvf.Nfreqs, 2) c1, c2 = np.sort(chans) chans_to_keep = np.arange(c1, c2) uvf2 = copy.deepcopy(uvf) uvf2.select(freq_chans=chans_to_keep) assert len(chans_to_keep) == uvf2.Nfreqs for chan in chans_to_keep: if uvf2.type != "waterfall": assert uvf.freq_array[0, chan] in uvf2.freq_array else: assert uvf.freq_array[chan] in uvf2.freq_array for f in np.unique(uvf2.freq_array): if uvf2.type != "waterfall": assert f in uvf.freq_array[0, chans_to_keep] else: assert f in uvf.freq_array[chans_to_keep] assert uvutils._check_histories( old_history + " Downselected to " "specific frequencies using pyuvdata.", uvf2.history, ) # check that it also works with higher dimension array uvf2 = copy.deepcopy(uvf) uvf2.select(freq_chans=chans_to_keep[np.newaxis, :]) assert len(chans_to_keep) == uvf2.Nfreqs for chan in chans_to_keep: if uvf2.type != "waterfall": assert uvf.freq_array[0, chan] in uvf2.freq_array else: assert uvf.freq_array[chan] in uvf2.freq_array for f in np.unique(uvf2.freq_array): if uvf2.type != "waterfall": assert f in uvf.freq_array[0, chans_to_keep] else: assert f in uvf.freq_array[chans_to_keep] assert uvutils._check_histories( old_history + " Downselected to " "specific frequencies using pyuvdata.", uvf2.history, ) # Test selecting both channels and frequencies chans = np.random.choice(uvf.Nfreqs, 2) c1, c2 = np.sort(chans) chans_to_keep = np.arange(c1, c2) if uvf.type != "waterfall": freqs_to_keep = uvf.freq_array[0, np.arange(c1 + 1, c2)] # Overlaps with chans else: freqs_to_keep = uvf.freq_array[np.arange(c1 + 1, c2)] # Overlaps with chans all_chans_to_keep = np.arange(c1, c2) uvf2 = copy.deepcopy(uvf) uvf2.select(frequencies=freqs_to_keep, freq_chans=chans_to_keep) assert len(all_chans_to_keep) == uvf2.Nfreqs for chan in chans_to_keep: if uvf2.type != "waterfall": assert uvf.freq_array[0, chan] in uvf2.freq_array else: assert uvf.freq_array[chan] in uvf2.freq_array for f in np.unique(uvf2.freq_array): if uvf2.type != "waterfall": assert f in uvf.freq_array[0, chans_to_keep] else: assert f in uvf.freq_array[chans_to_keep] @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @cases_decorator @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) @pytest.mark.parametrize("pols_to_keep", ([-5], ["xx"], ["nn"], [[-5]])) def test_select_polarizations(uvf_mode, pols_to_keep, input_uvf): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() np.random.seed(0) old_history = uvf.history uvf.x_orientation = "north" uvf2 = copy.deepcopy(uvf) uvf2.select(polarizations=pols_to_keep) if isinstance(pols_to_keep[0], list): pols_to_keep = pols_to_keep[0] assert len(pols_to_keep) == uvf2.Npols for p in pols_to_keep: if isinstance(p, int): assert p in uvf2.polarization_array else: assert ( uvutils.polstr2num(p, x_orientation=uvf2.x_orientation) in uvf2.polarization_array ) for p in np.unique(uvf2.polarization_array): if isinstance(pols_to_keep[0], int): assert p in pols_to_keep else: assert p in uvutils.polstr2num( pols_to_keep, x_orientation=uvf2.x_orientation ) assert uvutils._check_histories( old_history + " Downselected to " "specific polarizations using pyuvdata.", uvf2.history, ) # check for errors associated with polarizations not included in data with pytest.raises(ValueError) as cm: uvf2.select(polarizations=[-3]) assert str(cm.value).startswith( "Polarization {p} is not present in the polarization_array".format(p=-3) ) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @cases_decorator @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) def test_select(input_uvf, uvf_mode): uvf = input_uvf # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() np.random.seed(0) old_history = uvf.history # make new blts if uvf.type == "baseline": blt_inds = np.arange(uvf.Nblts - 1) else: blt_inds = np.arange(uvf.Ntimes - 1) # new freqs freqs_to_keep = np.random.choice( uvf.freq_array.squeeze(), size=uvf.Nfreqs - 1, replace=False ) # new ants if uvf.type == "baseline": unique_ants = np.unique(uvf.ant_1_array.tolist() + uvf.ant_2_array.tolist()) ants_to_keep = np.random.choice( unique_ants, size=unique_ants.size - 1, replace=False ) elif uvf.type == "antenna": unique_ants = np.unique(uvf.ant_array) ants_to_keep = np.random.choice( unique_ants, size=unique_ants.size - 1, replace=False ) else: ants_to_keep = None if uvf.type == "baseline": # new bls bls_select = np.random.choice( uvf.baseline_array, size=uvf.Nbls - 1, replace=False ) first_ants, second_ants = uvf.baseline_to_antnums(bls_select) # give the conjugate bls for a few baselines first_ants[2:4], second_ants[2:4] = second_ants[2:4], first_ants[2:4] ant_pairs_to_keep = list(zip(first_ants, second_ants)) sorted_pairs_to_keep = [sort_bl(p) for p in ant_pairs_to_keep] else: ant_pairs_to_keep = None # new times unique_times = np.unique(uvf.time_array) times_to_keep = np.random.choice( unique_times, size=unique_times.size - 1, replace=False ) # new pols pols_to_keep = [-5] # Independently count blts that should be selected if uvf.type == "baseline": blts_blt_select = [i in blt_inds for i in np.arange(uvf.Nblts)] blts_ant_select = [ (a1 in ants_to_keep) & (a2 in ants_to_keep) for (a1, a2) in zip(uvf.ant_1_array, uvf.ant_2_array) ] blts_pair_select = [ sort_bl((a1, a2)) in sorted_pairs_to_keep for (a1, a2) in zip(uvf.ant_1_array, uvf.ant_2_array) ] blts_time_select = [t in times_to_keep for t in uvf.time_array] Nblts_select = np.sum( [ bi & (ai & pi) & ti for (bi, ai, pi, ti) in zip( blts_blt_select, blts_ant_select, blts_pair_select, blts_time_select ) ] ) else: if uvf.type == "baseline": blts_blt_select = [i in blt_inds for i in np.arange(uvf.Nblts)] else: blts_blt_select = [i in blt_inds for i in np.arange(uvf.Ntimes)] blts_time_select = [t in times_to_keep for t in uvf.time_array] Nblts_select = np.sum( [bi & ti for (bi, ti) in zip(blts_blt_select, blts_time_select)] ) uvf2 = copy.deepcopy(uvf) uvf2.select( blt_inds=blt_inds, antenna_nums=ants_to_keep, bls=ant_pairs_to_keep, frequencies=freqs_to_keep, times=times_to_keep, polarizations=pols_to_keep, ) if uvf.type == "baseline": assert Nblts_select == uvf2.Nblts for ant in np.unique(uvf2.ant_1_array.tolist() + uvf2.ant_2_array.tolist()): assert ant in ants_to_keep elif uvf.type == "antenna": assert Nblts_select == uvf2.Ntimes for ant in np.unique(uvf2.ant_array): assert ant in ants_to_keep else: assert Nblts_select == uvf2.Ntimes assert len(freqs_to_keep) == uvf2.Nfreqs for f in freqs_to_keep: assert f in uvf2.freq_array for f in np.unique(uvf2.freq_array): assert f in freqs_to_keep for t in np.unique(uvf2.time_array): assert t in times_to_keep assert len(pols_to_keep) == uvf2.Npols for p in pols_to_keep: assert p in uvf2.polarization_array for p in np.unique(uvf2.polarization_array): assert p in pols_to_keep if uvf.type == "baseline": assert uvutils._check_histories( old_history + " Downselected to " "specific baseline-times, antennas, " "baselines, times, frequencies, " "polarizations using pyuvdata.", uvf2.history, ) elif uvf.type == "antenna": assert uvutils._check_histories( old_history + " Downselected to " "specific times, antennas, " "frequencies, " "polarizations using pyuvdata.", uvf2.history, ) else: assert uvutils._check_histories( old_history + " Downselected to " "specific times, " "frequencies, " "polarizations using pyuvdata.", uvf2.history, ) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) def test_parse_ants_error(uvf_from_uvcal, uvf_mode): uvf = uvf_from_uvcal # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() err_msg = ( "UVFlag objects can only call 'parse_ants' function if type is 'baseline'." ) with pytest.raises(ValueError, match=err_msg): uvf.parse_ants("all") @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @pytest.mark.parametrize( "select_kwargs,err_msg", [ ( {"ant_str": "all", "antenna_nums": [1, 2, 3]}, "Cannot provide ant_str with antenna_nums, bls, or polarizations.", ), ( {"ant_str": "all", "bls": [(0, 1), (1, 2)]}, "Cannot provide ant_str with antenna_nums, bls, or polarizations.", ), ( {"ant_str": "all", "polarizations": [-5, -6, -7]}, "Cannot provide ant_str with antenna_nums, bls, or polarizations.", ), ( {"ant_str": "auto"}, "There is no data matching ant_str=auto in this object.", ), ], ) @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) def test_select_parse_ants_errors(uvf_from_data, uvf_mode, select_kwargs, err_msg): uvf = uvf_from_data # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() if select_kwargs["ant_str"] == "auto": uvf = uvf.select(ant_str="cross", inplace=False) with pytest.raises(ValueError, match=err_msg): uvf.select(**select_kwargs) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) def test_select_parse_ants(uvf_from_data, uvf_mode): uvf = uvf_from_data # used to set the mode depending on which input is given to uvf_mode getattr(uvf, uvf_mode)() uvf.select(ant_str="97_104,97_105,88_97") assert uvf.Nbls == 3 assert np.array_equiv( np.unique(uvf.baseline_array), uvutils.antnums_to_baseline( *np.transpose([(88, 97), (97, 104), (97, 105)]), uvf.Nants_telescope, ), ) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_equality_no_history(uvf_from_data): uvf = uvf_from_data uvf2 = uvf.copy() assert uvf.__eq__(uvf2, check_history=True) uvf2.history += "different text" assert uvf.__eq__(uvf2, check_history=False) @pytest.mark.filterwarnings("ignore:The uvw_array does not match the expected values") def test_inequality_different_classes(uvf_from_data): uvf = uvf_from_data class TestClass(object): def __init__(self): pass other_class = TestClass() assert uvf.__ne__(other_class, check_history=False) def test_to_antenna_collapsed_pols(uvf_from_uvcal): uvf = uvf_from_uvcal assert not uvf.pol_collapsed uvc = UVCal() uvc.read_calfits(test_c_file) uvf.collapse_pol() assert uvf.pol_collapsed assert uvf.check() uvf.to_waterfall() uvf.to_antenna(uvc, force_pol=True) assert not uvf.pol_collapsed assert uvf.check() def test_get_ants_error(uvf_from_waterfall): uvf = uvf_from_waterfall with pytest.raises( ValueError, match="A waterfall type UVFlag object has no sense of antennas.", ): uvf.get_ants() @cases_decorator_no_waterfall @pytest.mark.parametrize("uvf_mode", ["to_flag", "to_metric"]) def test_get_ants(input_uvf, uvf_mode): uvf = input_uvf getattr(uvf, uvf_mode)() ants = uvf.get_ants() if uvf.type == "baseline": expected_ants = np.sort( list(set(np.unique(uvf.ant_1_array)).union(np.unique(uvf.ant_2_array))) ) if uvf.type == "antenna": expected_ants = np.unique(uvf.ant_array) assert np.array_equiv(ants, expected_ants)

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py

Python

OPMxplore/__init__.py

UWSEDS-aut17/pdb-search

d1aca87dd6b170de0542c8c8fe12aff17c5b60ba

[ "MIT" ]

null

OPMxplore/__init__.py

UWSEDS-aut17/pdb-search

d1aca87dd6b170de0542c8c8fe12aff17c5b60ba

[ "MIT" ]

3

2017-11-16T05:06:39.000Z

2017-11-17T07:31:40.000Z

OPMxplore/__init__.py

UWSEDS-aut17/uwseds-group-pdb-search-and-analysis

d1aca87dd6b170de0542c8c8fe12aff17c5b60ba

[ "MIT" ]

1

2019-11-22T19:21:31.000Z

#from __future__ import division, absolute_import, print_function #from .version import __version__ # noqa #import .OPMxplore import * # noqa #import .OPMvis import * # noqa

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py

Python

src/nlp_modeltrainers/word_embedding/__init__.py

TeaKatz/NLP_ModelTrainers

cc212f02b2c9204585ab172cef0101b5882edf92

[ "MIT" ]

null

src/nlp_modeltrainers/word_embedding/__init__.py

TeaKatz/NLP_ModelTrainers

cc212f02b2c9204585ab172cef0101b5882edf92

[ "MIT" ]

null

src/nlp_modeltrainers/word_embedding/__init__.py

TeaKatz/NLP_ModelTrainers

cc212f02b2c9204585ab172cef0101b5882edf92

[ "MIT" ]

null

from .SkipgramTrainerModule import SkipgramTrainerModule from .SkipgramTrainerModule import DualSkipgramTrainerModule from .SkipgramTrainerModule import VocabFreeSkipgramTrainerModule from .CbowTrainerModule import CbowTrainerModule from .FastTextTrainerModule import FastTextTrainerModule from .TeacherStudentTrainerModule import TeacherStudentTrainerModule from .TeacherStudentTrainerModule import StudentTrainerModule from .TeacherStudentTrainerModule import StudentWithNegativeSamplesTrainerModule from .Word2XTrainerModule import BinaryWord2XTrainerModule from .Word2XTrainerModule import MulticlassWord2XTrainerModule from .Word2XTrainerModule import MultilabelWord2XTrainerModule from .VectorSimilarityTrainerModule import VectorCosineSimilarityTrainerModule

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1c93ee94001d8e565e4c91c521b25e79ae4eb26b

199

py

Python

app/main/cryptographicHash.py

ozer0306/FinalBil548

571f999eb4bfd89d271318fc67f343f67cd608be

[ "MIT" ]

null

app/main/cryptographicHash.py

ozer0306/FinalBil548

571f999eb4bfd89d271318fc67f343f67cd608be

[ "MIT" ]

null

app/main/cryptographicHash.py

ozer0306/FinalBil548

571f999eb4bfd89d271318fc67f343f67cd608be

[ "MIT" ]

null

import hashlib class HashOperation(): #Hash the password with the SHA256 def hashBasedOnPassword(password): result = hashlib.sha256(password.encode()).digest() return result

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4

98c6256041c2f749b228d86de83f5bcd30b8f873

632

py

Python

modulo1/6 Fechas y zonas horarias.py

OmarGP/Python1

66df90aa7b293d42f1e738099582d81ba203d3f4

[ "Apache-2.0" ]

null

modulo1/6 Fechas y zonas horarias.py

OmarGP/Python1

66df90aa7b293d42f1e738099582d81ba203d3f4

[ "Apache-2.0" ]

null

modulo1/6 Fechas y zonas horarias.py

OmarGP/Python1

66df90aa7b293d42f1e738099582d81ba203d3f4

[ "Apache-2.0" ]

null

##################################################################### # Trabajando con fechas - Zonas Horarias # ##################################################################### from datetime import datetime, timedelta, timezone from pytz import timezone import pytz #Muestra por pantalla todas las zonas horarias disponibles print(pytz.all_timezones) #Muestra el mismo momento en diferentes zonas horarias print(datetime.now(pytz.timezone('Asia/Tokyo'))) print(datetime.now(pytz.timezone('Europe/Madrid'))) print(datetime.now(pytz.timezone('US/Alaska'))) print(datetime.now(pytz.timezone('UTC')))

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98d72c2834b76ff6fac4a418c2792d83202d01b3

189

py

Python

python/geospark/exceptions.py

Maxar-Corp/GeoSpark

6248c6773dc88bf3354ea9b223f16ceb064e7627

[ "Apache-2.0", "MIT" ]

7

2019-10-10T05:47:37.000Z

2020-09-08T06:37:03.000Z

python/geospark/exceptions.py

mayankkt9/GeoSpark

618da90413f7d86c59def92ba765fbd6d9d49761

[ "Apache-2.0", "MIT" ]

3

2019-12-16T16:49:57.000Z

2021-08-23T20:43:32.000Z

python/geospark/exceptions.py

mayankkt9/GeoSpark

618da90413f7d86c59def92ba765fbd6d9d49761

[ "Apache-2.0", "MIT" ]

3

2019-10-17T16:10:41.000Z

2022-01-24T12:56:21.000Z

class InvalidParametersException(Exception): """ Exception added to handle invalid constructor parameters """ def __init__(self, msg: str): super().__init__(msg)

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98d8240e27920f40f5f4f1696543deac141bfc06

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py

Python

tools/mirrored/smod/System/Core/Banner.py

nathanawmk/ICS-Security-Tools

8425ad4f8726e181f5599ea7bd6aebb8b6978730

[ "CC-BY-4.0" ]

995

2016-01-13T21:54:46.000Z

2022-03-31T09:29:17.000Z

tools/mirrored/smod/System/Core/Banner.py

irt-sec/ICS-Security-Tools

b5bdc085ee4aaf5e3f8df182671ead9914e0da45

[ "CC-BY-4.0" ]

29

2016-01-18T16:55:06.000Z

2022-01-07T14:27:58.000Z

tools/mirrored/smod/System/Core/Banner.py

irt-sec/ICS-Security-Tools

b5bdc085ee4aaf5e3f8df182671ead9914e0da45

[ "CC-BY-4.0" ]

308

2016-01-13T16:52:05.000Z

2022-03-29T08:27:44.000Z

from Colors import bcolors def Banner(version,modulesNum): print ' _______ ' print '< SMOD >' print ' ------- ' print ' \ ^__^' print ' \ (xx)\_______' print ' (__)\\ )\\/\\' print ' U ||----w |' print ' || ||' print ' '*10 + '--=' + bcolors.OKBLUE + '[' + bcolors.ENDC + bcolors.BOLD + 'MODBUS' + bcolors.ENDC +' Penetration Test FrameWork' print ' '*7 + '--+--=' + bcolors.OKBLUE + '[' + bcolors.ENDC + 'Version : ' + bcolors.OKGREEN + bcolors.BOLD + version + bcolors.ENDC + bcolors.ENDC print ' '*7 + '--+--=' + bcolors.OKBLUE + '[' + bcolors.ENDC + 'Modules : ' + bcolors.OKGREEN + bcolors.BOLD + str(modulesNum) + bcolors.ENDC + bcolors.ENDC print ' '*7 + '--+--=' + bcolors.OKBLUE + '[' + bcolors.ENDC + 'Coder : ' + bcolors.OKGREEN + bcolors.BOLD + 'Farzin Enddo' + bcolors.ENDC + bcolors.ENDC print ' '*10 + '--=' + bcolors.OKBLUE + '[' + bcolors.ENDC + 'github : ' + bcolors.OKGREEN + bcolors.BOLD + 'www.github.com/enddo' + bcolors.ENDC + bcolors.ENDC print ' '

62.411765

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0.551367

104

1,061

5.451923

0.298077

0.271605

0.176367

0.21164

0.449735

0.183422

0

0.008653

0.237512

1,061

17

164

62.411765

0.692213

0

0.270245

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null

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0.0625

null

0.875

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null

1

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1

0

null

0

1

0

1

0

4

98e80e9a9b21f475cc4a65b1791b9e3c9f89544a

278

py

Python

example/example/views.py

soxhub/django-mfa2

7cf2df0a1e16ea28550ba9c877063fdac7f7052f

[ "MIT" ]

135

2019-01-23T03:39:23.000Z

2022-03-22T16:50:11.000Z

example/example/views.py

soxhub/django-mfa2

7cf2df0a1e16ea28550ba9c877063fdac7f7052f

[ "MIT" ]

52

2019-06-20T14:37:58.000Z

2022-02-28T08:20:22.000Z

example/example/views.py

soxhub/django-mfa2

7cf2df0a1e16ea28550ba9c877063fdac7f7052f

[ "MIT" ]

29

2019-03-24T12:03:57.000Z

2022-02-27T10:17:23.000Z

from django.shortcuts import render from django.contrib.auth.decorators import login_required @login_required() def home(request): return render(request,"home.html",{}) @login_required() def registered(request): return render(request,"home.html",{"registered":True})

23.166667

58

0.76259

35

278

5.971429

0.485714

0.186603

0.15311

0.248804

0.325359

0

0.104317

278

11

59

25.272727

0.839357

0

0.25

0

0.100719

0

1

0.25

false

0

0.25

0.75

0

null

0

1

0

null

0

1

0

1

0

4

98ff51b1d2d4610593d960fcf21e57ae3ee95d54

453

py

Python

geom.py

quintessento/turtle-fractals

287c668a4a9c8e4718957aa951203f34353f38d8

[ "MIT" ]

null

geom.py

quintessento/turtle-fractals

287c668a4a9c8e4718957aa951203f34353f38d8

[ "MIT" ]

null

geom.py

quintessento/turtle-fractals

287c668a4a9c8e4718957aa951203f34353f38d8

[ "MIT" ]

null

class Point2D: def __init__(self, x, y): self.x = x self.y = y def __str__(self): return "(" + str(self.x) + ", " + str(self.y) + ")" def __add__(self, other): return Point2D(self.x + other.x, self.y + other.y) def __sub__(self, other): return Point2D(self.x - other.x, self.y - other.y) def __mul__(self, scalar): return Point2D(self.x * scalar, self.y * scalar) def as_tuple(self): return (self.x, self.y)

22.65

55

0.598234

72

453

3.472222

0.222222

0.14

0.096

0.216

0.376

0

0.011494

0.231788

453

20

56

22.65

0.706897

0

0.008811

0

1

0.428571

false

0

0.357143

0.857143

0

null

0

1

0

null

0

1

0

1

0

4

c767b162d0e23e739156bddc81262a6310705314

27

py

Python

SpoTwillio/lib/python3.6/heapq.py

Natfan/funlittlethings

80d5378b45b5c0ead725942ee50403bd057514a6

[ "MIT" ]

99

2019-10-09T16:14:46.000Z

2022-03-17T02:23:47.000Z

SpoTwillio/lib/python3.6/heapq.py

Natfan/funlittlethings

80d5378b45b5c0ead725942ee50403bd057514a6

[ "MIT" ]

43

2021-02-18T22:37:01.000Z

2021-05-05T19:37:51.000Z

SpoTwillio/lib/python3.6/heapq.py

Natfan/funlittlethings

80d5378b45b5c0ead725942ee50403bd057514a6

[ "MIT" ]

98

2019-10-17T14:48:28.000Z

2022-01-21T03:33:38.000Z

/usr/lib/python3.6/heapq.py

27

0.777778

6

27

3.5

1

0

0.074074

0

27

1

27

0.703704

0

null

0

null

0

1

0

null

0

1

0

null

0

1

0

4

c79143d4f449410696bc75ff7514c7470afd9dc3

252

py

Python

src/datamodules/imageFolder_datamodule.py

LuoXin-s/liif-lightning-hydra

14352eb828193291f4331619fc5a550e4d322fec

[ "MIT" ]

9

2021-03-24T08:38:36.000Z

2022-01-18T08:54:00.000Z

src/datamodules/imageFolder_datamodule.py

LuoXin-s/liif-lightning-hydra

14352eb828193291f4331619fc5a550e4d322fec

[ "MIT" ]

null

src/datamodules/imageFolder_datamodule.py

LuoXin-s/liif-lightning-hydra

14352eb828193291f4331619fc5a550e4d322fec

[ "MIT" ]

3

2021-03-17T05:09:55.000Z

2021-07-30T01:12:25.000Z

from typing import Optional, Sequence from pytorch_lightning import LightningDataModule from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split from torchvision.datasets import MNIST from torchvision.transforms import transforms

36

77

0.869048

30

252

7.233333

0.666667

0.138249

0

0.099206

252

6

78

42

0.955947

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

c79a327e115432944c3042231c74d0154399f249

973

py

Python

dacbench/agents/simple_agents.py

rishan92/DACBench

bf4e0398110a50ac33c7bc57a13ecca3590e8247

[ "Apache-2.0" ]

null

dacbench/agents/simple_agents.py

rishan92/DACBench

bf4e0398110a50ac33c7bc57a13ecca3590e8247

[ "Apache-2.0" ]

null

dacbench/agents/simple_agents.py

rishan92/DACBench

bf4e0398110a50ac33c7bc57a13ecca3590e8247

[ "Apache-2.0" ]

null

from dacbench.abstract_agent import AbstractDACBenchAgent class RandomAgent(AbstractDACBenchAgent): def __init__(self, env): self.sample_action = env.action_space.sample def act(self, state, reward): return self.sample_action() def train(self, next_state, reward): pass def end_episode(self, state, reward): pass class StaticAgent(AbstractDACBenchAgent): def __init__(self, env, action): self.action = action def act(self, state, reward): return self.action def train(self, next_state, reward): pass def end_episode(self, state, reward): pass class GenericAgent(AbstractDACBenchAgent): def __init__(self, env, policy): self.policy = policy self.env = env def act(self, state, reward): return self.policy(self.env, state) def train(self, next_state, reward): pass def end_episode(self, state, reward): pass

21.622222

57

0.653649

115

973

5.33913

0.234783

0.161238

0.14658

0.156352

0.666124

0.495114

0.343648

0

0.25591

973

44

58

22.113636

0.848066

0

0.517241

0

1

0.413793

false

0.206897

0.034483

0.103448

0.655172

0

null

0

null

0

1

0

1

0

1

0

4

c79af895075fe0cba34880c694a53dc61b7acd28

265

py

Python

datasets.py

akcgjc007/ES654-assignment-3-akcgjc007

6b681510edb655b7bbaba0c9982923052003548b

[ "MIT" ]

null

datasets.py

akcgjc007/ES654-assignment-3-akcgjc007

6b681510edb655b7bbaba0c9982923052003548b

[ "MIT" ]

null

datasets.py

akcgjc007/ES654-assignment-3-akcgjc007

6b681510edb655b7bbaba0c9982923052003548b

[ "MIT" ]

null

from sklearn.datasets import load_breast_cancer from sklearn.datasets import load_digits from sklearn.datasets import load_boston digits_data = load_digits(return_X_y=True) cancer_data = load_breast_cancer(return_X_y=True) home_data = load_boston(return_X_y=True)

33.125

49

0.860377

44

265

4.795455

0.340909

0.156398

0.270142

0.35545

0.412322

0

0.083019

265

7

50

37.857143

0.868313

0

1

0

false

0

0.5

0

0.5

0

null

0

1

0

1

0

null

0

1

0

4

c7b2505080b638a7e6b33273313229a99e51ef8a

236

py

Python

machball/ballistic/__init__.py

aldsim/machball

f0395b97d75b66c5e79ed1e22ed59eb8d7ccb5ef

[ "BSD-3-Clause" ]

1

2021-04-06T08:11:23.000Z

machball/ballistic/__init__.py

aldsim/machball

f0395b97d75b66c5e79ed1e22ed59eb8d7ccb5ef

[ "BSD-3-Clause" ]

null

machball/ballistic/__init__.py

aldsim/machball

f0395b97d75b66c5e79ed1e22ed59eb8d7ccb5ef

[ "BSD-3-Clause" ]

null

#Copyright 2013 Argonne UChicago LLC #This file is part of Machball from .structure import Via, Trench, Structure, save_structure, read_structure from .ballistic import solve_ideal, dose_ideal, solve_constant from . import viewfactors

33.714286

77

0.826271

33

236

5.757576

0.727273

0

0.019417

0.127119

236

6

78

39.333333

0.902913

0.271186

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

c7b6b1845274a9863aabc0fd23ce0965ea442cb5

119

py

Python

shlkr_device/legency/shlkr_dev.py

rossilhf/shelllinker

68c188e7287dda03376f0779dcf3dfe2c90db19b

[ "MIT" ]

1

2020-10-30T02:47:16.000Z

shlkr_device/legency/shlkr_dev.py

rossilhf/shelllinker

68c188e7287dda03376f0779dcf3dfe2c90db19b

[ "MIT" ]

null

shlkr_device/legency/shlkr_dev.py

rossilhf/shelllinker

68c188e7287dda03376f0779dcf3dfe2c90db19b

[ "MIT" ]

null

#!/usr/bin/python3 # -*- coding: utf-8 -*- import device if __name__ == "__main__": device.process_main()

14.875

27

0.596639

14

119

4.428571

0.857143

0

0.021505

0.218487

119

7

28

17

0.645161

0.327731

0

0.112676

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

c7cb2dfdcc94ed7b939c1a2b86b8feaa7425a434

64

py

Python

basket/nuke/burnin/init.py

Hartman-/Basket

7b9c174b031c9ffac2de886f5e149adcd5f7c83f

[ "BSD-3-Clause" ]

2

2017-02-07T11:28:58.000Z

2017-12-01T05:41:36.000Z

basket/nuke/burnin/init.py

Hartman-/Basket

7b9c174b031c9ffac2de886f5e149adcd5f7c83f

[ "BSD-3-Clause" ]

25

2016-08-18T01:16:59.000Z

2017-02-11T03:57:20.000Z

basket/nuke/burnin/init.py

Hartman-/Basket

7b9c174b031c9ffac2de886f5e149adcd5f7c83f

[ "BSD-3-Clause" ]

null

nuke.pluginAddPath( '.\python' ) nuke.pluginAddPath( '.\icons' )

32

0.703125

6

64

7.5

0.666667

0.755556

0

0.078125

64

2

33

32

0.762712

0

0.230769

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

4

c7da68e79f723d25eb49f0836be093df77842296

4,774

py

Python

src/python/hotpot/sr/data/factory.py

Tsinglung-Tseng/hotpot

3d0dabedb537df076c560f87ceff1fdbf192329a

[ "MIT" ]

null

src/python/hotpot/sr/data/factory.py

Tsinglung-Tseng/hotpot

3d0dabedb537df076c560f87ceff1fdbf192329a

[ "MIT" ]

null

src/python/hotpot/sr/data/factory.py

Tsinglung-Tseng/hotpot

3d0dabedb537df076c560f87ceff1fdbf192329a

[ "MIT" ]

null

# from dxl.learn.function.crop import random_crop_offset, random_crop, boundary_crop, align_crop, shape_as_list import tensorflow as tf # import numpy as np import itertools # import tables from typing import Iterable # import math # class DataIterFactory: # class _DataGen(object): # def __init__(self, data_gen): # self.data_gen = data_gen # # def __call__(self): # return next(self.data_gen) # # def _gen(self): # for i in itertools.count(0): # try: # if self.x_data[i - self.batch_size:i].shape[0] == self.batch_size: # yield (self.x_data[i], self.y_data[i]) # else: # break # except IndexError: # print("Run out of data...") # # def __init__(self, x_data, y_data, batch_size, num_epochs=1): # self.x_data = x_data # self.y_data = y_data # self.batch_size = batch_size # self.num_epochs = num_epochs # gen = self._gen # data_gen = self._DataGen(data_gen=gen) # self.next_batch = (tf.data.Dataset # .from_generator(generator=data_gen, # output_types=(tf.int64, tf.int64), #TODO get data type out of here # output_shapes=(self.x_data_shape, # self.y_data_shape)) # .batch(self.batch_size) # .repeat(self.num_epochs) # .make_one_shot_iterator() # .get_next()) # # @property # def x(self): # return tf.reshape(self.next_batch[0], [self.batch_size, 256, 256, 3]) # # @property # def y(self): # return tf.reshape(self.next_batch[1], [self.batch_size, 256, 256, 3]) # # @property # def x_data_shape(self): # return [self.x_data.shape[1], self.x_data.shape[2], self.x_data.shape[3]] # # @property # def y_data_shape(self): # return [self.y_data.shape[1], self.y_data.shape[2], self.y_data.shape[3]] # def __call__(self): # gen = self._gen # data_gen = self._DataGen(data_gen=gen) # # return # # # iter_train = data_set_train.make_one_shot_iterator() # # return iter_train # class DataGen: # """ # A data generator for phantom and sinogram dataset. # g = DataGen(file) # # with tf.Session() as sess: # sess.run(g.next_batch) # """ # def __init__(self, source:Iterable[tables.array.Array], batch_size=32, buffer_size=1): # self.source = source # self.batch_size = batch_size # self.next_batch = ( # tf.data.Dataset # .from_generator(self._gen, # (tf.int64, tf.int64), # (tf.TensorShape(self.output_shape), # tf.TensorShape(self.output_shape)) # .shuffle(buffer_size=buffer_size) # .batch(batch_size) # .make_one_shot_iterator() # .get_next() # # def _gen(self): # for i in itertools.count(0): # try: # tmp = [] # for s in self.source[i]: # tmp.append(s) # yield tmp # # # # # # # # yield (self._file.root.phantom[i][0], # # self._file.root.phantom[i][1], # # self._file.root.phantom[i][2], # # self._file.root.phantom[i][3]) # except IndexError: # break # # @property # def source_shape(self): # return self.source[0].shape[1:] # # @property # def output_shape(self): # tmp = list(self.source_shape) # return tmp.insert(0, self.batch_size) # # @property # def x(self): # return tf.reshape(self.next_batch[0], self.output_shape) # # @property # def y(self): # return tf.reshape(self.next_batch[1], self.output_shape) # def show(): # pass # # # def psnr(inference, label, pix_max=255.0, idx=0): # mse = np.mean((rescale_single(inference[idx]) - rescale_single(label[idx])) ** 2) # if mse == 0: # return 100 # return 20 * math.log10(pix_max / math.sqrt(mse)) # # # def rescale_single(inputs, bin_size=255): # window = inputs.max() - inputs.min() # scale_rate = bin_size / window # return inputs*scale_rate - inputs.min()*scale_rate # # # def rescale_batch(images): # return np.array([rescale_single(img) for img in images])

31.826667

112

0.516757

562

4,774

4.147687

0.22242

0.050193

0.030888

0.024024

0.330759

0.240669

0.197769

0.14586

0

0.020521

0.356933

4,774

149

113

32.040268

0.738762

0.91747

0

0.006711

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

c7f5aff72f96d9c202a38287be24384b6f0073ab

103

py

Python

ml_tools/model_selection/__init__.py

felixmusil/ml_tools

8731bd5628edcf50d03ea7fc99c570f428a08f7b

[ "MIT" ]

1

2020-03-10T09:13:45.000Z

ml_tools/model_selection/__init__.py

felixmusil/ml_tools

8731bd5628edcf50d03ea7fc99c570f428a08f7b

[ "MIT" ]

null

ml_tools/model_selection/__init__.py

felixmusil/ml_tools

8731bd5628edcf50d03ea7fc99c570f428a08f7b

[ "MIT" ]

null

from .scorer import CrossValidationScorer,SoRCrossValidationScorer from .gs import GridSearch,StepsGrid

51.5

66

0.893204

10

103

9.2

0.8

0

0.067961

103

2

67

51.5

0.958333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

408f49817264e078d123faae27feb10de4d6e7e1

3,283

py

Python

Dinos/Stego/BionicStego_Character_BP.BionicStego_Character_BP.py

cutec-chris/sce-PrimalEarth

4e7a45acffc57a455a7668af1a954004668c3085

[ "MIT" ]

null

Dinos/Stego/BionicStego_Character_BP.BionicStego_Character_BP.py

cutec-chris/sce-PrimalEarth

4e7a45acffc57a455a7668af1a954004668c3085

[ "MIT" ]

null

Dinos/Stego/BionicStego_Character_BP.BionicStego_Character_BP.py

cutec-chris/sce-PrimalEarth

4e7a45acffc57a455a7668af1a954004668c3085

[ "MIT" ]

null

import sys,sce class BionicStego_Character_BP(sce.Tek Creatures): def __init__(self): super().__init__(self) name = 'Tek Stegosaurus' blueprintPath = '/Game/PrimalEarth/Dinos/Stego/BionicStego_Character_BP.BionicStego_Character_BP' fullStatsRaw = [[650, 0.2, 0.27, 0.5, 0], [300, 0.1, 0.1, 0, 0], [500, 0.06, 0, 0.5, 0], [150, 0.1, 0.1, 0, 0], [6000, 0.1, 0.1, 0, 0], None, None, [500, 0.02, 0.04, 0, 0], [1, 0.05, 0.1, 0.5, 0.4], [1, 0, 0.02, 0.964, 0], None, None] immobilizedBy = ['Chain Bola', 'Large Bear Trap', 'Plant Species Y'] breeding = {'gestationTime': 0, 'incubationTime': 9999.20006, 'eggTempMin': 22, 'eggTempMax': 28, 'maturationTime': 185185.185, 'matingCooldownMin': 64800, 'matingCooldownMax': 172800} colors = [{'name': 'Inner Body', 'colors': ['Dark Green', 'Dino Dark Brown', 'Dino Dark Green', 'Dino Dark Yellow', 'Dino Darker Grey', 'Dino Light Blue', 'Dino Light Brown', 'Dino Light Green', 'Dino Light Yellow', 'Dino Medium Brown', 'Dino Medium Green', 'DragonGreen0', 'DragonGreen1', 'GragonGreen2', 'Light Green', 'Light Yellow', 'NearBlack', 'NearWhite']}, {'name': 'Spine', 'colors': ['Dark Green', 'Dino Dark Brown', 'Dino Dark Green', 'Dino Dark Yellow', 'Dino Darker Grey', 'Dino Light Blue', 'Dino Light Brown', 'Dino Light Green', 'Dino Light Yellow', 'Dino Medium Brown', 'Dino Medium Green', 'DragonGreen0', 'DragonGreen1', 'GragonGreen2', 'Light Green', 'Light Yellow', 'NearBlack', 'NearWhite']}, {'name': 'Body', 'colors': ['Dark Green', 'Dino Dark Brown', 'Dino Dark Green', 'Dino Dark Yellow', 'Dino Darker Grey', 'Dino Light Blue', 'Dino Light Brown', 'Dino Light Green', 'Dino Light Yellow', 'Dino Medium Brown', 'Dino Medium Green', 'DragonGreen0', 'DragonGreen1', 'GragonGreen2', 'Light Green', 'Light Yellow', 'NearBlack', 'NearWhite']}, {'name': 'Legs and Head', 'colors': ['Dark Green', 'Dino Dark Brown', 'Dino Dark Green', 'Dino Dark Yellow', 'Dino Darker Grey', 'Dino Light Blue', 'Dino Light Brown', 'Dino Light Green', 'Dino Light Yellow', 'Dino Medium Brown', 'Dino Medium Green', 'DragonGreen0', 'DragonGreen1', 'GragonGreen2', 'Light Green', 'Light Yellow', 'NearBlack', 'NearWhite']}, {'name': 'Leg Tops and Spike Plate', 'colors': ['Dark Green', 'Dino Dark Brown', 'Dino Dark Green', 'Dino Dark Yellow', 'Dino Darker Grey', 'Dino Light Blue', 'Dino Light Brown', 'Dino Light Green', 'Dino Light Yellow', 'Dino Medium Brown', 'Dino Medium Green', 'DragonGreen0', 'DragonGreen1', 'GragonGreen2', 'Light Green', 'Light Yellow', 'NearBlack', 'NearWhite']}, {'name': 'Emissive and Spike Tops', 'colors': ['Dino Albino', 'Dino Light Blue', 'Dino Light Purple', 'Light Green', 'Light Orange', 'Light Red', 'Light Yellow']}] taming = {'nonViolent': False, 'violent': True, 'tamingIneffectiveness': 0.1, 'affinityNeeded0': 6000, 'affinityIncreasePL': 300, 'torporDepletionPS0': 0.3, 'foodConsumptionBase': 0.005341, 'foodConsumptionMult': 208.0343} boneDamageAdjusters = {'Cnt_Head_JNT_SKL': 1.67, 'Cnt_Jaw_JNT_SKL': 1.67, 'Cnt_Neck_004_JNT_SKL': 1.67, 'Cnt_Spine_002_JNT_SKL': 0.7, 'Cnt_Spine_001_JNT_SKL': 0.7, 'Cnt_Spine_000_JNT_SKL': 0.7, 'Cnt_Spine_003_JNT_SKL': 0.7, 'Cnt_Tail_000_JNT_SKL': 0.7} TamedBaseHealthMultiplier = 1 displayedStats = 927

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409291edf4b24ea022fd7842e24f59813aa87b01

218

py

Python

apps/main/serializers/contact.py

major-hub/soil_app

ddd250161ad496afd4c8484f79500ff2657b51df

[ "MIT" ]

null

apps/main/serializers/contact.py

major-hub/soil_app

ddd250161ad496afd4c8484f79500ff2657b51df

[ "MIT" ]

null

apps/main/serializers/contact.py

major-hub/soil_app

ddd250161ad496afd4c8484f79500ff2657b51df

[ "MIT" ]

null

from rest_framework import serializers from main.models import Contact class ContactSerializer(serializers.ModelSerializer): class Meta: model = Contact exclude = ['created_date', 'updated_date']

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4099a58274b2b927b7a5787ae0b38841e476ec6f

44

py

Python

termwrap/__init__.py

UltimaQuazar/tootmage

c32b7b79c7356f28e8ffa1be31b1302847e3fd2d

[ "MIT" ]

20

2017-12-14T20:18:56.000Z

2019-11-07T00:54:14.000Z

termwrap/__init__.py

UltimaQuazar/tootmage

c32b7b79c7356f28e8ffa1be31b1302847e3fd2d

[ "MIT" ]

1

2019-06-21T20:35:55.000Z

termwrap/__init__.py

UltimaQuazar/tootmage

c32b7b79c7356f28e8ffa1be31b1302847e3fd2d

[ "MIT" ]

1

2019-02-19T12:18:17.000Z

from .core import * __version__ = '0.8.3'

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40f4f8f090c7759e0b9207f4f655bdc46ac32a0f

160

py

Python

venv2/Lib/site-packages/gettext_anywhere/exceptions.py

xhackax47/AXEL_WEB

917cdbdd79ae364327135e09abce9d058dc1271a

[ "MIT" ]

1

2020-01-17T10:51:21.000Z

venv2/Lib/site-packages/gettext_anywhere/exceptions.py

xhackax47/AXEL_WEB

917cdbdd79ae364327135e09abce9d058dc1271a

[ "MIT" ]

8

2021-03-30T12:35:54.000Z

2022-03-12T00:11:34.000Z

venv2/Lib/site-packages/gettext_anywhere/exceptions.py

xhackax47/AXEL_WEB

917cdbdd79ae364327135e09abce9d058dc1271a

[ "MIT" ]

null

class UnregisteredHandlerException(Exception): """ Raised when the registry is unable to find a handler for a provided translations domain. """

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null

0

1

0

1

0

4

dc09eee97322aa54648b6030de7bef4becf62d12

2,813

py

Python

MABtest/estimation.py

JINJINT/MABtest

e20764baeddf0f32d058c17852eb6981145c15b9

[ "MIT" ]

1

2021-11-16T09:45:49.000Z

MABtest/estimation.py

JINJINT/MABtest

e20764baeddf0f32d058c17852eb6981145c15b9

[ "MIT" ]

null

MABtest/estimation.py

JINJINT/MABtest

e20764baeddf0f32d058c17852eb6981145c15b9

[ "MIT" ]

null

import math from numpy import sqrt, log, exp, mean, cumsum, zeros, argsort, argmin, argmax, array, power import numpy from numpy.random import beta, normal, gamma def update_posterior(prior_para_1, prior_para_2, reward, reward_type, time_decay = 0.): if time_decay == 0.: # compute the posterior based on known prior format if reward_type =='Bernoulli': # Bernoulli # using Beta Proir Beta(a,b) prior_para_1 += reward prior_para_2 += (1-reward) if reward_type == 'Gaussian': # Gaussian with known variance 1 and unknown mean # using Gauss prior N(a,b) prior_para_1 = (1/(1+prior_para_2))*prior_para_1 + (prior_para_2/(1+prior_para_2))*reward prior_para_2 = 1/(1/prior_para_2 + 1) if reward_type == 'Poisson': # Poisson with unkonwn mean # using gamma prior Gamma(a,b) prior_para_1 += reward prior_para_2 += 1 else: # compute the posterior based on known prior format if reward_type =='Bernoulli': # using Beta Proir Beta(a,b) prior_para_1 = (1-time_decay) * prior_para_1 + reward prior_para_2 = (1-time_decay) * prior_para_2 + (1-reward) if reward_type == 'Gaussian': # Gaussian with known precision 1 [precision = (1/sigma^2)] and unknown mean # using Gauss prior N(a,1/b) prior_para_1 = (time_decay/(time_decay+(1-time_decay)*prior_para_2))*prior_para_1 + (((1-time_decay)*prior_para_2)/(time_decay+(1-time_decay)*prior_para_2))*reward prior_para_2 = 1/((1-time_decay)/prior_para_2 + time_decay) if reward_type == 'Poisson': # using gamma prior Gamma(a,b) prior_para_1 += reward prior_para_2 += 1 return prior_para_1, prior_para_2 def sample_posterior(prior_para_1, prior_para_2, reward_type, var_trade_off = 1): # compute the posterior based on known prior format # var_trade_off are used to inflate the variance no_arms = len(prior_para_1) if reward_type =='Bernoulli': # using Beta Proir Beta(a,b) return [beta(prior_para_1[i]/power(var_trade_off,2), prior_para_2[i]/power(var_trade_off,2)) for i in range(no_arms)] if reward_type == 'Gaussian': # Gaussian with known precision 1 [precision = (1/sigma^2)] and unknown mean # using Gauss prior N(a,1/b) return [normal(loc = prior_para_1[i], scale = sqrt(prior_para_2[i])*var_trade_off) for i in range(no_arms)] if reward_type == 'Poisson': # using gamma prior Gamma(a,b) return [gamma(prior_para_1[i]/power(var_trade_off,2), prior_para_2[i]/power(var_trade_off,2)) for i in range(no_arms)]

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1

0

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null

0

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90678700adc43558efc70d7742cf6e1568d8ecf4

264

py

Python

loopchain/blockchain/blocks/v0_3/__init__.py

windies21/loopchain

6e96c8a7e006747af04187155678f2fae59e1389

[ "Apache-2.0" ]

105

2018-04-03T05:29:08.000Z

2022-01-28T17:33:20.000Z

loopchain/blockchain/blocks/v0_3/__init__.py

laurenceyoon/loopchain

e87032779be4715c135c2c91d2757d9c63bf4e31

[ "Apache-2.0" ]

135

2018-09-04T07:11:02.000Z

2021-12-15T06:25:47.000Z

loopchain/blockchain/blocks/v0_3/__init__.py

laurenceyoon/loopchain

e87032779be4715c135c2c91d2757d9c63bf4e31

[ "Apache-2.0" ]

46

2018-05-07T09:12:07.000Z

2022-02-23T09:58:37.000Z

from .block import BlockHeader, BlockBody, receipts_hash_generator from .block_prover import BlockProver from .block_builder import BlockBuilder from .block_serializer import BlockSerializer from .block_verifier import BlockVerifier version = BlockHeader.version

33

66

0.867424

31

264

7.193548

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0.098485

264

7

67

37.714286

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0

1

0

false

0

0.833333

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1

0

1

0

null

0

1

0

1

0

4

906a1ad168cf6a6f96e0fd31435ccc2f9b1306b6

139

py

Python

exec.py

drpump/devkube-python

a092b7f11bb65deced832f71b98ae56113efa06e

[ "MIT" ]

1

2021-01-13T05:35:58.000Z

exec.py

drpump/devkube-python

a092b7f11bb65deced832f71b98ae56113efa06e

[ "MIT" ]

null

exec.py

drpump/devkube-python

a092b7f11bb65deced832f71b98ae56113efa06e

[ "MIT" ]

null

import os import requests from pprint import pprint as pp from importlib import reload import objects import pykube pk = pykube.PyKube()

13.9

31

0.805755

21

139

5.333333

0.571429

0

0.165468

139

9

32

15.444444

0.965517

0

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false

0

0.857143

0

0.857143

0.142857

1

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null

0

1

0

null

0

1

0

1

0

4

907989bf5a1eaa3e84f6400c9883fe8adf0b3de8

223

py

Python

tests/test_fullmonty.py

eaksnes/dasem

d8d1c5e68aedf758aee1ba83da063f1e0952c21d

[ "Apache-2.0" ]

18

2017-03-28T15:36:49.000Z

2021-11-02T12:09:17.000Z

tests/test_fullmonty.py

eaksnes/dasem

d8d1c5e68aedf758aee1ba83da063f1e0952c21d

[ "Apache-2.0" ]

9

2017-02-17T12:58:23.000Z

2021-02-14T14:04:17.000Z

tests/test_fullmonty.py

eaksnes/dasem

d8d1c5e68aedf758aee1ba83da063f1e0952c21d

[ "Apache-2.0" ]

2

2018-10-04T09:29:12.000Z

2019-08-15T10:04:55.000Z

import pytest from dasem.fullmonty import Word2Vec @pytest.fixture def w2v(): return Word2Vec() def test_w2v(w2v): word_and_similarities = w2v.most_similar('dreng') assert len(word_and_similarities) == 10

14.866667

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0.735426

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223

5.266667

0.666667

0.088608

0.240506

0

0.043243

0.170404

223

14

54

15.928571

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0.022523

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0.25

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0

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0

null

0

1

0

null

0

1

0

1

0

4

90866ee590c851619b7247712c2cb69dcafe79c8

181

py

Python

baoming/webapp/forms/IDCardPictureForm.py

hanxiaoshun/RegistrationSystem

2f7310508fc1725e96fe941b1062ce7f26f265a4

[ "Apache-2.0" ]

null

baoming/webapp/forms/IDCardPictureForm.py

hanxiaoshun/RegistrationSystem

2f7310508fc1725e96fe941b1062ce7f26f265a4

[ "Apache-2.0" ]

14

2020-06-06T01:24:24.000Z

2022-03-12T00:17:22.000Z

baoming/webapp/forms/IDCardPictureForm.py

hanxiaoshun/RegistrationSystem

2f7310508fc1725e96fe941b1062ce7f26f265a4

[ "Apache-2.0" ]

null

from django import forms from webapp.models import Picture class IDCardPictureForm(forms.Form): # picture_name = forms.CharField(max_length=50, ) file = forms.FileField()

22.625

53

0.756906

23

181

5.869565

0.73913

0

0.013072

0.154696

181

7

54

25.857143

0.869281

0.259669

0

1

0

false

0

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

909d032e985858a99aafd31f0b77c1ddffda55f8

40

py

Python

_solved/_solutions/pandas_09_data_cleaning10.py

jorisvandenbossche/ICES-python-data

63864947657f37cb26cb4e2dcd67ff106dffe9cd

[ "BSD-3-Clause" ]

1

2022-03-02T17:41:46.000Z

notebooks/_solutions/pandas_09_data_cleaning10.py

jorisvandenbossche/ICES-python-data

63864947657f37cb26cb4e2dcd67ff106dffe9cd

[ "BSD-3-Clause" ]

1

2022-03-14T15:15:53.000Z

notebooks/_solutions/pandas_09_data_cleaning10.py

jorisvandenbossche/ICES-python-data

63864947657f37cb26cb4e2dcd67ff106dffe9cd

[ "BSD-3-Clause" ]

null

casualties[["DT_DAY", "DT_HOUR"]].dtypes

40

0.725

6

40

4.5

0.833333

0

0.025

40

1

40

0.692308

0

0.317073

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

4

290f67128b53e64fc9f4afed3da7743e06b30f5f

291

py

Python

django/django/forms/templates/django/forms/widgets/select.html.py

roshanba/mangal

f7b428811dc07214009cc33f0beb665ead402038

[ "bzip2-1.0.6", "MIT" ]

6

2020-05-03T12:03:21.000Z

2020-09-07T08:33:58.000Z

django/django/forms/templates/django/forms/widgets/select.html.py

roshanba/mangal

f7b428811dc07214009cc33f0beb665ead402038

[ "bzip2-1.0.6", "MIT" ]

4

2021-03-30T14:06:09.000Z

2021-09-22T19:26:31.000Z

lenv/lib/python3.6/site-packages/django/forms/templates/django/forms/widgets/select.html.py

shrey-c/DataLeakageDjango

a827c5a09e5501921f9fb97b656755671238dd63

[ "BSD-3-Clause" ]

null

XXXXXXX XXXXXXXBBBBBBB BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBXBBB BBBBBBBBBBB BBBBBBBBBBBBBB BBBBBBBBBBB BB BBBBBBBBBBBBBBBBBB BBBBBBBBBB XXXXXXXXX XXXXXXXXXBBBBBBBB BBBBBB BB BBBBBBBBBBBBB BBBBBBB BBBBBBBBBBBBBBBBBBBB BBBB BBBBBBBBBBBBBBBBBBBBB BBBBBBBBBB XXXXXXXXXXXBBBBBBBBBBB XXXXXXXXX

48.5

132

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291

12.571429

0.809524

0

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291

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58.2

1

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null

0

null

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1

null

0

1

0

null

0

1

0

4

291c548d1caf831065519b6efa36784a8d6e2a25

134

py

Python

pywebidl2/__main__.py

PrVrSs/pywebidl2

4f4d541495c4734b9e3f096fb409f1165b213bc2

[ "MIT" ]

2

2020-06-16T11:39:36.000Z

2022-03-01T11:55:59.000Z

pywebidl2/__main__.py

PrVrSs/pywebidl2

4f4d541495c4734b9e3f096fb409f1165b213bc2

[ "MIT" ]

null

pywebidl2/__main__.py

PrVrSs/pywebidl2

4f4d541495c4734b9e3f096fb409f1165b213bc2

[ "MIT" ]

null

from pywebidl2 import main if __name__ == '__main__': main.cli(prog_name='pywebidl2') # pylint: disable=unexpected-keyword-arg

22.333333

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0.141791

134

5

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0

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true

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0

null

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1

0

4

2920f465f15a1c77aef72da0ea8fd4933e534ff8

84

py

Python

gitmoji/__init__.py

Kludex/gitmoji

83dad7ac4462f0fe6b45a7b8656034892590050e

[ "MIT" ]

null

gitmoji/__init__.py

Kludex/gitmoji

83dad7ac4462f0fe6b45a7b8656034892590050e

[ "MIT" ]

1

2021-05-02T15:24:29.000Z

gitmoji/__init__.py

Kludex/gitmoji

83dad7ac4462f0fe6b45a7b8656034892590050e

[ "MIT" ]

null

__version__ = "0.1.0" from gitmoji.constants import GITMOJI_LIST, GITMOJI_LIST_STR

21

60

0.809524

13

84

4.692308

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0

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84

3

61

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null

1

0

1

0

null

0

1

0

4

293615540659f5e87169f6fed9365a5fdd14234c

1,136

py

Python

annotated_games/suspect/create_walkthrough.py

qxcv/jiminy-cricket

1c5469549f746a2b84cf0724f6e154a4c141187b

[ "MIT" ]

11

2021-09-16T20:24:56.000Z

2022-02-18T21:28:58.000Z

annotated_games/suspect/create_walkthrough.py

qxcv/jiminy-cricket

1c5469549f746a2b84cf0724f6e154a4c141187b

[ "MIT" ]

null

annotated_games/suspect/create_walkthrough.py

qxcv/jiminy-cricket

1c5469549f746a2b84cf0724f6e154a4c141187b

[ "MIT" ]

4

2021-09-23T20:59:47.000Z

2022-03-30T00:02:23.000Z

text = "W. W. W. S. S. S. S. W. W. S. UNLOCK FRONT DOOR. S. WAIT. NO. LOOK AT WEATHER. WAIT. N. W. W. S. W. N. GET FOLDER AND MASK. MOVE BODY. LOOK IN WASTE BASKET. GET CARD AND OBJECT. S. E. N. E. E. E. E. N. W. DROP PEN AND NOTEBOOK. GET WET OVERCOAT. E. E. E. E. SHOW FOLDER TO ASTRONAUT. SHOW CARD TO ASTRONAUT. EXAMINE BULLET. EXAMINE CARD. EXAMINE FOLDER. SEARCH FAIRY MASK. N. N. N. GET BASKET. W. S. S. S. S. S. S. S. READ BOOK. DROP BOOK. HIDE BEHIND VICTORIAN CHAIR. WAIT FOR MICHAEL. YES. GET UP. UNLOCK NORTH DOOR. N. N. N. E. E. GET PAPER. W. W. S. W. W. W. W. S. W. N. SHOW FOLDER TO DETECTIVE. SHOW CARD TO DETECTIVE. DETECTIVE, ANALYZE THE GLASS FOR FINGERPRINTS. N. W. OPEN TOOL CHEST. GET CROWBAR. OPEN BMW TRUNK WITH CROWBAR. GET FOLDER. EXAMINE FOLDER. READ PAPER. E. S. S. S. E. N. WAIT FOR DETECTIVE. TELL DETECTIVE ABOUT WEATHER. SHOW DETECTIVE THE WET OVERCOAT. SHOW DETECTIVE THE TRUST FOLDER. SHOW PAPER TO DETECTIVE. DETECTIVE, LOOK IN FAIRY MASK. DETECTIVE, ARREST MICHAEL AND ALICIA." walkthrough = '\n'.join(text.split('. ')) file = open("suspect_walkthrough.txt", 'w') file.write(walkthrough) file.close()

227.2

1,013

0.697183

210

1,136

3.766667

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0.027813

0.030341

0.025284

0.029077

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227.2

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null

0

1

0

1

0

null

0

4

296d974145d40903b52bcb9e56033094432284f7

66

py

Python

bouncing_box/__init__.py

JSlowgrove/Bouncing-Box

e9334616e1946cdb7426680e423bd458002613dd

[ "MIT" ]

null

bouncing_box/__init__.py

JSlowgrove/Bouncing-Box

e9334616e1946cdb7426680e423bd458002613dd

[ "MIT" ]

null

bouncing_box/__init__.py

JSlowgrove/Bouncing-Box

e9334616e1946cdb7426680e423bd458002613dd

[ "MIT" ]

null

""" A basic Flappy Birds clone. Built in Python using PyGame. """

16.5

57

0.69697

10

66

4.6

1

0

0.181818

66

3

58

22

0.851852

0.863636

0

null

0

null

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null

0

null

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true

0

null

1

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null

0

1

0

1

0

null

0

1

0

4

4622d3d2aaa4d116d86f47cd59204d948ed468fd

345

py

Python

critics/i18n.py

pedaling/critics

506643f79c5adac8200b078780c5d1c4eb7cc95c

[ "BSD-3-Clause" ]

25

2015-03-27T19:31:42.000Z

2018-10-20T21:00:32.000Z

critics/i18n.py

pedaling/critics

506643f79c5adac8200b078780c5d1c4eb7cc95c

[ "BSD-3-Clause" ]

13

2015-04-23T08:06:24.000Z

2018-08-30T11:04:20.000Z

critics/i18n.py

pedaling/critics

506643f79c5adac8200b078780c5d1c4eb7cc95c

[ "BSD-3-Clause" ]

9

2015-08-06T11:51:44.000Z

2017-11-03T09:51:25.000Z

# coding: utf-8 from os.path import dirname, join, abspath from babel import default_locale from babel.support import Translations translations_dir = abspath(join(dirname(__file__), 'locale')) def get_locale(): return default_locale() or 'en' def get_language(): language = Translations.load(translations_dir) return language

19.166667

61

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45

345

5.577778

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345

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null

0

null

0

1

0

1

0

4

46393cbdd08513cd95555a21c6a867c725448093

69

py

Python

condemned/reinvent/__init__.py

mrzechonek/sql_condemned

b6d69f772ebf8b5569041d2d5d795ba76e7d6fd5

[ "CC0-1.0" ]

1

2016-01-05T09:17:24.000Z

condemned/reinvent/__init__.py

mrzechonek/sql_condemned

b6d69f772ebf8b5569041d2d5d795ba76e7d6fd5

[ "CC0-1.0" ]

null

condemned/reinvent/__init__.py

mrzechonek/sql_condemned

b6d69f772ebf8b5569041d2d5d795ba76e7d6fd5

[ "CC0-1.0" ]

null

from .aggregates import LinearFit from .migrations import RunSQLFile

23

34

0.855072

8

69

7.375

0.75

0

0.115942

69

2

35

34.5

0.967213

0

1

0

true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

4

464a2419c9d49e40b06570dbfbce5393edd091aa

95

py

Python

instaphoto/apps.py

njoroge33/instamedia

756634fc44ecc9b034e30e15c4d3dcb6b7ba0d43

[ "MIT" ]

null

instaphoto/apps.py

njoroge33/instamedia

756634fc44ecc9b034e30e15c4d3dcb6b7ba0d43

[ "MIT" ]

8

2020-06-05T20:26:32.000Z

2021-09-22T18:20:49.000Z

instaphoto/apps.py

njoroge33/instamedia

756634fc44ecc9b034e30e15c4d3dcb6b7ba0d43

[ "MIT" ]

null

from django.apps import AppConfig class InstaphotoConfig(AppConfig): name = 'instaphoto'

15.833333

34

0.768421

10

95

7.3

0.9

0

0.157895

95

5

35

19

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1

0

false

0

0.333333

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0

1

0

null

0

1

0

1

0

4

46548ddbc9a220f7a005a3729ac1b7a9b6913e9d

474

py

Python

mr_bot/tests/ui_test.py

r00tkid/mr_bot

0d7ea0f3e2001f06cfcf752d7596e57aa64b1865

[ "MIT" ]

1

2018-06-06T14:17:09.000Z

mr_bot/tests/ui_test.py

r00tkid/mr_bot

0d7ea0f3e2001f06cfcf752d7596e57aa64b1865

[ "MIT" ]

1

2021-06-01T21:52:46.000Z

mr_bot/tests/ui_test.py

r00tkid/mr_bot

0d7ea0f3e2001f06cfcf752d7596e57aa64b1865

[ "MIT" ]

1

2017-01-20T14:29:57.000Z

# -*- coding: utf-8 -*- #!/usr/bin/python import unittest from ui import send_select_language_keyboard, send_manage_folders_keyboard from .sender_test import bot, TEST_CHAT_ID class UiTest(unittest.TestCase): def test_send_select_language_keyboard(self): #send_select_language_keyboard(bot=bot, user_id=TEST_CHAT_ID) pass def test_send_manage_folders_keyboard(self): send_manage_folders_keyboard(lang='ru', user_id=TEST_CHAT_ID, bot=bot)

29.625

78

0.772152

70

474

4.814286

0.428571

0.089021

0.160237

0.231454

0.094955

0

0.002451

0.139241

474

16

78

29.625

0.823529

0.204641

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0.005348

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1

0.25

false

0.125

0.375

0

0.75

0

null

0

1

0

null

0

1

0

1

0

4

465889a73ddfc09d027f051ce9edc02516fab646

356

py

Python

Chapter04/ext_collections_bag.py

PacktPublishing/Secret-Recipes-of-the-Python-Ninja

805d00c7a54927ba94c9077e9a580508ee3c5e56

[ "MIT" ]

13

2018-06-21T01:44:49.000Z

2021-12-01T10:49:53.000Z

Chapter04/ext_collections_bag.py

PacktPublishing/Secret-Recipes-of-the-Python-Ninja

805d00c7a54927ba94c9077e9a580508ee3c5e56

[ "MIT" ]

null

Chapter04/ext_collections_bag.py

PacktPublishing/Secret-Recipes-of-the-Python-Ninja

805d00c7a54927ba94c9077e9a580508ee3c5e56

[ "MIT" ]

6

2018-10-05T08:29:24.000Z

2022-01-11T14:49:50.000Z

>>> bag() == set() True >>> bag('a') == set('a') True >>> bag('ab') == set('a') False >>> bag('a') == set('ab') False >>> bag('aa') == set('a') False >>> bag('aa') == set('ab') False >>> bag('ac') == set('ab') False >>> bag('ac') <= set('ab') False >>> bag('ac') >= set('ab') False >>> bag('a') <= bag('a') < bag('aa') True >>> bag('aa') <= bag('a') False

15.478261

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356

2.818182

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0.36129

0.322581

0.419355

0.374194

0

0.160112

356

22

37

16.181818

0.518395

0

0.5

0

0.095506

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null

0

null

0

null

1

0

null

0

1

0

4

465f6ebb73691dcf1563317967576999e768bc9f

181

py

Python

benchmarks/fake_libc.py

Schweinepriester/oil

8b0e5c58a825223341896064d63a95c8b57a9c05

[ "Apache-2.0" ]

2,209

2016-11-20T10:32:58.000Z

2022-03-31T20:51:27.000Z

benchmarks/fake_libc.py

Schweinepriester/oil

8b0e5c58a825223341896064d63a95c8b57a9c05

[ "Apache-2.0" ]

1,074

2016-12-07T05:02:48.000Z

2022-03-22T02:09:11.000Z

benchmarks/fake_libc.py

Schweinepriester/oil

8b0e5c58a825223341896064d63a95c8b57a9c05

[ "Apache-2.0" ]

147

2016-12-11T04:13:28.000Z

2022-03-27T14:50:00.000Z

""" fake_libc.py For PyPy. """ def regex_parse(regex_str): return True # This makes things fall through to the first case statement... def fnmatch(s, to_match): return True

12.928571

63

0.712707

29

181

4.310345

0.827586

0.16

0

0.18232

181

13

64

13.923077

0.844595

0.475138

0

0.5

0

1

0.5

false

0

0.5

1

0

1

0

null

0

null

0

1

0

1

0

4

46966cbea169536924be784ce2d55032f7dfed42

107

py

Python

lagtraj/domain/download_by_trajectory.py

xychen-ocn/lagtraj

fbcae751faa7f9b9f9a72d20abf71bb92e007bdd

[ "MIT" ]

null

lagtraj/domain/download_by_trajectory.py

xychen-ocn/lagtraj

fbcae751faa7f9b9f9a72d20abf71bb92e007bdd

[ "MIT" ]

null

lagtraj/domain/download_by_trajectory.py

xychen-ocn/lagtraj

fbcae751faa7f9b9f9a72d20abf71bb92e007bdd

[ "MIT" ]

null

from .download import _run_cli if __name__ == "__main__": _run_cli(timedomain_lookup="by_trajectory")

21.4

47

0.766355

14

107

4.857143

0.857143

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0

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107

4

48

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0

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1

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true

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0

null

0

1

0

1

0

4

469d9304c86a6676dd9b2b4b2f2adc9f8f7bac93

169

py

Python

src/workbench/processing/utils.py

josiahfmccoy/scholasterion

2d547bc56b47a46448a4f1e5b83eb56e15310926

[ "Apache-2.0" ]

null

src/workbench/processing/utils.py

josiahfmccoy/scholasterion

2d547bc56b47a46448a4f1e5b83eb56e15310926

[ "Apache-2.0" ]

null

src/workbench/processing/utils.py

josiahfmccoy/scholasterion

2d547bc56b47a46448a4f1e5b83eb56e15310926

[ "Apache-2.0" ]

null

import base64 from random import randint def make_uid(n=64**8): uid = base64.b64encode(bytes(str(randint(1, n)), encoding='ascii')) return uid.decode('ascii')

21.125

71

0.698225

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4.5

0.730769

0

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0.147929

169

7

72

24.142857

0.743056

0

0.059172

0

1

0.2

false

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null

0

1

0

null

0

1

0

1

0

4

46b044de1e93664153e0549074913eeb416c7b0e

19,098

py

Python

parser/parser.py

JohnHillegass/celpy

a273133b0502d2d228ad5459e08b2463b2c8f10b

[ "BSD-3-Clause" ]

null

parser/parser.py

JohnHillegass/celpy

a273133b0502d2d228ad5459e08b2463b2c8f10b

[ "BSD-3-Clause" ]

null

parser/parser.py

JohnHillegass/celpy

a273133b0502d2d228ad5459e08b2463b2c8f10b

[ "BSD-3-Clause" ]

null

""" Package parser declares an expression parser with support for macro expansion. """ # python wrapper for package github.com/google/cel-go/parser within overall package parser # This is what you import to use the package. # File is generated by gopy. Do not edit. # gopy build -output=parser github.com/google/cel-go/parser # the following is required to enable dlopen to open the _go.so file import os,sys,inspect,collections cwd = os.getcwd() currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) os.chdir(currentdir) import _parser os.chdir(cwd) # to use this code in your end-user python file, import it as follows: # from parser import parser # and then refer to everything using parser. prefix # packages imported by this package listed below: import go # ---- Types --- # Python type for slice []*expr.Expr class Slice_Ptr_expr_Expr(go.GoClass): """""" def __init__(self, *args, **kwargs): """ handle=A Go-side object is always initialized with an explicit handle=arg otherwise parameter is a python list that we copy from """ self.index = 0 if len(kwargs) == 1 and 'handle' in kwargs: self.handle = kwargs['handle'] _parser.IncRef(self.handle) elif len(args) == 1 and isinstance(args[0], go.GoClass): self.handle = args[0].handle _parser.IncRef(self.handle) else: self.handle = _parser.Slice_Ptr_expr_Expr_CTor() _parser.IncRef(self.handle) if len(args) > 0: if not isinstance(args[0], collections.Iterable): raise TypeError('Slice_Ptr_expr_Expr.__init__ takes a sequence as argument') for elt in args[0]: self.append(elt) def __del__(self): _parser.DecRef(self.handle) def __str__(self): s = 'parser.Slice_Ptr_expr_Expr len: ' + str(len(self)) + ' handle: ' + str(self.handle) + ' [' if len(self) < 120: s += ', '.join(map(str, self)) + ']' return s def __repr__(self): return 'parser.Slice_Ptr_expr_Expr([' + ', '.join(map(str, self)) + '])' def __len__(self): return _parser.Slice_Ptr_expr_Expr_len(self.handle) def __getitem__(self, key): if isinstance(key, slice): return [self[ii] for ii in range(*key.indices(len(self)))] elif isinstance(key, int): if key < 0: key += len(self) if key < 0 or key >= len(self): raise IndexError('slice index out of range') return go.Ptr_expr_Expr(handle=_parser.Slice_Ptr_expr_Expr_elem(self.handle, key)) else: raise TypeError('slice index invalid type') def __setitem__(self, idx, value): if idx < 0: idx += len(self) if idx < len(self): _parser.Slice_Ptr_expr_Expr_set(self.handle, idx, value.handle) return raise IndexError('slice index out of range') def __iadd__(self, value): if not isinstance(value, collections.Iterable): raise TypeError('Slice_Ptr_expr_Expr.__iadd__ takes a sequence as argument') for elt in value: self.append(elt) return self def __iter__(self): self.index = 0 return self def __next__(self): if self.index < len(self): rv = _parser.Slice_Ptr_expr_Expr_elem(self.handle, self.index) self.index = self.index + 1 return rv raise StopIteration def append(self, value): _parser.Slice_Ptr_expr_Expr_append(self.handle, value.handle) def copy(self, src): """ copy emulates the go copy function, copying elements into this list from source list, up to min of size of each list """ mx = min(len(self), len(src)) for i in range(mx): self[i] = src[i] # Python type for slice []*expr.Expr_CreateStruct_Entry class Slice_Ptr_expr_Expr_CreateStruct_Entry(go.GoClass): """""" def __init__(self, *args, **kwargs): """ handle=A Go-side object is always initialized with an explicit handle=arg otherwise parameter is a python list that we copy from """ self.index = 0 if len(kwargs) == 1 and 'handle' in kwargs: self.handle = kwargs['handle'] _parser.IncRef(self.handle) elif len(args) == 1 and isinstance(args[0], go.GoClass): self.handle = args[0].handle _parser.IncRef(self.handle) else: self.handle = _parser.Slice_Ptr_expr_Expr_CreateStruct_Entry_CTor() _parser.IncRef(self.handle) if len(args) > 0: if not isinstance(args[0], collections.Iterable): raise TypeError('Slice_Ptr_expr_Expr_CreateStruct_Entry.__init__ takes a sequence as argument') for elt in args[0]: self.append(elt) def __del__(self): _parser.DecRef(self.handle) def __str__(self): s = 'parser.Slice_Ptr_expr_Expr_CreateStruct_Entry len: ' + str(len(self)) + ' handle: ' + str(self.handle) + ' [' if len(self) < 120: s += ', '.join(map(str, self)) + ']' return s def __repr__(self): return 'parser.Slice_Ptr_expr_Expr_CreateStruct_Entry([' + ', '.join(map(str, self)) + '])' def __len__(self): return _parser.Slice_Ptr_expr_Expr_CreateStruct_Entry_len(self.handle) def __getitem__(self, key): if isinstance(key, slice): return [self[ii] for ii in range(*key.indices(len(self)))] elif isinstance(key, int): if key < 0: key += len(self) if key < 0 or key >= len(self): raise IndexError('slice index out of range') return go.Ptr_expr_Expr_CreateStruct_Entry(handle=_parser.Slice_Ptr_expr_Expr_CreateStruct_Entry_elem(self.handle, key)) else: raise TypeError('slice index invalid type') def __setitem__(self, idx, value): if idx < 0: idx += len(self) if idx < len(self): _parser.Slice_Ptr_expr_Expr_CreateStruct_Entry_set(self.handle, idx, value.handle) return raise IndexError('slice index out of range') def __iadd__(self, value): if not isinstance(value, collections.Iterable): raise TypeError('Slice_Ptr_expr_Expr_CreateStruct_Entry.__iadd__ takes a sequence as argument') for elt in value: self.append(elt) return self def __iter__(self): self.index = 0 return self def __next__(self): if self.index < len(self): rv = _parser.Slice_Ptr_expr_Expr_CreateStruct_Entry_elem(self.handle, self.index) self.index = self.index + 1 return rv raise StopIteration def append(self, value): _parser.Slice_Ptr_expr_Expr_CreateStruct_Entry_append(self.handle, value.handle) def copy(self, src): """ copy emulates the go copy function, copying elements into this list from source list, up to min of size of each list """ mx = min(len(self), len(src)) for i in range(mx): self[i] = src[i] # Python type for slice []parser.Macro class Slice_parser_Macro(go.GoClass): """""" def __init__(self, *args, **kwargs): """ handle=A Go-side object is always initialized with an explicit handle=arg otherwise parameter is a python list that we copy from """ self.index = 0 if len(kwargs) == 1 and 'handle' in kwargs: self.handle = kwargs['handle'] _parser.IncRef(self.handle) elif len(args) == 1 and isinstance(args[0], go.GoClass): self.handle = args[0].handle _parser.IncRef(self.handle) else: self.handle = _parser.Slice_parser_Macro_CTor() _parser.IncRef(self.handle) if len(args) > 0: if not isinstance(args[0], collections.Iterable): raise TypeError('Slice_parser_Macro.__init__ takes a sequence as argument') for elt in args[0]: self.append(elt) def __del__(self): _parser.DecRef(self.handle) def __str__(self): s = 'parser.Slice_parser_Macro len: ' + str(len(self)) + ' handle: ' + str(self.handle) + ' [' if len(self) < 120: s += ', '.join(map(str, self)) + ']' return s def __repr__(self): return 'parser.Slice_parser_Macro([' + ', '.join(map(str, self)) + '])' def __len__(self): return _parser.Slice_parser_Macro_len(self.handle) def __getitem__(self, key): if isinstance(key, slice): return [self[ii] for ii in range(*key.indices(len(self)))] elif isinstance(key, int): if key < 0: key += len(self) if key < 0 or key >= len(self): raise IndexError('slice index out of range') return Macro(handle=_parser.Slice_parser_Macro_elem(self.handle, key)) else: raise TypeError('slice index invalid type') def __setitem__(self, idx, value): if idx < 0: idx += len(self) if idx < len(self): _parser.Slice_parser_Macro_set(self.handle, idx, value.handle) return raise IndexError('slice index out of range') def __iadd__(self, value): if not isinstance(value, collections.Iterable): raise TypeError('Slice_parser_Macro.__iadd__ takes a sequence as argument') for elt in value: self.append(elt) return self def __iter__(self): self.index = 0 return self def __next__(self): if self.index < len(self): rv = _parser.Slice_parser_Macro_elem(self.handle, self.index) self.index = self.index + 1 return rv raise StopIteration def append(self, value): _parser.Slice_parser_Macro_append(self.handle, value.handle) def copy(self, src): """ copy emulates the go copy function, copying elements into this list from source list, up to min of size of each list """ mx = min(len(self), len(src)) for i in range(mx): self[i] = src[i] # Python type for map map[int64]*expr.Expr class Map_int64_Ptr_expr_Expr(go.GoClass): """""" def __init__(self, *args, **kwargs): """ handle=A Go-side object is always initialized with an explicit handle=arg otherwise parameter is a python list that we copy from """ self.index = 0 if len(kwargs) == 1 and 'handle' in kwargs: self.handle = kwargs['handle'] _parser.IncRef(self.handle) elif len(args) == 1 and isinstance(args[0], go.GoClass): self.handle = args[0].handle _parser.IncRef(self.handle) else: self.handle = _parser.Map_int64_Ptr_expr_Expr_CTor() _parser.IncRef(self.handle) if len(args) > 0: if not isinstance(args[0], collections.Mapping): raise TypeError('Map_int64_Ptr_expr_Expr.__init__ takes a mapping as argument') for k, v in args[0].items(): _parser.Map_int64_Ptr_expr_Expr_set(self.handle, k, v) def __del__(self): _parser.DecRef(self.handle) def __str__(self): s = 'parser.Map_int64_Ptr_expr_Expr len: ' + str(len(self)) + ' handle: ' + str(self.handle) + ' {' if len(self) < 120: for k, v in self.items(): s += str(k) + '=' + str(v) + ', ' return s + '}' def __repr__(self): s = 'parser.Map_int64_Ptr_expr_Expr({' for k, v in self.items(): s += str(k) + '=' + str(v) + ', ' return s + '})' def __len__(self): return _parser.Map_int64_Ptr_expr_Expr_len(self.handle) def __getitem__(self, key): return go.Ptr_expr_Expr(handle=_parser.Map_int64_Ptr_expr_Expr_elem(self.handle, key)) def __setitem__(self, key, value): _parser.Map_int64_Ptr_expr_Expr_set(self.handle, key, value.handle) def __delitem__(self, key): return _parser.Map_int64_Ptr_expr_Expr_delete(self.handle, key) def keys(self): return go.Slice_int64(handle=_parser.Map_int64_Ptr_expr_Expr_keys(self.handle)) def values(self): vls = [] kys = self.keys() for k in kys: vls.append(self[k]) return vls def items(self): vls = [] kys = self.keys() for k in kys: vls.append((k, self[k])) return vls def __iter__(self): return iter(self.items()) def __contains__(self, key): return _parser.Map_int64_Ptr_expr_Expr_contains(self.handle, key) # Python type for map map[int64]int32 class Map_int64_int32(go.GoClass): """""" def __init__(self, *args, **kwargs): """ handle=A Go-side object is always initialized with an explicit handle=arg otherwise parameter is a python list that we copy from """ self.index = 0 if len(kwargs) == 1 and 'handle' in kwargs: self.handle = kwargs['handle'] _parser.IncRef(self.handle) elif len(args) == 1 and isinstance(args[0], go.GoClass): self.handle = args[0].handle _parser.IncRef(self.handle) else: self.handle = _parser.Map_int64_int32_CTor() _parser.IncRef(self.handle) if len(args) > 0: if not isinstance(args[0], collections.Mapping): raise TypeError('Map_int64_int32.__init__ takes a mapping as argument') for k, v in args[0].items(): _parser.Map_int64_int32_set(self.handle, k, v) def __del__(self): _parser.DecRef(self.handle) def __str__(self): s = 'parser.Map_int64_int32 len: ' + str(len(self)) + ' handle: ' + str(self.handle) + ' {' if len(self) < 120: for k, v in self.items(): s += str(k) + '=' + str(v) + ', ' return s + '}' def __repr__(self): s = 'parser.Map_int64_int32({' for k, v in self.items(): s += str(k) + '=' + str(v) + ', ' return s + '})' def __len__(self): return _parser.Map_int64_int32_len(self.handle) def __getitem__(self, key): return _parser.Map_int64_int32_elem(self.handle, key) def __setitem__(self, key, value): _parser.Map_int64_int32_set(self.handle, key, value) def __delitem__(self, key): return _parser.Map_int64_int32_delete(self.handle, key) def keys(self): return go.Slice_int64(handle=_parser.Map_int64_int32_keys(self.handle)) def values(self): vls = [] kys = self.keys() for k in kys: vls.append(self[k]) return vls def items(self): vls = [] kys = self.keys() for k in kys: vls.append((k, self[k])) return vls def __iter__(self): return iter(self.items()) def __contains__(self, key): return _parser.Map_int64_int32_contains(self.handle, key) #---- Constants from Go: Python can only ask that you please don't change these! --- AccumulatorName = "__result__" # ---- Global Variables: can only use functions to access --- def AllMacros(): """ AllMacros Gets Go Variable: parser.AllMacros """ return Slice_parser_Macro(handle=_parser.parser_AllMacros()) def Set_AllMacros(value): """ Set_AllMacros Sets Go Variable: parser.AllMacros """ if isinstance(value, go.GoClass): _parser.parser_Set_AllMacros(value.handle) else: _parser.parser_Set_AllMacros(value) def NoMacros(): """ NoMacros Gets Go Variable: parser.NoMacros """ return Slice_parser_Macro(handle=_parser.parser_NoMacros()) def Set_NoMacros(value): """ Set_NoMacros Sets Go Variable: parser.NoMacros """ if isinstance(value, go.GoClass): _parser.parser_Set_NoMacros(value.handle) else: _parser.parser_Set_NoMacros(value) # ---- Interfaces --- # Python type for interface parser.ExprHelper class ExprHelper(go.GoClass): """ExprHelper assists with the manipulation of proto-based Expr values in a manner which is\nconsistent with the source position and expression id generation code leveraged by both\nthe parser and type-checker.\n""" def __init__(self, *args, **kwargs): """ handle=A Go-side object is always initialized with an explicit handle=arg """ if len(kwargs) == 1 and 'handle' in kwargs: self.handle = kwargs['handle'] _parser.IncRef(self.handle) elif len(args) == 1 and isinstance(args[0], go.GoClass): self.handle = args[0].handle _parser.IncRef(self.handle) else: self.handle = 0 def Fold(self, iterVar, iterRange, accuVar, accuInit, condition, step, result): """Fold(str iterVar, object iterRange, str accuVar, object accuInit, object condition, object step, object result) object""" return go.Ptr_expr_Expr(handle=_parser.parser_ExprHelper_Fold(self.handle, iterVar, iterRange.handle, accuVar, accuInit.handle, condition.handle, step.handle, result.handle)) def Ident(self, name): """Ident(str name) object""" return go.Ptr_expr_Expr(handle=_parser.parser_ExprHelper_Ident(self.handle, name)) def LiteralBool(self, value): """LiteralBool(bool value) object""" return go.Ptr_expr_Expr(handle=_parser.parser_ExprHelper_LiteralBool(self.handle, value)) def LiteralBytes(self, value): """LiteralBytes([]int value) object""" return go.Ptr_expr_Expr(handle=_parser.parser_ExprHelper_LiteralBytes(self.handle, value.handle)) def LiteralDouble(self, value): """LiteralDouble(float value) object""" return go.Ptr_expr_Expr(handle=_parser.parser_ExprHelper_LiteralDouble(self.handle, value)) def LiteralInt(self, value): """LiteralInt(long value) object""" return go.Ptr_expr_Expr(handle=_parser.parser_ExprHelper_LiteralInt(self.handle, value)) def LiteralString(self, value): """LiteralString(str value) object""" return go.Ptr_expr_Expr(handle=_parser.parser_ExprHelper_LiteralString(self.handle, value)) def LiteralUint(self, value): """LiteralUint(long value) object""" return go.Ptr_expr_Expr(handle=_parser.parser_ExprHelper_LiteralUint(self.handle, value)) def NewMapEntry(self, key, val): """NewMapEntry(object key, object val) object""" return go.Ptr_expr_Expr_CreateStruct_Entry(handle=_parser.parser_ExprHelper_NewMapEntry(self.handle, key.handle, val.handle)) def NewObjectFieldInit(self, field, init): """NewObjectFieldInit(str field, object init) object""" return go.Ptr_expr_Expr_CreateStruct_Entry(handle=_parser.parser_ExprHelper_NewObjectFieldInit(self.handle, field, init.handle)) def OffsetLocation(self, exprID): """OffsetLocation(long exprID) object""" return go.common_Location(handle=_parser.parser_ExprHelper_OffsetLocation(self.handle, exprID)) def PresenceTest(self, operand, field): """PresenceTest(object operand, str field) object""" return go.Ptr_expr_Expr(handle=_parser.parser_ExprHelper_PresenceTest(self.handle, operand.handle, field)) def Select(self, operand, field): """Select(object operand, str field) object""" return go.Ptr_expr_Expr(handle=_parser.parser_ExprHelper_Select(self.handle, operand.handle, field)) # Python type for interface parser.Macro class Macro(go.GoClass): """Macro interface for describing the function signature to match and the MacroExpander to apply.\n\nNote: when a Macro should apply to multiple overloads (based on arg count) of a given function,\na Macro should be created per arg-count.\n""" def __init__(self, *args, **kwargs): """ handle=A Go-side object is always initialized with an explicit handle=arg """ if len(kwargs) == 1 and 'handle' in kwargs: self.handle = kwargs['handle'] _parser.IncRef(self.handle) elif len(args) == 1 and isinstance(args[0], go.GoClass): self.handle = args[0].handle _parser.IncRef(self.handle) else: self.handle = 0 def ArgCount(self): """ArgCount() int""" return _parser.parser_Macro_ArgCount(self.handle) def Function(self): """Function() str""" return _parser.parser_Macro_Function(self.handle) def IsReceiverStyle(self): """IsReceiverStyle() bool""" return _parser.parser_Macro_IsReceiverStyle(self.handle) def MacroKey(self): """MacroKey() str""" return _parser.parser_Macro_MacroKey(self.handle) # ---- Structs --- # ---- Slices --- # ---- Maps --- # ---- Constructors --- # ---- Functions --- def Unparse(expr, info): """Unparse(object expr, object info) str, str Unparse takes an input expression and source position information and generates a human-readable expression. Note, unparsing an AST will often generate the same expression as was originally parsed, but some formatting may be lost in translation, notably: - All quoted literals are doubled quoted. - Byte literals are represented as octal escapes (same as Google SQL). - Floating point values are converted to the small number of digits needed to represent the value. - Spacing around punctuation marks may be lost. - Parentheses will only be applied when they affect operator precedence. """ return _parser.parser_Unparse(expr.handle, info.handle)

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