xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

33162412afcb1a45af8ff03715ec9e96750eac9d

44

py

Python

addons/stock_zebra/__init__.py

jjiege/odoo

fd5b8ad387c1881f349d125cbd56433f4d49398f

[ "MIT" ]

null

addons/stock_zebra/__init__.py

jjiege/odoo

fd5b8ad387c1881f349d125cbd56433f4d49398f

[ "MIT" ]

null

addons/stock_zebra/__init__.py

jjiege/odoo

fd5b8ad387c1881f349d125cbd56433f4d49398f

[ "MIT" ]

null

# -*- coding: utf-8 -*- from tests import *

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py

Python

rulesets/routes/blueprints/__init__.py

jdr-tools/rulesets

2bbfb280c84da6ef359d47fa6c24d34b84814eeb

[ "MIT" ]

null

rulesets/routes/blueprints/__init__.py

jdr-tools/rulesets

2bbfb280c84da6ef359d47fa6c24d34b84814eeb

[ "MIT" ]

3

2018-12-19T08:16:15.000Z

2018-12-19T08:16:47.000Z

rulesets/routes/blueprints/__init__.py

jdr-tools/rulesets

2bbfb280c84da6ef359d47fa6c24d34b84814eeb

[ "MIT" ]

null

from rulesets.routes.blueprints.rulesets import rulesets_blueprint

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6

687c56da3c8383d518959ddff88a8be0bbaa2a63

2,712

py

Python

b1.py

bigdraw715/IML_Assignment

8f3f07bf260f891e62dcc57bfbc4a740ab996f24

[ "Apache-2.0" ]

1

2021-12-03T13:38:15.000Z

b1.py

bigdraw715/IML_Assignment

8f3f07bf260f891e62dcc57bfbc4a740ab996f24

[ "Apache-2.0" ]

null

b1.py

bigdraw715/IML_Assignment

8f3f07bf260f891e62dcc57bfbc4a740ab996f24

[ "Apache-2.0" ]

null

import numpy as np from sklearn.model_selection import train_test_split import pandas as pd from preprocess import data_preprocess from sklearn.multiclass import OneVsRestClassifier from sklearn.multiclass import OneVsOneClassifier from sklearn.linear_model import LogisticRegression import time X_onehot,y_onehot = data_preprocess(method="onehot") X_class,y_class = data_preprocess(method="class") print("Use class encoded data") time.sleep(2) print("One vs One calssifier:") X = np.array(X_class) y = np.array(y_class["category"]).astype(int) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, shuffle=True, random_state=0) clf = OneVsOneClassifier( LogisticRegression(max_iter = 10000)).fit(X_train, y_train) # clf = LogisticRegression(max_iter = 10000).fit(X_train, y_train) print("Train Accuracy:",clf.score(X_train,y_train)) print("Test Accuracy:",clf.score(X_test,y_test)) print("One vs All calssifier:") X = np.array(X_class) y = np.array(y_class["category"]).astype(int) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, shuffle=True, random_state=0) clf = OneVsRestClassifier( LogisticRegression(max_iter = 10000)).fit(X_train, y_train) # clf = LogisticRegression(max_iter = 10000).fit(X_train, y_train) print("Train Accuracy:",clf.score(X_train,y_train)) print("Test Accuracy:",clf.score(X_test,y_test)) print("\n Use onehot encoded data") time.sleep(2) print("One vs One calssifier:") X = np.array(X_onehot) y = np.array(y_onehot["category"]).astype(int) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, shuffle=True, random_state=0) clf = OneVsOneClassifier( LogisticRegression(max_iter = 10000)).fit(X_train, y_train) # clf = LogisticRegression(max_iter = 10000).fit(X_train, y_train) print("Train Accuracy:",clf.score(X_train,y_train)) print("Test Accuracy:",clf.score(X_test,y_test)) print("One vs All calssifier:") X = np.array(X_onehot) y = np.array(y_onehot["category"]).astype(int) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, shuffle=True, random_state=0) clf = OneVsRestClassifier( LogisticRegression(max_iter = 10000)).fit(X_train, y_train) # clf = LogisticRegression(max_iter = 10000).fit(X_train, y_train) print("Train Accuracy:",clf.score(X_train,y_train)) print("Test Accuracy:",clf.score(X_test,y_test)) print( ''' ##### # ##### ## #### # # # # ## # # # # # # # # # # # # #### #### ##### # # ###### # # # # ### # # # # # # # # # ### # # # # #### # # ####### ### ##### ''' )

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d7b5d36586e40fedc4321edbe1784b5edbec40a1

13,849

py

Python

bugs/gcc-function-detection/gzip/x86/test.py

xbabka01/retdec-regression-tests

1ac40cca5165740364e6f7fb72b20820eac9bc7c

[ "MIT" ]

8

2017-12-14T14:25:17.000Z

2019-03-09T03:29:12.000Z

bugs/gcc-function-detection/gzip/x86/test.py

xbabka01/retdec-regression-tests

1ac40cca5165740364e6f7fb72b20820eac9bc7c

[ "MIT" ]

10

2019-06-14T09:12:55.000Z

2021-10-01T12:15:43.000Z

bugs/gcc-function-detection/gzip/x86/test.py

xbabka01/retdec-regression-tests

1ac40cca5165740364e6f7fb72b20820eac9bc7c

[ "MIT" ]

8

2019-05-10T14:59:48.000Z

2022-03-07T16:34:23.000Z

from regression_tests import * class Test(Test): settings = TestSettings( input='gzip-strip', args='-k' # TODO: matula, not sure if some functions are not called, or we just do not detect it. ) def test_check_for_all_currently_detected_strings(self): assert self.out_c.has_string_literal( ' %9lu %9lu ' ) assert self.out_c.has_string_literal( ' Copyright (C) 1992-1993 Jean-loup Gailly' ) assert self.out_c.has_string_literal( ' This program is free software; you can redistribute it and/or modify' ) assert self.out_c.has_string_literal( ' any later version.' ) assert self.out_c.has_string_literal( ' it under the terms of the GNU General Public License as published by' ) assert self.out_c.has_string_literal( ' the Free Software Foundation; either version 2, or (at your option)' ) assert self.out_c.has_string_literal( ' %s\\n' ) assert self.out_c.has_string_literal( ' (totals)' ) assert self.out_c.has_string_literal( ' -- replaced with %s' ) assert self.out_c.has_string_literal( ' -1 --fast compress faster' ) assert self.out_c.has_string_literal( ' -9 --best compress better' ) assert self.out_c.has_string_literal( ' -L --license display software license' ) assert self.out_c.has_string_literal( ' -N --name save or restore the original name and time stamp' ) assert self.out_c.has_string_literal( ' -S .suf --suffix .suf use suffix .suf on compressed files' ) assert self.out_c.has_string_literal( ' -V --version display version number' ) assert self.out_c.has_string_literal( ' -c --stdout write on standard output, keep original files unchanged' ) assert self.out_c.has_string_literal( ' -d --decompress decompress' ) assert self.out_c.has_string_literal( ' -f --force force overwrite of output file and compress links' ) assert self.out_c.has_string_literal( ' -h --help give this help' ) assert self.out_c.has_string_literal( ' -l --list list compressed file contents' ) assert self.out_c.has_string_literal( ' -n --no-name do not save or restore the original name and time stamp' ) assert self.out_c.has_string_literal( ' -q --quiet suppress all warnings' ) assert self.out_c.has_string_literal( ' -t --test test compressed file integrity' ) assert self.out_c.has_string_literal( ' -v --verbose verbose mode' ) assert self.out_c.has_string_literal( ' OK' ) assert self.out_c.has_string_literal( ' OK\\n' ) assert self.out_c.has_string_literal( ' do you wish to overwrite (y or n)? ' ) assert self.out_c.has_string_literal( ' file... files to (de)compress. If none given, use standard input.' ) assert self.out_c.has_string_literal( '%2ld.%1ld%%' ) assert self.out_c.has_string_literal( '%5s %08lx %11s ' ) assert self.out_c.has_string_literal( '%9ld %9ld ' ) assert self.out_c.has_string_literal( '%s %s (%s)\\n' ) assert self.out_c.has_string_literal( '%s: ' ) assert self.out_c.has_string_literal( '%s: %s already exists;' ) assert self.out_c.has_string_literal( '%s: %s already has %s suffix -- unchanged\\n' ) assert self.out_c.has_string_literal( '%s: %s and %s are the same file\\n' ) assert self.out_c.has_string_literal( '%s: %s compressed to %s\\n' ) assert self.out_c.has_string_literal( '%s: %s has %d other link%c -- unchanged\\n' ) assert self.out_c.has_string_literal( '%s: %s has flags 0x%x -- get newer version of gzip\\n' ) assert self.out_c.has_string_literal( '%s: %s has more than one entry -- unchanged\\n' ) assert self.out_c.has_string_literal( '%s: %s has more than one entry--rest ignored\\n' ) assert self.out_c.has_string_literal( '%s: %s is a a multi-part gzip file -- get newer version of gzip\\n' ) assert self.out_c.has_string_literal( '%s: %s is a directory -- ignored\\n' ) assert self.out_c.has_string_literal( '%s: %s is encrypted -- get newer version of gzip\\n' ) assert self.out_c.has_string_literal( '%s: %s is not a directory or a regular file - ignored\\n' ) assert self.out_c.has_string_literal( '%s: %s: cannot %scompress onto itself\\n' ) assert self.out_c.has_string_literal( '%s: %s: extra field of %u bytes ignored\\n' ) assert self.out_c.has_string_literal( '%s: %s: part number %u\\n' ) assert self.out_c.has_string_literal( '%s: %s: unknown method %d -- get newer version of gzip\\n' ) assert self.out_c.has_string_literal( '%s: %s: unknown suffix -- ignored\\n' ) assert self.out_c.has_string_literal( '%s: %s: warning, name truncated\\n' ) assert self.out_c.has_string_literal( '%s: %s: warning: %s%s\\n' ) assert self.out_c.has_string_literal( '%s: -Z not supported in this version\\n' ) assert self.out_c.has_string_literal( '%s: -r not supported on this system\\n' ) assert self.out_c.has_string_literal( '%s: compressed data not %s a terminal. Use -f to force %scompression.\\n' ) assert self.out_c.has_string_literal( '%s: time stamp restored\\n' ) assert self.out_c.has_string_literal( '%s:\\t%s' ) assert self.out_c.has_string_literal( '%s\\n' ) assert self.out_c.has_string_literal( '.exe' ) assert self.out_c.has_string_literal( '.tar' ) assert self.out_c.has_string_literal( '.taz' ) assert self.out_c.has_string_literal( '.tgz' ) assert self.out_c.has_string_literal( '1.2.4' ) assert self.out_c.has_string_literal( '18 Aug 93' ) assert self.out_c.has_string_literal( 'Bad table\\n' ) assert self.out_c.has_string_literal( 'Compilation options:\\n%s %s ' ) assert self.out_c.has_string_literal( 'For help, type: %s -h\\n' ) assert self.out_c.has_string_literal( 'HAVE_UNISTD_H ' ) assert self.out_c.has_string_literal( 'NO_DIR' ) assert self.out_c.has_string_literal( 'NO_MEMORY_H ' ) assert self.out_c.has_string_literal( 'UTIME' ) assert self.out_c.has_string_literal( '\\n%s: ' ) assert self.out_c.has_string_literal( '\\n%s: %s: %s\\n' ) assert self.out_c.has_string_literal( '\\n%s: %s: compressed with %d bits, can only handle %d bits\\n' ) assert self.out_c.has_string_literal( '\\n%s: %s: decompression OK, trailing garbage ignored\\n' ) assert self.out_c.has_string_literal( '\\n%s: %s: encrypted file -- use unzip\\n' ) assert self.out_c.has_string_literal( '\\n%s: %s: first entry not deflated or stored -- use unzip\\n' ) assert self.out_c.has_string_literal( '\\n%s: %s: not a valid zip file\\n' ) assert self.out_c.has_string_literal( '\\n%s: %s: not in gzip format\\n' ) assert self.out_c.has_string_literal( '\\n%s: %s: warning, unknown flags 0x%x\\n' ) assert self.out_c.has_string_literal( 'ab:cdfhH?lLmMnNqrS:tvVZ123456789' ) assert self.out_c.has_string_literal( 'argc<=0' ) assert self.out_c.has_string_literal( 'bad pack level' ) assert self.out_c.has_string_literal( "can't recover suffix\\n" ) assert self.out_c.has_string_literal( 'compressed uncompr. ratio uncompressed_name' ) assert self.out_c.has_string_literal( 'corrupt input.' ) assert self.out_c.has_string_literal( 'corrupt input. Use zcat to recover some data.' ) assert self.out_c.has_string_literal( 'corrupted input -- file name too large' ) assert self.out_c.has_string_literal( 'fstat(stdin)' ) assert self.out_c.has_string_literal( 'internal error, invalid method' ) assert self.out_c.has_string_literal( 'invalid compressed data -- Huffman code > 32 bits' ) assert self.out_c.has_string_literal( 'invalid compressed data--crc error' ) assert self.out_c.has_string_literal( 'invalid compressed data--format violated' ) assert self.out_c.has_string_literal( 'invalid compressed data--length error' ) assert self.out_c.has_string_literal( 'invalid compressed data--length mismatch' ) assert self.out_c.has_string_literal( 'len %ld, siz %ld\\n' ) assert self.out_c.has_string_literal( 'method crc date time ' ) assert self.out_c.has_string_literal( 'name too short' ) assert self.out_c.has_string_literal( 'out of memory' ) assert self.out_c.has_string_literal( 'output in compress .Z format not supported\\n' ) assert self.out_c.has_string_literal( 'read from' ) assert self.out_c.has_string_literal( 'stdout' ) assert self.out_c.has_string_literal( 'too many leaves in Huffman tree' ) assert self.out_c.has_string_literal( 'un' ) assert self.out_c.has_string_literal( 'usage: %s [-%scdfhlLnN%stvV19] [-S suffix] [file ...]\\n' ) assert self.out_c.has_string_literal( '%s: unexpected end of file\\n' ) assert self.out_c.has_string_literal( 'internal error in shorten_name' ) # Currently detected functions which have their named (from symbols) counterparts in not-stripped binary. # def test_check_for_all_currently_detected_functions(self): assert self.out_c.has_func( 'function_804897c' ) # assert self.out_c.has_func( 'entry_point' ) # assert self.out_c.has_func( 'function_8048c70' ) # assert self.out_c.has_func( 'function_8048c80' ) # assert self.out_c.has_func( 'function_8048cb0' ) # assert self.out_c.has_func( 'function_8048cf0' ) # assert self.out_c.has_func( 'function_8048d50' ) # assert self.out_c.has_func( 'function_8048d80' ) # assert self.out_c.has_func( 'function_8048da1' ) # assert self.out_c.has_func( 'function_8048e00' ) # assert self.out_c.has_func( 'function_8048e2f' ) # assert self.out_c.has_func( 'function_8048e64' ) # assert self.out_c.has_func( 'function_8048f64' ) # assert self.out_c.has_func( 'function_8049019' ) # assert self.out_c.has_func( 'function_8049053' ) # assert self.out_c.has_func( 'function_80491c9' ) # assert self.out_c.has_func( 'function_8049a5e' ) # assert self.out_c.has_func( 'function_8049d3d' ) # assert self.out_c.has_func( 'function_804b368' ) # assert self.out_c.has_func( 'function_804b70c' ) # assert self.out_c.has_func( 'function_804b829' ) # assert self.out_c.has_func( 'function_804b950' ) # assert self.out_c.has_func( 'function_804bbf2' ) # assert self.out_c.has_func( 'function_804c110' ) # assert self.out_c.has_func( 'function_804c19d' ) # assert self.out_c.has_func( 'function_804c270' ) # assert self.out_c.has_func( 'function_804c350' ) # assert self.out_c.has_func( 'function_804c4f5' ) # assert self.out_c.has_func( 'function_804c660' ) # assert self.out_c.has_func( 'function_804c6cc' ) # assert self.out_c.has_func( 'function_804cae5' ) # assert self.out_c.has_func( 'function_804cd1c' ) # assert self.out_c.has_func( 'function_804cfbe' ) # assert self.out_c.has_func( 'function_804d110' ) # assert self.out_c.has_func( 'function_804d13d' ) # assert self.out_c.has_func( 'function_804d216' ) # assert self.out_c.has_func( 'function_804d23a' ) # assert self.out_c.has_func( 'function_804d2ff' ) # assert self.out_c.has_func( 'function_804d430' ) # assert self.out_c.has_func( 'function_804da20' ) # assert self.out_c.has_func( 'function_804da4a' ) # assert self.out_c.has_func( 'function_804e048' ) # assert self.out_c.has_func( 'function_804e43f' ) # assert self.out_c.has_func( 'function_804e604' ) # assert self.out_c.has_func( 'function_804e7aa' ) # assert self.out_c.has_func( 'function_804eeaa' ) # assert self.out_c.has_func( 'function_804efa8' ) # assert self.out_c.has_func( 'function_804f040' ) # assert self.out_c.has_func( 'function_804f081' ) # assert self.out_c.has_func( 'function_804f0b4' ) # assert self.out_c.has_func( 'basename' ) # assert self.out_c.has_func( 'function_804f111' ) # assert self.out_c.has_func( 'error' ) # assert self.out_c.has_func( 'warn' ) # assert self.out_c.has_func( 'function_804f1de' ) # assert self.out_c.has_func( 'function_804f23d' ) # assert self.out_c.has_func( 'function_804f2d5' ) # assert self.out_c.has_func( 'function_804f30b' ) # assert self.out_c.has_func( 'function_804f34c' ) # assert self.out_c.has_func( 'function_804f3a9' ) # assert self.out_c.has_func( 'function_804f3e8' ) # assert self.out_c.has_func( 'function_804f47c' ) # assert self.out_c.has_func( 'function_804f52f' ) # assert self.out_c.has_func( 'function_804f558' ) # assert self.out_c.has_func( 'function_8050180' ) # assert self.out_c.has_func( 'function_8050220' ) # assert self.out_c.has_func( 'function_8050240' ) # assert self.out_c.has_func( 'function_8050478' ) # assert self.out_c.has_func( 'function_8050a60' ) # assert self.out_c.has_func( 'function_804b6b3' ) # file_read assert self.out_c.has_func( 'function_804d592' ) # unzip assert self.out_c.has_func( 'function_804f734' ) # lzw assert self.out_c.has_func( 'function_804f7a0' ) # unlzw assert self.out_c.has_func( 'function_804fd94' ) # unpack assert self.out_c.has_func( 'function_80505eb' ) # unlzh

70.658163

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6

d7bf7f7930e175c69a5da5f16a33a1472ac6496d

115

py

Python

run_test.py

Reuben481/tigre

f342aaa73da8204140fb48929c28cf2f75566a21

[ "BSD-3-Clause" ]

null

run_test.py

Reuben481/tigre

f342aaa73da8204140fb48929c28cf2f75566a21

[ "BSD-3-Clause" ]

null

run_test.py

Reuben481/tigre

f342aaa73da8204140fb48929c28cf2f75566a21

[ "BSD-3-Clause" ]

null

from __future__ import print_function from _Ax import Ax from tigre.Algorithms import SART print('hello world')

23

37

0.808696

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115

5.117647

0.647059

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0.147826

115

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true

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6

cc1c8f766b384e5ddcb7b4ff0ad39f829b62b3c8

73

py

Python

tests/context_processors/models.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

61,676

2015-01-01T00:05:13.000Z

2022-03-31T20:37:54.000Z

tests/context_processors/models.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

8,884

2015-01-01T00:12:05.000Z

2022-03-31T19:53:11.000Z

tests/context_processors/models.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

33,143

2015-01-01T02:04:52.000Z

2022-03-31T19:42:46.000Z

from django.db import models class DebugObject(models.Model): pass

12.166667

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6

0bc5d96966f648cadbfe6b3cbf5fb9b714a41b19

44

py

Python

src/sacred_scripts/__init__.py

jhrmnn/schnetpack

2f96dee7d184b8db8ee610d6743570daeb3763b9

[ "MIT" ]

null

src/sacred_scripts/__init__.py

jhrmnn/schnetpack

2f96dee7d184b8db8ee610d6743570daeb3763b9

[ "MIT" ]

null

src/sacred_scripts/__init__.py

jhrmnn/schnetpack

2f96dee7d184b8db8ee610d6743570daeb3763b9

[ "MIT" ]

null

from sacred_scripts.run_schnetpack import *

22

43

0.863636

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0

null

0

1

0

1

0

1

0

6

0befd131c790b6c5002f2d1c19cce8957b053cd7

99

py

Python

memories/__init__.py

jacobver/mem_seq2seq

1d87d2fb0884b825131d991e97aecc6d2bd31ce0

[ "MIT" ]

null

memories/__init__.py

jacobver/mem_seq2seq

1d87d2fb0884b825131d991e97aecc6d2bd31ce0

[ "MIT" ]

null

memories/__init__.py

jacobver/mem_seq2seq

1d87d2fb0884b825131d991e97aecc6d2bd31ce0

[ "MIT" ]

null

import memories.memory_model import memories.util as util from memories.Converser import Converser

24.75

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99

6.071429

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0.329412

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99

3

41

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true

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0

null

0

1

0

1

0

1

0

6

040f971d7c3a25aa2f6b3bc174e49f099822305b

1,303

py

Python

genrl/classical/bandit/__init__.py

ajaysub110/JigglypuffRL

083fd26d05b7eac018e6db7d32c4be4587461766

[ "MIT" ]

null

genrl/classical/bandit/__init__.py

ajaysub110/JigglypuffRL

083fd26d05b7eac018e6db7d32c4be4587461766

[ "MIT" ]

null

genrl/classical/bandit/__init__.py

ajaysub110/JigglypuffRL

083fd26d05b7eac018e6db7d32c4be4587461766

[ "MIT" ]

null

from genrl.classical.bandit.bandits import Bandit # noqa from genrl.classical.bandit.bandits import BernoulliBandit # noqa from genrl.classical.bandit.bandits import GaussianBandit # noqa from genrl.classical.bandit.contextual_bandits import BernoulliCB # noqa from genrl.classical.bandit.contextual_bandits import ContextualBandit # noqa from genrl.classical.bandit.contextual_bandits import GaussianCB # noqa from genrl.classical.bandit.contextual_policies import BayesianUCBCBPolicy # noqa from genrl.classical.bandit.contextual_policies import CBPolicy # noqa from genrl.classical.bandit.contextual_policies import EpsGreedyCBPolicy # noqa from genrl.classical.bandit.contextual_policies import GradientCBPolicy # noqa from genrl.classical.bandit.contextual_policies import ThompsonSamplingCBPolicy # noqa from genrl.classical.bandit.contextual_policies import UCBCBPolicy # noqa from genrl.classical.bandit.policies import BanditPolicy # noqa from genrl.classical.bandit.policies import BayesianUCBPolicy # noqa from genrl.classical.bandit.policies import EpsGreedyPolicy # noqa from genrl.classical.bandit.policies import GradientPolicy # noqa from genrl.classical.bandit.policies import ThompsonSamplingPolicy # noqa from genrl.classical.bandit.policies import UCBPolicy # noqa

68.578947

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0

1

0

1

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6

0457dcd17de9810388f967ac4a353a3433d957e5

8,232

py

Python

boto/redshift/exceptions.py

bopopescu/debpkg_python-boto

06f9b6f3693ba1933be8214da69cebcd5212cd97

[ "MIT" ]

15

2015-03-25T05:24:11.000Z

2021-12-18T04:24:06.000Z

boto/redshift/exceptions.py

bopopescu/debpkg_python-boto

06f9b6f3693ba1933be8214da69cebcd5212cd97

[ "MIT" ]

1

2021-09-11T14:30:32.000Z

boto/redshift/exceptions.py

bopopescu/debpkg_python-boto

06f9b6f3693ba1933be8214da69cebcd5212cd97

[ "MIT" ]

10

2015-04-26T17:56:37.000Z

2020-09-24T14:01:53.000Z

# Copyright (c) 2013 Amazon.com, Inc. or its affiliates. All Rights Reserved # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, dis- # tribute, sublicense, and/or sell copies of the Software, and to permit # persons to whom the Software is furnished to do so, subject to the fol- # lowing conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABIL- # ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT # SHALL THE AUTHOR BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. # from boto.exception import JSONResponseError class ClusterNotFoundFault(JSONResponseError): pass class InvalidClusterSnapshotStateFault(JSONResponseError): pass class ClusterSnapshotNotFoundFault(JSONResponseError): pass class ClusterNotFoundFault(JSONResponseError): pass class ClusterSecurityGroupQuotaExceededFault(JSONResponseError): pass class ReservedNodeOfferingNotFoundFault(JSONResponseError): pass class InvalidSubnet(JSONResponseError): pass class ClusterSubnetGroupQuotaExceededFault(JSONResponseError): pass class InvalidClusterStateFault(JSONResponseError): pass class InvalidClusterParameterGroupStateFault(JSONResponseError): pass class ClusterParameterGroupAlreadyExistsFault(JSONResponseError): pass class InvalidClusterSecurityGroupStateFault(JSONResponseError): pass class InvalidRestoreFault(JSONResponseError): pass class AuthorizationNotFoundFault(JSONResponseError): pass class ResizeNotFoundFault(JSONResponseError): pass class NumberOfNodesQuotaExceededFault(JSONResponseError): pass class ClusterSnapshotAlreadyExistsFault(JSONResponseError): pass class AuthorizationQuotaExceededFault(JSONResponseError): pass class AuthorizationAlreadyExistsFault(JSONResponseError): pass class ClusterSnapshotQuotaExceededFault(JSONResponseError): pass class ReservedNodeNotFoundFault(JSONResponseError): pass class ReservedNodeAlreadyExistsFault(JSONResponseError): pass class ClusterSecurityGroupAlreadyExistsFault(JSONResponseError): pass class ClusterParameterGroupNotFoundFault(JSONResponseError): pass class ReservedNodeQuotaExceededFault(JSONResponseError): pass class ClusterQuotaExceededFault(JSONResponseError): pass class ClusterSubnetQuotaExceededFault(JSONResponseError): pass class UnsupportedOptionFault(JSONResponseError): pass class InvalidVPCNetworkStateFault(JSONResponseError): pass class ClusterSecurityGroupNotFoundFault(JSONResponseError): pass class InvalidClusterSubnetGroupStateFault(JSONResponseError): pass class ClusterSubnetGroupAlreadyExistsFault(JSONResponseError): pass class NumberOfNodesPerClusterLimitExceededFault(JSONResponseError): pass class ClusterSubnetGroupNotFoundFault(JSONResponseError): pass class ClusterParameterGroupQuotaExceededFault(JSONResponseError): pass class ClusterAlreadyExistsFault(JSONResponseError): pass class InsufficientClusterCapacityFault(JSONResponseError): pass class InvalidClusterSubnetStateFault(JSONResponseError): pass class SubnetAlreadyInUse(JSONResponseError): pass class InvalidParameterCombinationFault(JSONResponseError): pass class AccessToSnapshotDeniedFault(JSONResponseError): pass class UnauthorizedOperationFault(JSONResponseError): pass class SnapshotCopyAlreadyDisabled(JSONResponseError): pass class ClusterNotFound(JSONResponseError): pass class UnknownSnapshotCopyRegion(JSONResponseError): pass class InvalidClusterSubnetState(JSONResponseError): pass class ReservedNodeQuotaExceeded(JSONResponseError): pass class InvalidClusterState(JSONResponseError): pass class HsmClientCertificateQuotaExceeded(JSONResponseError): pass class SubscriptionCategoryNotFound(JSONResponseError): pass class HsmClientCertificateNotFound(JSONResponseError): pass class SubscriptionEventIdNotFound(JSONResponseError): pass class ClusterSecurityGroupAlreadyExists(JSONResponseError): pass class HsmConfigurationAlreadyExists(JSONResponseError): pass class NumberOfNodesQuotaExceeded(JSONResponseError): pass class ReservedNodeOfferingNotFound(JSONResponseError): pass class BucketNotFound(JSONResponseError): pass class InsufficientClusterCapacity(JSONResponseError): pass class InvalidRestore(JSONResponseError): pass class UnauthorizedOperation(JSONResponseError): pass class ClusterQuotaExceeded(JSONResponseError): pass class InvalidVPCNetworkState(JSONResponseError): pass class ClusterSnapshotNotFound(JSONResponseError): pass class AuthorizationQuotaExceeded(JSONResponseError): pass class InvalidHsmClientCertificateState(JSONResponseError): pass class SNSTopicArnNotFound(JSONResponseError): pass class ResizeNotFound(JSONResponseError): pass class ClusterSubnetGroupNotFound(JSONResponseError): pass class SNSNoAuthorization(JSONResponseError): pass class ClusterSnapshotQuotaExceeded(JSONResponseError): pass class AccessToSnapshotDenied(JSONResponseError): pass class InvalidClusterSecurityGroupState(JSONResponseError): pass class NumberOfNodesPerClusterLimitExceeded(JSONResponseError): pass class ClusterSubnetQuotaExceeded(JSONResponseError): pass class SNSInvalidTopic(JSONResponseError): pass class ClusterSecurityGroupNotFound(JSONResponseError): pass class InvalidElasticIp(JSONResponseError): pass class InvalidClusterParameterGroupState(JSONResponseError): pass class InvalidHsmConfigurationState(JSONResponseError): pass class ClusterAlreadyExists(JSONResponseError): pass class HsmConfigurationQuotaExceeded(JSONResponseError): pass class ClusterSnapshotAlreadyExists(JSONResponseError): pass class SubscriptionSeverityNotFound(JSONResponseError): pass class SourceNotFound(JSONResponseError): pass class ReservedNodeAlreadyExists(JSONResponseError): pass class ClusterSubnetGroupQuotaExceeded(JSONResponseError): pass class ClusterParameterGroupNotFound(JSONResponseError): pass class InvalidS3BucketName(JSONResponseError): pass class InvalidS3KeyPrefix(JSONResponseError): pass class SubscriptionAlreadyExist(JSONResponseError): pass class HsmConfigurationNotFound(JSONResponseError): pass class AuthorizationNotFound(JSONResponseError): pass class ClusterSecurityGroupQuotaExceeded(JSONResponseError): pass class EventSubscriptionQuotaExceeded(JSONResponseError): pass class AuthorizationAlreadyExists(JSONResponseError): pass class InvalidClusterSnapshotState(JSONResponseError): pass class ClusterParameterGroupQuotaExceeded(JSONResponseError): pass class SnapshotCopyDisabled(JSONResponseError): pass class ClusterSubnetGroupAlreadyExists(JSONResponseError): pass class ReservedNodeNotFound(JSONResponseError): pass class HsmClientCertificateAlreadyExists(JSONResponseError): pass class InvalidClusterSubnetGroupState(JSONResponseError): pass class SubscriptionNotFound(JSONResponseError): pass class InsufficientS3BucketPolicy(JSONResponseError): pass class ClusterParameterGroupAlreadyExists(JSONResponseError): pass class UnsupportedOption(JSONResponseError): pass class CopyToRegionDisabled(JSONResponseError): pass class SnapshotCopyAlreadyEnabled(JSONResponseError): pass class IncompatibleOrderableOptions(JSONResponseError): pass

17.895652

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616

8,232

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459

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1

null

1

0

null

0

1

0

1

0

6

f09d91ddaea70f71b76289b850f48c08a528b370

29

py

Python

RestFlask-Hotels/MemoryMode/models/__init__.py

LucasBiason/FlaskStudies

a594846f6eaa1655267f84da73764716e22f719b

[ "MIT" ]

null

RestFlask-Hotels/MemoryMode/models/__init__.py

LucasBiason/FlaskStudies

a594846f6eaa1655267f84da73764716e22f719b

[ "MIT" ]

null

RestFlask-Hotels/MemoryMode/models/__init__.py

LucasBiason/FlaskStudies

a594846f6eaa1655267f84da73764716e22f719b

[ "MIT" ]

null

from .hotel import HotelModel

29

0.862069

4

29

6.25

1

0

0.103448

29

1

29

0.961538

0

1

0

true

0

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0

1

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1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f0c79622e39856b52ad56e96ebd9fff337670348

782

py

Python

python_helper/api/test/api/src/ModuleImportsTest.py

SamuelJansen/python_helper

1cd43f9ef64cdb84d3c22e56346dc3a1096ac809

[ "MIT" ]

null

python_helper/api/test/api/src/ModuleImportsTest.py

SamuelJansen/python_helper

1cd43f9ef64cdb84d3c22e56346dc3a1096ac809

[ "MIT" ]

null

python_helper/api/test/api/src/ModuleImportsTest.py

SamuelJansen/python_helper

1cd43f9ef64cdb84d3c22e56346dc3a1096ac809

[ "MIT" ]

null

from python_helper import log from python_helper import ObjectHelper from python_helper import SettingHelper from python_helper import StringHelper from python_helper import EnvironmentHelper from python_helper import ReflectionHelper from python_helper import RandomHelper from python_helper import Constant from python_helper import EnvironmentVariable from python_helper import Test from python_helper import Method from python_helper import Function from python_helper import ObjectHelperHelper from python_helper import SettingHelperHelper from python_helper import SettingHelperHelper from python_helper import LogHelperHelper from python_helper import RandomHelperHelper from python_helper import FileHelper @Test(inspectGlobals=False) def allImportedSuccesfuly() : ...

34

45

0.882353

95

782

7.073684

0.263158

0.267857

0.428571

0.589286

0.154762

0

0.104859

782

22

46

35.545455

0.96

0

0.095238

0

1

0.047619

true

0

0.904762

0

0.952381

0

null

1

0

null

0

1

0

1

0

1

0

6

f0ccb1d4136a01f5a1a46145d3c3722cb4ea2e83

142

py

Python

tests/example/core/tests.py

jacklinke/django-directed

8ef8cd8a71e9a03a8628dce6465351f676f542ff

[ "Apache-2.0" ]

2

2022-02-09T10:15:40.000Z

2022-02-22T14:11:03.000Z

tests/example/core/tests.py

jacklinke/django-directed

8ef8cd8a71e9a03a8628dce6465351f676f542ff

[ "Apache-2.0" ]

1

2022-02-20T14:49:37.000Z

tests/example/core/tests.py

jacklinke/django-directed

8ef8cd8a71e9a03a8628dce6465351f676f542ff

[ "Apache-2.0" ]

null

import pytest from django.conf import settings def test_account_is_configured(): assert "tests.example.core" in settings.INSTALLED_APPS

20.285714

58

0.809859

20

142

5.55

0.9

0

0.126761

142

6

59

23.666667

0.895161

0

0.126761

0

0.25

1

0.25

true

0

0.5

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0b12d5fac4c9dd60c1948ade841572faba00efe9

150

py

Python

BlockSim/settings.py

aminrd/BlockSim

cda6f119ab57b4db6e177a1095705c28d024c25e

[ "MIT" ]

null

BlockSim/settings.py

aminrd/BlockSim

cda6f119ab57b4db6e177a1095705c28d024c25e

[ "MIT" ]

null

BlockSim/settings.py

aminrd/BlockSim

cda6f119ab57b4db6e177a1095705c28d024c25e

[ "MIT" ]

null

import os root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) database_url = f'sqlite:///{os.path.join(root_dir, "database.db")}'

30

70

0.733333

25

150

4.12

0.56

0.23301

0.252427

0.291262

0.31068

0

0.066667

150

4

71

37.5

0.735714

0

0.326667

0.22

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

6

9bfaa1fe4ff1db73cf283a644fc9b01945728a13

36

py

Python

indra/assemblers/cag/__init__.py

zebulon2/indra

7727ddcab52ad8012eb6592635bfa114e904bd48

[ "BSD-2-Clause" ]

1

2020-12-27T14:37:10.000Z

indra/assemblers/cag/__init__.py

zebulon2/indra

7727ddcab52ad8012eb6592635bfa114e904bd48

[ "BSD-2-Clause" ]

null

indra/assemblers/cag/__init__.py

zebulon2/indra

7727ddcab52ad8012eb6592635bfa114e904bd48

[ "BSD-2-Clause" ]

null

from .assembler import CAGAssembler

18

35

0.861111

4

36

7.75

1

0

0.111111

36

1

36

0.96875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5041bbcb0f65bc2c0fad6c129c93427862929a0a

67

py

Python

hackathonbaobab2020/__init__.py

JaimeSotomayor/hackathonbaobab2020

0fd527a37adc110d4118c8d87f5448c677a31bba

[ "MIT" ]

null

hackathonbaobab2020/__init__.py

JaimeSotomayor/hackathonbaobab2020

0fd527a37adc110d4118c8d87f5448c677a31bba

[ "MIT" ]

null

hackathonbaobab2020/__init__.py

JaimeSotomayor/hackathonbaobab2020

0fd527a37adc110d4118c8d87f5448c677a31bba

[ "MIT" ]

null

from .core import * from .execution import * from .solver import *

16.75

24

0.731343

9

67

5.444444

0.555556

0.408163

0

0.179104

67

3

25

22.333333

0.890909

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

ac9d2f9fcab843a1a40d34f769236fb2c0df0800

4,800

py

Python

tests/api/one/test_accounts.py

stjordanis/python-client

2a04351ea4da9db491fd85c8f898bb8fbab542df

[ "MIT" ]

1

2018-12-07T22:42:06.000Z

tests/api/one/test_accounts.py

stjordanis/python-client

2a04351ea4da9db491fd85c8f898bb8fbab542df

[ "MIT" ]

null

tests/api/one/test_accounts.py

stjordanis/python-client

2a04351ea4da9db491fd85c8f898bb8fbab542df

[ "MIT" ]

null

import responses from client import ArkClient def test_balance_calls_correct_url_with_params(): responses.add( responses.GET, 'http://127.0.0.1:4002/accounts/getBalance', json={'success': True}, status=200 ) client = ArkClient('http://127.0.0.1:4002', api_version='v1') client.accounts.balance(address='spongebob') assert len(responses.calls) == 1 assert responses.calls[0].request.url == ( 'http://127.0.0.1:4002/accounts/getBalance?address=spongebob' ) def test_public_key_calls_correct_url_with_params(): responses.add( responses.GET, 'http://127.0.0.1:4002/accounts/getPublicKey', json={'success': True}, status=200 ) client = ArkClient('http://127.0.0.1:4002', api_version='v1') client.accounts.public_key(address='spongebob') assert len(responses.calls) == 1 assert responses.calls[0].request.url == ( 'http://127.0.0.1:4002/accounts/getPublicKey?address=spongebob' ) def test_delegates_calls_correct_url_with_params(): responses.add( responses.GET, 'http://127.0.0.1:4002/accounts/delegates', json={'success': True}, status=200 ) client = ArkClient('http://127.0.0.1:4002', api_version='v1') client.accounts.delegates(address='spongebob') assert len(responses.calls) == 1 assert responses.calls[0].request.url == ( 'http://127.0.0.1:4002/accounts/delegates?address=spongebob' ) def test_delegates_fee_calls_correct_url(): responses.add( responses.GET, 'http://127.0.0.1:4002/accounts/delegates/fee', json={'success': True}, status=200 ) client = ArkClient('http://127.0.0.1:4002', api_version='v1') client.accounts.delegates_fee() assert len(responses.calls) == 1 assert responses.calls[0].request.url == 'http://127.0.0.1:4002/accounts/delegates/fee' def test_get_correct_url_with_params(): responses.add( responses.GET, 'http://127.0.0.1:4002/accounts', json={'success': True}, status=200 ) client = ArkClient('http://127.0.0.1:4002', api_version='v1') client.accounts.get(address='spongebob') assert len(responses.calls) == 1 assert responses.calls[0].request.url == 'http://127.0.0.1:4002/accounts?address=spongebob' def test_all_calls_correct_url_with_default_params(): responses.add( responses.GET, 'http://127.0.0.1:4002/accounts/getAllAccounts', json={'success': True}, status=200 ) client = ArkClient('http://127.0.0.1:4002', api_version='v1') client.accounts.all() assert len(responses.calls) == 1 assert responses.calls[0].request.url == ( 'http://127.0.0.1:4002/accounts/getAllAccounts?limit=100' ) def test_all_calls_correct_url_with_passed_in_params(): responses.add( responses.GET, 'http://127.0.0.1:4002/accounts/getAllAccounts', json={'success': True}, status=200 ) client = ArkClient('http://127.0.0.1:4002', api_version='v1') client.accounts.all(limit=69, offset=123) assert len(responses.calls) == 1 assert responses.calls[0].request.url.startswith( 'http://127.0.0.1:4002/accounts/getAllAccounts?' ) assert 'limit=69' in responses.calls[0].request.url assert 'offset=123' in responses.calls[0].request.url def test_top_calls_correct_url_with_default_params(): responses.add( responses.GET, 'http://127.0.0.1:4002/accounts/top', json={'success': True}, status=200 ) client = ArkClient('http://127.0.0.1:4002', api_version='v1') client.accounts.top() assert len(responses.calls) == 1 assert responses.calls[0].request.url == 'http://127.0.0.1:4002/accounts/top?limit=100' def test_top_calls_correct_url_with_passed_in_params(): responses.add( responses.GET, 'http://127.0.0.1:4002/accounts/top', json={'success': True}, status=200 ) client = ArkClient('http://127.0.0.1:4002', api_version='v1') client.accounts.top(limit=69, offset=123) assert len(responses.calls) == 1 assert responses.calls[0].request.url.startswith('http://127.0.0.1:4002/accounts/top?') assert 'limit=69' in responses.calls[0].request.url assert 'offset=123' in responses.calls[0].request.url def test_count_calls_correct_url(): responses.add( responses.GET, 'http://127.0.0.1:4002/accounts/count', json={'success': True}, status=200 ) client = ArkClient('http://127.0.0.1:4002', api_version='v1') client.accounts.count() assert len(responses.calls) == 1 assert responses.calls[0].request.url == 'http://127.0.0.1:4002/accounts/count'

30.188679

95

0.645417

657

4,800

4.601218

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0.079391

0.089315

0.94608

0.924909

0.913993

0.884883

0.882898

0

0.100464

0.19125

4,800

158

96

30.379747

0.678259

0

0.571429

0

0.260417

0

0.190476

1

0.079365

false

0.015873

0

0.095238

0

null

0

1

0

null

0

6

acec77c98e9dde22241e1b4e2ec03f97e2bdf055

349

py

Python

nlproar/explain/importance_measures/__init__.py

AndreasMadsen/nlp-roar-interpretability

ad30f756cd744dfb05d1b57de744c5ff60d9f20c

[ "MIT" ]

17

2021-11-04T02:15:30.000Z

2021-12-26T16:31:27.000Z

nlproar/explain/importance_measures/__init__.py

AndreasMadsen/nlp-roar-interpretability

ad30f756cd744dfb05d1b57de744c5ff60d9f20c

[ "MIT" ]

null

nlproar/explain/importance_measures/__init__.py

AndreasMadsen/nlp-roar-interpretability

ad30f756cd744dfb05d1b57de744c5ff60d9f20c

[ "MIT" ]

1

2021-11-04T10:45:25.000Z

from .attention import AttentionImportanceMeasure from .gradient import GradientImportanceMeasure from .integrated_gradient import IntegratedGradientImportanceMeasure from .mutual_information import MutualInformationImportanceMeasure from .random import RandomImportanceMeasure from .input_times_gradient import InputTimesGradientImportanceMeasure

43.625

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6

4a16c50ac67ab1d7846d6e1d5c5e6c9ff2749df8

8,546

py

Python

UnityEngine/UI/GraphicRaycaster/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

UnityEngine/UI/GraphicRaycaster/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

UnityEngine/UI/GraphicRaycaster/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

from typing import overload from UdonPie import System from UdonPie import UnityEngine from UdonPie.Undefined import * class GraphicRaycaster: def __new__(cls, arg1=None): ''' :returns: GraphicRaycaster :rtype: UnityEngine.UI.GraphicRaycaster ''' pass @staticmethod def op_Implicit(arg1): ''' :param arg1: Object :type arg1: UnityEngine.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def op_Equality(arg1, arg2): ''' :param arg1: Object :type arg1: UnityEngine.Object :param arg2: Object :type arg2: UnityEngine.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def op_Inequality(arg1, arg2): ''' :param arg1: Object :type arg1: UnityEngine.Object :param arg2: Object :type arg2: UnityEngine.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def get_sortOrderPriority(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def get_renderOrderPriority(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def get_ignoreReversedGraphics(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def set_ignoreReversedGraphics(arg1): ''' :param arg1: Boolean :type arg1: System.Boolean or bool ''' pass @staticmethod def get_blockingObjects(): ''' :returns: GraphicRaycaster+BlockingObjects :rtype: UnityEngine.GraphicRaycaster+BlockingObjects ''' pass @staticmethod def set_blockingObjects(arg1): ''' :param arg1: BlockingObjects :type arg1: UnityEngine.BlockingObjects ''' pass @staticmethod def Raycast(arg1, arg2): ''' :param arg1: PointerEventData :type arg1: UnityEngine.PointerEventData :param arg2: Undefined variable :type arg2: SystemCollectionsGenericList.SystemCollectionsGenericList ''' pass @staticmethod def get_eventCamera(): ''' :returns: Camera :rtype: UnityEngine.Camera ''' pass @staticmethod def ToString(): ''' :returns: String :rtype: System.String ''' pass @staticmethod def IsActive(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def IsDestroyed(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def get_enabled(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def set_enabled(arg1): ''' :param arg1: Boolean :type arg1: System.Boolean or bool ''' pass @staticmethod def get_transform(): ''' :returns: Transform :rtype: UnityEngine.Transform ''' pass @staticmethod def get_gameObject(): ''' :returns: GameObject :rtype: UnityEngine.GameObject ''' pass @staticmethod @overload def GetComponent(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod @overload def GetComponent(arg1): ''' :param arg1: String :type arg1: System.String or str :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod def GetComponent(arg1=None): pass @staticmethod @overload def GetComponentInChildren(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Boolean :type arg2: System.Boolean or bool :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod @overload def GetComponentInChildren(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod def GetComponentInChildren(arg1=None, arg2=None): pass @staticmethod @overload def GetComponentsInChildren(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Boolean :type arg2: System.Boolean or bool :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInChildren(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInChildren(arg1, arg2): ''' :param arg1: Boolean :type arg1: System.Boolean or bool :param arg2: Undefined variable :type arg2: ListT.ListT ''' pass @staticmethod @overload def GetComponentsInChildren(arg1): ''' :param arg1: Undefined variable :type arg1: ListT.ListT ''' pass @staticmethod def GetComponentsInChildren(arg1=None, arg2=None): pass @staticmethod @overload def GetComponentInParent(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod def GetComponentInParent(arg1=None): pass @staticmethod @overload def GetComponentsInParent(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Boolean :type arg2: System.Boolean or bool :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInParent(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInParent(arg1, arg2): ''' :param arg1: Boolean :type arg1: System.Boolean or bool :param arg2: Undefined variable :type arg2: ListT.ListT ''' pass @staticmethod def GetComponentsInParent(arg1=None, arg2=None): pass @staticmethod @overload def GetComponents(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponents(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Undefined variable :type arg2: SystemCollectionsGenericList.SystemCollectionsGenericList ''' pass @staticmethod @overload def GetComponents(arg1): ''' :param arg1: Undefined variable :type arg1: ListT.ListT ''' pass @staticmethod def GetComponents(arg1=None, arg2=None): pass @staticmethod def GetInstanceID(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def GetHashCode(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def Equals(arg1): ''' :param arg1: Object :type arg1: System.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def get_name(): ''' :returns: String :rtype: System.String ''' pass @staticmethod def set_name(arg1): ''' :param arg1: String :type arg1: System.String or str ''' pass @staticmethod def GetType(): ''' :returns: Type :rtype: System.Type ''' pass

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null

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6

c580055bbc089ebf59edd6420a0ce51bf8ec2bef

101

py

Python

integration/common/openlineage/common/provider/great_expectations/__init__.py

tomassatka/OpenLineage

96b34d8ed2eb642dcc3a2ee53eca53f4455ac7c0

[ "Apache-2.0" ]

746

2020-10-26T16:45:54.000Z

2022-03-31T22:49:29.000Z

integration/common/openlineage/common/provider/great_expectations/__init__.py

tomassatka/OpenLineage

96b34d8ed2eb642dcc3a2ee53eca53f4455ac7c0

[ "Apache-2.0" ]

442

2020-10-26T12:34:58.000Z

2022-03-31T16:28:41.000Z

integration/common/openlineage/common/provider/great_expectations/__init__.py

tomassatka/OpenLineage

96b34d8ed2eb642dcc3a2ee53eca53f4455ac7c0

[ "Apache-2.0" ]

70

2020-12-28T18:52:35.000Z

2022-03-30T06:58:09.000Z

from openlineage.common.provider.great_expectations.action import OpenLineageValidationAction # noqa

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100

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101

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true

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0

6

c5ab4ea59dfac027c47bbe9f226bf71744ff3ad9

10,436

py

Python

example/layers/attention.py

OpenBMB/BMCook

de31035e4d58d67433647e7c676f56ede7ac8477

[ "Apache-2.0" ]

1

2022-03-29T09:06:17.000Z

example/layers/attention.py

OpenBMB/BMCook

de31035e4d58d67433647e7c676f56ede7ac8477

[ "Apache-2.0" ]

null

example/layers/attention.py

OpenBMB/BMCook

de31035e4d58d67433647e7c676f56ede7ac8477

[ "Apache-2.0" ]

1

2022-03-30T02:25:42.000Z

from typing import Optional import torch import bmtrain as bmt import cpm_kernels.torch as ct import math class Attention(bmt.DistributedModule): def __init__(self, dim_model : int, num_heads : int, dim_head : int, init_method : bmt.ParameterInitializer, int8=True, dtype=torch.half ): super().__init__() self.project_q = bmt.DistributedParameter( torch.empty(num_heads * dim_head, dim_model, dtype=dtype), init_method=init_method) self.project_k = bmt.DistributedParameter( torch.empty(num_heads * dim_head, dim_model, dtype=dtype), init_method=init_method) self.project_v = bmt.DistributedParameter( torch.empty(num_heads * dim_head, dim_model, dtype=dtype), init_method=init_method) self.attention_out = bmt.DistributedParameter( torch.empty(dim_model, num_heads * dim_head, dtype=dtype), init_method=init_method) self.relu = torch.nn.ReLU() self.dim_model = dim_model self.num_heads = num_heads self.dim_head = dim_head self.int8 = int8 def forward(self, hidden_q : torch.Tensor, # (batch, dim_model, len_q) hidden_kv : torch.Tensor, # (batch, dim_model, len_k) mask : torch.Tensor, # (batch, len_k, len_q) position_bias : Optional[torch.Tensor], # (num_heads, len_k, len_q) ): """ Args: hidden_q : (batch, dim_model, len_q) fp16 hidden_kv : (batch, dim_model, len_k) fp16 mask : (batch, len_k, len_q) fp16 position_bias : (num_heads, len_k, len_q) fp16 Returns: out : (batch, dim_model, len_q) fp16 """ # bmt.inspect.record_tensor(hidden_q, "attn_x") batch_size = hidden_q.size(0) len_q = hidden_q.size(2) len_k = hidden_kv.size(2) project_q = self.project_q project_k = self.project_k project_v = self.project_v attention_out = self.attention_out # (1#batch, num_heads * dim_head, dim_model) @ (batch, dim_model, len_q) = (batch, num_heads * dim_head, len_q) h_q = ct.bmm(project_q.unsqueeze(0), False, hidden_q, False, int8=self.int8) #/ math.sqrt(self.dim_model) h_k = ct.bmm(project_k.unsqueeze(0), False, hidden_kv, False, int8=self.int8) #/ math.sqrt(self.dim_model) h_v = ct.bmm(project_v.unsqueeze(0), False, hidden_kv, False, int8=self.int8) #/ math.sqrt(self.dim_model) # view (batch * num_heads, dim_head, length) h_q = h_q.view(batch_size * self.num_heads, self.dim_head, -1) h_k = h_k.view(batch_size * self.num_heads, self.dim_head, -1) h_v = h_v.view(batch_size * self.num_heads, self.dim_head, -1) # (batch * num_heads, dim_head, len_k)T @ (batch * num_heads, dim_head, len_q) = (batch * num_heads, len_k, len_q) score = ct.bmm( h_k, True, h_q, False, int8=False) # use FP 16 here score = score / math.sqrt(self.dim_head) # (batch, num_heads, len_k, len_q) score = score.view(batch_size, self.num_heads, len_k, len_q) if position_bias is not None: score = ct.batched_add( score, position_bias ) # (batch, num_heads, len_k * len_q) masked_score = ct.mask( score.view(batch_size, self.num_heads, -1), mask.view(batch_size, -1), float("-inf") ) # (batch * num_heads, len_k, len_q) masked_score = masked_score.view(batch_size * self.num_heads, len_k, len_q) # (batch * num_heads, len_k, len_q) masked_score = ct.softmax(masked_score) # softmax along len_k # (batch * num_heads, dim_head, len_k) @ (batch * num_heads, len_k, len_q) = (batch * num_heads, dim_head, len_q) attention_result = ct.bmm(h_v, False, masked_score, False, int8=False) # use FP 16 here attention_result = attention_result.view(batch_size, self.num_heads * self.dim_head, len_q) # (1#batch, dim_model, num_heads * dim_head) @ (batch, num_heads * dim_head, len_q) = (batch, dim_model, len_q) attention_out = ct.bmm(attention_out.unsqueeze(0), False, attention_result, False, int8=self.int8) #/ math.sqrt(self.dim_head * self.num_heads) return attention_out def fixed_pos_embedding(x, seq_dim=1, seq_len=None): dim = x.shape[-2] if seq_len is None: seq_len = x.shape[seq_dim] inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim)) sinusoid_inp = torch.einsum("j , i -> i j", torch.arange(seq_len), inv_freq).to(x.device).half() return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp) def rotate_every_two(x): x1 = x[:, ::2, :] x2 = x[:, 1::2, :] x = torch.stack((-x2, x1), axis=-2) return x.flatten(-3, -2) # in einsum notation: rearrange(x, '... d j -> ... (d j)') def apply_rotary_pos_emb(x, sincos, offset=0): sin, cos = map(lambda t: t[None, :, offset : x.shape[-1] + offset].repeat_interleave(2, 1), sincos) # einsum notation for lambda t: repeat(t[offset:x.shape[1]+offset,:], "n d -> () n () (d j)", j=2) return (x * cos) + (rotate_every_two(x) * sin) class GPTJAtt(bmt.DistributedModule): def __init__(self, dim_model : int, num_heads : int, dim_head : int, init_method: bmt.ParameterInitializer, int8=True, dtype=torch.half ): super().__init__() self.q_proj = bmt.DistributedParameter( torch.empty(num_heads * dim_head, dim_model, dtype=dtype), init_method=init_method) self.k_proj = bmt.DistributedParameter( torch.empty(num_heads * dim_head, dim_model, dtype=dtype), init_method=init_method) self.v_proj = bmt.DistributedParameter( torch.empty(num_heads * dim_head, dim_model, dtype=dtype), init_method=init_method) self.out_proj = bmt.DistributedParameter( torch.empty(dim_model, num_heads * dim_head, dtype=dtype), init_method=init_method) self.relu = torch.nn.ReLU() self.dim_model = dim_model self.num_heads = num_heads self.dim_head = dim_head self.int8 = int8 self.rotary_dim = 64 def forward(self, hidden_q : torch.Tensor, # (batch, dim_model, len_q) hidden_kv : torch.Tensor, # (batch, dim_model, len_k) mask : torch.Tensor, # (batch, len_k, len_q) position_bias : Optional[torch.Tensor], # (num_heads, len_k, len_q) ): """ Args: hidden_q : (batch, dim_model, len_q) fp16 hidden_kv : (batch, dim_model, len_k) fp16 mask : (batch, len_k, len_q) fp16 position_bias : (num_heads, len_k, len_q) fp16 Returns: out : (batch, dim_model, len_q) fp16 """ # bmt.inspect.record_tensor(hidden_q, "attn_x") batch_size = hidden_q.size(0) len_q = hidden_q.size(2) len_k = hidden_kv.size(2) project_q = self.q_proj project_k = self.k_proj project_v = self.v_proj attention_out = self.out_proj # (1#batch, num_heads * dim_head, dim_model) @ (batch, dim_model, len_q) = (batch, num_heads * dim_head, len_q) h_q = ct.bmm(project_q.unsqueeze(0), False, hidden_q, False, int8=self.int8) #/ math.sqrt(self.dim_model) h_k = ct.bmm(project_k.unsqueeze(0), False, hidden_kv, False, int8=self.int8) #/ math.sqrt(self.dim_model) h_v = ct.bmm(project_v.unsqueeze(0), False, hidden_kv, False, int8=self.int8) #/ math.sqrt(self.dim_model) # view (batch * num_heads, dim_head, length) h_q = h_q.view(batch_size * self.num_heads, self.dim_head, -1) h_k = h_k.view(batch_size * self.num_heads, self.dim_head, -1) h_v = h_v.view(batch_size * self.num_heads, self.dim_head, -1) k_rot = h_k[:, : self.rotary_dim, :] k_pass = h_k[:, self.rotary_dim :, :] q_rot = h_q[:, : self.rotary_dim, :] q_pass = h_q[:, self.rotary_dim :, :] seq_len = h_k.shape[-1] sincos = fixed_pos_embedding(k_rot, -1, seq_len=seq_len) k_rot = apply_rotary_pos_emb(k_rot, sincos, offset=0) q_rot = apply_rotary_pos_emb(q_rot, sincos, offset=0) h_k = torch.cat([k_rot, k_pass], dim=-2) h_q = torch.cat([q_rot, q_pass], dim=-2) # (batch * num_heads, dim_head, len_k)T @ (batch * num_heads, dim_head, len_q) = (batch * num_heads, len_k, len_q) score = ct.bmm( h_k, True, h_q, False, int8=False) # use FP 16 here score = score / math.sqrt(self.dim_head) # (batch, num_heads, len_k, len_q) score = score.view(batch_size, self.num_heads, len_k, len_q) # if position_bias is not None: # score = ct.batched_add( # score, # position_bias # ) # (batch, num_heads, len_k * len_q) masked_score = ct.mask( score.view(batch_size, self.num_heads, -1), mask.view(batch_size, -1), float("-inf") ) # (batch * num_heads, len_k, len_q) masked_score = masked_score.view(batch_size * self.num_heads, len_k, len_q) self.masked_score = masked_score # Intermediary values for KD # (batch * num_heads, len_k, len_q) masked_score = ct.softmax(masked_score) # softmax along len_k # (batch * num_heads, dim_head, len_k) @ (batch * num_heads, len_k, len_q) = (batch * num_heads, dim_head, len_q) attention_result = ct.bmm(h_v, False, masked_score, False, int8=False) # use FP 16 here attention_result = attention_result.view(batch_size, self.num_heads * self.dim_head, len_q) # (1#batch, dim_model, num_heads * dim_head) @ (batch, num_heads * dim_head, len_q) = (batch, dim_model, len_q) attention_out = ct.bmm(attention_out.unsqueeze(0), False, attention_result, False, int8=self.int8) #/ math.sqrt(self.dim_head * self.num_heads) return attention_out

41.744

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1,508

10,436

3.831565

0.090849

0.088612

0.062998

0.067497

0.834545

0.809969

0

0.015638

0.283059

10,436

249

152

41.911647

0.756616

0.258432

0

0.636943

0

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0

1

0.044586

false

0.025478

0.031847

0

0.121019

0

null

0

1

0

null

0

6

c5e4c89e80185f3850abfabe2724a9cd076c82b2

15,976

py

Python

lib/pytracking/ltr/data/processing.py

ngunnar/tracking_reg

71a1d22e53e277f36f961040f03e56efb163ded5

[ "MIT" ]

11

2020-11-25T16:19:23.000Z

2022-01-12T08:08:47.000Z

ltr/data/processing.py

tsingqguo/AttackTracker

054268d5afa0044675c7acf1ac13e621f1c9549e

[ "Apache-2.0" ]

null

ltr/data/processing.py

tsingqguo/AttackTracker

054268d5afa0044675c7acf1ac13e621f1c9549e

[ "Apache-2.0" ]

null

import torch import torchvision.transforms as transforms from pytracking import TensorDict import ltr.data.processing_utils as prutils class BaseProcessing: """ Base class for Processing. Processing class is used to process the data returned by a dataset, before passing it through the network. For example, it can be used to crop a search region around the object, apply various data augmentations, etc.""" def __init__(self, transform=transforms.ToTensor(), train_transform=None, test_transform=None, joint_transform=None): """ args: transform - The set of transformations to be applied on the images. Used only if train_transform or test_transform is None. train_transform - The set of transformations to be applied on the train images. If None, the 'transform' argument is used instead. test_transform - The set of transformations to be applied on the test images. If None, the 'transform' argument is used instead. joint_transform - The set of transformations to be applied 'jointly' on the train and test images. For example, it can be used to convert both test and train images to grayscale. """ self.transform = {'train': transform if train_transform is None else train_transform, 'test': transform if test_transform is None else test_transform, 'joint': joint_transform} def __call__(self, data: TensorDict): raise NotImplementedError class ATOMProcessing(BaseProcessing): """ The processing class used for training ATOM. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. A set of proposals are then generated for the test images by jittering the ground truth box. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, proposal_params, mode='pair', *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.proposal_params = proposal_params self.mode = mode def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * self.center_jitter_factor[mode]).item() jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generates proposals by adding noise to the input box args: box - input box returns: torch.Tensor - Array of shape (num_proposals, 4) containing proposals torch.Tensor - Array of shape (num_proposals,) containing IoU overlap of each proposal with the input box. The IoU is mapped to [-1, 1] """ # Generate proposals num_proposals = self.proposal_params['boxes_per_frame'] proposals = torch.zeros((num_proposals, 4)) gt_iou = torch.zeros(num_proposals) for i in range(num_proposals): proposals[i, :], gt_iou[i] = prutils.perturb_box(box, min_iou=self.proposal_params['min_iou'], sigma_factor=self.proposal_params['sigma_factor'] ) # Map to [-1, 1] gt_iou = gt_iou * 2 - 1 return proposals, gt_iou def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images' - 'test_images' - 'train_anno' - 'test_anno' - returns: TensorDict - output data block with following fields: 'train_images' - 'test_images' - 'train_anno' - 'test_anno' - 'test_proposals'- 'proposal_iou' - """ # Apply joint transforms if self.transform['joint'] is not None: num_train_images = len(data['train_images']) all_images = data['train_images'] + data['test_images'] all_images_trans = self.transform['joint'](*all_images) data['train_images'] = all_images_trans[:num_train_images] data['test_images'] = all_images_trans[num_train_images:] for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] # Crop image region centered at jittered_anno box crops, boxes = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) # Apply transforms data[s + '_images'] = [self.transform[s](x) for x in crops] data[s + '_anno'] = boxes # Generate proposals frame2_proposals, gt_iou = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = list(frame2_proposals) data['proposal_iou'] = list(gt_iou) # Prepare output if self.mode == 'sequence': data = data.apply(prutils.stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) return data class DiMPProcessing(BaseProcessing): """ The processing class used for training DiMP. The images are processed in the following way. First, the target bounding box is jittered by adding some noise. Next, a square region (called search region ) centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is always at the center of the search region. The search region is then resized to a fixed size given by the argument output_sz. A Gaussian label centered at the target is generated for each image. These label functions are used for computing the loss of the predicted classification model on the test images. A set of proposals are also generated for the test images by jittering the ground truth box. These proposals are used to train the bounding box estimating branch. """ def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor, crop_type='replicate', mode='pair', proposal_params=None, label_function_params=None, *args, **kwargs): """ args: search_area_factor - The size of the search region relative to the target size. output_sz - An integer, denoting the size to which the search region is resized. The search region is always square. center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before extracting the search region. See _get_jittered_box for how the jittering is done. scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before extracting the search region. See _get_jittered_box for how the jittering is done. crop_type - If 'replicate', the boundary pixels are replicated in case the search region crop goes out of image. If 'nopad', the search region crop is shifted/shrunk to fit completely inside the image. mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames proposal_params - Arguments for the proposal generation process. See _generate_proposals for details. label_function_params - Arguments for the label generation process. See _generate_label_function for details. """ super().__init__(*args, **kwargs) self.search_area_factor = search_area_factor self.output_sz = output_sz self.center_jitter_factor = center_jitter_factor self.scale_jitter_factor = scale_jitter_factor self.crop_type = crop_type self.mode = mode self.proposal_params = proposal_params self.label_function_params = label_function_params def _get_jittered_box(self, box, mode): """ Jitter the input box args: box - input bounding box mode - string 'train' or 'test' indicating train or test data returns: torch.Tensor - jittered box """ jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode]) max_offset = (jittered_size.prod().sqrt() * self.center_jitter_factor[mode]).item() jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5) return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0) def _generate_proposals(self, box): """ Generates proposals by adding noise to the input box args: box - input box returns: torch.Tensor - Array of shape (num_proposals, 4) containing proposals torch.Tensor - Array of shape (num_proposals,) containing IoU overlap of each proposal with the input box. The IoU is mapped to [-1, 1] """ # Generate proposals num_proposals = self.proposal_params['boxes_per_frame'] proposals = torch.zeros((num_proposals, 4)) gt_iou = torch.zeros(num_proposals) for i in range(num_proposals): proposals[i, :], gt_iou[i] = prutils.perturb_box(box, min_iou=self.proposal_params['min_iou'], sigma_factor=self.proposal_params['sigma_factor']) # Map to [-1, 1] gt_iou = gt_iou * 2 - 1 return proposals, gt_iou def _generate_label_function(self, target_bb): """ Generates the gaussian label function centered at target_bb args: target_bb - target bounding box (num_images, 4) returns: torch.Tensor - Tensor of shape (num_images, label_sz, label_sz) containing the label for each sample """ gauss_label = prutils.gaussian_label_function(target_bb.view(-1, 4), self.label_function_params['sigma_factor'], self.label_function_params['kernel_sz'], self.label_function_params['feature_sz'], self.output_sz, end_pad_if_even=self.label_function_params.get('end_pad_if_even', True)) return gauss_label def __call__(self, data: TensorDict): """ args: data - The input data, should contain the following fields: 'train_images' - 'test_images' - 'train_anno' - 'test_anno' - returns: TensorDict - output data block with following fields: 'train_images' - 'test_images' - 'train_anno' - 'test_anno' - 'test_proposals' (optional) - 'proposal_iou' (optional) - 'test_label' (optional) - 'train_label' (optional) - """ if self.transform['joint'] is not None: num_train_images = len(data['train_images']) all_images = data['train_images'] + data['test_images'] all_images_trans = self.transform['joint'](*all_images) data['train_images'] = all_images_trans[:num_train_images] data['test_images'] = all_images_trans[num_train_images:] for s in ['train', 'test']: assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \ "In pair mode, num train/test frames must be 1" # Add a uniform noise to the center pos jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']] if self.crop_type == 'replicate': crops, boxes = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) elif self.crop_type == 'nopad': crops, boxes = prutils.jittered_center_crop_nopad(data[s + '_images'], jittered_anno, data[s + '_anno'], self.search_area_factor, self.output_sz) else: raise ValueError('Unknown crop type {}'.format(self.crop_type)) data[s + '_images'] = [self.transform[s](x) for x in crops] data[s + '_anno'] = boxes # Generate proposals if self.proposal_params: frame2_proposals, gt_iou = zip(*[self._generate_proposals(a) for a in data['test_anno']]) data['test_proposals'] = list(frame2_proposals) data['proposal_iou'] = list(gt_iou) # Prepare output if self.mode == 'sequence': data = data.apply(prutils.stack_tensors) else: data = data.apply(lambda x: x[0] if isinstance(x, list) else x) # Generate label functions if self.label_function_params is not None: data['train_label'] = self._generate_label_function(data['train_anno']) data['test_label'] = self._generate_label_function(data['test_anno']) return data

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c5fd3ba9c1239d1be32b09988fbbc83d00c6584c

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py

Python

autoflow/feature_engineer/impute/__init__.py

auto-flow/autoflow

f5903424ad8694d57741a0bd6dfeaba320ea6517

[ "BSD-3-Clause" ]

49

2020-04-16T11:17:28.000Z

2020-05-06T01:32:44.000Z

autoflow/feature_engineer/impute/__init__.py

auto-flow/autoflow

f5903424ad8694d57741a0bd6dfeaba320ea6517

[ "BSD-3-Clause" ]

null

autoflow/feature_engineer/impute/__init__.py

auto-flow/autoflow

f5903424ad8694d57741a0bd6dfeaba320ea6517

[ "BSD-3-Clause" ]

3

2020-04-17T00:53:24.000Z

2020-04-23T03:04:26.000Z

from .knn_impute import KNNImputer from .miss_forest import MissForest from .simple import SimpleImputer from .gbt_impute import GBTImputer

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py

Python

tdirstat/__init__.py

apockill/tdirstat

2d2196432e3f2a861a24db86064cf36f093585c3

[ "MIT" ]

2

2020-02-03T18:11:55.000Z

2020-12-19T21:31:12.000Z

tdirstat/__init__.py

apockill/tdirstat

2d2196432e3f2a861a24db86064cf36f093585c3

[ "MIT" ]

1

2021-08-25T01:50:37.000Z

2021-08-30T04:44:35.000Z

tdirstat/__init__.py

apockill/tdirstat

2d2196432e3f2a861a24db86064cf36f093585c3

[ "MIT" ]

null

from .main import main as tdirstat

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6

a89c10ebebee62503ebcc5f14e8d005100b55d3e

146

py

Python

m16_mlutils/pipeline/__init__.py

messier16/m16_mlutils

868775f48106f2e3a2090e98b8508349ca278158

[ "MIT" ]

null

m16_mlutils/pipeline/__init__.py

messier16/m16_mlutils

868775f48106f2e3a2090e98b8508349ca278158

[ "MIT" ]

9

2018-10-13T06:50:05.000Z

2021-06-01T23:07:42.000Z

m16_mlutils/pipeline/__init__.py

messier16/m16_mlutils

868775f48106f2e3a2090e98b8508349ca278158

[ "MIT" ]

null

from .DataFrameSelector import DataFrameSelector from .MostFrequentImputer import MostFrequentImputer from .CategoryEncoder import CategoryEncoder

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1

0

1

0

6

a89fb7b3f701258a61b86789bfb6848889a90154

1,762

py

Python

shamanai/common/wrapper2.py

adaptationio/Shaman-RL

548fa847e6ba2105cc0a876b02db3f3d7c179c54

[ "MIT" ]

2

2020-06-13T04:38:08.000Z

2022-03-22T08:38:10.000Z

shamanai/common/wrapper2.py

adaptationio/Shaman-RL

548fa847e6ba2105cc0a876b02db3f3d7c179c54

[ "MIT" ]

1

2020-11-13T17:46:38.000Z

shamanai/common/wrapper2.py

adaptationio/Shaman-AI

548fa847e6ba2105cc0a876b02db3f3d7c179c54

[ "MIT" ]

null

import numpy as np from collections import deque import gym from gym import spaces import cv2 import tensorflow as tf import json class WarpFrame(gym.ObservationWrapper): def __init__(self, env): """Warp frames to 84x84 as done in the Nature paper and later work.""" gym.ObservationWrapper.__init__(self, env) self.width = 84 self.height = 84 self.observation_space = spaces.Box(low=0, high=255, shape=(self.height, self.width, 1), dtype=np.uint8) def observation(self, frame): frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA) return frame[:, :, None] class WarpFrameRGB(gym.ObservationWrapper): def __init__(self, env): """Warp frames to 84x84 as done in the Nature paper and later work.""" gym.ObservationWrapper.__init__(self, env) self.width = 84 self.height = 84 self.observation_space = spaces.Box(low=0, high=255, shape=(self.height, self.width, 3), dtype=np.uint8) def observation(self, frame): frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA) return frame[:, :, None] class WarpFrameRGBYolo(gym.ObservationWrapper): def __init__(self, env): """Warp frames to 84x84 as done in the Nature paper and later work.""" gym.ObservationWrapper.__init__(self, env) self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 24), dtype=np.uint8) # hack this part so that the graph is correctly built def observation(self, frame): frame = cv2.resize(frame, (224, 224), interpolation=cv2.INTER_AREA) return frame

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null

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1

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null

0

1

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6

765e536ba56c55528d56c015566fe130f4999b8d

122

py

Python

zmyy/zmyy.py

ZehnMilliarden/PublicPyTools

4671cac07de00ecbdf50b0ef577d847c4ee6f892

[ "Apache-2.0" ]

4

2021-11-14T07:50:42.000Z

2021-11-29T01:38:00.000Z

zmyy/zmyy.py

ZehnMilliarden/PublicPyTools

4671cac07de00ecbdf50b0ef577d847c4ee6f892

[ "Apache-2.0" ]

null

zmyy/zmyy.py

ZehnMilliarden/PublicPyTools

4671cac07de00ecbdf50b0ef577d847c4ee6f892

[ "Apache-2.0" ]

null

import zmyy_a import zmyy_b import zmyy_s if __name__ == '__main__': # zmyy_a.excute_main() zmyy_b.excute_main()

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6

767ab757b6c644bb0b13ae27db0c4c707931f56d

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py

Python

ddq_1/lang/fol_inference.py

jadnohra/connect

8eb21e6f122898094447bc3d5edb3053d5a2adf2

[ "Unlicense" ]

null

ddq_1/lang/fol_inference.py

jadnohra/connect

8eb21e6f122898094447bc3d5edb3053d5a2adf2

[ "Unlicense" ]

6

2021-03-19T12:06:56.000Z

2022-03-12T00:23:09.000Z

ddq_1/lang/fol_inference.py

jadnohra/connect

8eb21e6f122898094447bc3d5edb3053d5a2adf2

[ "Unlicense" ]

null

''' References: - Symbolic Logic, Copi, p.33, 396 ''' from typing import List from .fol_lang import Wff, PropVarWff, BinaryWff, PropositionalVariable, NegWff class Inference: def short_name(self): pass def possible_inferences(self, permisses: List[Wff]): pass class PropInference(Inference): def __init__(self, premisses: List[Wff], conclusion: List[Wff]): self.premisses = premisses self.conclusion = conclusion def short_name(self): pass class ModusPonens(PropInference): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) prem1 = BinaryWff.new_impl(p, q) prem2 = p concl = q super().__init__([prem1, prem2], [concl]) def short_name(self): return 'MP' def possible_inferences(self, permisses: List[Wff]): pass class ModusTollens(PropInference): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) prem1 = BinaryWff.new_impl(p, q) prem2 = NegWff(q) concl = NegWff(p) super().__init__([prem1, prem2], [concl]) def short_name(self): return 'MT' class HypotheticalSyllogism(PropInference): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) r = PropVarWff(PropositionalVariable('r')) prem1 = BinaryWff.new_impl(p, q) prem2 = BinaryWff.new_impl(q, r) concl = BinaryWff.new_impl(p, r) super().__init__([prem1, prem2], [concl]) def short_name(self): return 'HS' class DisjunctiveSyllogism(PropInference): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) prem1 = BinaryWff.new_disj(p, q) prem2 = NegWff(q) concl = q super().__init__([prem1, prem2], [concl]) def short_name(self): return 'DS' class ConstructiveDilemma(PropInference): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) r = PropVarWff(PropositionalVariable('r')) s = PropVarWff(PropositionalVariable('s')) prem1 = BinaryWff.new_disj( BinaryWff.new_impl(p, q), BinaryWff.new_impl(r, s)) prem2 = BinaryWff.new_disj(p, r) concl = BinaryWff.new_disj(q, s) super().__init__([prem1, prem2], [concl]) def short_name(self): return 'DS' class DistructuveDilemma(PropInference): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) r = PropVarWff(PropositionalVariable('r')) s = PropVarWff(PropositionalVariable('s')) prem1 = BinaryWff.new_disj( BinaryWff.new_impl(p, q), BinaryWff.new_impl(r, s)) prem2 = BinaryWff.new_disj( NegWff(q), NegWff(s)) concl = BinaryWff.new_disj( NegWff(p), NegWff(r)) super().__init__([prem1, prem2], [concl]) def short_name(self): return 'DD' class Simplification(PropInference): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) prem = BinaryWff.new_conj(p, q) concl = q super().__init__([prem], [concl]) def short_name(self): return 'SIMP' class Conjunction(PropInference): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) prem1 = p prem2 = q concl = BinaryWff.new_conj(p, q) super().__init__([prem1, prem2], [concl]) def short_name(self): return 'CONJ' class Addition(PropInference): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) prem = p concl = BinaryWff.new_disj(p, q) super().__init__([prem], [concl]) def short_name(self): return 'ADD' class Replacement: def __init__(self, pattern1: Wff, pattern2: Wff): self.pattern1 = pattern1 self.pattern2 = pattern2 def short_name(self): pass class DeMorganConj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) pat1 = NegWff(BinaryWff.new_conj(p, q)) pat2 = BinaryWff.new_disj( NegWff(p), NegWff(q)) super().__init__(pat1, pat2) def short_name(self): return 'DMc' class DeMorganDisj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) pat1 = NegWff(BinaryWff.new_disj(p, q)) pat2 = BinaryWff.new_conj( NegWff(p), NegWff(q)) super().__init__(pat1, pat2) def short_name(self): return 'DMd' class CommutationConj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) pat1 = BinaryWff.new_conj(p, q) pat2 = BinaryWff.new_conj(q, p) super().__init__(pat1, pat2) def short_name(self): return 'COMc' class CommutationDisj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) pat1 = BinaryWff.new_disj(p, q) pat2 = BinaryWff.new_disj(q, p) super().__init__(pat1, pat2) def short_name(self): return 'COMd' class AssociationConj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) r = PropVarWff(PropositionalVariable('r')) pat1 = BinaryWff.new_conj( BinaryWff.new_conj(p, q), r) pat2 = BinaryWff.new_conj( p, BinaryWff.new_conj(q, r)) super().__init__(pat1, pat2) def short_name(self): return 'ASCc' class AssociationDisj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) r = PropVarWff(PropositionalVariable('r')) pat1 = BinaryWff.new_disj( BinaryWff.new_disj(p, q), r) pat2 = BinaryWff.new_disj( p, BinaryWff.new_disj(q, r)) super().__init__(pat1, pat2) def short_name(self): return 'ASCd' class DistributionConj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) r = PropVarWff(PropositionalVariable('r')) pat1 = BinaryWff.new_conj( p, BinaryWff.new_disj(q, r)) pat2 = BinaryWff.new_disj( BinaryWff.new_conj(p, q), BinaryWff.new_conj(p, r)) super().__init__(pat1, pat2) def short_name(self): return 'DISTc' class DistributionDisj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) r = PropVarWff(PropositionalVariable('r')) pat1 = BinaryWff.new_disj( p, BinaryWff.new_conj(q, r)) pat2 = BinaryWff.new_conj( BinaryWff.new_disj(p, q), BinaryWff.new_disj(p, r)) super().__init__(pat1, pat2) def short_name(self): return 'DISTd' class DoubleNegation(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) pat1 = NegWff(NegWff(p)) pat2 = p super().__init__(pat1, pat2) def short_name(self): return 'DN' class Transposition(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) pat1 = BinaryWff.new_impl(p, q) pat2 = BinaryWff.new_impl(NegWff(q), NegWff(p)) super().__init__(pat1, pat2) def short_name(self): return 'TRANS' class MaterialImplication(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) pat1 = BinaryWff.new_impl(p, q) pat2 = BinaryWff.new_disj(NegWff(p), q) super().__init__(pat1, pat2) def short_name(self): return 'IMPL' class MaterialEquivalenceConj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) pat1 = BinaryWff.new_equiv(p, q) pat2 = BinaryWff.new_conj( BinaryWff.new_impl(p, q), BinaryWff.new_impl(q, p) ) super().__init__(pat1, pat2) def short_name(self): return 'EQUIVc' class MaterialEquivalenceDisj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) pat1 = BinaryWff.new_equiv(p, q) pat2 = BinaryWff.new_disj( BinaryWff.new_conj(p, q), BinaryWff.new_conj(NegWff(q), NegWff(q)) ) super().__init__(pat1, pat2) def short_name(self): return 'EQUIVd' class Exportation(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) q = PropVarWff(PropositionalVariable('q')) r = PropVarWff(PropositionalVariable('r')) pat1 = BinaryWff.new_impl( BinaryWff.new_conj(p, q), r) pat2 = BinaryWff.new_impl( p, BinaryWff.new_impl(q, r)) super().__init__(pat1, pat2) def short_name(self): return 'EXP' class TautologyConj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) pat1 = p pat2 = BinaryWff.new_conj(p, p) super().__init__(pat1, pat2) def short_name(self): return 'TAUTc' class TautologyDisj(Replacement): def __init__(self): p = PropVarWff(PropositionalVariable('p')) pat1 = p pat2 = BinaryWff.new_conj(p, p) super().__init__(pat1, pat2) def short_name(self): return 'TAUTd'

27.685567

79

0.592255

1,107

10,742

5.474255

0.088528

0.124752

0.055446

0.073927

0.828383

0.802805

0.761221

0.756601

0.733993

0.696205

0

0.013466

0.287935

10,742

387

80

27.757106

0.778795

0.004282

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false

0.016779

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0.375839

0

null

0

1

0

null

0

6

767c80a0b89e81a36d63833781ae88999338bf2b

19

py

Python

python/testData/resolve/multiFile/resolveInPkg/pkg/submodule.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/resolve/multiFile/resolveInPkg/pkg/submodule.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/resolve/multiFile/resolveInPkg/pkg/submodule.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def token(): pass

9.5

12

0.631579

3

19

4

1

0

0.210526

19

2

13

9.5

0.8

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

767c957bb26309d3466ca37f3047270b07f8f60f

35

py

Python

reqlog/api/requests/view/__init__.py

JFF-Bohdan/reqlog

a7ba7b6e12609d736b3cd8cd8bc2913d511848ee

[ "MIT" ]

null

reqlog/api/requests/view/__init__.py

JFF-Bohdan/reqlog

a7ba7b6e12609d736b3cd8cd8bc2913d511848ee

[ "MIT" ]

null

reqlog/api/requests/view/__init__.py

JFF-Bohdan/reqlog

a7ba7b6e12609d736b3cd8cd8bc2913d511848ee

[ "MIT" ]

null

from .api_view_all import * # noqa

35

0.742857

6

35

4

1

0

0.171429

35

1

35

0.827586

0.114286

0

1

0

true

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0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

76bcfa0ec30e0c395ca989eb1be41b2aa4dff255

3,312

py

Python

RNNs.py

ishine/RPN_KWS

b54d4010a701a6ec0a9ddf3ab6177a4be6dd6af5

[ "MIT" ]

53

2019-08-13T08:05:26.000Z

2022-02-27T15:44:59.000Z

RNNs.py

ishine/RPN_KWS

b54d4010a701a6ec0a9ddf3ab6177a4be6dd6af5

[ "MIT" ]

3

2019-10-31T09:25:38.000Z

2021-04-16T06:26:39.000Z

RNNs.py

ishine/RPN_KWS

b54d4010a701a6ec0a9ddf3ab6177a4be6dd6af5

[ "MIT" ]

19

2019-08-14T03:47:58.000Z

2022-02-14T08:49:38.000Z

#!/usr/bin/env python # Copyrigh 2018 houjingyong@gmail.com # MIT Licence import numpy as np import sys import torch import torch.nn as nn from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence from basic_nodes import * class GRU(nn.Module): def __init__(self, input_size, output_size, hidden_size, num_layers, bias=True, batch_first=True, dropout=0.0001, bidirectional=False, output_layer=False, init_weight=True): super(GRU, self).__init__() self.hidden_size = hidden_size self.num_layers = num_layers self.rnn = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bias=bias, batch_first=batch_first, dropout=dropout, bidirectional = bidirectional) if init_weight: self.init_weights() if output_layer: self.linear = LinearBlock(hidden_size, output_size, 'relu') else: self.linear = None def init_weights(self): for name, param in self.named_parameters(): #print("name: %s\n"%name) #print(param.shape) if 'weight_ih' in name: torch.nn.init.xavier_uniform_(param.data) elif 'weight_hh' in name: torch.nn.init.orthogonal_(param.data) elif 'bias' in name: param.data.fill_(0) def forward(self, x, length): batch_size = x.shape[0] total_length = x.shape[1] h0 = torch.randn(self.num_layers, batch_size, self.hidden_size).type_as(x) x = pack_padded_sequence(x, length, batch_first=True) output, hn = self.rnn(x, h0) output, _ = pad_packed_sequence(output, batch_first=True, total_length=total_length) if self.linear == None: return output output = self.linear(output) return output class LSTM(nn.Module): def __init__(self, input_size, output_size, hidden_size, num_layers, bias=True, batch_first=True, dropout=0.0001, bidirectional=False, output_layer=False, init_weight=False): super(LSTM, self).__init__() self.hidden_size = hidden_size self.num_layers = num_layers self.rnn = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bias=bias, batch_first=batch_first, dropout=dropout, bidirectional = bidirectional) if init_weight: print("Xavier init") for i in range(num_layers): nn.init.xavier_uniform_(self.rnn.all_weights[i][0]) nn.init.xavier_uniform_(self.rnn.all_weights[i][1]) if output_layer: self.linear = LinearBlock(hidden_size, output_size, 'relu') else: self.linear = None def forward(self, x, h0, length): batch_size = x.shape[0] total_length = x.shape[1] h0 = torch.randn(self.num_layers, batch_size, self.hidden_dim).type_as(x) x = pack_padded_sequence(x, length, batch_first=True) output, hn = self.rnn(x, h0) output, _ = pad_packed_sequence(output, batch_first=True, total_length=total_length) if self.linear == None: return output output = self.linear(output) return output

38.964706

178

0.632246

437

3,312

4.533181

0.210526

0.065623

0.056537

0.034326

0.750126

0.732963

0.695608

0

0.010744

0.269324

3,312

84

179

39.428571

0.807851

0.033213

0

0.537313

0

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0

null

0

0.089552

null

0.014925

0

null

0

1

0

null

0

1

0

6

4f12370126b2ab7c00276ce1e1cc4e5de67cd411

26

py

Python

undict/__init__.py

firstprayer/undict

94edc66c8243ee35746d830b80e8bd4a9f046f20

[ "MIT" ]

null

undict/__init__.py

firstprayer/undict

94edc66c8243ee35746d830b80e8bd4a9f046f20

[ "MIT" ]

null

undict/__init__.py

firstprayer/undict

94edc66c8243ee35746d830b80e8bd4a9f046f20

[ "MIT" ]

null

from undict import undict

13

25

0.846154

4

26

5.5

0.75

0

0.153846

26

1

26

1

0

1

0

true

0

1

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4f2646ef476417fa46e6a5536e7103e237123ff4

25,643

py

Python

projects/08/code_writer.py

Youngermaster/Nand2Tetris-Solutions

9fb4ac31a0558bcc2324696bfb451aac11232088

[ "MIT" ]

null

projects/08/code_writer.py

Youngermaster/Nand2Tetris-Solutions

9fb4ac31a0558bcc2324696bfb451aac11232088

[ "MIT" ]

null

projects/08/code_writer.py

Youngermaster/Nand2Tetris-Solutions

9fb4ac31a0558bcc2324696bfb451aac11232088

[ "MIT" ]

null

import os from parser import Parser class CodeWriter: def __init__(self, filepath, isfile=True): self.parser = Parser(filepath) self.isfile = isfile # * Performs the logic of the recommended setFileName constructor here if self.isfile: ind1 = path.find('/') ind2 = path.find('.') self.writefile = path[:ind1] + "/" + path[ind1+1:ind2] self.filename = self.writefile + '.asm' self.file = open(self.filename, 'w') self.writefile_ind = self.writefile.rfind('/') # useful in declaring static variables self.static_var = self.writefile[self.writefile_ind + 1:] self.function_list = [] else: inds = [i for i, x in enumerate(filepath) if x == '/'] self.writefolder = path[inds[-2]+1:inds[-1]] self.filename = self.writefolder + '.asm' writefile_ind = filepath.rfind('/') filepath_ = filepath[:writefile_ind] self.file = open(filepath_ + '/' + self.filename, 'w') self.static_var_dict = {} # useful in declaring static variables self.function_list = [] def writePushPop(self): # no need to pass in command as an argument assert self.parser.commandType() in ['C_PUSH', 'C_POP'] arg1 = self.parser.arg1() arg2 = self.parser.arg2() if self.parser.commandType() == 'C_PUSH': self.file.write('// push %s %s\n' % (arg1, arg2)) # stack operation if arg1 == 'constant': # e.g. push constant 7 self.file.write('@%s\n' % arg2) self.file.write('D=A\n') # D = 7 self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') # M[M[base_address]] = 7 elif arg1 in ['temp', 'pointer', 'local', 'argument', 'this', 'that']: self.file.write('@%s\n' % arg2) self.file.write('D=A\n') if arg1 == 'temp': self.file.write('@5\n') self.file.write('A=D+A\n') elif arg1 == 'pointer': self.file.write('@3\n') self.file.write('A=D+A\n') elif arg1 == 'local': self.file.write('@LCL\n') self.file.write('A=D+M\n') elif arg1 == 'argument': self.file.write('@ARG\n') self.file.write('A=D+M\n') elif arg1 == 'this': self.file.write('@THIS\n') self.file.write('A=D+M\n') elif arg1 == 'that': self.file.write('@THAT\n') self.file.write('A=D+M\n') else: pass self.file.write('D=M\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') elif arg1 == 'static': # declare a new symbol file.j in "push static j" if self.isfile: self.file.write('@%s.%s\n' % (self.static_var, arg2)) else: self.file.write('@%s.%s\n' % (self.static_var_dict[self.parser.i], arg2)) self.file.write('D=M\n') # push D's value to the stack self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') else: # TODO pass # increase address of stack top self.file.write('@SP\n') self.file.write('M=M+1\n') # M[base_address] = M[base_address] + 1 elif self.parser.commandType() == 'C_POP': # pop the stack value and store it in segment[index] # use general purpose RAM[13] to store the value of 'segment_base_address + index' self.file.write('// pop %s %s\n' % (arg1, arg2)) self.file.write('@%s\n' % arg2) self.file.write('D=A\n') if arg1 in ['temp', 'pointer', 'local', 'argument', 'this', 'that']: if arg1 == 'local': self.file.write('@LCL\n') self.file.write('D=D+M\n') elif arg1 == 'argument': self.file.write('@ARG\n') self.file.write('D=D+M\n') elif arg1 == 'this': self.file.write('@THIS\n') self.file.write('D=D+M\n') elif arg1 == 'that': self.file.write('@THAT\n') self.file.write('D=D+M\n') elif arg1 == 'temp': self.file.write('@5\n') self.file.write('D=D+A\n') elif arg1 == 'pointer': self.file.write('@3\n') self.file.write('D=D+A\n') else: # TODO pass # self.file.write('D=D+M\n') self.file.write('@13\n') # general purpose register self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') # pop command self.file.write('@13\n') self.file.write('A=M\n') self.file.write('M=D\n') # write to appropriate address self.file.write('@SP\n') self.file.write('M=M-1\n') # adjust address of stack top elif arg1 == 'static': self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') # pop command if self.isfile: self.file.write('@%s.%s\n' % (self.static_var, arg2)) else: self.file.write('@%s.%s\n' % (self.static_var_dict[self.parser.i], arg2)) self.file.write('M=D\n') # write to appropriate address self.file.write('@SP\n') self.file.write('M=M-1\n') # adjust address of stack top else: # TODO pass def writeArithmetic(self): # no need to pass in command as an argument assert self.parser.commandType() == 'C_ARITHMETIC' command = self.parser.arg1() self.file.write('// %s\n' % command) if command == 'add': # stack operation self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') self.file.write('A=A-1\n') self.file.write('D=D+M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M-1\n') elif command == 'sub': # stack operation self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') self.file.write('A=A-1\n') self.file.write('D=M-D\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M-1\n') elif command == 'eq': # stack operation self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') self.file.write('A=A-1\n') self.file.write('D=M-D\n') # there could be more than one 'eq' command self.file.write('@IF_TRUE_%s\n' % self.parser.i) self.file.write('D;JEQ\n') self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('A=A-1\n') self.file.write('M=0\n') # there could be more than one 'eq' command self.file.write('@END_%s\n' % self.parser.i) self.file.write('0;JMP\n') self.file.write('(IF_TRUE_%s)\n' % self.parser.i) self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('A=A-1\n') self.file.write('M=-1\n') self.file.write('(END_%s)\n' % self.parser.i) self.file.write('@SP\n') self.file.write('M=M-1\n') elif command == 'gt': # stack operation self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') self.file.write('A=A-1\n') self.file.write('D=M-D\n') # there could be more than one 'gt' command self.file.write('@IF_TRUE_%s\n' % self.parser.i) self.file.write('D;JGT\n') self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('A=A-1\n') self.file.write('M=0\n') # there could be more than one 'gt' command self.file.write('@END_%s\n' % self.parser.i) self.file.write('0;JMP\n') self.file.write('(IF_TRUE_%s)\n' % self.parser.i) self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('A=A-1\n') self.file.write('M=-1\n') self.file.write('(END_%s)\n' % self.parser.i) self.file.write('@SP\n') self.file.write('M=M-1\n') elif command == 'lt': # stack operation self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') self.file.write('A=A-1\n') self.file.write('D=M-D\n') # there could be more than one 'lt' command self.file.write('@IF_TRUE_%s\n' % self.parser.i) self.file.write('D;JLT\n') self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('A=A-1\n') self.file.write('M=0\n') # there could be more than one 'lt' command self.file.write('@END_%s\n' % self.parser.i) self.file.write('0;JMP\n') self.file.write('(IF_TRUE_%s)\n' % self.parser.i) self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('A=A-1\n') self.file.write('M=-1\n') self.file.write('(END_%s)\n' % self.parser.i) self.file.write('@SP\n') self.file.write('M=M-1\n') elif command == 'and': # stack operation self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') self.file.write('A=A-1\n') self.file.write('M=D&M\n') self.file.write('@SP\n') self.file.write('M=M-1\n') elif command == 'or': # stack operation self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') self.file.write('A=A-1\n') self.file.write('M=D|M\n') self.file.write('@SP\n') self.file.write('M=M-1\n') elif command == 'neg': # stack operation self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('M=-M\n') elif command == 'not': # stack operation self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('M=!M\n') else: raise ValueError( "Unrecognized command for C_ARITHMETIC command type") def writeInit(self): self.file.write('// init\n') # initially set the SP address to 256 (the address for the stack) self.file.write('@256\n') self.file.write('D=A\n') self.file.write('@SP\n') self.file.write('M=D\n') # set the local address to 300 self.file.write('@300\n') self.file.write('D=A\n') self.file.write('@LCL\n') self.file.write('M=D\n') # set the argument address to 400 self.file.write('@400\n') self.file.write('D=A\n') self.file.write('@ARG\n') self.file.write('M=D\n') # set the this address to 3000 self.file.write('@3000\n') self.file.write('D=A\n') self.file.write('@THIS\n') self.file.write('M=D\n') # set the that address to 3010 self.file.write('@3010\n') self.file.write('D=A\n') self.file.write('@THAT\n') self.file.write('M=D\n') def writeLabel(self): self.file.write('// label\n') # check if label was declared within function; if so, label should carry function name try: func_name = self.function_list[-1] + "$" except: func_name = '' label_name_input = self.parser.arg1() label_name = func_name + label_name_input self.file.write('(%s)\n' % label_name) def writeGoto(self): self.file.write('// goto\n') # check if goto was declared within function; if so, label should carry function name try: func_name = self.function_list[-1] + "$" except: func_name = '' label_name_input = self.parser.arg1() label_name = func_name + label_name_input self.file.write('@%s\n' % label_name) self.file.write('0;JMP\n') def writeIf(self): self.file.write('// if-goto\n') # check if 'if-goto' was declared within function; if so, label should carry function name try: func_name = self.function_list[-1] + "$" except: func_name = '' label_name_input = self.parser.arg1() label_name = func_name + label_name_input self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') self.file.write('@SP\n') # adjust stack top self.file.write('M=M-1\n') self.file.write('@%s\n' % label_name) self.file.write('D;JNE\n') def writeFunction(self): func_name = self.parser.arg1() self.function_list.append(func_name) num_locals = self.parser.arg2() self.file.write('// function %s %s\n' % (func_name, num_locals)) self.file.write('(%s)\n' % func_name) self.file.write('@%s\n' % num_locals) self.file.write('D=A\n') self.file.write('@13\n') self.file.write('M=D\n') self.file.write('(LOOP_%s)\n' % func_name) self.file.write('@13\n') self.file.write('D=M\n') self.file.write('@END_%s\n' % func_name) self.file.write('D;JEQ\n') # start logic for code to carry out while D != 0 self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=0\n') # M[M[base_address]] = 7 self.file.write('@SP\n') self.file.write('M=M+1\n') # M[base_address] = M[base_address] + 1 self.file.write('@13\n') self.file.write('M=M-1\n') # end logic for code to carry out while D != 0 self.file.write('@LOOP_%s\n' % func_name) self.file.write('0;JMP\n') self.file.write('(END_%s)\n' % func_name) def writeReturn(self): self.file.write('// return\n') # func_name = self.function_list.pop() # FRAME = LCL : store FRAME in a temp variable self.file.write('@LCL\n') self.file.write('D=M\n') self.file.write('@13\n') # address of the temp variable FRAME self.file.write('M=D\n') # RET = *(FRAME - 5) : store return address in another temp variable self.file.write('@13\n') self.file.write('D=M\n') self.file.write('@5\n') self.file.write('D=D-A\n') self.file.write('A=D\n') self.file.write('D=M\n') # D now equals *(FRAME - 5) self.file.write('@14\n') # address of the temp variable RET self.file.write('M=D\n') # *ARG = pop() self.file.write('@SP\n') self.file.write('A=M-1\n') self.file.write('D=M\n') self.file.write('@ARG\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M-1\n') ## SP = ARG + 1 self.file.write('@ARG\n') self.file.write('D=M+1\n') self.file.write('@SP\n') self.file.write('M=D\n') # THAT = *(FRAME - 1) self.file.write('@13\n') self.file.write('A=M-1\n') self.file.write('D=M\n') self.file.write('@THAT\n') self.file.write('M=D\n') # THIS = *(FRAME - 2) self.file.write('@13\n') self.file.write('D=M\n') self.file.write('@2\n') self.file.write('A=D-A\n') self.file.write('D=M\n') self.file.write('@THIS\n') self.file.write('M=D\n') # ARG = *(FRAME - 3) self.file.write('@13\n') self.file.write('D=M\n') self.file.write('@3\n') self.file.write('A=D-A\n') self.file.write('D=M\n') self.file.write('@ARG\n') self.file.write('M=D\n') # LCL = *(FRAME - 4) self.file.write('@13\n') self.file.write('D=M\n') self.file.write('@4\n') self.file.write('A=D-A\n') self.file.write('D=M\n') self.file.write('@LCL\n') self.file.write('M=D\n') # goto RET self.file.write('@14\n') # address of RET self.file.write('A=M\n') # address = RET self.file.write('0;JMP\n') def writeCall(self): func_name = self.parser.arg1() num_args = self.parser.arg2() self.file.write('// call %s %s\n' % (func_name, num_args)) # push return-address (using label declared below) self.file.write('// call : push return-address\n') # there could be more than one return_addresses in the entire code s = 'RETURN_ADDRESS_' + str(self.parser.i) self.file.write('@%s\n' % s) self.file.write('D=A\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M+1\n') # push LCL self.file.write('// call : push LCL\n') self.file.write('@LCL\n') self.file.write('D=M\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M+1\n') # push ARG self.file.write('// call : push ARG\n') self.file.write('@ARG\n') self.file.write('D=M\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M+1\n') # push THIS self.file.write('// call : push THIS\n') self.file.write('@THIS\n') self.file.write('D=M\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M+1\n') # push THAT self.file.write('// call : push THAT\n') self.file.write('@THAT\n') self.file.write('D=M\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M+1\n') # ARG = SP - n - 5 self.file.write('// call : ARG = SP - n - 5\n') self.file.write('@SP\n') self.file.write('D=M\n') self.file.write('@%s\n' % num_args) self.file.write('D=D-A\n') self.file.write('@5\n') self.file.write('D=D-A\n') self.file.write('@ARG\n') self.file.write('M=D\n') # LCL = SP self.file.write('// call : LCL = SP\n') self.file.write('@SP\n') self.file.write('D=M\n') self.file.write('@LCL\n') self.file.write('M=D\n') # goto f self.file.write('// call : goto f\n') self.file.write('@%s\n' % func_name) self.file.write('0;JMP\n') # declare a label for the return-address self.file.write('// call : declare label for return-address\n') self.file.write('(%s)\n' % s) def writeBootstrap(self): self.file.write('// boostrap\n') ## SP = 256 self.file.write('@256\n') self.file.write('D=A\n') self.file.write('@SP\n') self.file.write('M=D\n') # call Sys.init : call Sys.init 0 # push return-address sys_init_ret_add = 'return-address-sysinit' self.file.write('@%s\n' % sys_init_ret_add) self.file.write('D=A\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M+1\n') # push LCL self.file.write('@LCL\n') self.file.write('D=M\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M+1\n') # push ARG self.file.write('@ARG\n') self.file.write('D=M\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M+1\n') # push THIS self.file.write('@THIS\n') self.file.write('D=M\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M+1\n') # push THAT self.file.write('@THAT\n') self.file.write('D=M\n') self.file.write('@SP\n') self.file.write('A=M\n') self.file.write('M=D\n') self.file.write('@SP\n') self.file.write('M=M+1\n') # ARG = SP - n - 5 self.file.write('@SP\n') self.file.write('D=M\n') self.file.write('@5\n') self.file.write('D=D-A\n') self.file.write('@ARG\n') self.file.write('M=D\n') # LCL = SP self.file.write('@SP\n') self.file.write('D=M\n') self.file.write('@LCL\n') self.file.write('M=D\n') # goto f func_name = 'Sys.init' self.file.write('@%s\n' % func_name) self.file.write('0;JMP\n') # declare a label for the return-address self.file.write('(%s)\n' % sys_init_ret_add) def createOutput(self): if not self.isfile: self.writeBootstrap() else: pass # self.writeBootstrap() self.parser.i = -1 while self.parser.hasMoreCommands(): self.parser.advance() c_type = self.parser.commandType() if c_type in ['C_PUSH', 'C_POP']: self.writePushPop() elif c_type == 'C_ARITHMETIC': self.writeArithmetic() elif c_type == 'C_FUNCTION': self.writeFunction() elif c_type == 'C_LABEL': self.writeLabel() elif c_type == 'C_GOTO': self.writeGoto() elif c_type == 'C_IF': self.writeIf() elif c_type == 'C_RETURN': self.writeReturn() elif c_type == 'C_CALL': self.writeCall() # close file self.file.close() if __name__ == "__main__": for path in ["ProgramFlow/BasicLoop/BasicLoop.vm", "ProgramFlow/FibonacciSeries/FibonacciSeries.vm", "FunctionCalls/SimpleFunction/SimpleFunction.vm", "FunctionCalls/FibonacciElement", "FunctionCalls/StaticsTest"]: # handle the case where input path is a folder if os.path.isdir(path): files = [file_ for file_ in os.listdir( path) if file_.endswith(".vm")] d_file_codewriter = {} for f in files: f_input = path + '/%s' % f d_file_codewriter[f] = CodeWriter(f_input, False) codewriter = d_file_codewriter['Sys.vm'] tot_lines_sys = d_file_codewriter['Sys.vm'].parser.total_commands count_f = 0 for f in files: if f != 'Sys.vm': if count_f == 0: codewriter.static_var_dict = {i: f for i in range( d_file_codewriter[f].parser.total_commands)} prev_counts = d_file_codewriter[f].parser.total_commands else: new_dict = { i + prev_counts: f for i in range(d_file_codewriter[f].parser.total_commands)} codewriter.static_var_dict.update(new_dict) prev_counts += d_file_codewriter[f].parser.total_commands codewriter.parser.clean_lines = codewriter.parser.clean_lines + \ d_file_codewriter[f].parser.clean_lines codewriter.parser.total_commands = len( codewriter.parser.clean_lines) count_f += 1 # post-processing of clean_lines codewriter.parser.clean_lines = codewriter.parser.clean_lines[ tot_lines_sys:] + codewriter.parser.clean_lines[:tot_lines_sys] # handle the case where input path is a file elif os.path.isfile(path): codewriter = CodeWriter(path) codewriter.createOutput()

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0.626644

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0.337363

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1

0

1

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null

0

6

4f5d1809808d697e40d6c3b20491f75cf98a311a

214

py

Python

services/__init__.py

dev-11/mars-rover-challenge

67569fcc4b93e5ec4cbe466d7a2fd5b3e9a316b0

[ "MIT" ]

null

services/__init__.py

dev-11/mars-rover-challenge

67569fcc4b93e5ec4cbe466d7a2fd5b3e9a316b0

[ "MIT" ]

null

services/__init__.py

dev-11/mars-rover-challenge

67569fcc4b93e5ec4cbe466d7a2fd5b3e9a316b0

[ "MIT" ]

null

from .rover_runner_service import RoverRunnerService from .move_commands import get_move_commands, MoveCommandSelector from .turn_commands import get_turn_commands, TurnCommandSelector from .command import Command

42.8

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6

96e11fbf2414390c277f0a9f47b09043ce2bb218

35

py

Python

linux_plex_updater/api/__init__.py

amickael/Linux-Plex-Updater

a74dc480d374daf52ee4cc09b40ea34b9e6ffcd4

[ "MIT" ]

null

linux_plex_updater/api/__init__.py

amickael/Linux-Plex-Updater

a74dc480d374daf52ee4cc09b40ea34b9e6ffcd4

[ "MIT" ]

null

linux_plex_updater/api/__init__.py

amickael/Linux-Plex-Updater

a74dc480d374daf52ee4cc09b40ea34b9e6ffcd4

[ "MIT" ]

null

from .PlexClient import PlexClient

17.5

34

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35

7.5

0.75

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35

1

35

0.967742

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6

8c04cad192ddc2276a9700252dfd9cb17cf0b09e

135

py

Python

tests/__init__.py

g-simmons2/py2cytoscape

e2cd1c5d598e1da02f50273e958ddf574c523eb8

[ "MIT" ]

97

2018-01-23T00:20:51.000Z

2022-03-11T05:01:01.000Z

tests/__init__.py

g-simmons2/py2cytoscape

e2cd1c5d598e1da02f50273e958ddf574c523eb8

[ "MIT" ]

64

2018-01-24T14:51:20.000Z

2022-02-21T01:05:02.000Z

tests/__init__.py

g-simmons2/py2cytoscape

e2cd1c5d598e1da02f50273e958ddf574c523eb8

[ "MIT" ]

25

2018-01-20T20:29:39.000Z

2021-04-09T17:28:58.000Z

# -*- coding: utf-8 -*- """ Tests for py2cytoscape ------------------- """ import json print('============ Test Init =============')

13.5

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0.017241

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135

9

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null

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6

8c2179e387711c77d6977a6fa547da1822d10e82

198

py

Python

function/python/brightics/common/json/__init__.py

nohkwangsun/studio

b2dd7da1d73d83bef6c046d73fb85639d3006fc2

[ "Apache-2.0" ]

null

function/python/brightics/common/json/__init__.py

nohkwangsun/studio

b2dd7da1d73d83bef6c046d73fb85639d3006fc2

[ "Apache-2.0" ]

null

function/python/brightics/common/json/__init__.py

nohkwangsun/studio

b2dd7da1d73d83bef6c046d73fb85639d3006fc2

[ "Apache-2.0" ]

1

2020-11-19T06:44:15.000Z

def to_json(data, for_redis=False): from .encoder import encode return encode(data, for_redis) def from_json(json_str): from .decoder import decode return decode(json_str)

22

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0.222222

198

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null

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0

1

0

1

0

6

8c3f7564cb15fff935229ac0ac2f5e217d40d599

126

py

Python

mlpipe/data_reader/mongodb/__init__.py

j-o-d-o/MLPipe-Trainer

b686dc4d28e3d4cd2c6581487f8a2491a6d7cb60

[ "MIT" ]

null

mlpipe/data_reader/mongodb/__init__.py

j-o-d-o/MLPipe-Trainer

b686dc4d28e3d4cd2c6581487f8a2491a6d7cb60

[ "MIT" ]

null

mlpipe/data_reader/mongodb/__init__.py

j-o-d-o/MLPipe-Trainer

b686dc4d28e3d4cd2c6581487f8a2491a6d7cb60

[ "MIT" ]

null

from .mongodb_connect import MongoDBConnect from .mongodb_generator import MongoDBGenerator from .data_loader import load_ids

31.5

47

0.880952

16

126

6.6875

0.6875

0.205607

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0.095238

126

3

48

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6

8c5cb547cfc5e18d429cee44a2b29b3240bf6d91

195

py

Python

NewPee/tests.py

CMPUT404W19T3/NewPee

ba34341e0407746c12aec72689e50fbc2054ae77

[ "MIT" ]

2

2019-02-19T17:11:58.000Z

2019-02-19T17:19:28.000Z

NewPee/tests.py

CMPUT404W19T3/NewPee

ba34341e0407746c12aec72689e50fbc2054ae77

[ "MIT" ]

74

2019-02-01T17:15:02.000Z

2022-03-08T21:09:44.000Z

NewPee/tests.py

CMPUT404W19T3/NewPee

ba34341e0407746c12aec72689e50fbc2054ae77

[ "MIT" ]

1

2019-03-15T16:09:51.000Z

from django.test import TestCase from Tests.test_author import AuthorModelTests from Tests.test_frontend import FrontEndTests from Tests.test_post import PostModelTests # Create your tests here.

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py

Python

vnet_manager/settings/test.py

ppartarr/vnet-manager

e7e8dfc9014c98f34bce639f48e0baa603d83b67

[ "MIT" ]

null

vnet_manager/settings/test.py

ppartarr/vnet-manager

e7e8dfc9014c98f34bce639f48e0baa603d83b67

[ "MIT" ]

null

vnet_manager/settings/test.py

ppartarr/vnet-manager

e7e8dfc9014c98f34bce639f48e0baa603d83b67

[ "MIT" ]

null

from .base import * # /dev/log doesn't exist everywhere del LOGGING["handlers"]["syslog"]["address"] # Fixture config CONFIG = { "providers": { "lxc": { "supported_operating_systems": ["bionic", "focal"], "dns-nameserver": "1.1.1.1", "required_host_packages": ["lxd", "lxc", "bridge-utils", "tcpdump", "net-tools", "curl"], "guest_packages": ["man", "net-tools", "traceroute", "nano", "vim", "bridge-utils", "radvd", "frr", "frr-pythontools"], "base_image": {"os": "18.04", "server": "https://cloud-images.ubuntu.com/daily", "protocol": "simplestreams"}, } }, "switches": 2, "machines": { "router100": { "type": "router", "interfaces": {"eth12": {"ipv4": "192.168.0.2/24", "ipv6": "fd00:12::2/64", "mac": "00:00:00:00:01:11", "bridge": 0}}, "vlans": {"vlan.100": {"id": 100, "link": "eth12", "addresses": ["10.0.100.1/24"]},}, "files": {"router100": "/etc/frr/"}, }, "router101": { "type": "router", "interfaces": { "eth12": {"ipv4": "192.168.0.1/24", "ipv6": "fd00:12::1/64", "mac": "00:00:00:00:02:12", "bridge": 0}, "eth23": {"ipv4": "10.0.0.1/8", "ipv6": "fd00:23::1/64", "mac": "00:00:00:00:02:22", "bridge": 1}, }, "files": {"router101": "/etc/frr/"}, }, "host102": { "type": "host", "interfaces": {"eth23": {"ipv4": "10.0.0.2/8", "ipv6": "fd00:23::2/64", "mac": "00:00:00:00:03:23", "bridge": 1}}, "files": {"host102": "/etc/frr/"}, }, }, "veths": {"vnet-veth1": {"bridge": "vnet-br1", "stp": True}, "vnet-veth0": {"peer": "vnet-veth1", "bridge": "vnet-br0", "stp": False},}, } VALIDATED_CONFIG = { "providers": { "lxc": { "supported_operating_systems": ["bionic", "focal"], "dns-nameserver": "8.8.8.8", "required_host_packages": ["lxd", "lxc", "bridge-utils", "tcpdump", "net-tools", "curl"], "guest_packages": ["man", "net-tools", "traceroute", "nano", "vim", "bridge-utils", "radvd", "frr", "frr-pythontools"], "base_image": {"os": "18.04", "server": "https://cloud-images.ubuntu.com/daily", "protocol": "simplestreams"}, } }, "switches": 2, "machines": { "router100": { "type": "router", "interfaces": {"eth12": {"ipv4": "192.168.0.2/24", "ipv6": "fd00:12::2/64", "mac": "00:00:00:00:01:11", "bridge": 0}}, "files": {"/root/vnet-manager/config/ripng/router100": "/etc/frr/"}, }, "router101": { "type": "router", "interfaces": { "eth12": {"ipv4": "192.168.0.1/24", "ipv6": "fd00:12::1/64", "mac": "00:00:00:00:02:12", "bridge": 0}, "eth23": {"ipv4": "10.0.0.1/8", "ipv6": "fd00:23::1/64", "mac": "00:00:00:00:02:22", "bridge": 1}, }, "files": {"/root/vnet-manager/config/ripng/router101": "/etc/frr/"}, }, "host102": { "type": "host", "interfaces": {"eth23": {"ipv4": "10.0.0.2/8", "ipv6": "fd00:23::2/64", "mac": "00:00:00:00:03:23", "bridge": 1}}, }, }, "veths": { "vnet-veth3": {"bridge": "vnet-br2", "stp": True}, "vnet-veth2": {"peer": "vnet-veth3", "bridge": "vnet-br0"}, "vnet-veth1": {"bridge": "vnet-br1", "stp": True}, "vnet-veth0": {"peer": "vnet-veth1", "bridge": "vnet-br0", "stp": True}, "vnet-veth5": {"bridge": "vnet-br2"}, "vnet-veth4": {"peer": "vnet-veth5", "bridge": "vnet-br1"}, }, "config_dir": "/root/vnet-manager/config/ripng", } # Speed up testing LXC_MAX_STATUS_WAIT_ATTEMPTS = 2

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8b0602dde28463d89f06e3aeec4ac0d327387ee9

16,775

py

Python

huaweicloud-sdk-elb/huaweicloudsdkelb/v2/__init__.py

wuchen-huawei/huaweicloud-sdk-python-v3

3683d703f4320edb2b8516f36f16d485cff08fc2

[ "Apache-2.0" ]

64

2020-06-12T07:05:07.000Z

2022-03-30T03:32:50.000Z

huaweicloud-sdk-elb/huaweicloudsdkelb/v2/__init__.py

wuchen-huawei/huaweicloud-sdk-python-v3

3683d703f4320edb2b8516f36f16d485cff08fc2

[ "Apache-2.0" ]

11

2020-07-06T07:56:54.000Z

2022-01-11T11:14:40.000Z

huaweicloud-sdk-elb/huaweicloudsdkelb/v2/__init__.py

wuchen-huawei/huaweicloud-sdk-python-v3

3683d703f4320edb2b8516f36f16d485cff08fc2

[ "Apache-2.0" ]

24

2020-06-08T11:42:13.000Z

2022-03-04T06:44:08.000Z

# coding: utf-8 from __future__ import absolute_import # import ElbClient from huaweicloudsdkelb.v2.elb_client import ElbClient from huaweicloudsdkelb.v2.elb_async_client import ElbAsyncClient # import models into sdk package from huaweicloudsdkelb.v2.model.action_match import ActionMatch from huaweicloudsdkelb.v2.model.action_tag import ActionTag from huaweicloudsdkelb.v2.model.batch_create_listener_tags_request import BatchCreateListenerTagsRequest from huaweicloudsdkelb.v2.model.batch_create_listener_tags_request_body import BatchCreateListenerTagsRequestBody from huaweicloudsdkelb.v2.model.batch_create_listener_tags_response import BatchCreateListenerTagsResponse from huaweicloudsdkelb.v2.model.batch_create_loadbalancer_tags_request import BatchCreateLoadbalancerTagsRequest from huaweicloudsdkelb.v2.model.batch_create_loadbalancer_tags_request_body import BatchCreateLoadbalancerTagsRequestBody from huaweicloudsdkelb.v2.model.batch_create_loadbalancer_tags_response import BatchCreateLoadbalancerTagsResponse from huaweicloudsdkelb.v2.model.batch_delete_listener_tags_request import BatchDeleteListenerTagsRequest from huaweicloudsdkelb.v2.model.batch_delete_listener_tags_request_body import BatchDeleteListenerTagsRequestBody from huaweicloudsdkelb.v2.model.batch_delete_listener_tags_response import BatchDeleteListenerTagsResponse from huaweicloudsdkelb.v2.model.batch_delete_loadbalancer_tags_request import BatchDeleteLoadbalancerTagsRequest from huaweicloudsdkelb.v2.model.batch_delete_loadbalancer_tags_request_body import BatchDeleteLoadbalancerTagsRequestBody from huaweicloudsdkelb.v2.model.batch_delete_loadbalancer_tags_response import BatchDeleteLoadbalancerTagsResponse from huaweicloudsdkelb.v2.model.certificate_resp import CertificateResp from huaweicloudsdkelb.v2.model.create_certificate_request import CreateCertificateRequest from huaweicloudsdkelb.v2.model.create_certificate_request_body import CreateCertificateRequestBody from huaweicloudsdkelb.v2.model.create_certificate_response import CreateCertificateResponse from huaweicloudsdkelb.v2.model.create_healthmonitor_req import CreateHealthmonitorReq from huaweicloudsdkelb.v2.model.create_healthmonitor_request import CreateHealthmonitorRequest from huaweicloudsdkelb.v2.model.create_healthmonitor_request_body import CreateHealthmonitorRequestBody from huaweicloudsdkelb.v2.model.create_healthmonitor_response import CreateHealthmonitorResponse from huaweicloudsdkelb.v2.model.create_l7policy_req import CreateL7policyReq from huaweicloudsdkelb.v2.model.create_l7policy_request import CreateL7policyRequest from huaweicloudsdkelb.v2.model.create_l7policy_request_body import CreateL7policyRequestBody from huaweicloudsdkelb.v2.model.create_l7policy_response import CreateL7policyResponse from huaweicloudsdkelb.v2.model.create_l7rule_req import CreateL7ruleReq from huaweicloudsdkelb.v2.model.create_l7rule_req_in_policy import CreateL7ruleReqInPolicy from huaweicloudsdkelb.v2.model.create_l7rule_request import CreateL7ruleRequest from huaweicloudsdkelb.v2.model.create_l7rule_request_body import CreateL7ruleRequestBody from huaweicloudsdkelb.v2.model.create_l7rule_response import CreateL7ruleResponse from huaweicloudsdkelb.v2.model.create_listener_req import CreateListenerReq from huaweicloudsdkelb.v2.model.create_listener_request import CreateListenerRequest from huaweicloudsdkelb.v2.model.create_listener_request_body import CreateListenerRequestBody from huaweicloudsdkelb.v2.model.create_listener_response import CreateListenerResponse from huaweicloudsdkelb.v2.model.create_listener_tags_request import CreateListenerTagsRequest from huaweicloudsdkelb.v2.model.create_listener_tags_request_body import CreateListenerTagsRequestBody from huaweicloudsdkelb.v2.model.create_listener_tags_response import CreateListenerTagsResponse from huaweicloudsdkelb.v2.model.create_loadbalancer_req import CreateLoadbalancerReq from huaweicloudsdkelb.v2.model.create_loadbalancer_request import CreateLoadbalancerRequest from huaweicloudsdkelb.v2.model.create_loadbalancer_request_body import CreateLoadbalancerRequestBody from huaweicloudsdkelb.v2.model.create_loadbalancer_response import CreateLoadbalancerResponse from huaweicloudsdkelb.v2.model.create_loadbalancer_tags_request import CreateLoadbalancerTagsRequest from huaweicloudsdkelb.v2.model.create_loadbalancer_tags_request_body import CreateLoadbalancerTagsRequestBody from huaweicloudsdkelb.v2.model.create_loadbalancer_tags_response import CreateLoadbalancerTagsResponse from huaweicloudsdkelb.v2.model.create_member_req import CreateMemberReq from huaweicloudsdkelb.v2.model.create_member_request import CreateMemberRequest from huaweicloudsdkelb.v2.model.create_member_request_body import CreateMemberRequestBody from huaweicloudsdkelb.v2.model.create_member_response import CreateMemberResponse from huaweicloudsdkelb.v2.model.create_pool_req import CreatePoolReq from huaweicloudsdkelb.v2.model.create_pool_request import CreatePoolRequest from huaweicloudsdkelb.v2.model.create_pool_request_body import CreatePoolRequestBody from huaweicloudsdkelb.v2.model.create_pool_response import CreatePoolResponse from huaweicloudsdkelb.v2.model.create_whitelist_req import CreateWhitelistReq from huaweicloudsdkelb.v2.model.create_whitelist_request import CreateWhitelistRequest from huaweicloudsdkelb.v2.model.create_whitelist_request_body import CreateWhitelistRequestBody from huaweicloudsdkelb.v2.model.create_whitelist_response import CreateWhitelistResponse from huaweicloudsdkelb.v2.model.delete_certificate_request import DeleteCertificateRequest from huaweicloudsdkelb.v2.model.delete_certificate_response import DeleteCertificateResponse from huaweicloudsdkelb.v2.model.delete_healthmonitor_request import DeleteHealthmonitorRequest from huaweicloudsdkelb.v2.model.delete_healthmonitor_response import DeleteHealthmonitorResponse from huaweicloudsdkelb.v2.model.delete_l7policy_request import DeleteL7policyRequest from huaweicloudsdkelb.v2.model.delete_l7policy_response import DeleteL7policyResponse from huaweicloudsdkelb.v2.model.delete_l7rule_request import DeleteL7ruleRequest from huaweicloudsdkelb.v2.model.delete_l7rule_response import DeleteL7ruleResponse from huaweicloudsdkelb.v2.model.delete_listener_request import DeleteListenerRequest from huaweicloudsdkelb.v2.model.delete_listener_response import DeleteListenerResponse from huaweicloudsdkelb.v2.model.delete_listener_tags_request import DeleteListenerTagsRequest from huaweicloudsdkelb.v2.model.delete_listener_tags_response import DeleteListenerTagsResponse from huaweicloudsdkelb.v2.model.delete_loadbalancer_request import DeleteLoadbalancerRequest from huaweicloudsdkelb.v2.model.delete_loadbalancer_response import DeleteLoadbalancerResponse from huaweicloudsdkelb.v2.model.delete_loadbalancer_tags_request import DeleteLoadbalancerTagsRequest from huaweicloudsdkelb.v2.model.delete_loadbalancer_tags_response import DeleteLoadbalancerTagsResponse from huaweicloudsdkelb.v2.model.delete_member_request import DeleteMemberRequest from huaweicloudsdkelb.v2.model.delete_member_response import DeleteMemberResponse from huaweicloudsdkelb.v2.model.delete_pool_request import DeletePoolRequest from huaweicloudsdkelb.v2.model.delete_pool_response import DeletePoolResponse from huaweicloudsdkelb.v2.model.delete_whitelist_request import DeleteWhitelistRequest from huaweicloudsdkelb.v2.model.delete_whitelist_response import DeleteWhitelistResponse from huaweicloudsdkelb.v2.model.healthmonitor_resp import HealthmonitorResp from huaweicloudsdkelb.v2.model.healthmonitors_in_status_resp import HealthmonitorsInStatusResp from huaweicloudsdkelb.v2.model.insert_header import InsertHeader from huaweicloudsdkelb.v2.model.l7policies_in_status_resp import L7policiesInStatusResp from huaweicloudsdkelb.v2.model.l7policy_resp import L7policyResp from huaweicloudsdkelb.v2.model.l7rule_resp import L7ruleResp from huaweicloudsdkelb.v2.model.l7rules_in_status_resp import L7rulesInStatusResp from huaweicloudsdkelb.v2.model.list_certificates_request import ListCertificatesRequest from huaweicloudsdkelb.v2.model.list_certificates_response import ListCertificatesResponse from huaweicloudsdkelb.v2.model.list_healthmonitors_request import ListHealthmonitorsRequest from huaweicloudsdkelb.v2.model.list_healthmonitors_response import ListHealthmonitorsResponse from huaweicloudsdkelb.v2.model.list_l7policies_request import ListL7policiesRequest from huaweicloudsdkelb.v2.model.list_l7policies_response import ListL7policiesResponse from huaweicloudsdkelb.v2.model.list_l7rules_request import ListL7rulesRequest from huaweicloudsdkelb.v2.model.list_l7rules_response import ListL7rulesResponse from huaweicloudsdkelb.v2.model.list_listener_tags_request import ListListenerTagsRequest from huaweicloudsdkelb.v2.model.list_listener_tags_response import ListListenerTagsResponse from huaweicloudsdkelb.v2.model.list_listeners_by_tags_request import ListListenersByTagsRequest from huaweicloudsdkelb.v2.model.list_listeners_by_tags_request_body import ListListenersByTagsRequestBody from huaweicloudsdkelb.v2.model.list_listeners_by_tags_response import ListListenersByTagsResponse from huaweicloudsdkelb.v2.model.list_listeners_request import ListListenersRequest from huaweicloudsdkelb.v2.model.list_listeners_response import ListListenersResponse from huaweicloudsdkelb.v2.model.list_loadbalancer_tags_request import ListLoadbalancerTagsRequest from huaweicloudsdkelb.v2.model.list_loadbalancer_tags_response import ListLoadbalancerTagsResponse from huaweicloudsdkelb.v2.model.list_loadbalancers_by_tags_request import ListLoadbalancersByTagsRequest from huaweicloudsdkelb.v2.model.list_loadbalancers_by_tags_request_body import ListLoadbalancersByTagsRequestBody from huaweicloudsdkelb.v2.model.list_loadbalancers_by_tags_response import ListLoadbalancersByTagsResponse from huaweicloudsdkelb.v2.model.list_loadbalancers_request import ListLoadbalancersRequest from huaweicloudsdkelb.v2.model.list_loadbalancers_response import ListLoadbalancersResponse from huaweicloudsdkelb.v2.model.list_members_request import ListMembersRequest from huaweicloudsdkelb.v2.model.list_members_response import ListMembersResponse from huaweicloudsdkelb.v2.model.list_pools_request import ListPoolsRequest from huaweicloudsdkelb.v2.model.list_pools_response import ListPoolsResponse from huaweicloudsdkelb.v2.model.list_tag import ListTag from huaweicloudsdkelb.v2.model.list_whitelists_request import ListWhitelistsRequest from huaweicloudsdkelb.v2.model.list_whitelists_response import ListWhitelistsResponse from huaweicloudsdkelb.v2.model.listener_resp import ListenerResp from huaweicloudsdkelb.v2.model.listeners_in_status_resp import ListenersInStatusResp from huaweicloudsdkelb.v2.model.loadbalancer_in_status_resp import LoadbalancerInStatusResp from huaweicloudsdkelb.v2.model.loadbalancer_resp import LoadbalancerResp from huaweicloudsdkelb.v2.model.member_resp import MemberResp from huaweicloudsdkelb.v2.model.members_in_status_resp import MembersInStatusResp from huaweicloudsdkelb.v2.model.pool_resp import PoolResp from huaweicloudsdkelb.v2.model.pools_in_status_resp import PoolsInStatusResp from huaweicloudsdkelb.v2.model.resource_list import ResourceList from huaweicloudsdkelb.v2.model.resource_tag import ResourceTag from huaweicloudsdkelb.v2.model.resources_by_tag import ResourcesByTag from huaweicloudsdkelb.v2.model.session_persistence import SessionPersistence from huaweicloudsdkelb.v2.model.show_certificate_request import ShowCertificateRequest from huaweicloudsdkelb.v2.model.show_certificate_response import ShowCertificateResponse from huaweicloudsdkelb.v2.model.show_healthmonitors_request import ShowHealthmonitorsRequest from huaweicloudsdkelb.v2.model.show_healthmonitors_response import ShowHealthmonitorsResponse from huaweicloudsdkelb.v2.model.show_l7policy_request import ShowL7policyRequest from huaweicloudsdkelb.v2.model.show_l7policy_response import ShowL7policyResponse from huaweicloudsdkelb.v2.model.show_l7rule_request import ShowL7ruleRequest from huaweicloudsdkelb.v2.model.show_l7rule_response import ShowL7ruleResponse from huaweicloudsdkelb.v2.model.show_listener_request import ShowListenerRequest from huaweicloudsdkelb.v2.model.show_listener_response import ShowListenerResponse from huaweicloudsdkelb.v2.model.show_listener_tags_request import ShowListenerTagsRequest from huaweicloudsdkelb.v2.model.show_listener_tags_response import ShowListenerTagsResponse from huaweicloudsdkelb.v2.model.show_loadbalancer_request import ShowLoadbalancerRequest from huaweicloudsdkelb.v2.model.show_loadbalancer_response import ShowLoadbalancerResponse from huaweicloudsdkelb.v2.model.show_loadbalancer_tags_request import ShowLoadbalancerTagsRequest from huaweicloudsdkelb.v2.model.show_loadbalancer_tags_response import ShowLoadbalancerTagsResponse from huaweicloudsdkelb.v2.model.show_loadbalancers_status_request import ShowLoadbalancersStatusRequest from huaweicloudsdkelb.v2.model.show_loadbalancers_status_response import ShowLoadbalancersStatusResponse from huaweicloudsdkelb.v2.model.show_member_request import ShowMemberRequest from huaweicloudsdkelb.v2.model.show_member_response import ShowMemberResponse from huaweicloudsdkelb.v2.model.show_pool_request import ShowPoolRequest from huaweicloudsdkelb.v2.model.show_pool_response import ShowPoolResponse from huaweicloudsdkelb.v2.model.show_whitelist_request import ShowWhitelistRequest from huaweicloudsdkelb.v2.model.show_whitelist_response import ShowWhitelistResponse from huaweicloudsdkelb.v2.model.status_resp import StatusResp from huaweicloudsdkelb.v2.model.update_certificate_request import UpdateCertificateRequest from huaweicloudsdkelb.v2.model.update_certificate_request_body import UpdateCertificateRequestBody from huaweicloudsdkelb.v2.model.update_certificate_response import UpdateCertificateResponse from huaweicloudsdkelb.v2.model.update_healthmonitor_req import UpdateHealthmonitorReq from huaweicloudsdkelb.v2.model.update_healthmonitor_request import UpdateHealthmonitorRequest from huaweicloudsdkelb.v2.model.update_healthmonitor_request_body import UpdateHealthmonitorRequestBody from huaweicloudsdkelb.v2.model.update_healthmonitor_response import UpdateHealthmonitorResponse from huaweicloudsdkelb.v2.model.update_l7policies_request import UpdateL7policiesRequest from huaweicloudsdkelb.v2.model.update_l7policies_request_body import UpdateL7policiesRequestBody from huaweicloudsdkelb.v2.model.update_l7policies_response import UpdateL7policiesResponse from huaweicloudsdkelb.v2.model.update_l7policy_req import UpdateL7policyReq from huaweicloudsdkelb.v2.model.update_l7rule_req import UpdateL7ruleReq from huaweicloudsdkelb.v2.model.update_l7rule_request import UpdateL7ruleRequest from huaweicloudsdkelb.v2.model.update_l7rule_request_body import UpdateL7ruleRequestBody from huaweicloudsdkelb.v2.model.update_l7rule_response import UpdateL7ruleResponse from huaweicloudsdkelb.v2.model.update_listener_req import UpdateListenerReq from huaweicloudsdkelb.v2.model.update_listener_request import UpdateListenerRequest from huaweicloudsdkelb.v2.model.update_listener_request_body import UpdateListenerRequestBody from huaweicloudsdkelb.v2.model.update_listener_response import UpdateListenerResponse from huaweicloudsdkelb.v2.model.update_loadbalancer_req import UpdateLoadbalancerReq from huaweicloudsdkelb.v2.model.update_loadbalancer_request import UpdateLoadbalancerRequest from huaweicloudsdkelb.v2.model.update_loadbalancer_request_body import UpdateLoadbalancerRequestBody from huaweicloudsdkelb.v2.model.update_loadbalancer_response import UpdateLoadbalancerResponse from huaweicloudsdkelb.v2.model.update_member_req import UpdateMemberReq from huaweicloudsdkelb.v2.model.update_member_request import UpdateMemberRequest from huaweicloudsdkelb.v2.model.update_member_request_body import UpdateMemberRequestBody from huaweicloudsdkelb.v2.model.update_member_response import UpdateMemberResponse from huaweicloudsdkelb.v2.model.update_pool_req import UpdatePoolReq from huaweicloudsdkelb.v2.model.update_pool_request import UpdatePoolRequest from huaweicloudsdkelb.v2.model.update_pool_request_body import UpdatePoolRequestBody from huaweicloudsdkelb.v2.model.update_pool_response import UpdatePoolResponse from huaweicloudsdkelb.v2.model.update_whitelist_req import UpdateWhitelistReq from huaweicloudsdkelb.v2.model.update_whitelist_request import UpdateWhitelistRequest from huaweicloudsdkelb.v2.model.update_whitelist_request_body import UpdateWhitelistRequestBody from huaweicloudsdkelb.v2.model.update_whitelist_response import UpdateWhitelistResponse from huaweicloudsdkelb.v2.model.whitelist_resp import WhitelistResp

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6

8cf0e53859e40d148324cd066673c6a4a76c4bdf

22,829

py

Python

from_config/dev/model_dev.py

astrockragh/IceCube

eba09e9f9a3c351dbf05496821bcd7d29ac0261c

[ "MIT" ]

null

from_config/dev/model_dev.py

astrockragh/IceCube

eba09e9f9a3c351dbf05496821bcd7d29ac0261c

[ "MIT" ]

null

from_config/dev/model_dev.py

astrockragh/IceCube

eba09e9f9a3c351dbf05496821bcd7d29ac0261c

[ "MIT" ]

2

2021-03-03T20:39:38.000Z

2021-06-09T11:58:00.000Z

import os import numpy as np from spektral.layers.convolutional.gcn_conv import GCNConv os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2' import tensorflow as tf from spektral.layers import ECCConv, GraphSageConv, MessagePassing from spektral.layers.pooling.global_pool import GlobalMaxPool, GlobalAvgPool, GlobalSumPool from tensorflow.keras import Model, Input, Sequential from tensorflow.keras.layers import Dense, LeakyReLU, BatchNormalization, Dropout, multiply from tensorflow.keras.activations import tanh, sigmoid from tensorflow.sparse import SparseTensor eps=1e-5 print('loading model') d_act=LeakyReLU(alpha=0.15) def no_norm(x, training): return x #copy over from other model class GAT(Model): def __init__(self, n_out = 4, hidden_states=64, gat_layers=2, gat_activation='relu', decode_layers=3, decode_activation='relu', regularization=None, dropout=0.2, batch_norm=True, forward=True): super().__init__() self.n_out=n_out self.hidden_states=hidden_states self.gat_activation=conv_activation self.forward=forward self.dropout=dropout self.gat_layers=gat_layers self.regularize=regularization if type(decode_activation)==str: self.decode_activation=tf.keras.activations.get(decode_activation) else: self.decode_activation=decode_activation self.batch_norm=batch_norm # Define layers of the model if self.edgeconv: self.ECC1 = ECCConv(hidden_states, [hidden_states, hidden_states, hidden_states], n_out = hidden_states, activation = "relu", kernel_regularizer=self.regularize) self.GCNs = [GCNConv(hidden_states*int(i), activation=self.conv_activation, kernel_regularizer=self.regularize) for i in 2**np.arange(self.conv_layers)] self.Pool1 = GlobalMaxPool() self.Pool2 = GlobalAvgPool() self.Pool3 = GlobalSumPool() self.decode = [Dense(i * hidden_states, activation=self.decode_activation) for i in 2**np.arange(decode_layers)] self.dropout_layers = [Dropout(dropout) for i in range(len(self.decode))] if self.batch_norm: self.norm_layers = [BatchNormalization() for i in range(len(self.decode))] else: self.norm_layers = [no_norm for i in range(len(self.decode))] self.final = Dense(n_out) def call(self, inputs, training = False): x, a, i = inputs if self.edgeconv: a, e = self.generate_edge_features(x, a) x = self.ECC1([x, a, e]) for GCN_layer in self.GCNs: x=GCN_layer([x,a]) x1 = self.Pool1([x, i]) x2 = self.Pool2([x, i]) x3 = self.Pool3([x, i]) x = tf.concat([x1, x2, x3], axis = 1) for decode_layer, dropout_layer, norm_layer in zip(self.decode, self.dropout_layers, self.norm_layers): x = dropout_layer(x, training = training) x = self.decode_activation(decode_layer(x)) x = norm_layer(x, training = training) x = self.final(x) # tf.print(tf.shape(x)) return x def generate_edge_features(self, x, a): send = a.indices[:, 0] receive = a.indices[:, 1] if self.forward == True: forwards = tf.gather(x[:, 3], send) <= tf.gather(x[:, 3], receive) send = tf.cast(send[forwards], tf.int64) receive = tf.cast(receive[forwards], tf.int64) a = SparseTensor(indices = tf.stack([send, receive], axis = 1), values = tf.ones(tf.shape(send), dtype = tf.float32), dense_shape = tf.cast(tf.shape(a), tf.int64)) diff_x = tf.subtract(tf.gather(x, receive), tf.gather(x, send)) dists = tf.sqrt( tf.reduce_sum( tf.square( diff_x[:, :3] ), axis = 1 )) vects = tf.math.divide_no_nan(diff_x[:, :3], tf.expand_dims(dists, axis = -1)) e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects], axis = 1) return a, e class DevEdge(Model): def __init__(self, edgeconv, edgenorm, hidden_states=64, edgetype=0, forward=True, K=[1,2], agg_method='min',regularization=None, dropout=0.025): super().__init__() self.n_out=3 self.n_sigs=2 self.hidden_states=hidden_states self.conv_activation='relu' self.forward=forward self.dropout=dropout self.Ks=K self.agg_method=agg_method self.conv_layers=2 self.decode_layers=2 self.edgeconv=edgeconv self.edgenorm=edgenorm self.edgetype=edgetype self.regularize=regularization self.decode_activation=d_act self.batch_norm=True # Define layers of the model if self.edgenorm: self.norm_edge = BatchNormalization() self.MPs = [SGConv(self.hidden_states, self.hidden_states, K=K, agg_method=self.agg_method, dropout = self.dropout) for K in self.Ks] if self.edgeconv: self.ECC1 = ECCConv(self.hidden_states, [self.hidden_states, self.hidden_states, self.hidden_states], n_out = self.hidden_states, activation = "relu", kernel_regularizer=self.regularize) self.GCNs = [GraphSageConv(self.hidden_states*int(i), activation=self.conv_activation, kernel_regularizer=self.regularize) for i in 4*2**np.arange(self.conv_layers)] self.Pool1 = GlobalMaxPool() self.Pool2 = GlobalAvgPool() self.Pool3 = GlobalSumPool() self.decode = [Dense(i * self.hidden_states) for i in 2*2**np.arange(self.decode_layers+1,1,-1)] self.dropout_layers = [Dropout(self.dropout) for i in range(len(self.decode))] if self.batch_norm: self.norm_layers = [BatchNormalization() for i in range(len(self.decode))] else: self.norm_layers = [no_norm for i in range(len(self.decode))] self.loge = [Dense(self.hidden_states) for _ in range(2)] self.loge_out = Dense(1) self.angles = [Dense(self.hidden_states) for _ in range(2)] self.angles_out = Dense(2) self.angle_scale= Dense(2) if self.n_sigs > 0: self.sigs = [Dense(self.hidden_states) for _ in range(2)] self.sigs_out = Dense(self.n_sigs) def call(self, inputs, training = False): x, a, i = inputs glob_avg=tf.math.segment_mean(x,i) glob_var=abs(tf.math.subtract(tf.math.segment_mean(multiply([x,x]),i),multiply([glob_avg, glob_avg]))) glob_max=tf.math.segment_max(x,i) glob_min=tf.math.segment_min(x,i) xglob=tf.concat([glob_avg, glob_var, glob_max, glob_min], axis=1) a, e = self.generate_edge_features(x, a) if self.edgenorm: e=self.norm_edge(e) for MP in self.MPs: x = MP([x, a, e]) if self.edgeconv: x = self.ECC1([x, a, e]) for conv in self.GCNs: x=conv([x,a]) x1 = self.Pool1([x, i]) x2 = self.Pool2([x, i]) x3 = self.Pool3([x, i]) x = tf.concat([x1, x2, x3], axis = 1) x=tf.concat([x, xglob], axis=1) for decode_layer, dropout_layer, norm_layer in zip(self.decode, self.dropout_layers, self.norm_layers): x = dropout_layer(x, training = training) x = self.decode_activation(decode_layer(x)) x = norm_layer(x, training = training) x_loge = self.loge[0](x) x_loge = self.loge[1](x_loge) x_loge = self.loge_out(x_loge) x_angles = self.angles[0](x) x_angles = self.angles[1](x_angles) x_angles = self.angles_out(x_angles) zeniazi=sigmoid(self.angle_scale(x_angles)) if self.n_sigs > 0: x_sigs = self.sigs[0](x) x_sigs = self.sigs[1](x_sigs) x_sigs = tf.abs(self.sigs_out(x_sigs)) + eps #could add correlation here xs=tf.stack([x_loge[:,0], zeniazi[:,0]*np.pi, zeniazi[:,1]*2*np.pi], axis = 1) if self.n_sigs > 0: return tf.concat([xs, x_sigs], axis=1) else: return xs def generate_edge_features(self, x, a): send = a.indices[:, 0] receive = a.indices[:, 1] if self.forward == True: #could maybe be improved forwards = tf.gather(x[:, 3], send) <= tf.gather(x[:, 3], receive) send = tf.cast(send[forwards], tf.int64) receive = tf.cast(receive[forwards], tf.int64) a = SparseTensor(indices = tf.stack([send, receive], axis = 1), values = tf.ones(tf.shape(send), dtype = tf.float32), dense_shape = tf.cast(tf.shape(a), tf.int64)) ##distance vectors diff_x = tf.subtract(tf.gather(x, receive), tf.gather(x, send)) dists = tf.sqrt( tf.reduce_sum( tf.square( diff_x[:, :3] ), axis = 1 )) vects = tf.math.divide_no_nan(diff_x[:, :3], tf.expand_dims(dists, axis = -1)) if self.edgetype==0: e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects], axis = 1) if self.edgetype==1: ## SRT, could make this is a mask prod_x = tf.math.multiply(tf.gather(x, receive), tf.gather(x, send)) srt = prod_x[:,5] e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects, tf.expand_dims(srt, -1)], axis = 1) if self.edgetype==2: st=2699 # time scale, database specific c=tf.constant(0.000299792458) #speed of light in km pr nanosec speed = tf.math.divide_no_nan(dists, st*diff_x[:,3]) #could add fudge factor to account for ice c lower than vacuum c speed = tf.math.greater_equal(speed, c) speed = tf.cast(tf.where(speed, 0,1), tf.float32) e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects, tf.expand_dims(speed,-1)], axis = 1) if self.edgetype==3: #srt comm? prod_x = tf.math.multiply(tf.gather(x, receive), tf.gather(x, send)) srt = prod_x[:,5] #higher than c? st=2699 # time scale, database specific c=tf.constant(0.000299792458) #speed of light in km pr nanosec speed = tf.math.divide_no_nan(dists, st*diff_x[:,3]) #could add fudge factor to account for ice c lower than vacuum c speed = tf.math.greater_equal(speed, c) speed = tf.cast(tf.where(speed, 0,1), tf.float32) e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects, tf.expand_dims(srt, -1), tf.expand_dims(speed,-1)], axis = 1) return a, e class MLP(Model): def __init__(self, output, hidden=256, layers=2, batch_norm=True, dropout=0.0, activation='relu', final_activation=None): super().__init__() self.batch_norm = batch_norm self.dropout_rate = dropout self.mlp = Sequential() for i in range(layers): # Linear self.mlp.add(Dense(hidden if i < layers - 1 else output, activation = activation)) if dropout > 0: self.mlp.add(Dropout(dropout)) def call(self, inputs, training = False): return self.mlp(inputs, training = training) class SGConv(MessagePassing): # note that the D^-1/2 norm is not implemented since it is irrelevant for us def __init__(self, n_out, hidden_states, K=2, agg_method='sum', dropout = 0): """Agg_method supports "sum": scatter_sum, "mean": scatter_mean, "max": scatter_max, "min": scatter_min, "prod": scatter_prod""" super().__init__() self.n_out = n_out self.agg_method=agg_method self.K=K self.hidden_states = hidden_states self.message_mlps = [MLP(hidden_states * 2, hidden = hidden_states * 4, layers = 2, dropout = dropout) for _ in range(self.K)] self.update_mlp = MLP(hidden_states * 1, hidden = hidden_states * 2, layers = 2, dropout = dropout) ##inverted structure since tf requires output func to be propagate def prop_khop(self, x, a, k, e=None, training = False, **kwargs): self.n_nodes = tf.shape(x)[0] self.index_i = a.indices[:, 1] self.index_j = a.indices[:, 0] # Message # print(x, a, e) # msg_kwargs = self.get_kwargs(x, a, e, self.msg_signature, kwargs) messages = self.message(x, a, k, e, training = training) # Aggregate # agg_kwargs = self.get_kwargs(x, a, e, self.agg_signature, kwargs) ## make own aggregate embeddings = self.aggregate(messages, training = training) return embeddings def propagate(self, x, a, e, training=False): for hop in range(self.K): x=self.prop_khop(x,a, hop, e, training = training) return self.update(x, training = training) def message(self, x, a, k, e, training = False): # print([self.get_i(x), self.get_j(x), e]) out = tf.concat([self.get_i(x), self.get_j(x), e], axis = 1) out = self.message_mlps[k](out, training = training) return out def update(self, embeddings, training = False): out = self.update_mlp(embeddings, training = training) return out class KHop(Model): def __init__(self, n_out = 3, n_sigs=2, K=[1,2,3], agg_method='sum', hidden_states=64, glob=True, conv_layers=1, conv_activation='relu', decode_layers=2, decode_activation=1, regularization=None, dropout=0.2, batch_norm=True, forward=True): super().__init__() self.n_out=n_out self.n_sigs=n_sigs self.hidden_states=hidden_states self.conv_activation=conv_activation self.forward=forward self.dropout=dropout self.glob=glob self.Ks=K self.agg_method=agg_method self.conv_layers=conv_layers self.regularize=regularization if type(decode_activation)==str: self.decode_activation=tf.keras.activations.get(decode_activation) else: self.decode_activation=d_act self.batch_norm=batch_norm # Define layers of the model self.MPs = [SGConv(hidden_states, hidden_states, K=K, agg_method=self.agg_method, dropout = dropout) for K in self.Ks] self.GCNs = [GraphSageConv(hidden_states*int(i), activation=self.conv_activation, kernel_regularizer=self.regularize) for i in 2*2**np.arange(self.conv_layers)] self.Pool1 = GlobalMaxPool() self.Pool2 = GlobalAvgPool() self.Pool3 = GlobalSumPool() self.decode = [Dense(i * hidden_states) for i in 2*2**np.arange(decode_layers+1,1,-1)] self.dropout_layers = [Dropout(dropout) for i in range(len(self.decode))] if self.batch_norm: self.norm_layers = [BatchNormalization() for i in range(len(self.decode))] else: self.norm_layers = [no_norm for i in range(len(self.decode))] self.loge = [Dense(hidden_states) for _ in range(2)] self.loge_out = Dense(1) self.angles = [Dense(hidden_states) for _ in range(2)] self.angles_out = Dense(2) self.angle_scale= Dense(2) if n_sigs > 0: self.sigs = [Dense(hidden_states) for i in range(2)] self.sigs_out = Dense(n_sigs) def call(self, inputs, training = False): x, a, i = inputs glob_avg=tf.math.segment_mean(x,i) glob_var=abs(tf.math.subtract(tf.math.segment_mean(multiply([x,x]),i),multiply([glob_avg, glob_avg]))) glob_max=tf.math.segment_max(x,i) glob_min=tf.math.segment_min(x,i) xglob=tf.concat([glob_avg, glob_var, glob_max, glob_min], axis=1) a, e = self.generate_edge_features(x, a) for MP in self.MPs: x = MP([x, a, e]) for conv in self.GCNs: x=conv([x,a]) x1 = self.Pool1([x, i]) x2 = self.Pool2([x, i]) x3 = self.Pool3([x, i]) x = tf.concat([x1, x2, x3], axis = 1) x=tf.concat([x, xglob], axis=1) for decode_layer, dropout_layer, norm_layer in zip(self.decode, self.dropout_layers, self.norm_layers): x = dropout_layer(x, training = training) x = self.decode_activation(decode_layer(x)) x = norm_layer(x, training = training) x_loge = self.loge[0](x) x_loge = self.loge[1](x_loge) x_loge = self.loge_out(x_loge) x_angles = self.angles[0](x) x_angles = self.angles[1](x_angles) x_angles = self.angles_out(x_angles) zeniazi=sigmoid(self.angle_scale(x_angles)) if self.n_sigs > 0: x_sigs = self.sigs[0](x) x_sigs = self.sigs[1](x_sigs) x_sigs = tf.abs(self.sigs_out(x_sigs)) + eps #could add correlation here xs=tf.stack([x_loge[:,0], zeniazi[:,0]*np.pi, zeniazi[:,1]*2*np.pi], axis = 1) if self.n_sigs > 0: return tf.concat([xs, x_sigs], axis=1) else: return xs def generate_edge_features(self, x, a): send = a.indices[:, 0] receive = a.indices[:, 1] if self.forward == True: forwards = tf.gather(x[:, 3], send) <= tf.gather(x[:, 3], receive) send = tf.cast(send[forwards], tf.int64) receive = tf.cast(receive[forwards], tf.int64) a = SparseTensor(indices = tf.stack([send, receive], axis = 1), values = tf.ones(tf.shape(send), dtype = tf.float32), dense_shape = tf.cast(tf.shape(a), tf.int64)) diff_x = tf.subtract(tf.gather(x, receive), tf.gather(x, send)) dists = tf.sqrt( tf.reduce_sum( tf.square( diff_x[:, :3] ), axis = 1 )) vects = tf.math.divide_no_nan(diff_x[:, :3], tf.expand_dims(dists, axis = -1)) e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects], axis = 1) return a, e class KHopSplit(Model): def __init__(self, n_out = 3, n_sigs=2, K=[1,2,3], agg_method='sum', hidden_states=64, glob=True, conv_layers=1, conv_activation='relu', decode_layers=2, decode_activation=1, regularization=None, dropout=0.2, batch_norm=True, forward=True): super().__init__() self.n_out=n_out self.n_sigs=n_sigs self.hidden_states=hidden_states self.conv_activation=conv_activation self.forward=forward self.dropout=dropout self.glob=glob self.Ks=K self.agg_method=agg_method self.conv_layers=conv_layers self.regularize=regularization if type(decode_activation)==str: self.decode_activation=tf.keras.activations.get(decode_activation) else: self.decode_activation=d_act self.batch_norm=batch_norm # Define layers of the model self.MPs = [SGConv(hidden_states, hidden_states, K=K, agg_method=self.agg_method, dropout = dropout) for K in self.Ks] self.GCNs = [GraphSageConv(hidden_states*int(i), activation=self.conv_activation, kernel_regularizer=self.regularize) for i in 2*2**np.arange(self.conv_layers)] self.Pool1 = GlobalMaxPool() self.Pool2 = GlobalAvgPool() self.Pool3 = GlobalSumPool() self.decode = [Dense(i * hidden_states) for i in 2*2**np.arange(decode_layers+1,1,-1)] self.dropout_layers = [Dropout(dropout) for i in range(len(self.decode))] if self.batch_norm: self.norm_layers = [BatchNormalization() for i in range(len(self.decode))] else: self.norm_layers = [no_norm for i in range(len(self.decode))] self.loge = [Dense(hidden_states) for _ in range(2)] self.loge_out = Dense(1) self.zeni = [Dense(hidden_states) for _ in range(2)] self.zeni_out = Dense(1) self.azi = [Dense(hidden_states) for _ in range(2)] self.azi_out = Dense(1) self.zeni_scale= Dense(1) self.azi_scale= Dense(1) self.sig_zeni = [Dense(hidden_states) for i in range(2)] self.sig_zeni_out = Dense(1) self.sig_azi = [Dense(hidden_states) for i in range(2)] self.sig_azi_out = Dense(1) def call(self, inputs, training = False): x, a, i = inputs glob_avg=tf.math.segment_mean(x,i) glob_var=abs(tf.math.subtract(tf.math.segment_mean(multiply([x,x]),i),multiply([glob_avg, glob_avg]))) glob_max=tf.math.segment_max(x,i) glob_min=tf.math.segment_min(x,i) xglob=tf.concat([glob_avg, glob_var, glob_max, glob_min], axis=1) a, e = self.generate_edge_features(x, a) for MP in self.MPs: x = MP([x, a, e]) for conv in self.GCNs: x=conv([x,a]) x1 = self.Pool1([x, i]) x2 = self.Pool2([x, i]) x3 = self.Pool3([x, i]) x = tf.concat([x1, x2, x3], axis = 1) x=tf.concat([x, xglob], axis=1) for decode_layer, dropout_layer, norm_layer in zip(self.decode, self.dropout_layers, self.norm_layers): x = dropout_layer(x, training = training) x = self.decode_activation(decode_layer(x)) x = norm_layer(x, training = training) x_loge = self.loge[0](x) x_loge = self.loge[1](x_loge) x_loge = self.loge_out(x_loge) x_zeni = self.zeni[0](x) x_zeni = self.zeni[1](x_zeni) x_zeni = self.zeni_out(x_zeni) zeni=sigmoid(self.zeni_scale(x_zeni)) x_azi = self.azi[0](x) x_azi = self.azi[1](x_azi) x_azi = self.azi_out(x_azi) azi=sigmoid(self.azi_scale(x_azi)) sig_z = self.sig_zeni[0](x) sig_z = self.sig_zeni[1](sig_z) sig_z = tf.abs(self.sig_zeni_out(sig_z)) + eps sig_az = self.sig_azi[0](x) sig_az = self.sig_azi[1](sig_az) sig_az = tf.abs(self.sig_azi_out(sig_az)) + eps #could add correlation here x=tf.stack([x_loge[:,0], zeni[:,0]*np.pi, azi[:,0]*2*np.pi, sig_z[:,0], sig_az[:,0]], axis = 1) return x def generate_edge_features(self, x, a): send = a.indices[:, 0] receive = a.indices[:, 1] if self.forward == True: forwards = tf.gather(x[:, 3], send) <= tf.gather(x[:, 3], receive) send = tf.cast(send[forwards], tf.int64) receive = tf.cast(receive[forwards], tf.int64) a = SparseTensor(indices = tf.stack([send, receive], axis = 1), values = tf.ones(tf.shape(send), dtype = tf.float32), dense_shape = tf.cast(tf.shape(a), tf.int64)) diff_x = tf.subtract(tf.gather(x, receive), tf.gather(x, send)) dists = tf.sqrt( tf.reduce_sum( tf.square( diff_x[:, :3] ), axis = 1 )) vects = tf.math.divide_no_nan(diff_x[:, :3], tf.expand_dims(dists, axis = -1)) e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects], axis = 1) return a, e

40.766071

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0.705949

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22,829

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1

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null

0

6

8cf957b985cbf2f565da3a545f37b6007d69179b

10,247

py

Python

ore/tests/tests_permissions.py

lukegb/Ore-python

1d1c73795406fa52ae969726feb89f7aedbc4afc

[ "MIT" ]

1

2016-05-24T14:49:42.000Z

ore/tests/tests_permissions.py

gratimax/ore-old

1d1c73795406fa52ae969726feb89f7aedbc4afc

[ "MIT" ]

null

ore/tests/tests_permissions.py

gratimax/ore-old

1d1c73795406fa52ae969726feb89f7aedbc4afc

[ "MIT" ]

null

from django.contrib.contenttypes.models import ContentType from ore.accounts.models import OreUser from ore.core.models import Permission, Organization from django.test import TestCase from ore.projects.models import Project from ore.teams.models import OrganizationTeam class PermissionsTestCase(TestCase): def make_john(self): user_john = OreUser.objects.create_user( 'john', 'password', 'john@ore.spongepowered.org') user_john.is_superuser = False user_john.save() return user_john def setUp(self): org_content_type = ContentType.objects.get_for_model(Organization) self.org_permission_foo = Permission.objects.create( slug='org.foo.do', name='Do Foo', description='Performs foo', applies_to_model=org_content_type) self.org_permission_bar = Permission.objects.create( slug='org.foo.bar', name='Bar', description='Bars foo', applies_to_model=org_content_type) self.org_permission_baz = Permission.objects.create( slug='org.baz.do', name='Do Baz', description='Bazzes the widget', applies_to_model=org_content_type) proj_content_type = ContentType.objects.get_for_model(Organization) self.proj_permission_foo = Permission.objects.create( slug='proj.foo.do', name='Do Foo', description='Performs foo', applies_to_model=proj_content_type) self.proj_permission_bar = Permission.objects.create( slug='proj.foo.bar', name='Bar', description='Bars foo', applies_to_model=proj_content_type) self.proj_permission_baz = Permission.objects.create( slug='proj.baz.do', name='Do Baz', description='Bazzes the widget', applies_to_model=proj_content_type) def test_unrelated_people_cant_do_anything_on_organization(self): organization_sponge = Organization.objects.create(name='Sponge') user_john = self.make_john() self.assertFalse(organization_sponge.user_has_permission( user_john, 'org.foo.do'), 'John can\'t foo.do') self.assertFalse(organization_sponge.user_has_permission( user_john, 'org.foo.bar'), 'John can\'t foo.bar') self.assertFalse(organization_sponge.user_has_permission( user_john, 'org.baz.do'), 'John can\'t baz.do') def test_unrelated_people_cant_do_anything_on_organization_project(self): organization_sponge = Organization.objects.create(name='Sponge') project_sponge = Project.objects.create( name='Sponge', namespace=organization_sponge, description='Sponge', ) user_john = self.make_john() self.assertFalse(project_sponge.user_has_permission( user_john, 'proj.foo.do'), 'John can\'t foo.do') self.assertFalse(project_sponge.user_has_permission( user_john, 'proj.foo.bar'), 'John can\'t foo.bar') self.assertFalse(project_sponge.user_has_permission( user_john, 'proj.baz.do'), 'John can\'t baz.do') def test_organization_owner_can_do_everything_on_organization(self): organization_sponge = Organization.objects.create(name='Sponge') team = organization_sponge.teams.get(is_owner_team=True) user_john = self.make_john() team.users = [user_john] self.assertTrue(organization_sponge.user_has_permission( user_john, 'org.foo.do'), 'John can foo.do') self.assertTrue(organization_sponge.user_has_permission( user_john, 'org.foo.bar'), 'John can foo.bar') self.assertTrue(organization_sponge.user_has_permission( user_john, 'org.baz.do'), 'John can baz.do') def test_organization_owner_can_do_everything_on_project(self): organization_sponge = Organization.objects.create(name='Sponge') project_sponge = Project.objects.create( name='Sponge', namespace=organization_sponge, description='Sponge' ) team = organization_sponge.teams.get(is_owner_team=True) user_john = self.make_john() team.users = [user_john] self.assertTrue(project_sponge.user_has_permission( user_john, 'proj.foo.do'), 'John can foo.do') self.assertTrue(project_sponge.user_has_permission( user_john, 'proj.foo.bar'), 'John can foo.bar') self.assertTrue(project_sponge.user_has_permission( user_john, 'proj.baz.do'), 'John can baz.do') def test_project_owner_can_do_everything_on_project(self): user_john = self.make_john() project_sponge = Project.objects.create( name='Sponge', namespace=user_john, description='Sponge' ) self.assertTrue(project_sponge.user_has_permission( user_john, 'proj.foo.do'), 'John can foo.do') self.assertTrue(project_sponge.user_has_permission( user_john, 'proj.foo.bar'), 'John can foo.bar') self.assertTrue(project_sponge.user_has_permission( user_john, 'proj.baz.do'), 'John can baz.do') def test_organization_all_project_teams_grant_permissions_on_projects(self): organization_sponge = Organization.objects.create(name='Sponge') project_sponge = Project.objects.create( name='Sponge', namespace=organization_sponge, description='Sponge' ) user_john = self.make_john() team = OrganizationTeam.objects.create( name='People', organization=organization_sponge, is_all_projects=True, is_owner_team=False, ) team.users = [user_john] team.permissions = [self.proj_permission_foo, self.proj_permission_bar] self.assertTrue(project_sponge.user_has_permission( user_john, 'proj.foo.do'), 'John can foo.do') self.assertTrue(project_sponge.user_has_permission( user_john, 'proj.foo.bar'), 'John can foo.bar') self.assertFalse(project_sponge.user_has_permission( user_john, 'proj.baz.do'), 'John can\'t baz.do') def test_organization_all_project_teams_grant_permissions_on_organisations(self): organization_sponge = Organization.objects.create(name='Sponge') user_john = self.make_john() team = OrganizationTeam.objects.create( name='People', organization=organization_sponge, is_all_projects=True, is_owner_team=False, ) team.users = [user_john] team.permissions = [self.org_permission_foo, self.org_permission_bar] self.assertTrue(organization_sponge.user_has_permission( user_john, 'org.foo.do'), 'John can foo.do') self.assertTrue(organization_sponge.user_has_permission( user_john, 'org.foo.bar'), 'John can foo.bar') self.assertFalse(organization_sponge.user_has_permission( user_john, 'org.baz.do'), 'John can\'t baz.do') def test_organization_limited_project_teams_grant_permissions_on_selected_projects(self): organization_sponge = Organization.objects.create(name='Sponge') project_sponge = Project.objects.create( name='Sponge', namespace=organization_sponge, description='Sponge' ) user_john = self.make_john() team = OrganizationTeam.objects.create( name='People', organization=organization_sponge, is_all_projects=False, is_owner_team=False, ) team.users = [user_john] team.permissions = [self.proj_permission_foo, self.proj_permission_bar] team.projects = [project_sponge] self.assertTrue(project_sponge.user_has_permission( user_john, 'proj.foo.do'), 'John can foo.do') self.assertTrue(project_sponge.user_has_permission( user_john, 'proj.foo.bar'), 'John can foo.bar') self.assertFalse(project_sponge.user_has_permission( user_john, 'proj.baz.do'), 'John can\'t baz.do') def test_organization_limited_project_teams_dont_grant_permissions_on_unselected_projects(self): organization_sponge = Organization.objects.create(name='Sponge') project_sponge = Project.objects.create( name='Sponge', namespace=organization_sponge, description='Sponge' ) project_spongeapi = Project.objects.create( name='SpongeAPI', namespace=organization_sponge, description='Sponge' ) user_john = self.make_john() team = OrganizationTeam.objects.create( name='People', organization=organization_sponge, is_all_projects=False, is_owner_team=False, ) team.users = [user_john] team.permissions = [self.proj_permission_foo, self.proj_permission_bar] team.projects = [project_spongeapi] self.assertFalse(project_sponge.user_has_permission( user_john, 'proj.foo.do'), 'John can\'t foo.do') self.assertFalse(project_sponge.user_has_permission( user_john, 'proj.foo.bar'), 'John can\'t foo.bar') self.assertFalse(project_sponge.user_has_permission( user_john, 'proj.baz.do'), 'John can\'t baz.do') def test_organization_limited_project_teams_dont_grant_permissions_on_organisations(self): organization_sponge = Organization.objects.create(name='Sponge') project_sponge = Project.objects.create( name='Sponge', namespace=organization_sponge, description='Sponge' ) user_john = self.make_john() team = OrganizationTeam.objects.create( name='People', organization=organization_sponge, is_all_projects=False, is_owner_team=False, ) team.users = [user_john] team.permissions = [self.org_permission_foo, self.org_permission_bar] team.projects = [project_sponge] self.assertFalse(organization_sponge.user_has_permission( user_john, 'org.foo.do'), 'John can\'t foo.do') self.assertFalse(organization_sponge.user_has_permission( user_john, 'org.foo.bar'), 'John can\'t foo.bar') self.assertFalse(organization_sponge.user_has_permission( user_john, 'orgl.baz.do'), 'John can\'t baz.do')

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10,247

5.360097

0.068127

0.064155

0.05901

0.104403

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0.876381

0.864881

0.857013

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0

0.215185

10,247

221

116

46.366516

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false

0.005319

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0

0.106383

0

null

0

1

0

null

0

6

5069f67c5c7bc019b3bb913925c7782b29dba34f

56

py

Python

test.py

anhlt59/firebase-tutorial

0044873cbbdb4c75769941af6df2b0f2de473cbc

[ "MIT" ]

null

test.py

anhlt59/firebase-tutorial

0044873cbbdb4c75769941af6df2b0f2de473cbc

[ "MIT" ]

null

test.py

anhlt59/firebase-tutorial

0044873cbbdb4c75769941af6df2b0f2de473cbc

[ "MIT" ]

null

import firebase_admin # from firebase_admin import db

11.2

31

0.821429

8

56

5.5

0.625

0.590909

0

0.160714

56

4

32

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0.93617

0.517857

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null

0

1

0

1

0

1

0

6

506fcb5f72f8f72d20284d842495b20339901545

353

py

Python

scripts/whitelist.py

marcofavorito/google-hashcode-2021

d12ea986343d27bf531247e7e70e6bea030116fd

[ "MIT" ]

null

scripts/whitelist.py

marcofavorito/google-hashcode-2021

d12ea986343d27bf531247e7e70e6bea030116fd

[ "MIT" ]

null

scripts/whitelist.py

marcofavorito/google-hashcode-2021

d12ea986343d27bf531247e7e70e6bea030116fd

[ "MIT" ]

null

# type: ignore o # unused variable (hashcode/common/base.py:38) write_input # unused function (hashcode/common/base.py:59) obj # unused variable (hashcode/common/base.py:59) read_output # unused function (hashcode/common/base.py:70) obj # unused variable (hashcode/common/base.py:80) _._module_name # unused attribute (hashcode/common/core.py:22)

44.125

63

0.76204

53

353

4.981132

0.45283

0.318182

0.340909

0.378788

0.681818

0.666667

0.280303

0

0.038217

0.110482

353

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1

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1

0

null

0

1

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6

507aa04f87a5c2f1fcc7be8f05e51279ca73612f

30,476

py

Python

scripts/DR_comparison_stats_SG41_52.py

hhuang2018/HLAWholeGeneAnalysis

9cdd2e062a6cc2eed2ebfa84e1888687b2b98cf3

[ "MIT" ]

2

2018-03-28T19:06:40.000Z

2020-08-06T08:32:09.000Z

scripts/DR_comparison_stats_SG41_52.py

hhuang2018/HLAWholeGeneAnalysis

9cdd2e062a6cc2eed2ebfa84e1888687b2b98cf3

[ "MIT" ]

null

scripts/DR_comparison_stats_SG41_52.py

hhuang2018/HLAWholeGeneAnalysis

9cdd2e062a6cc2eed2ebfa84e1888687b2b98cf3

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 3 14:42:06 2017 @author: hhuang2 """ import glob import sqlite3 as sql # from utils import phase_block_check as ps from utils import IMGTdbIO, CompareSeq import os import re fname = '../Output/SG41_52/2018/IMGTv3310/SG41_52_DRpair_Stats/SG41_52_pairedCases_Stats.pkl' Matching_cases_stats = IMGTdbIO.load_pickle2dict(fname) ## 'All_paired' groupType = 'fiveLoci_paired' # groupType = 'ClassI_paired' # groupType = 'All_paired' group_caseIDs = Matching_cases_stats[groupType] All_loci = ['A', 'B', 'C', 'DRB1', 'DQB1']#, 'DPB1'] ClassI_loci = ['A', 'B', 'C'] ClassII_loci = ['DRB1', 'DQB1'] CaseStats = {} LocusStats = {} #MatchStats = {} for caseID in group_caseIDs: # for locus in ClassI_loci: ARSregion = ['Exon2', 'Exon3'] bothMM_output = "../Output/SG41_52/2018/IMGTv3310/SG41_52_bothMisMatched_locus_" + locus + "_0125_TargetedAlignment/" # "_1218_TargetedAlignment/" singleMM_output = "../Output/SG41_52/2018/IMGTv3310/SG41_52_singleMisMatched_" + locus + "_0125_TargetedAlignment/" ### Cases where both sequences don't match if caseID in Matching_cases_stats[locus+'_both_Seqmm']: mm_file_PS1 = bothMM_output+ 'CaseID_'+ caseID + '_Locus_' + locus + '_annotation_PS1.pkl' mm_file_PS2 = bothMM_output+ 'CaseID_'+ caseID + '_Locus_' + locus + '_annotation_PS2.pkl' mm_locus_stats_PS1 = IMGTdbIO.load_pickle2dict(mm_file_PS1) mm_locus_stats_PS1 = CompareSeq.rmRefAln(mm_locus_stats_PS1) mm_locus_stats_PS2 = IMGTdbIO.load_pickle2dict(mm_file_PS2) mm_locus_stats_PS2 = CompareSeq.rmRefAln(mm_locus_stats_PS2) #if len(mm_locus_stats_PS1['MMpos']) > 20 or len(mm_locus_stats_PS2['MMpos']) > 20: if CompareSeq.isARSmm(mm_locus_stats_PS1['MMannotation'].values(), ARSregion) and CompareSeq.isARSmm(mm_locus_stats_PS2['MMannotation'].values(), ARSregion): # probably phase set swap. seq_ps1 = mm_locus_stats_PS1['seq'] seq_ps2 = mm_locus_stats_PS2['seq'] params_ps1 = mm_locus_stats_PS1['params'] params_ps2 = mm_locus_stats_PS2['params'] #tp = params_ps2['HLAtyping'] #tp = [tp[1], tp[0]] #params_ps2['HLAtyping'] = tp swapped_alignment = CompareSeq.swapPS_comparison(seq_ps1, params_ps1, seq_ps2, params_ps2, caseID) #if max([len(swapped_alignment['PS1']['MMpos']), len(swapped_alignment['PS2']['MMpos'])]) < max([len(mm_locus_stats_PS1['MMpos']), len(mm_locus_stats_PS2['MMpos'])]): if not CompareSeq.isARSmm(swapped_alignment['PS1']['MMannotation'].values(), ARSregion) or not CompareSeq.isARSmm(swapped_alignment['PS2']['MMannotation'].values(), ARSregion): # if swapped case is better, then use the swapped case mm_locus_stats_PS1 = swapped_alignment['PS1'] mm_locus_stats_PS2 = swapped_alignment['PS2'] params_ps1 = mm_locus_stats_PS1['params'] params_ps2 = mm_locus_stats_PS2['params'] # caseStats if caseID in CaseStats.keys(): CaseStats[caseID][locus] = {'PS1': CompareSeq.RegionCount(mm_locus_stats_PS1['MMannotation'], locus, True), 'PS2': CompareSeq.RegionCount(mm_locus_stats_PS2['MMannotation'], locus, True)} CaseStats[caseID][locus]['HLAtyping'] = params_ps1['HLAtyping'] + params_ps2['HLAtyping'] else: CaseStats[caseID] = {locus:{'PS1': CompareSeq.RegionCount(mm_locus_stats_PS1['MMannotation'], locus, True), 'PS2': CompareSeq.RegionCount(mm_locus_stats_PS2['MMannotation'], locus, True), 'HLAtyping':params_ps1['HLAtyping'] + params_ps2['HLAtyping']}} if len(mm_locus_stats_PS1['params']['HLAtyping']) == 1: typing = mm_locus_stats_PS1['params']['HLAtyping'][0] if typing in LocusStats.keys(): if CaseStats[caseID][locus]['PS1']['ARS'] > 0: if 'ARS' in LocusStats[typing].keys(): LocusStats[typing]['ARS'].append(caseID) else: LocusStats[typing] = {'ARS': [caseID]} #LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS1']['Non_ARS_exon'] >0: if 'Non_ARS_exon' in LocusStats[typing].keys(): LocusStats[typing]['Non_ARS_exon'].append(caseID) else: LocusStats[typing] = {'Non_ARS_exon': [caseID]} #LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS1']['Intron'] > 0: if 'Intron' in LocusStats[typing].keys(): LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'Intron': [caseID]} #LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'ARS': [], 'Non_ARS_exon': [], 'Intron': []} if CaseStats[caseID][locus]['PS1']['ARS'] > 0: LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS1']['Non_ARS_exon'] >0: LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS1']['Intron'] > 0: LocusStats[typing]['Intron'].append(caseID) for key, item in mm_locus_stats_PS1['MMannotation'].items(): if key.isdigit(): if item in LocusStats[typing].keys(): LocusStats[typing][item].append(caseID) else: LocusStats[typing] = {item: [caseID]} if len(mm_locus_stats_PS2['params']['HLAtyping']) == 1: typing = mm_locus_stats_PS2['params']['HLAtyping'][0] if typing in LocusStats.keys(): if CaseStats[caseID][locus]['PS2']['ARS'] > 0: if 'ARS' in LocusStats[typing].keys(): LocusStats[typing]['ARS'].append(caseID) else: LocusStats[typing] = {'ARS': [caseID]} #LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS2']['Non_ARS_exon'] >0: if 'Non_ARS_exon' in LocusStats[typing].keys(): LocusStats[typing]['Non_ARS_exon'].append(caseID) else: LocusStats[typing] = {'Non_ARS_exon': [caseID]} #LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS2']['Intron'] > 0: if 'Intron' in LocusStats[typing].keys(): LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'Intron': [caseID]} #LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'ARS': [], 'Non_ARS_exon': [], 'Intron': []} if CaseStats[caseID][locus]['PS2']['ARS'] > 0: LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS2']['Non_ARS_exon'] >0: LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS2']['Intron'] > 0: LocusStats[typing]['Intron'].append(caseID) for key, item in mm_locus_stats_PS2['MMannotation'].items(): if key.isdigit(): if item in LocusStats[typing].keys(): LocusStats[typing][item].append(caseID) else: LocusStats[typing] = {item: [caseID]} elif caseID in Matching_cases_stats[locus+'_one_Seqmm']: ### Cases where only one sequence doesn't match mm_file = singleMM_output+ 'CaseID_'+ caseID + '_Locus_' + locus + '_annotation.pkl' mm_locus_stats = IMGTdbIO.load_pickle2dict(mm_file) mm_locus_stats = CompareSeq.rmRefAln(mm_locus_stats) params_singmm = mm_locus_stats['params'] # caseStats if caseID in CaseStats.keys(): CaseStats[caseID][locus] = {'PS1': CompareSeq.RegionCount(mm_locus_stats['MMannotation'], locus, True)} CaseStats[caseID][locus]['HLAtyping'] = params_singmm['HLAtyping'] else: CaseStats[caseID] = {locus:{'PS1': CompareSeq.RegionCount(mm_locus_stats['MMannotation'], locus, True), 'HLAtyping':params_singmm['HLAtyping']}} if len(params_singmm['HLAtyping']) == 1: typing = params_singmm['HLAtyping'][0] if typing in LocusStats.keys(): if CaseStats[caseID][locus]['PS1']['ARS'] > 0: if 'ARS' in LocusStats[typing].keys(): LocusStats[typing]['ARS'].append(caseID) else: LocusStats[typing] = {'ARS': [caseID]} if CaseStats[caseID][locus]['PS1']['Non_ARS_exon'] >0: if 'Non_ARS_exon' in LocusStats[typing].keys(): LocusStats[typing]['Non_ARS_exon'].append(caseID) else: LocusStats[typing] = {'Non_ARS_exon': [caseID]} if CaseStats[caseID][locus]['PS1']['Intron'] > 0: if 'Intron' in LocusStats[typing].keys(): LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'Intron': [caseID]} else: LocusStats[typing] = {'ARS': [], 'Non_ARS_exon': [], 'Intron': []} if CaseStats[caseID][locus]['PS1']['ARS'] > 0: LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS1']['Non_ARS_exon'] >0: LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS1']['Intron'] > 0: LocusStats[typing]['Intron'].append(caseID) for key, item in mm_locus_stats['MMannotation'].items(): if key.isdigit(): if item in LocusStats[typing].keys(): LocusStats[typing][item].append(caseID) else: LocusStats[typing] = {item: [caseID]} ClassI_stats = {'CaseStats': CaseStats, 'LocusStats': LocusStats} IMGTdbIO.save_dict2pickle(ClassI_stats, '../Output/SG41_52/2018/IMGTv3310/SG41_52_DRpair_Stats/ClassI_Stats_0125_'+groupType) #1220_'+groupType) # Class II #Group_fname = '../Output/SG41_52/2018/IMGTv3310/SG41_52_DRpair_Stats/ClassI_Stats_0125_' + groupType + '.pkl' #Stats_Dict = IMGTdbIO.load_pickle2dict(Group_fname) #CaseStats = Stats_Dict['CaseStats'] #LocusStats = Stats_Dict['LocusStats'] for caseID in group_caseIDs: # for locus in ClassII_loci: bothMM_output = "../Output/SG41_52/2018/IMGTv3310/SG41_52_bothMisMatched_locus_" + locus + "_0125_TargetedAlignment/" singleMM_output = "../Output/SG41_52/2018/IMGTv3310/SG41_52_singleMisMatched_" + locus + "_0125_TargetedAlignment/" ### Cases where both sequences don't match if caseID in Matching_cases_stats[locus+'_both_Seqmm']: mm_file_PS1 = glob.glob(bothMM_output+ 'CaseID_'+ caseID + '_Locus_' + locus + '_annotation_PS1*.pkl') for file_id in mm_file_PS1: mm_locus_stats_PS1 = IMGTdbIO.load_pickle2dict(file_id) mm_locus_stats_PS1 = CompareSeq.rmRefAln(mm_locus_stats_PS1) #if mm_locus_stats_PS1['SameSeqs']: # if the exons are the same if 'Exon' in file_id: for key, item in mm_locus_stats_PS1['MMannotation'].items(): if key.isdigit(): tempItem = item.split('.') if int(tempItem[1]) <0 and int(tempItem[0][-1])== int(file_id.split('_')[-2][-1]): tempItem[0] = 'Intron'+ str(int(file_id.split('_')[-2][-1])-1) mm_locus_stats_PS1['MMannotation'][key] = '.'.join(tempItem) if caseID in CaseStats.keys(): if locus not in CaseStats[caseID].keys(): CaseStats[caseID][locus] = {} if 'PS1' not in CaseStats[caseID][locus].keys(): CaseStats[caseID][locus]['PS1'] = CompareSeq.RegionCount(mm_locus_stats_PS1['MMannotation'], locus) else: tempStats = CompareSeq.RegionCount(mm_locus_stats_PS1['MMannotation'], locus) for key, item in tempStats.items(): CaseStats[caseID][locus]['PS1'][key] += item if 'HLAtyping' not in CaseStats[caseID][locus].keys(): CaseStats[caseID][locus]['HLAtyping'] = mm_locus_stats_PS1['params']['HLAtyping'] else: CaseStats[caseID] = {locus:{'PS1': CompareSeq.RegionCount(mm_locus_stats_PS1['MMannotation'], locus), 'HLAtyping':mm_locus_stats_PS1['params']['HLAtyping']}} if len(mm_locus_stats_PS1['params']['HLAtyping']) == 1: typing = mm_locus_stats_PS1['params']['HLAtyping'][0] if typing in LocusStats.keys(): if CaseStats[caseID][locus]['PS1']['ARS'] > 0: if 'ARS' in LocusStats[typing].keys(): LocusStats[typing]['ARS'].append(caseID) else: LocusStats[typing] = {'ARS': [caseID]} #LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS1']['Non_ARS_exon'] >0: if 'Non_ARS_exon' in LocusStats[typing].keys(): LocusStats[typing]['Non_ARS_exon'].append(caseID) else: LocusStats[typing] = {'Non_ARS_exon': [caseID]} #LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS1']['Intron'] > 0: if 'Intron' in LocusStats[typing].keys(): LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'Intron': [caseID]} #LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'ARS': [], 'Non_ARS_exon': [], 'Intron': []} if CaseStats[caseID][locus]['PS1']['ARS'] > 0: LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS1']['Non_ARS_exon'] >0: LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS1']['Intron'] > 0: LocusStats[typing]['Intron'].append(caseID) for key, item in mm_locus_stats_PS1['MMannotation'].items(): if key.isdigit(): if item in LocusStats[typing].keys(): LocusStats[typing][item].append(caseID) else: LocusStats[typing] = {item: [caseID]} mm_file_PS2 = glob.glob(bothMM_output+ 'CaseID_'+ caseID + '_Locus_' + locus + '_annotation_PS2*.pkl') for file_id in mm_file_PS2: mm_locus_stats_PS2 = IMGTdbIO.load_pickle2dict(file_id) mm_locus_stats_PS2 = CompareSeq.rmRefAln(mm_locus_stats_PS2) if 'Exon' in file_id: for key, item in mm_locus_stats_PS2['MMannotation'].items(): if key.isdigit(): tempItem = item.split('.') if int(tempItem[1]) <0 and int(tempItem[0][-1])== int(file_id.split('_')[-2][-1]): tempItem[0] = 'Intron'+ str(int(file_id.split('_')[-2][-1])-1) mm_locus_stats_PS2['MMannotation'][key] = '.'.join(tempItem) if caseID in CaseStats.keys(): if locus not in CaseStats[caseID].keys(): CaseStats[caseID][locus] = {} if 'PS2' not in CaseStats[caseID][locus].keys(): CaseStats[caseID][locus]['PS2'] = CompareSeq.RegionCount(mm_locus_stats_PS2['MMannotation'], locus) else: tempStats = CompareSeq.RegionCount(mm_locus_stats_PS2['MMannotation'], locus) for key, item in tempStats.items(): CaseStats[caseID][locus]['PS2'][key] += item CaseStats[caseID][locus]['HLAtyping'] += mm_locus_stats_PS2['params']['HLAtyping'] else: CaseStats[caseID] = {locus:{'PS2': CompareSeq.RegionCount(mm_locus_stats_PS2['MMannotation'], locus), 'HLAtyping':mm_locus_stats_PS2['params']['HLAtyping']}} if len(mm_locus_stats_PS2['params']['HLAtyping']) == 1: typing = mm_locus_stats_PS2['params']['HLAtyping'][0] if typing in LocusStats.keys(): if CaseStats[caseID][locus]['PS2']['ARS'] > 0: if 'ARS' in LocusStats[typing].keys(): LocusStats[typing]['ARS'].append(caseID) else: LocusStats[typing] = {'ARS': [caseID]} #LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS2']['Non_ARS_exon'] >0: if 'Non_ARS_exon' in LocusStats[typing].keys(): LocusStats[typing]['Non_ARS_exon'].append(caseID) else: LocusStats[typing] = {'Non_ARS_exon': [caseID]} #LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS2']['Intron'] > 0: if 'Intron' in LocusStats[typing].keys(): LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'Intron': [caseID]} #LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'ARS': [], 'Non_ARS_exon': [], 'Intron': []} if CaseStats[caseID][locus]['PS2']['ARS'] > 0: LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS2']['Non_ARS_exon'] >0: LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS2']['Intron'] > 0: LocusStats[typing]['Intron'].append(caseID) for key, item in mm_locus_stats_PS2['MMannotation'].items(): if key.isdigit(): if item in LocusStats[typing].keys(): LocusStats[typing][item].append(caseID) else: LocusStats[typing] = {item: [caseID]} # Single mismatch cases elif caseID in Matching_cases_stats[locus+'_one_Seqmm']: ### Cases where only one sequence doesn't match mm_file_PS = glob.glob(singleMM_output+ 'CaseID_'+ caseID + '_Locus_' + locus + '_annotation*.pkl') for file_id in mm_file_PS: mm_locus_stats_PS = IMGTdbIO.load_pickle2dict(file_id) mm_locus_stats_PS = CompareSeq.rmRefAln(mm_locus_stats_PS) if 'Exon' in file_id: for key, item in mm_locus_stats_PS['MMannotation'].items(): if key.isdigit(): tempItem = item.split('.') if int(tempItem[1]) <0 and int(tempItem[0][-1])== int(file_id.split('_')[-2][-1]): tempItem[0] = 'Intron'+ str(int(file_id.split('_')[-2][-1])-1) mm_locus_stats_PS['MMannotation'][key] = '.'.join(tempItem) if caseID in CaseStats.keys(): if locus not in CaseStats[caseID].keys(): CaseStats[caseID][locus] = {} if 'PS1' not in CaseStats[caseID][locus].keys(): CaseStats[caseID][locus]['PS1'] = CompareSeq.RegionCount(mm_locus_stats_PS['MMannotation'], locus) else: tempStats = CompareSeq.RegionCount(mm_locus_stats_PS['MMannotation'], locus) for key, item in tempStats.items(): CaseStats[caseID][locus]['PS1'][key] += item if 'HLAtyping' not in CaseStats[caseID][locus].keys(): CaseStats[caseID][locus]['HLAtyping'] = mm_locus_stats_PS['params']['HLAtyping'] else: CaseStats[caseID] = {locus:{'PS1': CompareSeq.RegionCount(mm_locus_stats_PS['MMannotation'], locus), 'HLAtyping':mm_locus_stats_PS['params']['HLAtyping']}} if len(mm_locus_stats_PS['params']['HLAtyping']) == 1: typing = mm_locus_stats_PS['params']['HLAtyping'][0] if typing in LocusStats.keys(): if CaseStats[caseID][locus]['PS1']['ARS'] > 0: if 'ARS' in LocusStats[typing].keys(): LocusStats[typing]['ARS'].append(caseID) else: LocusStats[typing] = {'ARS': [caseID]} #LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS1']['Non_ARS_exon'] >0: if 'Non_ARS_exon' in LocusStats[typing].keys(): LocusStats[typing]['Non_ARS_exon'].append(caseID) else: LocusStats[typing] = {'Non_ARS_exon': [caseID]} #LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS1']['Intron'] > 0: if 'Intron' in LocusStats[typing].keys(): LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'Intron': [caseID]} #LocusStats[typing]['Intron'].append(caseID) else: LocusStats[typing] = {'ARS': [], 'Non_ARS_exon': [], 'Intron': []} if CaseStats[caseID][locus]['PS1']['ARS'] > 0: LocusStats[typing]['ARS'].append(caseID) if CaseStats[caseID][locus]['PS1']['Non_ARS_exon'] >0: LocusStats[typing]['Non_ARS_exon'].append(caseID) if CaseStats[caseID][locus]['PS1']['Intron'] > 0: LocusStats[typing]['Intron'].append(caseID) for key, item in mm_locus_stats_PS['MMannotation'].items(): if key.isdigit(): if item in LocusStats[typing].keys(): LocusStats[typing][item].append(caseID) else: LocusStats[typing] = {item: [caseID]} ClassII_stats = {'CaseStats': CaseStats, 'LocusStats': LocusStats} IMGTdbIO.save_dict2pickle(ClassII_stats, '../Output/SG41_52/2018/IMGTv3310/SG41_52_DRpair_Stats/ClassII_Stats_0125_'+groupType) #ClassI_stats = IMGTdbIO.load_pickle2dict('../Output/SG41_52/2018/IMGTv3310/SG41_52_DRpair_Stats/ClassI_Stats_0125_fiveLoci_paired.pkl') #ClassII_stats = IMGTdbIO.load_pickle2dict('../Output/SG41_52/2018/IMGTv3310/SG41_52_DRpair_Stats/ClassII_Stats_0125_fiveLoci_paired.pkl') fiveLociPaired_stats = {'CaseStats': CaseStats, 'LocusStats': LocusStats} IMGTdbIO.save_dict2pickle(fiveLociPaired_stats, '../Output/SG41_52/2018/IMGTv3310/SG41_52_DRpair_Stats/fiveLoci_paired_Stats_0125_'+groupType) ####### Swapped cases for DQB1 ## Swapped case check if 'PS1' in CaseStats[caseID][locus].keys() and 'PS2' in CaseStats[caseID][locus].keys(): if CaseStats[caseID][locus]['PS1']['ARS'] > 5 and CaseStats[caseID][locus]['PS2']['ARS'] > 5: DB_fp = '../Output/SG39_DRpairs/SG39_HLA_'+ locus +'_paired.db' con = sql.connect(DB_fp) con.row_factory = sql.Row cur = con.cursor() t = (caseID,) cur.execute('SELECT * FROM OriginalSeqs WHERE BMT_caseID = ?', t) case_records = cur.fetchall() Sequence = {} Params = {} for ind in range(2): seq1_ID = 'Recipient-PS'+str(ind+1) seq2_ID = 'Donor-PS'+str(ind+1) seq1 = case_records[ind][seq1_ID.split('-')[0]] seq2 = case_records[ind][seq2_ID.split('-')[0]] HLAtyping_list = case_records[ind]['HLATyping'] tplist = HLAtyping_list.split("+") HLAtyping = [] for tp in tplist: if tp.find('[') == -1: if tp.find('/') != -1: ambTPlist = tp.split('/') HLAtyping.extend(ambTPlist) else: HLAtyping.append(tp) else: possTPlist = re.sub('[\[\'\]]', '',tp) # remove possible characters possTPlist = possTPlist.split(",") for item in possTPlist: if item.find('/') != -1: item_pos = item.replace(" ", "") ambTPlist = item_pos.split('/') HLAtyping.extend(ambTPlist) else: #HLAtyping.extend(possTPlist) HLAtyping.append(item.replace(" ", "")) # HLAtyping.append(tp) Sequence[str(ind)]= {seq1_ID: seq1, seq2_ID:seq2} if ind == 0: algn_file = mm_locus_stats_PS1['params']['algn_file'] else: algn_file = mm_locus_stats_PS2['params']['algn_file'] Params[str(ind)] = {'algn_file': algn_file, 'saveFile': True, 'HLAtyping': HLAtyping} swapped_alignment = CompareSeq.swapPS_comparison(Sequence['0'], Params['0'], Sequence['1'], Params['1'], caseID) if any("Exon" in s for s in alignment.keys()): ## save results # for multiple Exons for itemID, itemDict in alignment.items(): saveOBJ = {'seq': Sequence, 'params': params, 'alignment':itemDict, 'MMannotation': annotation[itemID], 'SameSeqs': annotation[itemID]['SameSeqs']} if annotation[itemID]['SameSeqs']: # same seqs Output_fname = bothMM_output + 'CaseID_'+ caseID + '_Locus_' + locus + '_annotation_PS'+str(ind+1)+'_'+itemID+'_SameSeqs' else: Output_fname = bothMM_output + 'CaseID_'+ caseID + '_Locus_' + locus + '_annotation_PS'+str(ind+1)+'_'+itemID+'_MisMatchSeqs' IMGTdbIO.save_dict2pickle(saveOBJ, Output_fname) else: ## save results -- for one single sequence saveOBJ = {'seq': Sequence, 'params': params, 'alignment':alignment, 'MMannotation': annotation} Output_fname = bothMM_output + 'CaseID_'+ caseID + '_Locus_' + locus + '_annotation_PS'+str(ind+1) IMGTdbIO.save_dict2pickle(saveOBJ, Output_fname)

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5080d273a610511207399cb356f74e6810f43f4f

2,313

py

Python

models.py

water-vapor/how-can-we-be-so-dense-pytorch

76132ee80dfc29c9c60e7ca614f8f9415d133b5e

[ "MIT" ]

null

models.py

water-vapor/how-can-we-be-so-dense-pytorch

76132ee80dfc29c9c60e7ca614f8f9415d133b5e

[ "MIT" ]

null

models.py

water-vapor/how-can-we-be-so-dense-pytorch

76132ee80dfc29c9c60e7ca614f8f9415d133b5e

[ "MIT" ]

null

import torch from layers import SparseLinear, KWinner, SparseConv2D class DenseMLP(torch.nn.Module): def __init__(self): super().__init__() self.layers_stack = torch.nn.Sequential( torch.nn.Flatten(), torch.nn.Linear(784, 128), torch.nn.ReLU(), torch.nn.Linear(128, 64), torch.nn.ReLU(), torch.nn.Linear(64, 10) ) def forward(self, inputs): return self.layers_stack(inputs) class SparseMLP(torch.nn.Module): def __init__(self): super().__init__() self.layers_stack = torch.nn.Sequential( torch.nn.Flatten(), SparseLinear(784, 128), KWinner(k=40), SparseLinear(128, 64), KWinner(k=20), torch.nn.Linear(64, 10) ) def forward(self, inputs): return self.layers_stack(inputs) class DenseCNN(torch.nn.Module): def __init__(self): super().__init__() self.layers_stack = torch.nn.Sequential( torch.nn.Conv2d(1, 30, (3, 3)), torch.nn.MaxPool2d(2, stride=2), torch.nn.ReLU(), torch.nn.Flatten(), torch.nn.Linear(5070, 150), torch.nn.ReLU(), torch.nn.Linear(150, 10) ) def forward(self, inputs): return self.layers_stack(inputs) class HybridCNN(torch.nn.Module): def __init__(self): super().__init__() self.layers_stack = torch.nn.Sequential( torch.nn.Conv2d(1, 30, (3, 3)), torch.nn.MaxPool2d(2, stride=2), KWinner(k=400), torch.nn.Flatten(), SparseLinear(5070, 150), torch.nn.ReLU(), torch.nn.Linear(150, 10) ) def forward(self, inputs): return self.layers_stack(inputs) class SparseCNN(torch.nn.Module): def __init__(self): super().__init__() self.layers_stack = torch.nn.Sequential( SparseConv2D(1, 30, (3, 3)), torch.nn.MaxPool2d(2, stride=2), KWinner(k=400), torch.nn.Flatten(), SparseLinear(5070, 150), KWinner(k=50), torch.nn.Linear(150, 10) ) def forward(self, inputs): return self.layers_stack(inputs)

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6

50a2011cfd3821bb6f5b4c71ac548be24eaacfec

100

py

Python

django/mysite/tourit/context_processors.py

rishiraj-rpg/MPR--Tour-It

923dc55f49848583898b6402824c7bcf6d8ebe7b

[ "MIT" ]

null

django/mysite/tourit/context_processors.py

rishiraj-rpg/MPR--Tour-It

923dc55f49848583898b6402824c7bcf6d8ebe7b

[ "MIT" ]

null

django/mysite/tourit/context_processors.py

rishiraj-rpg/MPR--Tour-It

923dc55f49848583898b6402824c7bcf6d8ebe7b

[ "MIT" ]

1

2022-03-22T17:43:33.000Z

from tourit.models import PlaceType def sidenav(request): return{'sn':PlaceType.objects.all()}

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6

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25

py

Python

sniffpy/__init__.py

asifmallik/sniffpy

0214a2b899e9bd169f782e363a836dfb4dd94bf2

[ "MIT" ]

null

sniffpy/__init__.py

asifmallik/sniffpy

0214a2b899e9bd169f782e363a836dfb4dd94bf2

[ "MIT" ]

null

sniffpy/__init__.py

asifmallik/sniffpy

0214a2b899e9bd169f782e363a836dfb4dd94bf2

[ "MIT" ]

null

from .sniff import sniff

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0fa7cbab5735418c9ff13c86764cde19cc52444d

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py

Python

pulse2percept/implants/tests/test_prima.py

tanyabhatia/pulse2percept

b322c7daf22154d60f7abd8adb039c5982824a7c

[ "BSD-3-Clause" ]

null

pulse2percept/implants/tests/test_prima.py

tanyabhatia/pulse2percept

b322c7daf22154d60f7abd8adb039c5982824a7c

[ "BSD-3-Clause" ]

null

pulse2percept/implants/tests/test_prima.py

tanyabhatia/pulse2percept

b322c7daf22154d60f7abd8adb039c5982824a7c

[ "BSD-3-Clause" ]

null

import numpy as np import pytest import numpy.testing as npt from matplotlib.patches import Circle, RegularPolygon from pulse2percept.implants import (PhotovoltaicPixel, PRIMA, PRIMA75, PRIMA55, PRIMA40) def test_PhotovoltaicPixel(): electrode = PhotovoltaicPixel(0, 1, 2, 3, 4) npt.assert_almost_equal(electrode.x, 0) npt.assert_almost_equal(electrode.y, 1) npt.assert_almost_equal(electrode.z, 2) npt.assert_almost_equal(electrode.r, 3) npt.assert_almost_equal(electrode.a, 4) # Slots: npt.assert_equal(hasattr(electrode, '__slots__'), True) npt.assert_equal(hasattr(electrode, '__dict__'), False) # Plots: ax = electrode.plot() npt.assert_equal(len(ax.texts), 0) npt.assert_equal(len(ax.patches), 2) npt.assert_equal(isinstance(ax.patches[0], RegularPolygon), True) npt.assert_equal(isinstance(ax.patches[1], Circle), True) PhotovoltaicPixel(0, 1, 2, 3, 4) @pytest.mark.parametrize('ztype', ('float', 'list')) @pytest.mark.parametrize('x', (-100, 200)) @pytest.mark.parametrize('y', (-200, 400)) @pytest.mark.parametrize('r', (-45, 60)) def test_PRIMA(ztype, x, y, r): # 85 um pixel with 15 um trenches: spacing = 100 # Roughly a 12x15 grid, but edges are trimmed off: n_elec = 378 # Create an Prima and make sure location is correct # Height `z` can either be a float or a list z = -100 if ztype == 'float' else -np.ones(378) * 20 # Convert rotation angle to rad rot = r * np.pi / 180 prima = PRIMA(x, y, z=z, rot=rot) # Slots: npt.assert_equal(hasattr(prima, '__slots__'), True) npt.assert_equal(hasattr(prima, '__dict__'), False) # Make sure number of electrodes is correct npt.assert_equal(prima.n_electrodes, n_elec) npt.assert_equal(len(prima.earray.electrodes), n_elec) # Coordinates of A6 when device is not rotated: xy = np.array([-616.99, -925.0]).T # Rotate R = np.array([np.cos(rot), -np.sin(rot), np.sin(rot), np.cos(rot)]).reshape((2, 2)) xy = np.matmul(R, xy) # Then off-set: Make sure first electrode is placed # correctly npt.assert_almost_equal(prima['A6'].x, xy[0] + x, decimal=2) npt.assert_almost_equal(prima['A6'].y, xy[1] + y, decimal=2) # Make sure the radius is correct for e in ['A7', 'B3', 'C5', 'D7', 'E9', 'F11', 'G13', 'H14']: npt.assert_almost_equal(prima[e].r, 14) # Make sure the pitch is correct: distF6E6 = np.sqrt((prima['E6'].x - prima['F6'].x) ** 2 + (prima['E6'].y - prima['F6'].y) ** 2) npt.assert_almost_equal(distF6E6, spacing) distF6E7 = np.sqrt((prima['E7'].x - prima['F6'].x) ** 2 + (prima['E7'].y - prima['F6'].y) ** 2) npt.assert_almost_equal(distF6E7, spacing) with pytest.raises(ValueError): PRIMA(0, 0, z=np.ones(16)) @pytest.mark.parametrize('ztype', ('float', 'list')) @pytest.mark.parametrize('x', (-100, 200)) @pytest.mark.parametrize('y', (-200, 400)) @pytest.mark.parametrize('r', (-45, 60)) def test_PRIMA75(ztype, x, y, r): # 70 um pixel with 5 um trenches: spacing = 75 # Roughly a 12x15 grid, but edges are trimmed off: n_elec = 142 # Create an Prima and make sure location is correct # Height `z` can either be a float or a list z = -100 if ztype == 'float' else -np.ones(142) * 20 # Convert rotation angle to rad rot = r * np.pi / 180 prima = PRIMA75(x, y, z=z, rot=rot) # Slots: npt.assert_equal(hasattr(prima, '__slots__'), True) npt.assert_equal(hasattr(prima, '__dict__'), False) # Make sure number of electrodes is correct npt.assert_equal(len(prima.earray.electrodes), n_elec) npt.assert_equal(prima.n_electrodes, n_elec) # Coordinates of A6 when device is not rotated: xy = np.array([-200.24, -431.25]).T # Rotate R = np.array([np.cos(rot), -np.sin(rot), np.sin(rot), np.cos(rot)]).reshape((2, 2)) xy = np.matmul(R, xy) # Then off-set: Make sure first electrode is placed # correctly npt.assert_almost_equal(prima['A6'].x, xy[0] + x, decimal=2) npt.assert_almost_equal(prima['A6'].y, xy[1] + y, decimal=2) # Make sure the radius is correct for e in ['A6', 'B4', 'C5', 'D7', 'E9', 'F11', 'G13', 'H14']: npt.assert_almost_equal(prima[e].r, 10) # Make sure the pitch is correct: distF6E6 = np.sqrt((prima['E6'].x - prima['F6'].x) ** 2 + (prima['E6'].y - prima['F6'].y) ** 2) npt.assert_almost_equal(distF6E6, spacing) distF6E7 = np.sqrt((prima['E7'].x - prima['F6'].x) ** 2 + (prima['E7'].y - prima['F6'].y) ** 2) npt.assert_almost_equal(distF6E7, spacing) with pytest.raises(ValueError): PRIMA75(0, 0, z=np.ones(16)) @pytest.mark.parametrize('ztype', ('float', 'list')) @pytest.mark.parametrize('x', (-100, 200)) @pytest.mark.parametrize('y', (-200, 400)) @pytest.mark.parametrize('r', (-45, 60)) def test_PRIMA55(ztype, x, y, r): # 50 um pixels with 5 um trenches: spacing = 55 # Roughly a 18x21 grid, but edges are trimmed off: n_elec = 273 # Create an Prima and make sure location is correct # Height `z` can either be a float or a list z = -100 if ztype == 'float' else -np.ones(273) * 20 # Convert rotation angle to rad rot = r * np.pi / 180 prima = PRIMA55(x, y, z=z, rot=rot) # Slots: npt.assert_equal(hasattr(prima, '__slots__'), True) npt.assert_equal(hasattr(prima, '__dict__'), False) # Make sure number of electrodes is correct npt.assert_equal(len(prima.earray.electrodes), n_elec) npt.assert_equal(prima.n_electrodes, n_elec) # Coordinates of C8 when device is not rotated: xy = np.array([-216.58, -371.25]).T # Rotate R = np.array([np.cos(rot), -np.sin(rot), np.sin(rot), np.cos(rot)]).reshape((2, 2)) xy = np.matmul(R, xy) # Then off-set: Make sure first electrode is placed # correctly npt.assert_almost_equal(prima['C8'].x, xy[0] + x, decimal=2) npt.assert_almost_equal(prima['C8'].y, xy[1] + y, decimal=2) # Make sure the radius is correct for e in ['B12', 'C15', 'D17', 'E19', 'F11', 'G13', 'H14']: npt.assert_almost_equal(prima[e].r, 8) # Make sure the pitch is correct: distF6E6 = np.sqrt((prima['E6'].x - prima['F6'].x) ** 2 + (prima['E6'].y - prima['F6'].y) ** 2) npt.assert_almost_equal(distF6E6, spacing) distF6E7 = np.sqrt((prima['E7'].x - prima['F6'].x) ** 2 + (prima['E7'].y - prima['F6'].y) ** 2) npt.assert_almost_equal(distF6E7, spacing) with pytest.raises(ValueError): PRIMA55(0, 0, z=np.ones(16)) @pytest.mark.parametrize('ztype', ('float', 'list')) @pytest.mark.parametrize('x', (-100, 200)) @pytest.mark.parametrize('y', (-200, 400)) @pytest.mark.parametrize('r', (-45, 60)) def test_PRIMA40(ztype, x, y, r): # 35 um pixel with 5 um trenches: spacing = 40 # Roughly a 25x28 grid, but edges are trimmed off: n_elec = 532 # Create an Prima and make sure location is correct # Height `z` can either be a float or a list z = -100 if ztype == 'float' else -np.ones(532) * 20 # Convert rotation angle to rad rot = r * np.pi / 180 prima = PRIMA40(x, y, z=z, rot=rot) # Slots: npt.assert_equal(hasattr(prima, '__slots__'), True) npt.assert_equal(hasattr(prima, '__dict__'), False) # Make sure number of electrodes is correct npt.assert_equal(len(prima.earray.electrodes), n_elec) npt.assert_equal(prima.n_electrodes, n_elec) # Coordinates of D16 when device is not rotated: xy = np.array([-20.38, -370.0]).T # Rotate R = np.array([np.cos(rot), -np.sin(rot), np.sin(rot), np.cos(rot)]).reshape((2, 2)) xy = np.matmul(R, xy) # Then off-set: Make sure first electrode is placed # correctly npt.assert_almost_equal(prima['D16'].x, xy[0] + x, decimal=2) npt.assert_almost_equal(prima['D16'].y, xy[1] + y, decimal=2) # Make sure the radius is correct for e in ['B14', 'C15', 'D17', 'E19', 'F11', 'G13', 'H14']: npt.assert_almost_equal(prima[e].r, 8) # Make sure the pitch is correct: distF6E6 = np.sqrt((prima['E6'].x - prima['F6'].x) ** 2 + (prima['E6'].y - prima['F6'].y) ** 2) npt.assert_almost_equal(distF6E6, spacing) distF6E7 = np.sqrt((prima['E7'].x - prima['F6'].x) ** 2 + (prima['E7'].y - prima['F6'].y) ** 2) npt.assert_almost_equal(distF6E7, spacing) with pytest.raises(ValueError): PRIMA40(0, 0, z=np.ones(16))

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null

0

6

0fa991bee0b5cc0c29e9f2e3c50d359bb1b104ac

106

py

Python

python/pytraph/core/__init__.py

toyteam/traph

ae80d4e205e447fd8688dc95b76a43507b7fe568

[ "MIT" ]

1

2019-07-05T05:41:00.000Z

python/pytraph/core/__init__.py

jstzwj/traph

ae80d4e205e447fd8688dc95b76a43507b7fe568

[ "MIT" ]

null

python/pytraph/core/__init__.py

jstzwj/traph

ae80d4e205e447fd8688dc95b76a43507b7fe568

[ "MIT" ]

null

# -*- coding: utf-8 -*- import pytraph.core.dtype import pytraph.core.tensor __all__ = ["dtype", "tensor"]

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6

ba06d6e941fd37fb51bb507ff0294bf1e5632c1b

157

py

Python

chrome_dino/carregar_sprites.py

jjpaulo2/chrome-dino-pygame

f9fd40de343cd6d0e075e302d120f4ba9e09874d

[ "MIT" ]

null

chrome_dino/carregar_sprites.py

jjpaulo2/chrome-dino-pygame

f9fd40de343cd6d0e075e302d120f4ba9e09874d

[ "MIT" ]

null

chrome_dino/carregar_sprites.py

jjpaulo2/chrome-dino-pygame

f9fd40de343cd6d0e075e302d120f4ba9e09874d

[ "MIT" ]

null

import pygame, pathlib def carregar_imagem(imagem: str): return pygame.image.load(str(pathlib.Path(__file__).parent.absolute()) + "/sprites/" + imagem)

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py

Python

sesame/jacobian2.py

haney411/sesame

866aefb048143c5df131310253ce67b4a24283fc

[ "BSD-3-Clause" ]

2

2018-04-06T14:50:20.000Z

2021-01-19T16:16:15.000Z

sesame/jacobian2.py

haney411/sesame

866aefb048143c5df131310253ce67b4a24283fc

[ "BSD-3-Clause" ]

null

sesame/jacobian2.py

haney411/sesame

866aefb048143c5df131310253ce67b4a24283fc

[ "BSD-3-Clause" ]

null

# Copyright 2017 University of Maryland. # # This file is part of Sesame. It is subject to the license terms in the file # LICENSE.rst found in the top-level directory of this distribution. import numpy as np from itertools import chain from .observables import * from .defects import defectsJ def getJ(sys, v, efn, efp): ########################################################################### # organization of the Jacobian matrix # ########################################################################### # A site with coordinates (i,j) corresponds to a site number s as follows: # j = s//Nx # i = s - j*Nx # # Rows for (efn_s, efp_s, v_s) # ---------------------------- # fn_row = 3*s # fp_row = 3*s+1 # fv_row = 3*s+2 # # Columns for (efn_s, efp_s, v_s) # ------------------------------- # efn_smN_col = 3*(s-Nx) # efn_sm1_col = 3*(s-1) # efn_s_col = 3*s # efn_sp1_col = 3*(s+1) # efn_spN_col = 3*(s+Nx) # # efp_smN_col = 3*(s-Nx)+1 # efp_sm1_col = 3*(s-1)+1 # efp_s_col = 3*s+1 # efp_sp1_col = 3*(s+1)+1 # efp_spN_col = 3*(s+Nx)+1 # # v_smN_col = 3*(s-Nx)+2 # v_sm1_col = 3*(s-1)+2 # v_s_col = 3*s+2 # v_sp1_col = 3*(s+1)+2 # v_spN_col = 3*(s+Nx)+2 Nx, Ny = sys.xpts.shape[0], sys.ypts.shape[0] # lists of rows, columns and data that will create the sparse Jacobian global rows, columns, data rows = [] columns = [] data = [] ########################################################################### # For all sites in the system # ########################################################################### # carrier densities n = sys.Nc * np.exp(+sys.bl + efn + v) p = sys.Nv * exp(-sys.Eg - sys.bl - efp - v) # bulk charges drho_defn_s = - n drho_defp_s = - p drho_dv_s = - n - p # derivatives of the bulk recombination rates dr_defn_s, dr_defp_s, dr_dv_s = get_bulk_rr_derivs(sys, n, p) # charge defects if len(sys.defects_list) != 0: defectsJ(sys, sys.defects_list, n, p, drho_dv_s, drho_defn_s,\ drho_defp_s, dr_defn_s, dr_defp_s, dr_dv_s) # reshape the array as array[y-indices, x-indices] _sites = np.arange(Nx*Ny, dtype=int).reshape(Ny, Nx) def update(r, c, d): global rows, columns, data rows.extend(chain.from_iterable(r)) columns.extend(chain.from_iterable(c)) data.extend(chain.from_iterable(d)) def f_derivatives(carriers, djx_s, djx_sm1, djy_s, djy_smN, dxbar, dybar, sites): # The function is written with p indices but is valid for both n and p # currents derivatives djx_s_def_s, djx_s_def_sp1, djx_s_dv_s, djx_s_dv_sp1 = djx_s djx_sm1_def_sm1, djx_sm1_def_s, djx_sm1_dv_sm1, djx_sm1_dv_s = djx_sm1 djy_s_def_s, djy_s_def_spN, djy_s_dv_s, djy_s_dv_spN = djy_s djy_smN_def_smN, djy_smN_def_s, djy_smN_dv_smN, djy_smN_dv_s = djy_smN # compute the derivatives of fp def_smN = - djy_smN_def_smN / dybar dv_smN = - djy_smN_dv_smN / dybar def_sm1 = - djx_sm1_def_sm1 / dxbar dv_sm1 = - djx_sm1_dv_sm1 / dxbar dv_s = (djx_s_dv_s - djx_sm1_dv_s) / dxbar + \ (djy_s_dv_s - djy_smN_dv_s) / dybar if carriers == 'holes': defn_s = dr_defn_s[sites] defp_s = (djx_s_def_s - djx_sm1_def_s) / dxbar + \ (djy_s_def_s - djy_smN_def_s) / dybar + dr_defp_s[sites] dv_s = dv_s + dr_dv_s[sites] if carriers == 'electrons': defn_s = (djx_s_def_s - djx_sm1_def_s) / dxbar + \ (djy_s_def_s - djy_smN_def_s) / dybar - dr_defn_s[sites] defp_s = - dr_defp_s[sites] dv_s = dv_s - dr_dv_s[sites] def_sp1 = djx_s_def_sp1 / dxbar dv_sp1 = djx_s_dv_sp1 / dxbar def_spN = djy_s_def_spN / dybar dv_spN = djy_s_dv_spN / dybar return def_smN, dv_smN, def_sm1, dv_sm1, defn_s, defp_s, dv_s,\ def_sp1, dv_sp1, def_spN, dv_spN def fv_derivatives(dx, dy, dxm1, dym1, epsilon, sites): dxbar = (dx + dxm1) / 2 dybar = (dy + dym1) / 2 p1y_ind = np.mod(sites + Nx, Nx*Ny) m1y_ind = np.mod(sites - Nx, Nx*Ny) eps_m1x = .5 * (epsilon[sites - 1] + epsilon[sites]) eps_p1x = .5 * (epsilon[sites + 1] + epsilon[sites]) eps_m1y = .5 * (epsilon[sites - Nx] + epsilon[sites]) eps_p1y = .5 * (epsilon[sites + Nx] + epsilon[sites]) # compute the derivatives #dvmN = -1./(dym1 * dybar) #dvm1 = -1./(dxm1 * dxbar) #dv = 2./(dx * dxm1) + 2./(dy * dym1) - drho_dv_s[sites] #dvp1 = -1./(dx * dxbar) #dvpN = -1./(dy * dybar) dvmN = -eps_m1y * 1. / (dym1 * dybar) dvm1 = -eps_m1x * 1. / (dxm1 * dxbar) dv = eps_m1x/(dxm1 * dxbar) + eps_p1x/(dx * dxbar) + eps_m1y/(dym1 * dybar) + eps_p1y/(dy * dybar) - drho_dv_s[sites] dvp1 = -eps_p1x * 1. / (dx * dxbar) dvpN = -eps_p1y * 1. / (dy * dybar) defn = - drho_defn_s[sites] defp = - drho_defp_s[sites] return dvmN, dvm1, dv, defn, defp, dvp1, dvpN def bn_derivatives(carriers, djx_sm1, djy_s, djy_smN, dxbar, dybar, sites): djx_sm1_def_sm1, djx_sm1_def_s, djx_sm1_dv_sm1, djx_sm1_dv_s = djx_sm1 djy_s_def_s, djy_s_def_spN, djy_s_dv_s, djy_s_dv_spN = djy_s djy_smN_def_smN, djy_smN_def_s, djy_smN_dv_smN, djy_smN_dv_s = djy_smN # compute bn derivatives def_smN = dxbar/dybar * djy_smN_def_smN dv_smN = dxbar/dybar * djy_smN_dv_smN def_sm1 = djx_sm1_def_sm1 dv_sm1 = djx_sm1_dv_sm1 if carriers == 'electrons': defn_s = djx_sm1_def_s + dxbar * (dr_defn_s[sites]\ - (djy_s_def_s - djy_smN_def_s) / dybar) + sys.Scn[1] * n[sites] defp_s = dxbar * dr_defp_s[sites] dv_s = djx_sm1_dv_s + dxbar * (dr_dv_s[sites]\ - (djy_s_dv_s - djy_smN_dv_s) / dybar) + sys.Scn[1] * n[sites] if carriers == 'holes': defn_s = - dxbar * dr_defn_s[sites] defp_s = djx_sm1_def_s + dxbar * (-dr_defp_s[sites]\ - (djy_s_def_s - djy_smN_def_s) / dybar) + sys.Scp[1] * p[sites] dv_s = djx_sm1_dv_s + dxbar * (-dr_dv_s[sites] \ - (djy_s_dv_s - djy_smN_dv_s) / dybar) + sys.Scp[1] * p[sites] def_spN = - dxbar/dybar * djy_s_def_spN dv_spN = - dxbar/dybar * djy_s_dv_spN return def_smN, dv_smN, def_sm1, dv_sm1, defn_s, defp_s, dv_s,\ def_spN, dv_spN ########################################################################### # inside the system: 0 < i < Nx-1 and 0 < j < Ny-1 # ########################################################################### # We compute fn, fp, fv derivatives. Those functions are only defined on the # inner part of the system. All the edges containing boundary conditions. # list of the sites inside the system sites = _sites[1:Ny-1, 1:Nx-1].flatten() # lattice distances dx = np.tile(sys.dx[1:], Ny-2) dxm1 = np.tile(sys.dx[:-1], Ny-2) dy = np.repeat(sys.dy[1:], Nx-2) dym1 = np.repeat(sys.dy[:-1], Nx-2) dxbar = (dxm1 + dx) / 2. dybar = (dym1 + dy) / 2. #------------------------ fn derivatives ---------------------------------- # get the derivatives of jx_s, jx_sm1, jy_s, jy_smN djx_s = get_jn_derivs(sys, efn, v, sites, sites + 1, dx) djx_sm1 = get_jn_derivs(sys, efn, v, sites - 1, sites, dxm1) djy_s = get_jn_derivs(sys, efn, v, sites, sites + Nx, dy) djy_smN = get_jn_derivs(sys, efn, v, sites - Nx, sites, dym1) defn_smN, dv_smN, defn_sm1, dv_sm1, defn_s, defp_s, dv_s, defn_sp1, dv_sp1,\ defn_spN, dv_spN = \ f_derivatives('electrons', djx_s, djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns for the inner part of the system dfn_rows = np.reshape(np.repeat(3*sites, 11), (len(sites), 11)).tolist() dfn_cols = zip(3*(sites-Nx), 3*(sites-Nx)+2, 3*(sites-1), 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2, 3*(sites+1), 3*(sites+1)+2,\ 3*(sites+Nx), 3*(sites+Nx)+2) dfn_data = zip(defn_smN, dv_smN, defn_sm1, dv_sm1, defn_s, defp_s, dv_s,\ defn_sp1, dv_sp1, defn_spN, dv_spN) update(dfn_rows, dfn_cols, dfn_data) #------------------------ fp derivatives ---------------------------------- # get the derivatives of jx_s, jx_sm1, jy_s, jy_smN djx_s = get_jp_derivs(sys, efp, v, sites, sites + 1, dx) djx_sm1 = get_jp_derivs(sys, efp, v, sites - 1, sites, dxm1) djy_s = get_jp_derivs(sys, efp, v, sites, sites + Nx, dy) djy_smN = get_jp_derivs(sys, efp, v, sites - Nx, sites, dym1) defp_smN, dv_smN, defp_sm1, dv_sm1, defn_s, defp_s, dv_s, defp_sp1, dv_sp1,\ defp_spN, dv_spN = \ f_derivatives('holes', djx_s, djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns for the inner part of the system dfp_rows = np.reshape(np.repeat(3*sites+1, 11), (len(sites), 11)).tolist() dfp_cols = zip(3*(sites-Nx)+1, 3*(sites-Nx)+2, 3*(sites-1)+1, 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2, 3*(sites+1)+1, 3*(sites+1)+2,\ 3*(sites+Nx)+1, 3*(sites+Nx)+2) dfp_data = zip(defp_smN, dv_smN, defp_sm1, dv_sm1, defn_s, defp_s, dv_s,\ defp_sp1, dv_sp1, defp_spN, dv_spN) update(dfp_rows, dfp_cols, dfp_data) #---------------- fv derivatives inside the system ------------------------ dvmN, dvm1, dv, defn, defp, dvp1, dvpN = fv_derivatives(dx, dy, dxm1, dym1, sys.epsilon, sites) # update the sparse matrix row and columns for the inner part of the system dfv_rows = np.reshape(np.repeat(3*sites+2, 7), (len(sites), 7)).tolist() dfv_cols = zip(3*(sites-Nx)+2, 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2, 3*(sites+1)+2, 3*(sites+Nx)+2) dfv_data = zip(dvmN, dvm1, defn, defp, dv, dvp1, dvpN) update(dfv_rows, dfv_cols, dfv_data) ########################################################################### # left boundary: i = 0 and 0 <= j <= Ny-1 # ########################################################################### # We compute an, ap, av derivatives. Those functions are only defined on the # left boundary of the system. # list of the sites on the left side sites = _sites[:, 0].flatten() #-------------------------- an derivatives -------------------------------- # s_sp1 = [i for i in zip(sites, sites + 1)] defn_s, defn_sp1, dv_s, dv_sp1 = get_jn_derivs(sys, efn, v, sites, sites+1, sys.dx[0]) defn_s -= sys.Scn[0] * n[sites] dv_s -= sys.Scn[0] * n[sites] # update the sparse matrix row and columns dan_rows = zip(3*sites, 3*sites, 3*sites, 3*sites) dan_cols = zip(3*sites, 3*sites+2, 3*(sites+1), 3*(sites+1)+2) dan_data = zip(defn_s, dv_s, defn_sp1, dv_sp1) update(dan_rows, dan_cols, dan_data) #-------------------------- ap derivatives -------------------------------- defp_s, defp_sp1, dv_s, dv_sp1 = get_jp_derivs(sys, efp, v, sites, sites+1, sys.dx[0]) defp_s -= sys.Scp[0] * p[sites] dv_s -= sys.Scp[0] * p[sites] # update the sparse matrix row and columns dap_rows = zip(3*sites+1, 3*sites+1, 3*sites+1, 3*sites+1) dap_cols = zip(3*sites+1, 3*sites+2, 3*(sites+1)+1, 3*(sites+1)+2) dap_data = zip(defp_s, dv_s, defp_sp1, dv_sp1) update(dap_rows, dap_cols, dap_data) #-------------------------- av derivatives -------------------------------- dav_rows = (3*sites+2).tolist() dav_cols = (3*sites+2).tolist() dav_data = np.ones((len(sites,))).tolist() rows += dav_rows columns += dav_cols data += dav_data ########################################################################### # right boundary: i = Nx-1 and 0 < j < Ny-1 # ########################################################################### # We compute bn, bp, bv derivatives. Those functions are only defined on the # right boundary of the system. # list of the sites on the right side sites = _sites[1:Ny-1, Nx-1].flatten() # dxbar and dybar dxm1 = sys.dx[-1] dy = sys.dy[1:] dym1 = sys.dy[:-1] dxbar = np.tile(sys.dx[-1], Ny-2) dybar = (dy + dym1) / 2. #-------------------------- bn derivatives -------------------------------- # compute the currents derivatives djx_sm1 = get_jn_derivs(sys, efn, v, sites - 1, sites, dxm1) djy_s = get_jn_derivs(sys, efn, v, sites, sites + Nx, dy) djy_smN = get_jn_derivs(sys, efn, v, sites - Nx, sites, dym1) defn_smN, dv_smN, defn_sm1, dv_sm1, defn_s, defp_s, dv_s,\ defn_spN, dv_spN =\ bn_derivatives('electrons', djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns dbn_rows = np.reshape(np.repeat(3*sites, 9), (len(sites), 9)).tolist() dbn_cols = zip(3*(sites-Nx), 3*(sites-Nx)+2, 3*(sites-1), 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2, 3*(sites+Nx), 3*(sites+Nx)+2) dbn_data = zip(defn_smN, dv_smN, defn_sm1, dv_sm1, defn_s, defp_s, \ dv_s, defn_spN, dv_spN) update(dbn_rows, dbn_cols, dbn_data) #-------------------------- bp derivatives -------------------------------- # compute the currents derivatives djx_sm1 = get_jp_derivs(sys, efp, v, sites - 1, sites, dxm1) djy_s = get_jp_derivs(sys, efp, v, sites, sites + Nx, dy) djy_smN = get_jp_derivs(sys, efp, v, sites - Nx, sites, dym1) defp_smN, dv_smN, defp_sm1, dv_sm1, defn_s, defp_s, dv_s,\ defp_spN, dv_spN =\ bn_derivatives('holes', djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns dbp_rows = np.reshape(np.repeat(3*sites+1, 9), (len(sites), 9)).tolist() dbp_cols = zip(3*(sites-Nx)+1, 3*(sites-Nx)+2, 3*(sites-1)+1, 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2, 3*(sites+Nx)+1, 3*(sites+Nx)+2) dbp_data = zip(defp_smN, dv_smN, defp_sm1, dv_sm1, defn_s, defp_s, \ dv_s, defp_spN, dv_spN) update(dbp_rows, dbp_cols, dbp_data) #-------------------------- bv derivatives -------------------------------- dbv_rows = (3*sites+2).tolist() dbv_cols = (3*sites+2).tolist() dbv_data = np.ones((len(sites,))).tolist() # dv_s = 0 rows += dbv_rows columns += dbv_cols data += dbv_data ########################################################################### # right boundary: i = Nx-1 and j = 0 # ########################################################################### # list of the sites sites = np.array([Nx-1]) # dxbar and dybar dxm1 = sys.dx[-1] dy = sys.dy[0] dym1 = (sys.dy[0] + sys.dy[-1]) / 2. dxbar = sys.dx[-1] dybar = (dy + dym1) / 2. #-------------------------- bn derivatives -------------------------------- # compute the currents derivatives djx_sm1 = get_jn_derivs(sys, efn, v, Nx*Ny-1, Nx-1, dxm1) djy_s = get_jn_derivs(sys, efn, v, Nx-1, 2*Nx-1, dy) djy_smN = get_jn_derivs(sys, efn, v, Nx*Ny-1, Nx-1, dym1) defn_smN, dv_smN, defn_sm1, dv_sm1, defn_s, defp_s, dv_s,\ defn_spN, dv_spN =\ bn_derivatives('electrons', djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns dbn_rows = np.reshape(np.repeat(3*sites, 9), (len(sites), 9)).tolist() dbn_cols = [3*(sites-1), 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2, 3*(sites+Nx), 3*(sites+Nx)+2, 3*(sites+Nx*(Ny-1)), 3*(sites+Nx*(Ny-1))+2] dbn_data = [defn_sm1, dv_sm1, defn_s[0], defp_s[0], dv_s[0], defn_spN, dv_spN,\ defn_smN, dv_smN] update(dbn_rows, dbn_cols, [dbn_data]) #-------------------------- bp derivatives -------------------------------- # compute the currents derivatives djx_sm1 = get_jp_derivs(sys, efp, v, Nx*Ny-1, Nx-1, dxm1) djy_s = get_jp_derivs(sys, efp, v, Nx-1, 2*Nx-1, dy) djy_smN = get_jp_derivs(sys, efp, v, Nx*Ny-1, Nx-1, dym1) defp_smN, dv_smN, defp_sm1, dv_sm1, defn_s, defp_s, dv_s,\ defp_spN, dv_spN =\ bn_derivatives('holes', djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns dbp_rows = np.reshape(np.repeat(3*sites+1, 9), (len(sites), 9)).tolist() dbp_cols = [3*(sites-1)+1, 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2,\ 3*(sites+Nx)+1, 3*(sites+Nx)+2, 3*(sites+Nx*(Ny-1))+1, 3*(sites+Nx*(Ny-1))+2] dbp_data = [defp_sm1, dv_sm1, defn_s[0], defp_s[0], dv_s[0], defp_spN, dv_spN,\ defp_smN, dv_smN] update(dbp_rows, dbp_cols, [dbp_data]) #-------------------------- bv derivatives -------------------------------- dbv_rows = (3*sites+2).tolist() dbv_cols = (3*sites+2).tolist() dbv_data = np.ones((len(sites,))).tolist() # dv_s = 0 rows += dbv_rows columns += dbv_cols data += dbv_data ########################################################################### # right boundary: i = Nx-1 and j = Ny-1 # ########################################################################### # list of the sites sites = np.array([Nx*Ny-1]) # dxbar and dybar dxm1 = sys.dx[-1] dy = (sys.dy[0] + sys.dy[-1]) / 2. dym1 = sys.dy[-1] dxbar = sys.dx[-1] dybar = (dy + dym1) / 2. #-------------------------- bn derivatives -------------------------------- # compute the currents derivatives djx_sm1 = get_jn_derivs(sys, efn, v, Nx*Ny-2, Nx*Ny-1, dxm1) djy_s = get_jn_derivs(sys, efn, v, Nx*Ny-1, Nx-1, dy) djy_smN = get_jn_derivs(sys, efn, v, Nx*(Ny-1)-1, Nx*Ny-1, dym1) defn_smN, dv_smN, defn_sm1, dv_sm1, defn_s, defp_s, dv_s,\ defn_spN, dv_spN =\ bn_derivatives('electrons', djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns dbn_rows = np.reshape(np.repeat(3*sites, 9), (len(sites), 9)).tolist() dbn_cols = [3*(sites-Nx*(Ny-1)), 3*(sites-Nx*(Ny-1))+2, 3*(sites-Nx), 3*(sites-Nx)+2, 3*(sites-1), 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2] dbn_data = [defn_spN, dv_spN, defn_smN, dv_smN, defn_sm1, dv_sm1,\ defn_s[0], defp_s[0], dv_s[0]] update(dbn_rows, dbn_cols, [dbn_data]) #-------------------------- bp derivatives -------------------------------- # compute the currents derivatives djx_sm1 = get_jp_derivs(sys, efp, v, Nx*Ny-2, Nx*Ny-1, dxm1) djy_s = get_jp_derivs(sys, efp, v, Nx*Ny-1, Nx-1, dy) djy_smN = get_jp_derivs(sys, efp, v, Nx*(Ny-1)-1, Nx*Ny-1, dym1) defp_smN, dv_smN, defp_sm1, dv_sm1, defn_s, defp_s, dv_s,\ defp_spN, dv_spN =\ bn_derivatives('holes', djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns dbp_rows = np.reshape(np.repeat(3*sites+1, 9), (len(sites), 9)).tolist() dbp_cols = [3*(sites-Nx*(Ny-1))+1, 3*(sites-Nx*(Ny-1))+2, 3*(sites-Nx)+1, 3*(sites-Nx)+2, 3*(sites-1)+1, 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2] dbp_data = [defp_spN, dv_spN, defp_smN, dv_smN, defp_sm1, dv_sm1,\ defn_s[0], defp_s[0], dv_s[0]] update(dbp_rows, dbp_cols, [dbp_data]) #-------------------------- bv derivatives -------------------------------- dbv_rows = (3*sites+2).tolist() dbv_cols = (3*sites+2).tolist() dbv_data = np.ones((len(sites,))).tolist() # dv_s = 0 rows += dbv_rows columns += dbv_cols data += dbv_data ########################################################################### # boundary: 0 < i < Nx-1 and j = 0 # ########################################################################### # We apply drift diffusion equations with the periodic boundary conditions. # list of the sites inside the system sites = _sites[0, 1:Nx-1].flatten() # lattice distances dx = sys.dx[1:] dxm1 = sys.dx[:-1] dy = np.repeat(sys.dy[0], Nx-2) dym1 = np.repeat((sys.dy[0] + sys.dy[-1])/2., Nx-2) dxbar = (dxm1 + dx) / 2. dybar = (dym1 + dy) / 2. #------------------------ fn derivatives ---------------------------------- # get the derivatives of jx_s, jx_sm1, jy_s, jy_smN djx_s = get_jn_derivs(sys, efn, v, sites, sites + 1, dx) djx_sm1 = get_jn_derivs(sys, efn, v, sites - 1, sites, dxm1) djy_s = get_jn_derivs(sys, efn, v, sites, sites + Nx, dy) djy_smN = get_jn_derivs(sys, efn, v, sites + Nx*(Ny-1), sites, dym1) defn_smN, dv_smN, defn_sm1, dv_sm1, defn_s, defp_s, dv_s, defn_sp1, dv_sp1,\ defn_spN, dv_spN = \ f_derivatives('electrons', djx_s, djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns for the inner part of the system dfn_rows = np.reshape(np.repeat(3*sites, 11), (len(sites), 11)).tolist() dfn_cols = zip(3*(sites-1), 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2, 3*(sites+1), 3*(sites+1)+2, 3*(sites+Nx), 3*(sites+Nx)+2,\ 3*(sites+Nx*(Ny-1)), 3*(sites+Nx*(Ny-1))+2) dfn_data = zip(defn_sm1, dv_sm1, defn_s, defp_s, dv_s, defn_sp1, dv_sp1,\ defn_spN, dv_spN, defn_smN, dv_smN) update(dfn_rows, dfn_cols, dfn_data) #------------------------ fp derivatives ---------------------------------- # get the derivatives of jx_s, jx_sm1, jy_s, jy_smN djx_s = get_jp_derivs(sys, efp, v, sites, sites+1, dx) djx_sm1 = get_jp_derivs(sys, efp, v, sites - 1, sites, dxm1) djy_s = get_jp_derivs(sys, efp, v, sites, sites+Nx, dy) djy_smN = get_jp_derivs(sys, efp, v, sites + Nx*(Ny-1), sites, dym1) defp_smN, dv_smN, defp_sm1, dv_sm1, defn_s, defp_s, dv_s, defp_sp1, dv_sp1,\ defp_spN, dv_spN = \ f_derivatives('holes', djx_s, djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns for the inner part of the system dfp_rows = np.reshape(np.repeat(3*sites+1, 11), (len(sites), 11)).tolist() dfp_cols = zip(3*(sites-1)+1, 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2, 3*(sites+1)+1, 3*(sites+1)+2, 3*(sites+Nx)+1,\ 3*(sites+Nx)+2, 3*(sites+Nx*(Ny-1))+1, 3*(sites+Nx*(Ny-1))+2) dfp_data = zip(defp_sm1, dv_sm1, defn_s, defp_s, dv_s, defp_sp1, dv_sp1,\ defp_spN, dv_spN, defp_smN, dv_smN) update(dfp_rows, dfp_cols, dfp_data) #---------------- fv derivatives inside the system ------------------------ eps_m1x = .5 * (sys.epsilon[sites - 1] + sys.epsilon[sites]) eps_p1x = .5 * (sys.epsilon[sites + 1] + sys.epsilon[sites]) eps_m1y = .5 * (sys.epsilon[sites + Nx * (Ny - 1)] + sys.epsilon[sites]) eps_p1y = .5 * (sys.epsilon[sites + Nx] + sys.epsilon[sites]) dvmN = -eps_m1y * 1. / (dym1 * dybar) dvm1 = -eps_m1x * 1. / (dxm1 * dxbar) dv = eps_m1x / (dxm1 * dxbar) + eps_p1x / (dx * dxbar) + eps_m1y / (dym1 * dybar) + eps_p1y / (dy * dybar) - \ drho_dv_s[sites] dvp1 = -eps_p1x * 1. / (dx * dxbar) dvpN = -eps_p1y * 1. / (dy * dybar) defn = - drho_defn_s[sites] defp = - drho_defp_s[sites] #dvmN, dvm1, dv, defn, defp, dvp1, dvpN = fv_derivatives(dx, dy, dxm1, dym1, sys.epsilon, sites) # update the sparse matrix row and columns for the inner part of the system dfv_rows = np.reshape(np.repeat(3*sites+2, 7), (len(sites), 7)).tolist() dfv_cols = zip(3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2, 3*(sites+1)+2,\ 3*(sites+Nx)+2, 3*(sites+Nx*(Ny-1))+2) dfv_data = zip(dvm1, defn, defp, dv, dvp1, dvpN, dvmN) update(dfv_rows, dfv_cols, dfv_data) ########################################################################### # boundary: 0 < i < Nx-1 and j = Ny-1 # ########################################################################### # We apply drift diffusion equations with the periodic boundary conditions. # list of the sites inside the system sites = _sites[Ny-1, 1:Nx-1].flatten() # lattice distances dx = sys.dx[1:] dxm1 = sys.dx[:-1] dy = np.repeat((sys.dy[0] + sys.dy[-1])/2., Nx-2) dym1 = np.repeat(sys.dy[-1], Nx-2) dxbar = (dxm1 + dx) / 2. dybar = (dym1 + dy) / 2. #------------------------ fn derivatives ---------------------------------- # get the derivatives of jx_s, jx_sm1, jy_s, jy_smN djx_s = get_jn_derivs(sys, efn, v, sites, sites+1, dx) djx_sm1 = get_jn_derivs(sys, efn, v, sites-1, sites, dxm1) djy_s = get_jn_derivs(sys, efn, v, sites, sites - Nx*(Ny-1), dy) djy_smN = get_jn_derivs(sys, efn, v, sites-Nx, sites, dym1) defn_smN, dv_smN, defn_sm1, dv_sm1, defn_s, defp_s, dv_s, defn_sp1, dv_sp1,\ defn_spN, dv_spN = \ f_derivatives('electrons', djx_s, djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns for the inner part of the system dfn_rows = np.reshape(np.repeat(3*sites, 11), (len(sites), 11)).tolist() dfn_cols = zip(3*(sites-Nx*(Ny-1)), 3*(sites-Nx*(Ny-1))+2, 3*(sites-Nx), 3*(sites-Nx)+2, 3*(sites-1), 3*(sites-1)+2, 3*sites,\ 3*sites+1, 3*sites+2, 3*(sites+1), 3*(sites+1)+2) dfn_data = zip(defn_spN, dv_spN, defn_smN, dv_smN, defn_sm1, dv_sm1,\ defn_s, defp_s, dv_s, defn_sp1, dv_sp1) update(dfn_rows, dfn_cols, dfn_data) #------------------------ fp derivatives ---------------------------------- # get the derivatives of jx_s, jx_sm1, jy_s, jy_smN djx_s = get_jp_derivs(sys, efp, v, sites, sites+1, dx) djx_sm1 = get_jp_derivs(sys, efp, v, sites-1, sites, dxm1) djy_s = get_jp_derivs(sys, efp, v, sites, sites - Nx*(Ny-1), dy) djy_smN = get_jp_derivs(sys, efp, v, sites-Nx, sites, dym1) defp_smN, dv_smN, defp_sm1, dv_sm1, defn_s, defp_s, dv_s, defp_sp1, dv_sp1,\ defp_spN, dv_spN = \ f_derivatives('holes', djx_s, djx_sm1, djy_s, djy_smN, dxbar, dybar, sites) # update the sparse matrix row and columns for the inner part of the system dfp_rows = np.reshape(np.repeat(3*sites+1, 11), (len(sites), 11)).tolist() dfp_cols = zip(3*(sites-Nx*(Ny-1))+1, 3*(sites-Nx*(Ny-1))+2,\ 3*(sites-Nx)+1,3*(sites-Nx)+2, 3*(sites-1)+1, 3*(sites-1)+2,\ 3*sites, 3*sites+1, 3*sites+2, 3*(sites+1)+1, 3*(sites+1)+2) dfp_data = zip(defp_spN, dv_spN, defp_smN, dv_smN, defp_sm1, dv_sm1,\ defn_s, defp_s, dv_s, defp_sp1, dv_sp1,) update(dfp_rows, dfp_cols, dfp_data) #---------------- fv derivatives inside the system ------------------------ eps_m1x = .5 * (sys.epsilon[sites - 1] + sys.epsilon[sites]) eps_p1x = .5 * (sys.epsilon[sites + 1] + sys.epsilon[sites]) eps_m1y = .5 * (sys.epsilon[sites - Nx] + sys.epsilon[sites]) eps_p1y = .5 * (sys.epsilon[sites - Nx * (Ny - 1)] + sys.epsilon[sites]) dvmN = -eps_m1y * 1. / (dym1 * dybar) dvm1 = -eps_m1x * 1. / (dxm1 * dxbar) dv = eps_m1x / (dxm1 * dxbar) + eps_p1x / (dx * dxbar) + eps_m1y / (dym1 * dybar) + eps_p1y / (dy * dybar) - \ drho_dv_s[sites] dvp1 = -eps_p1x * 1. / (dx * dxbar) dvpN = -eps_p1y * 1. / (dy * dybar) defn = - drho_defn_s[sites] defp = - drho_defp_s[sites] #dvmN, dvm1, dv, defn, defp, dvp1, dvpN = fv_derivatives(dx, dy, dxm1, dym1, sys.epsilon, sites) # update the sparse matrix row and columns for the inner part of the system dfv_rows = np.reshape(np.repeat(3*sites+2, 7), (len(sites), 7)).tolist() dfv_cols = zip(3*(sites-Nx*(Ny-1))+2, 3*(sites-Nx)+2, 3*(sites-1)+2, 3*sites, 3*sites+1, 3*sites+2, 3*(sites+1)+2) dfv_data = zip(dvpN, dvmN, dvm1, defn, defp, dv, dvp1) update(dfv_rows, dfv_cols, dfv_data) return rows, columns, data

40.896403

125

0.535517

4,504

28,423

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0.076072

0.0355

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false

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0

null

0

1

0

null

0

6

e84eabbd460f2f829dad15e2a6f8d4a4f2a0739f

14,581

py

Python

gnocchi/indexer/alembic/versions/1c98ac614015_initial_base.py

NeCTAR-RC/gnocchi

aa2e5d1ce03291d492808b60c674537733d3f1a9

[ "Apache-2.0" ]

null

gnocchi/indexer/alembic/versions/1c98ac614015_initial_base.py

NeCTAR-RC/gnocchi

aa2e5d1ce03291d492808b60c674537733d3f1a9

[ "Apache-2.0" ]

null

gnocchi/indexer/alembic/versions/1c98ac614015_initial_base.py

NeCTAR-RC/gnocchi

aa2e5d1ce03291d492808b60c674537733d3f1a9

[ "Apache-2.0" ]

null

# # Copyright 2015 OpenStack Foundation # # 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. """Initial base for Gnocchi 1.0.0 Revision ID: 1c98ac614015 Revises: Create Date: 2015-04-27 16:05:13.530625 """ # revision identifiers, used by Alembic. revision = '1c98ac614015' down_revision = None branch_labels = None depends_on = None from alembic import op import sqlalchemy as sa import sqlalchemy_utils import gnocchi.indexer.sqlalchemy_base def upgrade(): op.create_table('resource', sa.Column('type', sa.Enum('generic', 'instance', 'swift_account', 'volume', 'ceph_account', 'network', 'identity', 'ipmi', 'stack', 'image', name='resource_type_enum'), nullable=False), sa.Column('created_by_user_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('created_by_project_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('started_at', gnocchi.indexer.sqlalchemy_base.PreciseTimestamp(), nullable=False), sa.Column('revision_start', gnocchi.indexer.sqlalchemy_base.PreciseTimestamp(), nullable=False), sa.Column('ended_at', gnocchi.indexer.sqlalchemy_base.PreciseTimestamp(), nullable=True), sa.Column('user_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('project_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.PrimaryKeyConstraint('id'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_resource_id', 'resource', ['id'], unique=False) op.create_table('archive_policy', sa.Column('name', sa.String(length=255), nullable=False), sa.Column('back_window', sa.Integer(), nullable=False), sa.Column('definition', gnocchi.indexer.sqlalchemy_base.ArchivePolicyDefinitionType(), nullable=False), sa.Column('aggregation_methods', gnocchi.indexer.sqlalchemy_base.SetType(), nullable=False), sa.PrimaryKeyConstraint('name'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_archive_policy_name', 'archive_policy', ['name'], unique=False) op.create_table('volume', sa.Column('display_name', sa.String(length=255), nullable=False), sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.ForeignKeyConstraint(['id'], ['resource.id'], name="fk_volume_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('id'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_volume_id', 'volume', ['id'], unique=False) op.create_table('instance', sa.Column('flavor_id', sa.Integer(), nullable=False), sa.Column('image_ref', sa.String(length=255), nullable=False), sa.Column('host', sa.String(length=255), nullable=False), sa.Column('display_name', sa.String(length=255), nullable=False), sa.Column('server_group', sa.String(length=255), nullable=True), sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.ForeignKeyConstraint(['id'], ['resource.id'], name="fk_instance_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('id'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_instance_id', 'instance', ['id'], unique=False) op.create_table('stack', sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.ForeignKeyConstraint(['id'], ['resource.id'], name="fk_stack_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('id'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_stack_id', 'stack', ['id'], unique=False) op.create_table('archive_policy_rule', sa.Column('name', sa.String(length=255), nullable=False), sa.Column('archive_policy_name', sa.String(length=255), nullable=False), sa.Column('metric_pattern', sa.String(length=255), nullable=False), sa.ForeignKeyConstraint(['archive_policy_name'], ['archive_policy.name'], name="fk_archive_policy_rule_archive_policy_name_archive_policy_name", ondelete='RESTRICT'), sa.PrimaryKeyConstraint('name'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_archive_policy_rule_name', 'archive_policy_rule', ['name'], unique=False) op.create_table('swift_account', sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.ForeignKeyConstraint(['id'], ['resource.id'], name="fk_swift_account_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('id'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_swift_account_id', 'swift_account', ['id'], unique=False) op.create_table('ceph_account', sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.ForeignKeyConstraint(['id'], ['resource.id'], name="fk_ceph_account_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('id'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_ceph_account_id', 'ceph_account', ['id'], unique=False) op.create_table('ipmi', sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.ForeignKeyConstraint(['id'], ['resource.id'], name="fk_ipmi_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('id'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_ipmi_id', 'ipmi', ['id'], unique=False) op.create_table('image', sa.Column('name', sa.String(length=255), nullable=False), sa.Column('container_format', sa.String(length=255), nullable=False), sa.Column('disk_format', sa.String(length=255), nullable=False), sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.ForeignKeyConstraint(['id'], ['resource.id'], name="fk_image_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('id'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_image_id', 'image', ['id'], unique=False) op.create_table('resource_history', sa.Column('type', sa.Enum('generic', 'instance', 'swift_account', 'volume', 'ceph_account', 'network', 'identity', 'ipmi', 'stack', 'image', name='resource_type_enum'), nullable=False), sa.Column('created_by_user_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('created_by_project_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('started_at', gnocchi.indexer.sqlalchemy_base.PreciseTimestamp(), nullable=False), sa.Column('revision_start', gnocchi.indexer.sqlalchemy_base.PreciseTimestamp(), nullable=False), sa.Column('ended_at', gnocchi.indexer.sqlalchemy_base.PreciseTimestamp(), nullable=True), sa.Column('user_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('project_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('revision', sa.Integer(), nullable=False), sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.Column('revision_end', gnocchi.indexer.sqlalchemy_base.PreciseTimestamp(), nullable=False), sa.ForeignKeyConstraint(['id'], ['resource.id'], name="fk_resource_history_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('revision'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_resource_history_id', 'resource_history', ['id'], unique=False) op.create_table('identity', sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.ForeignKeyConstraint(['id'], ['resource.id'], name="fk_identity_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('id'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_identity_id', 'identity', ['id'], unique=False) op.create_table('network', sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.ForeignKeyConstraint(['id'], ['resource.id'], name="fk_network_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('id'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_network_id', 'network', ['id'], unique=False) op.create_table('metric', sa.Column('id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=False), sa.Column('archive_policy_name', sa.String(length=255), nullable=False), sa.Column('created_by_user_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('created_by_project_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('resource_id', sqlalchemy_utils.types.uuid.UUIDType(binary=False), nullable=True), sa.Column('name', sa.String(length=255), nullable=True), sa.ForeignKeyConstraint(['archive_policy_name'], ['archive_policy.name'], name="fk_metric_archive_policy_name_archive_policy_name", ondelete='RESTRICT'), sa.ForeignKeyConstraint(['resource_id'], ['resource.id'], name="fk_metric_resource_id_resource_id", ondelete='CASCADE'), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('resource_id', 'name', name='uniq_metric0resource_id0name'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_metric_id', 'metric', ['id'], unique=False) op.create_table('identity_history', sa.Column('revision', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['revision'], ['resource_history.revision'], name="fk_identity_history_resource_history_revision", ondelete='CASCADE'), sa.PrimaryKeyConstraint('revision'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_identity_history_revision', 'identity_history', ['revision'], unique=False) op.create_table('instance_history', sa.Column('flavor_id', sa.Integer(), nullable=False), sa.Column('image_ref', sa.String(length=255), nullable=False), sa.Column('host', sa.String(length=255), nullable=False), sa.Column('display_name', sa.String(length=255), nullable=False), sa.Column('server_group', sa.String(length=255), nullable=True), sa.Column('revision', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['revision'], ['resource_history.revision'], name="fk_instance_history_resource_history_revision", ondelete='CASCADE'), sa.PrimaryKeyConstraint('revision'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_instance_history_revision', 'instance_history', ['revision'], unique=False) op.create_table('network_history', sa.Column('revision', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['revision'], ['resource_history.revision'], name="fk_network_history_resource_history_revision", ondelete='CASCADE'), sa.PrimaryKeyConstraint('revision'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_network_history_revision', 'network_history', ['revision'], unique=False) op.create_table('swift_account_history', sa.Column('revision', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['revision'], ['resource_history.revision'], name="fk_swift_account_history_resource_history_revision", ondelete='CASCADE'), sa.PrimaryKeyConstraint('revision'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_swift_account_history_revision', 'swift_account_history', ['revision'], unique=False) op.create_table('ceph_account_history', sa.Column('revision', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['revision'], ['resource_history.revision'], name="fk_ceph_account_history_resource_history_revision", ondelete='CASCADE'), sa.PrimaryKeyConstraint('revision'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_ceph_account_history_revision', 'ceph_account_history', ['revision'], unique=False) op.create_table('ipmi_history', sa.Column('revision', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['revision'], ['resource_history.revision'], name="fk_ipmi_history_resource_history_revision", ondelete='CASCADE'), sa.PrimaryKeyConstraint('revision'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_ipmi_history_revision', 'ipmi_history', ['revision'], unique=False) op.create_table('image_history', sa.Column('name', sa.String(length=255), nullable=False), sa.Column('container_format', sa.String(length=255), nullable=False), sa.Column('disk_format', sa.String(length=255), nullable=False), sa.Column('revision', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['revision'], ['resource_history.revision'], name="fk_image_history_resource_history_revision", ondelete='CASCADE'), sa.PrimaryKeyConstraint('revision'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_image_history_revision', 'image_history', ['revision'], unique=False) op.create_table('stack_history', sa.Column('revision', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['revision'], ['resource_history.revision'], name="fk_stack_history_resource_history_revision", ondelete='CASCADE'), sa.PrimaryKeyConstraint('revision'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_stack_history_revision', 'stack_history', ['revision'], unique=False) op.create_table('volume_history', sa.Column('display_name', sa.String(length=255), nullable=False), sa.Column('revision', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['revision'], ['resource_history.revision'], name="fk_volume_history_resource_history_revision", ondelete='CASCADE'), sa.PrimaryKeyConstraint('revision'), mysql_charset='utf8', mysql_engine='InnoDB' ) op.create_index('ix_volume_history_revision', 'volume_history', ['revision'], unique=False)

54.406716

189

0.729031

1,810

14,581

5.643646

0.095028

0.054038

0.077827

0.06373

0.84464

0.842095

0.812237

0.767597

0.738815

0.723642

0

0.010786

0.1098

14,581

267

190

54.610487

0.776194

0.048419

0

0.548117

0

0.275464

0.107269

0

1

0.004184

false

0

0.016736

0

0.020921

0

null

0

1

0

null

0

6

e8591f3ade3921c59c209e435e1a1cd0a5035ea4

2,247

py

Python

transliteration.py

HALLOWe3n/UA-to-EN-Transliteration

dfdf2baa2396a4d329e79e65000a26afe7818088

[ "MIT" ]

null

transliteration.py

HALLOWe3n/UA-to-EN-Transliteration

dfdf2baa2396a4d329e79e65000a26afe7818088

[ "MIT" ]

null

transliteration.py

HALLOWe3n/UA-to-EN-Transliteration

dfdf2baa2396a4d329e79e65000a26afe7818088

[ "MIT" ]

null

def transliteration(text: str): return text.replace("іє", 'ie') \ .replace("Іє", 'Ie') \ .replace("ія", 'ia') \ .replace("Ія", 'Ia') \ .replace("зг", 'zgh') \ .replace("Зг", 'Zgh') \ .replace("ьо", 'io') \ .replace("а", 'a') \ .replace("б", 'b') \ .replace("в", 'v') \ .replace("г", 'h') \ .replace("ґ", 'g') \ .replace("д", 'd') \ .replace("е", 'e') \ .replace("є", 'ie') \ .replace("ж", 'zh') \ .replace("з", 'z') \ .replace("и", 'y') \ .replace("і", 'i') \ .replace("ї", 'i') \ .replace("й", 'i') \ .replace("к", 'k') \ .replace("л", 'l') \ .replace("м", 'm') \ .replace("н", 'n') \ .replace("о", 'o') \ .replace("п", 'p') \ .replace("р", 'r') \ .replace("с", 's') \ .replace("т", 't') \ .replace("у", 'u') \ .replace("ф", 'f') \ .replace("х", 'kh') \ .replace("ц", 'ts') \ .replace("ч", 'ch') \ .replace("ш", 'sh') \ .replace("щ", 'sch') \ .replace("ь", '') \ .replace("ю", 'iu') \ .replace("я", 'ia') \ .replace("А", 'A') \ .replace("Б", 'B') \ .replace("В", 'V') \ .replace("Г", 'H') \ .replace("Ґ", 'G') \ .replace("Д", 'D') \ .replace("Е", 'E') \ .replace("Є", 'Ie') \ .replace("Ж", 'Zh') \ .replace("З", 'Z') \ .replace("И", 'Y') \ .replace("І", 'I') \ .replace("Ї", 'I') \ .replace("Й", 'I') \ .replace("К", 'K') \ .replace("Л", 'L') \ .replace("М", 'M') \ .replace("Н", 'N') \ .replace("О", 'O') \ .replace("П", 'P') \ .replace("Р", 'R') \ .replace("С", 'S') \ .replace("Т", 'T') \ .replace("У", 'U') \ .replace("Ф", 'F') \ .replace("Х", 'Kh') \ .replace("Ц", 'Ts') \ .replace("Ч", 'Ch') \ .replace("Ш", 'Sh') \ .replace("Щ", 'Sch') \ .replace("Ь", '') \ .replace("Ю", 'Iu') \ .replace("Я", 'Ia') if __name__ == '__main__': print(translit("Ірпінь"))

28.443038

37

0.339564

229

2,247

3.296943

0.353712

0.063576

0.029139

0.047682

0.810596

0

0.361816

2,247

78

38

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0

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0

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false

0

0.013158

0.026316

0.013158

0

null

0

1

0

null

0

6

e85cda2ddfb268ae2c5888ae2565ff5f50640c7b

233

py

Python

juliany_pizza/orders/admin.py

kzborisov/Juliany-Pizza

4ebc0b21e314b244048df79e4858f30447b43f8b

[ "MIT" ]

null

juliany_pizza/orders/admin.py

kzborisov/Juliany-Pizza

4ebc0b21e314b244048df79e4858f30447b43f8b

[ "MIT" ]

9

2022-03-23T13:13:23.000Z

2022-03-28T13:40:20.000Z

juliany_pizza/orders/admin.py

kzborisov/Juliany-Pizza

4ebc0b21e314b244048df79e4858f30447b43f8b

[ "MIT" ]

null

from django.contrib import admin from juliany_pizza.orders.models import Order @admin.register(Order) class OrderAdmin(admin.ModelAdmin): pass # @admin.register(OrderItem) # class OrderItemAdmin(admin.ModelAdmin): # pass

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py

Python

test/test_objects.py

vercity/czsc

7a372baa3a550b18ff319008ac3fcab0f3faa684

[ "MIT" ]

1

2022-02-22T06:31:40.000Z

test/test_objects.py

vercity/czsc

7a372baa3a550b18ff319008ac3fcab0f3faa684

[ "MIT" ]

1

2021-09-25T02:32:39.000Z

test/test_objects.py

vercity/czsc

7a372baa3a550b18ff319008ac3fcab0f3faa684

[ "MIT" ]

null

# coding: utf-8 from collections import OrderedDict import pandas as pd from czsc.objects import Signal, Factor, Event, Freq, Operate, PositionLong, PositionShort def test_signal(): s = Signal(k1="1分钟", k3="倒1形态", v1="类一买", v2="七笔", v3="基础型", score=3) assert str(s) == "Signal('1分钟_任意_倒1形态_类一买_七笔_基础型_3')" assert s.key == "1分钟_倒1形态" s1 = Signal(signal='1分钟_任意_倒1形态_类一买_七笔_基础型_3') assert s == s1 assert s.is_match({"1分钟_倒1形态": "类一买_七笔_基础型_3"}) assert not s.is_match({"1分钟_倒1形态": "类一买_七笔_特例一_3"}) assert not s.is_match({"1分钟_倒1形态": "类一买_九笔_基础型_3"}) s = Signal(k1="1分钟", k2="倒1形态", k3="类一买", score=3) assert str(s) == "Signal('1分钟_倒1形态_类一买_任意_任意_任意_3')" assert s.key == "1分钟_倒1形态_类一买" try: s = Signal(k1="1分钟", k2="倒1形态", k3="类一买", score=101) except ValueError as e: assert str(e) == 'score 必须在0~100之间' def test_factor(): freq = Freq.F15 s = OrderedDict() default_signals = [ Signal(k1=str(freq.value), k2="倒0笔", k3="方向", v1="向上", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒0笔", k3="长度", v1="大于5", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒0笔", k3="三K形态", v1="顶分型", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒1笔", k3="表里关系", v1="其他", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒1笔", k3="RSQ状态", v1="小于0.2", v2='其他', v3='其他'), ] for signal in default_signals: s[signal.key] = signal.value factor = Factor( name="单测", signals_all=[ Signal(k1=str(freq.value), k2="倒0笔", k3="方向", v1="向上", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒0笔", k3="长度", v1="大于5", v2='其他', v3='其他') ] ) assert factor.is_match(s) factor = Factor( name="单测", signals_all=[ Signal(k1=str(freq.value), k2="倒0笔", k3="方向", v1="向上", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒0笔", k3="长度", v1="大于5", v2='其他', v3='其他') ], signals_any=[ Signal(k1=str(freq.value), k2="倒1笔", k3="RSQ状态", v1="小于0.2", v2='其他', v3='其他') ] ) assert factor.is_match(s) factor = Factor( name="单测", signals_all=[ Signal(k1=str(freq.value), k2="倒0笔", k3="方向", v1="向上", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒0笔", k3="长度", v1="大于5", v2='其他', v3='其他') ], signals_any=[ Signal(k1=str(freq.value), k2="倒1笔", k3="RSQ状态", v1="小于0.8", v2='其他', v3='其他') ] ) assert not factor.is_match(s) factor = Factor( name="单测", signals_all=[ Signal(k1=str(freq.value), k2="倒0笔", k3="方向", v1="向上", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒0笔", k3="长度", v1="大于5", v2='其他', v3='其他') ], signals_any=[ Signal(k1=str(freq.value), k2="倒1笔", k3="RSQ状态", v1="小于0.2", v2='其他', v3='其他') ], signals_not=[ Signal(k1=str(freq.value), k2="倒0笔", k3="三K形态", v1="顶分型", v2='其他', v3='其他'), ] ) assert not factor.is_match(s) def test_event(): freq = Freq.F15 s = OrderedDict() default_signals = [ Signal(k1=str(freq.value), k2="倒0笔", k3="方向", v1="向上", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒0笔", k3="长度", v1="大于5", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒0笔", k3="三K形态", v1="顶分型", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒1笔", k3="表里关系", v1="其他", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒1笔", k3="RSQ状态", v1="小于0.2", v2='其他', v3='其他'), ] for signal in default_signals: s[signal.key] = signal.value event = Event(name="单测", operate=Operate.LO, factors=[ Factor( name="测试", signals_all=[ Signal(k1=str(freq.value), k2="倒0笔", k3="方向", v1="向上", v2='其他', v3='其他'), Signal(k1=str(freq.value), k2="倒0笔", k3="长度", v1="大于5", v2='其他', v3='其他')] ) ]) m, f = event.is_match(s) assert m and f event = Event(name="单测", operate=Operate.LO, factors=[ Factor( name="测试", signals_all=[ Signal('15分钟_倒0笔_方向_向上_其他_其他_0'), Signal('15分钟_倒0笔_长度_任意_其他_其他_0') ] ) ]) m, f = event.is_match(s) assert m and f event = Event(name="单测", operate=Operate.LO, factors=[ Factor( name="测试", signals_all=[ Signal('15分钟_倒0笔_方向_向上_其他_其他_20'), Signal('15分钟_倒0笔_长度_任意_其他_其他_0') ] ) ]) m, f = event.is_match(s) assert not m and not f event = Event(name="单测", operate=Operate.LO, factors=[ Factor( name="测试", signals_all=[ Signal('15分钟_倒0笔_方向_向下_其他_其他_0'), Signal('15分钟_倒0笔_长度_任意_其他_其他_0') ] ) ]) m, f = event.is_match(s) assert not m and not f def test_position_long(): pos_long = PositionLong(symbol="000001.XSHG") pos_long.update(dt=pd.to_datetime('2021-01-01'), op=Operate.HO, price=100, bid=0) assert not pos_long.pos_changed and pos_long.pos == 0 pos_long.update(dt=pd.to_datetime('2021-01-02'), op=Operate.LO, price=100, bid=1, op_desc="首次开仓测试") assert pos_long.pos_changed and pos_long.pos == 0.5 pos_long.update(dt=pd.to_datetime('2021-01-03'), op=Operate.LO, price=100, bid=2, op_desc="首次开仓测试") assert not pos_long.pos_changed and pos_long.pos == 0.5 pos_long.update(dt=pd.to_datetime('2021-01-04'), op=Operate.LA1, price=100, bid=3) assert pos_long.pos_changed and pos_long.pos == 0.8 pos_long.update(dt=pd.to_datetime('2021-01-05'), op=Operate.LA1, price=100, bid=4) assert not pos_long.pos_changed and pos_long.pos == 0.8 pos_long.update(dt=pd.to_datetime('2021-01-06'), op=Operate.LA2, price=100, bid=5) assert pos_long.pos_changed and pos_long.pos == 1 pos_long.update(dt=pd.to_datetime('2021-01-07'), op=Operate.LR1, price=100, bid=6) assert pos_long.pos_changed and pos_long.pos == 0.8 pos_long.update(dt=pd.to_datetime('2021-01-08'), op=Operate.LR2, price=100, bid=7) assert pos_long.pos_changed and pos_long.pos == 0.5 pos_long.update(dt=pd.to_datetime('2021-01-08'), op=Operate.LR2, price=100, bid=7) assert not pos_long.pos_changed and pos_long.pos == 0.5 pos_long.update(dt=pd.to_datetime('2021-01-09'), op=Operate.LA2, price=100, bid=8) assert not pos_long.pos_changed and pos_long.pos == 0.5 pos_long.update(dt=pd.to_datetime('2021-01-10'), op=Operate.LA1, price=100, bid=9) assert pos_long.pos_changed and pos_long.pos == 0.8 pos_long.update(dt=pd.to_datetime('2021-01-11'), op=Operate.LE, price=100, bid=10) assert pos_long.pos_changed and pos_long.pos == 0 assert len(pos_long.pairs) == 1 assert pos_long.pairs[0]['持仓天数'] == 9 pos_long.evaluate_operates() def test_position_long_t0(): """测试T0逻辑""" pos_long = PositionLong(symbol="000001.XSHG", T0=False) pos_long.update(dt=pd.to_datetime('2021-01-01'), op=Operate.HO, price=100, bid=0) assert not pos_long.pos_changed and pos_long.pos == 0 pos_long.update(dt=pd.to_datetime('2021-01-02'), op=Operate.LO, price=100, bid=1, op_desc="首次开仓测试") assert pos_long.pos_changed and pos_long.pos == 0.5 pos_long.update(dt=pd.to_datetime('2021-01-02'), op=Operate.LA1, price=100, bid=3) assert pos_long.pos_changed and pos_long.pos == 0.8 pos_long.update(dt=pd.to_datetime('2021-01-02'), op=Operate.LA2, price=100, bid=5) assert pos_long.pos_changed and pos_long.pos == 1 # T0 平仓信号不生效 pos_long.update(dt=pd.to_datetime('2021-01-02'), op=Operate.LE, price=100, bid=8) assert not pos_long.pos_changed and pos_long.pos == 1 pos_long.update(dt=pd.to_datetime('2021-01-03'), op=Operate.LE, price=100, bid=10) assert pos_long.pos_changed and pos_long.pos == 0 try: pos_long.update(dt=pd.to_datetime('2021-01-03'), op=Operate.SO, price=100, bid=11) except AssertionError as e: print(e) assert len(pos_long.pairs) == 1 pos_long.evaluate_operates() def test_position_long_min_interval(): """测试T0逻辑""" pos_long = PositionLong(symbol="000001.XSHG", T0=False, long_min_interval=3600*72) pos_long.update(dt=pd.to_datetime('2021-01-01'), op=Operate.HO, price=100, bid=0) assert not pos_long.pos_changed and pos_long.pos == 0 pos_long.update(dt=pd.to_datetime('2021-01-02'), op=Operate.LO, price=100, bid=1, op_desc="首次开仓测试") assert pos_long.pos_changed and pos_long.pos == 0.5 pos_long.update(dt=pd.to_datetime('2021-01-02'), op=Operate.LA1, price=100, bid=3) assert pos_long.pos_changed and pos_long.pos == 0.8 pos_long.update(dt=pd.to_datetime('2021-01-02'), op=Operate.LA2, price=100, bid=5) assert pos_long.pos_changed and pos_long.pos == 1 # T0 平仓信号不生效 pos_long.update(dt=pd.to_datetime('2021-01-02'), op=Operate.LE, price=100, bid=8) assert not pos_long.pos_changed and pos_long.pos == 1 pos_long.update(dt=pd.to_datetime('2021-01-03'), op=Operate.LE, price=100, bid=10) assert pos_long.pos_changed and pos_long.pos == 0 assert len(pos_long.pairs) == 1 pos_long.update(dt=pd.to_datetime('2021-01-04'), op=Operate.LE, price=100, bid=11) assert not pos_long.pos_changed and pos_long.pos == 0 # 测试最小开仓间隔 pos_long.update(dt=pd.to_datetime('2021-01-04'), op=Operate.LO, price=100, bid=12, op_desc="第二次开仓测试") assert not pos_long.pos_changed and pos_long.pos == 0 pos_long.update(dt=pd.to_datetime('2021-01-05'), op=Operate.LO, price=100, bid=13, op_desc="第二次开仓测试") assert not pos_long.pos_changed and pos_long.pos == 0 pos_long.update(dt=pd.to_datetime('2021-01-06'), op=Operate.LO, price=100, bid=14, op_desc="第二次开仓测试") assert pos_long.pos_changed and pos_long.pos == 0.5 pos_long.update(dt=pd.to_datetime('2021-01-09'), op=Operate.LA1, price=100, bid=15) assert pos_long.pos_changed and pos_long.pos == 0.8 pos_long.update(dt=pd.to_datetime('2021-01-10'), op=Operate.LA2, price=100, bid=16) assert pos_long.pos_changed and pos_long.pos == 1 assert len(pos_long.pairs) == 1 print(pos_long.evaluate_operates()) def test_position_short(): pos_short = PositionShort(symbol="000001.XSHG") pos_short.update(dt=pd.to_datetime('2021-01-01'), op=Operate.HO, price=100, bid=0) assert not pos_short.pos_changed and pos_short.pos == 0 pos_short.update(dt=pd.to_datetime('2021-01-02'), op=Operate.SO, price=100, bid=1, op_desc="首次开仓测试") assert pos_short.pos_changed and pos_short.pos == 0.5 pos_short.update(dt=pd.to_datetime('2021-01-03'), op=Operate.SO, price=100, bid=2, op_desc="首次开仓测试") assert not pos_short.pos_changed and pos_short.pos == 0.5 pos_short.update(dt=pd.to_datetime('2021-01-04'), op=Operate.SA1, price=100, bid=3) assert pos_short.pos_changed and pos_short.pos == 0.8 pos_short.update(dt=pd.to_datetime('2021-01-05'), op=Operate.SA1, price=100, bid=4) assert not pos_short.pos_changed and pos_short.pos == 0.8 pos_short.update(dt=pd.to_datetime('2021-01-06'), op=Operate.SA2, price=100, bid=5) assert pos_short.pos_changed and pos_short.pos == 1 pos_short.update(dt=pd.to_datetime('2021-01-07'), op=Operate.SR1, price=100, bid=6) assert pos_short.pos_changed and pos_short.pos == 0.8 pos_short.update(dt=pd.to_datetime('2021-01-08'), op=Operate.SR2, price=100, bid=7) assert pos_short.pos_changed and pos_short.pos == 0.5 pos_short.update(dt=pd.to_datetime('2021-01-08'), op=Operate.SR2, price=100, bid=7) assert not pos_short.pos_changed and pos_short.pos == 0.5 pos_short.update(dt=pd.to_datetime('2021-01-09'), op=Operate.SA2, price=100, bid=8) assert not pos_short.pos_changed and pos_short.pos == 0.5 pos_short.update(dt=pd.to_datetime('2021-01-10'), op=Operate.SA1, price=100, bid=9) assert pos_short.pos_changed and pos_short.pos == 0.8 pos_short.update(dt=pd.to_datetime('2021-01-11'), op=Operate.SE, price=100, bid=10) assert pos_short.pos_changed and pos_short.pos == 0 assert len(pos_short.pairs) == 1 assert pos_short.pairs[0]['持仓天数'] == 9 pos_short.evaluate_operates() def test_position_short_t0(): """测试T0逻辑""" pos_short = PositionShort(symbol="000001.XSHG", T0=False) pos_short.update(dt=pd.to_datetime('2021-01-01'), op=Operate.HO, price=100, bid=0) assert not pos_short.pos_changed and pos_short.pos == 0 pos_short.update(dt=pd.to_datetime('2021-01-02'), op=Operate.SO, price=100, bid=1, op_desc="首次开仓测试") assert pos_short.pos_changed and pos_short.pos == 0.5 pos_short.update(dt=pd.to_datetime('2021-01-02'), op=Operate.SA1, price=100, bid=3) assert pos_short.pos_changed and pos_short.pos == 0.8 pos_short.update(dt=pd.to_datetime('2021-01-02'), op=Operate.SA2, price=100, bid=5) assert pos_short.pos_changed and pos_short.pos == 1 # T0 平仓信号不生效 pos_short.update(dt=pd.to_datetime('2021-01-02'), op=Operate.SE, price=100, bid=8) assert not pos_short.pos_changed and pos_short.pos == 1 pos_short.update(dt=pd.to_datetime('2021-01-03'), op=Operate.SE, price=100, bid=10) assert pos_short.pos_changed and pos_short.pos == 0 try: pos_short.update(dt=pd.to_datetime('2021-01-03'), op=Operate.LO, price=100, bid=11) except AssertionError as e: print(e) assert len(pos_short.pairs) == 1 pos_short.evaluate_operates() def test_position_short_min_interval(): """测试T0逻辑""" pos_short = PositionShort(symbol="000001.XSHG", T0=False, short_min_interval=3600*72) pos_short.update(dt=pd.to_datetime('2021-01-01'), op=Operate.HO, price=100, bid=0) assert not pos_short.pos_changed and pos_short.pos == 0 pos_short.update(dt=pd.to_datetime('2021-01-02'), op=Operate.SO, price=100, bid=1, op_desc="首次开仓测试") assert pos_short.pos_changed and pos_short.pos == 0.5 pos_short.update(dt=pd.to_datetime('2021-01-02'), op=Operate.SA1, price=100, bid=3) assert pos_short.pos_changed and pos_short.pos == 0.8 pos_short.update(dt=pd.to_datetime('2021-01-02'), op=Operate.SA2, price=100, bid=5) assert pos_short.pos_changed and pos_short.pos == 1 # T0 平仓信号不生效 pos_short.update(dt=pd.to_datetime('2021-01-02'), op=Operate.SE, price=100, bid=8) assert not pos_short.pos_changed and pos_short.pos == 1 pos_short.update(dt=pd.to_datetime('2021-01-03'), op=Operate.SE, price=100, bid=10) assert pos_short.pos_changed and pos_short.pos == 0 assert len(pos_short.pairs) == 1 pos_short.update(dt=pd.to_datetime('2021-01-04'), op=Operate.SE, price=100, bid=11) assert not pos_short.pos_changed and pos_short.pos == 0 # 测试最小开仓间隔 pos_short.update(dt=pd.to_datetime('2021-01-04'), op=Operate.SO, price=100, bid=12, op_desc="第二次开仓测试") assert not pos_short.pos_changed and pos_short.pos == 0 pos_short.update(dt=pd.to_datetime('2021-01-05'), op=Operate.SO, price=100, bid=13, op_desc="第二次开仓测试") assert not pos_short.pos_changed and pos_short.pos == 0 pos_short.update(dt=pd.to_datetime('2021-01-06'), op=Operate.SO, price=100, bid=14, op_desc="第二次开仓测试") assert pos_short.pos_changed and pos_short.pos == 0.5 pos_short.update(dt=pd.to_datetime('2021-01-09'), op=Operate.SA1, price=100, bid=15) assert pos_short.pos_changed and pos_short.pos == 0.8 pos_short.update(dt=pd.to_datetime('2021-01-10'), op=Operate.SA2, price=100, bid=16) assert pos_short.pos_changed and pos_short.pos == 1 assert len(pos_short.pairs) == 1 print(pos_short.evaluate_operates())

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6

e8886f2bf86837ee354ec37211859aa3541d896e

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py

Python

advex_uar/__init__.py

nraghuraman/advex-uar

b2bd5c2bf3ae07d3d5c65b81e4a6c5e21284fa43

[ "Apache-2.0" ]

75

2019-08-22T04:56:17.000Z

2022-03-28T02:32:55.000Z

advex_uar/__init__.py

nraghuraman/advex-uar

b2bd5c2bf3ae07d3d5c65b81e4a6c5e21284fa43

[ "Apache-2.0" ]

7

2019-10-08T16:27:48.000Z

2022-02-18T01:36:02.000Z

advex_uar/__init__.py

nraghuraman/advex-uar

b2bd5c2bf3ae07d3d5c65b81e4a6c5e21284fa43

[ "Apache-2.0" ]

18

2019-08-22T15:55:22.000Z

2022-02-17T19:32:10.000Z

from . import attacks

11

21

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3

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5.666667

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0

6

e8915dda0203d11b42a8e571e29d17404d45bccb

32

py

Python

qutip/solver/ode/__init__.py

jakelishman/qutip

fbb7fad5bc205910228db622d90601c82db45e4b

[ "BSD-3-Clause" ]

null

qutip/solver/ode/__init__.py

jakelishman/qutip

fbb7fad5bc205910228db622d90601c82db45e4b

[ "BSD-3-Clause" ]

2

2020-07-13T12:11:30.000Z

2020-08-09T22:45:05.000Z

qutip/solver/ode/__init__.py

jakelishman/qutip

fbb7fad5bc205910228db622d90601c82db45e4b

[ "BSD-3-Clause" ]

null

from .scipy_integrator import *

16

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0

null

0

1

0

1

0

1

0

6

fa38a9f27834f3a988a97022e44848896c2c206a

148

py

Python

examples/more/ED/28orb/get_fock.py

danielballan/edrixs

57fbd11ba9aaeaa393c3e2f06af41e4e386749e4

[ "BSD-3-Clause" ]

null

examples/more/ED/28orb/get_fock.py

danielballan/edrixs

57fbd11ba9aaeaa393c3e2f06af41e4e386749e4

[ "BSD-3-Clause" ]

null

examples/more/ED/28orb/get_fock.py

danielballan/edrixs

57fbd11ba9aaeaa393c3e2f06af41e4e386749e4

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python from edrixs.fock_basis import write_fock_dec_by_N if __name__ == "__main__": write_fock_dec_by_N(28, 14, "fock_i.in")

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fa3aa658b62b752c19ebe536c74ad96d2358d2c1

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py

Python

datasets.py

vauxgomes/ml-datasets

e9bb187bb049eccd176d25cf215836770bd0352b

[ "MIT" ]

null

datasets.py

vauxgomes/ml-datasets

e9bb187bb049eccd176d25cf215836770bd0352b

[ "MIT" ]

null

datasets.py

vauxgomes/ml-datasets

e9bb187bb049eccd176d25cf215836770bd0352b

[ "MIT" ]

null

import numpy as np import pandas as pd # Constants PATH = '~/Projects/datasets/data/' # def load_bcw(dropna=True, verbosity=False): ''' Breast Cancer Winsconsin COLUMNS ------------------------------------------- ID* Clump Thickness Uniformity of Cell Size Uniformity of Cell Shape Marginal Adhesion Single Epithelial Cell Size Bare Nuclei** Bland Chromatin Normal Nucleoli Mitoses Class * Delete ** Drop NaN -- https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data ''' NAME = 'bcw.data' COLUMNS = [ 'ID', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df.drop(['ID'], axis=1, inplace=True) df.replace('?', np.NaN, inplace=True) df.dropna(inplace=True) df['Bare Nuclei'] = df['Bare Nuclei'].astype('int') if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_car(dropna=True, verbosity=False): ''' Car Evaluation Database Marko Bohanec COLUMNS ------------------------------------------- Buying Maint Doors Persons Luggage Boot Safety Note: All data items was categorized -- https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data ''' NAME = 'car.data' COLUMNS = [ 'Buying', 'Maint', 'Doors', 'Persons', 'Luggage Boot', 'Safety', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) for c in df.columns: df[c] = pd.Categorical(df[c]).codes if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_cortex_nuclear(dropna=True, verbosity=False): ''' Data Cortex Nuclear COLUMNS ------------------------------------------- MouseID*, DYRK1A_N, ITSN1_N, BDNF_N, NR1_N, NR2A_N, pAKT_N, pBRAF_N, pCAMKII_N, pCREB_N, pELK_N, pERK_N, pJNK_N, PKCA_N, pMEK_N, pNR1_N, pNR2A_N, pNR2B_N, pPKCAB_N, pRSK_N, AKT_N, BRAF_N, CAMKII_N, CREB_N, ELK_N, ERK_N, GSK3B_N, JNK_N, MEK_N, TRKA_N, RSK_N, APP_N, Bcatenin_N, SOD1_N, MTOR_N, P38_N, pMTOR_N, DSCR1_N, AMPKA_N, NR2B_N, pNUMB_N, RAPTOR_N, TIAM1_N, pP70S6_N, NUMB_N, P70S6_N, pGSK3B_N, pPKCG_N, CDK5_N, S6_N, ADARB1_N, AcetylH3K9_N, RRP1_N, BAX_N, ARC_N, ERBB4_N, nNOS_N, Tau_N, GFAP_N, GluR3_N, GluR4_N, IL1B_N, P3525_N, pCASP9_N, PSD95_N, SNCA_N, Ubiquitin_N, pGSK3B_Tyr216_N, SHH_N, BAD_N, BCL2_N, pS6_N, pCFOS_N, SYP_N, H3AcK18_N, EGR1_N, H3MeK4_N, CaNA_N, Genotype, Treatment, Behavior, Class *Delete -- https://archive.ics.uci.edu/ml/machine-learning-databases/00342/Data_Cortex_Nuclear.xls ''' NAME = 'cortex_nuclear.data' COLUMNS = [ 'MouseID', 'DYRK1A_N', 'ITSN1_N', 'BDNF_N', 'NR1_N', 'NR2A_N', 'pAKT_N', 'pBRAF_N', 'pCAMKII_N', 'pCREB_N', 'pELK_N', 'pERK_N', 'pJNK_N', 'PKCA_N', 'pMEK_N', 'pNR1_N', 'pNR2A_N', 'pNR2B_N', 'pPKCAB_N', 'pRSK_N', 'AKT_N', 'BRAF_N', 'CAMKII_N', 'CREB_N', 'ELK_N', 'ERK_N', 'GSK3B_N', 'JNK_N', 'MEK_N', 'TRKA_N', 'RSK_N', 'APP_N', 'Bcatenin_N', 'SOD1_N', 'MTOR_N', 'P38_N', 'pMTOR_N', 'DSCR1_N', 'AMPKA_N', 'NR2B_N', 'pNUMB_N', 'RAPTOR_N', 'TIAM1_N', 'pP70S6_N', 'NUMB_N', 'P70S6_N', 'pGSK3B_N', 'pPKCG_N', 'CDK5_N', 'S6_N', 'ADARB1_N', 'AcetylH3K9_N', 'RRP1_N', 'BAX_N', 'ARC_N', 'ERBB4_N', 'nNOS_N', 'Tau_N', 'GFAP_N', 'GluR3_N', 'GluR4_N', 'IL1B_N', 'P3525_N', 'pCASP9_N', 'PSD95_N', 'SNCA_N', 'Ubiquitin_N', 'pGSK3B_Tyr216_N', 'SHH_N', 'BAD_N', 'BCL2_N', 'pS6_N', 'pCFOS_N', 'SYP_N', 'H3AcK18_N', 'EGR1_N', 'H3MeK4_N', 'CaNA_N', 'Genotype', 'Treatment', 'Behavior', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df.replace('?', np.NaN, inplace=True) df.drop('MouseID', axis=1, inplace=True) df['Genotype'] = df['Genotype'].astype('category').cat.codes df['Treatment'] = df['Treatment'].astype('category').cat.codes df['Behavior'] = df['Behavior'].astype('category').cat.codes df['Class'] = df['Class'].astype('category').cat.codes if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_credit_card_clients(dropna=True, verbosity=False): ''' Credit Card Clients COLUMNS ------------------------------------------- ID*, Limit_bal, Sex, Education, Marriage, Age, Pay_0, Pay_2, Pay_3, Pay_4, Pay_5, Pay_6, Bill_amt1, Bill_amt2, Bill_amt3, Bill_amt4, Bill_amt5, Bill_amt6, Pay_amt1, Pay_amt2, Pay_amt3, Pay_amt4, Pay_amt5, Pay_amt6, Class * Delete -- https://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls ''' NAME = 'credit_card_clients.data' COLUMNS = [ 'ID', 'Limit_bal', 'Sex', 'Education', 'Marriage', 'Age', 'Pay_0', 'Pay_2', 'Pay_3', 'Pay_4', 'Pay_5', 'Pay_6', 'Bill_amt1', 'Bill_amt2', 'Bill_amt3', 'Bill_amt4', 'Bill_amt5', 'Bill_amt6', 'Pay_amt1', 'Pay_amt2', 'Pay_amt3', 'Pay_amt4', 'Pay_amt5', 'Pay_amt6', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df.drop(['ID'], axis=1, inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_dermatology(dropna=True, verbosity=False): ''' Dermatology Dataset COLUMNS ------------------------------------------- Erythema, Scaling, Definite Borders, Itching, Koebner Phenomenon, Polygonal Papules, Follicular Papules, Oral Mucosal Involvement, Knee And Elbow Involvement, Scalp Involvement, Family History, Melanin Incontinence, Eosinophils In The Infiltrate, Pnl Infiltrate, Fibrosis Of The Papillary Dermis, Exocytosis, Acanthosis, Hyperkeratosis, Parakeratosis, Clubbing Of The Rete Ridges, Elongation Of The Rete Ridges, Thinning Of The Suprapapillary Epidermis, Spongiform Pustule, Munro Microabcess, Focal Hypergranulosis, Disappearance Of The Granular Layer, Vacuolisation And Damage Of Basal Layer, Spongiosis, Saw-Tooth Appearance Of Retes, Follicular Horn Plug, Perifollicular Parakeratosis, Inflammatory Monoluclear Inflitrate, Band-Like Infiltrate, Age**, Class **Missing Values -- https://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data ''' NAME = 'dermatology.data' COLUMNS = [ 'Erythema', 'Scaling', 'Definite Borders', 'Itching', 'Koebner Phenomenon', 'Polygonal Papules', 'Follicular Papules', 'Oral Mucosal Involvement', 'Knee And Elbow Involvement', 'Scalp Involvement', 'Family History', 'Melanin Incontinence', 'Eosinophils In The Infiltrate', 'Pnl Infiltrate', 'Fibrosis Of The Papillary Dermis', 'Exocytosis', 'Acanthosis', 'Hyperkeratosis', 'Parakeratosis', 'Clubbing Of The Rete Ridges', 'Elongation Of The Rete Ridges', 'Thinning Of The Suprapapillary Epidermis', 'Spongiform Pustule', 'Munro Microabcess', 'Focal Hypergranulosis', 'Disappearance Of The Granular Layer', 'Vacuolisation And Damage Of Basal Layer', 'Spongiosis', 'Saw-Tooth Appearance Of Retes', 'Follicular Horn Plug', 'Perifollicular Parakeratosis', 'Inflammatory Monoluclear Inflitrate', 'Band-Like Infiltrate', 'Age', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df.replace('?', np.NaN, inplace=True) if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_ecoli(dropna=True, verbosity=False): ''' Ecoli Data Set COLUMNS ------------------------------------------- Sequence Name*, MCG, GVH, LIP, CHG, AAC, ALM1, ALM2, Class, *Delete -- https://archive.ics.uci.edu/ml/machine-learning-databases/ecoli/ecoli.data ''' NAME = 'ecoli.data' COLUMNS = [ 'Sequence Name', 'MCG', 'GVH', 'LIP', 'CHG', 'AAC', 'ALM1', 'ALM2', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df.replace('?', np.NaN, inplace=True) df.drop('Sequence Name', axis=1, inplace=True) if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_eeg_eye_state(dropna=True, verbosity=False): ''' Data Cortex Nuclear COLUMNS ------------------------------------------- AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4, Class -- https://archive.ics.uci.edu/ml/machine-learning-databases/00264/EEG%20Eye%20State.arff ''' NAME = 'eeg_eye_state.data' COLUMNS = [ 'AF3', 'F7', 'F3', 'FC5', 'T7', 'P7', 'O1', 'O2', 'P8', 'T8', 'FC6', 'F4', 'F8', 'AF4', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_glass(dropna=True, verbosity=False): ''' Glass Type Dataset COLUMNS ------------------------------------------- ID*, Refractive Index, Na, Mg, Al, Si, K, Ca, Ba, Fe, Class * Delete -- https://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data ''' NAME = 'glass.data' COLUMNS = [ 'ID', 'Refractive Index', 'Na', 'Mg', 'Al', 'Si', 'K', 'Ca', 'Ba', 'Fe', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_haberman(dropna=True, verbosity=False): ''' Haberman's Survival Data Set COLUMNS ------------------------------------------- Age, Years of Operation, Positive Axillary Nodes, Class -- https://archive.ics.uci.edu/ml/machine-learning-databases/00264/EEG%20Eye%20State.arff ''' NAME = 'haberman.data' COLUMNS = [ 'Age', 'Years of Operation', 'Positive Axillary Nodes', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_ionosphere(dropna=True, verbosity=False): ''' Ionosphere Data Set COLUMNS ------------------------------------------- ATT 1, ATT 2, ATT 3, ATT 4, ATT 5, ATT 6, ATT 7, ATT 8, ATT 9, ATT 10, ATT 11, ATT 12, ATT 13, ATT 14, ATT 15, ATT 16, ATT 17, ATT 18, ATT 19, ATT 20, ATT 21, ATT 22, ATT 23, ATT 24, ATT 25, ATT 26, ATT 27, ATT 28, ATT 29, ATT 30, ATT 31, ATT 32, ATT 33, ATT 34, Class -- https://archive.ics.uci.edu/ml/machine-learning-databases/ionosphere/ionosphere.data ''' NAME = 'ionosphere.data' COLUMNS = [ 'ATT 1', 'ATT 2', 'ATT 3', 'ATT 4', 'ATT 5', 'ATT 6', 'ATT 7', 'ATT 8', 'ATT 9', 'ATT 10', 'ATT 11', 'ATT 12', 'ATT 13', 'ATT 14', 'ATT 15', 'ATT 16', 'ATT 17', 'ATT 18', 'ATT 19', 'ATT 20', 'ATT 21', 'ATT 22', 'ATT 23', 'ATT 24', 'ATT 25', 'ATT 26', 'ATT 27', 'ATT 28', 'ATT 29', 'ATT 30', 'ATT 31', 'ATT 32', 'ATT 33', 'ATT 34', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df['Class'] = df['Class'].astype('category').cat.codes if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_iris(dropna=True, verbosity=False): ''' Iris Plants Database R.A. Fisher COLUMNS ------------------------------------------- Sepal Length Sepal Width Petal Length Petal Width Class 0. Iris-setosa: 50 1. Iris-versicolor: 50 2. Iris-virginica: 50 -- https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data ''' NAME = 'iris.data' COLUMNS = [ 'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df['Class'] = df['Class'].astype('category').cat.codes if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_messidor(dropna=True, verbosity=False): ''' Diabetic Retinopathy Debrecen COLUMNS ------------------------------------------- Quality assessment, Pre-screening, MA Detection 0.5, MA Detection 0.6, MA Detection 0.7, MA Detection 0.8, MA Detection 0.9, MA Detection 1.0, MA detection Exut 1, MA detection Exut 2, MA detection Exut 3, MA detection Exut 4, MA detection Exut 5, MA detection Exut 6, MA detection Exut 7, MA detection Exut 8, Distance, Diameter, AmFm Classification, Class, -- https://archive.ics.uci.edu/ml/machine-learning-databases/00329/messidor_features.arff ''' NAME = 'messidor_features.data' COLUMNS = [ 'Quality Assessment', 'Pre-screening', 'MA Detection 0.5', 'MA Detection 0.6', 'MA Detection 0.7', 'MA Detection 0.8', 'MA Detection 0.9', 'MA Detection 1.0', 'MA detection Exut 1', 'MA detection Exut 2', 'MA detection Exut 3', 'MA detection Exut 4', 'MA detection Exut 5', 'MA detection Exut 6', 'MA detection Exut 7', 'MA detection Exut 8', 'Distance', 'Diameter', 'AmFm Classification', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df.replace('?', np.NaN, inplace=True) if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_nursery(dropna=True, verbosity=False): ''' Nursery Data Set COLUMNS ------------------------------------------- Parents Has Nurs Form Children Housing Finance Social Health Class -- https://archive.ics.uci.edu/ml/machine-learning-databases/nursery/nursery.data ''' NAME = 'nursery.data' COLUMNS = [ 'Parents', 'Has Nurs', 'Form', 'Children', 'Housing', 'Finance', 'Social', 'Health', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) for c in df.columns: df[c] = df[c].astype('category').cat.codes if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_phishing_websites(dropna=True, verbosity=False): ''' Phishing Websites Data Set COLUMNS ------------------------------------------- Sfh, Popupwidnow, Sslfinal_state, Request_url, Url_of_anchor, Web_traffic, Url_length, Age_of_domain, Having_ip_address, Class, -- https://archive.ics.uci.edu/ml/machine-learning-databases/00379/PhishingData.arff ''' NAME = 'phishing.data' COLUMNS = [ 'Sfh', 'Popupwidnow', 'Sslfinal_state', 'Request_url', 'Url_of_anchor', 'Web_traffic', 'Url_length', 'Age_of_domain', 'Having_ip_address', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df.replace('?', np.NaN, inplace=True) if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_seeds(dropna=True, verbosity=False): ''' Seeds Data Set COLUMNS ------------------------------------------- Area, Perimeter, Compactness, Length of kernel, Width of kernel, Asymmetry coefficient, Length of kernel groove, Class -- https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt ''' NAME = 'seeds.data' COLUMNS = [ 'Area', 'Perimeter', 'Compactness', 'Length of Kernel', 'Width of Kernel', 'Asymmetry Coefficient', 'Length of Kernel Groove', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', sep='\t', names=COLUMNS) for c in df.columns: df[c] = df[c].astype('category').cat.codes if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_seismic_bumps(dropna=True, verbosity=False): ''' Seismic Bumps Data Set COLUMNS ------------------------------------------- Seismic, Seismoacoustic, Shift, Genergy, Gpuls, Gdenergy, Gdpuls, Ghazard, Nbumps, Nbumps2, Nbumps3, Nbumps4, Nbumps5, Nbumps6, Nbumps7, Nbumps89, Energy, Maxenergy, Class -- https://archive.ics.uci.edu/ml/machine-learning-databases/00266/seismic-bumps.arff ''' NAME = 'seismic_bumps.data' COLUMNS = [ 'Seismic', 'Seismoacoustic', 'Shift', 'Genergy', 'Gpuls', 'Gdenergy', 'Gdpuls', 'Ghazard', 'Nbumps', 'Nbumps2', 'Nbumps3', 'Nbumps4', 'Nbumps5', 'Nbumps6', 'Nbumps7', 'Nbumps89', 'Energy', 'Maxenergy', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df['Seismic'] = df['Seismic'].astype('category').cat.codes df['Seismoacoustic'] = df['Seismoacoustic'].astype('category').cat.codes df['Shift'] = df['Shift'].astype('category').cat.codes df['Ghazard'] = df['Ghazard'].astype('category').cat.codes df['Class'] = df['Class'].astype('category').cat.codes if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_soybean(dropna=True, verbosity=False): ''' Soybean Large Data Set COLUMNS ------------------------------------------- Date, Plant-Stand, Precip, Temp, Hail, Crop-Hist, Area-Damaged, Severity, Seed-Tmt, Germination, Plant-Growth, Leaves, Leafspots-Halo, Leafspots-Marg, Leafspot-Size, Leaf-Shread, Leaf-Malf, Leaf-Mild, Stem, Lodging, Stem-Cankers, Canker-Lesion, Fruiting-Bodies, External Decay, Mycelium, Int-Discolor, Sclerotia, Fruit-Pods, Fruit Spots, Seed, Mold-Growth, Seed-Discolor, Seed-Size, Shriveling, Roots, Class -- https://archive.ics.uci.edu/ml/machine-learning-databases/soybean/soybean-large.data ''' NAME = 'soybean.data' COLUMNS = [ 'Class', 'Date', 'Plant-Stand', 'Precip', 'Temp', 'Hail', 'Crop-Hist', 'Area-Damaged', 'Severity', 'Seed-Tmt', 'Germination', 'Plant-Growth', 'Leaves', 'Leafspots-Halo', 'Leafspots-Marg', 'Leafspot-Size', 'Leaf-Shread', 'Leaf-Malf', 'Leaf-Mild', 'Stem', 'Lodging', 'Stem-Cankers', 'Canker-Lesion', 'Fruiting-Bodies', 'External Decay', 'Mycelium', 'Int-Discolor', 'Sclerotia', 'Fruit-Pods', 'Fruit Spots', 'Seed', 'Mold-Growth', 'Seed-Discolor', 'Seed-Size', 'Shriveling', 'Roots' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df.replace('?', np.NaN, inplace=True) if dropna: df.dropna(inplace=True) for c in df.columns: df[c] = df[c].astype('category').cat.codes class_col = df.pop('Class') df['Class'] = class_col if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_tae(dropna=True, verbosity=False): ''' Teaching Assistant Evaluation Data Set COLUMNS ------------------------------------------- Native English Speaker, Instructor, Course, Summer/Regular, Class Size (numerical), Class -- https://archive.ics.uci.edu/ml/machine-learning-databases/tae/tae.data ''' NAME = 'tae.data' COLUMNS = [ 'Native English Speaker', 'Instructor', 'Course', 'Summer/Regular', 'Class Size', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df.replace('?', np.NaN, inplace=True) if dropna: df.dropna(inplace=True) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_wholesale(dropna=True, verbosity=False): ''' Wholesale customers Data Set COLUMNS ------------------------------------------- Channel** Region* Fresh Milk Grocery Frozen Detergents_Paper Delicassen *Chosen class **Could be class -- https://archive.ics.uci.edu/ml/machine-learning-databases/00292/Wholesale%20customers%20data.csv ''' NAME = 'wholesale.data' COLUMNS = [ 'Channel', 'Region', 'Fresh,' 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df.replace('?', np.NaN, inplace=True) if dropna: df.dropna(inplace=True) df['Class'] = df.pop('Channel') if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_wine(dropna=True, verbosity=False): ''' Wine Quality P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. COLUMNS ------------------------------------------- Fixed Acidity Volatile Acidity Citric Acid Residual Sugar Chlorides Free Sulfur Dioxide Total Sulfur Dioxide Density Ph Sulphates Alcohol Class (Quality) -- https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv ''' NAME = 'wine.data' COLUMNS = [ 'Fixed Acidity', 'Volatile Acidity', 'Citric Acid', 'Residual Sugar', 'Chlorides', 'Free Sulfur Dioxide', 'Total Sulfur Dioxide', 'Density', 'Ph', 'Sulphates', 'Alcohol', 'Class' ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS, sep=';') #, skiprows=1) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df # def load_titanic(dropna=True, verbosity=False): ''' Titanic COLUMNS ------------------------------------------- Survived Pclass Name Sex Age Siblings/Spouses Aboard, Parents/Children Aboard Fare *New -- https://www.kaggle.com/c/titanic/data?select=train.csv ''' NAME = 'titanic.data' COLUMNS = [ 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SS Aboard', 'PC Aboard', 'Fare', ] df = pd.read_csv(f'{PATH}{NAME}', names=COLUMNS) df['Sex'] = df['Sex'].astype('category').cat.codes #df['Title'] = df['Name'].apply(lambda x: x.split('.')[0]).astype('category').cat.codes columns = list(df.columns) if verbosity: aux = '\n ' print(f'Data: {NAME}') print(f'Lines: {df.shape[0]}') print(f'Columns:\n {aux.join(df.columns)}') return df[columns[1:] + columns[0:1]]

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d72773cb3f6e14f15d6ff0d24a1a9437a90f37c5

136

py

Python

OpenCart/pages/__init__.py

turovod/Otus

57433c6944bca155177b07ff361139ff30f7f692

[ "MIT" ]

null

OpenCart/pages/__init__.py

turovod/Otus

57433c6944bca155177b07ff361139ff30f7f692

[ "MIT" ]

null

OpenCart/pages/__init__.py

turovod/Otus

57433c6944bca155177b07ff361139ff30f7f692

[ "MIT" ]

null

from .base_page import BasePage from .common_page import CommonPage from .login_page import LoginLogout from .main_page import MainPage

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d773e429daa9526ecb1f71d424d914614fd4e568

174

py

Python

tests/unit/test_random_port.py

danni-m/RLTest

85c09592e96e26edab94a22077a582fa425b62fa

[ "BSD-3-Clause" ]

15

2018-09-06T12:07:47.000Z

2022-03-02T05:27:31.000Z

tests/unit/test_random_port.py

danni-m/RLTest

85c09592e96e26edab94a22077a582fa425b62fa

[ "BSD-3-Clause" ]

79

2018-09-04T13:25:56.000Z

2022-03-31T22:48:26.000Z

tests/unit/test_random_port.py

danni-m/RLTest

85c09592e96e26edab94a22077a582fa425b62fa

[ "BSD-3-Clause" ]

12

2018-09-04T23:17:04.000Z

2021-07-18T12:33:54.000Z

from unittest import TestCase class Test(TestCase): def test_register_port(self): pass class Test(TestCase): def test_get_random_port(self): pass

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d792ea9172cd0ad6ba515cb3f4de8f42b7942d67

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py

Python

src/filters/__init__.py

raboakye/python-packages-intro

ef17ad08ad7b822d900762bc1c320028096b0523

[ "MIT" ]

null

src/filters/__init__.py

raboakye/python-packages-intro

ef17ad08ad7b822d900762bc1c320028096b0523

[ "MIT" ]

null

src/filters/__init__.py

raboakye/python-packages-intro

ef17ad08ad7b822d900762bc1c320028096b0523

[ "MIT" ]

null

from app.src.filters import main

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6

ad06034c82d1f4d7dee1a88657a9094a3b3c96c5

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py

Python

packages/pyright-scip/snapshots/input/aliased_import/actual.py

sourcegraph/pyright

f6a94a47f7e61172fd108ee9a4c62f748e1d24af

[ "MIT" ]

null

packages/pyright-scip/snapshots/input/aliased_import/actual.py

sourcegraph/pyright

f6a94a47f7e61172fd108ee9a4c62f748e1d24af

[ "MIT" ]

19

2022-03-17T03:20:34.000Z

2022-03-31T02:53:12.000Z

packages/pyright-scip/snapshots/input/aliased_import/actual.py

sourcegraph/pyright

f6a94a47f7e61172fd108ee9a4c62f748e1d24af

[ "MIT" ]

null

import aliased import aliased as A print(A.SOME_CONSTANT)

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ad4f085a7e8fc8d52f1dae9b983c384c82286474

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py

Python

scraper/__init__.py

u-aaa/Vilnius-Apartment-Predictions

de9ab9433aa71891b6d19cc4deecef33b0453ac3

[ "MIT" ]

null

scraper/__init__.py

u-aaa/Vilnius-Apartment-Predictions

de9ab9433aa71891b6d19cc4deecef33b0453ac3

[ "MIT" ]

null

scraper/__init__.py

u-aaa/Vilnius-Apartment-Predictions

de9ab9433aa71891b6d19cc4deecef33b0453ac3

[ "MIT" ]

null

from .aruodas_scraper import AruodasScraper

22

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1

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6

ad903555804ce744773c13a81923e8bf04dbb36f

10,039

py

Python

ChineseReverseDictionary/code/result_analysis_Ch.py

thunlp/MultiRD

fe72148c00a72eaebcd22e58104e9588dfb72fa4

[ "MIT" ]

82

2019-12-08T05:01:45.000Z

2022-03-09T06:32:44.000Z

ChineseReverseDictionary/code/result_analysis_Ch.py

thunlp/MultiRD

fe72148c00a72eaebcd22e58104e9588dfb72fa4

[ "MIT" ]

3

2021-03-28T15:02:07.000Z

2022-03-21T01:29:48.000Z

ChineseReverseDictionary/code/result_analysis_Ch.py

thunlp/MultiRD

fe72148c00a72eaebcd22e58104e9588dfb72fa4

[ "MIT" ]

20

2019-12-19T08:18:17.000Z

2022-03-21T01:37:15.000Z

import argparse import json, os import numpy as np def evaluate_test(ground_truth, prediction): accu_1 = 0. accu_10 = 0. accu_100 = 0. length = len(ground_truth) pred_rank = [] for i in range(length): try: pred_rank.append(prediction[i][:].index(ground_truth[i])) except: pred_rank.append(1000) if ground_truth[i] in prediction[i][:100]: accu_100 += 1 if ground_truth[i] in prediction[i][:10]: accu_10 += 1 if ground_truth[i] == prediction[i][0]: accu_1 += 1 return pred_rank, accu_1/length*100, accu_10/length*100, accu_100/length*100, np.median(pred_rank), np.sqrt(np.var(pred_rank)) def evaluate_synset(ground_truth, prediction): # one batch accu_1 = 0. accu_10 = 0. accu_100 = 0. length = len(ground_truth) # batch size for i in range(length): if prediction[i][0] in ground_truth[i]: accu_1 += 1 accu_10 += 1 accu_100 += 1 elif set(prediction[i][:10]).intersection(set(ground_truth[i])): accu_10 += 1 accu_100 += 1 elif set(prediction[i][:100]).intersection(set(ground_truth[i])): accu_100 += 1 return accu_1/length*100, accu_10/length*100, accu_100/length*100 def evaluate_1stChar(ground_truth, prediction): accu_1 = 0. accu_10 = 0. accu_100 = 0. length = len(ground_truth) prediction_char = [[]]*length i = 0 for gt in ground_truth: if len(gt)==1: # 中文中要排除只有一个字的情况，当只有一个字时，仍然用原来的预测结果，不进行已知字的筛选。 prediction_char[i] = prediction[i] i += 1 continue char1st = gt[0] prediction_char[i] = [] for wd in prediction[i]: if wd[0] == char1st: prediction_char[i].append(wd) i += 1 pred_rank = [] for i in range(length): try: pred_rank.append(prediction_char[i][:].index(ground_truth[i])) except: pred_rank.append(1000) if ground_truth[i] in prediction_char[i][:100]: accu_100 += 1 if ground_truth[i] in prediction_char[i][:10]: accu_10 += 1 if ground_truth[i] == prediction_char[i][0]: accu_1 += 1 return accu_1/length*100, accu_10/length*100, accu_100/length*100, np.median(pred_rank), np.sqrt(np.var(pred_rank)) def evaluate_len(ground_truth, prediction): accu_1 = 0. accu_10 = 0. accu_100 = 0. length = len(ground_truth) prediction_len = [[]]*length i = 0 for gt in ground_truth: leng = len(gt) prediction_len[i] = [] for wd in prediction[i]: if len(wd) == leng: prediction_len[i].append(wd) i += 1 pred_rank = [] for i in range(length): try: pred_rank.append(prediction_len[i][:].index(ground_truth[i])) except: pred_rank.append(1000) if ground_truth[i] in prediction_len[i][:100]: accu_100 += 1 if ground_truth[i] in prediction_len[i][:10]: accu_10 += 1 if ground_truth[i] == prediction_len[i][0]: accu_1 += 1 return accu_1/length*100, accu_10/length*100, accu_100/length*100, np.median(pred_rank), np.sqrt(np.var(pred_rank)) def evaluate_POS(ground_truth, prediction, word_pos): accu_1 = 0. accu_10 = 0. accu_100 = 0. length = len(ground_truth) prediction_pos = [[]]*length i = 0 for gt in ground_truth: pos = set(word_pos[gt]) prediction_pos[i] = [] for wd in prediction[i]: try: if (set(word_pos[wd]) & pos): prediction_pos[i].append(wd) except: prediction_pos[i].append(wd) # 为什么会有没词性的？ #print(wd) i += 1 pred_rank = [] for i in range(length): try: pred_rank.append(prediction_pos[i][:].index(ground_truth[i])) except: pred_rank.append(1000) if ground_truth[i] in prediction_pos[i][:100]: accu_100 += 1 if ground_truth[i] in prediction_pos[i][:10]: accu_10 += 1 if ground_truth[i] == prediction_pos[i][0]: accu_1 += 1 return accu_1/length*100, accu_10/length*100, accu_100/length*100, np.median(pred_rank), np.sqrt(np.var(pred_rank)) def main(mode): label_list_wd = json.load(open(mode+'_label_list.json')) print('load file : '+mode+'_label_list.json'+' [OK]') pred_list_wd = json.load(open(mode+'_pred_list.json')) print('load file : '+mode+'_pred_list.json'+' [OK]') synset_all = dict() with open('../data/word2synset_synset.txt') as f: for line in f.readlines(): wd_l = line.split() synset_all[wd_l[0]] = wd_l # it must include itself synset = [] for wd in label_list_wd: if wd in synset_all: synset.append(synset_all[wd]) else: synset.append(wd) diction = json.load(open('../data/dictionary_sense.json')) word_pos = {} word_pos['<OOV>'] = [] for wd in diction: if diction[wd]['POS'] == []: word_pos[wd] = ['介', '副', '数', '连', '助', '动', '形', '代', '拟声', '量', '名', '叹'] else: word_pos[wd] = diction[wd]['POS'] print('Test on 2000: ') pred_rank_list, test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_test(label_list_wd[:2000], pred_list_wd[:2000]) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 2000 synset: ') test_accu_1, test_accu_10, test_accu_100 = evaluate_synset(synset[:2000], pred_list_wd[:2000]) print('test_accu(1/10/100): %.2f %.2F %.2f'%(test_accu_1, test_accu_10, test_accu_100)) print('Test on 2000 char1st: ') test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_1stChar(label_list_wd[:2000], pred_list_wd[:2000]) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 2000 wordLen: ') test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_len(label_list_wd[:2000], pred_list_wd[:2000]) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 2000 POS: ') test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_POS(label_list_wd[:2000], pred_list_wd[:2000], word_pos) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 200: ') pred_rank_list, test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_test(label_list_wd[2000:2200], pred_list_wd[2000:2200]) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 200 synset: ') test_accu_1, test_accu_10, test_accu_100 = evaluate_synset(synset[2000:2200], pred_list_wd[2000:2200]) print('test_accu(1/10/100): %.2f %.2F %.2f'%(test_accu_1, test_accu_10, test_accu_100)) print('Test on 200 char1st: ') test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_1stChar(label_list_wd[2000:2200], pred_list_wd[2000:2200]) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 200 wordLen: ') test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_len(label_list_wd[2000:2200], pred_list_wd[2000:2200]) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 200 POS: ') test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_POS(label_list_wd[2000:2200], pred_list_wd[2000:2200], word_pos) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 272: ') pred_rank_list, test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_test(label_list_wd[2200:], pred_list_wd[2200:]) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 272 synset: ') test_accu_1, test_accu_10, test_accu_100 = evaluate_synset(synset[2200:], pred_list_wd[2200:]) print('test_accu(1/10/100): %.2f %.2F %.2f'%(test_accu_1, test_accu_10, test_accu_100)) print('Test on 272 char1st: ') test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_1stChar(label_list_wd[2200:], pred_list_wd[2200:]) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 272 wordLen: ') test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_len(label_list_wd[2200:], pred_list_wd[2200:]) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) print('Test on 272 POS: ') test_accu_1, test_accu_10, test_accu_100, median, variance = evaluate_POS(label_list_wd[2200:], pred_list_wd[2200:], word_pos) print('test_accu(1/10/100): %.2f %.2F %.2f %.1f %.2f'%(test_accu_1, test_accu_10, test_accu_100, median, variance)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-m', '--mode', type=str, default='[mode]') args = parser.parse_args() main(args.mode)

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null

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1

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null

0

6

d10957b0da9d2b12cbe5cddb20608a916b170a7e

33

py

Python

app/Watch/__init__.py

LonglyCode/flask-blog

b7f36e8798c61aa1669ede59452f3ca446f5b9ce

[ "MIT" ]

2

2016-10-04T14:53:27.000Z

2019-01-11T02:08:47.000Z

app/Watch/__init__.py

LonglyCode/flask-blog

b7f36e8798c61aa1669ede59452f3ca446f5b9ce

[ "MIT" ]

null

app/Watch/__init__.py

LonglyCode/flask-blog

b7f36e8798c61aa1669ede59452f3ca446f5b9ce

[ "MIT" ]

null

from .file_watch import init_app

16.5

32

0.848485

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1

0

1

0

1

0

6

d10b3ece23ed9f0d45baa9b433a739d7832b9008

74

py

Python

algo/__init__.py

rwbfd/rl_lab

a402b4c595c8abf5659e2493614d9890e62ff7b6

[ "MIT" ]

43

2018-09-18T02:36:30.000Z

2022-03-09T09:41:11.000Z

algo/__init__.py

rwbfd/rl_lab

a402b4c595c8abf5659e2493614d9890e62ff7b6

[ "MIT" ]

1

2019-05-30T06:46:22.000Z

algo/__init__.py

rwbfd/rl_lab

a402b4c595c8abf5659e2493614d9890e62ff7b6

[ "MIT" ]

8

2018-09-21T16:01:50.000Z

2020-11-30T11:42:09.000Z

from .a2c_acktr import A2C_ACKTR from .ppo import PPO from .sil import SIL

24.666667

32

0.810811

14

74

4.142857

0.428571

0.275862

0

0.031746

0.148649

74

3

33

24.666667

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true

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null

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1

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6

d110a58ff31c9ea059663346de63755ad5a3a3dd

25,907

py

Python

models/model_builder.py

norton-chris/MARS-Net

6f671837d0629422680c78adf9b643894debae70

[ "MIT" ]

null

models/model_builder.py

norton-chris/MARS-Net

6f671837d0629422680c78adf9b643894debae70

[ "MIT" ]

null

models/model_builder.py

norton-chris/MARS-Net

6f671837d0629422680c78adf9b643894debae70

[ "MIT" ]

null

''' Author Junbong Jang Date 6/2/2021 To build model for train.py and predict.py ''' from deeplabv3 import Deeplabv3 from deep_neural_net_classifier import * from deep_neural_net_MTL import * from deep_neural_net import * from deep_neural_net_3D import * from deep_neural_net_attn import * from deep_neural_net_layer import * from model_utils import get_MTL_weights, get_MTL_auto_remove_task import loss import numpy as np from tensorflow.keras import backend as K from tensorflow.keras.optimizers import Adam, SGD if tf.__version__.split('.')[0] == '2': import tensorflow_addons as tfa from sam import SAMModel def build_model_predict(constants, frame, repeat_index, model_name, image_rows, image_cols, orig_rows, orig_cols): weights_path = constants.get_trained_weights_path(str(frame), model_name, str(repeat_index)) if "VGG19_MTL" in str(constants.strategy_type): model = VGG19_MTL(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19_classifier_regressor" in str(constants.strategy_type): model = VGG19_classifier_regressor(image_rows, image_cols, weights_path=weights_path) elif "VGG19_classifier" in str(constants.strategy_type): model = VGG19_classifier(image_rows, image_cols, weights_path=weights_path) elif "VGG19D_classifier" in str(constants.strategy_type): model = VGG19D_classifier(image_rows, image_cols, weights_path=weights_path) elif "EFF_B7_classifier" in str(constants.strategy_type): model = EFF_B7_classifier(image_rows, image_cols, weights_path=weights_path) elif "vit_classifier" in str(constants.strategy_type): model = vit_classifier(image_rows, image_cols, 1, weights_path=weights_path) # -------------------------------------------------------------------------------- elif "Res50V2" in str(constants.strategy_type): model = ResNet50V2Keras(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "Dense201" in str(constants.strategy_type): model = DenseNet201Keras(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "InceptionResV2" in str(constants.strategy_type): model = InceptionResV2(image_rows, image_cols, 0, image_cols - orig_cols, image_rows - orig_rows, weights_path=weights_path) elif "deeplabv3" in str(constants.strategy_type): model = Deeplabv3(input_shape=(image_rows, image_cols, 3), output_shape=(orig_rows, orig_cols)) model.load_weights(weights_path, by_name=True) elif "VGG16_dropout" in str(constants.strategy_type): model = VGG16_dropout(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG16_batchnorm" in str(constants.strategy_type): model = VGG16_batchnorm(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG16_instancenorm" in str(constants.strategy_type): model = VGG16_instancenorm(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "movie3" in str(constants.strategy_type): model = VGG16_movie(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG16_dac_input256" in constants.strategy_type: model = VGG16_dac(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG16_spp_input256" in constants.strategy_type: model = VGG16_spp(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG16" in str(constants.strategy_type): model = VGG16(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "spheroid_test_VGG19" in str(constants.strategy_type): # model = VGG19(image_rows, image_cols, int((image_cols-orig_cols)/2), 0, 0, weights_path=weights_path, encoder_weights=None) model = VGG19(image_rows, image_cols, 64, image_cols-orig_cols-64, image_rows-orig_rows-64, weights_path=weights_path, encoder_weights=None) elif "VGG19D_temporal_context_residual" in str(constants.strategy_type): model = VGG19D_temporal_context_residual(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19D_temporal_distributed_v2" in str(constants.strategy_type): model = VGG19D_temporal_distributed_v2(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19D_temporal_distributed" in str(constants.strategy_type): model = VGG19D_temporal_distributed(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19D_temporal_attn_v3" in str(constants.strategy_type): model = VGG19D_temporal_attn_v3(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19D_temporal_attn_v2" in str(constants.strategy_type): model = VGG19D_temporal_attn_v2(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19D_temporal_attn" in str(constants.strategy_type): model = VGG19D_temporal_attn(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19D_crop_first" in str(constants.strategy_type): model = VGG19D_crop_first(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19D_se" in str(constants.strategy_type): model = VGG19D_se(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19_dropout_gelu" in str(constants.strategy_type): model = VGG19_dropout_gelu(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19_dropout_swish" in str(constants.strategy_type): model = VGG19_dropout_swish(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19_dropout_dac" in str(constants.strategy_type): model = VGG19_dropout_dac(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19_dropout_feature_extractor" in str(constants.strategy_type): model = VGG19_dropout_feature_extractor(image_rows, image_cols, 0, image_cols - orig_cols, image_rows - orig_rows, weights_path=weights_path) elif "VGG19_batchnorm_dropout" in str(constants.strategy_type): model = VGG19_batchnorm_dropout(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19_batchnorm" in str(constants.strategy_type): model = VGG19_batchnorm(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19_dropout" in str(constants.strategy_type) or "VGG19D" in str(constants.strategy_type): model = VGG19_dropout(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19_freeze" in str(constants.strategy_type): model = VGG19_freeze(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path, encoder_weights=None) elif "VGG19_imagenet_pretrained" in str(constants.strategy_type): model = VGG19_imagenet_pretrained(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "VGG19" in str(constants.strategy_type): model = VGG19(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path, encoder_weights=None) elif "EFF_B7" in str(constants.strategy_type): model = EFF_B7(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "unet_3D" in str(constants.strategy_type): model = UNet_3D(32, image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "unet_feature_extractor" in str(constants.strategy_type): model = UNet_feature_extractor(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "unet_imagenet_pretrained" in str(constants.strategy_type): model = UNet_imagenet_pretrained(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) elif "unet" in str(constants.strategy_type) or "Unet" in str(constants.strategy_type): model = UNet(image_rows, image_cols, 0, image_cols-orig_cols, image_rows-orig_rows, weights_path=weights_path) return model def build_model_train(constants, args, frame, model_name): pretrained_weights_path = constants.get_pretrained_weights_path(frame, model_name) if "VGG19_MTL_auto" in str(constants.strategy_type): removed_tasks = get_MTL_auto_remove_task(constants.strategy_type) model = VGG19_MTL_auto(args.input_size, args.input_size, args.cropped_boundary, 0, 0, removed_tasks, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=None) elif "VGG19_MTL" in str(constants.strategy_type): model = VGG19_MTL(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) cls, reg, aut, seg = get_MTL_weights(constants.strategy_type) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy', tf.keras.losses.MeanSquaredError(), tf.keras.losses.MeanAbsoluteError(), tfa.losses.sigmoid_focal_crossentropy], loss_weights={"segmentation": seg, "autoencoder": aut, "regressor": reg, "classifier": cls}) elif "VGG19_classifier_regressor" in str(constants.strategy_type): model = VGG19_classifier_regressor(args.input_size, args.input_size, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=[tf.keras.losses.MeanAbsoluteError(), tfa.losses.sigmoid_focal_crossentropy], loss_weights={"regressor":0.01,"classifier":1}) elif "VGG19_classifier_custom_loss" in str(constants.strategy_type): model = VGG19_classifier_custom_loss(args.input_size, args.input_size, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=None) elif "VGG19_classifier_binary" in str(constants.strategy_type): model = VGG19_classifier(args.input_size, args.input_size, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=[tf.keras.losses.BinaryCrossentropy()], metrics=['accuracy']) elif "VGG19_classifier" in str(constants.strategy_type): model = VGG19_classifier(args.input_size, args.input_size, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=[tfa.losses.SigmoidFocalCrossEntropy(alpha=0.5)], metrics=['accuracy']) elif "VGG19D_classifier" in str(constants.strategy_type): model = VGG19D_classifier(args.input_size, args.input_size, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=[tfa.losses.SigmoidFocalCrossEntropy(alpha=0.5)], metrics=['accuracy']) elif "EFF_B7_classifier" in str(constants.strategy_type): model = EFF_B7_classifier(args.input_size, args.input_size, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=[tfa.losses.SigmoidFocalCrossEntropy(alpha=0.5)], metrics=['accuracy']) elif "vit_classifier" in str(constants.strategy_type): model = vit_classifier(args.input_size, args.input_size, 1, weights_path=pretrained_weights_path) # model = SAMModel(model) model.compile(optimizer=Adam(lr=1e-5), loss=[tfa.losses.SigmoidFocalCrossEntropy(alpha=0.5)], metrics=['accuracy']) # -------------------------------------------------------------------------------- elif "Res50V2" in str(constants.strategy_type): model = ResNet50V2Keras(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "InceptionResV2" in str(constants.strategy_type): model = InceptionResV2(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "Dense201" in str(constants.strategy_type): model = DenseNet201Keras(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "deeplabv3" in str(constants.strategy_type): model = Deeplabv3(input_shape=(args.input_size, args.input_size, 3), output_shape=(68, 68), right_crop=0, bottom_crop=0) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG16_dropout" in str(constants.strategy_type): model = VGG16_dropout(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG16_batchnorm" in str(constants.strategy_type): model = VGG16_batchnorm(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG16_instancenorm" in str(constants.strategy_type): model = VGG16_instancenorm(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG16_movie3" in str(constants.strategy_type): model = VGG16_movie(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=loss.temporal_cross_entropy, metrics=[loss.dice_coef]) elif "VGG16_dice" in str(constants.strategy_type): model = VGG16(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=[loss.dice_coef], metrics=['binary_crossentropy']) elif "VGG16_l2" in str(constants.strategy_type): model = VGG16_l2(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG16_dac_input256" in constants.strategy_type: model = VGG16_dac(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG16_spp_input256" in constants.strategy_type: model = VGG16_spp(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG16_no_pretrain" in str(constants.strategy_type): model = VGG16(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path, encoder_weights=None) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG16" in str(constants.strategy_type): model = VGG16(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19D_crop_first" in str(constants.strategy_type): model = VGG19D_crop_first(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19D_se" in str(constants.strategy_type): model = VGG19D_se(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19D_temporal_se" in str(constants.strategy_type): model = VGG19D_temporal_se(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19D_temporal_distributed_v2" in str(constants.strategy_type): model = VGG19D_temporal_distributed_v2(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19D_temporal_distributed" in str(constants.strategy_type): model = VGG19D_temporal_distributed(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19D_temporal_context_residual" in str(constants.strategy_type): model = VGG19D_temporal_context_residual(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19D_temporal_attn_v3" in str(constants.strategy_type): model = VGG19D_temporal_attn_v3(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19D_temporal_attn_v2" in str(constants.strategy_type): model = VGG19D_temporal_attn_v2(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19D_temporal_attn" in str(constants.strategy_type): model = VGG19D_temporal_attn(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19_dropout_dac_input256" in str(constants.strategy_type): model = VGG19_dropout_dac(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19_dropout_feature_extractor" in str(constants.strategy_type): model = VGG19_dropout_feature_extractor(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy', loss.zero_loss], metrics=[loss.dice_coef, loss.zero_loss]) elif "VGG19_batchnorm_dropout" in str(constants.strategy_type): model = VGG19_batchnorm_dropout(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19_dropout_gelu" in str(constants.strategy_type): model = VGG19_dropout_gelu(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19_dropout_swish" in str(constants.strategy_type): model = VGG19_dropout_swish(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19_dropout" in str(constants.strategy_type) or "VGG19D" in str(constants.strategy_type): model = VGG19_dropout(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19_batchnorm" in str(constants.strategy_type): model = VGG19_batchnorm(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19_no_pretrain_freeze" in str(constants.strategy_type): model = VGG19_freeze(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path, encoder_weights=None) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19_no_pretrain" in str(constants.strategy_type): model = VGG19(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path, encoder_weights=None) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19_imagenet_pretrained" in str(constants.strategy_type): model = VGG19_imagenet_pretrained(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19_freeze" in str(constants.strategy_type): model = VGG19_freeze(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "VGG19" in str(constants.strategy_type): model = VGG19(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "EFF_B7" in str(constants.strategy_type): model = EFF_B7(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "unet_3D" in str(constants.strategy_type): model = UNet_3D(args.input_depth, args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "unet_feature_extractor" in str(constants.strategy_type): model = UNet_feature_extractor(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy', loss.zero_loss], metrics=[loss.dice_coef, loss.zero_loss]) elif "unet_imagenet_pretrained" in str(constants.strategy_type): model = UNet_imagenet_pretrained(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) elif "unet" in str(constants.strategy_type): model = UNet(args.input_size, args.input_size, args.cropped_boundary, 0, 0, weights_path=pretrained_weights_path) model.compile(optimizer=Adam(lr=1e-5), loss=['binary_crossentropy'], metrics=[loss.dice_coef]) return model

69.642473

189

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5.050203

0.048172

0.11567

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0.135954

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null

0

6

d13905354b9f40e14fa9941c71b7a41e4774328a

5,021

py

Python

multiflap/ms_package/integrator_2.py

vortexlab-uclouvain/multiflap

6de0a9ceabf8c42b72b2a82943fb78e105480636

[ "Apache-2.0" ]

13

2020-12-05T15:35:57.000Z

2022-03-14T09:09:03.000Z

multiflap/ms_package/integrator_2.py

vortexlab-uclouvain/multiflap

6de0a9ceabf8c42b72b2a82943fb78e105480636

[ "Apache-2.0" ]

1

2021-04-26T18:36:12.000Z

2021-04-27T14:20:43.000Z

multiflap/ms_package/integrator_2.py

vortexlab-uclouvain/multiflap

6de0a9ceabf8c42b72b2a82943fb78e105480636

[ "Apache-2.0" ]

null

import numpy as np import collections class Integrator: def __init__(self, ode_system, x0, time_array): self.ode_system = ode_system self.x0 = x0 self.time_array = time_array def rk4(self): """ Runge-Kutta 4 Integrator. Inputs: VelocityFunction: Function name to integrate this function must have two inputs namely state space vector and time. For example: velocity(ssp, t) InitialCondition: Initial condition, 1xd NumPy array, where d is the dimension of the state space TimeArray: 1 x Nt NumPy array which contains instances for the solution to be returned. Outputs: solution: Nt x d NumPy array which contains numerical solution of the ODE. """ rk_sol = collections.namedtuple('rk_sol',['x', 't']) #Generate the solution array to fill in: solution = np.zeros((np.size(self.time_array, 0), np.size(self.x0, 0))) #Assign the initial condition to the first element: solution[0, :] = self.x0 for i in range(0, np.size(self.time_array) - 1): #Read time element: deltat = self.time_array[i + 1] - self.time_array[i] #Runge Kutta k's: k1 = deltat * self.ode_system(solution[i], self.time_array[i]) k2 = deltat * self.ode_system(solution[i]+k1/2.0, self.time_array[i]+deltat/2.0) k3 = deltat * self.ode_system(solution[i]+k2/2.0, self.time_array[i]+deltat/2.0) k4 = deltat * self.ode_system(solution[i]+k3, self.time_array[i]+deltat) #Next integration step: solution[i + 1] = solution[i] + ((k1 +2*k2 + 2*k3 + k4)/6.0) sol = rk_sol(solution, self.time_array) return sol def rk3(self): """ Runge-Kutta 3 Integrator. Inputs: VelocityFunction: Function name to integrate this function must have two inputs namely state space vector and time. For example: velocity(ssp, t) InitialCondition: Initial condition, 1xd NumPy array, where d is the dimension of the state space TimeArray: 1 x Nt NumPy array which contains instances for the solution to be returned. Outputs: solution: Nt x d NumPy array which contains numerical solution of the ODE. """ rk_sol = collections.namedtuple('rk_sol',['x', 't']) #Generate the solution array to fill in: solution = np.zeros((np.size(self.time_array, 0), np.size(self.x0, 0))) #Assign the initial condition to the first element: solution[0, :] = self.x0 for i in range(0, np.size(self.time_array) - 1): #Read time element: deltat = self.time_array[i + 1] - self.time_array[i] #Runge Kutta k's: k1 = deltat * self.ode_system(solution[i], self.time_array[i]) k2 = deltat * self.ode_system(solution[i]+k1/2.0, self.time_array[i]+deltat/2.0) k3 = deltat * self.ode_system(solution[i] -k1 + 2*k2, self.time_array[i]+deltat) #Next integration step: solution[i + 1] = solution[i] + ((k1 +4*k2 + k3)/6.0) sol = rk_sol(solution, self.time_array) return sol def rk2(self): """ Runge-Kutta 2 Integrator. Inputs: VelocityFunction: Function name to integrate this function must have two inputs namely state space vector and time. For example: velocity(ssp, t) InitialCondition: Initial condition, 1xd NumPy array, where d is the dimension of the state space TimeArray: 1 x Nt NumPy array which contains instances for the solution to be returned. Outputs: solution: Nt x d NumPy array which contains numerical solution of the ODE. """ rk_sol = collections.namedtuple('rk_sol',['x', 't']) #Generate the solution array to fill in: solution = np.zeros((np.size(self.time_array, 0), np.size(self.x0, 0))) #Assign the initial condition to the first element: solution[0, :] = self.x0 for i in range(0, np.size(self.time_array)-1): #Read time element: deltat = self.time_array[i + 1] - self.time_array[i] #Runge Kutta k's: k1 = deltat * self.ode_system(solution[i], self.time_array[i]) k2 = deltat * self.ode_system(solution[i]+k1*(2./3.), self.time_array[i]+deltat*(2./3.)) #Next integration step: solution[i + 1] = solution[i] + (k1/4. + (3./4.)*k2) sol = rk_sol(solution, self.time_array) return sol

42.550847

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0

null

0

1

0

null

0

6

d143b8eb90116bd3627e8f5565cdd2a5b0f44511

19,466

py

Python

experiments/0_setup.py

helenacuesta/multif0-estimation-polyvocals

4960f5415f8a170f2ff8d5b776bfd4cb5576d3ba

[ "MIT" ]

36

2020-09-13T12:30:41.000Z

2022-02-15T08:52:58.000Z

experiments/0_setup.py

helenacuesta/multif0-estimation-polyvocals

4960f5415f8a170f2ff8d5b776bfd4cb5576d3ba

[ "MIT" ]

6

2020-09-04T11:14:14.000Z

2022-02-09T23:49:59.000Z

experiments/0_setup.py

helenacuesta/multif0-estimation-polyvocals

4960f5415f8a170f2ff8d5b776bfd4cb5576d3ba

[ "MIT" ]

null

''' This script creates the audio mixtures for all the working datasets. ''' import sox import os import pandas as pd import librosa import soundfile import scipy from experiments import config import utils def combine_audio_files(params): cmb = sox.Combiner() cmb.convert(samplerate=22050) cmb.build( [ os.path.join(params['audio_folder'], params['filenames'][0]), os.path.join(params['audio_folder'], params['filenames'][1]), os.path.join(params['audio_folder'], params['filenames'][2]), os.path.join(params['audio_folder'], params['filenames'][3]) ], os.path.join(config.audio_save_folder, params['output_fname']), 'mix') # , 'mix', input_volumes=[0.6, 0.3, 0.3, 0.3]) # if the reverb option is active, this creates the reverb audio files using an IR from Isophonics if params['reverb']: y_ir, sr_ir = librosa.load('./ir/IR_greathall.wav', sr=params['sr']) y_sig, sr_sig = librosa.load(os.path.join(config.audio_save_folder, params['output_fname']), sr=params['sr']) y_rev = scipy.signal.convolve(y_sig, y_ir, mode="full") soundfile.write(os.path.join(config.audio_save_folder, 'reverb', params['output_fname']), y_rev, samplerate=params['sr']) def create_dict_entry(diction, audiopath, audiofname, annot_files, annot_folder): diction[audiofname] = dict() diction[audiofname]['audiopath'] = audiopath diction[audiofname]['annot_files'] = annot_files diction[audiofname]['annot_folder'] = annot_folder return diction def create_full_dataset_mixes(dataset, mixes_wavpath, reverb=True, compute_audio_mix=True, compute_metadata=True): mtracks = dict() # ------------ Process Choral Singing Dataset ------------ # print("Processing Choral Singing Dataset...") for song in dataset['CSD']['songs']: for combo in dataset['CSD']['combos']: params = {} params['audio_folder'] = config.csd_folder params['annot_folder'] = config.csd_folder params['sr'] = 44100 params['reverb'] = True params['filenames'] = [ '{}_soprano_{}.wav'.format(song, combo[0]), '{}_alto_{}.wav'.format(song, combo[1]), '{}_tenor_{}.wav'.format(song, combo[2]), '{}_bass_{}.wav'.format(song, combo[3]), ] params['output_fname'] = '{}_{}_{}_{}_{}.wav'.format(song, combo[0], combo[1], combo[2], combo[3]) if compute_audio_mix and not os.path.exists(os.path.join(mixes_wavpath, params['output_fname'])): # create audio mixture and its reverb version if indicated combine_audio_files(params) if compute_metadata: print("Annotations for {}".format(song)) # create_dict_entry(diction, audiopath, audiofname, annot_files, annot_folder) annotation_files = [ '{}_soprano_{}.jams'.format(song, combo[0]), '{}_alto_{}.jams'.format(song, combo[1]), '{}_tenor_{}.jams'.format(song, combo[2]), '{}_bass_{}.jams'.format(song, combo[3]) ] mtracks = create_dict_entry(mtracks, mixes_wavpath, params['output_fname'], annotation_files, params['annot_folder']) if reverb: idx=-1 for annot in annotation_files: idx+=1 utils.shift_annotations(params['annot_folder'], annot, params['audio_folder'], params['filenames'][idx]) print("Mixtures for {} have been created.".format(song)) # ------------ Process ESMUC ChoralSet ------------ # print("Processing ESMUC Choral Dataset...") # Der Greis for song in dataset['ECS']['DG_songs']: for combo in dataset['ECS']['DG_combos']: params = {} params['audio_folder'] = config.ecs_folder params['annot_folder'] = config.ecs_folder params['sr'] = 22050 params['reverb'] = True params['filenames'] = [ "{}_S{}.wav".format(song, combo[0]), "{}_A{}.wav".format(song, combo[1]), "{}_T{}.wav".format(song, combo[2]), "{}_B{}.wav".format(song, combo[3]) ] params['output_fname'] = '{}_{}_{}_{}_{}.wav'.format(song, combo[0], combo[1], combo[2], combo[3]) if compute_audio_mix and not os.path.exists(os.path.join(mixes_wavpath, params['output_fname'])): # create audio mixture and its reverb version if indicated combine_audio_files(params) if compute_metadata: print("Annotations for {}".format(song)) # create_dict_entry(diction, audiopath, audiofname, annot_files, annot_folder) annotation_files = [ '{}_S{}.jams'.format(song, combo[0]), '{}_A{}.jams'.format(song, combo[1]), '{}_T{}.jams'.format(song, combo[2]), '{}_B{}.jams'.format(song, combo[3]) ] mtracks = create_dict_entry(mtracks, mixes_wavpath, params['output_fname'], annotation_files, params['annot_folder']) if reverb: idx=-1 for annot in annotation_files: idx+=1 utils.shift_annotations(params['annot_folder'], annot, params['audio_folder'], params['filenames'][idx]) #print("Reverb annotations not created for the reverb versions. Working on annotation shift.") print('{} quartets mixed and exported'.format(song)) # Die Himmel for song in dataset['ECS']['DH_songs']: for combo in dataset['ECS']['DG_combos']: params = {} params['audio_folder'] = config.ecs_folder params['annot_folder'] = config.ecs_folder params['sr'] = 22050 params['reverb'] = True params['filenames'] = [ "{}_{}.wav".format(song, dataset['ECS']['DH_singers'][combo[0]-1]), "{}_{}.wav".format(song, dataset['ECS']['DH_singers'][combo[1]-1+5]), "{}_{}.wav".format(song, dataset['ECS']['DH_singers'][combo[2]-1+7]), "{}_{}.wav".format(song, dataset['ECS']['DH_singers'][combo[3]-1+10]) ] params['output_fname'] = '{}_{}_{}_{}_{}.wav'.format(song, combo[0], combo[1], combo[2], combo[3]) if compute_audio_mix and not os.path.exists(os.path.join(mixes_wavpath, params['output_fname'])): # create audio mixture and its reverb version if indicated combine_audio_files(params) if compute_metadata: print("Annotations for {}".format(song)) # create_dict_entry(diction, audiopath, audiofname, annot_files, annot_folder) annotation_files = [ '{}_{}.jams'.format(song, dataset['ECS']['DH_singers'][combo[0]-1]), '{}_{}.jams'.format(song, dataset['ECS']['DH_singers'][combo[1]-1+5]), '{}_{}.jams'.format(song, dataset['ECS']['DH_singers'][combo[2]-1+7]), '{}_{}.jams'.format(song, dataset['ECS']['DH_singers'][combo[3]-1+10]) ] mtracks = create_dict_entry(mtracks, mixes_wavpath, params['output_fname'], annotation_files, params['annot_folder']) if reverb: idx=-1 for annot in annotation_files: idx+=1 utils.shift_annotations(params['annot_folder'], annot, params['audio_folder'], params['filenames'][idx]) print('{} quartets mixed and exported'.format(song)) # Seele Christi for song in dataset['ECS']['SC_songs']: for combo in dataset['ECS']['SC_combos']: params = {} params['audio_folder'] = config.ecs_folder params['annot_folder'] = config.ecs_folder params['sr'] = 22050 params['reverb'] = True params['filenames'] = [ "{}_S{}.wav".format(song, combo[0]), "{}_A{}.wav".format(song, combo[1]), "{}_T{}.wav".format(song, combo[2]), "{}_B{}.wav".format(song, combo[3]) ] params['output_fname'] = '{}_{}_{}_{}_{}.wav'.format(song, combo[0], combo[1], combo[2], combo[3]) if compute_audio_mix and not os.path.exists(os.path.join(mixes_wavpath, params['output_fname'])): # create audio mixture and its reverb version if indicated combine_audio_files(params) if compute_metadata: print("Annotations for {}".format(song)) # create_dict_entry(diction, audiopath, audiofname, annot_files, annot_folder) annotation_files = [ "{}_S{}.jams".format(song, combo[0]), "{}_A{}.jams".format(song, combo[1]), "{}_T{}.jams".format(song, combo[2]), "{}_B{}.jams".format(song, combo[3]) ] mtracks = create_dict_entry(mtracks, mixes_wavpath, params['output_fname'], annotation_files, params['annot_folder']) if reverb: idx = -1 for annot in annotation_files: idx += 1 utils.shift_annotations(params['annot_folder'], annot, params['audio_folder'], params['filenames'][idx]) print('{} quartets mixed and exported'.format(song)) # ------------ Process Dagstuhl ChoirSet ------------ # print("Processing Dagstuhl ChoirSet...") # Full Choir setting for song in dataset['DCS']['FC_songs']: params = {} params['audio_folder'] = config.dcs_folder_audio params['annot_folder'] = config.dcs_folder_annot params['sr'] = 22050 params['reverb'] = True params['filenames'] = [ "{}_{}.wav".format(song, dataset['DCS']['FC_singers'][0]), "{}_{}.wav".format(song, dataset['DCS']['FC_singers'][1]), "{}_{}.wav".format(song, dataset['DCS']['FC_singers'][2]), "{}_{}.wav".format(song, dataset['DCS']['FC_singers'][3]) ] # no combos here, there are only four singers per song params['output_fname'] = "{}_1_2_2_2.wav".format(song) if compute_audio_mix and not os.path.exists(os.path.join(mixes_wavpath, params['output_fname'])): combine_audio_files(params) if compute_metadata: print("Annotations for {}".format(song)) annotation_files = [ "{}_{}.jams".format(song, dataset['DCS']['FC_singers'][0]), "{}_{}.jams".format(song, dataset['DCS']['FC_singers'][1]), "{}_{}.jams".format(song, dataset['DCS']['FC_singers'][2]), "{}_{}.jams".format(song, dataset['DCS']['FC_singers'][3]) ] mtracks = create_dict_entry(mtracks, mixes_wavpath, params['output_fname'], annotation_files, params['annot_folder']) if reverb: idx = -1 for annot in annotation_files: idx += 1 utils.shift_annotations(params['annot_folder'], annot, params['audio_folder'], params['filenames'][idx]) print('{} quartets mixed and exported'.format(song)) # Quartet A setting for song in dataset['DCS']['QA_songs']: params = {} params['audio_folder'] = config.dcs_folder_audio params['annot_folder'] = config.dcs_folder_annot params['sr'] = 22050 params['reverb'] = True params['filenames'] = [ "{}_{}.wav".format(song, dataset['DCS']['QA_singers'][0]), "{}_{}.wav".format(song, dataset['DCS']['QA_singers'][1]), "{}_{}.wav".format(song, dataset['DCS']['QA_singers'][2]), "{}_{}.wav".format(song, dataset['DCS']['QA_singers'][3]) ] # no combos here, there are only four singers per song params['output_fname'] = "{}_2_1_1_1.wav".format(song) if compute_audio_mix and not os.path.exists(os.path.join(mixes_wavpath, params['output_fname'])): combine_audio_files(params) if compute_metadata: print("Annotations for {}".format(song)) annotation_files = [ "{}_{}.jams".format(song, dataset['DCS']['QA_singers'][0]), "{}_{}.jams".format(song, dataset['DCS']['QA_singers'][1]), "{}_{}.jams".format(song, dataset['DCS']['QA_singers'][2]), "{}_{}.jams".format(song, dataset['DCS']['QA_singers'][3]) ] mtracks = create_dict_entry(mtracks, mixes_wavpath, params['output_fname'], annotation_files, params['annot_folder']) if reverb: idx = -1 for annot in annotation_files: idx += 1 utils.shift_annotations(params['annot_folder'], annot, params['audio_folder'], params['filenames'][idx]) print('{} quartets mixed and exported'.format(song)) # Quartet B setting for song in dataset['DCS']['QB_songs']: params = {} params['audio_folder'] = config.dcs_folder_audio params['annot_folder'] = config.dcs_folder_annot params['sr'] = 22050 params['reverb'] = True params['filenames'] = [ "{}_{}.wav".format(song, dataset['DCS']['QB_singers'][0]), "{}_{}.wav".format(song, dataset['DCS']['QB_singers'][1]), "{}_{}.wav".format(song, dataset['DCS']['QB_singers'][2]), "{}_{}.wav".format(song, dataset['DCS']['QB_singers'][3]) ] # no combos here, there are only four singers per song params['output_fname'] = "{}_1_2_2_2.wav".format(song) if compute_audio_mix and not os.path.exists(os.path.join(mixes_wavpath, params['output_fname'])): combine_audio_files(params) if compute_metadata: print("Annotations for {}".format(song)) annotation_files = [ "{}_{}.jams".format(song, dataset['DCS']['QB_singers'][0]), "{}_{}.jams".format(song, dataset['DCS']['QB_singers'][1]), "{}_{}.jams".format(song, dataset['DCS']['QB_singers'][2]), "{}_{}.jams".format(song, dataset['DCS']['QB_singers'][3]) ] mtracks = create_dict_entry(mtracks, mixes_wavpath, params['output_fname'], annotation_files, params['annot_folder']) if reverb: idx = -1 for annot in annotation_files: idx += 1 utils.shift_annotations(params['annot_folder'], annot, params['audio_folder'], params['filenames'][idx]) print('{} quartets mixed and exported'.format(song)) # ------------ Process Barbershop Quartets ------------ # print("Processing Barbershop Quartets...") song_idx = -1 for song in dataset['BSQ']['songs']: song_idx += 1 parts = dataset['BSQ']['num_parts'][song_idx] params = {} params['audio_folder'] = config.bsq_folder_audio params['annot_folder'] = config.bsq_folder_annot params['sr'] = 44100 params['reverb'] = True params['filenames'] = [ "{}_part{}_s_1ch.wav".format(song, parts), "{}_part{}_a_1ch.wav".format(song, parts), "{}_part{}_t_1ch.wav".format(song, parts), "{}_part{}_b_1ch.wav".format(song, parts) ] params['output_fname'] = "{}_{}_satb.wav".format(song, parts) if compute_audio_mix and not os.path.exists(os.path.join(mixes_wavpath, params['output_fname'])): combine_audio_files(params) if compute_metadata: print("Annotations for {}".format(song)) annotation_files = [ "{}_part{}_s_1ch_pyin.jams".format(song, parts), "{}_part{}_a_1ch_pyin.jams".format(song, parts), "{}_part{}_t_1ch_pyin.jams".format(song, parts), "{}_part{}_b_1ch_pyin.jams".format(song, parts) ] mtracks = create_dict_entry(mtracks, mixes_wavpath, params['output_fname'], annotation_files, params['annot_folder']) if reverb: idx = -1 for annot in annotation_files: idx += 1 utils.shift_annotations(params['annot_folder'], annot, params['audio_folder'], params['filenames'][idx]) print('{} quartets mixed and exported'.format(song)) # ------------ Process Bach Chorales ------------ # print("Processing Bach Chorales...") song_idx = -1 for song in dataset['BC']['songs']: song_idx += 1 parts = dataset['BC']['num_parts'][song_idx] params = {} params['audio_folder'] = config.bc_folder_audio params['annot_folder'] = config.bc_folder_annot params['sr'] = 44100 params['reverb'] = True params['filenames'] = [ "{}_part{}_s_1ch.wav".format(song, parts), "{}_part{}_a_1ch.wav".format(song, parts), "{}_part{}_t_1ch.wav".format(song, parts), "{}_part{}_b_1ch.wav".format(song, parts) ] params['output_fname'] = "{}_{}_satb.wav".format(song, parts) if compute_audio_mix and not os.path.exists(os.path.join(mixes_wavpath, params['output_fname'])): combine_audio_files(params) if compute_metadata: print("Annotations for {}".format(song)) annotation_files = [ "{}_part{}_s_1ch_pyin.jams".format(song, parts), "{}_part{}_a_1ch_pyin.jams".format(song, parts), "{}_part{}_t_1ch_pyin.jams".format(song, parts), "{}_part{}_b_1ch_pyin.jams".format(song, parts) ] mtracks = create_dict_entry(mtracks, mixes_wavpath, params['output_fname'], annotation_files, params['annot_folder']) if reverb: idx = -1 for annot in annotation_files: idx += 1 utils.shift_annotations(params['annot_folder'], annot, params['audio_folder'], params['filenames'][idx]) print('{} quartets mixed and exported'.format(song)) # Store the metadata file if compute_metadata: utils.save_json_data(mtracks, os.path.join(mixes_wavpath, 'mtracks_info.json')) def main(): # load the dataset info dataset = config.dataset print("Dataset info loaded.") # use the dataset information to create audio mixtures and annotations create_full_dataset_mixes(dataset, config.audio_save_folder, reverb=True, compute_audio_mix=True, compute_metadata=True) if __name__ == '__main__': main()

40.136082

133

0.545823

2,122

19,466

4.773798

0.084354

0.09773

0.057749

0.047384

0.862685

0.82616

0.800888

0.728529

0.705133

0.68154

0

0.014578

0.295233

19,466

484

134

40.219008

0.723814

0.075465

0

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0.012308

0

1

0.012195

false

0

0.02439

0

0.039634

0.073171

0

null

0

1

0

null

0

6

d16568ace50afca4c1410f303eb90d9ff022f87d

6,667

py

Python

ElectroWeakAnalysis/WMuNu/test/WMuNuCandidateHistogrammer.py

m-sedghi/cmssw

859df8affee372c53be79cdd2d8a5ff001eae841

[ "Apache-2.0" ]

1

2019-12-19T13:43:44.000Z

ElectroWeakAnalysis/WMuNu/test/WMuNuCandidateHistogrammer.py

m-sedghi/cmssw

859df8affee372c53be79cdd2d8a5ff001eae841

[ "Apache-2.0" ]

7

2020-02-10T18:55:34.000Z

2022-01-16T20:08:44.000Z

ElectroWeakAnalysis/WMuNu/test/WMuNuCandidateHistogrammer.py

m-sedghi/cmssw

859df8affee372c53be79cdd2d8a5ff001eae841

[ "Apache-2.0" ]

1

2020-12-17T23:09:17.000Z

import FWCore.ParameterSet.Config as cms # Process, how many events, inout files, ... process = cms.Process("wmunuplots") process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) ) process.source = cms.Source("PoolSource", debugVerbosity = cms.untracked.uint32(0), debugFlag = cms.untracked.bool(False), # fileNames = cms.untracked.vstring( # '/store/user/cepeda/mytestSkim_PTR_Wmunu_10pb/EWK_WMuNu_SubSkim_31Xv3_1.root', # '/store/user/cepeda/mytestSkim_PTR_Wmunu_10pb/EWK_WMuNu_SubSkim_31Xv3_2.root', # '/store/user/cepeda/mytestSkim_PTR_Wmunu_10pb/EWK_WMuNu_SubSkim_31Xv3_3.root', # '/store/user/cepeda/mytestSkim_PTR_Wmunu_10pb/EWK_WMuNu_SubSkim_31Xv3_4.root', # '/store/user/cepeda/mytestSkim_PTR_Wmunu_10pb/EWK_WMuNu_SubSkim_31Xv3_5.root', # '/store/user/cepeda/mytestSkim_PTR_Wmunu_10pb/EWK_WMuNu_SubSkim_31Xv3_6.root', # '/store/user/cepeda/mytestSkim_PTR_Wmunu_10pb/EWK_WMuNu_SubSkim_31Xv3_7.root', # '/store/user/cepeda/mytestSkim_PTR_Wmunu_10pb/EWK_WMuNu_SubSkim_31Xv3_8.root', # '/store/user/cepeda/mytestSkim_PTR_Wmunu_10pb/EWK_WMuNu_SubSkim_31Xv3_9.root', # '/store/user/cepeda/mytestSkim_PTR_Wmunu_10pb/EWK_WMuNu_SubSkim_31Xv3_10.root' #) fileNames = cms.untracked.vstring( "file:EWK_WMuNu_SubSkim_31Xv3.root" # "file:AOD_with_WCandidates.root" ) ) # Debug/info printouts process.MessageLogger = cms.Service("MessageLogger", cerr = cms.untracked.PSet( enable = cms.untracked.bool(False) ), cout = cms.untracked.PSet( default = cms.untracked.PSet( limit = cms.untracked.int32(-1) ), enable = cms.untracked.bool(True), threshold = cms.untracked.string('DEBUG') ), debugModules = cms.untracked.vstring( 'corMetWMuNus', 'selcorMet' ) ) process.selcorMet = cms.EDFilter("WMuNuSelector", # Fill Basc Histograms? -> plotHistograms = cms.untracked.bool(True), # Input collections -> MuonTag = cms.untracked.InputTag("muons"), TrigTag = cms.untracked.InputTag("TriggerResults::HLT8E29"), JetTag = cms.untracked.InputTag("antikt5CaloJets"), WMuNuCollectionTag = cms.untracked.InputTag("corMetWMuNus"), # Preselection! MuonTrig = cms.untracked.string("HLT_Mu9"), PtThrForZ1 = cms.untracked.double(20.0), PtThrForZ2 = cms.untracked.double(10.0), EJetMin = cms.untracked.double(40.), NJetMax = cms.untracked.int32(999999), # Main cuts -> PtCut = cms.untracked.double(25.0), EtaCut = cms.untracked.double(2.1), IsRelativeIso = cms.untracked.bool(True), IsCombinedIso = cms.untracked.bool(False), IsoCut03 = cms.untracked.double(0.1), MtMin = cms.untracked.double(50.0), MtMax = cms.untracked.double(200.0), MetMin = cms.untracked.double(-999999.), MetMax = cms.untracked.double(999999.), AcopCut = cms.untracked.double(2.), # Muon quality cuts -> DxyCut = cms.untracked.double(0.2), NormalizedChi2Cut = cms.untracked.double(10.), TrackerHitsCut = cms.untracked.int32(11), IsAlsoTrackerMuon = cms.untracked.bool(True), # Select only W-, W+ ( default is all Ws) SelectByCharge=cms.untracked.int32(0) ) process.selpfMet = cms.EDFilter("WMuNuSelector", # Fill Basc Histograms? -> plotHistograms = cms.untracked.bool(True), # Preselection! MuonTrig = cms.untracked.string("HLT_Mu9"), PtThrForZ1 = cms.untracked.double(20.0), PtThrForZ2 = cms.untracked.double(10.0), EJetMin = cms.untracked.double(40.), NJetMax = cms.untracked.int32(999999), # Input collections -> MuonTag = cms.untracked.InputTag("muons"), TrigTag = cms.untracked.InputTag("TriggerResults::HLT8E29"), JetTag = cms.untracked.InputTag("antikt5CaloJets"), WMuNuCollectionTag = cms.untracked.InputTag("pfMetWMuNus"), # Main cuts -> UseTrackerPt = cms.untracked.bool(True), PtCut = cms.untracked.double(25.0), EtaCut = cms.untracked.double(2.1), IsRelativeIso = cms.untracked.bool(True), IsCombinedIso = cms.untracked.bool(False), IsoCut03 = cms.untracked.double(0.1), MtMin = cms.untracked.double(50.0), MtMax = cms.untracked.double(200.0), MetMin = cms.untracked.double(-999999.), MetMax = cms.untracked.double(999999.), AcopCut = cms.untracked.double(2.), # Muon quality cuts -> DxyCut = cms.untracked.double(0.2), NormalizedChi2Cut = cms.untracked.double(10.), TrackerHitsCut = cms.untracked.int32(11), IsAlsoTrackerMuon = cms.untracked.bool(True), # Select only W-, W+ ( default is all Ws) SelectByCharge=cms.untracked.int32(0) ) process.seltcMet = cms.EDFilter("WMuNuSelector", # Fill Basc Histograms? -> plotHistograms = cms.untracked.bool(True), # Input collections -> MuonTag = cms.untracked.InputTag("muons"), TrigTag = cms.untracked.InputTag("TriggerResults::HLT8E29"), JetTag = cms.untracked.InputTag("antikt5CaloJets"), WMuNuCollectionTag = cms.untracked.InputTag("tcMetWMuNus"), # Preselection! MuonTrig = cms.untracked.string("HLT_Mu9"), PtThrForZ1 = cms.untracked.double(20.0), PtThrForZ2 = cms.untracked.double(10.0), EJetMin = cms.untracked.double(40.), NJetMax = cms.untracked.int32(999999), # Main cuts -> UseTrackerPt = cms.untracked.bool(True), PtCut = cms.untracked.double(25.0), EtaCut = cms.untracked.double(2.1), IsRelativeIso = cms.untracked.bool(True), IsCombinedIso = cms.untracked.bool(False), IsoCut03 = cms.untracked.double(0.1), MtMin = cms.untracked.double(50.0), MtMax = cms.untracked.double(200.0), MetMin = cms.untracked.double(-999999.), MetMax = cms.untracked.double(999999.), AcopCut = cms.untracked.double(2.), # Muon quality cuts -> DxyCut = cms.untracked.double(0.2), NormalizedChi2Cut = cms.untracked.double(10.), TrackerHitsCut = cms.untracked.int32(11), IsAlsoTrackerMuon = cms.untracked.bool(True), # Select only W-, W+ ( default is all Ws) SelectByCharge=cms.untracked.int32(0) ) process.TFileService = cms.Service("TFileService", fileName = cms.string('WMuNuBasicPlots.root') ) # Steering the process process.path1 = cms.Path(process.selcorMet) process.path2 = cms.Path(process.selpfMet) process.path3 = cms.Path(process.seltcMet)

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null

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6

66fa8e02daaf7ea6f0e12b5a4f99e7170223ff9f

85

py

Python

rA9/synapses/__init__.py

junhoyeo/rA9

6ab5537880f842b36ae666f0ef5645acc62c236e

[ "MIT" ]

2

2020-10-09T00:36:06.000Z

2020-10-20T06:20:19.000Z

rA9/synapses/__init__.py

junhoyeo/rA9

6ab5537880f842b36ae666f0ef5645acc62c236e

[ "MIT" ]

null

rA9/synapses/__init__.py

junhoyeo/rA9

6ab5537880f842b36ae666f0ef5645acc62c236e

[ "MIT" ]

1

2020-10-09T00:36:08.000Z

from .Conv import * from .pooling import * from .Linear import * from .loss import *

17

22

0.717647

12

85

5.083333

0.5

0.491803

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0.188235

85

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null

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0

1

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1

0

6

66fab7ec7906c810a48e49907a5ed0f0e65a9b43

108

py

Python

indexedconv/__init__.py

vuillaut/IndexedConv

781f6252248fc80ce80524389c51b8bb74de3052

[ "MIT" ]

13

2018-11-05T13:17:44.000Z

2022-01-08T12:01:09.000Z

indexedconv/__init__.py

vuillaut/IndexedConv

781f6252248fc80ce80524389c51b8bb74de3052

[ "MIT" ]

12

2018-10-20T13:31:20.000Z

2019-10-23T10:55:05.000Z

indexedconv/__init__.py

vuillaut/IndexedConv

781f6252248fc80ce80524389c51b8bb74de3052

[ "MIT" ]

7

2018-11-26T16:49:08.000Z

2020-07-28T01:58:56.000Z

import indexedconv.utils import indexedconv.engine import indexedconv.nets from .version import __version__

21.6

32

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108

6.923077

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null

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6

0f19c06d28b709e4056741947f49ef6303871738

252

bzl

Python

test/proto_cross_repo_boundary/repo.bzl

blorente/rules_scala

0c1ed832f2db5fa1069c7b21d546f234d078d210

[ "Apache-2.0" ]

326

2016-02-24T18:28:10.000Z

2022-03-30T08:51:08.000Z

test/proto_cross_repo_boundary/repo.bzl

blorente/rules_scala

0c1ed832f2db5fa1069c7b21d546f234d078d210

[ "Apache-2.0" ]

1,157

2016-02-24T04:26:27.000Z

2022-03-31T05:59:14.000Z

test/proto_cross_repo_boundary/repo.bzl

ConsultingMD/rules_scala

75b0bef95a2ced6062229e5ea4cfce7047eead30

[ "Apache-2.0" ]

262

2016-02-24T18:29:21.000Z

2022-03-24T21:39:20.000Z

def proto_cross_repo_boundary_repository(): native.new_local_repository( name = "proto_cross_repo_boundary", path = "test/proto_cross_repo_boundary/repo", build_file = "test/proto_cross_repo_boundary/repo/BUILD.repo", )

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0.166667

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null

1

0

1

0

1

null

0

1

0

6

0f4f366719f239c45d1f49a9aa45daa0b054431d

76

py

Python

codes/deeplearning/data/__init__.py

sarvai/proposals

578c0094db52594cd85acb843df82fe3c19db46d

[ "Apache-2.0" ]

null

codes/deeplearning/data/__init__.py

sarvai/proposals

578c0094db52594cd85acb843df82fe3c19db46d

[ "Apache-2.0" ]

null

codes/deeplearning/data/__init__.py

sarvai/proposals

578c0094db52594cd85acb843df82fe3c19db46d

[ "Apache-2.0" ]

null

from .workbench import workbench from .pose_workbench import pose_workbench

25.333333

42

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10

76

6.4

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null

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1

0

1

0

1

0

6

0f68704d2b40022d35a3796f70e08e96b8d765b3

4,109

py

Python

tests/test_factoryboy_state.py

hrother/pytest-factoryboy-state

66661a0f608d2174e3996a0ccb6c3a27bf35284f

[ "MIT" ]

1

2021-06-15T21:17:30.000Z

tests/test_factoryboy_state.py

hrother/pytest-factoryboy-state

66661a0f608d2174e3996a0ccb6c3a27bf35284f

[ "MIT" ]

2

2022-03-20T22:45:57.000Z

2022-03-21T00:06:07.000Z

tests/test_factoryboy_state.py

hrother/pytest-factoryboy-state

66661a0f608d2174e3996a0ccb6c3a27bf35284f

[ "MIT" ]

null

# -*- coding: utf-8 -*- def test_help_message(testdir): result = testdir.runpytest( "--help", ) # fnmatch_lines does an assertion internally result.stdout.fnmatch_lines( [ "factoryboy-state:", "*--show-state*Show factoryboy state for failures.", "*--set-state=FACTORYBOY_STATE", "*Set factoryboy state.", ] ) def test_does_nothing_when_not_explicitly_called(testdir): testdir.makepyfile( """ def test_failure(): assert False """ ) result = testdir.runpytest("") result.stdout.no_fnmatch_line("=*= factory-boy random state =*=") def test_shows_state_on_failure(testdir): testdir.makepyfile( """ def test_failure(): assert False """ ) result = testdir.runpytest("--show-state") result.stdout.fnmatch_lines(["=*= factory-boy random state =*="]) def test_shows_state_on_failure_from_environment_variable(testdir, monkeypatch): monkeypatch.setenv("SHOW_FACTORYBOY_STATE", "True") testdir.makepyfile( """ def test_failure(): assert False """ ) result = testdir.runpytest() result.stdout.fnmatch_lines(["=*= factory-boy random state =*="]) def test_shows_state_on_error(testdir): testdir.makepyfile( """ import pytest @pytest.fixture def foo(): raise Exception def test_failure(foo): assert True """ ) result = testdir.runpytest("--show-state") result.stdout.fnmatch_lines(["=*= factory-boy random state =*="]) def test_shows_state_on_error_for_environment_variable(testdir, monkeypatch): monkeypatch.setenv("SHOW_FACTORYBOY_STATE", "True") testdir.makepyfile( """ import pytest @pytest.fixture def foo(): raise Exception def test_failure(foo): assert True """ ) result = testdir.runpytest() result.stdout.fnmatch_lines(["=*= factory-boy random state =*="]) def test_uses_set_state(testdir, state): testdir.makepyfile( """ import factory class User: def __init__(self, name): self.name = name class UserFactory(factory.Factory): class Meta: model = User name = factory.Faker("first_name") def test_user_name(): user = UserFactory() assert user.name == "Sara" """ ) result = testdir.runpytest("-v", f"--set-state={state}") result.stdout.fnmatch_lines( [ "*::test_user_name PASSED*", ] ) assert result.ret == 0 def test_uses_set_state_from_environment(testdir, state, monkeypatch): monkeypatch.setenv("FACTORYBOY_STATE", state) testdir.makepyfile( """ import factory class User: def __init__(self, name): self.name = name class UserFactory(factory.Factory): class Meta: model = User name = factory.Faker("first_name") def test_user_name(): user = UserFactory() assert user.name == "Sara" """ ) result = testdir.runpytest("-v") result.stdout.fnmatch_lines( [ "*::test_user_name PASSED*", ] ) assert result.ret == 0 def test_ignores_invalid_state(testdir): testdir.makepyfile( """ import factory class User: def __init__(self, name): self.name = name class UserFactory(factory.Factory): class Meta: model = User name = factory.Faker("first_name") def test_user_name(): user = UserFactory() assert user.name == "Sara" """ ) result = testdir.runpytest("-v", "--set-state=x") result.stdout.fnmatch_lines( [ "*::test_user_name FAILED*", ] ) assert result.ret != 0

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4,109

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0

null

0

1

0

null

0

6

7e67dae07dd26619ffb58032d4d5f68712f7e834

711

py

Python

test_utils.py

zakomo/GMReorganizer

9253e80cf89bbad84d868bd7aa191e6b6b18b5d8

[ "MIT" ]

null

test_utils.py

zakomo/GMReorganizer

9253e80cf89bbad84d868bd7aa191e6b6b18b5d8

[ "MIT" ]

null

test_utils.py

zakomo/GMReorganizer

9253e80cf89bbad84d868bd7aa191e6b6b18b5d8

[ "MIT" ]

null

import utils def test_sanitize_names_html_entity_with_hash(): assert utils.sanitize_name("c'erano") == "c'erano" def test_sanitize_names_html_entity_with_name(): assert utils.sanitize_name("hello&g'day") == "hello&g'day" def test_sanitize_names_with_pathsep_linux(): assert utils.sanitize_name("hello/world") == "hello-world" def test_sanitize_names_with_pathsep_win(): assert utils.sanitize_name("hello\\world") == "hello-world" def test_sanitize_names_with_spaces(): assert utils.sanitize_name(" hello world \t") == "hello world" def test_sanitize_names_mixed(): assert utils.sanitize_name(" The night & the day are 'rad") == "The night & the day are 'rad"

27.346154

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0.183299

0.244399

0.674134

0.617108

0.411405

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0

0.006452

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711

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true

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null

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1

0

1

0

1

0

6

7e9d404a592c5e253f882f7b8330b61e4612fd2e

43

py

Python

horsepics/__init__.py

Mason-McGough/HorsePics

deacc3bedb69147b1584bf8b159624789fbdd5c9

[ "MIT" ]

null

horsepics/__init__.py

Mason-McGough/HorsePics

deacc3bedb69147b1584bf8b159624789fbdd5c9

[ "MIT" ]

null

horsepics/__init__.py

Mason-McGough/HorsePics

deacc3bedb69147b1584bf8b159624789fbdd5c9

[ "MIT" ]

null

from .stitch import * from .adjust import *

21.5

21

0.744186

6

43

5.333333

0.666667

0

0.162791

43

2

22

21.5

0.888889

0

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true

0

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null

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null

0

1

0

1

0

1

0

6

0e2f40c94ea37960f3215f15956564b4b827f8ad

107

py

Python

src/repository/__init__.py

DiceNameIsMy/fastapi-registration

ea1d0f69bb6fcdac77adbc136a8061ca56e05e18

[ "MIT" ]

1

2022-01-12T14:29:51.000Z

src/repository/__init__.py

DiceNameIsMy/fastapi-registration

ea1d0f69bb6fcdac77adbc136a8061ca56e05e18

[ "MIT" ]

null

src/repository/__init__.py

DiceNameIsMy/fastapi-registration

ea1d0f69bb6fcdac77adbc136a8061ca56e05e18

[ "MIT" ]

null

from .repository import Repository, get_repository_class __all__ = ["Repository", "get_repository_class"]

26.75

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107

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0

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35.666667

0.814433

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false

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null

1

0

1

0

null

0

1

0

6

0e6c575a9082626a215abb10d8b10b1bb63f4eca

11,719

py

Python

cogs/logging.py

Joystickplays/GoMod

f48af46b08e095136cb048d9dbb76a5f539f4ea1

[ "Apache-2.0" ]

1

2022-02-25T04:25:21.000Z

cogs/logging.py

Joystickplays/GoMod

f48af46b08e095136cb048d9dbb76a5f539f4ea1

[ "Apache-2.0" ]

null

cogs/logging.py

Joystickplays/GoMod

f48af46b08e095136cb048d9dbb76a5f539f4ea1

[ "Apache-2.0" ]

null

import discord import asyncio from discord.ext import commands from bot import GoModBot class Logging(commands.Cog): def __init__(self, bot): self.bot = bot async def cog_check(self, ctx): lookup = await self.bot.db.fetchrow("SELECT * FROM modules WHERE server = $1 AND module = $2", ctx.guild.id, "lg") return lookup is not None @commands.Cog.listener() async def on_member_join(self, member): for record in self.bot.logcache: if record["guildid"] == member.guild.id and record["loggingtype"] == "m": channel = self.bot.get_channel(record["channelid"]) if channel is None: return embed = discord.Embed(title="Member joined", description=f"{member.name} has joined {member.guild.name}", color=discord.Color.green()).add_field(name="Member count", value=f"{member.guild.member_count}") await channel.send(embed=embed) return logs = await self.bot.db.fetch("SELECT * FROM logch") for log in logs: tempdict = {} tempdict["guildid"] = log["guildid"] tempdict["channelid"] = log["channelid"] tempdict["loggingtype"] = log["loggingtype"] self.bot.logcache.append(tempdict) if log["guildid"] == member.guild.id and log["loggingtype"] == "m": channel = self.bot.get_channel(record["channelid"]) if channel is None: return embed = discord.Embed(title="Member joined", description=f"{member.name} has joined {member.guild.name}", color=discord.Color.green()).add_field(name="Member count", value=f"{member.guild.member_count}") await channel.send(embed=embed) return @commands.Cog.listener() async def on_member_remove(self, member): for record in self.bot.logcache: if record["guildid"] == member.guild.id and record["loggingtype"] == "m": channel = self.bot.get_channel(record["channelid"]) if channel is None: return embed = discord.Embed(title="Member left", description=f"{member.name} has left {member.guild.name}", color=discord.Color.orange()).add_field(name="Member count", value=f"{member.guild.member_count}") await channel.send(embed=embed) return logs = await self.bot.db.fetch("SELECT * FROM logch") for log in logs: tempdict = {} tempdict["guildid"] = log["guildid"] tempdict["channelid"] = log["channelid"] tempdict["loggingtype"] = log["loggingtype"] self.bot.logcache.append(tempdict) if log["guildid"] == member.guild.id and log["loggingtype"] == "m": channel = self.bot.get_channel(record["channelid"]) if channel is None: return embed = discord.Embed(title="Member left", description=f"{member.name} has left {member.guild.name}", color=discord.Color.orange()).add_field(name="Member count", value=f"{member.guild.member_count}") await channel.send(embed=embed) return @commands.Cog.listener() async def on_message_edit(self, messagebefore, messageafter): if messagebefore.author.bot: return if messagebefore.guild is None: return for ign in self.bot.logign: if ign["channel"] == messagebefore.channel.id: return ignore = await self.bot.db.fetch("SELECT * FROM ignoredlogs") for ign in ignore: tempdict = {} tempdict["server"] = ign["server"] tempdict["channel"] = ign["channel"] self.bot.logign.append(tempdict) if ign["server"] == messagebefore.guild.id and ign["channel"] == messagebefore.channel.id: return for record in self.bot.logcache: if record["guildid"] == messagebefore.guild.id and record["loggingtype"] == "e": channel = self.bot.get_channel(record["channelid"]) if channel is None: return embed = discord.Embed(title="Message edited", description=f"The following message was edited in `{messagebefore.channel.name}` by `{messagebefore.author.name}`:\n\nFrom:\n```\n{messagebefore.content}\n```\nTo:\n```{messageafter.content}```", color=discord.Color.orange()) await channel.send(embed=embed) return logs = await self.bot.db.fetch("SELECT * FROM logch") for log in logs: tempdict = {} tempdict["guildid"] = log["guildid"] tempdict["channelid"] = log["channelid"] tempdict["loggingtype"] = log["loggingtype"] self.bot.logcache.append(tempdict) if log["guildid"] == messagebefore.guild.id and log["loggingtype"] == "e": channel = self.bot.get_channel(record["channelid"]) if channel is None: return embed = discord.Embed(title="Message edited", description=f"The following message was edited in `{messagebefore.channel.name}` by `{messagebefore.author.name}`:\n\nFrom:\n```\n{messagebefore.content}\n```\nTo:\n```{messageafter.content}```", color=discord.Color.orange()) await channel.send(embed=embed) return @commands.Cog.listener() async def on_message_delete(self, message): if message.author.bot: return if message.guild is None: return for record in self.bot.logcache: if record["guildid"] == message.guild.id and record["loggingtype"] == "d": channel = self.bot.get_channel(record["channelid"]) if channel is None: return embed = discord.Embed(title="Message deleted", description=f"The following message was deleted in `{message.channel.name}` by `{message.author.name}`:\n\n```\n{message.content}\n```", color=discord.Color.red()) await channel.send(embed=embed) return logs = await self.bot.db.fetch("SELECT * FROM logch") for log in logs: tempdict = {} tempdict["guildid"] = log["guildid"] tempdict["channelid"] = log["channelid"] tempdict["loggingtype"] = log["loggingtype"] self.bot.logcache.append(tempdict) if log["guildid"] == message.guild.id and log["loggingtype"] == "d": channel = self.bot.get_channel(log["channelid"]) if channel is None: return embed = discord.Embed(title="Message deleted", description=f"The following message was deleted in `{message.channel.name}` by `{message.author.name}`:\n\n```\n{message.content}\n```", color=discord.Color.red()) await channel.send(embed=embed) return @commands.Cog.listener() async def on_guild_channel_delete(self, channel): for record in self.bot.logcache: if record["channelid"] == channel.id: await self.bot.db.execute("DELETE FROM logch WHERE channelid = $1", channel.id) self.bot.logcache.remove(record) return logs = await self.bot.db.fetch("SELECT * FROM logch") for log in logs: tempdict = {} tempdict["guildid"] = log["guildid"] tempdict["channelid"] = log["channelid"] tempdict["loggingtype"] = log["loggingtype"] self.bot.logcache.append(tempdict) if log["channelid"] == channel.id: await self.bot.db.execute("DELETE FROM logch WHERE channelid = $1", channel.id) self.bot.logcache.remove(log) return @commands.command() @commands.has_permissions(manage_messages=True) async def createlogging(self, ctx): embed = discord.Embed(title="Logging setup", description=f"You will setup the channel {ctx.channel.mention}. Continue?", color=0x00b2ff) msg = await ctx.send(embed=embed) await msg.add_reaction("✅") await msg.add_reaction("❌") def check(reaction, user): return user == ctx.author and reaction.message.id == msg.id and str(reaction.emoji) in ["✅", "❌"] try: reaction, user = await self.bot.wait_for('reaction_add', check=check, timeout=60) except asyncio.TimeoutError: await msg.edit(embed=discord.Embed(title="Logging setup", description="Timed out.", color=0x00b2ff)) return if str(reaction.emoji) == "❌": await msg.edit(embed=discord.Embed(title="Logging setup", description="Cancelled.", color=0x00b2ff)) return embed = discord.Embed(title="Logging setup", description="Do you want to make the channel a deletion log, edit log or member log?", color=0x00b2ff) msg = await ctx.send(embed=embed) await msg.add_reaction("🗑") await msg.add_reaction("📝") await msg.add_reaction("👤") def check(reaction, user): return user == ctx.author and reaction.message.id == msg.id and str(reaction.emoji) in ["🗑", "📝", "👤"] try: reaction, user = await self.bot.wait_for('reaction_add', check=check, timeout=60) except asyncio.TimeoutError: await msg.edit(embed=discord.Embed(title="Logging setup", description="Timed out.", color=0x00b2ff)) return if str(reaction.emoji) == "🗑": embed = discord.Embed(title="Setting...", description="Setting up deletion logging...", color=0x00b2ff) await ctx.send(embed=embed) await self.bot.db.execute("INSERT INTO logch (channelid, loggingtype, guildid) VALUES ($1, $2, $3)", ctx.channel.id, "d", ctx.guild.id) embed = discord.Embed(title="Logging setup", description="Complete! To test, try deleting a message.", color=0x00b2ff) await ctx.send(embed=embed) elif str(reaction.emoji) == "📝": embed = discord.Embed(title="Setting...", description="Setting up edit logging...", color=0x00b2ff) await ctx.send(embed=embed) await self.bot.db.execute("INSERT INTO logch (channelid, loggingtype, guildid) VALUES ($1, $2, $3)", ctx.channel.id, "e", ctx.guild.id) embed = discord.Embed(title="Logging setup", description="Complete! To test, try editing a message.", color=0x00b2ff) await ctx.send(embed=embed) elif str(reaction.emoji) == "👤": embed = discord.Embed(title="Setting...", description="Setting up member logging...", color=0x00b2ff) await ctx.send(embed=embed) await self.bot.db.execute("INSERT INTO logch (channelid, loggingtype, guildid) VALUES ($1, $2, $3)", ctx.channel.id, "m", ctx.guild.id) embed = discord.Embed(title="Logging setup", description="Complete! To test, try adding a member.", color=0x00b2ff) await ctx.send(embed=embed) @commands.command() @commands.has_permissions(manage_messages=True) async def ignorelogging(self, ctx, channel: discord.TextChannel): await self.bot.db.execute("INSERT INTO ignoredlogs (server, channel) VALUES ($1, $2)", ctx.guild.id, channel.id) embed = discord.Embed(title="Logging setup", description=f"Channel {channel.mention} has been added to the ignore list.", color=0x00b2ff) await ctx.send(embed=embed) def setup(bot:GoModBot): bot.add_cog(Logging(bot))

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0

null

0

1

0

null

0

6

0e9661a2f2bfcede9d59ddb5cec3cf02ca62cdbd

48

py

Python

waterbutler/providers/dataverse/__init__.py

alexschiller/waterbutler

24014d7705aca3e99a6565fc3b9b4075ec6ec563

[ "Apache-2.0" ]

65

2015-01-23T03:22:04.000Z

2022-01-11T22:33:19.000Z

waterbutler/providers/dataverse/__init__.py

alexschiller/waterbutler

24014d7705aca3e99a6565fc3b9b4075ec6ec563

[ "Apache-2.0" ]

300

2015-02-16T16:45:02.000Z

2022-01-31T14:49:07.000Z

waterbutler/providers/dataverse/__init__.py

Johnetordoff/waterbutler

b505cdbcffadaba12984dcb19c9139068e6c314d

[ "Apache-2.0" ]

76

2015-01-20T20:45:17.000Z

2021-07-30T13:18:10.000Z

from .provider import DataverseProvider # noqa

24

47

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null

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1

0

1

0

1

0

6

7eba36971a1b3c4583004d613ef4fdb0410f2235

3,127

py

Python

pyspark_proxy/sql/column.py

abronte/PysparkProxy

cc28bacb0d4ee6fb87ced763a73e9ea791612414

[ "Apache-2.0" ]

3

2018-09-06T18:37:35.000Z

2018-09-07T17:49:44.000Z

pyspark_proxy/sql/column.py

abronte/PysparkProxy

cc28bacb0d4ee6fb87ced763a73e9ea791612414

[ "Apache-2.0" ]

29

2018-09-04T23:53:42.000Z

2018-12-12T21:46:59.000Z

pyspark_proxy/sql/column.py

abronte/PysparkProxy

cc28bacb0d4ee6fb87ced763a73e9ea791612414

[ "Apache-2.0" ]

null

from pyspark_proxy.proxy import Proxy __all__ = ['Column'] class Column(Proxy): def alias(self, *args, **kwargs): return self._call(self._id, 'alias', (args, kwargs)) def cast(self, *args, **kwargs): return self._call(self._id, 'cast', (args, kwargs)) def __repr__(self): return self._call(self._id, '__repr__', ((), {})) # better way to define these? def _op_func(self, name, *args, **kwargs): return self._call(self._id, '__neg__', (args, kwargs)) def __add__(self, *args, **kwargs): return self._call(self._id, '__add__', (args, kwargs)) def __sub__(self, *args, **kwargs): return self._call(self._id, '__sub__', (args, kwargs)) def __mul__(self, *args, **kwargs): return self._call(self._id, '__mul__', (args, kwargs)) def __div__(self, *args, **kwargs): return self._call(self._id, '__div__', (args, kwargs)) def __truediv__(self, *args, **kwargs): return self._call(self._id, '__truediv__', (args, kwargs)) def __mod__(self, *args, **kwargs): return self._call(self._id, '__mod__', (args, kwargs)) def __radd__(self, *args, **kwargs): return self._call(self._id, '__radd__', (args, kwargs)) def __rsub__(self, *args, **kwargs): return self._call(self._id, '__rsub__', (args, kwargs)) def __rmul__(self, *args, **kwargs): return self._call(self._id, '__rmul__', (args, kwargs)) def __rdiv__(self, *args, **kwargs): return self._call(self._id, '__rdiv__', (args, kwargs)) def __rtruediv__(self, *args, **kwargs): return self._call(self._id, '__rdiv__', (args, kwargs)) def __rmod__(self, *args, **kwargs): return self._call(self._id, '__rmod__', (args, kwargs)) def __pow__(self, *args, **kwargs): return self._call(self._id, '__pow__', (args, kwargs)) def __rpow__(self, *args, **kwargs): return self._call(self._id, '__rpow__', (args, kwargs)) def __eq__(self, *args, **kwargs): return self._call(self._id, '__eq__', (args, kwargs)) def __ne__(self, *args, **kwargs): return self._call(self._id, '__ne__', (args, kwargs)) def __lt__(self, *args, **kwargs): return self._call(self._id, '__lt__', (args, kwargs)) def __le__(self, *args, **kwargs): return self._call(self._id, '__le__', (args, kwargs)) def __ge__(self, *args, **kwargs): return self._call(self._id, '__ge__', (args, kwargs)) def __gt__(self, *args, **kwargs): return self._call(self._id, '__gt__', (args, kwargs)) def __and__(self, *args, **kwargs): return self._call(self._id, '__and__', (args, kwargs)) def __or__(self, *args, **kwargs): return self._call(self._id, '__or__', (args, kwargs)) def __invert__(self, *args, **kwargs): return self._call(self._id, '__invert__', (args, kwargs)) def __rand__(self, *args, **kwargs): return self._call(self._id, '__rand__', (args, kwargs)) def __ror__(self, *args, **kwargs): return self._call(self._id, '__ror__', (args, kwargs))

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null

1

0

null

0

1

0

1

0

6

7ec1b8e4f45c838696db7b30d3d3dad0ea0b0e22

32

py

Python

src/core/app/app/services/__init__.py

exytab/FrontLineLiveUA

733bb0c84062e3a3d8eec3cf988add7e1470d392

[ "MIT" ]

null

src/core/app/app/services/__init__.py

exytab/FrontLineLiveUA

733bb0c84062e3a3d8eec3cf988add7e1470d392

[ "MIT" ]

null

src/core/app/app/services/__init__.py

exytab/FrontLineLiveUA

733bb0c84062e3a3d8eec3cf988add7e1470d392

[ "MIT" ]

null

from . import map, need, supply

16

31

0.71875

5

32

4.6

1

0

0.1875

32

1

32

0.884615

0

1

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true

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1

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null

0

1

0

1

0

1

0

6

addd1e083b1fc8ffd73e1ffa6c9c6b3dff9d8e53

38

py

Python

ezhost/__main__.py

zhexiao/ezhost1

4146bc0be14bb1bfe98ec19283d19fab420871b3

[ "MIT" ]

4

2016-12-16T20:22:44.000Z

2018-10-31T07:12:34.000Z

ezhost/__main__.py

zhexiao/ezhost1

4146bc0be14bb1bfe98ec19283d19fab420871b3

[ "MIT" ]

null

ezhost/__main__.py

zhexiao/ezhost1

4146bc0be14bb1bfe98ec19283d19fab420871b3

[ "MIT" ]

1

2017-07-19T05:36:58.000Z

import ezhost.main ezhost.main.main()

12.666667

18

0.789474

6

38

5

0.5

0.666667

0

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38

2

19

0.857143

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1

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true

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1

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1

0

null

0

1

0

1

0

6

ade56c6eca6b0d226dc5be00b20de6149339cf64

105

py

Python

module/__init__.py

liurenfeng007/DSRE

7b3b257c68b1991b8b12c817a245af022a5fbeaa

[ "MIT" ]

null

module/__init__.py

liurenfeng007/DSRE

7b3b257c68b1991b8b12c817a245af022a5fbeaa

[ "MIT" ]

null

module/__init__.py

liurenfeng007/DSRE

7b3b257c68b1991b8b12c817a245af022a5fbeaa

[ "MIT" ]

null

from .embedding import Embedding from .encoder import * from .selector import * from .classifier import *

26.25

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0.790476

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105

6.384615

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4

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26.25

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0

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true

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1

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1

0

null

0

1

0

1

0

1

0

6

bc05d5098b93ea3846c65ff6a9527cd48f7c9d95

42

py

Python

recognition/src/models/__init__.py

AlexeyZhuravlev/OCR-experiments

8493045054678a2e13cafce6d9e85c7581086c7a

[ "MIT" ]

2

2020-05-28T18:46:37.000Z

2020-08-29T12:49:57.000Z

recognition/src/models/__init__.py

AlexeyZhuravlev/OCR-experiments

8493045054678a2e13cafce6d9e85c7581086c7a

[ "MIT" ]

null

recognition/src/models/__init__.py

AlexeyZhuravlev/OCR-experiments

8493045054678a2e13cafce6d9e85c7581086c7a

[ "MIT" ]

null

from .multi_head import MultiHeadOcrModel

21

41

0.880952

5

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7.2

1

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1

42

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true

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0

null

0

1

0

1

0

1

0

6

bc37af5a16986b639b10ae157cdf53ebf4cfc204

21,116

py

Python

pyfuzzy_toolbox/features/count.py

matheuscas/pyfuzzy_toolbox

57885f3ff53d1b7ab3559c7ff6197ceb97f67c3b

[ "BSD-3-Clause" ]

null

pyfuzzy_toolbox/features/count.py

matheuscas/pyfuzzy_toolbox

57885f3ff53d1b7ab3559c7ff6197ceb97f67c3b

[ "BSD-3-Clause" ]

null

pyfuzzy_toolbox/features/count.py

matheuscas/pyfuzzy_toolbox

57885f3ff53d1b7ab3559c7ff6197ceb97f67c3b

[ "BSD-3-Clause" ]

null

from . import pre from . import set_pos_tags_codes from . import set_ngram_polarity_statement from . import ADJS, ADVS, VERBS, ALL, ADJS_AND_ADVS, ADJS_AND_VERBS, ADVS_AND_VERBS,\ ADJS_AND_BI_ADV_ADJ, ADVS_AND_BI_ADV_ADV, VERBS_AND_BI_ADV_VERB, ALL_NON_GENERAL_BIGRAMS """ ------------------------------ Base functions ------------------------------ """ def count_of_unigrams_scores(bow_sentences, unigram=ADJS, positive=True): pos_tags_codes = set_pos_tags_codes(unigram) polarity_eval_stm = set_ngram_polarity_statement(positive=positive) _count = 0 for bs in bow_sentences: for ngram in bs: if pre.is_unigram(ngram) and ngram.pos_tag in pos_tags_codes and eval(polarity_eval_stm): _count += 1 return _count def count_of_bigrams_scores(bow_sentences, bigram_word_1=ADVS, bigram_word_2=ADJS, positive=True): pos_tags_codes_word_1 = set_pos_tags_codes(bigram_word_1) pos_tags_codes_word_2 = set_pos_tags_codes(bigram_word_2) polarity_eval_stm = set_ngram_polarity_statement(positive=positive) _count = 0 for bs in bow_sentences: for ngram in bs: if pre.is_bigram(ngram) and \ (ngram.word_1.pos_tag in pos_tags_codes_word_1) and \ (ngram.word_2.pos_tag in pos_tags_codes_word_2) and \ eval(polarity_eval_stm): _count += 1 return _count def positive_to_negative_ratio_count_unigrams_scores(bow_sentences, unigram=ADJS): positive_sum = count_of_unigrams_scores(bow_sentences, unigram=unigram) negative_sum = count_of_unigrams_scores( bow_sentences, unigram=unigram, positive=False) return positive_sum - negative_sum def count_of_unigrams_and_bigrams_scores(bow_sentences, unigram=ADJS, bigram_word_1=ADVS, bigram_word_2=ADJS, positive=True): unigrams_count = count_of_unigrams_scores( bow_sentences, unigram=unigram, positive=positive) bigrams_count = count_of_bigrams_scores( bow_sentences, bigram_word_1=bigram_word_1, bigram_word_2=bigram_word_2, positive=positive) return unigrams_count + bigrams_count def positive_to_negative_ratio_count_unigrams_and_bigrams_scores(bow_sentences, unigram=ADJS, bigram_word_1=ADVS, bigram_word_2=ADJS): positive_unigrams_and_bigrams_count = count_of_unigrams_and_bigrams_scores( bow_sentences, unigram=unigram, bigram_word_1=bigram_word_1, bigram_word_2=bigram_word_2) negative_unigrams_and_bigrams_count = count_of_unigrams_and_bigrams_scores( bow_sentences, unigram=unigram, bigram_word_1=bigram_word_1, bigram_word_2=bigram_word_2, positive=False) return positive_unigrams_and_bigrams_count - negative_unigrams_and_bigrams_count # UNTESTED def count_of_trigrams_scores(bow_sentences, trigram_word_1=ADVS, trigram_word_2=ADVS, trigram_word_3=ADJS, positive=True): pos_tags_codes_word_1 = set_pos_tags_codes(trigram_word_1) pos_tags_codes_word_2 = set_pos_tags_codes(trigram_word_2) pos_tags_codes_word_3 = set_pos_tags_codes(trigram_word_3) polarity_eval_stm = set_ngram_polarity_statement(positive=positive) _count = 0 for bs in bow_sentences: for ngram in bs: if pre.is_trigram(ngram) and \ (ngram.word_1.pos_tag in pos_tags_codes_word_1) and \ (ngram.word_2.pos_tag in pos_tags_codes_word_2) and \ (ngram.word_3.pos_tag in pos_tags_codes_word_3) and \ eval(polarity_eval_stm): _count += 1 return _count # UNTESTED def count_of_unigrams_bigrams_and_trigrams_scores(bow_sentences, unigram=ADJS, bigram_word_1=ADVS, bigram_word_2=ADJS, trigram_word_1=ADVS, trigram_word_2=ADVS, trigram_word_3=ADJS, positive=True): unigrams_count_and_bigrams_count = count_of_unigrams_and_bigrams_scores(bow_sentences, unigram=unigram, bigram_word_1=bigram_word_1, bigram_word_2=bigram_word_2, positive=positive) return unigrams_count_and_bigrams_count + count_of_trigrams_scores(bow_sentences, trigram_word_1=trigram_word_1, trigram_word_2=trigram_word_2, trigram_word_3=trigram_word_3, positive=positive) # UNTESTED def positive_to_negative_ratio_count_unigrams_bigrams_and_trigrams_scores(bow_sentences, unigram=ADJS, bigram_word_1=ADVS, bigram_word_2=ADJS, trigram_word_1=ADVS, trigram_word_2=ADVS, trigram_word_3=ADJS): all_ratio_positives = count_of_unigrams_bigrams_and_trigrams_scores(bow_sentences, unigram=unigram, bigram_word_1=bigram_word_1, bigram_word_2=bigram_word_2, trigram_word_1=trigram_word_1, trigram_word_2=trigram_word_2, trigram_word_3=trigram_word_3, positive=True) return all_ratio_positives - count_of_unigrams_bigrams_and_trigrams_scores(bow_sentences, unigram=unigram, bigram_word_1=bigram_word_1, bigram_word_2=bigram_word_2, trigram_word_1=trigram_word_1, trigram_word_2=trigram_word_2, trigram_word_3=trigram_word_3, positive=False) def count_selected_ngrams(bow_sentences): ngrams_selected = 0 for bs in bow_sentences: ngrams_selected = ngrams_selected + len(bs) return ngrams_selected def document_size(bow_sentences): for bs in bow_sentences: for ngram in bs: if pre.is_unigram(ngram): if ngram.doc_word_count: return ngram.doc_word_count else: return 0 elif pre.is_bigram(ngram): if ngram.word_2.doc_word_count: return ngram.word_2.doc_word_count else: return 0 elif pre.is_trigram(ngram): if ngram.word_3.doc_word_count: return ngram.word_3.doc_word_count else: return 0 return 0 def percentage_of_negated_ngrams_by_document_size(bow_sentences): _count = 0 _doc_words_count = 0 for bs in bow_sentences: for ngram in bs: if pre.is_bigram(ngram) and ngram.word_1.word in pre.NEGATION_WORDS or \ pre.is_trigram(ngram) and ngram.word_1.word in pre.NEGATION_WORDS: _doc_words_count = ngram.word_1.doc_word_count _count += 1 _doc_words_count = float(_doc_words_count) if _doc_words_count > 0: return {'value': _count / float(_doc_words_count), 'name': 'percentage_of_negated_ngrams_by_document_size'} else: return {'value': 0.0, 'name': 'percentage_of_negated_ngrams_by_document_size'} """ ------------------------------ Features functions ------------------------------ """ def positive_adjectives_count(bow_sentences): return {'value': count_of_unigrams_scores( bow_sentences, unigram=ADJS, positive=True), 'name': 'positive_adjectives_count'} def negative_adjectives_count(bow_sentences): return {'value': count_of_unigrams_scores( bow_sentences, unigram=ADJS, positive=False), 'name': 'negative_adjectives_count'} def positive_adverbs_count(bow_sentences): return {'value': count_of_unigrams_scores( bow_sentences, unigram=ADVS, positive=True), 'name': 'positive_adverbs_count'} def negative_adverbs_count(bow_sentences): return {'value': count_of_unigrams_scores( bow_sentences, unigram=ADVS, positive=False), 'name': 'negative_adverbs_count'} def positive_verbs_count(bow_sentences): return {'value': count_of_unigrams_scores( bow_sentences, unigram=VERBS, positive=True), 'name': 'positive_verbs_count'} def negative_verbs_count(bow_sentences): return {'value': count_of_unigrams_scores( bow_sentences, unigram=VERBS, positive=False), 'name': 'negative_verbs_count'} def positive_to_negative_ratio_of_adjectives_count(bow_sentences): return {'value': positive_to_negative_ratio_count_unigrams_scores( bow_sentences, unigram=ADJS), 'name': 'positive_to_negative_ratio_of_adjectives_count'} def positive_to_negative_ratio_of_adverbs_count(bow_sentences): return {'value': positive_to_negative_ratio_count_unigrams_scores( bow_sentences, unigram=ADVS), 'name': 'positive_to_negative_ratio_of_adverbs_count'} def positive_to_negative_ratio_of_verbs_count(bow_sentences): return {'value': positive_to_negative_ratio_count_unigrams_scores( bow_sentences, unigram=VERBS), 'name': 'positive_to_negative_ratio_of_verbs_count'} def positive_adjectives_count_and_bigrams_with_adjectives(bow_sentences): return {'value': count_of_unigrams_and_bigrams_scores( bow_sentences, unigram=ADJS, bigram_word_1=ADVS, bigram_word_2=ADJS, positive=True), 'name': 'positive_adjectives_count_and_bigrams_with_adjectives'} def negative_adjectives_count_and_bigrams_with_adjectives(bow_sentences): return {'value': count_of_unigrams_and_bigrams_scores( bow_sentences, unigram=ADJS, bigram_word_1=ADVS, bigram_word_2=ADJS, positive=False), 'name': 'negative_adjectives_count_and_bigrams_with_adjectives'} def positive_adverbs_count_and_bigrams_with_adverbs(bow_sentences): return {'value': count_of_unigrams_and_bigrams_scores( bow_sentences, unigram=ADVS, bigram_word_1=ADVS, bigram_word_2=ADVS, positive=True), 'name': 'positive_adverbs_count_and_bigrams_with_adverbs'} def negative_adverbs_count_and_bigrams_with_adverbs(bow_sentences): return {'value': count_of_unigrams_and_bigrams_scores( bow_sentences, unigram=ADVS, bigram_word_1=ADVS, bigram_word_2=ADVS, positive=False), 'name': 'negative_adverbs_count_and_bigrams_with_adverbs'} def positive_verbs_count_and_bigrams_with_verbs(bow_sentences): return {'value': count_of_unigrams_and_bigrams_scores( bow_sentences, unigram=VERBS, bigram_word_1=ADVS, bigram_word_2=VERBS, positive=True), 'name': 'positive_verbs_count_and_bigrams_with_verbs'} def negative_verbs_count_and_bigrams_with_verbs(bow_sentences): return {'value': count_of_unigrams_and_bigrams_scores( bow_sentences, unigram=VERBS, bigram_word_1=ADVS, bigram_word_2=VERBS, positive=False), 'name': 'negative_verbs_count_and_bigrams_with_verbs'} def positive_unigrams_and_bigrams_count(bow_sentences): return {'value': count_of_unigrams_and_bigrams_scores( bow_sentences, unigram=ALL, bigram_word_1=ALL, bigram_word_2=ALL, positive=True), 'name': 'positive_unigrams_and_bigrams_count'} def negative_unigrams_and_bigrams_count(bow_sentences): return {'value': count_of_unigrams_and_bigrams_scores( bow_sentences, unigram=ALL, bigram_word_1=ALL, bigram_word_2=ALL, positive=False), 'name': 'negative_unigrams_and_bigrams_count'} def positive_unigrams_bigrams_and_trigrams_count(bow_sentences): return {'value': count_of_unigrams_bigrams_and_trigrams_scores( bow_sentences, unigram=ALL, bigram_word_1=ALL, bigram_word_2=ALL, positive=True, trigram_word_1=ALL, trigram_word_2=ALL, trigram_word_3=ALL), 'name': 'positive_unigrams_bigrams_and_trigrams_count'} def negative_unigrams_bigrams_and_trigrams_count(bow_sentences): return {'value': count_of_unigrams_bigrams_and_trigrams_scores( bow_sentences, unigram=ALL, bigram_word_1=ALL, bigram_word_2=ALL, positive=False, trigram_word_1=ALL, trigram_word_2=ALL, trigram_word_3=ALL), 'name': 'negative_unigrams_bigrams_and_trigrams_count'} def positive_to_negative_ratio_of_adjectives_count_and_bigrams_with_adjectives(bow_sentences): return {'value': positive_to_negative_ratio_count_unigrams_and_bigrams_scores( bow_sentences, unigram=ADJS, bigram_word_1=ADVS, bigram_word_2=ADJS), 'name': 'positive_to_negative_ratio_of_adjectives_count_and_bigrams_with_adjectives'} def positive_to_negative_ratio_of_adverbs_count_and_bigrams_with_adverbs(bow_sentences): return {'value': positive_to_negative_ratio_count_unigrams_and_bigrams_scores( bow_sentences, unigram=ADVS, bigram_word_1=ADVS, bigram_word_2=ADVS), 'name': 'positive_to_negative_ratio_of_adverbs_count_and_bigrams_with_adverbs'} def positive_to_negative_ratio_of_verbs_count_and_bigrams_with_verbs(bow_sentences): return {'value': positive_to_negative_ratio_count_unigrams_and_bigrams_scores( bow_sentences, unigram=VERBS, bigram_word_1=ADVS, bigram_word_2=VERBS), 'name': 'positive_to_negative_ratio_of_verbs_count_and_bigrams_with_verbs'} def positive_to_negative_ratio_of_unigrams_and_bigrams_count(bow_sentences): return {'value': positive_to_negative_ratio_count_unigrams_and_bigrams_scores( bow_sentences, unigram=ALL, bigram_word_1=ALL, bigram_word_2=ALL), 'name': 'positive_to_negative_ratio_of_unigrams_and_bigrams_count'} def positive_to_negative_ratio_of_unigrams_bigrams_and_trigrams_count(bow_sentences): return {'value': positive_to_negative_ratio_count_unigrams_bigrams_and_trigrams_scores(bow_sentences, unigram=ALL, bigram_word_1=ALL, bigram_word_2=ALL, trigram_word_1=ALL, trigram_word_2=ALL, trigram_word_3=ALL), 'name': 'positive_to_negative_ratio_of_unigrams_bigrams_and_trigrams_count'} def selected_ngrams_count(bow_sentences): return {'value': count_selected_ngrams(bow_sentences), 'name': 'selected_ngrams_count'} def original_document_size(bow_sentences): return {'value': document_size(bow_sentences), 'name': 'original_document_size'} def all(bow_sentences, unigrams_only=True, unigrams_only_ratio=True, unigram_type=ALL, non_general_unigrams_and_bigrams=True, non_general_unigrams_and_bigrams_ratio=True, non_general_bigram_type=ALL_NON_GENERAL_BIGRAMS, ngrams_count=True, general_unigrams_and_bigrams=True, general_unigrams_and_bigrams_ratio=True, unigrams_and_bigrams_trigram=True, unigrams_and_bigrams_trigram_ratio=True): features_list = [] if unigrams_only: if unigram_type == ADJS: features_list.append(positive_adjectives_count(bow_sentences)) features_list.append(negative_adjectives_count(bow_sentences)) elif unigram_type == ADJS_AND_ADVS: features_list.append(positive_adjectives_count(bow_sentences)) features_list.append(negative_adjectives_count(bow_sentences)) features_list.append(positive_adverbs_count(bow_sentences)) features_list.append(negative_adverbs_count(bow_sentences)) elif unigram_type == ADJS_AND_VERBS: features_list.append(positive_adjectives_count(bow_sentences)) features_list.append(negative_adjectives_count(bow_sentences)) features_list.append(positive_verbs_count(bow_sentences)) features_list.append(negative_verbs_count(bow_sentences)) elif unigram_type == ADVS_AND_VERBS: features_list.append(positive_adverbs_count(bow_sentences)) features_list.append(negative_adverbs_count(bow_sentences)) features_list.append(positive_verbs_count(bow_sentences)) features_list.append(negative_verbs_count(bow_sentences)) elif unigram_type == ADVS: features_list.append(positive_adverbs_count(bow_sentences)) features_list.append(negative_adverbs_count(bow_sentences)) elif unigram_type == VERBS: features_list.append(positive_verbs_count(bow_sentences)) features_list.append(negative_verbs_count(bow_sentences)) else: features_list.append(positive_adjectives_count(bow_sentences)) features_list.append(negative_adjectives_count(bow_sentences)) features_list.append(positive_adverbs_count(bow_sentences)) features_list.append(negative_adverbs_count(bow_sentences)) features_list.append(positive_verbs_count(bow_sentences)) features_list.append(negative_verbs_count(bow_sentences)) if unigrams_only_ratio: features_list.append( positive_to_negative_ratio_of_adjectives_count(bow_sentences)) features_list.append( positive_to_negative_ratio_of_adverbs_count(bow_sentences)) features_list.append( positive_to_negative_ratio_of_verbs_count(bow_sentences)) if non_general_unigrams_and_bigrams: if non_general_bigram_type == ADVS_AND_BI_ADV_ADV: features_list.append( positive_adverbs_count_and_bigrams_with_adverbs(bow_sentences)) features_list.append( negative_adverbs_count_and_bigrams_with_adverbs(bow_sentences)) elif non_general_bigram_type == VERBS_AND_BI_ADV_VERB: features_list.append( positive_verbs_count_and_bigrams_with_verbs(bow_sentences)) features_list.append( negative_verbs_count_and_bigrams_with_verbs(bow_sentences)) elif non_general_bigram_type == ADJS_AND_BI_ADV_ADJ: features_list.append( positive_adjectives_count_and_bigrams_with_adjectives(bow_sentences)) features_list.append( negative_adjectives_count_and_bigrams_with_adjectives(bow_sentences)) else: features_list.append( positive_adverbs_count_and_bigrams_with_adverbs(bow_sentences)) features_list.append( negative_adverbs_count_and_bigrams_with_adverbs(bow_sentences)) features_list.append( positive_verbs_count_and_bigrams_with_verbs(bow_sentences)) features_list.append( negative_verbs_count_and_bigrams_with_verbs(bow_sentences)) features_list.append( positive_adjectives_count_and_bigrams_with_adjectives(bow_sentences)) features_list.append( negative_adjectives_count_and_bigrams_with_adjectives(bow_sentences)) if non_general_unigrams_and_bigrams_ratio: features_list.append( positive_to_negative_ratio_of_adjectives_count_and_bigrams_with_adjectives(bow_sentences)) features_list.append( positive_to_negative_ratio_of_adverbs_count_and_bigrams_with_adverbs(bow_sentences)) features_list.append( positive_to_negative_ratio_of_verbs_count_and_bigrams_with_verbs(bow_sentences)) if ngrams_count: features_list.append(selected_ngrams_count(bow_sentences)) features_list.append(original_document_size(bow_sentences)) if general_unigrams_and_bigrams: features_list.append( positive_unigrams_and_bigrams_count(bow_sentences)) features_list.append( negative_unigrams_and_bigrams_count(bow_sentences)) features_list.append( percentage_of_negated_ngrams_by_document_size(bow_sentences)) if general_unigrams_and_bigrams_ratio: features_list.append( positive_to_negative_ratio_of_unigrams_and_bigrams_count(bow_sentences)) if unigrams_and_bigrams_trigram: features_list.append( positive_unigrams_bigrams_and_trigrams_count(bow_sentences)) features_list.append( negative_unigrams_bigrams_and_trigrams_count(bow_sentences)) if unigrams_and_bigrams_trigram_ratio: features_list.append( positive_to_negative_ratio_of_unigrams_bigrams_and_trigrams_count(bow_sentences)) return features_list

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Python

pyid3tagger/__init__.py

pkla6/pyid3tagger

ca61136b319474d9c77339e514e5615f7343a30e

[ "MIT" ]

1

2019-01-21T03:45:00.000Z

pyid3tagger/__init__.py

pkla6/pyid3tagger

ca61136b319474d9c77339e514e5615f7343a30e

[ "MIT" ]

null

pyid3tagger/__init__.py

pkla6/pyid3tagger

ca61136b319474d9c77339e514e5615f7343a30e

[ "MIT" ]

null

# coding=utf-8 from const import * from utilities import * from tags import ID3v1Tag from tags import ID3v1_1Tag from tags import ID3v2_3Tag from file_tags import FileTags from id3v2_3frames import ID3v2_3_APIC_Frame from id3v2_3frames import ID3v2_3_COMM_Frame from id3v2_3frames import ID3v2_3_GEOB_Frame from id3v2_3frames import ID3v2_3_TALB_Frame from id3v2_3frames import ID3v2_3_TCON_Frame from id3v2_3frames import ID3v2_3_TDAT_Frame from id3v2_3frames import ID3v2_3_TIT2_Frame from id3v2_3frames import ID3v2_3_TPE1_Frame from id3v2_3frames import ID3v2_3_TPE2_Frame from id3v2_3frames import ID3v2_3_TPOS_Frame from id3v2_3frames import ID3v2_3_TRCK_Frame from id3v2_3frames import ID3v2_3_TYER_Frame from id3v2_3frames import ID3v2_3_WPUB_Frame

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py

Python

src/__init__.py

paul-buechner/magic-illustrator

afab2391c318f800128fad886372cc5f1601bd27

[ "MIT" ]

null

src/__init__.py

paul-buechner/magic-illustrator

afab2391c318f800128fad886372cc5f1601bd27

[ "MIT" ]

null

src/__init__.py

paul-buechner/magic-illustrator

afab2391c318f800128fad886372cc5f1601bd27

[ "MIT" ]

null

from src.illustrator_config import * def main(): # Initialize illustrator thread initialize()

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py

Python

Chapter09/testset1.py

ibiscum/Python-Parallel-Programming-Cookbook-Second-Edition

8fd583019778b4d797d4f948d091b5564e23f732

[ "MIT" ]

null

Chapter09/testset1.py

ibiscum/Python-Parallel-Programming-Cookbook-Second-Edition

8fd583019778b4d797d4f948d091b5564e23f732

[ "MIT" ]

null

Chapter09/testset1.py

ibiscum/Python-Parallel-Programming-Cookbook-Second-Edition

8fd583019778b4d797d4f948d091b5564e23f732

[ "MIT" ]

null

# testset.py from nose.tools import eq_ import unittest def test_sum(): eq_(2 + 2, 4)

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py

Python

doc/epsilon_pretty_gdb_load.py

Jojendersie/Epsilon-Intersection

23f020ddc9832742dc156c7dac038276070707b2

[ "MIT" ]

14

2015-01-18T21:13:02.000Z

2022-01-19T17:24:29.000Z

doc/epsilon_pretty_gdb_load.py

Jojendersie/Epsilon-Intersection

23f020ddc9832742dc156c7dac038276070707b2

[ "MIT" ]

28

2015-08-06T14:27:35.000Z

2022-03-21T09:03:44.000Z

doc/epsilon_pretty_gdb_load.py

Jojendersie/Epsilon-Intersection

23f020ddc9832742dc156c7dac038276070707b2

[ "MIT" ]

5

2018-11-15T11:35:34.000Z

2021-08-16T03:38:41.000Z

import gdb.printing import epsilon_pretty_printing gdb.printing.register_pretty_printer( gdb.current_objfile(), epsilon_pretty_printing.build_pretty_printer())

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py

Python

app/main/__init__.py

VirginiaNdungu1/Taarifa-App

0a04bd0ddffd43a59cb92a136645cd9c8d4a1768

[ "MIT" ]

null

app/main/__init__.py

VirginiaNdungu1/Taarifa-App

0a04bd0ddffd43a59cb92a136645cd9c8d4a1768

[ "MIT" ]

null

app/main/__init__.py

VirginiaNdungu1/Taarifa-App

0a04bd0ddffd43a59cb92a136645cd9c8d4a1768

[ "MIT" ]

null

# import Blueprint class from flask import Blueprint # initialise the Blueprint class main = Blueprint('main', __name__) # import views module from . import views

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py

Python

3_team/tests/sample_doctest_ok.py

pyfirst/pymook-samplecode

82321237c34515d287f28bd51ea86f870c1f5514

[ "MIT" ]

31

2017-09-27T14:54:39.000Z

2021-05-26T14:03:44.000Z

3_team/tests/sample_doctest_ok.py

pyfirst/pymook-samplecode

82321237c34515d287f28bd51ea86f870c1f5514

[ "MIT" ]

11

2018-03-11T05:28:14.000Z

2022-03-11T23:19:36.000Z

3_team/tests/sample_doctest_ok.py

pyfirst/pymook-samplecode

82321237c34515d287f28bd51ea86f870c1f5514

[ "MIT" ]

41

2017-10-21T04:45:56.000Z

2021-07-16T14:12:33.000Z

def get_ok(): """ 文字列 'OK' を返す >>> get_ok() 'OK' """ return 'OK'

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1dcfcfe873ef4b985d677df7eb2f2763cc77a4c7

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py

Python

{{cookiecutter.repo_name}}/app.py

xhlulu/dash-template

c76debd0e7c8cf119b46bf6e7233ea967851cb78

[ "MIT" ]

1

2021-04-07T17:27:26.000Z

{{cookiecutter.repo_name}}/app.py

xhlulu/dash-template

c76debd0e7c8cf119b46bf6e7233ea967851cb78

[ "MIT" ]

null

{{cookiecutter.repo_name}}/app.py

xhlulu/dash-template

c76debd0e7c8cf119b46bf6e7233ea967851cb78

[ "MIT" ]

null

{%- if cookiecutter.format == 'bootstrap' -%} {%- include 'cookiecutter_templates/app_bootstrap.py' -%} {%- elif cookiecutter.format == 'regular' -%} {%- include 'cookiecutter_templates/app_regular.py' -%} {%- endif -%}

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1dd6a3d04a7d00be735a4344b52b63f7d4e00faa

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py

Python

atpg/utils/__init__.py

jstavr/SDN_Project

9fe5a65f46eadf15e1da43d9f8125b8c15161bbd

[ "Apache-2.0" ]

null

atpg/utils/__init__.py

jstavr/SDN_Project

9fe5a65f46eadf15e1da43d9f8125b8c15161bbd

[ "Apache-2.0" ]

null

atpg/utils/__init__.py

jstavr/SDN_Project

9fe5a65f46eadf15e1da43d9f8125b8c15161bbd

[ "Apache-2.0" ]

null

''' Created on Aug 14, 2011 '''

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380909b6935f2ea342b89b999d1e6343dc2c6c30

271

py

Python

flask_unchained/_code_templates/project/app/extensions/__init__.py

achiang/flask-unchained

12788a6e618904a25ff2b571eb05ff1dc8f1840f

[ "MIT" ]

69

2018-10-10T01:59:11.000Z

2022-03-29T17:29:30.000Z

flask_unchained/_code_templates/project/app/extensions/__init__.py

achiang/flask-unchained

12788a6e618904a25ff2b571eb05ff1dc8f1840f

[ "MIT" ]

18

2018-11-17T12:42:02.000Z

2021-05-22T18:45:27.000Z

flask_unchained/_code_templates/project/app/extensions/__init__.py

achiang/flask-unchained

12788a6e618904a25ff2b571eb05ff1dc8f1840f

[ "MIT" ]

7

2018-10-12T16:20:25.000Z

2021-10-06T12:18:21.000Z

# from vendor import ExtensionName # extension_instance = ExtensionName() EXTENSIONS = { # 'extension_name': extension_instance, # or, if an extension depends on other extension(s): # 'extension_name': (extension_instance, ['ext', 'dependency', 'names']), }

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69cb4e0b611917997928e326ebefaae54a7252b8

113

py

Python

test/resources/hello_virtual/python/develop/hello.py

Manu343726/biicode-common

91b32c6fd1e4a72ce5451183f1766d313cd0e420

[ "MIT" ]

17

2015-04-15T09:40:23.000Z

2017-05-17T20:34:49.000Z

test/resources/hello_virtual/python/develop/hello.py

Manu343726/biicode-common

91b32c6fd1e4a72ce5451183f1766d313cd0e420

[ "MIT" ]

2

2015-04-22T11:29:36.000Z

2018-09-25T09:31:09.000Z

test/resources/hello_virtual/python/develop/hello.py

bowlofstew/common

45e9ca902be7bbbdd73dafe3ab8957bc4a006020

[ "MIT" ]

22

2015-04-15T09:46:00.000Z

2020-09-29T17:03:31.000Z

''' Created on 17/07/2013 @author: drodri ''' import sys def hello(): sys.stdout.write("Develop: %PRINT%")

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69cb8c70545ac2529f5d07b142a59d1bb203fbe5

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py

Python

src/oop/Zoo/__init__.py

tborzyszkowski/TestAutomationInPython

843c71df796588e181466d9b9b549f03dd907a6e

[ "MIT" ]

2

2020-10-08T09:44:12.000Z

2021-10-08T08:32:19.000Z

src/oop/Zoo/__init__.py

tborzyszkowski/TestAutomationInPython

843c71df796588e181466d9b9b549f03dd907a6e

[ "MIT" ]

null

src/oop/Zoo/__init__.py

tborzyszkowski/TestAutomationInPython

843c71df796588e181466d9b9b549f03dd907a6e

[ "MIT" ]

1

2020-10-19T14:08:00.000Z

from .World import * from .Position import *

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