Datasets:
xszheng2020
/
the_stack_dedup_python_hits_6

Dataset card Data Studio Files
xet
Community
hexsha
string
size
int64
ext
string
lang
string
max_stars_repo_path
string
max_stars_repo_name
string
max_stars_repo_head_hexsha
string
max_stars_repo_licenses
list
max_stars_count
int64
max_stars_repo_stars_event_min_datetime
string
max_stars_repo_stars_event_max_datetime
string
max_issues_repo_path
string
max_issues_repo_name
string
max_issues_repo_head_hexsha
string
max_issues_repo_licenses
list
max_issues_count
int64
max_issues_repo_issues_event_min_datetime
string
max_issues_repo_issues_event_max_datetime
string
max_forks_repo_path
string
max_forks_repo_name
string
max_forks_repo_head_hexsha
string
max_forks_repo_licenses
list
max_forks_count
int64
max_forks_repo_forks_event_min_datetime
string
max_forks_repo_forks_event_max_datetime
string
content
string
avg_line_length
float64
max_line_length
int64
alphanum_fraction
float64
qsc_code_num_words_quality_signal
int64
qsc_code_num_chars_quality_signal
float64
qsc_code_mean_word_length_quality_signal
float64
qsc_code_frac_words_unique_quality_signal
float64
qsc_code_frac_chars_top_2grams_quality_signal
float64
qsc_code_frac_chars_top_3grams_quality_signal
float64
qsc_code_frac_chars_top_4grams_quality_signal
float64
qsc_code_frac_chars_dupe_5grams_quality_signal
float64
qsc_code_frac_chars_dupe_6grams_quality_signal
float64
qsc_code_frac_chars_dupe_7grams_quality_signal
float64
qsc_code_frac_chars_dupe_8grams_quality_signal
float64
qsc_code_frac_chars_dupe_9grams_quality_signal
float64
qsc_code_frac_chars_dupe_10grams_quality_signal
float64
qsc_code_frac_chars_replacement_symbols_quality_signal
float64
qsc_code_frac_chars_digital_quality_signal
float64
qsc_code_frac_chars_whitespace_quality_signal
float64
qsc_code_size_file_byte_quality_signal
float64
qsc_code_num_lines_quality_signal
float64
qsc_code_num_chars_line_max_quality_signal
float64
qsc_code_num_chars_line_mean_quality_signal
float64
qsc_code_frac_chars_alphabet_quality_signal
float64
qsc_code_frac_chars_comments_quality_signal
float64
qsc_code_cate_xml_start_quality_signal
float64
qsc_code_frac_lines_dupe_lines_quality_signal
float64
qsc_code_cate_autogen_quality_signal
float64
qsc_code_frac_lines_long_string_quality_signal
float64
qsc_code_frac_chars_string_length_quality_signal
float64
qsc_code_frac_chars_long_word_length_quality_signal
float64
qsc_code_frac_lines_string_concat_quality_signal
float64
qsc_code_cate_encoded_data_quality_signal
float64
qsc_code_frac_chars_hex_words_quality_signal
float64
qsc_code_frac_lines_prompt_comments_quality_signal
float64
qsc_code_frac_lines_assert_quality_signal
float64
qsc_codepython_cate_ast_quality_signal
float64
qsc_codepython_frac_lines_func_ratio_quality_signal
float64
qsc_codepython_cate_var_zero_quality_signal
bool
qsc_codepython_frac_lines_pass_quality_signal
float64
qsc_codepython_frac_lines_import_quality_signal
float64
qsc_codepython_frac_lines_simplefunc_quality_signal
float64
qsc_codepython_score_lines_no_logic_quality_signal
float64
qsc_codepython_frac_lines_print_quality_signal
float64
qsc_code_num_words
int64
qsc_code_num_chars
int64
qsc_code_mean_word_length
int64
qsc_code_frac_words_unique
null
qsc_code_frac_chars_top_2grams
int64
qsc_code_frac_chars_top_3grams
int64
qsc_code_frac_chars_top_4grams
int64
qsc_code_frac_chars_dupe_5grams
int64
qsc_code_frac_chars_dupe_6grams
int64
qsc_code_frac_chars_dupe_7grams
int64
qsc_code_frac_chars_dupe_8grams
int64
qsc_code_frac_chars_dupe_9grams
int64
qsc_code_frac_chars_dupe_10grams
int64
qsc_code_frac_chars_replacement_symbols
int64
qsc_code_frac_chars_digital
int64
qsc_code_frac_chars_whitespace
int64
qsc_code_size_file_byte
int64
qsc_code_num_lines
int64
qsc_code_num_chars_line_max
int64
qsc_code_num_chars_line_mean
int64
qsc_code_frac_chars_alphabet
int64
qsc_code_frac_chars_comments
int64
qsc_code_cate_xml_start
int64
qsc_code_frac_lines_dupe_lines
int64
qsc_code_cate_autogen
int64
qsc_code_frac_lines_long_string
int64
qsc_code_frac_chars_string_length
int64
qsc_code_frac_chars_long_word_length
int64
qsc_code_frac_lines_string_concat
null
qsc_code_cate_encoded_data
int64
qsc_code_frac_chars_hex_words
int64
qsc_code_frac_lines_prompt_comments
int64
qsc_code_frac_lines_assert
int64
qsc_codepython_cate_ast
int64
qsc_codepython_frac_lines_func_ratio
int64
qsc_codepython_cate_var_zero
int64
qsc_codepython_frac_lines_pass
int64
qsc_codepython_frac_lines_import
int64
qsc_codepython_frac_lines_simplefunc
int64
qsc_codepython_score_lines_no_logic
int64
qsc_codepython_frac_lines_print
int64
effective
string
hits
int64
119bf70de472ba3cab193d2bf98c10883d62420b
30
py
Python
build/lib/AccountManager/test doc.py
maartenelgar/Block_Fund_Trading
0ced0f4ac5bb8785ca1b75e55dee7df1db5030a8
[ "MIT" ]
null
null
null
build/lib/AccountManager/test doc.py
maartenelgar/Block_Fund_Trading
0ced0f4ac5bb8785ca1b75e55dee7df1db5030a8
[ "MIT" ]
4
2020-03-24T16:17:31.000Z
2021-06-01T22:48:07.000Z
build/lib/AccountManager/test doc.py
maartenelgar/Block_Fund_Trading
0ced0f4ac5bb8785ca1b75e55dee7df1db5030a8
[ "MIT" ]
null
null
null
from Keys import keys
6
23
0.6
4
30
4.5
0.75
0
0
0
0
0
0
0
0
0
0
0
0.4
30
4
24
7.5
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
11ffa6aa23050da19bff683dc02e8ea752daf62c
161
py
Python
by-session/class-921/week2/input-output.py
amiraliakbari/sharif-mabani-python
5d14a08d165267fe71c28389ddbafe29af7078c5
[ "MIT" ]
2
2015-04-29T20:59:35.000Z
2018-09-26T13:33:43.000Z
by-session/class-921/week2/input-output.py
amiraliakbari/sharif-mabani-python
5d14a08d165267fe71c28389ddbafe29af7078c5
[ "MIT" ]
null
null
null
by-session/class-921/week2/input-output.py
amiraliakbari/sharif-mabani-python
5d14a08d165267fe71c28389ddbafe29af7078c5
[ "MIT" ]
null
null
null
a = input("?") print a b = raw_input("?") print b c = raw_input("please input an integer") print int(c) d = raw_input("please input an float") print float(d)
13.416667
40
0.658385
29
161
3.551724
0.413793
0.23301
0.271845
0.368932
0.407767
0
0
0
0
0
0
0
0.180124
161
11
41
14.636364
0.780303
0
0
0
0
0
0.285714
0
0
0
0
0
0
0
null
null
0
0
null
null
0.5
1
0
0
null
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
1
0
0
0
0
0
0
1
0
6
ee9a1158727d2281c8375b20ff0b934706e0a84e
43
py
Python
__init__.py
manishrawat4u/plugin.video.bloimediaplayer
d561c095fd0862bbe21620daef80d0c5fde36ca5
[ "MIT" ]
1
2019-01-27T23:49:49.000Z
2019-01-27T23:49:49.000Z
__init__.py
manishrawat4u/plugin.video.bloimediaplayer
d561c095fd0862bbe21620daef80d0c5fde36ca5
[ "MIT" ]
null
null
null
__init__.py
manishrawat4u/plugin.video.bloimediaplayer
d561c095fd0862bbe21620daef80d0c5fde36ca5
[ "MIT" ]
null
null
null
print('calling init from home directory')
14.333333
41
0.767442
6
43
5.5
1
0
0
0
0
0
0
0
0
0
0
0
0.139535
43
2
42
21.5
0.891892
0
0
0
0
0
0.761905
0
0
0
0
0
0
1
0
true
0
0
0
0
1
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
null
0
0
0
0
0
0
1
0
0
0
0
1
0
6
e107c95e09a7b7a3ece413c2cae93649bc1ece1d
254
py
Python
goodsongs/errors.py
italopaiva/goodsongs
90182e5b372e9736517989b74ca637e02b52a688
[ "BSD-3-Clause" ]
null
null
null
goodsongs/errors.py
italopaiva/goodsongs
90182e5b372e9736517989b74ca637e02b52a688
[ "BSD-3-Clause" ]
null
null
null
goodsongs/errors.py
italopaiva/goodsongs
90182e5b372e9736517989b74ca637e02b52a688
[ "BSD-3-Clause" ]
null
null
null
"""Module to define specific errors.""" class NotFoundError(ValueError): """Raised when some application object could not be found.""" class InvalidRecordError(ValueError): """Raised when some application object did not passed validation."""
25.4
72
0.740157
29
254
6.482759
0.724138
0.170213
0.212766
0.255319
0.43617
0.43617
0
0
0
0
0
0
0.153543
254
9
73
28.222222
0.874419
0.598425
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
0
0
1
0
1
0
0
null
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
0
0
1
0
0
6
e123321d7b022876f6d68813b71b9d34fc114dea
58,372
py
Python
tests/regressiontests/livre_journal.py
Starou/Colbert
a2b4abaebe7e0606f90c09b98c9267e76d3fd3fc
[ "BSD-3-Clause" ]
1
2015-09-30T20:18:14.000Z
2015-09-30T20:18:14.000Z
tests/regressiontests/livre_journal.py
Starou/Colbert
a2b4abaebe7e0606f90c09b98c9267e76d3fd3fc
[ "BSD-3-Clause" ]
6
2015-01-17T10:02:12.000Z
2020-05-09T15:19:55.000Z
tests/regressiontests/livre_journal.py
Starou/Colbert
a2b4abaebe7e0606f90c09b98c9267e76d3fd3fc
[ "BSD-3-Clause" ]
null
null
null
# -*- coding: utf-8 -*- import os import sys import unittest import codecs import io import datetime import json from colbert.livre_journal import livre_journal_to_list from decimal import Decimal CURRENT_DIR = os.path.dirname(__file__) VERSION_INFO = sys.version_info class LivreJournalTestCase(unittest.TestCase): def test_check_livre_journal(self): from colbert.livre_journal import check_livre_journal livre_journal = codecs.open(os.path.join(CURRENT_DIR, "livre-journal.txt"), mode="r", encoding="utf-8") result = check_livre_journal(livre_journal) self.assertEqual(result, [ ['18/03/2011 - Frais de constitution de la société CFE Paris.', 'OK : débit = crédit (90.45).'], ['18/03/2011 - Frais de constitution de la société - Annonce légale.', 'OK : débit = crédit (99.00).'], ['31/03/2011 - Facture 2011-01 MyClient1', 'OK : débit = crédit (5 980.00).'], ['01/04/2011 - Résultat arrêté compte', 'OK : débit = crédit (48.00).'], ['02/04/2011 - Capital initial', 'OK : débit = crédit (1 500.00).'], ['04/04/2011 - Facture 2011-02 MyClient2', 'OK : débit = crédit (1 794.00).'], ['28/04/2011 - Cotisation Option PRO LCL', 'OK : débit = crédit (15.00).'], ['02/05/2011 - Abonnement LCL Access', 'OK : débit = crédit (3.00).'], ['11/06/2011 - BHV - Fournitures des bureau (Livres comptables).', 'OK : débit = crédit (25.65).'], ['15/06/2011 - Remise chèque XXXXXXX règlement facture 2011-02', 'OK : débit = crédit (2 088.00).'], ['05/07/2011 - Traitement mois de juin gérant.', 'OK : débit = crédit (3 000.00).'], ['08/08/2011 - Chèque XXXXXXY', 'OK : débit = crédit (393.00).'], ['02/09/2011 - Virement MyClient1 ZZZZZZZZZZZ', 'OK : débit = crédit (6 960.00).'], ['03/09/2011 - Abonnement LCL Access', 'OK : débit = crédit (3.00).'], ['28/09/2011 - Facture 2011-04 MyClient1', 'OK : débit = crédit (13 156.00).'], ['30/09/2011 - Solde des comptes de TVA du 01/03/2011 au 30/09/2011', 'OK : débit = crédit (1 274.00).'], ['06/10/2011 - Chèque WWWWWWW', 'OK : débit = crédit (1 240.00).'], ['01/11/2011 - Facture 2011-05 MyClient1', 'OK : débit = crédit (5 382.00).'], ['17/11/2011 - Chèque ZZZZZZZ', 'OK : débit = crédit (402.00).'], ['01/12/2011 - Abonnement LCL Access', 'OK : débit = crédit (3.00).'], ['01/12/2011 - Virement MyClient1 WWWWWWWWWW', 'OK : débit = crédit (21 576.00).'], ['01/12/2011 - Facture 2011-06 MyClient3', 'OK : débit = crédit (8 372.00).'], ['31/12/2011 - Solde des comptes de TVA du 01/10/2011 au 31/12/2011', 'OK : débit = crédit (3 038.00).'], ['31/12/2011 - Prestation MyClient1 décembre 2011', 'OK : débit = crédit (13 156.00).'], ['01/01/2012 - Prestation MyClient1 décembre 2011', 'OK : débit = crédit (13 156.00).'], ['03/01/2012 - Facture 2012-01 MyClient1', 'OK : débit = crédit (13 156.00).'], ["01/02/2012 - Restaurant La Tour d'argent", 'OK : débit = crédit (49.80).'] ]) def test_check_ecriture_livre_journal(self): from colbert.livre_journal import check_ecriture_livre_journal ecriture = { 'date': datetime.date(2011, 3, 18), 'numero_ligne_debut': 13, 'numero_ligne_fin': 17, 'intitule': [' Frais de constitution de la société CFE Paris.'], 'ecritures': [ { 'credit': Decimal('0.00'), 'debit': Decimal('80.00'), 'nom_compte': "Achats - Frais d'actes et de contentieux", 'numero_compte_credit': '', 'numero_compte_debit': '6227' }, { 'credit': Decimal('0.00'), 'debit': Decimal('10.45'), 'nom_compte': 'T.V.A. déductible sur autres biens et services', 'numero_compte_credit': '', 'numero_compte_debit': '44566' }, { 'credit': Decimal('90.45'), 'debit': Decimal('0.00'), 'nom_compte': 'Associés - Comptes courants', 'numero_compte_credit': '455', 'numero_compte_debit': '' } ], } self.assertEqual( check_ecriture_livre_journal(ecriture), ['18/03/2011 - Frais de constitution de la société CFE Paris.', 'OK : débit = crédit (90.45).'] ) # Erreur dans la colonne du compte. ecriture = { 'date': datetime.date(2011, 3, 18), 'numero_ligne_debut': 13, 'numero_ligne_fin': 17, 'intitule': [' Frais de constitution de la société CFE Paris.'], 'ecritures': [ { 'credit': Decimal('0.00'), 'debit': Decimal('80.00'), 'nom_compte': "Achats - Frais d'actes et de contentieux", 'numero_compte_credit': '', 'numero_compte_debit': '6227' }, { 'credit': Decimal('0.00'), 'debit': Decimal('10.45'), 'nom_compte': 'T.V.A. déductible sur autres biens et services', 'numero_compte_credit': '', 'numero_compte_debit': '44566' }, { 'credit': Decimal('90.45'), 'debit': Decimal('0.00'), 'nom_compte': 'Associés - Comptes courants', 'numero_compte_credit': '', 'numero_compte_debit': '455' } ], } self.assertEqual( check_ecriture_livre_journal(ecriture), ['18/03/2011 - Frais de constitution de la société CFE Paris.', 'ERREUR : incohérence entre les colonnes numéro de compte et montant'] ) ecriture = { 'date': datetime.date(2011, 3, 18), 'numero_ligne_debut': 13, 'numero_ligne_fin': 17, 'intitule': [' Frais de constitution de la société CFE Paris.'], 'ecritures': [ { 'credit': Decimal('0.00'), 'debit': Decimal('80.00'), 'nom_compte': "Achats - Frais d'actes et de contentieux", 'numero_compte_credit': '6227', 'numero_compte_debit': '' }, { 'credit': Decimal('0.00'), 'debit': Decimal('10.45'), 'nom_compte': 'T.V.A. déductible sur autres biens et services', 'numero_compte_credit': '', 'numero_compte_debit': '44566' }, { 'credit': Decimal('90.45'), 'debit': Decimal('0.00'), 'nom_compte': 'Associés - Comptes courants', 'numero_compte_credit': '455', 'numero_compte_debit': '' } ], } self.assertEqual( check_ecriture_livre_journal(ecriture), ['18/03/2011 - Frais de constitution de la société CFE Paris.', 'ERREUR : incohérence entre les colonnes numéro de compte et montant'] ) def test_ecritures_de_cloture(self): from colbert.livre_journal import ecritures_de_cloture balance_des_comptes = codecs.open(os.path.join(CURRENT_DIR, "balance_des_comptes-2011.json"), mode="r", encoding="utf-8") edc = ecritures_de_cloture(json.loads(balance_des_comptes.read())) self.maxDiff = None self.assertEqual( edc, [{'date': datetime.date(2011, 12, 31), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('40000.00'), 'nom_compte': 'Produits - prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '706'}, {'credit': Decimal('0.00'), 'debit': Decimal('0.34'), 'nom_compte': 'Produits divers de gestion courante', 'numero_compte_credit': '', 'numero_compte_debit': '758'}, {'credit': Decimal('40000.34'), 'debit': Decimal('0.0'), 'nom_compte': 'Regroupement des comptes de produits', 'numero_compte_credit': '127', 'numero_compte_debit': ''}], 'intitule': ['Ecritures de clôture des comptes.']}, {'date': datetime.date(2011, 12, 31), 'ecritures': [{'credit': Decimal('0.0'), 'debit': Decimal('4048.44'), 'nom_compte': 'Regroupement des comptes de charges', 'numero_compte_credit': '', 'numero_compte_debit': '126'}, {'credit': Decimal('21.44'), 'debit': Decimal('0.00'), 'nom_compte': 'Achats - Fournitures de bureau', 'numero_compte_credit': '60225', 'numero_compte_debit': ''}, {'credit': Decimal('160.00'), 'debit': Decimal('0.00'), 'nom_compte': "Achats - Frais d'actes et de contentieux", 'numero_compte_credit': '6227', 'numero_compte_debit': ''}, {'credit': Decimal('72.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Autres frais de commission sur prestations de services', 'numero_compte_credit': '6278-LCL', 'numero_compte_debit': ''}, {'credit': Decimal('3000.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Charges - Salaires et appointements', 'numero_compte_credit': '6411', 'numero_compte_debit': ''}, {'credit': Decimal('393.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Charges - cotisations RSI', 'numero_compte_credit': '6411-RSI', 'numero_compte_debit': ''}, {'credit': Decimal('161.80'), 'debit': Decimal('0.00'), 'nom_compte': 'Charges - cotisations URSSAF - Allocations familliales', 'numero_compte_credit': '6411-URSF1', 'numero_compte_debit': ''}, {'credit': Decimal('153.31'), 'debit': Decimal('0.00'), 'nom_compte': 'Charges - cotisations URSSAF - CSG/RDS déductible', 'numero_compte_credit': '6411-URSF2', 'numero_compte_debit': ''}, {'credit': Decimal('86.89'), 'debit': Decimal('0.00'), 'nom_compte': 'Charges - cotisations URSSAF - CSG/RDS non-déductible', 'numero_compte_credit': '6411-URSF3', 'numero_compte_debit': ''}], 'intitule': ['Ecritures de clôture des comptes.']}, {'date': datetime.date(2011, 12, 31), 'ecritures': [{'credit': Decimal('0.0'), 'debit': Decimal('40000.34'), 'nom_compte': 'Regroupement des comptes de produits', 'numero_compte_credit': '', 'numero_compte_debit': '127'}, {'credit': Decimal('4048.44'), 'debit': Decimal('0.0'), 'nom_compte': 'Regroupement des comptes de charges', 'numero_compte_credit': '126', 'numero_compte_debit': ''}, {'credit': Decimal('35951.90'), 'debit': Decimal('0.0'), 'nom_compte': "résultat de l'exercice (bénéfice)", 'numero_compte_credit': '120', 'numero_compte_debit': ''}], 'intitule': ["Enregistrement du résultat net de l'exercice"]}] ) def test_ecritures_to_livre_journal(self): from colbert.livre_journal import ecritures_to_livre_journal ecritures = codecs.open(os.path.join(CURRENT_DIR, "ecritures_de_cloture-2011.json"), mode="r", encoding="utf-8") output = io.StringIO() ecritures_to_livre_journal(json.loads(ecritures.read()), output) self.maxDiff = None self.assertEqual(output.getvalue(), """+---------------------------------------------------------------------------------------------------------------------------------------------------------+ | Ecritures pour le Livre-journal | +=============+=================+=================+==============================================================+=================+=================+====+ || 31/12/2011 || || || Ecritures de clôture des comptes. || || | | || || 706 || || Produits - prestations de services || 40 000.00 || | | || || 758 || || Produits divers de gestion courante || 0.34 || | | || || || 127 || Regroupement des comptes de produits || || 40 000.34 | | +-------------+-----------------+-----------------+--------------------------------------------------------------+-----------------+-----------------+----+ || 31/12/2011 || || || Ecritures de clôture des comptes. || || | | || || 126 || || Regroupement des comptes de charges || 4 048.44 || | | || || || 60225 || Achats - Fournitures de bureau || || 21.44 | | || || || 6227 || Achats - Frais d'actes et de contentieux || || 160.00 | | || || || 6278-LCL || Autres frais de commission sur prestations de services || || 72.00 | | || || || 6411 || Charges - Salaires et appointements || || 3 000.00 | | || || || 6411-RSI || Charges - cotisations RSI || || 393.00 | | || || || 6411-URSF1 || Charges - cotisations URSSAF - Allocations familliales || || 161.80 | | || || || 6411-URSF2 || Charges - cotisations URSSAF - CSG/RDS déductible || || 153.31 | | || || || 6411-URSF3 || Charges - cotisations URSSAF - CSG/RDS non-déductible || || 86.89 | | +-------------+-----------------+-----------------+--------------------------------------------------------------+-----------------+-----------------+----+ || 31/12/2011 || || || Enregistrement du résultat net de l'exercice || || | | || || 127 || || Regroupement des comptes de produits || 40 000.34 || | | || || || 126 || Regroupement des comptes de charges || || 4 048.44 | | || || || 120 || résultat de l'exercice (bénéfice) || || 35 951.90 | | +-------------+-----------------+-----------------+--------------------------------------------------------------+-----------------+-----------------+----+ """) def test_get_solde_compte(self): from colbert.livre_journal import get_solde_compte livre_journal = codecs.open(os.path.join(CURRENT_DIR, "livre-journal.txt"), mode="r", encoding="utf-8") livre_journal_list = livre_journal_to_list(livre_journal) date_debut = datetime.date(2011, 1, 1) date_fin = datetime.date(2011, 12, 31) debit, credit = get_solde_compte(livre_journal_list, "512", date_debut, date_fin) # TODO verifier. self.assertEqual(debit, Decimal("22679.35")) self.assertEqual(credit, Decimal("0.00")) def test_livre_journal_to_list(self): from colbert.livre_journal import RX_DATE_INTITULE, RX_SUITE_INTITULE, RX_ECRITURE self.maxDiff = None # Première ligne d'écriture. s = "|| 31/03/2011 || || || Facture 2011-01 AdenClassifieds || || | | " if VERSION_INFO >= (2, 7): self.assertRegex(s, RX_DATE_INTITULE) m = RX_DATE_INTITULE.match(s) self.assertEqual(m.groupdict(), {'intitule': ' Facture 2011-01 AdenClassifieds ', 'credit': ' ', 'debit': ' ', 'date': '31/03/2011', 'numero_compte_credit': ' ', 'numero_compte_debit': ' ', 'checked': ' '}) # Ligne supplementaire d'intitulé. s = "|| || || || suite et fin de l'intitulé || || | |" if VERSION_INFO >= (2, 7): self.assertRegex(s, RX_SUITE_INTITULE) m = RX_SUITE_INTITULE.match(s) # Ecriture. s = "|| || 4111-clie || || Clients - ventes de biens ou prestations de services || 8 372 || | |" if VERSION_INFO >= (2, 7): self.assertRegex(s, RX_ECRITURE) m = RX_ECRITURE.match(s) self.assertEqual(m.groupdict(), {'nom_compte': 'Clients - ventes de biens ou prestations de services ', 'credit': ' ', 'debit': ' 8 372 ', 'date': ' ', 'numero_compte_credit': ' ', 'numero_compte_debit': ' 4111-clie ', 'checked': ' '}) # Conversion du livre journal. livre_journal = codecs.open(os.path.join(CURRENT_DIR, "livre-journal.txt"), mode="r", encoding="utf-8") livre_journal_list = livre_journal_to_list(livre_journal) self.assertEqual(livre_journal_list, [ {'date': datetime.date(2011, 3, 18), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('80.00'), 'nom_compte': "Achats - Frais d'actes et de contentieux", 'numero_compte_credit': '', 'numero_compte_debit': '6227'}, {'credit': Decimal('0.00'), 'debit': Decimal('10.45'), 'nom_compte': 'T.V.A. déductible sur autres biens et services', 'numero_compte_credit': '', 'numero_compte_debit': '44566'}, {'credit': Decimal('90.45'), 'debit': Decimal('0.00'), 'nom_compte': 'Associés - Comptes courants', 'numero_compte_credit': '455', 'numero_compte_debit': ''}], 'intitule': [' Frais de constitution de la société CFE Paris.'], 'numero_ligne_debut': 13, 'numero_ligne_fin': 17}, {'date': datetime.date(2011, 3, 18), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('80.00'), 'nom_compte': "Achats - Frais d'actes et de contentieux MONTANT à vérifier", 'numero_compte_credit': '', 'numero_compte_debit': '6227'}, {'credit': Decimal('0.00'), 'debit': Decimal('19.00'), 'nom_compte': 'T.V.A. déductible sur autres biens et services', 'numero_compte_credit': '', 'numero_compte_debit': '44566'}, {'credit': Decimal('99.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Associés - Comptes courants', 'numero_compte_credit': '455', 'numero_compte_debit': ''}], 'intitule': [' Frais de constitution de la société - Annonce légale.'], 'numero_ligne_debut': 18, 'numero_ligne_fin': 22}, {'date': datetime.date(2011, 3, 31), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('5980.00'), 'nom_compte': 'Clients - ventes de biens ou prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '4111-CL1'}, {'credit': Decimal('5000.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Produits - prestations de services', 'numero_compte_credit': '706', 'numero_compte_debit': ''}, {'credit': Decimal('980.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '44587', 'numero_compte_debit': ''}], 'intitule': [' Facture 2011-01 MyClient1', ' Prestation MyClient1 mars 2011'], 'numero_ligne_debut': 23, 'numero_ligne_fin': 28}, {'date': datetime.date(2011, 4, 1), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('48.00'), 'nom_compte': 'Autres frais de commission sur prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '6278-LCL'}, {'credit': Decimal('48.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Résultat arrêté compte'], 'numero_ligne_debut': 31, 'numero_ligne_fin': 34}, {'date': datetime.date(2011, 4, 2), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('1500.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '', 'numero_compte_debit': '512'}, {'credit': Decimal('1500.00'), 'debit': Decimal('0.00'), 'nom_compte': "Capital et compte de l'exploitant", 'numero_compte_credit': '100', 'numero_compte_debit': ''}], 'intitule': [' Capital initial', ' Dépôt de 1500 € par Stanislas Guerra', ' au LCL Ledru Rollin'], 'numero_ligne_debut': 35, 'numero_ligne_fin': 40}, {'date': datetime.date(2011, 4, 4), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('1794.00'), 'nom_compte': 'Clients - ventes de biens ou prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '4111-CL2'}, {'credit': Decimal('1500.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Produits - prestations de services', 'numero_compte_credit': '706', 'numero_compte_debit': ''}, {'credit': Decimal('294.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '44587', 'numero_compte_debit': ''}], 'intitule': [' Facture 2011-02 MyClient2', ' Prestation MyClient2'], 'numero_ligne_debut': 41, 'numero_ligne_fin': 46}, {'date': datetime.date(2011, 4, 28), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('15.00'), 'nom_compte': 'Autres frais de commission sur prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '6278-LCL'}, {'credit': Decimal('15.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Cotisation Option PRO LCL'], 'numero_ligne_debut': 47, 'numero_ligne_fin': 50}, {'date': datetime.date(2011, 5, 2), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('3.00'), 'nom_compte': 'Autres frais de commission sur prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '6278-LCL'}, {'credit': Decimal('3.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Abonnement LCL Access'], 'numero_ligne_debut': 53, 'numero_ligne_fin': 56}, {'date': datetime.date(2011, 6, 11), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('21.44'), 'nom_compte': 'Achats - Fournitures de bureau', 'numero_compte_credit': '', 'numero_compte_debit': '60225'}, {'credit': Decimal('0.00'), 'debit': Decimal('4.21'), 'nom_compte': 'T.V.A. déductible sur autres biens et services', 'numero_compte_credit': '', 'numero_compte_debit': '44566'}, {'credit': Decimal('25.65'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' BHV - Fournitures des bureau (Livres comptables).'], 'numero_ligne_debut': 59, 'numero_ligne_fin': 63}, {'date': datetime.date(2011, 6, 15), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('1794.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '', 'numero_compte_debit': '512'}, {'credit': Decimal('0.00'), 'debit': Decimal('294.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '', 'numero_compte_debit': '44587'}, {'credit': Decimal('1794.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Clients - ventes de biens ou prestations de services', 'numero_compte_credit': '4111-CL2', 'numero_compte_debit': ''}, {'credit': Decimal('294.00'), 'debit': Decimal('0.00'), 'nom_compte': 'T.V.A. Collectée', 'numero_compte_credit': '44571', 'numero_compte_debit': ''}], 'intitule': [' Remise chèque XXXXXXX règlement facture 2011-02'], 'numero_ligne_debut': 64, 'numero_ligne_fin': 69}, {'date': datetime.date(2011, 7, 5), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('3000.00'), 'nom_compte': 'Charges - Salaires et appointements', 'numero_compte_credit': '', 'numero_compte_debit': '6411'}, {'credit': Decimal('3000.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Traitement mois de juin gérant.'], 'numero_ligne_debut': 72, 'numero_ligne_fin': 75}, {'date': datetime.date(2011, 8, 8), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('393.00'), 'nom_compte': 'Charges - cotisations RSI', 'numero_compte_credit': '', 'numero_compte_debit': '6411-RSI'}, {'credit': Decimal('393.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Chèque XXXXXXY', ' Cotisation trimestrielle RSI/Prévadiès.'], 'numero_ligne_debut': 78, 'numero_ligne_fin': 82}, {'date': datetime.date(2011, 9, 2), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('5980.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '', 'numero_compte_debit': '512'}, {'credit': Decimal('0.00'), 'debit': Decimal('980.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '', 'numero_compte_debit': '44587'}, {'credit': Decimal('5980.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Clients - ventes de biens ou prestations de services', 'numero_compte_credit': '4111-CL1', 'numero_compte_debit': ''}, {'credit': Decimal('980.00'), 'debit': Decimal('0.00'), 'nom_compte': 'T.V.A. Collectée', 'numero_compte_credit': '44571', 'numero_compte_debit': ''}], 'intitule': [' Virement MyClient1 ZZZZZZZZZZZ', ' Facture 2011-01'], 'numero_ligne_debut': 85, 'numero_ligne_fin': 91}, {'date': datetime.date(2011, 9, 3), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('3.00'), 'nom_compte': 'Autres frais de commission sur prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '6278-LCL'}, {'credit': Decimal('3.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Abonnement LCL Access'], 'numero_ligne_debut': 92, 'numero_ligne_fin': 95}, {'date': datetime.date(2011, 9, 28), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('13156.00'), 'nom_compte': 'Clients - ventes de biens ou prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '4111-CL1'}, {'credit': Decimal('11000.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Produits - prestations de services', 'numero_compte_credit': '706', 'numero_compte_debit': ''}, {'credit': Decimal('2156.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '44587', 'numero_compte_debit': ''}], 'intitule': [' Facture 2011-04 MyClient1', ' Prestation aout 2011'], 'numero_ligne_debut': 96, 'numero_ligne_fin': 101}, {'date': datetime.date(2011, 9, 30), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('1274.00'), 'nom_compte': 'TVA collecté', 'numero_compte_credit': '', 'numero_compte_debit': '44571'}, {'credit': Decimal('33.66'), 'debit': Decimal('0.00'), 'nom_compte': 'TVA déductible sur autres biens et services', 'numero_compte_credit': '44566', 'numero_compte_debit': ''}, {'credit': Decimal('1240.00'), 'debit': Decimal('0.00'), 'nom_compte': 'TVA à décaisser', 'numero_compte_credit': '44551', 'numero_compte_debit': ''}, {'credit': Decimal('0.34'), 'debit': Decimal('0.00'), 'nom_compte': 'Produits divers de gestion courante', 'numero_compte_credit': '758', 'numero_compte_debit': ''}], 'intitule': [' Solde des comptes de TVA du 01/03/2011 au 30/09/2011'], 'numero_ligne_debut': 104, 'numero_ligne_fin': 109}, {'date': datetime.date(2011, 10, 6), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('1240.00'), 'nom_compte': 'TVA à décaisser', 'numero_compte_credit': '', 'numero_compte_debit': '44551'}, {'credit': Decimal('1240.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Chèque WWWWWWW', ' Règlement de la TVA trimestrielle'], 'numero_ligne_debut': 112, 'numero_ligne_fin': 116}, {'date': datetime.date(2011, 11, 1), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('5382.00'), 'nom_compte': 'Clients - ventes de biens ou prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '4111-CL1'}, {'credit': Decimal('4500.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Produits - prestations de services', 'numero_compte_credit': '706', 'numero_compte_debit': ''}, {'credit': Decimal('882.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '44587', 'numero_compte_debit': ''}], 'intitule': [' Facture 2011-05 MyClient1', ' Prestation septembre 2011'], 'numero_ligne_debut': 119, 'numero_ligne_fin': 124}, {'date': datetime.date(2011, 11, 17), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('161.80'), 'nom_compte': 'Charges - cotisations URSSAF - Allocations familliales', 'numero_compte_credit': '', 'numero_compte_debit': '6411-URSF1'}, {'credit': Decimal('0.00'), 'debit': Decimal('153.31'), 'nom_compte': 'Charges - cotisations URSSAF - CSG/RDS déductible', 'numero_compte_credit': '', 'numero_compte_debit': '6411-URSF2'}, {'credit': Decimal('0.00'), 'debit': Decimal('86.89'), 'nom_compte': 'Charges - cotisations URSSAF - CSG/RDS non-déductible', 'numero_compte_credit': '', 'numero_compte_debit': '6411-URSF3'}, {'credit': Decimal('402.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Chèque ZZZZZZZ', ' Cotisation sociales Urssaf 4ème trimestre.'], 'numero_ligne_debut': 125, 'numero_ligne_fin': 131}, {'date': datetime.date(2011, 12, 1), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('3.00'), 'nom_compte': 'Autres frais de commission sur prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '6278-LCL'}, {'credit': Decimal('3.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Abonnement LCL Access'], 'numero_ligne_debut': 134, 'numero_ligne_fin': 137}, {'date': datetime.date(2011, 12, 1), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('18538.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '', 'numero_compte_debit': '512'}, {'credit': Decimal('0.00'), 'debit': Decimal('2156.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '', 'numero_compte_debit': '44587'}, {'credit': Decimal('0.00'), 'debit': Decimal('882.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '', 'numero_compte_debit': '44587'}, {'credit': Decimal('18538.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Clients - ventes de biens ou prestations de services', 'numero_compte_credit': '4111-CL1', 'numero_compte_debit': ''}, {'credit': Decimal('2156.00'), 'debit': Decimal('0.00'), 'nom_compte': 'T.V.A. Collectée', 'numero_compte_credit': '44571', 'numero_compte_debit': ''}, {'credit': Decimal('882.00'), 'debit': Decimal('0.00'), 'nom_compte': 'T.V.A. Collectée', 'numero_compte_credit': '44571', 'numero_compte_debit': ''}], 'intitule': [' Virement MyClient1 WWWWWWWWWW', ' Facture 2011-04, 2011-05'], 'numero_ligne_debut': 138, 'numero_ligne_fin': 146}, {'date': datetime.date(2011, 12, 1), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('8372.00'), 'nom_compte': 'Clients - ventes de biens ou prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '4111-CL3'}, {'credit': Decimal('7000.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Produits - prestations de services', 'numero_compte_credit': '706', 'numero_compte_debit': ''}, {'credit': Decimal('1372.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '44587', 'numero_compte_debit': ''}], 'intitule': [' Facture 2011-06 MyClient3'], 'numero_ligne_debut': 147, 'numero_ligne_fin': 151}, {'date': datetime.date(2011, 12, 31), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('3038.00'), 'nom_compte': 'TVA collecté', 'numero_compte_credit': '', 'numero_compte_debit': '44571'}, {'credit': Decimal('3038.00'), 'debit': Decimal('0.00'), 'nom_compte': 'TVA à décaisser', 'numero_compte_credit': '44551', 'numero_compte_debit': ''}], 'intitule': [' Solde des comptes de TVA du 01/10/2011 au 31/12/2011'], 'numero_ligne_debut': 154, 'numero_ligne_fin': 157}, {'date': datetime.date(2011, 12, 31), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('13156.00'), 'nom_compte': 'Clients - Factures à établir', 'numero_compte_credit': '', 'numero_compte_debit': '4181'}, {'credit': Decimal('11000.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Produits - prestations de services', 'numero_compte_credit': '706', 'numero_compte_debit': ''}, {'credit': Decimal('2156.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '44587', 'numero_compte_debit': ''}], 'intitule': [' Prestation MyClient1 décembre 2011'], 'numero_ligne_debut': 160, 'numero_ligne_fin': 164}, {'date': datetime.date(2012, 1, 1), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('11000.00'), 'nom_compte': 'Produits - prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '706'}, {'credit': Decimal('0.00'), 'debit': Decimal('2156.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '', 'numero_compte_debit': '44587'}, {'credit': Decimal('13156.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Clients - Factures à établir', 'numero_compte_credit': '4181', 'numero_compte_debit': ''}], 'intitule': [' Prestation MyClient1 décembre 2011'], 'numero_ligne_debut': 169, 'numero_ligne_fin': 173}, {'date': datetime.date(2012, 1, 3), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('13156.00'), 'nom_compte': 'Clients - ventes de biens ou prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '4111-CL1'}, {'credit': Decimal('11000.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Produits - prestations de services', 'numero_compte_credit': '706', 'numero_compte_debit': ''}, {'credit': Decimal('2156.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Taxes sur le CA sur factures à établir', 'numero_compte_credit': '44587', 'numero_compte_debit': ''}], 'intitule': [' Facture 2012-01 MyClient1', ' Prestation décembre 2011'], 'numero_ligne_debut': 176, 'numero_ligne_fin': 181}, {'date': datetime.date(2012, 2, 1), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('46.02'), 'nom_compte': 'Charges - Réceptions', 'numero_compte_credit': '', 'numero_compte_debit': '6257'}, {'credit': Decimal('0.00'), 'debit': Decimal('3.78'), 'nom_compte': 'T.V.A. déductible sur autres biens et services', 'numero_compte_credit': '', 'numero_compte_debit': '44566'}, {'credit': Decimal('49.80'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [" Restaurant La Tour d'argent", " Déjeuner d'affaire avec Steve Jobs", ' 0.88€ TVA 19.6% ; 2.90€ TVA 7.0%'], 'numero_ligne_debut': 184, 'numero_ligne_fin': 190} ]) def test_update_ecriture(self): from colbert.livre_journal import update_ecriture ecriture = { 'date': "12/11/2014", 'intitule': "Restaurant La Tour d'argent Déjeuner d'affaire avec Vladimir P.", 'ecritures': [ {'credit': '0.00', 'debit': '49.80', 'nom_compte': 'Charges - Réceptions', 'numero_compte_credit': '', 'numero_compte_debit': '6257'}, {'credit': '49.80', 'debit': '0.00', 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''} ] } update_ecriture(ecriture, date="23/12/2014", montants=["33.40"]) self.assertEqual(ecriture, { 'date': "23/12/2014", 'intitule': "Restaurant La Tour d'argent Déjeuner d'affaire avec Vladimir P.", 'ecritures': [ {'credit': '0.00', 'debit': '33.40', 'nom_compte': 'Charges - Réceptions', 'numero_compte_credit': '', 'numero_compte_debit': '6257'}, {'credit': '33.40', 'debit': '0.00', 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''} ] }) # Avec plusieurs montants. ecriture = { 'date': "12/11/2014", 'intitule': "Restaurant La Tour d'argent Déjeuner d'affaire avec Vladimir P.", 'ecritures': [ {'credit': '0.00', 'debit': '46.02', 'nom_compte': 'Charges - Réceptions', 'numero_compte_credit': '', 'numero_compte_debit': '6257'}, {'credit': '0.00', 'debit': '3.78', 'nom_compte': 'T.V.A. déductible sur autres biens et services', 'numero_compte_credit': '', 'numero_compte_debit': '44566'}, {'credit': '49.80', 'debit': '0.00', 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''} ] } update_ecriture(ecriture, date="23/12/2014", montants=["30.10", "3.30", "33.40"]) self.assertEqual(ecriture, { 'date': "23/12/2014", 'intitule': "Restaurant La Tour d'argent Déjeuner d'affaire avec Vladimir P.", 'ecritures': [ {'credit': '0.00', 'debit': '30.10', 'nom_compte': 'Charges - Réceptions', 'numero_compte_credit': '', 'numero_compte_debit': '6257'}, {'credit': '0.00', 'debit': '3.30', 'nom_compte': 'T.V.A. déductible sur autres biens et services', 'numero_compte_credit': '', 'numero_compte_debit': '44566'}, {'credit': '33.40', 'debit': '0.00', 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''} ] }) def test_rechercher_ecriture(self): from colbert.livre_journal import rechercher_ecriture livre_journal = codecs.open(os.path.join(CURRENT_DIR, "livre-journal.txt"), mode="r", encoding="utf-8") livre_journal_list = livre_journal_to_list(livre_journal) self.assertEqual(list(rechercher_ecriture("lcl", livre_journal_list)), [ {'date': datetime.date(2011, 4, 2), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('1500.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '', 'numero_compte_debit': '512'}, {'credit': Decimal('1500.00'), 'debit': Decimal('0.00'), 'nom_compte': "Capital et compte de l'exploitant", 'numero_compte_credit': '100', 'numero_compte_debit': ''}], 'intitule': [' Capital initial', ' Dépôt de 1500 € par Stanislas Guerra', ' au LCL Ledru Rollin'], 'numero_ligne_debut': 35, 'numero_ligne_fin': 40}, {'date': datetime.date(2011, 4, 28), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('15.00'), 'nom_compte': 'Autres frais de commission sur prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '6278-LCL'}, {'credit': Decimal('15.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Cotisation Option PRO LCL'], 'numero_ligne_debut': 47, 'numero_ligne_fin': 50}, {'date': datetime.date(2011, 5, 2), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('3.00'), 'nom_compte': 'Autres frais de commission sur prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '6278-LCL'}, {'credit': Decimal('3.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Abonnement LCL Access'], 'numero_ligne_debut': 53, 'numero_ligne_fin': 56}, {'date': datetime.date(2011, 9, 3), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('3.00'), 'nom_compte': 'Autres frais de commission sur prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '6278-LCL'}, {'credit': Decimal('3.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Abonnement LCL Access'], 'numero_ligne_debut': 92, 'numero_ligne_fin': 95}, {'date': datetime.date(2011, 12, 1), 'ecritures': [{'credit': Decimal('0.00'), 'debit': Decimal('3.00'), 'nom_compte': 'Autres frais de commission sur prestations de services', 'numero_compte_credit': '', 'numero_compte_debit': '6278-LCL'}, {'credit': Decimal('3.00'), 'debit': Decimal('0.00'), 'nom_compte': 'Banques', 'numero_compte_credit': '512', 'numero_compte_debit': ''}], 'intitule': [' Abonnement LCL Access'], 'numero_ligne_debut': 134, 'numero_ligne_fin': 137} ]) def suite(): suite = unittest.TestLoader().loadTestsFromTestCase(LivreJournalTestCase) return suite
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4.821518
0.08236
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0
0
0
0
0
6
014f40ddff30525556d2ec687b6f4e7d387eca21
733
py
Python
dFL/Utils/config.py
a-dirir/decentralized_FL
5a2e75d02ec77f7bb0f124d2498e0087b1bc1f0e
[ "MIT" ]
null
null
null
dFL/Utils/config.py
a-dirir/decentralized_FL
5a2e75d02ec77f7bb0f124d2498e0087b1bc1f0e
[ "MIT" ]
null
null
null
dFL/Utils/config.py
a-dirir/decentralized_FL
5a2e75d02ec77f7bb0f124d2498e0087b1bc1f0e
[ "MIT" ]
null
null
null
config = { "root_directory": "D:\\dFL", "main_server": { "ip": "127.0.0.1", "port": 5000, "url": "http://127.0.0.1:5000", "encryption_key": "2d2d2d2d2d424547494e205055424c4943204b45592d2d2d2d2d0a4d436f77425159444b325675417945413531744d7137345949476543742b5a5059554b6d364e526f7a697470477467576f564c6d662f694d4d6a413d0a2d2d2d2d2d454e44205055424c4943204b45592d2d2d2d2d0a", "signature_key": "2d2d2d2d2d424547494e205055424c4943204b45592d2d2d2d2d0a4d436f77425159444b32567741794541494b52736d323769723269384d7251696573364173554734646b323657473158536f4354312b39325742553d0a2d2d2d2d2d454e44205055424c4943204b45592d2d2d2d2d0a" }, "database": { "ip": "127.0.0.1", "port": 27017, "name": "dFL" } }
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0.103683
733
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252
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6
0184e6dfc8d5507b4ffde3cb1c395e87919bf667
139
py
Python
other/ipypublish/filters/replace_string.py
KGerring/metaproj
e957de611f5268978df10184e4cedbd229ef617a
[ "MIT" ]
2
2021-04-11T01:43:09.000Z
2021-07-08T00:17:57.000Z
other/ipypublish/filters/replace_string.py
KGerring/metaproj
e957de611f5268978df10184e4cedbd229ef617a
[ "MIT" ]
1
2021-08-21T23:39:26.000Z
2021-08-21T23:39:26.000Z
other/ipypublish/filters/replace_string.py
KGerring/metaproj
e957de611f5268978df10184e4cedbd229ef617a
[ "MIT" ]
null
null
null
#!/usr/bin/env python from __future__ import annotations def replace_string(line, find, replace): return line.replace(find, replace)
19.857143
40
0.76259
19
139
5.315789
0.736842
0.217822
0
0
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0
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0.136691
139
6
41
23.166667
0.841667
0.143885
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0.333333
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0
0
1
1
0
0
0
6
018b710f7ebec73a1adb46f00adf4743bd6b0cc1
4,613
py
Python
mcmctools/pytorch/data_generation/batchconfigdatagenerator.py
statphysandml/MCMCEvaluationLib
f722b2c7df88b1b33cd29335a22eef53bdad9665
[ "MIT" ]
2
2021-06-04T04:52:04.000Z
2021-06-04T19:32:58.000Z
mcmctools/pytorch/data_generation/batchconfigdatagenerator.py
statphysandml/MCMCEvaluationLib
f722b2c7df88b1b33cd29335a22eef53bdad9665
[ "MIT" ]
null
null
null
mcmctools/pytorch/data_generation/batchconfigdatagenerator.py
statphysandml/MCMCEvaluationLib
f722b2c7df88b1b33cd29335a22eef53bdad9665
[ "MIT" ]
null
null
null
from mcmctools.pytorch.data_generation.configdatagenerator import ConfigDataGenerator # Avoids that samples are loaded one by another from the self.data dataframe - instead, a batch is extracted directly # This leads to a performance boost since the underlying data frame is accessed via slicing # i.e: batch = self.data.iloc[i:i+batch_size].values instead of # : batch = np.stack([self.data.iloc[j] for j in range(i, i+batch_size]) class BatchConfigDataGenerator(ConfigDataGenerator): def __init__(self, **kwargs): super().__init__(**kwargs) self.batch_size = kwargs.pop('batch_size') def sample_target_config(self): if self.iterator >= len(self.data): self.iterator = 0 # Reset iterator self.data = self.get_next_chunk_collection(resample=True) # load data # Needs to be set again if get_next_chunk_collection is called here for the first time self.determine_target_and_input_size() self.iterator += self.batch_size if self.iterator > len(self.data): batch, target = list(self.data[self.labels].iloc[self.iterator - self.batch_size:len(self.data)].values.reshape((-1, self.inp_size))) , \ list(self.data["Config"].iloc[self.iterator - self.batch_size:len(self.data)].values.reshape((-1, self.tar_size))) if self.chunk_iterator < self.total_chunks: # Load next chunk and reset iterator n_missing_configs = self.iterator - len(self.data) self.iterator = n_missing_configs # Reset iterator self.data = self.get_next_chunk_collection(resample=True) # load data batch += list(self.data[self.labels].iloc[0:n_missing_configs].values.reshape((-1, self.inp_size))) target += list(self.data["Config"].iloc[0:n_missing_configs].values.reshape((-1, self.tar_size))) return batch, target else: # End of files has been reached # Prepare next data iteration self.iterator = 0 # Reset iterator self.data = self.get_next_chunk_collection(resample=True) # load data # Return last samples of previous data iteration return batch, target else: return list(self.data[self.labels].iloc[self.iterator - self.batch_size:self.iterator].values.reshape((-1, self.inp_size))) , \ list(self.data["Config"].iloc[self.iterator - self.batch_size:self.iterator].values.reshape((-1, self.tar_size))) def sample_target_param(self): if self.iterator == len(self.data): # Load next chunk and reset iterator self.iterator = 0 # Reset iterator self.data = self.get_next_chunk_collection(resample=True) # load data # Needs to be set again if get_next_chunk_collection is called here for the first time self.determine_target_and_input_size() self.iterator += self.batch_size if self.iterator > len(self.data): batch, target = list(self.data["Config"].iloc[self.iterator - self.batch_size:len(self.data)].values.reshape(-1, self.inp_size)), \ list(self.data[self.labels].iloc[self.iterator - self.batch_size:len(self.data)].values.reshape((-1, self.tar_size))) if self.chunk_iterator < self.total_chunks: # Load next chunk and reset iterator n_missing_configs = self.iterator - len(self.data) self.iterator = n_missing_configs # Reset iterator self.data = self.get_next_chunk_collection(resample=True) # load data batch += list(self.data["Config"].iloc[0:n_missing_configs].values.reshape(-1, self.inp_size)) target += list(self.data[self.labels].iloc[0:n_missing_configs].values.reshape((-1, self.tar_size))) return batch, target else: # End of files has been reached # Prepare next data iteration self.iterator = 0 # Reset iterator self.data = self.get_next_chunk_collection(resample=True) # load data # Return last samples of previous data iteration return batch, target else: return list(self.data["Config"].iloc[self.iterator - self.batch_size:self.iterator].values.reshape(-1, self.inp_size)), \ list(self.data[self.labels].iloc[self.iterator - self.batch_size:self.iterator].values.reshape((-1, self.tar_size)))
58.392405
149
0.636679
599
4,613
4.751252
0.171953
0.08714
0.063247
0.075896
0.827477
0.827477
0.817287
0.796908
0.796908
0.796908
0
0.005836
0.2571
4,613
79
150
58.392405
0.824628
0.211576
0
0.588235
0
0
0.012746
0
0
0
0
0
0
1
0.058824
false
0
0.019608
0
0.215686
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
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0
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0
0
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0
0
0
0
0
0
6
6df2506f3625ea63c6bc971197b8a1fac0029dca
239
py
Python
expfactory/database/__init__.py
YanivD/expfactory
a34ba21016ef01a44998764935be20ec99fdd0a8
[ "BSD-3-Clause" ]
26
2016-09-02T22:25:39.000Z
2021-02-03T16:09:33.000Z
expfactory/database/__init__.py
YanivD/expfactory
a34ba21016ef01a44998764935be20ec99fdd0a8
[ "BSD-3-Clause" ]
157
2016-08-09T20:17:58.000Z
2022-03-23T21:20:01.000Z
expfactory/database/__init__.py
YanivD/expfactory
a34ba21016ef01a44998764935be20ec99fdd0a8
[ "BSD-3-Clause" ]
14
2016-09-02T22:25:42.000Z
2022-03-04T11:40:48.000Z
from expfactory.defaults import EXPFACTORY_DATABASE if EXPFACTORY_DATABASE == "filesystem": from .filesystem import * else: from .relational import * if EXPFACTORY_DATABASE.startswith("sqlite"): from .sqlite import *
23.9
51
0.732218
25
239
6.88
0.44
0.313953
0.232558
0
0
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0.188285
239
9
52
26.555556
0.886598
0
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0.066946
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1
0
true
0
0.571429
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0
1
0
0
0
0
6
0970bdaa09faf6aac7a2ee9f8663253eaca5bc6c
49
py
Python
question28.py
gusenov/test-tech-mail-ru-python2
70e37a3de447b6f7c4da5add75f65df1b51405fe
[ "MIT" ]
null
null
null
question28.py
gusenov/test-tech-mail-ru-python2
70e37a3de447b6f7c4da5add75f65df1b51405fe
[ "MIT" ]
null
null
null
question28.py
gusenov/test-tech-mail-ru-python2
70e37a3de447b6f7c4da5add75f65df1b51405fe
[ "MIT" ]
null
null
null
s = "\nAlice\n" s.rstrip() print s # \nAlice\n
9.8
20
0.571429
9
49
3.111111
0.555556
0.5
0.571429
0
0
0
0
0
0
0
0
0
0.204082
49
4
21
12.25
0.717949
0.183673
0
0
0
0
0.236842
0
0
0
0
0
0
0
null
null
0
0
null
null
0.333333
1
1
0
null
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
1
0
0
0
0
0
0
0
0
6
09b5410337055cb5d9ae000a32984d3ed5535dd4
113
py
Python
fixtures/__init__.py
daryasary/instagram-graph-api
8a3fdf0b1bfb15d198da9889ac0bfe747f8bc019
[ "Apache-2.0" ]
11
2019-03-05T18:41:33.000Z
2020-10-16T13:54:06.000Z
fixtures/__init__.py
daryasary/instagram-graph-api
8a3fdf0b1bfb15d198da9889ac0bfe747f8bc019
[ "Apache-2.0" ]
1
2019-05-30T11:52:56.000Z
2019-05-31T19:51:14.000Z
fixtures/__init__.py
daryasary/instagram-graph-api
8a3fdf0b1bfb15d198da9889ac0bfe747f8bc019
[ "Apache-2.0" ]
6
2019-04-30T10:23:46.000Z
2020-05-12T17:26:53.000Z
try: from fixtures.local_variables import * except ImportError: from fixtures.default_variables import *
22.6
44
0.778761
13
113
6.615385
0.692308
0.27907
0
0
0
0
0
0
0
0
0
0
0.168142
113
4
45
28.25
0.914894
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
0.75
0
0.75
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
09c9406be5d4b2e3aa21d4a7eb381b39d7a5a284
24,741
py
Python
examples/SendSales/tax_code_list.py
avasachinbaijal/AvaTax-REST-V2-Python-SDK
b6e12550fa11b08cd8f57195c41d9b31000553de
[ "Apache-2.0" ]
13
2018-04-13T07:07:24.000Z
2021-05-06T21:08:03.000Z
examples/SendSales/tax_code_list.py
avasachinbaijal/AvaTax-REST-V2-Python-SDK
b6e12550fa11b08cd8f57195c41d9b31000553de
[ "Apache-2.0" ]
22
2018-03-21T18:44:20.000Z
2021-06-11T18:42:16.000Z
examples/SendSales/tax_code_list.py
avasachinbaijal/AvaTax-REST-V2-Python-SDK
b6e12550fa11b08cd8f57195c41d9b31000553de
[ "Apache-2.0" ]
27
2017-12-27T21:21:00.000Z
2022-03-29T17:00:51.000Z
"""Hold tax code list.""" tax_codes = ["D0000000", "D9999999", "DA010000", "DA030000", "DA040000", "DA040100", "DA051011", "DA051012", "DA051013", "DA059399", "DB010000", "DB020000", "DB031013", "DB031014", "DB031015", "DB031016", "DC010000", "DC010100", "DC010200", "DC010300", "DC010400", "DC010500", "DC010600", "DC011000", "DC020000", "DC020100", "DC020200", "DC020300", "DC020400", "DC020402", "DC020500", "DC020501", "DC020502", "DC020600", "DC060000", "DC070000", "DD020000", "DD040000", "DG010000", "DG010100", "DG010200", "DG010201", "DG010300", "DG010301", "DG010302", "DG020000", "DI010000", "DI010100", "DI010200", "DI010201", "DL020000", "DM010100", "DM020000", "DM020100", "DM020200", "DM030000", "DM030100", "DM030200", "DM030201", "DM040000", "DM040100", "DM040200", "DM040201", "DN010000", "DO010000", "DP010000", "DP010100", "DP010200", "DP010201", "DV010000", "DV010100", "DV010200", "DV010201", "DV017194", "DV021007", "DV021008", "DV021009", "DV021010", "DV029398", "FR000000", "FR010000", "FR010100", "FR010200", "FR020000", "FR020100", "FR020200", "FR020400", "FR020500", "FR020800", "FR020900", "FR021004", "FR022000", "FR030000", "FR030700", "FR040000", "FR059314", "FR060000", "FR070100", "FR999999", "O0000000", "O9999999", "OA020000", "OA020100", "OA020200", "OA020300", "OA020400", "OA020500", "OA020600", "OA020700", "OA026346", "OA029338", "OC030000", "OC040000", "OC040100", "OC040200", "OD010000", "OD020000", "OD020400", "OD020500", "OD030000", "OE010100", "OF020000", "OF030000", "OF040002", "OF040003", "OF040004", "OH010000", "OM010000", "ON010000", "ON030000", "OO028842", "OR040000", "OR070000", "OT010100", "OT010300", "OT010400", "P0000000", "P000000H", "P000100H", "P9999999", "PA020003", "PA020100", "PA020111", "PA020113", "PA020659", "PA020661", "PA020662", "PA020664", "PA020665", "PA020668", "PA020738", "PA021078", "PA028802", "PA028858", "PA029612", "PA029613", "PA029614", "PA100000", "PA200522", "PA200546", "PA300741", "PB100000", "PB100200", "PB100300", "PB100400", "PB100817", "PB100818", "PB100819", "PB200742", "PB200743", "PB308786", "PC002386", "PC010000", "PC020000", "PC030000", "PC030100", "PC030101", "PC030103", "PC030105", "PC030106", "PC030108", "PC030109", "PC030110", "PC030111", "PC030113", "PC030115", "PC030116", "PC030117", "PC030118", "PC030119", "PC030120", "PC030121", "PC030122", "PC030123", "PC030124", "PC030125", "PC030126", "PC030127", "PC030128", "PC030129", "PC030130", "PC030131", "PC030133", "PC030134", "PC030135", "PC030137", "PC030138", "PC030139", "PC030140", "PC030142", "PC030143", "PC030144", "PC030145", "PC030146", "PC030147", "PC030148", "PC030150", "PC030151", "PC030152", "PC030153", "PC030155", "PC030156", "PC030157", "PC030158", "PC030168", "PC030200", "PC030201", "PC030202", "PC030203", "PC030204", "PC030205", "PC030206", "PC030207", "PC030208", "PC030209", "PC030210", "PC030300", "PC030301", "PC030302", "PC030303", "PC030304", "PC030305", "PC030306", "PC030307", "PC030308", "PC030309", "PC030310", "PC030311", "PC030312", "PC030313", "PC030314", "PC030315", "PC030316", "PC030400", "PC030401", "PC030402", "PC030403", "PC030404", "PC030405", "PC030406", "PC030407", "PC030408", "PC030409", "PC030410", "PC030411", "PC030412", "PC030413", "PC030414", "PC030415", "PC030500", "PC030600", "PC030601", "PC030602", "PC030605", "PC031034", "PC031042", "PC031050", "PC031098", "PC031114", "PC031138", "PC031146", "PC031210", "PC031226", "PC031234", "PC031242", "PC039328", "PC040000", "PC040065", "PC040100", "PC040101", "PC040103", "PC040105", "PC040106", "PC040108", "PC040109", "PC040110", "PC040111", "PC040113", "PC040115", "PC040116", "PC040117", "PC040118", "PC040119", "PC040120", "PC040121", "PC040122", "PC040123", "PC040124", "PC040125", "PC040126", "PC040127", "PC040128", "PC040129", "PC040130", "PC040131", "PC040132", "PC040133", "PC040134", "PC040135", "PC040137", "PC040138", "PC040139", "PC040140", "PC040142", "PC040143", "PC040144", "PC040145", "PC040146", "PC040147", "PC040148", "PC040150", "PC040151", "PC040152", "PC040153", "PC040155", "PC040156", "PC040157", "PC040158", "PC040168", "PC040200", "PC040201", "PC040202", "PC040203", "PC040204", "PC040205", "PC040206", "PC040207", "PC040208", "PC040209", "PC040210", "PC040300", "PC040301", "PC040302", "PC040303", "PC040304", "PC040305", "PC040306", "PC040307", "PC040308", "PC040309", "PC040310", "PC040311", "PC040312", "PC040313", "PC040314", "PC040315", "PC040316", "PC040400", "PC040401", "PC040402", "PC040403", "PC040404", "PC040405", "PC040406", "PC040407", "PC040408", "PC040409", "PC040410", "PC040411", "PC040412", "PC040413", "PC040414", "PC040415", "PC040500", "PC040501", "PC040502", "PC040503", "PC040600", "PC040601", "PC040602", "PC040605", "PC041034", "PC041042", "PC041050", "PC041098", "PC041114", "PC041138", "PC041146", "PC041210", "PC041226", "PC041234", "PC041242", "PC049328", "PC060000", "PC070000", "PC070100", "PC070200", "PC070300", "PC070400", "PC070500", "PC070600", "PC070601", "PC077426", "PC078954", "PC080000", "PC080100", "PC080200", "PC080300", "PC080400", "PC080500", "PC080600", "PC080601", "PC087426", "PC088954", "PC098212", "PC098213", "PC100000", "PC168330", "PC168346", "PC168354", "PD010000", "PD071521", "PD078658", "PD078666", "PD078690", "PD078738", "PD078746", "PE020100", "PE070000", "PE070200", "PE070201", "PE070202", "PE070203", "PE070204", "PE070205", "PE070206", "PE070209", "PE080000", "PE080100", "PE080101", "PE080200", "PE080201", "PE080772", "PE089124", "PE200706", "PE308914", "PF012394", "PF030011", "PF030746", "PF040100", "PF040723", "PF040724", "PF040726", "PF040727", "PF040728", "PF040729", "PF040730", "PF040731", "PF040732", "PF040733", "PF040739", "PF041200", "PF049391", "PF050001", "PF050002", "PF050012", "PF050032", "PF050062", "PF050064", "PF050067", "PF050068", "PF050069", "PF050070", "PF050071", "PF050072", "PF050073", "PF050075", "PF050076", "PF050077", "PF050078", "PF050079", "PF050080", "PF050081", "PF050082", "PF050083", "PF050084", "PF050085", "PF050086", "PF050087", "PF050088", "PF050089", "PF050091", "PF050092", "PF050093", "PF050094", "PF050095", "PF050096", "PF050097", "PF050099", "PF050100", "PF050101", "PF050102", "PF050103", "PF050104", "PF050105", "PF050106", "PF050107", "PF050108", "PF050109", "PF050110", "PF050111", "PF050112", "PF050113", "PF050114", "PF050115", "PF050117", "PF050118", "PF050119", "PF050120", "PF050121", "PF050122", "PF050123", "PF050124", "PF050125", "PF050126", "PF050200", "PF050201", "PF050202", "PF050203", "PF050204", "PF050205", "PF050206", "PF050208", "PF05020H", "PF050211", "PF050214", "PF050215", "PF050216", "PF050218", "PF050219", "PF050220", "PF050221", "PF050222", "PF050224", "PF050226", "PF050227", "PF050232", "PF050233", "PF050236", "PF050237", "PF050300", "PF050301", "PF050302", "PF050303", "PF050304", "PF050305", "PF050306", "PF050307", "PF050309", "PF050311", "PF050313", "PF050314", "PF050323", "PF050401", "PF050402", "PF050403", "PF050404", "PF050405", "PF050406", "PF050407", "PF050408", "PF050409", "PF050410", "PF050411", "PF050412", "PF050413", "PF050414", "PF050415", "PF050416", "PF050417", "PF050500", "PF050501", "PF050502", "PF050503", "PF050507", "PF050600", "PF050612", "PF050613", "PF050654", "PF050655", "PF050667", "PF050669", "PF050670", "PF050671", "PF050672", "PF050673", "PF050674", "PF050675", "PF050676", "PF050677", "PF050685", "PF050686", "PF050687", "PF050700", "PF050701", "PF050702", "PF050707", "PF050709", "PF050711", "PF050712", "PF050713", "PF050714", "PF050715", "PF050716", "PF050717", "PF050718", "PF050720", "PF050721", "PF050722", "PF050801", "PF050802", "PF050806", "PF050807", "PF050900", "PF050901", "PF050902", "PF050903", "PF050904", "PF050905", "PF050908", "PF050909", "PF051000", "PF051004", "PF051100", "PF051101", "PF051102", "PF051103", "PF051104", "PF051105", "PF051338", "PF051346", "PF051354", "PF051370", "PF051378", "PF051410", "PF051418", "PF051419", "PF051426", "PF051427", "PF051428", "PF051434", "PF051450", "PF051458", "PF051459", "PF051474", "PF051475", "PF051490", "PF051498", "PF051506", "PF051507", "PF051514", "PF051522", "PF051530", "PF051531", "PF051538", "PF051546", "PF051554", "PF051562", "PF051570", "PF051571", "PF051578", "PF051579", "PF051586", "PF051594", "PF051690", "PF051698", "PF051706", "PF051714", "PF051722", "PF051730", "PF051778", "PF051786", "PF051794", "PF051802", "PF051810", "PF051811", "PF051818", "PF051826", "PF051834", "PF051842", "PF051850", "PF051858", "PF051866", "PF051874", "PF051882", "PF051890", "PF051898", "PF051906", "PF051914", "PF051930", "PF051946", "PF051954", "PF051962", "PF051970", "PF051971", "PF051977", "PF051978", "PF051986", "PF051994", "PF052002", "PF052018", "PF052025", "PF052026", "PF052034", "PF052050", "PF052058", "PF052066", "PF052074", "PF052082", "PF052090", "PF052098", "PF052106", "PF052114", "PF052122", "PF052130", "PF052131", "PF052132", "PF052133", "PF052138", "PF052139", "PF052140", "PF052141", "PF052142", "PF052143", "PF052144", "PF052145", "PF052146", "PF052147", "PF052154", "PF052162", "PF052167", "PF052168", "PF052169", "PF052170", "PF052178", "PF052202", "PF052210", "PF052234", "PF052242", "PF052250", "PF052322", "PF055195", "PF058754", "PF058762", "PF058770", "PF059392", "PF090123", "PF0F0215", "PF101000", "PF110000", "PF110300", "PF110301", "PF110302", "PF110303", "PF110304", "PF110305", "PF110400", "PF110401", "PF110402", "PF110403", "PF110404", "PF110405", "PF110700", "PF110701", "PF110702", "PF110703", "PF110704", "PF110705", "PF110800", "PF110801", "PF110802", "PF110803", "PF110804", "PF110805", "PF120000", "PF120012", "PF120101", "PF120603", "PF120900", "PF160014", "PF160015", "PF160016", "PF160017", "PF160019", "PF160020", "PF160021", "PF160022", "PF160023", "PF160024", "PF160025", "PF160026", "PF160027", "PF160030", "PF160036", "PF160039", "PF160040", "PF160041", "PF160042", "PF160043", "PF160045", "PF160046", "PF160047", "PF160048", "PF160049", "PF160050", "PF160051", "PF160052", "PF160053", "PF160054", "PF160055", "PF160056", "PF160057", "PF160058", "PF160059", "PF170000", "PF190677", "PF199605", "PF199606", "PF199607", "PF199608", "PF199609", "PF199610", "PF199611", "PF220744", "PF220745", "PF232402", "PG050000", "PG050715", "PG050716", "PG050717", "PG068810", "PG076546", "PG081610", "PG081618", "PH000794", "PH000802", "PH050000", "PH050051", "PH050100", "PH050101", "PH050102", "PH050103", "PH050104", "PH050105", "PH050106", "PH050107", "PH050108", "PH050109", "PH050110", "PH050111", "PH050112", "PH050113", "PH050114", "PH050115", "PH050116", "PH050117", "PH050118", "PH050122", "PH050124", "PH050125", "PH050126", "PH050127", "PH050140", "PH050141", "PH050142", "PH050143", "PH050144", "PH050145", "PH050146", "PH050147", "PH050148", "PH050149", "PH050150", "PH050151", "PH050152", "PH050153", "PH050154", "PH050155", "PH050200", "PH050201", "PH050202", "PH050207", "PH050208", "PH050213", "PH050214", "PH050215", "PH050216", "PH050217", "PH050218", "PH050219", "PH050220", "PH050221", "PH050222", "PH050223", "PH050224", "PH050225", "PH050226", "PH050227", "PH050228", "PH050229", "PH050230", "PH050231", "PH050232", "PH050233", "PH050234", "PH050235", "PH050236", "PH050237", "PH050238", "PH050239", "PH050240", "PH050241", "PH050242", "PH050243", "PH050244", "PH050245", "PH050246", "PH050247", "PH050300", "PH050301", "PH050302", "PH050310", "PH050311", "PH050312", "PH050313", "PH050400", "PH050401", "PH050402", "PH050407", "PH050408", "PH050413", "PH050414", "PH050419", "PH050420", "PH050425", "PH050426", "PH050440", "PH050441", "PH050442", "PH050443", "PH050444", "PH050445", "PH050446", "PH050447", "PH050448", "PH050449", "PH050450", "PH050451", "PH050452", "PH050453", "PH050454", "PH050455", "PH050456", "PH050457", "PH050458", "PH050459", "PH050460", "PH050461", "PH050462", "PH050463", "PH050464", "PH050465", "PH050500", "PH050501", "PH050507", "PH050508", "PH050509", "PH050511", "PH050514", "PH050526", "PH050527", "PH050528", "PH050529", "PH050530", "PH050531", "PH050532", "PH050533", "PH050600", "PH050601", "PH050623", "PH050639", "PH050676", "PH050677", "PH050700", "PH050709", "PH050720", "PH050730", "PH050731", "PH050732", "PH050733", "PH050859", "PH050860", "PH058922", "PH060754", "PH060762", "PH060770", "PH060771", "PH101010", "PH101011", "PH101012", "PH101013", "PH101014", "PH101015", "PH101016", "PH101017", "PH101018", "PH101020", "PH101030", "PH101040", "PH101050", "PH101060", "PH101070", "PH101080", "PH101090", "PH101100", "PH101110", "PH101120", "PH101130", "PH101140", "PH108866", "PH150101", "PH150102", "PH150103", "PH150104", "PH150105", "PH150106", "PH150107", "PH150108", "PH150111", "PH150112", "PH150113", "PH150114", "PH150115", "PH150116", "PH150117", "PH150118", "PH150121", "PH150122", "PH150123", "PH150124", "PH150125", "PH150126", "PH150127", "PH150128", "PH150131", "PH150132", "PH150133", "PH150134", "PH150135", "PH150136", "PH150137", "PH150138", "PH150141", "PH150142", "PH150143", "PH150144", "PH150145", "PH150146", "PH150147", "PH150148", "PH400004", "PH400005", "PH400007", "PH400008", "PH400009", "PH400014", "PH400031", "PH400033", "PH400035", "PH400036", "PH400038", "PH400044", "PH400167", "PH400652", "PH400653", "PH400656", "PH400778", "PH400787", "PH400788", "PH400789", "PH400826", "PH400827", "PH400834", "PH400842", "PH400850", "PH400874", "PH400882", "PH400898", "PH400899", "PH400906", "PH400914", "PH400922", "PH400923", "PH400924", "PH400930", "PH400938", "PH400946", "PH400954", "PH400955", "PH400956", "PH400957", "PH400958", "PH400962", "PH400963", "PH400970", "PH400972", "PH400986", "PH400994", "PH401002", "PH401010", "PH401011", "PH401018", "PH401019", "PH401020", "PH401021", "PH401022", "PH401023", "PH402810", "PH402818", "PH402826", "PH402834", "PH402842", "PH402850", "PH402858", "PH402874", "PH402882", "PH402891", "PH402898", "PH402906", "PH402914", "PH402922", "PH402923", "PH402930", "PH402938", "PH402946", "PH402954", "PH402962", "PH402970", "PH402978", "PH402986", "PH402994", "PH403002", "PH403003", "PH403004", "PH403010", "PH403018", "PH403026", "PH403034", "PH403042", "PH403050", "PH403058", "PH403066", "PH403067", "PH403074", "PH403082", "PH403090", "PH403098", "PH403106", "PH403114", "PH403122", "PH403130", "PH403138", "PH403210", "PH403218", "PH403234", "PH403242", "PH403250", "PH403274", "PH403282", "PH403290", "PH403298", "PH403306", "PH403314", "PH403322", "PH403330", "PH403338", "PH403346", "PH403362", "PH403370", "PH403378", "PH403386", "PH403394", "PH403402", "PH403410", "PH403418", "PH403426", "PH403434", "PH403442", "PH403450", "PH403458", "PH403466", "PH403474", "PH403482", "PH403490", "PH403498", "PH403506", "PH403522", "PH403530", "PH403538", "PH403546", "PH403554", "PH403562", "PH403570", "PH403578", "PH403586", "PH403594", "PH403602", "PH403610", "PH403618", "PH403626", "PH403642", "PH403650", "PH403658", "PH403666", "PH403674", "PH403682", "PH403683", "PH403684", "PH403690", "PH403691", "PH403698", "PH403699", 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"PH404227", "PH404228", "PH404234", "PH404242", "PH404250", "PH404258", "PH404266", "PH404274", "PH404282", "PH404290", "PH404298", "PH404306", "PH404314", "PH404322", "PH404330", "PH404338", "PH404346", "PH404354", "PH404362", "PH404370", "PH404378", "PH404386", "PH404394", "PH404402", "PH404410", "PH404418", "PH404426", "PH404434", "PH404442", "PH404450", "PH404458", "PH404466", "PH404474", "PH404482", "PH404490", "PH404498", "PH404506", "PH404514", "PH404522", "PH404530", "PH404538", "PH404546", "PH404554", "PH404562", "PH404570", "PH404578", "PH404586", "PH404594", "PH404602", "PH404610", "PH404618", "PH404626", "PH404634", "PH404642", "PH404650", "PH404658", "PH404666", "PH404674", "PH404682", "PH404690", "PH404698", "PH404706", "PH404714", "PH404722", "PH404730", "PH404738", "PH404746", "PH404754", "PH404762", "PH404770", "PH404778", "PH404786", "PH404802", "PH404810", "PH404818", "PH404826", "PH404834", "PH404842", "PH404850", "PH404858", "PH404866", "PH404874", "PH404882", "PH404890", "PH404898", "PH404906", "PH404914", "PH404922", "PH404930", "PH404938", "PH404946", "PH404954", "PH404962", "PH404970", "PH404978", "PH404986", "PH404994", "PH405002", "PH405010", "PH405018", "PH405026", "PH405034", "PH405042", "PH405050", "PH405051", "PH405058", "PH405059", "PH405066", "PH405082", "PH405090", "PH405098", "PH405106", "PH405114", "PH405122", "PH405123", "PH405130", "PH405138", "PH405146", "PH405154", "PH405170", "PH405179", "PH405186", "PH405194", "PH405202", "PH405210", "PH405218", "PH405226", "PH405234", "PH405235", "PH405242", "PH405243", "PH405250", "PH405258", "PH405266", "PH405274", "PH405282", "PH405290", "PH405298", "PH405306", "PH405314", "PH405322", "PH405323", "PH405324", "PH405330", "PH405338", "PH405354", "PH405370", "PH405378", "PH405386", "PH405394", "PH405402", "PH405410", "PH405418", "PH405426", "PH405434", "PH405442", "PH405450", "PH405458", "PH405466", "PH405474", "PH408930", "PH409601", "PH409618", "PI011642", "PI011650", "PI011658", "PI011666", "PI040006", "PI040578", "PI040594", "PI040597", "PI040599", "PI040600", "PI040601", "PI040610", "PI041073", "PI041074", "PI041075", "PI041076", "PI041077", "PI041078", "PI041079", "PI041080", "PI041082", "PI041090", "PI041106", "PI041107", "PL018834", "PM008826", "PM020400", "PM020404", "PM020700", "PM020704", "PM030000", "PM030100", "PM030108", "PM030110", "PM030811", "PM030812", "PM039393", "PM039394", "PM039395", "PM039396", "PM039397", "PM062306", "PN050814", "PN052314", "PN058970", "PN060815", "PN070816", "PO100000", "PP030000", "PP030001", "PP030100", "PP030106", "PP030107", "PP030108", "PP030109", "PP030110", "PP030113", "PP030771", "PP050736", "PP050737", "PP051195", "PP051196", "PP140029", "PP140037", "PP140235", "PP140236", "PP148090", "PP148211", "PP198874", "PP208778", "PR058882", "PR060298", "PR061200", "PR062482", "PR062498", "PR082546", "PR082626", "PR082658", "PR082666", "PR097018", "PS050000", "PS050100", "PS050101", "PS050102", "PS050103", "PS050104", "PS050105", "PS050106", "PS050107", "PS050108", "PS050109", "PS050110", "PS050111", "PS050112", "PS050113", "PS050114", "PS050115", "PS050116", "PS050117", "PS050118", "PS050119", "PS050120", "PS050121", "PS050122", "PS050123", "PS050124", "PS050125", "PS050126", "PS050127", "PS050300", "PS050302", "PS050303", "PS050401", "PS050402", "PS050403", "PS050404", "PS050405", "PS060000", "PS060100", "PS060101", "PS060102", "PS060103", "PS060104", "PS060105", "PS060106", "PS060107", "PS060108", "PS060109", "PS060110", "PS060111", "PS060112", "PS060113", "PS060114", "PS060115", "PS060116", "PS060117", "PS060118", "PS060119", "PS060120", "PS060121", "PS060122", "PS060123", "PS060124", "PS060125", "PS060126", "PS060127", "PS060300", "PS060302", "PS060303", "PS060401", "PS060404", "PS060405", "PS078938", "PS080101", "PS081250", "PS081258", "PS081266", "PS081274", "PS081282", "PS101298", "PS116242", "PS118898", "PS160747", "PS160748", "PS160749", "PS160750", "PS160751", "PS160800", "PS160801", "PS160802", "PT030000", "PT030400", "PT030500", "PT030501", "PT030600", "PT118906", "PW032474", "S0000000", "S0000001", "S0557082", "S9999999", "SA010000", "SA010100", "SA010200", "SA010300", "SA010400", "SA011060", "SA016850", "SA030000", "SA030200", "SA030300", "SA030400", "SA030401", "SA035898", "SA035906", "SA035914", "SA035922", "SA035930", "SA035938", "SA035946", "SA035954", "SA036298", "SA046706", "SA046866", "SA070000", "SA080000", "SA090000", "SA090101", "SA090102", "SA090103", "SA090400", "SA090500", "SA090700", "SA097130", "SA186314", "SB010000", "SB010100", "SB010200", "SB010300", "SB016682", "SB030000", "SB040100", "SB045978", "SB045994", "SB046002", "SB046003", "SB046004", "SB046010", "SB070000", "SB070400", "SB070500", "SB070700", "SB071000", "SB156266", "SC010000", "SC060000", "SC070000", "SC070100", "SC070101", "SC070102", "SC070103", "SC070104", "SC070121", "SC070131", "SC070200", "SC070201", "SC070202", "SC070203", "SC070204", "SC070300", "SC070301", "SC070302", "SC070303", "SC070304", "SC070305", "SC070306", "SC070321", "SC070331", "SC080000", "SC080100", "SC080101", "SC080102", "SC080103", "SC080121", "SC080200", "SC080201", "SC080202", "SC080203", "SC080300", "SC080301", "SC080302", "SC080303", "SC080304", "SC080305", "SC080306", "SC080307", "SC080308", "SC080309", "SC080321", "SC090000", "SC090100", "SC090101", "SC090102", "SC090103", "SC090200", "SC090201", "SC090202", "SC090300", "SC090301", "SC090302", "SC090303", "SC090400", "SC090401", "SC090402", "SC090500", "SC090501", "SC090502", "SC090503", "SC090600", "SC090601", "SC090602", "SC100000", "SC100100", "SC100101", "SC100102", "SC100103", "SC100121", "SC100122", "SC100200", "SC100201", "SC100202", "SC100203", "SC100221", "SC100222", "SC100300", "SC100301", "SC100302", "SC100303", "SC100400", "SC100401", "SC100402", "SC100500", "SC100501", "SC100502", "SC100503", "SC100600", "SC100601", "SC100602", "SC100603", "SC117274", "SC117314", "SC120000", "SC120100", "SC120101", "SC120102", "SC120200", "SC120201", "SC120202", "SC120300", "SC120301", "SC120302", "SC120400", "SC120401", "SC120402", "SC120500", "SC120501", "SC120502", "SC120600", "SC120601", "SC120602", "SC130000", "SC130100", "SC130101", "SC130102", "SC130121", "SC130122", "SC130200", "SC130201", "SC130202", "SC130221", "SC130222", "SC130300", "SC130301", "SC130302", "SC130400", "SC130401", "SC130402", "SC130500", "SC130501", "SC130502", "SC130600", "SC130601", "SC130602", "SC150100", "SC150156", "SC150157", "SC150158", "SC150200", "SC150205", "SC150300", "SC150305", "SC150306", "SC155866", "SC155874", "SC155882", "SC155890", "SC156194", "SC156210", "SC160000", "SC160500", "SC160900", "SC161000", "SC161100", "SC166938", "SC210000", "SC223100", "SD016410", "SD020100", "SD020900", "SD020901", "SD020902", "SD021100", "SD036434", "SD040100", "SD050000", "SD086570", "SD110100", "SD140000", "SE020000", "SE040200", "SE040301", "SE050000", "SE050100", "SE050200", "SE050300", "SE050400", "SE066490", "SE076498", "SE090000", "SE100100", "SE109350", "SF010000", "SF072442", "SF072450", "SF086522", "SF086530", "SF096362", "SF096370", "SF096386", "SF106514", "SG016250", "SG016962", "SG017226", "SG030000", "SG030100", "SG030200", "SG046826", "SH020400", "SH026466", "SH026626", "SI010001", "SI010002", "SI010003", "SI010004", "SI016642", "SI020100", "SI020200", "SI026666", "SI046674", "SI060000", "SI080000", "SI086690", "SJ010000", "SJ010100", "SJ010300", "SL006714", "SL020159", "SL026730", "SL027170", "SL066754", "SM016778", "SM020200", "SM020400", "SM050200", "SM060000", "SM060400", "SM060500", "SM060700", "SM076818", "SM146794", "SM146802", "SP010000", "SP036858", "SP040000", "SP060000", "SP076882", "SP076890", "SP102800", "SP106394", "SP106914", "SP140000", "SP156226", "SP186834", "SR060000", "SR060100", "SR060101", "SR060200", "SR060201", "SR066978", "SR066986", "SR067002", "SR087010", "SS020000", "SS040000", "SS040200", "SS040300", "SS040400", "SS040500", "SS046322", "SS046330", "SS046634", "SS050200", "SS050400", "SS066906", "SS087058", "SS087066", "SS127090", "SS130000", "SS186898", "SS197074", "SS227026", "ST017098", "ST027106", "ST037114", "ST040100", "ST050100", "ST080000", "ST087634", "ST096306", "ST096738", "ST097154", "ST112506", "ST117162", "ST147146", "ST157138", "SW019478", "SW037234", "SW040000", "SW040400", "SW040500", "SW040700", "SW050000", "SW050300", "SW050400", "SW050401", "SW050500", "SW052000", "SW052010", "SW052020", "SW052201", "SW052202", "SW052301", "SW052302", "SW052400", "SW053000", "SW053001", "SW053002", "SW054000", "SW054001", "SW054002", "SW054100", "SW054101", "SW054102", "SW057242", "SW077250", "SW077260", "SW090000", "SW110000", "SW117202", "SW117210", "SW117218", "SY010100", "SY010200"] # Slice out sample list of codes to import to send_sales. sample_codes = tax_codes[0:50]
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09f96beb17de21326a86a1bc1e0deade8b798629
30,455
py
Python
remodet_repository_wdh_part/Projects/PyLib/NetLib/PvaNet.py
UrwLee/Remo_experience
a59d5b9d6d009524672e415c77d056bc9dd88c72
[ "MIT" ]
null
null
null
remodet_repository_wdh_part/Projects/PyLib/NetLib/PvaNet.py
UrwLee/Remo_experience
a59d5b9d6d009524672e415c77d056bc9dd88c72
[ "MIT" ]
null
null
null
remodet_repository_wdh_part/Projects/PyLib/NetLib/PvaNet.py
UrwLee/Remo_experience
a59d5b9d6d009524672e415c77d056bc9dd88c72
[ "MIT" ]
null
null
null
# -*- coding: utf-8 -*- import os import sys import caffe from caffe import layers as L from caffe import params as P from caffe.proto import caffe_pb2 sys.dont_write_bytecode = True def smCReLULayer(net, from_layer, out_layer, channels=32, use_reduced_layer=False, reduced_layers=[], \ lr=1, decay=1): bn_kwargs = { 'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)], 'batch_norm_param': dict(use_global_stats=True), } scale_kwargs = { 'bias_term': True, 'param': [dict(lr_mult=lr, decay_mult=0), dict(lr_mult=lr, decay_mult=0)], } power_kwargs = {'power': 1, 'scale': -1.0, 'shift': 0} conv_kwargs = { 'param': [dict(lr_mult=lr, decay_mult=decay)], 'weight_filler': dict(type='xavier'), 'bias_term': False, } start_layer = from_layer # 1x1 convLayer if use_reduced_layer: name = "{}/reduced/conv".format(out_layer) net[name] = L.Convolution(net[start_layer], num_output=reduced_layers[0], \ kernel_size=1, pad=0, stride=1, **conv_kwargs) start_layer = name name = "{}/reduced/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], in_place=True, **bn_kwargs) start_layer = name name = "{}/reduced/scale".format(out_layer) net[name] = L.Scale(net[start_layer], in_place=True, **scale_kwargs) start_layer = name name = "{}/reduced/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], in_place=True) start_layer = name # 3x3 convLayer if use_reduced_layer: name = "{}/inter/conv".format(out_layer) net[name] = L.Convolution(net[start_layer], num_output=reduced_layers[1], \ kernel_size=3, pad=1, stride=1, **conv_kwargs) start_layer = name name = "{}/inter/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], in_place=False, **bn_kwargs) start_layer = name neg_name = "{}/inter/neg".format(out_layer) net[neg_name] = L.Power(net[start_layer], **power_kwargs) name = "{}/inter/concat".format(out_layer) net[name] = L.Concat(net[start_layer], net[neg_name], axis=1) start_layer = name name = "{}/inter/scale".format(out_layer) net[name] = L.Scale(net[start_layer], in_place=True, **scale_kwargs) start_layer = name name = "{}/inter/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], in_place=True) start_layer = name else: name = "{}/conv".format(out_layer) net[name] = L.Convolution(net[start_layer], num_output=channels, \ kernel_size=3, pad=1, stride=1, **conv_kwargs) start_layer = name name = "{}/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], in_place=False, **bn_kwargs) start_layer = name neg_name = "{}/neg".format(out_layer) net[neg_name] = L.Power(net[start_layer], **power_kwargs) name = "{}/concat".format(out_layer) net[name] = L.Concat(net[start_layer], net[neg_name], axis=1) start_layer = name name = "{}/scale".format(out_layer) net[name] = L.Scale(net[start_layer], in_place=True, **scale_kwargs) start_layer = name name = "{}/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], in_place=True) start_layer = name # 1x1 if use_reduced_layer: name = "{}/out/conv".format(out_layer) net[name] = L.Convolution(net[start_layer], num_output=reduced_layers[2], \ kernel_size=1, pad=0, stride=1, **conv_kwargs) start_layer = name name = "{}/out/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], in_place=True, **bn_kwargs) start_layer = name name = "{}/out/scale".format(out_layer) net[name] = L.Scale(net[start_layer], in_place=True, **scale_kwargs) start_layer = name name = "{}/out/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], in_place=True) start_layer = name return net def smCReLULayer_NBN(net, from_layer, out_layer, channels=32, use_reduced_layer=False, reduced_layers=[], \ lr=1, decay=1): bn_kwargs = { 'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)], 'batch_norm_param': dict(use_global_stats=True), } scale_kwargs = { 'bias_term': True, 'param': [dict(lr_mult=lr, decay_mult=0), dict(lr_mult=lr, decay_mult=0)], } power_kwargs = {'power': 1, 'scale': -1.0, 'shift': 0} conv_kwargs = { 'param': [dict(lr_mult=lr, decay_mult=decay), dict(lr_mult=2*lr, decay_mult=0)], 'weight_filler': dict(type='xavier'), 'bias_filler': dict(type='constant', value=0) } conv_nb_kwargs = { 'param': [dict(lr_mult=lr, decay_mult=decay)], 'weight_filler': dict(type='xavier'), 'bias_term': False, } start_layer = from_layer # 1x1 convLayer if use_reduced_layer: name = "{}/reduced/conv".format(out_layer) net[name] = L.Convolution(net[start_layer], num_output=reduced_layers[0], \ kernel_size=1, pad=0, stride=1, **conv_kwargs) start_layer = name name = "{}/reduced/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], in_place=True) start_layer = name # 3x3 convLayer if use_reduced_layer: name = "{}/inter/conv".format(out_layer) net[name] = L.Convolution(net[start_layer], num_output=reduced_layers[1], \ kernel_size=3, pad=1, stride=1, **conv_nb_kwargs) start_layer = name name = "{}/inter/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], in_place=False, **bn_kwargs) start_layer = name neg_name = "{}/inter/neg".format(out_layer) net[neg_name] = L.Power(net[start_layer], **power_kwargs) name = "{}/inter/concat".format(out_layer) net[name] = L.Concat(net[start_layer], net[neg_name], axis=1) start_layer = name name = "{}/inter/scale".format(out_layer) net[name] = L.Scale(net[start_layer], in_place=True, **scale_kwargs) start_layer = name name = "{}/inter/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], in_place=True) start_layer = name else: name = "{}/conv".format(out_layer) net[name] = L.Convolution(net[start_layer], num_output=channels, \ kernel_size=3, pad=1, stride=1, **conv_nb_kwargs) start_layer = name name = "{}/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], in_place=False, **bn_kwargs) start_layer = name neg_name = "{}/neg".format(out_layer) net[neg_name] = L.Power(net[start_layer], **power_kwargs) name = "{}/concat".format(out_layer) net[name] = L.Concat(net[start_layer], net[neg_name], axis=1) start_layer = name name = "{}/scale".format(out_layer) net[name] = L.Scale(net[start_layer], in_place=True, **scale_kwargs) start_layer = name name = "{}/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], in_place=True) start_layer = name # 1x1 if use_reduced_layer: name = "{}/out/conv".format(out_layer) net[name] = L.Convolution(net[start_layer], num_output=reduced_layers[2], \ kernel_size=1, pad=0, stride=1, **conv_kwargs) start_layer = name name = "{}/out/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], in_place=True) start_layer = name return net def mCReLULayer(net, from_layer, out_layer, reduced_channels=24, \ inter_channels=24, output_channels=48, lr=1, decay=1, \ use_prior_bn=True, cross_stage=False, has_pool=False): bn_kwargs = { 'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)], 'batch_norm_param': dict(use_global_stats=True), } scale_kwargs = { 'bias_term': True, 'param': [dict(lr_mult=lr, decay_mult=0), dict(lr_mult=lr, decay_mult=0)], } power_kwargs = {'power': 1, 'scale': -1.0, 'shift': 0} input_kwargs = {'power': 1, 'scale': 1, 'shift': 0} conv_kwargs = { 'param': [dict(lr_mult=lr, decay_mult=decay), dict(lr_mult=2*lr, decay_mult=0)], 'weight_filler': dict(type='xavier'), 'bias_filler': dict(type='constant', value=0) } eltwise_kwargs = {'operation': 1, 'coeff': [1, 1]} # conv/1: bn/scale/relu/conv start_layer = from_layer if use_prior_bn: layer_name = "{}/1/bn".format(out_layer) name = "{}/1/pre".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=False, **bn_kwargs) start_layer = name layer_name = "{}/1/bn_scale".format(out_layer) name = "{}/1/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/1/relu".format(out_layer) name = "{}/1/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) start_layer = name layer_name = "{}/1/conv".format(out_layer) name = "{}/1".format(out_layer) if has_pool: stride = 2 else: stride = 1 net[name] = L.Convolution(net[start_layer], name=layer_name, num_output=reduced_channels, \ kernel_size=1, pad=0, stride=stride, **conv_kwargs) start_layer = name # conv/2: bn/scale/relu/conv layer_name = "{}/2/bn".format(out_layer) name = "{}/2/pre".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=False, **bn_kwargs) start_layer = name layer_name = "{}/2/bn_scale".format(out_layer) name = "{}/2/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/2/relu".format(out_layer) name = "{}/2/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) start_layer = name layer_name = "{}/2/conv".format(out_layer) name = "{}/2".format(out_layer) net[name] = L.Convolution(net[start_layer], name=layer_name, num_output=inter_channels, \ kernel_size=3, pad=1, stride=1, **conv_kwargs) start_layer = name # conv/3: bn/neg/concat/scale/relu/conv feaLayers = [] bn_layer = "{}/3/bn".format(out_layer) bn_name = "{}/3/pre".format(out_layer) net[bn_name] = L.BatchNorm(net[start_layer], name=bn_layer, in_place=False, **bn_kwargs) feaLayers.append(net[bn_name]) start_layer = bn_name neg_layer = "{}/3/neg".format(out_layer) neg_name = "{}/3/neg".format(out_layer) net[neg_name] = L.Power(net[start_layer], name=neg_layer, **power_kwargs) feaLayers.append(net[neg_name]) concat_layer = "{}/3/concat".format(out_layer) concat_name = "{}/3/preAct".format(out_layer) net[concat_name] = L.Concat(*feaLayers, name=concat_layer, axis=1) layer_name = "{}/3/scale".format(out_layer) name = "{}/3/scale".format(out_layer) net[name] = L.Scale(net[concat_name], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/3/relu".format(out_layer) name = "{}/3/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) start_layer = name layer_name = "{}/3/conv".format(out_layer) name = "{}/3".format(out_layer) net[name] = L.Convolution(net[start_layer], name=layer_name, num_output=output_channels, \ kernel_size=1, pad=0, stride=1, **conv_kwargs) start_layer = name mlayers = [] mlayers.append(net[name]) # proj or input if cross_stage: layer_name = "{}/proj".format(out_layer) name = "{}/proj".format(out_layer) if has_pool: start_layer = "{}/1/pre".format(out_layer) stride = 2 else: start_layer = from_layer stride = 1 net[name] = L.Convolution(net[start_layer], name=layer_name, num_output=output_channels, \ kernel_size=1, pad=0, stride=stride, **conv_kwargs) mlayers.append(net[name]) else: layer_name = "{}/input".format(out_layer) name = "{}/input".format(out_layer) start_layer = from_layer net[name] = L.Power(net[start_layer], name=layer_name, **input_kwargs) mlayers.append(net[name]) # eltwise layer_name = out_layer name = out_layer net[name] = L.Eltwise(*mlayers, name=layer_name, **eltwise_kwargs) return net def ResInceptionLayer(net, from_layer, out_layer, cross_stage=False, channels_1=64, \ channels_3=[48,128], channels_5=[24,48,128],channels_pool=128, \ channels_output=256, lr=1, decay=1, out_bn=False): assert len(channels_3) == 2 assert len(channels_5) == 3 bn_kwargs = { 'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)], 'batch_norm_param': dict(use_global_stats=True), } scale_kwargs = { 'bias_term': True, 'param': [dict(lr_mult=lr, decay_mult=0), dict(lr_mult=lr, decay_mult=0)], } input_kwargs = {'power': 1, 'scale': 1, 'shift': 0} conv_kwargs = { 'param': [dict(lr_mult=lr, decay_mult=decay)], 'weight_filler': dict(type='xavier'), 'bias_term': False, } convbias_kwargs = { 'param': [dict(lr_mult=lr, decay_mult=decay), dict(lr_mult=2*lr, decay_mult=0)], 'weight_filler': dict(type='xavier'), 'bias_filler': dict(type='constant', value=0) } eltwise_kwargs = {'operation': 1, 'coeff': [1, 1]} start_layer = from_layer if cross_stage: stride = 2 else: stride = 1 # pre-stage: bn/scale/relu layer_name = "{}/incep/bn".format(out_layer) name = "{}/incep/pre".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=False, **bn_kwargs) start_layer = name layer_name = "{}/incep/bn_scale".format(out_layer) name = "{}/incep/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/incep/relu".format(out_layer) name = "{}/incep/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) fea_layer = name mlayers = [] # conv-1x1 layer_name = "{}/incep/0/conv".format(out_layer) name = "{}/incep/0".format(out_layer) net[name] = L.Convolution(net[fea_layer], name=layer_name, num_output=channels_1, \ kernel_size=1, pad=0, stride=stride, **conv_kwargs) start_layer = name layer_name = "{}/incep/0/bn".format(out_layer) name = "{}/incep/0/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=True, **bn_kwargs) start_layer = name layer_name = "{}/incep/0/bn_scale".format(out_layer) name = "{}/incep/0/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/incep/0/relu".format(out_layer) name = "{}/incep/0/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) mlayers.append(net[name]) # conv-3x3 layer_name = "{}/incep/1_reduce/conv".format(out_layer) name = "{}/incep/1_reduce".format(out_layer) net[name] = L.Convolution(net[fea_layer], name=layer_name, num_output=channels_3[0], \ kernel_size=1, pad=0, stride=stride, **conv_kwargs) start_layer = name layer_name = "{}/incep/1_reduce/bn".format(out_layer) name = "{}/incep/1_reduce/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=True, **bn_kwargs) start_layer = name layer_name = "{}/incep/1_reduce/bn_scale".format(out_layer) name = "{}/incep/1_reduce/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/incep/1_reduce/relu".format(out_layer) name = "{}/incep/1_reduce/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) start_layer = name layer_name = "{}/incep/1_0/conv".format(out_layer) name = "{}/incep/1_0".format(out_layer) net[name] = L.Convolution(net[start_layer], name=layer_name, num_output=channels_3[1], \ kernel_size=3, pad=1, stride=1, **conv_kwargs) start_layer = name layer_name = "{}/incep/1_0/bn".format(out_layer) name = "{}/incep/1_0/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=True, **bn_kwargs) start_layer = name layer_name = "{}/incep/1_0/bn_scale".format(out_layer) name = "{}/incep/1_0/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/incep/1_0/relu".format(out_layer) name = "{}/incep/1_0/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) mlayers.append(net[name]) # conv-5x5 layer_name = "{}/incep/2_reduce/conv".format(out_layer) name = "{}/incep/2_reduce".format(out_layer) net[name] = L.Convolution(net[fea_layer], name=layer_name, num_output=channels_5[0], \ kernel_size=1, pad=0, stride=stride, **conv_kwargs) start_layer = name layer_name = "{}/incep/2_reduce/bn".format(out_layer) name = "{}/incep/2_reduce/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=True, **bn_kwargs) start_layer = name layer_name = "{}/incep/2_reduce/bn_scale".format(out_layer) name = "{}/incep/2_reduce/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/incep/2_reduce/relu".format(out_layer) name = "{}/incep/2_reduce/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) start_layer = name layer_name = "{}/incep/2_0/conv".format(out_layer) name = "{}/incep/2_0".format(out_layer) net[name] = L.Convolution(net[start_layer], name=layer_name, num_output=channels_5[1], \ kernel_size=3, pad=1, stride=1, **conv_kwargs) start_layer = name layer_name = "{}/incep/2_0/bn".format(out_layer) name = "{}/incep/2_0/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=True, **bn_kwargs) start_layer = name layer_name = "{}/incep/2_0/bn_scale".format(out_layer) name = "{}/incep/2_0/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/incep/2_0/relu".format(out_layer) name = "{}/incep/2_0/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) start_layer = name layer_name = "{}/incep/2_1/conv".format(out_layer) name = "{}/incep/2_1".format(out_layer) net[name] = L.Convolution(net[start_layer], name=layer_name, num_output=channels_5[2], \ kernel_size=3, pad=1, stride=1, **conv_kwargs) start_layer = name layer_name = "{}/incep/2_1/bn".format(out_layer) name = "{}/incep/2_1/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=True, **bn_kwargs) start_layer = name layer_name = "{}/incep/2_1/bn_scale".format(out_layer) name = "{}/incep/2_1/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/incep/2_1/relu".format(out_layer) name = "{}/incep/2_1/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) mlayers.append(net[name]) # pool if cross_stage: layer_name = "{}/incep/pool".format(out_layer) name = "{}/incep/pool".format(out_layer) net[name] = L.Pooling(net[fea_layer], pool=P.Pooling.MAX, kernel_size=3, stride=2) start_layer = name layer_name = "{}/incep/poolproj/conv".format(out_layer) name = "{}/incep/poolproj".format(out_layer) net[name] = L.Convolution(net[start_layer], name=layer_name, num_output=channels_pool, \ kernel_size=1, pad=0, stride=1, **conv_kwargs) start_layer = name layer_name = "{}/incep/poolproj/bn".format(out_layer) name = "{}/incep/poolproj/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=True, **bn_kwargs) start_layer = name layer_name = "{}/incep/poolproj/bn_scale".format(out_layer) name = "{}/incep/poolproj/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/incep/poolproj/relu".format(out_layer) name = "{}/incep/poolproj/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) mlayers.append(net[name]) # incep layer_name = "{}/incep".format(out_layer) name = "{}/incep".format(out_layer) net[name] = L.Concat(*mlayers, name=layer_name, axis=1) start_layer = name # out-conv scLayers = [] if not out_bn: layer_name = "{}/out/conv".format(out_layer) name = "{}/out".format(out_layer) net[name] = L.Convolution(net[start_layer], name=layer_name, num_output=channels_output, \ kernel_size=1, pad=0, stride=1, **convbias_kwargs) scLayers.append(net[name]) else: layer_name = "{}/out/conv".format(out_layer) name = "{}/out".format(out_layer) net[name] = L.Convolution(net[start_layer], name=layer_name, num_output=channels_output, \ kernel_size=1, pad=0, stride=1, **conv_kwargs) start_layer = name layer_name = "{}/out/bn".format(out_layer) name = "{}/out/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=True, **bn_kwargs) start_layer = name layer_name = "{}/out/bn_scale".format(out_layer) name = "{}/out/bn_scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) scLayers.append(net[name]) # proj or input if cross_stage: layer_name = "{}/proj".format(out_layer) name = "{}/proj".format(out_layer) net[name] = L.Convolution(net[from_layer], name=layer_name, num_output=channels_output, \ kernel_size=1, pad=0, stride=2, **convbias_kwargs) scLayers.append(net[name]) else: layer_name = "{}/input".format(out_layer) name = "{}/input".format(out_layer) net[name] = L.Power(net[from_layer], name=layer_name, **input_kwargs) scLayers.append(net[name]) # Eltwise layer_name = out_layer name = out_layer net[name] = L.Eltwise(*scLayers, name=layer_name, **eltwise_kwargs) return net def pva_convHeader(net, from_layer, out_layer, use_pool=True, lr=1, decay=1): bn_kwargs = { 'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)], 'batch_norm_param': dict(use_global_stats=True), } scale_kwargs = { 'bias_term': True, 'param': [dict(lr_mult=lr, decay_mult=0), dict(lr_mult=lr, decay_mult=0)], } power_kwargs = {'power': 1, 'scale': -1.0, 'shift': 0} conv_kwargs = { 'param': [dict(lr_mult=lr, decay_mult=decay)], 'weight_filler': dict(type='xavier'), 'bias_term': False, } layer_name = "{}/conv".format(out_layer) name = "{}/conv".format(out_layer) net[name] = L.Convolution(net[from_layer], name=layer_name, num_output=16, \ kernel_size=7, pad=3, stride=2, **conv_kwargs) start_layer = name layer_name = "{}/bn".format(out_layer) name = "{}/bn".format(out_layer) net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=True, **bn_kwargs) feaLayers = [] feaLayers.append(net[name]) start_layer = name neg_layer = "{}/neg".format(out_layer) neg_name = "{}/neg".format(out_layer) net[neg_name] = L.Power(net[start_layer], name=neg_layer, **power_kwargs) feaLayers.append(net[neg_name]) concat_layer = "{}/concat".format(out_layer) concat_name = out_layer net[concat_name] = L.Concat(*feaLayers, name=concat_layer, axis=1) start_layer = concat_name layer_name = "{}/scale".format(out_layer) name = "{}/scale".format(out_layer) net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "{}/relu".format(out_layer) name = "{}/relu".format(out_layer) net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) start_layer = name # pool if use_pool: layer_name = "pool1" name = "pool1" net[name] = L.Pooling(net[start_layer], pool=P.Pooling.MAX, kernel_size=3, stride=2) return net def PvaNet(net, from_layer="data", lr=1, decay=1): # input Layer pva_convHeader(net, from_layer, "conv1_1", use_pool=True, lr=lr, decay=decay) # conv2_1 mCReLULayer(net, "pool1", "conv2_1", reduced_channels=24, \ inter_channels=24, output_channels=64, lr=lr, decay=decay, \ use_prior_bn=False, cross_stage=True, has_pool=False) # conv2_2 mCReLULayer(net, "conv2_1", "conv2_2", reduced_channels=24, \ inter_channels=24, output_channels=64, lr=lr, decay=decay, \ use_prior_bn=True, cross_stage=False, has_pool=False) # conv2_3 mCReLULayer(net, "conv2_2", "conv2_3", reduced_channels=24, \ inter_channels=24, output_channels=64, lr=lr, decay=decay, \ use_prior_bn=True, cross_stage=False, has_pool=False) # conv3_1 mCReLULayer(net, "conv2_3", "conv3_1", reduced_channels=48, \ inter_channels=48, output_channels=128, lr=lr, decay=decay, \ use_prior_bn=True, cross_stage=True, has_pool=True) # conv3_2 mCReLULayer(net, "conv3_1", "conv3_2", reduced_channels=48, \ inter_channels=48, output_channels=128, lr=lr, decay=decay, \ use_prior_bn=True, cross_stage=False, has_pool=False) # conv3_3 mCReLULayer(net, "conv3_2", "conv3_3", reduced_channels=48, \ inter_channels=48, output_channels=128, lr=lr, decay=decay, \ use_prior_bn=True, cross_stage=False, has_pool=False) # conv3_4 mCReLULayer(net, "conv3_3", "conv3_4", reduced_channels=48, \ inter_channels=48, output_channels=128, lr=lr, decay=decay, \ use_prior_bn=True, cross_stage=False, has_pool=False) # conv4_1 ResInceptionLayer(net, "conv3_4", "conv4_1", cross_stage=True, channels_1=64, \ channels_3=[48,128], channels_5=[24,48,48],channels_pool=128, \ channels_output=256, lr=lr, decay=decay) # conv4_2 ResInceptionLayer(net, "conv4_1", "conv4_2", cross_stage=False, channels_1=64, \ channels_3=[64,128], channels_5=[24,48,48], \ channels_output=256, lr=lr, decay=decay) # conv4_3 ResInceptionLayer(net, "conv4_2", "conv4_3", cross_stage=False, channels_1=64, \ channels_3=[64,128], channels_5=[24,48,48], \ channels_output=256, lr=lr, decay=decay) # conv4_4 ResInceptionLayer(net, "conv4_3", "conv4_4", cross_stage=False, channels_1=64, \ channels_3=[64,128], channels_5=[24,48,48], \ channels_output=256, lr=lr, decay=decay) # conv5_1 ResInceptionLayer(net, "conv4_4", "conv5_1", cross_stage=True, channels_1=64, \ channels_3=[96,192], channels_5=[32,64,64],channels_pool=128, \ channels_output=384, lr=lr, decay=decay) # conv5_2 ResInceptionLayer(net, "conv5_1", "conv5_2", cross_stage=False, channels_1=64, \ channels_3=[96,192], channels_5=[32,64,64], \ channels_output=384, lr=lr, decay=decay) # conv5_3 ResInceptionLayer(net, "conv5_2", "conv5_3", cross_stage=False, channels_1=64, \ channels_3=[96,192], channels_5=[32,64,64], \ channels_output=384, lr=lr, decay=decay) # conv5_4 ResInceptionLayer(net, "conv5_3", "conv5_4", cross_stage=False, channels_1=64, \ channels_3=[96,192], channels_5=[32,64,64], \ channels_output=384, lr=lr, decay=decay, out_bn=True) # build last bn/scale/relu bn_kwargs = { 'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)], 'batch_norm_param': dict(use_global_stats=True), } scale_kwargs = { 'bias_term': True, 'param': [dict(lr_mult=lr, decay_mult=0), dict(lr_mult=lr, decay_mult=0)], } start_layer = net.keys()[-1] layer_name = "conv5_4/last_bn" name = layer_name net[name] = L.BatchNorm(net[start_layer], name=layer_name, in_place=True, **bn_kwargs) start_layer = name layer_name = "conv5_4/last_bn_scale" name = layer_name net[name] = L.Scale(net[start_layer], name=layer_name, in_place=True, **scale_kwargs) start_layer = name layer_name = "conv5_4/last_relu" name = layer_name net[name] = L.ReLU(net[start_layer], name=layer_name, in_place=True) return net
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6
61f3e40393e4b4ac252ffdd140e4f37b9c407899
44
py
Python
tests/test_base.py
djvaroli/samsung_oct
83924a36d18a56b6cdaadffaf47a9218c7084264
[ "MIT" ]
2
2021-07-04T16:34:08.000Z
2021-07-07T23:55:18.000Z
tests/test_base.py
janhavi-giri/samsung_oct
83924a36d18a56b6cdaadffaf47a9218c7084264
[ "MIT" ]
null
null
null
tests/test_base.py
janhavi-giri/samsung_oct
83924a36d18a56b6cdaadffaf47a9218c7084264
[ "MIT" ]
3
2021-07-10T01:14:00.000Z
2021-09-03T04:22:28.000Z
def test_always_true(): assert 1 == 1
8.8
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0.613636
7
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0
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6
61ffc61776967eaabe37ad03a4601f921af591c1
34,891
py
Python
tasks-deploy/zip-crypt/generate.py
chankruze/qctf-school-2018
1e732cf264ee0a94bc2fc1fd8cf3a20660d57605
[ "MIT" ]
null
null
null
tasks-deploy/zip-crypt/generate.py
chankruze/qctf-school-2018
1e732cf264ee0a94bc2fc1fd8cf3a20660d57605
[ "MIT" ]
null
null
null
tasks-deploy/zip-crypt/generate.py
chankruze/qctf-school-2018
1e732cf264ee0a94bc2fc1fd8cf3a20660d57605
[ "MIT" ]
null
null
null
TITLE = 'Заброшенный архив' STATEMENT = ''' Рассказывают, что старое полуразваленное здание возле пруда когда-то было не просто служебным помещением, а использовалось как самый настоящий архив материалов для служебного пользования. Порядок приёма документов был полностью автоматизирован: вся входящая корреспонденция сперва сжималась, а затем надёжно шифровалась секретным ключом. Это позволяло сохранять в тайне все материалы, не занимая при этом много места. Как только стало ясно, что продолжать деятельность АЭС не представляется возможным, все архивы были вывезены. Всё что сохранилось — [система шифрования](https://zip-crypt.contest.qctf.ru/{token}/) и [часть её исходного кода](/static/files/27a5v3gnz3/utils.py). ''' tokens = ['HzQt8U0ytJ9MoDkJQSAG42BHBZFy1KzP4-PXlIYmQ0RNyTJ0Zr8SwNsPJvrrkev1', 'HzQt8U0ytJ9MoDkJQSAG42lGz-LgebFxeqrkeI-7sFWhkoWwPHss53EOu3o0BhrA', 'HzQt8U0ytJ9MoDkJQSAG47SnvW3mBiu9bjKcsRDa2Htf48TbgkAoQ1LcSrwQ-pVa', 'HzQt8U0ytJ9MoDkJQSAG44hHu-Lm609I0dO3VF34Y4IWRB0mRfyG21FtoZ5LDG4C', 'HzQt8U0ytJ9MoDkJQSAG46P_4_EyLjLe8iymBrHfOvK7jwEALDeEB2ykdmMEu4Q5', 'HzQt8U0ytJ9MoDkJQSAG443N_c4z-G41mQ6Sn6OaQfNsU9Bhy_Ge0iKvztuCuCIl', 'HzQt8U0ytJ9MoDkJQSAG4w0HzItpDBX62nZxtz1TewUPJylpzYMP63vRRJxxN2S8', 'HzQt8U0ytJ9MoDkJQSAG4xFY6xASSkXzP57DwePGCmvOqs1WxwPX0HcuAZeqYKPr', 'HzQt8U0ytJ9MoDkJQSAG41hlUz5-UltJoJYWophxvGXXDlFlIvcl8_P3W2VG7dFO', 'HzQt8U0ytJ9MoDkJQSAG4-z-WrI0SoS51jJ-7BJ66rBbciR1Wk0aRAoqImMDPhx6', 'HzQt8U0ytJ9MoDkJQSAG40VC_Z8feUO_z-nDKeZiTT2szDuFrCPGWCfHVO2ck9gP', 'HzQt8U0ytJ9MoDkJQSAG4yp60a1_sB0nzpWWGmP-L72Uwn4pH8JnFCMRQGJ36rAu', 'HzQt8U0ytJ9MoDkJQSAG4yr7mCrlb2eeMDY7wExbKZDJLhN8n9rmHteLxoLPOmpW', 'HzQt8U0ytJ9MoDkJQSAG4yuy1dj7FMcfWR0UrSUT7ZPkIz3Pr4teTqNosx2a4xqT', 'HzQt8U0ytJ9MoDkJQSAG46d2xKrpHJJdkMsdpSpFpBkm0JP6P1F7Mg8dntaOzE9C', 'HzQt8U0ytJ9MoDkJQSAG4-EZgCg5UGJsxN9-f720FpzPxwhTUY4dkO9yQJX-kytt', 'HzQt8U0ytJ9MoDkJQSAG46j4lGgCI-7cpphVGWA5uBvofSS2o6snVFTlcjGehzU0', 'HzQt8U0ytJ9MoDkJQSAG413Z6vQugJDuo1enORqUt3jSZ4ss5-bo_uu4cUXssgsT', 'HzQt8U0ytJ9MoDkJQSAG4wsPXIamYX9nbcwsoGjodcbWfXRAVbpevdmp27rY-6Bl', 'HzQt8U0ytJ9MoDkJQSAG44nE0MK8bVfVO03rVJa9Qdu49JQtBnT7FwwPi4mIGok2', 'HzQt8U0ytJ9MoDkJQSAG41n9VcewDU54tlkt9njfXu3CEEGg9WuWpnuyMoxffXyD', 'HzQt8U0ytJ9MoDkJQSAG44nkdit6P9PEL1nkFWzfUqZkP7XpVMib2XVanx50AtIX', 'HzQt8U0ytJ9MoDkJQSAG416eTbwZzXwojKqm5MQ9FJ1FWtBrdVZAUe1GbhFpfZ5H', 'HzQt8U0ytJ9MoDkJQSAG4zMLfuyraiidlX7E1NnvSUh_vgiE9vJMhGEWKecIRn1S', 'HzQt8U0ytJ9MoDkJQSAG4-RFEuFvZie9SBzXHLtyv5HKk2ZCdXr7QRQxOKcau2lf', 'HzQt8U0ytJ9MoDkJQSAG40WXhIek0_OwT0uSBmlqCcphRfAwTOUtNMweEjv2e4b1', 'HzQt8U0ytJ9MoDkJQSAG4x1AZGXHLNj46yEiwB7M4wb_CWC_f3G0YdezdUzmwQ72', 'HzQt8U0ytJ9MoDkJQSAG45sv36tARax2XlznpMUzndDnVfFoQKSH3OHcvJ3dLJQg', 'HzQt8U0ytJ9MoDkJQSAG46BQawiCFA4bWcfBWtuobGMTyn7xjCZibOsgnK94Biou', 'HzQt8U0ytJ9MoDkJQSAG4yFr1NGOzq55RGfjIUFtCQ_5RYkD2-yqJH77aXBIxDE5', 'HzQt8U0ytJ9MoDkJQSAG4xV11e6YjiZE78Kp3wetUhvzYzaxMYCKoAPGrfTYqHAh', 'HzQt8U0ytJ9MoDkJQSAG4wk2OW4mV0ci_JBNswpR2Kl5NsekYnjQpxNAaQCnFqMi', 'HzQt8U0ytJ9MoDkJQSAG4xNP4S8n-NyZoyxYR4xP0atqtzwGv-6b9gwLKh5_f7hR', 'HzQt8U0ytJ9MoDkJQSAG442yW7fEhBcKEt6-kcTnQh3YINywtke9PaixebluAd0_', 'HzQt8U0ytJ9MoDkJQSAG46260-rAuN9YvCPc0b9SB3NNPki2nI--fTvhY3-NhrKM', 'HzQt8U0ytJ9MoDkJQSAG45y5sdeaYBu_MhqOjlFxAbLhqgtHLVR9xM0_Kq0QMU5L', 'HzQt8U0ytJ9MoDkJQSAG41iH7brfIJhGKTsl3O6kw81R0nYMV1JJ3GpwYOuAyYR8', 'HzQt8U0ytJ9MoDkJQSAG4-jMmsGAud5hQmEkHI7HCuQaBqqNZGw2oxlEe6KsoifC', 'HzQt8U0ytJ9MoDkJQSAG45w-lkQJ6I1Bogrla_YCVhXuw-UfKwaNXdVs__jglZUu', 'HzQt8U0ytJ9MoDkJQSAG4z7YaO0Ae4mKNU2B2QPq4tJx1viLXlyQMprfZ2B17aoc', 'HzQt8U0ytJ9MoDkJQSAG40KMEbeT0TQwhDI8zGtPwfhrX4WEC9cEqoFGgx5wtBIn', 'HzQt8U0ytJ9MoDkJQSAG40ZqndhuLEvULxmuIWDDUUDLQbzbGkDpDkiuH81IjXYr', 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'HzQt8U0ytJ9MoDkJQSAG488pkdGaeKyUVsAlLaXzgdAcrpBQcuBU7WZn3iHGPVmd', 'HzQt8U0ytJ9MoDkJQSAG488pkdGaeKyUVsAlLaXzgdCwN4CkM0kJAFuPco96eRT2', 'HzQt8U0ytJ9MoDkJQSAG488pkdGaeKyUVsAlLaXzgdD_zEpQy7YB6DrNwDppFhKl', 'HzQt8U0ytJ9MoDkJQSAG488pkdGaeKyUVsAlLaXzgdDDR4bFFCusgaEybF7lMZBG', 'HzQt8U0ytJ9MoDkJQSAG488pkdGaeKyUVsAlLaXzgdB70ySWDpVSTHnHHkzrzr9h', 'HzQt8U0ytJ9MoDkJQSAG488pkdGaeKyUVsAlLaXzgdAbIya5m8PXq_tEw-nxqm1s', 'HzQt8U0ytJ9MoDkJQSAG488pkdGaeKyUVsAlLaXzgdA7gLCjc83vaDo5m6Y4l9-F', 'HzQt8U0ytJ9MoDkJQSAG488pkdGaeKyUVsAlLaXzgdDbYJMF0W5Hn3pPNiyMlZLH', 'HzQt8U0ytJ9MoDkJQSAG488pkdGaeKyUVsAlLaXzgdC5yMq8whk6vQEHsFPaNQp6', 'HzQt8U0ytJ9MoDkJQSAG488pkdGaeKyUVsAlLaXzgdDbwWOOltixp0G3bkvaQhOu'] def generate(context): token = tokens[context['participant'].id % len(tokens)] return TaskStatement(TITLE, STATEMENT.format(token=token))
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6
1141330cc1bb597a77b364aba3a5c2cfbed9b633
26
py
Python
src/exts/storage/__init__.py
Pix-00/olea-v2_flask_1_
7ddfa83a7a2a7dfbe55b78da002c1193f38781c0
[ "Apache-2.0" ]
null
null
null
src/exts/storage/__init__.py
Pix-00/olea-v2_flask_1_
7ddfa83a7a2a7dfbe55b78da002c1193f38781c0
[ "Apache-2.0" ]
null
null
null
src/exts/storage/__init__.py
Pix-00/olea-v2_flask_1_
7ddfa83a7a2a7dfbe55b78da002c1193f38781c0
[ "Apache-2.0" ]
null
null
null
from .main import Storage
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25
0.807692
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26
5.25
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1
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26
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1
0
1
0
1
0
0
6
3a0cc9b48789c9619fb42ce7294ca0fb422aa73f
325
py
Python
react_comments_django/context_processors.py
studyhub-co/react-comments-django
00a69da4197a1f641bf520828b34c193b3c2f5a9
[ "Apache-2.0" ]
null
null
null
react_comments_django/context_processors.py
studyhub-co/react-comments-django
00a69da4197a1f641bf520828b34c193b3c2f5a9
[ "Apache-2.0" ]
6
2021-04-22T08:54:19.000Z
2022-02-10T08:07:44.000Z
react_comments_django/context_processors.py
physics-is-beautiful/react-comments-django
00a69da4197a1f641bf520828b34c193b3c2f5a9
[ "Apache-2.0" ]
1
2021-07-15T02:37:12.000Z
2021-07-15T02:37:12.000Z
# from django.conf import settings # # # def react_comments_django_settings(request): # if hasattr(settings, 'REACT_COMMENTS_DJANGO_BASE_TEMPLATE'): # return dict(BASE_TEMPLATE=settings.REACT_COMMENTS_DJANGO_BASE_TEMPLATE) # else: # return dict(BASE_TEMPLATE='react-comments-django/react_index.html')
36.111111
81
0.76
40
325
5.825
0.45
0.223176
0.32618
0.23176
0.334764
0.334764
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0
0
0
0.141538
325
8
82
40.625
0.835125
0.947692
0
null
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null
true
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1
0
0
0
0
0
0
6
28ab7dd4bb1bd35201da9dea4b4e7dc9cb81ae98
164
py
Python
borgweb/views/index.py
audeoudh/borgweb
58bb2d97c58c5e78da723f42967d8f9c752b8f5d
[ "BSD-3-Clause" ]
331
2015-06-15T09:31:38.000Z
2022-03-24T18:10:50.000Z
borgweb/views/index.py
audeoudh/borgweb
58bb2d97c58c5e78da723f42967d8f9c752b8f5d
[ "BSD-3-Clause" ]
91
2015-06-15T20:16:19.000Z
2022-03-09T19:24:22.000Z
borgweb/views/index.py
audeoudh/borgweb
58bb2d97c58c5e78da723f42967d8f9c752b8f5d
[ "BSD-3-Clause" ]
62
2015-06-15T09:31:46.000Z
2022-02-27T03:51:28.000Z
""" index / main view """ from flask import render_template from . import blueprint @blueprint.route('/') def index(): return render_template('index.html')
12.615385
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0.695122
20
164
5.6
0.65
0.25
0
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0.164634
164
12
41
13.666667
0.817518
0.103659
0
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0.2
true
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0.8
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0
0
1
0
1
1
1
0
0
6
28ba174daebd28fb586e4cad71d48abefc7a9353
142
py
Python
dataset/__init__.py
rcap107/holoclean
d4f5929a8e4d92d4f41eb058c04c96cdcb0af767
[ "Apache-2.0" ]
468
2018-11-11T15:40:12.000Z
2022-03-30T13:21:48.000Z
dataset/__init__.py
rcap107/holoclean
d4f5929a8e4d92d4f41eb058c04c96cdcb0af767
[ "Apache-2.0" ]
43
2018-11-10T20:03:49.000Z
2020-10-20T16:39:03.000Z
dataset/__init__.py
rcap107/holoclean
d4f5929a8e4d92d4f41eb058c04c96cdcb0af767
[ "Apache-2.0" ]
118
2018-11-12T19:11:42.000Z
2022-03-23T18:25:29.000Z
from .dataset import Dataset from .dataset import AuxTables from .dataset import CellStatus __all__ = ['Dataset', 'AuxTables', 'CellStatus']
23.666667
48
0.774648
16
142
6.625
0.375
0.311321
0.481132
0
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142
5
49
28.4
0.854839
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false
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null
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0
0
0
0
1
0
1
0
0
6
28d527b44406edeef9a67426835fb33e89b3421b
88
py
Python
fixtures/gotodef/approximate-resource-imports/gotodeflib2.py
gliviu/hyperclick-robot-framework
ff76a2c07829c4a0b12856e4925c8f9f8d741385
[ "MIT" ]
3
2017-02-18T11:55:59.000Z
2020-02-03T18:02:03.000Z
fixtures/gotodef/approximate-resource-imports/gotodeflib2.py
gliviu/hyperclick-robot-framework
ff76a2c07829c4a0b12856e4925c8f9f8d741385
[ "MIT" ]
4
2017-02-13T13:18:02.000Z
2020-08-14T16:26:13.000Z
fixtures/gotodef/approximate-resource-imports/gotodeflib2.py
gliviu/hyperclick-robot-framework
ff76a2c07829c4a0b12856e4925c8f9f8d741385
[ "MIT" ]
null
null
null
def impkw(): print('impkw2') def my_third_keyword(): print('my_third_keyword2')
17.6
30
0.681818
12
88
4.666667
0.666667
0.25
0
0
0
0
0
0
0
0
0
0.027027
0.159091
88
4
31
22
0.72973
0
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0.5
true
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0.5
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1
0
0
0
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1
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6
e93ac497fc091e3c82e2ab1911a26325c3b2e250
34
py
Python
dtuf/__main__.py
davedoesdev/dtuf
590f42e8ccee1b3f02af153fba34b0ba3b9b7850
[ "MIT" ]
13
2016-01-05T01:48:01.000Z
2022-02-20T14:53:04.000Z
dtuf/__main__.py
davedoesdev/dtuf
590f42e8ccee1b3f02af153fba34b0ba3b9b7850
[ "MIT" ]
3
2015-12-10T21:32:22.000Z
2016-03-09T22:38:02.000Z
dtuf/__main__.py
davedoesdev/dtuf
590f42e8ccee1b3f02af153fba34b0ba3b9b7850
[ "MIT" ]
2
2018-02-02T21:29:08.000Z
2020-05-27T10:50:35.000Z
import dtuf.main dtuf.main.main()
11.333333
16
0.764706
6
34
4.333333
0.5
0.615385
0
0
0
0
0
0
0
0
0
0
0.088235
34
2
17
17
0.83871
0
0
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1
0
true
0
0.5
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0.5
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1
1
0
null
1
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null
0
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1
0
0
0
0
6
aaeb6e7029cb574f5d29091c0ccd8c4ad17b8148
137
py
Python
src/base/__init__.py
shaliniiit/CVDD-PyTorch
c07e1bd24fad81c1a1c51a70d90474b333d19f57
[ "MIT" ]
48
2019-07-30T12:34:41.000Z
2022-02-23T10:56:42.000Z
src/base/__init__.py
Wuliyuanulb/CVDD-PyTorch
aa2b033ed8216ce132ef6977da1e4fae665fb0c0
[ "MIT" ]
4
2019-11-28T14:26:38.000Z
2021-11-16T14:53:17.000Z
src/base/__init__.py
Wuliyuanulb/CVDD-PyTorch
aa2b033ed8216ce132ef6977da1e4fae665fb0c0
[ "MIT" ]
19
2019-07-30T02:44:57.000Z
2022-02-02T00:39:13.000Z
from .base_dataset import * from .torchnlp_dataset import * from .base_net import * from .base_trainer import * from .embedding import *
22.833333
31
0.781022
19
137
5.421053
0.421053
0.38835
0.330097
0
0
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0
0
0.145985
137
5
32
27.4
0.880342
0
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true
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1
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6
aaebb1ad92f7af47a7d010a1f197d41ab01d5438
40
py
Python
fftbg/bird/msg_types.py
rainbowbismuth/birb-brains-bot
f168ec06c5c5cc8d41589437c6f91f0d97289167
[ "MIT" ]
1
2020-12-01T01:31:31.000Z
2020-12-01T01:31:31.000Z
fftbg/bird/msg_types.py
rainbowbismuth/birb-brains-bot
f168ec06c5c5cc8d41589437c6f91f0d97289167
[ "MIT" ]
2
2021-05-30T21:10:16.000Z
2021-05-30T21:10:44.000Z
fftbg/bird/msg_types.py
rainbowbismuth/birb-brains-bot
f168ec06c5c5cc8d41589437c6f91f0d97289167
[ "MIT" ]
null
null
null
BIRD_GOING_ALL_IN = 'BIRD_GOING_ALL_IN'
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6
c923c7d31b4c0467042ba3aa9347bf83a94695a6
16,376
py
Python
data.py
IVRL/FG-NIC
b2338f5dfd10883150fc415d149b1f080e5a344d
[ "MIT" ]
2
2021-05-31T22:46:12.000Z
2021-06-01T01:24:41.000Z
data.py
IVRL/FG-NIC
b2338f5dfd10883150fc415d149b1f080e5a344d
[ "MIT" ]
null
null
null
data.py
IVRL/FG-NIC
b2338f5dfd10883150fc415d149b1f080e5a344d
[ "MIT" ]
2
2021-12-13T08:57:18.000Z
2021-12-29T06:38:47.000Z
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Project : FG-NIC # @Author : Xiaoyu LIN # @File : data.py # @Description : This file is used to genterate Pytorch dataset for caltech-256 and caltech-101. from PIL import Image from typing import Any, Callable, List, Optional, Union, Tuple from torchvision.datasets.vision import VisionDataset from torchvision.datasets.utils import check_integrity, verify_str_arg import copy import gdown import pickle import random import os import tarfile class Caltech256(VisionDataset): """ Caltech 256 Dataset. Args: root (string): Root directory of dataset where directory ``caltech256`` exists or will be saved to if download is set to True. phase (string): ['train', 'valid', 'test'] load data for different phase. is_return_origin (bool): If true, return target is label for classification, if false, return target both label and the original image for restoration. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop``. target_transform (callable, optional): A function/transform that takes in the target and transforms it. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. train_size (int): The number of images in train and validation set per class. valid_ratio (float): The ratio of validation image in train and validation set per class. """ def __init__(self, root: str, phase: str = 'train', is_return_origin: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, train_size: int = 60, valid_ratio: float = 0.2, ) -> None: super(Caltech256, self).__init__(root, transform=transform, target_transform=target_transform) os.makedirs(self.root, exist_ok=True) self.is_return_origin = is_return_origin if download: self.download() if not self._check_integrity(): raise RuntimeError('Dataset not found or corrupted.' + ' You can use download=True to download it') self.categories = sorted(os.listdir(os.path.join(self.root, "256_ObjectCategories"))) # check previous train and validation indices if os.path.isfile(os.path.join(self.root, 'train_dic.pickle')) and os.path.isfile( os.path.join(self.root, 'valid_dic.pickle')): with open(os.path.join(self.root, 'train_dic.pickle'), 'rb') as file: train_dic = pickle.load(file) with open(os.path.join(self.root, 'valid_dic.pickle'), 'rb') as file: valid_dic = pickle.load(file) # if no previous train and validation indices, sample train and validation data else: train_dic = {} valid_dic = {} for c in self.categories: fileslist = os.listdir(os.path.join(self.root, "256_ObjectCategories", c)) n = len(list(filter(lambda file: file.endswith(".jpg"), fileslist))) # select 60 images randomly as training images per class train_index = random.sample(range(1, n + 1), k=train_size) valid_index = random.sample(train_index, k=int(train_size * valid_ratio)) train_index = list(set(train_index).difference(set(valid_index))) train_dic[c] = train_index valid_dic[c] = valid_index with open(os.path.join(self.root, 'train_dic.pickle'), 'wb') as file: pickle.dump(train_dic, file) with open(os.path.join(self.root, 'valid_dic.pickle'), 'wb') as file: pickle.dump(valid_dic, file) # generate new index, label(y), and map (between label number and text label) self.index: List[int] = [] self.y = [] self.map = {} for (i, c) in enumerate(self.categories): if 'train' in phase.lower(): self.index.extend(train_dic[c]) self.y.extend(len(train_dic[c]) * [i]) if 'valid' in phase.lower(): self.index.extend(valid_dic[c]) self.y.extend(len(valid_dic[c]) * [i]) if 'test' in phase.lower(): fileslist = os.listdir(os.path.join(self.root, "256_ObjectCategories", c)) n = len(list(filter(lambda file: file.endswith(".jpg"), fileslist))) self.index.extend( list(set(range(1, n + 1)).difference(set(train_dic[c])).difference(set(valid_dic[c])))) self.y.extend((n - train_size) * [i]) self.map[i] = c.split('.')[-1] def __getitem__(self, index: int ) -> Tuple[Any, Any]: """ Args: index (int): Index Returns: tuple: (image, target) where target is index of the target class for classification task or the same image for restoration task. """ img = Image.open(os.path.join(self.root, "256_ObjectCategories", self.categories[self.y[index]], "{:03d}_{:04d}.jpg".format(self.y[index] + 1, self.index[index]))) if img.mode != 'RGB': img = img.convert('RGB') origin = copy.deepcopy(img) target = self.y[index] if self.is_return_origin and self.transform is not None: img, origin = self.transform(img) elif self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target) if self.is_return_origin: return img, origin, target, else: return img, target, def _check_integrity(self) -> bool: # can be more robust and check hash of files return os.path.exists(os.path.join(self.root, "256_ObjectCategories")) def __len__(self) -> int: return len(self.index) def download(self) -> None: if self._check_integrity(): print('Files already downloaded and verified') return download_root = self.root extract_root = download_root filename = "256_ObjectCategories.tar" url = "https://drive.google.com/uc?id=1r6o0pSROcV1_VwT4oSjA2FBUSCWGuxLK" archive = os.path.join(download_root, filename) gdown.download(url, archive, quiet=False) # extract file print("Extracting {} to {}".format(archive, extract_root)) cwd = os.getcwd() tar = tarfile.open(archive, "r") os.chdir(extract_root) tar.extractall() tar.close() os.chdir(cwd) print("Extraction done!") class Caltech101(VisionDataset): """`Caltech 101 <http://www.vision.caltech.edu/Image_Datasets/Caltech101/>`_ Dataset. .. warning:: This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format. Args: root (string): Root directory of dataset where directory ``caltech101`` exists or will be saved to if download is set to True. target_type (string or list, optional): Type of target to use, ``category`` or ``annotation``. Can also be a list to output a tuple with all specified target types. ``category`` represents the target class, and ``annotation`` is a list of points from a hand-generated outline. Defaults to ``category``. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. """ def __init__(self, root: str, phase: str = 'train', is_return_origin: bool = True, target_type: Union[List[str], str] = "category", transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, train_size: int = 30, valid_ratio: float = 0.2, ) -> None: super(Caltech101, self).__init__(root, transform=transform, target_transform=target_transform) os.makedirs(self.root, exist_ok=True) self.is_return_origin = is_return_origin if not isinstance(target_type, list): target_type = [target_type] self.target_type = [verify_str_arg(t, "target_type", ("category", "annotation")) for t in target_type] if download: self.download() if not self._check_integrity(): raise RuntimeError('Dataset not found or corrupted.' + ' You can use download=True to download it') self.categories = sorted(os.listdir(os.path.join(self.root, "101_ObjectCategories"))) self.categories.remove("BACKGROUND_Google") # this is not a real class # For some reason, the category names in "101_ObjectCategories" and # "Annotations" do not always match. This is a manual map between the # two. Defaults to using same name, since most names are fine. name_map = {"Faces": "Faces_2", "Faces_easy": "Faces_3", "Motorbikes": "Motorbikes_16", "airplanes": "Airplanes_Side_2"} self.annotation_categories = list(map(lambda x: name_map[x] if x in name_map else x, self.categories)) self.index: List[int] = [] self.y = [] for (i, c) in enumerate(self.categories): n = len(os.listdir(os.path.join(self.root, "101_ObjectCategories", c))) self.index.extend(range(1, n + 1)) self.y.extend(n * [i]) # check previous train and validation indices if os.path.isfile(os.path.join(self.root, 'train_dic.pickle')) and os.path.isfile( os.path.join(self.root, 'valid_dic.pickle')): with open(os.path.join(self.root, 'train_dic.pickle'), 'rb') as file: train_dic = pickle.load(file) with open(os.path.join(self.root, 'valid_dic.pickle'), 'rb') as file: valid_dic = pickle.load(file) # if no previous train and validation indices, sample train and validation data else: train_dic = {} valid_dic = {} for c in self.categories: fileslist = os.listdir(os.path.join(self.root, "101_ObjectCategories", c)) n = len(list(filter(lambda file: file.endswith(".jpg"), fileslist))) # select 60 images randomly as training images per class train_index = random.sample(range(1, n + 1), k=train_size) valid_index = random.sample(train_index, k=int(train_size * valid_ratio)) train_index = list(set(train_index).difference(set(valid_index))) train_dic[c] = train_index valid_dic[c] = valid_index with open(os.path.join(self.root, 'train_dic.pickle'), 'wb') as file: pickle.dump(train_dic, file) with open(os.path.join(self.root, 'valid_dic.pickle'), 'wb') as file: pickle.dump(valid_dic, file) # generate new index, label(y), and map (between label number and text label) self.index: List[int] = [] self.y = [] self.map = {} for (i, c) in enumerate(self.categories): if 'train' in phase.lower(): self.index.extend(train_dic[c]) self.y.extend(len(train_dic[c]) * [i]) if 'valid' in phase.lower(): self.index.extend(valid_dic[c]) self.y.extend(len(valid_dic[c]) * [i]) if 'test' in phase.lower(): fileslist = os.listdir(os.path.join(self.root, "101_ObjectCategories", c)) n = len(list(filter(lambda file: file.endswith(".jpg"), fileslist))) self.index.extend( list(set(range(1, n + 1)).difference(set(train_dic[c])).difference(set(valid_dic[c])))) self.y.extend((n - train_size) * [i]) self.map[i] = c.split('.')[-1] def __getitem__(self, index: int) -> Tuple[Any, Any]: """ Args: index (int): Index Returns: tuple: (image, target) where the type of target specified by target_type. """ import scipy.io img = Image.open(os.path.join(self.root, "101_ObjectCategories", self.categories[self.y[index]], "image_{:04d}.jpg".format(self.index[index]))) if img.mode != 'RGB': img = img.convert('RGB') target: Any = [] for t in self.target_type: if t == "category": target.append(self.y[index]) elif t == "annotation": data = scipy.io.loadmat(os.path.join(self.root, "Annotations", self.annotation_categories[self.y[index]], "annotation_{:04d}.mat".format(self.index[index]))) target.append(data["obj_contour"]) target = tuple(target) if len(target) > 1 else target[0] if self.is_return_origin and self.transform is not None: img, origin = self.transform(img) elif self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target) if self.is_return_origin: return img, origin, target else: return img, target def _check_integrity(self) -> bool: # can be more robust and check hash of files return os.path.exists(os.path.join(self.root, "101_ObjectCategories")) def __len__(self) -> int: return len(self.index) def download(self) -> None: if self._check_integrity(): print('Files already downloaded and verified') return download_root = self.root extract_root = download_root filename = "101_ObjectCategories.tar" url = "https://drive.google.com/uc?id=137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp" archive = os.path.join(download_root, filename) gdown.download(url, archive, quiet=False) # extract file print("Extracting {} to {}".format(archive, extract_root)) cwd = os.getcwd() tar = tarfile.open(archive, "r") os.chdir(extract_root) tar.extractall() tar.close() os.chdir(cwd) print("Extraction done!") def extra_repr(self) -> str: return "Target type: {target_type}".format(**self.__dict__)
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6
a33d9a1c079733143e68fd42b039458a49962730
1,381
py
Python
djangophysics/units/permissions.py
fmeurou/django-physics
0f67efa1b6bd547e0b80191e7a2624c2c971bdc0
[ "MIT" ]
1
2021-06-15T20:51:45.000Z
2021-06-15T20:51:45.000Z
djangophysics/units/permissions.py
fmeurou/django-physics
0f67efa1b6bd547e0b80191e7a2624c2c971bdc0
[ "MIT" ]
null
null
null
djangophysics/units/permissions.py
fmeurou/django-physics
0f67efa1b6bd547e0b80191e7a2624c2c971bdc0
[ "MIT" ]
1
2021-12-01T00:01:29.000Z
2021-12-01T00:01:29.000Z
""" Permissions for CustomUnit APIs """ from rest_framework import permissions class CustomUnitObjectPermission(permissions.BasePermission): """ Permissions for CustomUnit API """ def has_object_permission(self, request, view, obj): """ Limit creation and modification tu logged in users """ if not request.user or not request.user.is_authenticated: return False if request.method in permissions.SAFE_METHODS: return True if request.method == 'POST': return True elif request.method.lower() in ['put', 'patch', 'delete'] and \ request.user == obj.user: return True return False class CustomDimensionObjectPermission(permissions.BasePermission): """ Permissions for CustomDimension API """ def has_object_permission(self, request, view, obj): """ Limit creation and modification tu logged in users """ if not request.user or not request.user.is_authenticated: return False if request.method in permissions.SAFE_METHODS: return True if request.method == 'POST': return True elif request.method.lower() in ['put', 'patch', 'delete'] and \ request.user == obj.user: return True return False
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6
a382b3dbcbcb81d02ece6aaac5616012124d63e5
13,022
py
Python
snn/core/cnn_fn.py
KaroliShp/pacbayes-opt
fa30b897fd6c3763a4bdb66a9fc9518165841c18
[ "Apache-2.0" ]
20
2019-04-03T11:33:45.000Z
2022-01-16T03:30:44.000Z
snn/core/cnn_fn.py
KaroliShp/pacbayes-opt
fa30b897fd6c3763a4bdb66a9fc9518165841c18
[ "Apache-2.0" ]
3
2020-05-06T09:22:12.000Z
2021-12-07T17:46:07.000Z
snn/core/cnn_fn.py
KaroliShp/pacbayes-opt
fa30b897fd6c3763a4bdb66a9fc9518165841c18
[ "Apache-2.0" ]
8
2019-06-10T08:16:45.000Z
2021-12-05T16:50:49.000Z
from __future__ import division, print_function, unicode_literals import functools import tensorflow as tf tf.logging.set_verbosity(tf.logging.ERROR) # Remove tf warnings import numpy as np from time import time import os, shutil, random NUM_CLASSES = 10 def CNN_withnoise(images, param_placeholders, scopes_list,layers,params_mean_values, graph=tf.Graph(), trainable=True): with graph.as_default(): param_tensor_list = [] with tf.variable_scope(scopes_list[0]) as scope: kernel = variable_initializer('weights', [5, 5, 3, 64], tf.constant_initializer(params_mean_values[0]), trainable = trainable) conv = tf.nn.conv2d(images, kernel+param_placeholders[0], [1, 1, 1, 1], padding='SAME') biases = variable_initializer('biases', [64], tf.constant_initializer(params_mean_values[1]), trainable = trainable) pre_activation = tf.nn.bias_add(conv, biases+param_placeholders[1]) conv1 = tf.nn.relu(pre_activation, name=scope.name) param_tensor_list.append(kernel) param_tensor_list.append(biases) # pool1 pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1') # norm1 norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1') # conv2 with tf.variable_scope(scopes_list[1]) as scope: kernel = variable_initializer('weights', [5, 5, 64, 64], tf.constant_initializer(params_mean_values[2]), trainable = trainable) conv = tf.nn.conv2d(norm1, kernel+param_placeholders[2], [1, 1, 1, 1], padding='SAME') biases = variable_initializer('biases', [64], tf.constant_initializer(params_mean_values[3]), trainable = trainable) pre_activation = tf.nn.bias_add(conv, biases+param_placeholders[3], name=scope.name) conv2 = tf.nn.relu(pre_activation, name=scope.name) param_tensor_list.append(kernel) param_tensor_list.append(biases) # norm2 norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2') # pool2 pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2') # local3 with tf.variable_scope(scopes_list[2]) as scope: # Move everything into depth so we can perform a single matrix multiply. reshape = tf.reshape(pool2, [-1, 4096]) dim = reshape.get_shape()[1].value weights = variable_initializer('weights', [dim, 384], tf.constant_initializer(params_mean_values[4]), trainable = trainable) biases = variable_initializer('biases', [384], tf.constant_initializer(params_mean_values[5]), trainable = trainable) local3 = tf.nn.relu(tf.matmul(reshape, weights+param_placeholders[4]) + biases+param_placeholders[5], name=scope.name) param_tensor_list.append(weights) param_tensor_list.append(biases) # local4 with tf.variable_scope(scopes_list[3]) as scope: weights = variable_initializer('weights', [384, 192], tf.constant_initializer(params_mean_values[6]), trainable = trainable) biases = variable_initializer('biases', [192], tf.constant_initializer(params_mean_values[7]), trainable = trainable) local4 = tf.nn.relu(tf.matmul(local3, weights+param_placeholders[6]) + biases + param_placeholders[7], name=scope.name) param_tensor_list.append(weights) param_tensor_list.append(biases) with tf.variable_scope(scopes_list[4]) as scope: weights = variable_initializer('weights', [192, NUM_CLASSES], tf.constant_initializer(params_mean_values[8]), trainable = trainable) biases = variable_initializer('biases', [NUM_CLASSES], tf.constant_initializer(params_mean_values[9]), trainable = trainable) softmax_linear = tf.add(tf.matmul(local4, weights+param_placeholders[8]), biases+param_placeholders[9], name=scope.name) param_tensor_list.append(weights) param_tensor_list.append(biases) return softmax_linear, param_tensor_list def lazy_property(function): """ Create a property such that defining model classes is easier with tf """ attribute = '_cache_' + function.__name__ @property @functools.wraps(function) def decorator(self): if not hasattr(self, attribute): setattr(self, attribute, function(self)) return getattr(self, attribute) return decorator def variable_initializer(name, shape, initializer, trainable=True): return tf.get_variable(name, shape, initializer=initializer, dtype=tf.float32, trainable=trainable) def convolutional_net(images, scopes_list = ['conv1','conv2','local3','local4','softmax_linear']): """Build the CIFAR-10 model. Args: x: Images placeholder. Returns: Logits. """ # We instantiate all variables using tf.get_variable() instead of # tf.Variable() in order to share variables across multiple GPU training runs. # If we only ran this model on a single GPU, we could simplify this function # by replacing all instances of tf.get_variable() with tf.Variable(). # # conv1 with tf.variable_scope(scopes_list[0]) as scope: kernel = variable_initializer('weights', [5, 5, 3, 64], tf.truncated_normal_initializer(stddev=5e-2, dtype=tf.float32)) conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME') biases = variable_initializer('biases', [64], tf.constant_initializer(0.0)) pre_activation = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(pre_activation, name=scope.name) # pool1 pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1') # norm1 norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1') # conv2 with tf.variable_scope(scopes_list[1]) as scope: kernel = variable_initializer('weights', [5, 5, 64, 64], tf.truncated_normal_initializer(stddev=5e-2, dtype=tf.float32)) conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME') biases = variable_initializer('biases', [64], tf.constant_initializer(0.1)) pre_activation = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(pre_activation, name=scope.name) # norm2 norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2') # pool2 pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2') # local3 with tf.variable_scope(scopes_list[2]) as scope: # Move everything into depth so we can perform a single matrix multiply. reshape = tf.reshape(pool2, [-1, 4096]) dim = reshape.get_shape()[1].value weights = variable_initializer('weights', [dim, 384], tf.truncated_normal_initializer(stddev=0.04, dtype=tf.float32)) biases = variable_initializer('biases', [384], tf.constant_initializer(0.1)) local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) # local4 with tf.variable_scope(scopes_list[3]) as scope: weights = variable_initializer('weights', [384, 192], tf.truncated_normal_initializer(stddev=0.04, dtype=tf.float32)) biases = variable_initializer('biases', [192], tf.constant_initializer(0.1)) local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name) with tf.variable_scope(scopes_list[4]) as scope: weights = variable_initializer('weights', [192, NUM_CLASSES], tf.truncated_normal_initializer(stddev=1/192.0, dtype=tf.float32) ) biases = variable_initializer('biases', [NUM_CLASSES], tf.constant_initializer(0.0)) softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name) return softmax_linear def convolutional_net_init(images, params_mean_values=None, scopes_list=['conv1', 'conv2', 'local3', 'local4', 'softmax_linear']): """ Build CIFAR10 model with provided initial values """ # We instantiate all variables using tf.get_variable() instead of # tf.Variable() in order to share variables across multiple GPU training runs. # If we only ran this model on a single GPU, we could simplify this function # by replacing all instances of tf.get_variable() with tf.Variable(). # # conv1 param_tensor_list = [] with tf.variable_scope(scopes_list[0]) as scope: kernel = variable_initializer('weights', [5, 5, 3, 64], tf.constant_initializer(params_mean_values[0])) conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME') biases = variable_initializer('biases', [64], tf.constant_initializer(params_mean_values[1])) pre_activation = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(pre_activation, name=scope.name) # _activation_summary(conv1) param_tensor_list.append(kernel) param_tensor_list.append(biases) # pool1 pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1') # norm1 norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1') # conv2 with tf.variable_scope(scopes_list[1]) as scope: kernel = variable_initializer('weights', [5, 5, 64, 64], tf.constant_initializer(params_mean_values[2])) conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME') biases = variable_initializer('biases', [64], tf.constant_initializer(params_mean_values[3])) pre_activation = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(pre_activation, name=scope.name) param_tensor_list.append(kernel) param_tensor_list.append(biases) # norm2 norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2') # pool2 pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2') # local3 with tf.variable_scope(scopes_list[2]) as scope: # Move everything into depth so we can perform a single matrix multiply. reshape = tf.reshape(pool2, [-1, 4096]) dim = reshape.get_shape()[1].value weights = variable_initializer('weights', [dim, 384], tf.constant_initializer(params_mean_values[4])) biases = variable_initializer('biases', [384], tf.constant_initializer(params_mean_values[5])) local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) param_tensor_list.append(weights) param_tensor_list.append(biases) # local4 with tf.variable_scope(scopes_list[3]) as scope: weights = variable_initializer('weights', [384, 192], tf.constant_initializer(params_mean_values[6])) biases = variable_initializer('biases', [192], tf.constant_initializer(params_mean_values[7])) local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name) param_tensor_list.append(weights) param_tensor_list.append(biases) with tf.variable_scope(scopes_list[4]) as scope: weights = variable_initializer('weights', [192, NUM_CLASSES], tf.constant_initializer(params_mean_values[8])) biases = variable_initializer('biases', [NUM_CLASSES], tf.constant_initializer(params_mean_values[9])) softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name) # _activation_summary(softmax_linear) param_tensor_list.append(weights) param_tensor_list.append(biases) return softmax_linear, param_tensor_list def weight_diff(w1, w2): """ Calculates the array of differences between the weights in arrays """ # Expand and flatten arrays _w1 = np.hstack([x.flatten() for x in w1]) _w2 = np.hstack([x.flatten() for x in w2]) return _w1 - _w2 def l2_norm(w1, w2): return np.linalg.norm(weight_diff(w1, w2))
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py
Python
python/tests/test_import_protobuf.py
foxglove/ws-server-example-python
9ca831a96206ff38e0f46d0510527019a1358993
[ "MIT" ]
null
null
null
python/tests/test_import_protobuf.py
foxglove/ws-server-example-python
9ca831a96206ff38e0f46d0510527019a1358993
[ "MIT" ]
null
null
null
python/tests/test_import_protobuf.py
foxglove/ws-server-example-python
9ca831a96206ff38e0f46d0510527019a1358993
[ "MIT" ]
null
null
null
def test_import_protobuf(): """ Ensure the generated protobuf file is successfully importable in a dev environment. """ from foxglove_websocket.examples.proto.ExampleMsg_pb2 import ExampleMsg _ = ExampleMsg
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py
Python
navec/__init__.py
FreedomSlow/navec
b9add7f6661d5da44a2e1ed42364e0c3bc4b00f1
[ "MIT" ]
115
2019-06-13T09:06:41.000Z
2022-03-22T12:15:11.000Z
navec/__init__.py
FreedomSlow/navec
b9add7f6661d5da44a2e1ed42364e0c3bc4b00f1
[ "MIT" ]
4
2020-02-13T06:40:00.000Z
2021-11-24T13:58:11.000Z
navec/__init__.py
FreedomSlow/navec
b9add7f6661d5da44a2e1ed42364e0c3bc4b00f1
[ "MIT" ]
13
2019-06-13T06:31:25.000Z
2022-03-20T19:20:58.000Z
from .navec import Navec # noqa
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py
Python
Python Games/Breakout/States/Baseclass.py
lazydinoz/HackFest21
84bfbfbb2c75a6511226a87d2e947984db878ba1
[ "MIT" ]
1
2021-11-12T10:51:19.000Z
2021-11-12T10:51:19.000Z
Python Games/Breakout/States/Baseclass.py
lazydinoz/HackFest21
84bfbfbb2c75a6511226a87d2e947984db878ba1
[ "MIT" ]
null
null
null
Python Games/Breakout/States/Baseclass.py
lazydinoz/HackFest21
84bfbfbb2c75a6511226a87d2e947984db878ba1
[ "MIT" ]
null
null
null
import pygame class Base: def __init__(self): pass def render(self) : pass def update(self, params) : pass def enter(self) : pass
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42bd9bd4494f3544721fc186b5d3de319b3092a0
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py
Python
tests/unit/test_global_config.py
iwanbolzern/ConfMe
2b91dac318e499b7e25a40fb2abaf0f15a604301
[ "MIT" ]
21
2020-03-04T07:40:12.000Z
2022-03-25T15:35:29.000Z
tests/unit/test_global_config.py
iwanbolzern/ConfMe
2b91dac318e499b7e25a40fb2abaf0f15a604301
[ "MIT" ]
9
2020-03-05T12:38:40.000Z
2021-12-22T14:37:33.000Z
tests/unit/test_global_config.py
iwanbolzern/ConfMe
2b91dac318e499b7e25a40fb2abaf0f15a604301
[ "MIT" ]
null
null
null
import os import uuid from pathlib import Path import pytest from tests.unit.config_model import GlobalRootConfig, RootConfig @pytest.fixture def test_config_yaml(tmp_path: str): config_content = 'rootValue: 1\n' \ 'rangeValue: 5\n' \ 'childNode:\n' \ ' testStr: "test-env"\n' \ ' testInt: 42\n' \ ' testFloat: 42.42\n' \ ' anyEnum: value2' config_path = Path(tmp_path) / f'{uuid.uuid4()}_test.yaml' with open(config_path, 'w') as config_file: config_file.write(config_content) return str(config_path) @pytest.fixture def prod_config_yaml(tmp_path: str): config_content = 'rootValue: 1\n' \ 'rangeValue: 5\n' \ 'childNode:\n' \ ' testStr: "prod-env"\n' \ ' testInt: 42\n' \ ' testFloat: 42.42\n' \ ' anyEnum: value2' config_path = Path(tmp_path) / f'{uuid.uuid4()}_prod.yaml' with open(config_path, 'w') as config_file: config_file.write(config_content) return str(config_path) def test_load_global_config(prod_config_yaml: str, test_config_yaml: str): os.environ['highSecure'] = 'superSecureSecret' GlobalRootConfig.register_folder(Path(prod_config_yaml).parent) os.environ['ENV'] = 'test' root_config = GlobalRootConfig.get() assert root_config.childNode.testStr == 'test-env' os.environ['ENV'] = 'prod' root_config = GlobalRootConfig.get() assert root_config.childNode.testStr == 'prod-env' def test_load_config_by_env(prod_config_yaml: str, test_config_yaml: str): os.environ['highSecure'] = 'superSecureSecret' RootConfig.register_folder(Path(prod_config_yaml).parent) os.environ['ENV'] = 'test' root_config = RootConfig.get() assert root_config.childNode.testStr == 'test-env' os.environ['ENV'] = 'prod' root_config = RootConfig.get() assert root_config.childNode.testStr == 'prod-env'
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6
42c0f1bf08d85f82660fbe016388bfb70b691d73
123
py
Python
mmt/models/clip/__init__.py
jianzhnie/MultimodalTransformer
6cd4ca8034a53da361149745aecead68fbe304a0
[ "Apache-2.0" ]
1
2021-11-08T14:32:24.000Z
2021-11-08T14:32:24.000Z
mmt/models/clip/__init__.py
jianzhnie/MultimodalTransformer
6cd4ca8034a53da361149745aecead68fbe304a0
[ "Apache-2.0" ]
null
null
null
mmt/models/clip/__init__.py
jianzhnie/MultimodalTransformer
6cd4ca8034a53da361149745aecead68fbe304a0
[ "Apache-2.0" ]
null
null
null
''' Author: jianzhnie Date: 2021-12-03 12:05:26 LastEditTime: 2021-12-03 12:05:27 LastEditors: jianzhnie Description: '''
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6e1a578c79d553a442e86b8c12861bda68d2515e
148
py
Python
Desktop10.4.1/python/gdalconst.py
Esri/raster2gpkg
d10ebb3038786ecddf41072ba5b2c49baad97c5a
[ "Apache-2.0" ]
13
2015-11-18T18:26:34.000Z
2021-05-09T13:59:46.000Z
Desktop10.4.1/python/gdalconst.py
Esri/raster2gpkg
d10ebb3038786ecddf41072ba5b2c49baad97c5a
[ "Apache-2.0" ]
4
2015-12-26T03:16:25.000Z
2016-08-23T17:18:11.000Z
Desktop10.4.1/python/gdalconst.py
Esri/raster2gpkg
d10ebb3038786ecddf41072ba5b2c49baad97c5a
[ "Apache-2.0" ]
5
2015-10-22T13:28:53.000Z
2020-12-12T13:07:52.000Z
# import osgeo.gdalconst as a convenience from osgeo.gdal import deprecation_warn deprecation_warn('gdalconst') from osgeo.gdalconst import *
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6e429e3963717941792f36ffbca568cbdbc21f51
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py
Python
plugins/flytekit-dolt/flytekitplugins/dolt/__init__.py
bstadlbauer/flytekit
12ef34d7b6d777088ab87f9cf0d5c32355895852
[ "Apache-2.0" ]
null
null
null
plugins/flytekit-dolt/flytekitplugins/dolt/__init__.py
bstadlbauer/flytekit
12ef34d7b6d777088ab87f9cf0d5c32355895852
[ "Apache-2.0" ]
null
null
null
plugins/flytekit-dolt/flytekitplugins/dolt/__init__.py
bstadlbauer/flytekit
12ef34d7b6d777088ab87f9cf0d5c32355895852
[ "Apache-2.0" ]
null
null
null
""" .. currentmodule:: flytekitplugins.dolt This package contains things that are useful when extending Flytekit. .. autosummary:: :template: custom.rst :toctree: generated/ DoltConfig DoltTable DoltTableNameTransformer """ from .schema import DoltConfig, DoltTable, DoltTableNameTransformer
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59
py
Python
service_monitor/__init__.py
soltanoff/systemd_watcher
11550f0760c2c654c4f57e11295ec03b9e8ee181
[ "MIT" ]
5
2018-12-05T09:22:45.000Z
2020-03-17T15:36:21.000Z
service_monitor/__init__.py
soltanoff/systemd_watcher
11550f0760c2c654c4f57e11295ec03b9e8ee181
[ "MIT" ]
10
2018-10-29T09:45:27.000Z
2021-09-22T17:43:45.000Z
service_monitor/__init__.py
soltanoff/systemd_watcher
11550f0760c2c654c4f57e11295ec03b9e8ee181
[ "MIT" ]
null
null
null
from service_monitor.service_monitor import ServiceMonitor
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py
Python
analysis/prep/network/lib/nhd/barriers.py
astutespruce/sarp
7ce503380440c47b762ed1a8efd1d3e3aab6605e
[ "MIT" ]
5
2020-07-10T16:13:26.000Z
2022-03-02T05:06:30.000Z
analysis/prep/network/lib/nhd/barriers.py
astutespruce/sarp
7ce503380440c47b762ed1a8efd1d3e3aab6605e
[ "MIT" ]
23
2019-06-02T14:37:53.000Z
2019-10-23T17:59:40.000Z
analysis/prep/network/lib/nhd/barriers.py
astutespruce/sarp
7ce503380440c47b762ed1a8efd1d3e3aab6605e
[ "MIT" ]
2
2020-05-27T23:28:36.000Z
2020-12-14T22:10:24.000Z
from pyogrio import read_dataframe from analysis.lib.geometry import make_valid BARRIER_COLS = ["NHDPlusID", "FType", "FCode", "GNIS_Name", "geometry"] # Dam, reservoir, waterfall POINT_FTYPES = [343, 436, 487] # Dam, Gate, Lock Chamber, Waterfall LINE_FTYPES = [343, 369, 398, 487] # Dam, Lock, Spillway POLY_FTYPES = [343, 398, 455] def extract_barrier_points(gdb_path, target_crs): """Extract NHDPoint records that are barrier types. Parameters ---------- gdb_path : str path to the NHD HUC4 Geodatabase target_crs: GeoPandas CRS object target CRS to project NHD to for analysis, like length calculations. Must be a planar projection. Returns ------- GeoDataFrame """ df = read_dataframe( gdb_path, layer="NHDPoint", columns=BARRIER_COLS, force_2d=True, where=f"FType in {tuple(POINT_FTYPES)}", ) df.NHDPlusID = df.NHDPlusID.astype("uint64") df["id"] = df.index.values.astype("uint32") + 1 if len(df): df = df.to_crs(target_crs) df.geometry = make_valid(df.geometry.values.data) return df def extract_barrier_lines(gdb_path, target_crs): """Extract NHDLine records that are barrier types. Parameters ---------- gdb_path : str path to the NHD HUC4 Geodatabase target_crs: GeoPandas CRS object target CRS to project NHD to for analysis, like length calculations. Must be a planar projection. Returns ------- GeoDataFrame """ df = read_dataframe( gdb_path, layer="NHDLine", columns=BARRIER_COLS, force_2d=True, where=f"FType in {tuple(LINE_FTYPES)}", ) df.NHDPlusID = df.NHDPlusID.astype("uint64") df["id"] = df.index.values.astype("uint32") + 1 if len(df): df = df.to_crs(target_crs) df.geometry = make_valid(df.geometry.values.data) return df def extract_barrier_polygons(gdb_path, target_crs): """Extract NHDArea records that are barrier types. Parameters ---------- gdb_path : str path to the NHD HUC4 Geodatabase target_crs: GeoPandas CRS object target CRS to project NHD to for analysis, like length calculations. Must be a planar projection. Returns ------- GeoDataFrame """ df = read_dataframe( gdb_path, layer="NHDArea", columns=BARRIER_COLS, force_2d=True, where=f"FType in {tuple(POLY_FTYPES)}", ) df.NHDPlusID = df.NHDPlusID.astype("uint64") df["id"] = df.index.values.astype("uint32") + 1 if len(df): df = df.to_crs(target_crs) df.geometry = make_valid(df.geometry.values.data) return df
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py
Python
enthought/envisage/ui/single_project/action/new_project_action.py
enthought/etsproxy
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
[ "BSD-3-Clause" ]
3
2016-12-09T06:05:18.000Z
2018-03-01T13:00:29.000Z
enthought/envisage/ui/single_project/action/new_project_action.py
enthought/etsproxy
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
[ "BSD-3-Clause" ]
1
2020-12-02T00:51:32.000Z
2020-12-02T08:48:55.000Z
enthought/envisage/ui/single_project/action/new_project_action.py
enthought/etsproxy
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
[ "BSD-3-Clause" ]
null
null
null
# proxy module from __future__ import absolute_import from envisage.ui.single_project.action.new_project_action import *
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9579a8b128b6c295843eb776ef772560204798a1
206
py
Python
test_data/scraper_follower3.py
digawp/MyScraper
1f0bcb47a1b81002bf70f0869949e16ab10c90e6
[ "MIT" ]
null
null
null
test_data/scraper_follower3.py
digawp/MyScraper
1f0bcb47a1b81002bf70f0869949e16ab10c90e6
[ "MIT" ]
null
null
null
test_data/scraper_follower3.py
digawp/MyScraper
1f0bcb47a1b81002bf70f0869949e16ab10c90e6
[ "MIT" ]
null
null
null
import scrapy def generate_next_urls(response): ''' Sample crawler. Replace with your own implementation. https://doc.scrapy.org/en/1.3/intro/tutorial.html#following-links ''' return []
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6
2500a8001e30053ec2e117af0ad5f6533705f9d5
33
py
Python
tweerator/__init__.py
Parassharmaa/tweerator
9ed281e05734ef3cb3532f56d18ff9450f5dde46
[ "MIT" ]
null
null
null
tweerator/__init__.py
Parassharmaa/tweerator
9ed281e05734ef3cb3532f56d18ff9450f5dde46
[ "MIT" ]
null
null
null
tweerator/__init__.py
Parassharmaa/tweerator
9ed281e05734ef3cb3532f56d18ff9450f5dde46
[ "MIT" ]
null
null
null
from .tweerator import Tweerator
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6
252aaf2ab821edfdc8d04f4d44b1948a21ae2822
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py
Python
tests/test_recipient_algs_ecdh_aes_key_wrap.py
dajiaji/python-cwt
61723510663dc4cd5a5171ff3a78994cac5f5213
[ "MIT" ]
11
2021-04-29T13:48:15.000Z
2022-01-31T22:27:14.000Z
tests/test_recipient_algs_ecdh_aes_key_wrap.py
dajiaji/python-cwt
61723510663dc4cd5a5171ff3a78994cac5f5213
[ "MIT" ]
185
2021-04-23T22:14:50.000Z
2022-03-28T06:27:35.000Z
tests/test_recipient_algs_ecdh_aes_key_wrap.py
dajiaji/python-cwt
61723510663dc4cd5a5171ff3a78994cac5f5213
[ "MIT" ]
5
2021-08-09T02:21:18.000Z
2022-01-05T11:39:08.000Z
""" Tests for Direct. """ import pytest from cwt.cose import COSE from cwt.cose_key import COSEKey from cwt.exceptions import DecodeError, EncodeError from cwt.recipient import Recipient from cwt.recipient_algs.ecdh_aes_key_wrap import ECDH_AESKeyWrap @pytest.fixture(scope="session", autouse=True) def sender_key_es(): return COSEKey.from_jwk( { "kty": "EC", "alg": "ECDH-ES+A128KW", "crv": "P-256", } ) @pytest.fixture(scope="session", autouse=True) def recipient_public_key(): return COSEKey.from_jwk( { "kty": "EC", "kid": "01", "crv": "P-256", "x": "Ze2loSV3wrroKUN_4zhwGhCqo3Xhu1td4QjeQ5wIVR0", "y": "HlLtdXARY_f55A3fnzQbPcm6hgr34Mp8p-nuzQCE0Zw", } ) @pytest.fixture(scope="session", autouse=True) def recipient_private_key(): return COSEKey.from_jwk( { "kty": "EC", "alg": "ECDH-ES+A128KW", "kid": "01", "crv": "P-256", "x": "Ze2loSV3wrroKUN_4zhwGhCqo3Xhu1td4QjeQ5wIVR0", "y": "HlLtdXARY_f55A3fnzQbPcm6hgr34Mp8p-nuzQCE0Zw", "d": "r_kHyZ-a06rmxM3yESK84r1otSg-aQcVStkRhA-iCM8", } ) class TestECDH_AESKeyWrap: """ Tests for ECDH_AESKeyWrap. """ def test_ecdh_aes_key_wrap_constructor_with_ecdh_es_a128kw(self): ctx = ECDH_AESKeyWrap({1: -29}, {4: b"01"}) assert isinstance(ctx, ECDH_AESKeyWrap) assert ctx.alg == -29 assert ctx.kid == b"01" def test_ecdh_aes_key_wrap_constructor_with_ecdh_es_a192kw(self): ctx = ECDH_AESKeyWrap({1: -30}, {4: b"01"}) assert ctx.alg == -30 assert ctx.kid == b"01" def test_ecdh_aes_key_wrap_constructor_with_ecdh_es_a256kw(self): ctx = ECDH_AESKeyWrap({1: -31}, {4: b"01"}) assert ctx.alg == -31 assert ctx.kid == b"01" def test_ecdh_aes_key_wrap_constructor_with_ecdh_ss_a128kw(self): ctx = ECDH_AESKeyWrap({1: -32}, {4: b"01"}) assert ctx.alg == -32 assert ctx.kid == b"01" def test_ecdh_aes_key_wrap_constructor_with_ecdh_ss_a192kw(self): ctx = ECDH_AESKeyWrap({1: -33}, {4: b"01"}) assert ctx.alg == -33 assert ctx.kid == b"01" def test_ecdh_aes_key_wrap_constructor_with_ecdh_ss_a256kw(self): ctx = ECDH_AESKeyWrap({1: -34}, {4: b"01"}) assert ctx.alg == -34 assert ctx.kid == b"01" def test_ecdh_aes_key_wrap_constructor_with_invalid_alg(self): with pytest.raises(ValueError) as err: ECDH_AESKeyWrap({1: -1}, {4: b"01"}) pytest.fail("ECDH_AESKeyWrap() should fail.") assert "Unknown alg(1) for ECDH with key wrap: -1." in str(err.value) def test_ecdh_aes_key_wrap_encode_and_extract_with_ecdh_es( self, sender_key_es, recipient_public_key, recipient_private_key ): enc_key = COSEKey.from_symmetric_key(alg="ChaCha20/Poly1305") sender = ECDH_AESKeyWrap({1: -29}, {4: b"01"}, sender_key=sender_key_es) sender.apply(enc_key, recipient_key=recipient_public_key, context={"alg": "A128GCM"}) assert sender.ciphertext is not None encoded = sender.to_list() recipient = Recipient.from_list(encoded) decoded_key = recipient.extract(recipient_private_key, alg="ChaCha20/Poly1305", context={"alg": "A128GCM"}) assert enc_key.key == decoded_key.key def test_ecdh_aes_key_wrap_through_cose_api(self, recipient_public_key, recipient_private_key): enc_key = COSEKey.from_symmetric_key(alg="ChaCha20/Poly1305") rec = Recipient.from_jwk({"kty": "EC", "crv": "P-256", "alg": "ECDH-ES+A128KW"}) rec.apply(enc_key, recipient_key=recipient_public_key, context={"alg": "A128GCM"}) ctx = COSE.new(alg_auto_inclusion=True) encoded = ctx.encode_and_encrypt(b"Hello world!", enc_key, recipients=[rec]) assert b"Hello world!" == ctx.decode(encoded, recipient_private_key, context={"alg": "A128GCM"}) def test_ecdh_aes_key_wrap_through_cose_api_without_kid(self): enc_key = COSEKey.from_symmetric_key(alg="ChaCha20/Poly1305") rec = Recipient.from_jwk({"kty": "EC", "crv": "P-256", "alg": "ECDH-ES+A128KW"}) pub_key = COSEKey.from_jwk( { "kty": "EC", # "kid": "01", "crv": "P-256", "x": "Ze2loSV3wrroKUN_4zhwGhCqo3Xhu1td4QjeQ5wIVR0", "y": "HlLtdXARY_f55A3fnzQbPcm6hgr34Mp8p-nuzQCE0Zw", } ) rec.apply(enc_key, recipient_key=pub_key, context={"alg": "A128GCM"}) ctx = COSE.new(alg_auto_inclusion=True) priv_key = COSEKey.from_jwk( { "kty": "EC", # "kid": "01", "alg": "ECDH-ES+A128KW", "crv": "P-256", "x": "Ze2loSV3wrroKUN_4zhwGhCqo3Xhu1td4QjeQ5wIVR0", "y": "HlLtdXARY_f55A3fnzQbPcm6hgr34Mp8p-nuzQCE0Zw", "d": "r_kHyZ-a06rmxM3yESK84r1otSg-aQcVStkRhA-iCM8", } ) encoded = ctx.encode_and_encrypt(b"Hello world!", enc_key, recipients=[rec]) assert b"Hello world!" == ctx.decode(encoded, priv_key, context={"alg": "A128GCM"}) def test_ecdh_aes_key_wrap_apply_without_key(self, sender_key_es): sender = ECDH_AESKeyWrap({1: -29}, {4: b"01"}, sender_key=sender_key_es) with pytest.raises(ValueError) as err: sender.apply(recipient_key=recipient_public_key, context={"alg": "A128GCM"}) pytest.fail("apply() should fail.") assert "key should be set." in str(err.value) def test_ecdh_aes_key_wrap_apply_without_sender_key(self, recipient_public_key): enc_key = COSEKey.from_symmetric_key(alg="ChaCha20/Poly1305") sender = ECDH_AESKeyWrap({1: -29}, {4: b"01"}) with pytest.raises(ValueError) as err: sender.apply(enc_key, recipient_key=recipient_public_key, context={"alg": "A128GCM"}) pytest.fail("apply() should fail.") assert "sender_key should be set in advance." in str(err.value) def test_ecdh_aes_key_wrap_apply_without_recipient_key(self, sender_key_es): enc_key = COSEKey.from_symmetric_key(alg="ChaCha20/Poly1305") sender = ECDH_AESKeyWrap({1: -29}, {4: b"01"}, sender_key=sender_key_es) with pytest.raises(ValueError) as err: sender.apply(enc_key, context={"alg": "A128GCM"}) pytest.fail("apply() should fail.") assert "recipient_key should be set in advance." in str(err.value) def test_ecdh_aes_key_wrap_apply_without_context(self, sender_key_es): enc_key = COSEKey.from_symmetric_key(alg="ChaCha20/Poly1305") sender = ECDH_AESKeyWrap({1: -29}, {4: b"01"}, sender_key=sender_key_es) with pytest.raises(ValueError) as err: sender.apply(enc_key, recipient_key=recipient_public_key) pytest.fail("apply() should fail.") assert "context should be set." in str(err.value) def test_ecdh_aes_key_wrap_apply_with_invalid_recipient_key(self, sender_key_es, recipient_private_key): enc_key = COSEKey.from_symmetric_key(alg="ChaCha20/Poly1305") rec = Recipient.new(protected={"alg": "ECDH-ES+A128KW"}, sender_key=sender_key_es) with pytest.raises(ValueError) as err: rec.apply(enc_key, recipient_key=recipient_private_key, context={"alg": "A128GCM"}) pytest.fail("apply() should fail.") assert "public_key should be elliptic curve public key." in str(err.value) def test_ecdh_aes_key_wrap_apply_with_invalid_key_to_wrap(self, sender_key_es, recipient_public_key): mac_key = COSEKey.from_symmetric_key(key="xxx", alg="HS256") rec = Recipient.new(protected={"alg": "ECDH-ES+A128KW"}, sender_key=sender_key_es) with pytest.raises(EncodeError) as err: rec.apply(mac_key, recipient_key=recipient_public_key, context={"alg": "A128GCM"}) pytest.fail("apply() should fail.") assert "Failed to wrap key." in str(err.value) def test_ecdh_aes_key_wrap_extract_without_alg(self): enc_key = COSEKey.from_symmetric_key(alg="ChaCha20/Poly1305") ctx = ECDH_AESKeyWrap({1: -29}, {4: b"01"}) with pytest.raises(ValueError) as err: ctx.extract(enc_key) pytest.fail("extract() should fail.") assert "alg should be set." in str(err.value) def test_ecdh_aes_key_wrap_extract_without_context(self): enc_key = COSEKey.from_symmetric_key(alg="ChaCha20/Poly1305") ctx = ECDH_AESKeyWrap({1: -29}, {4: b"01"}) with pytest.raises(ValueError) as err: ctx.extract(enc_key, alg="ChaCha20/Poly1305") pytest.fail("extract() should fail.") assert "context should be set." in str(err.value) def test_ecdh_aes_key_wrap_extract_with_invalid_recipient_private_key(self, recipient_public_key): enc_key = COSEKey.from_symmetric_key(alg="ChaCha20/Poly1305") rec = Recipient.from_jwk({"kty": "EC", "crv": "P-256", "alg": "ECDH-ES+A128KW"}) rec.apply(enc_key, recipient_key=recipient_public_key, context={"alg": "A128GCM"}) ctx = COSE.new(alg_auto_inclusion=True) recipient_private_key = COSEKey.from_jwk( { "kty": "EC", "kid": "01", # "alg": "ECDH-ES+A128KW", "crv": "P-256", "x": "Ze2loSV3wrroKUN_4zhwGhCqo3Xhu1td4QjeQ5wIVR0", "y": "HlLtdXARY_f55A3fnzQbPcm6hgr34Mp8p-nuzQCE0Zw", "d": "r_kHyZ-a06rmxM3yESK84r1otSg-aQcVStkRhA-iCM8", } ) encoded = ctx.encode_and_encrypt(b"Hello world!", enc_key, recipients=[rec]) with pytest.raises(DecodeError) as err: ctx.decode(encoded, recipient_private_key, context={"alg": "A128GCM"}) pytest.fail("extract() should fail.") assert "Failed to decode key." in str(err.value)
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6
254ea764a61f90a0a6be45290318eae97e984971
152
py
Python
tests/__init__.py
dgpv/bip32_template_python_implementation
299e87d7827a6e7b0b650fb394f269aaa3e061f7
[ "MIT" ]
5
2020-10-26T16:49:54.000Z
2021-11-06T10:46:06.000Z
tests/__init__.py
dgpv/bip32_template_python_implementation
299e87d7827a6e7b0b650fb394f269aaa3e061f7
[ "MIT" ]
1
2020-10-25T09:40:46.000Z
2020-10-25T10:00:28.000Z
tests/__init__.py
dgpv/bip32_template_python_implementation
299e87d7827a6e7b0b650fb394f269aaa3e061f7
[ "MIT" ]
1
2022-01-06T07:30:18.000Z
2022-01-06T07:30:18.000Z
try: import micropython # type: ignore # only needed for micropython's unittest from .test_templates import * except ImportError: pass
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256bdd15f9465a4084f6529ce7ef2bc8b3dff1fb
22
py
Python
networks/__init__.py
naivete5656/Mitosis_Detection_MLM
dfdadd7dfafab7e931f13a84c27e221498c9f959
[ "MIT" ]
2
2020-07-14T02:47:32.000Z
2020-07-15T09:38:01.000Z
networks/__init__.py
naivete5656/Mitosis_Detection_MLM
dfdadd7dfafab7e931f13a84c27e221498c9f959
[ "MIT" ]
2
2021-12-17T13:04:09.000Z
2022-01-03T01:20:25.000Z
networks/__init__.py
naivete5656/Mitosis_Detection_MLM
dfdadd7dfafab7e931f13a84c27e221498c9f959
[ "MIT" ]
2
2021-05-16T03:47:08.000Z
2021-12-28T16:56:23.000Z
from .vnet import VNet
22
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0.818182
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22
4.5
0.75
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0
0
0
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0
0.136364
22
1
22
22
0.947368
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0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
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0
0
0
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null
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0
0
1
0
1
0
1
0
0
6
c27830aae37019bfa65e406eaf4e44663387f8e0
25
py
Python
tabledict/core/exceptions/__init__.py
DolphDev/LDictionary
a8c44d40f70c7d7243ea3440743dfb9c68d319b5
[ "MIT" ]
null
null
null
tabledict/core/exceptions/__init__.py
DolphDev/LDictionary
a8c44d40f70c7d7243ea3440743dfb9c68d319b5
[ "MIT" ]
5
2018-01-08T14:32:02.000Z
2020-08-11T13:12:20.000Z
psv/core/exceptions/__init__.py
DolphDev/PSV
1cb22e20b15e10b01f104b879debb4864b93bfe9
[ "MIT" ]
null
null
null
from . import messages
6.25
22
0.72
3
25
6
1
0
0
0
0
0
0
0
0
0
0
0
0.24
25
3
23
8.333333
0.947368
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true
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null
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0
1
0
1
0
1
0
0
6
6c5636a859756dc88d73234155f3e5cb260e0c8c
47
py
Python
Applications/price_GEF_14/top_level.py
nagadakos/online-learning
3be9a59b56d4b7147b7efa4175448e74731cd005
[ "Apache-2.0" ]
null
null
null
Applications/price_GEF_14/top_level.py
nagadakos/online-learning
3be9a59b56d4b7147b7efa4175448e74731cd005
[ "Apache-2.0" ]
4
2018-10-25T20:53:07.000Z
2018-10-30T16:20:50.000Z
Applications/price_GEF_14/top_level.py
nagadakos/online-learning
3be9a59b56d4b7147b7efa4175448e74731cd005
[ "Apache-2.0" ]
1
2018-10-26T13:48:31.000Z
2018-10-26T13:48:31.000Z
import sys print("Hello from price_GEF_14!")
9.4
33
0.744681
8
47
4.125
1
0
0
0
0
0
0
0
0
0
0
0.05
0.148936
47
4
34
11.75
0.775
0
0
0
0
0
0.510638
0
0
0
0
0
0
1
0
true
0
0.5
0
0.5
0.5
1
1
0
null
0
0
0
0
0
0
0
0
0
0
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1
0
0
0
0
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null
0
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0
0
1
0
1
0
0
1
0
6
6c8ac8654891cf438d4cf9012cf1a39879ff0346
19,320
py
Python
test/test_slimta_smtp_server.py
nanojob/python-slimta
70b9c633756a56afaf1fdd53c5ead6d0001036e7
[ "MIT" ]
141
2015-01-24T23:59:18.000Z
2022-01-30T16:36:37.000Z
test/test_slimta_smtp_server.py
nanojob/python-slimta
70b9c633756a56afaf1fdd53c5ead6d0001036e7
[ "MIT" ]
106
2015-01-13T22:49:07.000Z
2021-02-17T15:14:11.000Z
test/test_slimta_smtp_server.py
nanojob/python-slimta
70b9c633756a56afaf1fdd53c5ead6d0001036e7
[ "MIT" ]
43
2015-07-29T14:55:09.000Z
2021-09-24T22:30:38.000Z
import unittest from mox3.mox import MoxTestBase, IsA from gevent.ssl import SSLSocket, SSLContext, SSLError from pysasl import SASLAuth from slimta.smtp.server import Server from slimta.smtp.auth import AuthSession from slimta.smtp import ConnectionLost class TestSmtpServer(MoxTestBase, unittest.TestCase): def setUp(self): super(TestSmtpServer, self).setUp() self.sock = self.mox.CreateMock(SSLSocket) self.sock.fileno = lambda: -1 self.sock.getpeername = lambda: ('test', 0) self.context = self.mox.CreateMock(SSLContext) self.context.session_stats = lambda: {} def test_starttls_extension(self): s = Server(None, None) self.assertFalse('STARTTLS' in s.extensions) s = Server(None, None, context=self.context) self.assertTrue('STARTTLS' in s.extensions) s = Server(None, None, context=self.context, tls_immediately=True) self.assertFalse('STARTTLS' in s.extensions) def test_recv_command(self): self.sock.recv(IsA(int)).AndReturn(b'cmd ARG\r\n') self.mox.ReplayAll() s = Server(self.sock, None) cmd, arg = s._recv_command() self.assertEqual(b'CMD', cmd) self.assertEqual(b'ARG', arg) def test_get_message_data(self): expected_reply = b'250 2.6.0 Message accepted for delivery\r\n' self.sock.recv(IsA(int)).AndReturn(b'one\r\n') self.sock.recv(IsA(int)).AndReturn(b'.\r\n') self.sock.sendall(expected_reply) self.mox.ReplayAll() s = Server(self.sock, None) s._get_message_data() self.assertFalse(s.have_mailfrom) self.assertFalse(s.have_rcptto) def test_call_custom_handler(self): class TestHandler(object): def TEST(self, arg): return arg.lower() s = Server(None, TestHandler()) self.assertEqual(b'stuff', s._call_custom_handler('TEST', b'STUFF')) def test_banner_quit(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() def test_unhandled_error(self): class TestHandler(object): def BANNER_(self, reply): raise Exception('test') self.sock.sendall(b'421 4.3.0 Unhandled system error\r\n') self.mox.ReplayAll() s = Server(self.sock, TestHandler()) with self.assertRaises(Exception) as cm: s.handle() self.assertEqual(('test', ), cm.exception.args) def test_banner_command(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'BANNER\r\n') self.sock.sendall(b'500 5.5.2 Syntax error, command unrecognized\r\n') self.sock.recv(IsA(int)).AndReturn(b'BANNER_\r\n') self.sock.sendall(b'500 5.5.2 Syntax error, command unrecognized\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() def test_tls_immediately(self): self.context.wrap_socket(self.sock, server_side=True).AndReturn(self.sock) self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None, context=self.context, tls_immediately=True) s.handle() def test_tls_immediately_sslerror(self): self.context.wrap_socket(self.sock, server_side=True).AndRaise(SSLError()) self.sock.sendall(b'421 4.7.0 TLS negotiation failed\r\n') self.mox.ReplayAll() s = Server(self.sock, None, context=self.context, tls_immediately=True) s.handle() def test_ehlo(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'EHLO there\r\n') self.sock.sendall(b'250-Hello there\r\n250 TEST\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.extensions.reset() s.extensions.add('TEST') s.handle() self.assertEqual('there', s.ehlo_as) def test_ehlo_empty(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'EHLO\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() self.assertEqual(None, s.ehlo_as) def test_ehlo_empty_with_helo(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'EHLO\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'HELO there\r\n') self.sock.sendall(b'250 Hello there\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() self.assertEqual('there', s.ehlo_as) def test_helo(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'HELO there\r\n') self.sock.sendall(b'250 Hello there\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() self.assertEqual('there', s.ehlo_as) def test_helo_empty(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'HELO\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() self.assertEqual(None, s.ehlo_as) def test_helo_empty_with_ehlo(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'HELO\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'EHLO there\r\n') self.sock.sendall(b'250-Hello there\r\n250 TEST\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.extensions.reset() s.extensions.add('TEST') s.handle() self.assertEqual('there', s.ehlo_as) def test_starttls(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'EHLO there\r\n') self.sock.sendall(b'250-Hello there\r\n250 STARTTLS\r\n') self.sock.recv(IsA(int)).AndReturn(b'STARTTLS\r\n') self.sock.sendall(b'220 2.7.0 Go ahead\r\n') self.context.wrap_socket(self.sock, server_side=True).AndReturn(self.sock) self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None, context=self.context) s.extensions.reset() s.extensions.add('STARTTLS') s.handle() self.assertEqual(None, s.ehlo_as) def test_starttls_bad(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'STARTTLS\r\n') self.sock.sendall(b'503 5.5.1 Bad sequence of commands\r\n') self.sock.recv(IsA(int)).AndReturn(b'STARTTLS badarg\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'EHLO there\r\n') self.sock.sendall(b'250-Hello there\r\n250 STARTTLS\r\n') self.sock.recv(IsA(int)).AndReturn(b'STARTTLS\r\n') self.sock.sendall(b'220 2.7.0 Go ahead\r\n') self.context.wrap_socket(self.sock, server_side=True).AndRaise(SSLError()) self.sock.sendall(b'421 4.7.0 TLS negotiation failed\r\n') self.mox.ReplayAll() s = Server(self.sock, None, context=self.context) s.extensions.reset() s.extensions.add('STARTTLS') s.handle() self.assertEqual('there', s.ehlo_as) def test_auth(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'EHLO there\r\n') self.sock.sendall(b'250-Hello there\r\n250 AUTH PLAIN\r\n') self.sock.recv(IsA(int)).AndReturn(b'AUTH PLAIN dGVzdHppZAB0ZXN0dXNlcgB0ZXN0cGFzc3dvcmQ=\r\n') self.sock.sendall(b'235 2.7.0 Authentication successful\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.extensions.reset() s.extensions.add('AUTH', AuthSession(SASLAuth([b'PLAIN']), s.io)) s.handle() self.assertTrue(s.authed) def test_mailfrom(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'HELO there\r\n') self.sock.sendall(b'250 Hello there\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FROM:<test">"addr>\r\n') self.sock.sendall(b'250 2.1.0 Sender <test">"addr> Ok\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() self.assertTrue(s.have_mailfrom) def test_mailfrom_bad(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FROM:<test>\r\n') self.sock.sendall(b'503 5.5.1 Bad sequence of commands\r\n') self.sock.recv(IsA(int)).AndReturn(b'HELO there\r\n') self.sock.sendall(b'250 Hello there\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FROM:<test1> SIZE=5\r\n') self.sock.sendall(b'504 5.5.4 Command parameter not implemented\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FRM:<addr>\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FROM:<addr\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FROM:<test1>\r\n') self.sock.sendall(b'250 2.1.0 Sender <test1> Ok\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FROM:<test2>\r\n') self.sock.sendall(b'503 5.5.1 Bad sequence of commands\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() self.assertTrue(s.have_mailfrom) def test_mailfrom_send_extension(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'EHLO there\r\n') self.sock.sendall(b'250-Hello there\r\n250 SIZE 10\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FROM:<test1> SIZE=ASDF\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FROM:<test1> SIZE=20\r\n') self.sock.sendall(b'552 5.3.4 Message size exceeds 10 limit\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FROM:<test1> SIZE=5\r\n') self.sock.sendall(b'250 2.1.0 Sender <test1> Ok\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.extensions.reset() s.extensions.add('SIZE', 10) s.handle() self.assertTrue(s.have_mailfrom) def test_rcptto(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'RCPT TO:<test">"addr>\r\n') self.sock.sendall(b'250 2.1.5 Recipient <test">"addr> Ok\r\n') self.sock.recv(IsA(int)).AndReturn(b'RCPT TO:<test2>\r\n') self.sock.sendall(b'250 2.1.5 Recipient <test2> Ok\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.ehlo_as = b'test' s.have_mailfrom = True s.handle() self.assertTrue(s.have_rcptto) def test_rcptto_bad(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'RCPT TO:<test>\r\n') self.sock.sendall(b'503 5.5.1 Bad sequence of commands\r\n') self.sock.recv(IsA(int)).AndReturn(b'HELO there\r\n') self.sock.sendall(b'250 Hello there\r\n') self.sock.recv(IsA(int)).AndReturn(b'RCPT TO:<test>\r\n') self.sock.sendall(b'503 5.5.1 Bad sequence of commands\r\n') self.sock.recv(IsA(int)).AndReturn(b'MAIL FROM:<test1>\r\n') self.sock.sendall(b'250 2.1.0 Sender <test1> Ok\r\n') self.sock.recv(IsA(int)).AndReturn(b'RCPT T:<test1>\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'RCPT TO:<test1\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() self.assertFalse(s.have_rcptto) def test_data(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'DATA\r\n') self.sock.sendall(b'354 Start mail input; end with <CRLF>.<CRLF>\r\n') self.sock.recv(IsA(int)).AndReturn(b'.\r\nQUIT\r\n') self.sock.sendall(b'250 2.6.0 Message accepted for delivery\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.ehlo_as = b'test' s.have_mailfrom = True s.have_rcptto = True s.handle() def test_data_bad(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'DATA arg\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'DATA\r\n') self.sock.sendall(b'503 5.5.1 Bad sequence of commands\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.ehlo_as = b'test' s.have_mailfrom = True s.handle() def test_data_connectionlost(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'DATA\r\n') self.sock.sendall(b'354 Start mail input; end with <CRLF>.<CRLF>\r\n') self.sock.recv(IsA(int)).AndReturn(b'') self.mox.ReplayAll() s = Server(self.sock, None) s.ehlo_as = b'test' s.have_mailfrom = True s.have_rcptto = True self.assertRaises(ConnectionLost, s.handle) def test_noop(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'NOOP\r\n') self.sock.sendall(b'250 2.0.0 Ok\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() def test_rset(self): class TestHandlers(object): server = None def NOOP(self2, reply): self.assertEqual(b'test', self2.server.ehlo_as) self.assertFalse(self2.server.have_mailfrom) self.assertFalse(self2.server.have_rcptto) self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'RSET arg\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'RSET\r\n') self.sock.sendall(b'250 2.0.0 Ok\r\n') self.sock.recv(IsA(int)).AndReturn(b'NOOP\r\n') self.sock.sendall(b'250 2.0.0 Ok\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() h = TestHandlers() s = h.server = Server(self.sock, h) s.ehlo_as = b'test' s.have_mailfrom = True s.have_rcptto = True s.handle() def test_quit_bad(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT arg\r\n') self.sock.sendall(b'501 5.5.4 Syntax error in parameters or arguments\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() def test_custom_command(self): class TestHandlers(object): def TEST(self2, reply, arg, server): self.assertTrue(server.have_mailfrom) reply.code = '250' reply.message = 'Doing '+arg.decode() self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'TEST stuff\r\n') self.sock.sendall(b'250 2.0.0 Doing stuff\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, TestHandlers()) s.have_mailfrom = True s.handle() def test_bad_commands(self): self.sock.sendall(b'220 ESMTP server\r\n') self.sock.recv(IsA(int)).AndReturn(b'\r\n') self.sock.sendall(b'500 5.5.2 Syntax error, command unrecognized\r\n') self.sock.recv(IsA(int)).AndReturn(b'BADCMD\r\n') self.sock.sendall(b'500 5.5.2 Syntax error, command unrecognized\r\n') self.sock.recv(IsA(int)).AndReturn(b'STARTTLS\r\n') self.sock.sendall(b'500 5.5.2 Syntax error, command unrecognized\r\n') self.sock.recv(IsA(int)).AndReturn(b'AUTH\r\n') self.sock.sendall(b'500 5.5.2 Syntax error, command unrecognized\r\n') self.sock.recv(IsA(int)).AndReturn(b'QUIT\r\n') self.sock.sendall(b'221 2.0.0 Bye\r\n') self.mox.ReplayAll() s = Server(self.sock, None) s.handle() def test_gather_params(self): s = Server(None, None) self.assertEqual({b'ONE': b'1'}, s._gather_params(b' ONE=1')) self.assertEqual({b'TWO': True}, s._gather_params(b'TWO')) self.assertEqual({b'THREE': b'foo', b'FOUR': b'bar'}, s._gather_params(b' THREE=foo FOUR=bar')) self.assertEqual({b'FIVE': True}, s._gather_params(b'five')) # vim:et:fdm=marker:sts=4:sw=4:ts=4
43.611738
102
0.616511
3,130
19,320
3.764537
0.0623
0.152084
0.094204
0.132394
0.844097
0.827209
0.808538
0.796656
0.791988
0.780956
0
0.038847
0.220549
19,320
442
103
43.710407
0.743608
0.001708
0
0.684864
0
0
0.227327
0.003422
0
0
0
0
0.081886
1
0.091811
false
0
0.01737
0.002481
0.124069
0
0
0
0
null
0
0
0
1
1
1
1
1
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0
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0
0
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0
0
6
66685c1b4c5f59fd325683a2eb2a24c69a61e101
29
py
Python
thealot/__init__.py
nCrazed/TheAlot
dc1bf88019c4f0ef08924d6f7bb39ff1e799940e
[ "MIT" ]
null
null
null
thealot/__init__.py
nCrazed/TheAlot
dc1bf88019c4f0ef08924d6f7bb39ff1e799940e
[ "MIT" ]
1
2016-02-25T11:22:46.000Z
2016-02-25T11:22:46.000Z
thealot/__init__.py
nCrazed/TheAlot
dc1bf88019c4f0ef08924d6f7bb39ff1e799940e
[ "MIT" ]
null
null
null
from .thealot import TheAlot
14.5
28
0.827586
4
29
6
0.75
0
0
0
0
0
0
0
0
0
0
0
0.137931
29
1
29
29
0.96
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
667e7c4daf1ab809f643a72558b8514957d82aff
11,856
py
Python
tests/classes/wrappers/test_map_result.py
vahndi/ux
8acb3c07327e547ee948788536b6d6d1d7815bb2
[ "MIT" ]
null
null
null
tests/classes/wrappers/test_map_result.py
vahndi/ux
8acb3c07327e547ee948788536b6d6d1d7815bb2
[ "MIT" ]
43
2019-05-30T12:26:52.000Z
2020-08-02T21:57:24.000Z
tests/classes/wrappers/test_map_result.py
vahndi/ux
8acb3c07327e547ee948788536b6d6d1d7815bb2
[ "MIT" ]
null
null
null
from itertools import product from typing import List from unittest import TestCase from pandas import Series, Index, DataFrame from ux.wrappers.map_result import MapResult class TestMapResult(TestCase): def setUp(self) -> None: self.mr_single_single: MapResult = MapResult( data={'a': 1, 'b': 2, 'c': 3}, key_names='letters', value_names='numbers' ) self.s_single_single: Series = Series( index=Index(data=['a', 'b', 'c'], name='letters'), data=[1, 2, 3], name='numbers' ) self.mr_single_fixed: MapResult = MapResult( data={'a': [1, 2, 3], 'b': [4, 5, 6]}, key_names='letters', value_names='numbers' ) self.s_single_fixed: Series = Series( index=Index(data=['a', 'a', 'a', 'b', 'b', 'b'], name='letters'), data=[1, 2, 3, 4, 5, 6], name='numbers' ) self.d_data_fixed_wide: DataFrame = DataFrame( data={'a': [1, 2, 3], 'b': [4, 5, 6]} ) self.mr_single_variable: MapResult = MapResult( data={'a': [1, 2], 'b': [3, 4, 5]}, key_names='letters', value_names='numbers' ) self.s_single_variable: Series = Series( index=Index(data=['a', 'a', 'b', 'b', 'b'], name='letters'), data=[1, 2, 3, 4, 5], name='numbers' ) self.mr_tuple_single: MapResult = MapResult( data={('a', 'b'): 1, ('c', 'd'): 2, ('e', 'f'): 3}, key_names=['letter_1', 'letter_2'], value_names='numbers' ) self.s_tuple_single: Series = Series( index=Index(data=[('a', 'b'), ('c', 'd'), ('e', 'f')], names=['letter_1', 'letter_2']), data=[1, 2, 3], name='numbers' ) self.mr_tuple_fixed: MapResult = MapResult( data={('a', 'b'): [1, 2, 3], ('c', 'd'): [4, 5, 6]}, key_names=['letter_1', 'letter_2'], value_names='numbers' ) self.s_tuple_fixed: Series = Series( index=Index(data=[('a', 'b'), ('a', 'b'), ('a', 'b'), ('c', 'd'), ('c', 'd'), ('c', 'd')], names=['letter_1', 'letter_2']), data=[1, 2, 3, 4, 5, 6], name='numbers' ) self.mr_tuple_variable: MapResult = MapResult( data={('a', 'b'): [1, 2], ('c', 'd'): [3, 4, 5]}, key_names=['letter_1', 'letter_2'], value_names='numbers' ) self.s_tuple_variable: Series = Series( index=Index(data=[('a', 'b'), ('a', 'b'), ('c', 'd'), ('c', 'd'), ('c', 'd')], names=['letter_1', 'letter_2']), data=[1, 2, 3, 4, 5], name='numbers' ) self.mr_single_key: List[MapResult] = [ self.mr_single_single, self.mr_single_fixed, self.mr_single_variable ] self.mr_tuple_key: List[MapResult] = [ self.mr_tuple_single, self.mr_tuple_fixed, self.mr_tuple_variable ] @staticmethod def series_equivalent(data_1: Series, data_2: Series) -> bool: return ( data_1.index.tolist() == data_2.index.tolist() and data_1.to_list() == data_2.to_list() ) @staticmethod def frames_equivalent(data_1: DataFrame, data_2: DataFrame) -> bool: return ( sorted(data_1.columns) == sorted(data_2.columns) and data_1.index.to_list() == data_2.index.to_list() and all(data_1[column].to_list() == data_2[column].to_list() for column in data_1.columns) ) def test_to_series(self): self.assertTrue(self.series_equivalent(self.s_single_single, self.mr_single_single.to_series())) self.assertTrue(self.series_equivalent(self.s_single_fixed, self.mr_single_fixed.to_series())) self.assertTrue(self.series_equivalent(self.s_single_variable, self.mr_single_variable.to_series())) self.assertTrue(self.series_equivalent(self.s_tuple_single, self.mr_tuple_single.to_series())) self.assertTrue(self.series_equivalent(self.s_tuple_fixed, self.mr_tuple_fixed.to_series())) self.assertTrue(self.series_equivalent(self.s_tuple_variable, self.mr_tuple_variable.to_series())) def test_to_frame(self): self.assertTrue( self.frames_equivalent(self.s_single_single.reset_index(), self.mr_single_single.to_frame()) ) self.assertTrue( self.frames_equivalent(self.s_single_fixed.reset_index(), self.mr_single_fixed.to_frame()) ) self.assertTrue( self.frames_equivalent(self.s_single_variable.reset_index(), self.mr_single_variable.to_frame()) ) self.assertTrue( self.frames_equivalent(self.s_tuple_single.reset_index(), self.mr_tuple_single.to_frame()) ) self.assertTrue( self.frames_equivalent(self.s_tuple_fixed.reset_index(), self.mr_tuple_fixed.to_frame()) ) self.assertTrue( self.frames_equivalent(self.s_tuple_variable.reset_index(), self.mr_tuple_variable.to_frame()) ) def test_to_frame_wide(self): self.assertTrue(self.frames_equivalent(self.d_data_fixed_wide, self.mr_single_fixed.to_frame(wide=True))) def test_add_works(self): self.assertEqual( self.mr_single_single + self.mr_single_single, MapResult( data={'a': 2, 'b': 4, 'c': 6}, key_names='letters', value_names='numbers' ) ) self.assertEqual( self.mr_single_fixed + self.mr_single_fixed, MapResult( data={'a': [1, 2, 3, 1, 2, 3], 'b': [4, 5, 6, 4, 5, 6]}, key_names='letters', value_names='numbers' ) ) self.assertEqual( self.mr_single_variable + self.mr_single_variable, MapResult( data={'a': [1, 2, 1, 2], 'b': [3, 4, 5, 3, 4, 5]}, key_names='letters', value_names='numbers' ) ) self.assertEqual( self.mr_tuple_single + self.mr_tuple_single, MapResult( data={('a', 'b'): 2, ('c', 'd'): 4, ('e', 'f'): 6}, key_names=['letter_1', 'letter_2'], value_names='numbers' ) ) self.assertEqual( self.mr_tuple_fixed + self.mr_tuple_fixed, MapResult( data={('a', 'b'): [1, 2, 3, 1, 2, 3], ('c', 'd'): [4, 5, 6, 4, 5, 6]}, key_names=['letter_1', 'letter_2'], value_names='numbers' ) ) self.assertEqual( self.mr_tuple_variable + self.mr_tuple_variable, MapResult( data={('a', 'b'): [1, 2, 1, 2], ('c', 'd'): [3, 4, 5, 3, 4, 5]}, key_names=['letter_1', 'letter_2'], value_names='numbers' ) ) self.assertEqual( self.mr_single_fixed + self.mr_single_variable, MapResult( data={'a': [1, 2, 3, 1, 2], 'b': [4, 5, 6, 3, 4, 5]}, key_names=['letters'], value_names='numbers' ) ) self.assertEqual( self.mr_tuple_fixed + self.mr_tuple_variable, MapResult( data={('a', 'b'): [1, 2, 3, 1, 2], ('c', 'd'): [4, 5, 6, 3, 4, 5]}, key_names=['letter_1', 'letter_2'], value_names='numbers' ) ) def test_add_fails(self): # mismatching key types for mr_1, mr_2 in product( self.mr_single_key, self.mr_tuple_key ): self.assertRaises(KeyError, lambda: mr_1 + mr_2) # unaddable value types for mr_1, mr_2 in [ (self.mr_single_single, self.mr_single_fixed), (self.mr_single_single, self.mr_single_variable), (self.mr_tuple_single, self.mr_tuple_fixed), (self.mr_tuple_single, self.mr_tuple_variable), ]: self.assertRaises(TypeError, lambda: mr_1 + mr_2) def test_sub_works(self): self.assertEqual( self.mr_single_single - self.mr_single_single, MapResult( data={'a': 0, 'b': 0, 'c': 0}, key_names='letters', value_names='numbers' ) ) self.assertEqual( self.mr_tuple_single - self.mr_tuple_single, MapResult( data={('a', 'b'): 0, ('c', 'd'): 0, ('e', 'f'): 0}, key_names=['letter_1', 'letter_2'], value_names='numbers' ) ) def test_sub_fails(self): # mismatching key types for mr_1, mr_2 in product( self.mr_single_key, self.mr_tuple_key ): self.assertRaises(KeyError, lambda: mr_1 - mr_2) # unsubtractable value types for mr_1, mr_2 in [ (self.mr_single_single, self.mr_single_fixed), (self.mr_single_single, self.mr_single_variable), (self.mr_single_fixed, self.mr_single_variable), (self.mr_tuple_single, self.mr_tuple_fixed), (self.mr_tuple_single, self.mr_tuple_variable), (self.mr_tuple_fixed, self.mr_tuple_variable) ]: self.assertRaises(TypeError, lambda: mr_1 - mr_2) def test_mul_works(self): self.assertEqual( self.mr_single_single * self.mr_single_single, MapResult( data={'a': 1, 'b': 4, 'c': 9}, key_names='letters', value_names='numbers' ) ) self.assertEqual( self.mr_tuple_single * self.mr_tuple_single, MapResult( data={('a', 'b'): 1, ('c', 'd'): 4, ('e', 'f'): 9}, key_names=['letter_1', 'letter_2'], value_names='numbers' ) ) def test_mul_fails(self): # mismatching key types for mr_1, mr_2 in product( self.mr_single_key, self.mr_tuple_key ): self.assertRaises(KeyError, lambda: mr_1 * mr_2) # unmultipliable value types for mr_1, mr_2 in [ (self.mr_single_single, self.mr_single_fixed), (self.mr_single_single, self.mr_single_variable), (self.mr_single_fixed, self.mr_single_variable), (self.mr_tuple_single, self.mr_tuple_fixed), (self.mr_tuple_single, self.mr_tuple_variable), (self.mr_tuple_fixed, self.mr_tuple_variable) ]: self.assertRaises(TypeError, lambda: mr_1 * mr_2) def test_div_works(self): self.assertEqual( self.mr_single_single / self.mr_single_single, MapResult( data={'a': 1, 'b': 1, 'c': 1}, key_names='letters', value_names='numbers' ) ) self.assertEqual( self.mr_tuple_single / self.mr_tuple_single, MapResult( data={('a', 'b'): 1, ('c', 'd'): 1, ('e', 'f'): 1}, key_names=['letter_1', 'letter_2'], value_names='numbers' ) ) def test_div_fails(self): # mismatching key types for mr_1, mr_2 in product( self.mr_single_key, self.mr_tuple_key ): self.assertRaises(KeyError, lambda: mr_1 / mr_2) # indivisible value types for mr_1, mr_2 in [ (self.mr_single_single, self.mr_single_fixed), (self.mr_single_single, self.mr_single_variable), (self.mr_single_fixed, self.mr_single_variable), (self.mr_tuple_single, self.mr_tuple_fixed), (self.mr_tuple_single, self.mr_tuple_variable), (self.mr_tuple_fixed, self.mr_tuple_variable) ]: self.assertRaises(TypeError, lambda: mr_1 / mr_2)
38.245161
113
0.54251
1,492
11,856
4.020777
0.058981
0.107018
0.108018
0.06001
0.88048
0.820303
0.780963
0.744791
0.713619
0.661777
0
0.027557
0.311319
11,856
309
114
38.368932
0.707165
0.015773
0
0.420074
0
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0.051458
0
0
0
0
0
0.130112
1
0.052045
false
0
0.018587
0.007435
0.081784
0
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null
0
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1
1
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0
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0
0
0
0
0
0
6
66dd8d3a4d0c6b8b9cd781c4991c94896208c2d5
3,668
py
Python
tests/test_compat.py
sharov/dd-trace-py
d0995b49cf7147ab463d0a67a38779fad3f539b4
[ "BSD-3-Clause" ]
1
2019-11-24T23:09:29.000Z
2019-11-24T23:09:29.000Z
tests/test_compat.py
sharov/dd-trace-py
d0995b49cf7147ab463d0a67a38779fad3f539b4
[ "BSD-3-Clause" ]
null
null
null
tests/test_compat.py
sharov/dd-trace-py
d0995b49cf7147ab463d0a67a38779fad3f539b4
[ "BSD-3-Clause" ]
2
2017-05-27T05:58:36.000Z
2019-02-07T13:38:53.000Z
# -*- coding: utf-8 -*- # Define source file encoding to support raw unicode characters in Python 2 # Third party from nose.tools import eq_ # Project from ddtrace.compat import to_unicode, PY2 # Use different test suites for each Python version, this allows us to test the expected # results for each Python version rather than writing a generic "works for both" test suite if PY2: class TestCompatPY2(object): def test_to_unicode_string(self): # Calling `compat.to_unicode` on a non-unicode string res = to_unicode('test') eq_(type(res), unicode) eq_(res, 'test') def test_to_unicode_unicode_encoded(self): # Calling `compat.to_unicode` on a unicode encoded string res = to_unicode('\xc3\xbf') eq_(type(res), unicode) eq_(res, u'ÿ') def test_to_unicode_unicode_double_decode(self): # Calling `compat.to_unicode` on a unicode decoded string # This represents the double-decode issue, which can cause a `UnicodeEncodeError` # `'\xc3\xbf'.decode('utf-8').decode('utf-8')` res = to_unicode('\xc3\xbf'.decode('utf-8')) eq_(type(res), unicode) eq_(res, u'ÿ') def test_to_unicode_unicode_string(self): # Calling `compat.to_unicode` on a unicode string res = to_unicode(u'ÿ') eq_(type(res), unicode) eq_(res, u'ÿ') def test_to_unicode_bytearray(self): # Calling `compat.to_unicode` with a `bytearray` containing unicode res = to_unicode(bytearray('\xc3\xbf')) eq_(type(res), unicode) eq_(res, u'ÿ') def test_to_unicode_bytearray_double_decode(self): # Calling `compat.to_unicode` with an already decoded `bytearray` # This represents the double-decode issue, which can cause a `UnicodeEncodeError` # `bytearray('\xc3\xbf').decode('utf-8').decode('utf-8')` res = to_unicode(bytearray('\xc3\xbf').decode('utf-8')) eq_(type(res), unicode) eq_(res, u'ÿ') def test_to_unicode_non_string(self): # Calling `compat.to_unicode` on non-string types eq_(to_unicode(1), u'1') eq_(to_unicode(True), u'True') eq_(to_unicode(None), u'None') eq_(to_unicode(dict(key='value')), u'{\'key\': \'value\'}') else: class TestCompatPY3(object): def test_to_unicode_string(self): # Calling `compat.to_unicode` on a non-unicode string res = to_unicode('test') eq_(type(res), str) eq_(res, 'test') def test_to_unicode_unicode_encoded(self): # Calling `compat.to_unicode` on a unicode encoded string res = to_unicode('\xff') eq_(type(res), str) eq_(res, 'ÿ') def test_to_unicode_unicode_string(self): # Calling `compat.to_unicode` on a unicode string res = to_unicode('ÿ') eq_(type(res), str) eq_(res, 'ÿ') def test_to_unicode_bytearray(self): # Calling `compat.to_unicode` with a `bytearray` containing unicode """ res = to_unicode(bytearray('\xff', 'utf-8')) eq_(type(res), str) eq_(res, 'ÿ') def test_to_unicode_non_string(self): # Calling `compat.to_unicode` on non-string types eq_(to_unicode(1), '1') eq_(to_unicode(True), 'True') eq_(to_unicode(None), 'None') eq_(to_unicode(dict(key='value')), '{\'key\': \'value\'}')
38.610526
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475
3,668
4.298947
0.187368
0.18952
0.052889
0.094025
0.803624
0.767385
0.751714
0.700784
0.683154
0.683154
0
0.00887
0.293075
3,668
94
94
39.021277
0.778635
0.333969
0
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0
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0
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0.214286
false
0
0.035714
0
0.285714
0
0
0
0
null
0
0
0
1
1
1
1
0
1
0
0
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null
0
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0
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0
0
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0
0
0
6
66eb57287a0dafaa40e5c7029f4f95ff6c3153ba
299
py
Python
webapi/youtube/__init__.py
RKSCapul/demo-hermitcraft-concept-site-backend-r2
ef1f2c7ee16be0301445a2e2902def0df2d08546
[ "MIT" ]
null
null
null
webapi/youtube/__init__.py
RKSCapul/demo-hermitcraft-concept-site-backend-r2
ef1f2c7ee16be0301445a2e2902def0df2d08546
[ "MIT" ]
null
null
null
webapi/youtube/__init__.py
RKSCapul/demo-hermitcraft-concept-site-backend-r2
ef1f2c7ee16be0301445a2e2902def0df2d08546
[ "MIT" ]
null
null
null
from .channels import getYouTubeChannelDataAll from .channels import getYouTubeChannelDataUser from .channels import getYouTubeAccountPictureAll from .channels import getYouTubeChannelLivestreamDataAll from .videos import getYouTubeChannelVideos from .videos import getAllRecentYouTubeChannelVideos
42.714286
56
0.899666
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dd65b870998d80f2ebebb6853507b2956888465a
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py
Python
TEMPy/Cluster.py
OniDaito/ChimeraXTempy
a32ef6c54a403580f3a530ab36d91e475bf4b2dc
[ "MIT" ]
2
2020-04-03T03:38:08.000Z
2020-06-21T02:31:38.000Z
TEMPy/Cluster.py
OniDaito/ChimeraXTempy
a32ef6c54a403580f3a530ab36d91e475bf4b2dc
[ "MIT" ]
16
2017-06-16T20:06:14.000Z
2017-07-31T17:32:32.000Z
TEMPy/Cluster.py
OniDaito/ChimeraXTempy
a32ef6c54a403580f3a530ab36d91e475bf4b2dc
[ "MIT" ]
1
2020-06-21T02:31:44.000Z
2020-06-21T02:31:44.000Z
#=============================================================================== # This file is part of TEMPy. # # TEMPy is a software designed to help the user in the manipulation # and analyses of macromolecular assemblies using 3D electron microscopy maps. # # Copyright 2015 Birkbeck College University of London. # # Authors: Maya Topf, Daven Vasishtan, Arun Prasad Pandurangan, # Irene Farabella, Agnel-Praveen Joseph, Harpal Sahota # # This software is made available under GPL V3 license # http://www.gnu.org/licenses/gpl-3.0.html # # # Please cite your use of TEMPy in published work: # # Farabella, I., Vasishtan, D., Joseph, A.P., Pandurangan, A.P., Sahota, H. & Topf, M. (2015). J. Appl. Cryst. 48. # #=============================================================================== from TEMPy.StructureBlurrer import StructureBlurrer from TEMPy.ScoringFunctions import ScoringFunctions from numpy import zeros import sys class Cluster: """A class to clustering an ensemble of structure instance""" def __init__(self): pass def _print_results_cluster(self,models,class_num,number_top_mod,score,write=False): """ private function used in Cluster_Ensemble """ out_list=[] if write==True: outp = open("top"+str(number_top_mod)+str(score)+"_classes.txt", "w") outp.write("pdb_name\tscore\tlrms\tclass\n") for i in range(1,class_num+1): # print the fits of each class ordered by the highest score for ipdb in models: if (ipdb[-1] == i): out_list.append([ipdb[0],ipdb[2],ipdb[3],ipdb[4]]) outp.write("%s\t%.5f\t%.3f\t%d\n" %(ipdb[0],ipdb[2],ipdb[3],ipdb[4])) outp.close() else: for i in range(1,class_num+1): for ipdb in models: if (ipdb[-1] == i): out_list.append([ipdb[0],ipdb[2],ipdb[3],ipdb[4]]) return out_list def _print_results_cluster2(self,models,write=True): """ private function used in Cluster_Ensemble """ out_list=[] if write==True: outp = open("top_rank.txt", "w") outp.write("pdb_name\tscore\tlrms\tclass\n") for i in models: #[name_mod,mod,score_mod,int(0),int(0)] # print the fits of each class ordered by the highest score outp.write("%s\t%.5f\n" %(i[0],i[2])) outp.close() else: print('this is for print!!!') def cluster_fit_ensemble_top_fit(self,ensemble_list,score,rms_cutoff,res_target_map,sigma_coeff,number_top_mod=0,write=False,targetMap=False): """ RMSD clustering of the multiple "fits" starting from the best scoring model accordingly with a chosen score. Cluster the fits based on Calpha RMSD (starting from the best scoring model) Arguments: *ensemble_list* Input list of Structure Instances. *targetMap* Target Map Instance. *score* Scoring function to use. See ScoringFunctions class for a list of the available Scoring Function. E.g. set score='CCC' to use the Cross-correlation coefficient. Score option are: i 'CCC' - Cross-correlation coefficient; ii 'LAP' - Laplacian-filtered cross-correlation coefficient: useful for maps with resolutions worse than 10-15 A; iii 'MI' - Mutual information score: a good and robust score but relatively slow to calculate; iv 'ENV' - Envelope score: the fastest score to calculate due to binarisation of the map. v-vii 'NV','NV_Sobel','NV_Laplace'- Normal vector score: a vector-based surface superimposition score with or without Sobel/Laplace filter. viii 'CD' - Chamfer Distance: a score used in computer vision algorithms as a fast similarity metric *rms_cutoff* float, the Calpha RMSD cutoff based on which you want to cluster the solutions. For example 3.5 (for 3.5 A). *res_target_map* the resolution, in Angstroms, of the target Map. *sigma_coeff* the sigma value (multiplied by the resolution) that controls the width of the Gaussian. Default values is 0.356. Other values used : 0.187R corresponding with the Gaussian width of the Fourier transform falling to half the maximum at 1/resolution, as used in Situs (Wriggers et al, 1999); 0.225R which makes the Fourier transform of the distribution fall to 1/e of its maximum value at wavenumber 1/resolution, the default in Chimera (Petterson et al, 2004) 0.356R corresponding to the Gaussian width at 1/e maximum height equaling the resolution, an option in Chimera (Petterson et al, 2004); 0.425R the fullwidth half maximum being equal to the resolution, as used by FlexEM (Topf et al, 2008); 0.5R the distance between the two inflection points being the same length as the resolution, an option in Chimera (Petterson et al, 2004); 1R where the sigma value simply equal to the resolution, as used by NMFF (Tama et al, 2004). *number_top_mod* Number of Fits to cluster. Default is all. *write* True will write out a file that contains the list of the structure instances representing different fits scored and clustered. note the lrms column is the Calpha RMSD of each fit from the first fit in its class """ blurrer = StructureBlurrer() scorer = ScoringFunctions() cluster=Cluster() count=0 dict_ensembl={} list_ordered=cluster.rank_fit_ensemble(ensemble_list,score,res_target_map,sigma_coeff,number_top_mod=0,write=False,targetMap=targetMap.copy()) #cluster fits by local rmsd if number_top_mod==0: ini_num = 0 end_num = len(list_ordered) fit_class = 0 for ipdb in list_ordered: print("model num %d: %s\n", list_ordered.index(ipdb)+1, ipdb[0]) ini_num1 = list_ordered.index(ipdb) mod1=ipdb[1] print('next index ' + str(ini_num1)) if ipdb[-1] == 0: fit_class+=1 for ipdb1 in list_ordered[ini_num1 : end_num]: mod2=ipdb1[1] if ipdb1[-1] == 0: rmsd_val=float(mod1.RMSD_from_same_structure(mod2,CA=True)) ipdb1[3]=rmsd_val print("rmsd of %s from best local fit (%s)= %.2f", ipdb1[0], ipdb[0], rmsd_val) if rmsd_val < rms_cutoff: ipdb1[-1] = fit_class print('class= ' + str(ipdb1[-1])) else: continue else: continue return cluster._print_results_cluster(list_ordered,fit_class,number_top_mod,score,write) else: x=int(number_top_mod) ini_num = 0 end_num = len(list_ordered[:x]) fit_class = 0 for ipdb in list_ordered[:x]: print("model num %d: %s\n", list_ordered.index(ipdb)+1, ipdb[0]) ini_num1 = list_ordered.index(ipdb) mod1=ipdb[1] print('next index ' + str(ini_num1)) if ipdb[-1] == 0: fit_class+=1 for ipdb1 in list_ordered[ini_num1 : end_num]: mod2=ipdb1[1] if ipdb1[-1] == 0: rmsd_val=float(mod1.RMSD_from_same_structure(mod2,CA=True)) print("rms of %s from best local fit (%s)= %.2f", ipdb1[0], ipdb[0], rmsd_val) ipdb1[3]=rmsd_val if rmsd_val < rms_cutoff: ipdb1[-1] = fit_class print('class= ' + str(ipdb1[-1])) else: continue else: continue return cluster._print_results_cluster(list_ordered[:x],fit_class,number_top_mod,score,write) def RMSD_ensemble(self,rank_fit_ensemble,ensemble_list,CA=True): """ Calculates the pairwise RMSD matrix for all Structure Instance in the ensemble. Arguments: *rank_fit_ensemble* Ensemble of Structure Instance ranked using cluster.rank_fit_ensemble *ensemble_list* Input list of Structure Instances *CA is set to True if only CA-RMSD is needed* Return: A numpy array """ list_rotate_models_dict={} for i in ensemble_list: list_rotate_models_dict[i[0]]=i[1] sorted_rank=rank_fit_ensemble mxRMSD = zeros(shape=(len(sorted_rank),len(sorted_rank))) for mod1 in sorted_rank: for mod2 in sorted_rank: print(mod1[0],mod2[0]) rmsd_val=float(list_rotate_models_dict[mod1[0]].RMSD_from_same_structure(list_rotate_models_dict[mod2[0]],CA=CA)) m1=sorted_rank.index(mod1) m2=sorted_rank.index(mod2) mxRMSD[m1][m2]=rmsd_val return mxRMSD def rank_fit_ensemble(self,ensemble_list,score,res_target_map,sigma_coeff,number_top_mod=0,\ write=False,targetMap=False,cont_targetMap=None): """ RMSD clustering of the multiple "fits" accordingly with a chosen score. Cluster the fits based on Calpha RMSD (starting from the best scoring model) Arguments: *ensemble_list* Input list of Structure Instances. *targetMap* Target Map Instance. *score* Scoring function to use. See ScoringFunctions class for a list of the available Scoring Function. E.g. set score='CCC' to use the Cross-correlation coefficient. Score option are: i 'CCC' - Cross-correlation coefficient; ii 'LAP' - Laplacian-filtered cross-correlation coefficient: useful for maps with resolutions worse than 10-15 A; iii 'MI' - Mutual information score: a good and robust score but relatively slow to calculate; iv 'ENV' - Envelope score: the fastest score to calculate due to binarisation of the map. v-vii 'NV','NV_Sobel','NV_Laplace'- Normal vector score: a vector-based surface superimposition score with or without Sobel/Laplace filter. viii 'CD' - Chamfer Distance: a score used in computer vision algorithms as a fast similarity metric *rms_cutoff* float, the Calpha RMSD cutoff based on which you want to cluster the solutions. For example 3.5 (for 3.5 A). *res_target_map* the resolution, in Angstroms, of the target Map. *sigma_coeff* the sigma value (multiplied by the resolution) that controls the width of the Gaussian. Default values is 0.356. Other values used : 0.187R corresponding with the Gaussian width of the Fourier transform falling to half the maximum at 1/resolution, as used in Situs (Wriggers et al, 1999); 0.225R which makes the Fourier transform of the distribution fall to 1/e of its maximum value at wavenumber 1/resolution, the default in Chimera (Petterson et al, 2004) 0.356R corresponding to the Gaussian width at 1/e maximum height equaling the resolution, an option in Chimera (Petterson et al, 2004); 0.425R the fullwidth half maximum being equal to the resolution, as used by FlexEM (Topf et al, 2008); 0.5R the distance between the two inflection points being the same length as the resolution, an option in Chimera (Petterson et al, 2004); 1R where the sigma value simply equal to the resolution, as used by NMFF (Tama et al, 2004). *number_top_mod* Number of Fits to cluster. Default is all. *write* True will write out a file that contains the list of the structure instances representing different fits scored and clustered. note the lrms column is the Calpha RMSD of each fit from the first fit in its class """ blurrer = StructureBlurrer() scorer = ScoringFunctions() cluster=Cluster() count=0 dict_ensembl={} list_to_order=[] #print targetMap if targetMap==False: #targetMap = self.protMap(prot, min(resolution/4., 3.5), resolution) print("WARNING:Need target map") sys.exit() if score not in ['CCC','LAP','MI','NV','NV_Sobel','NV_Laplace','ENV','CD']: print('Incorrect Scoring Function: %s', score) print('Please select from one of the following scoring functions: %s', ''.join(['CCC','LAP','MI','NV','NV_Sobel','NV_Laplace','ENV','CD'])) sys.exit() targetMap=targetMap.copy() if score=='CCC': for mod1 in ensemble_list: count+=1 name_mod=mod1[0] mod=mod1[1] sim_map = blurrer.gaussian_blur(mod, res_target_map,densMap=targetMap,sigma_coeff=sigma_coeff) if not cont_targetMap is None: score_mod=scorer.CCC_map(sim_map,targetMap,0.5*sim_map.fullMap.std(),cont_targetMap,2,True)[0]#CCC(sim_map,targetMap) else: score_mod=scorer.CCC_map(sim_map,targetMap,0.0,0.0,True)[0] #else: score_mod=scorer.CCC(sim_map,targetMap) #'name_file','structure_instance','score','lrmsd','class' list_to_order.append([name_mod,mod,score_mod,0,0]) if score=='LAP': for mod1 in ensemble_list: count+=1 name_mod=mod1[0] mod=mod1[1] sim_map = blurrer.gaussian_blur(mod, res_target_map,densMap=targetMap,sigma_coeff=sigma_coeff) score_mod=scorer.laplace_CCC(sim_map,targetMap) #'name_file','structure_instance','score','lrmsd','class' list_to_order.append([name_mod,mod,score_mod,0,0]) if score=='MI': for mod1 in ensemble_list: count+=1 name_mod=mod1[0] mod=mod1[1] sim_map = blurrer.gaussian_blur(mod, res_target_map,densMap=targetMap,sigma_coeff=sigma_coeff) if not cont_targetMap is None: score_mod=scorer.MI(sim_map,targetMap,0.5*sim_map.fullMap.std(),cont_targetMap,1) else: score_mod=scorer.MI(sim_map,targetMap) list_to_order.append([name_mod,mod,score_mod,0,0]) if score=='NV': for mod1 in ensemble_list: count+=1 name_mod=mod1[0] mod=mod1[1] #These two values should be calculated for the experimental map, and only #need to be calculated once, at the beginning sim_map = blurrer.gaussian_blur(mod, res_target_map,densMap=targetMap,sigma_coeff=sigma_coeff) if not cont_targetMap is None: score_mod=scorer.normal_vector_score(targetMap,sim_map, cont_targetMap-(0.1*targetMap.std()), cont_targetMap+(0.1*targetMap.std()),Filter=None) else: min_thr=targetMap.get_primary_boundary(mod.get_prot_mass_from_atoms(), targetMap.min(), targetMap.max()) points=targetMap.get_point_map(min_thr,percentage=0.2) max_thr=targetMap.get_second_boundary(min_thr, points, min_thr, targetMap.max(),err_percent=1) score_mod=scorer.normal_vector_score(targetMap,sim_map, min_thr, max_thr,Filter=None) score_mod = 1 - (score_mod/3.14) list_to_order.append([name_mod,mod,score_mod,0,0]) if score=='NV_Sobel': for mod1 in ensemble_list: count+=1 name_mod=mod1[0] mod=mod1[1] sim_map = blurrer.gaussian_blur(mod, res_target_map,densMap=targetMap,sigma_coeff=sigma_coeff) if not cont_targetMap is None: score_mod=scorer.normal_vector_score(targetMap,sim_map, cont_targetMap-(0.1*targetMap.std()), cont_targetMap+(0.1*targetMap.std()),Filter='Sobel') else: min_thr=targetMap.get_primary_boundary(mod.get_prot_mass_from_atoms(), targetMap.min(), targetMap.max()) points=targetMap.get_point_map(min_thr,percentage=0.2) max_thr=targetMap.get_second_boundary(min_thr, points, min_thr, targetMap.max(),err_percent=1) score_mod=scorer.normal_vector_score(targetMap,sim_map, min_thr, max_thr,Filter='Sobel') score_mod = 1 - (score_mod/3.14) list_to_order.append([name_mod,mod,score_mod,0,0]) if score=='NV_Laplace': for mod1 in ensemble_list: count+=1 name_mod=mod1[0] mod=mod1[1] sim_map = blurrer.gaussian_blur(mod, res_target_map,densMap=targetMap,sigma_coeff=sigma_coeff) if not cont_targetMap is None: score_mod=scorer.normal_vector_score(targetMap,sim_map, cont_targetMap-(0.1*targetMap.std()), cont_targetMap+(0.1*targetMap.std()),Filter='Laplace') else: min_thr=targetMap.get_primary_boundary(mod.get_prot_mass_from_atoms(), targetMap.min(), targetMap.max()) points=targetMap.get_point_map(min_thr,percentage=0.2) max_thr=targetMap.get_second_boundary(min_thr, points, min_thr, targetMap.max(),err_percent=1) score_mod=scorer.normal_vector_score(targetMap,sim_map, min_thr, max_thr,Filter='Laplace') score_mod = 1 - (score_mod/3.14) list_to_order.append([name_mod,mod,score_mod,0,0]) if score=='ENV': for mod1 in ensemble_list: count+=1 name_mod=mod1[0] mod=mod1[1] min_thr=targetMap.get_primary_boundary(mod.get_prot_mass_from_atoms(), targetMap.min(), targetMap.max()) score_mod=scorer.envelope_score(targetMap,min_thr,mod) #'name_file','structure_instance','score','lrmsd','class' list_to_order.append([name_mod,mod,score_mod,0,0]) if score=='CD': for mod1 in ensemble_list: count+=1 name_mod=mod1[0] mod=mod1[1] sim_map = blurrer.gaussian_blur(mod, res_target_map,densMap=targetMap,sigma_coeff=sigma_coeff) if not cont_targetMap is None: score_mod=scorer._surface_distance_score(sim_map,targetMap,0.5*sim_map.fullMap.std(),cont_targetMap,'Minimum') else: min_thr=targetMap.get_primary_boundary(mod.get_prot_mass_from_atoms(), targetMap.min(), targetMap.max()) points=targetMap.get_point_map(min_thr,percentage=0.2) max_thr=targetMap.get_second_boundary(min_thr, points, min_thr, targetMap.max(),err_percent=1) score_mod=scorer.chamfer_distance(sim_map,targetMap, min_thr, max_thr, kdtree=None) score_mod = 1/score_mod list_to_order.append([name_mod,mod,score_mod,0,0]) if score in ['NV','NV_Sobel','NV_Laplace']: list_ordered=sorted(list_to_order, key=lambda x: x[2],reverse=True)#was false when NV was negative else: list_ordered=sorted(list_to_order, key=lambda x: x[2],reverse=True) if number_top_mod==0: if write==True: return cluster._print_results_cluster2(list_ordered,write) return list_ordered else: x=int(number_top_mod) if write==True: return cluster._print_results_cluster2(list_ordered[:x],write) return list_ordered[:x]
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06db35de3fccd81c6b0762bb58b7518314063cf5
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py
Python
sdr-py/util/__init__.py
jdstmporter/SDRAudio
40392ab443de2e565f8e6b448af6cc3012c906db
[ "BSD-3-Clause" ]
null
null
null
sdr-py/util/__init__.py
jdstmporter/SDRAudio
40392ab443de2e565f8e6b448af6cc3012c906db
[ "BSD-3-Clause" ]
null
null
null
sdr-py/util/__init__.py
jdstmporter/SDRAudio
40392ab443de2e565f8e6b448af6cc3012c906db
[ "BSD-3-Clause" ]
null
null
null
from .log import SYSLOG
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py
Python
dexp/utils/backends/_cupy/texture/_test/test_texture.py
haesleinhuepf/dexp
2ea84f3db323724588fac565fae56f0d522bc5ca
[ "BSD-3-Clause" ]
16
2021-04-21T14:09:19.000Z
2022-03-22T02:30:59.000Z
dexp/utils/backends/_cupy/texture/_test/test_texture.py
haesleinhuepf/dexp
2ea84f3db323724588fac565fae56f0d522bc5ca
[ "BSD-3-Clause" ]
28
2021-04-15T17:43:08.000Z
2022-03-29T16:08:35.000Z
dexp/utils/backends/_cupy/texture/_test/test_texture.py
haesleinhuepf/dexp
2ea84f3db323724588fac565fae56f0d522bc5ca
[ "BSD-3-Clause" ]
3
2022-02-08T17:41:30.000Z
2022-03-18T15:32:27.000Z
from arbol import aprint, asection from dexp.utils.backends import CupyBackend from dexp.utils.backends._cupy.texture.texture import create_cuda_texture def test_cupy_texture_4channels(): try: import cupy with CupyBackend(): source = r""" extern "C"{ __global__ void copyKernel(float* output, cudaTextureObject_t texObj, int width, int height) { unsigned int x = blockIdx.x * blockDim.x + threadIdx.x; unsigned int y = blockIdx.y * blockDim.y + threadIdx.y; // Read from texture and write to global memory float u = x+0.5f; float v = y+0.5f; if (x < width && y < height) { output[y * 4 *width + 4 *x +0] = tex2D<float4>(texObj, u, v).x; output[y * 4 *width + 4 *x +1] = tex2D<float4>(texObj, u, v).y; output[y * 4 *width + 4 *x +2] = tex2D<float4>(texObj, u, v).z; output[y * 4 *width + 4 *x +3] = tex2D<float4>(texObj, u, v).w; } } } """ width = 3 height = 5 # allocate input/output arrays tex_data = cupy.arange(width * height * 4, dtype=cupy.float32).reshape(height, width, 4) # set up a texture object texobj, cuda_array = create_cuda_texture( tex_data, num_channels=4, sampling_mode="nearest", dtype=cupy.float32 ) real_output = cupy.zeros_like(tex_data) expected_output = tex_data.copy() # get the kernel, which copies from texture memory kernel = cupy.RawKernel(source, "copyKernel") # launch it block_x = 4 block_y = 4 grid_x = (width + block_x - 1) // block_x grid_y = (height + block_y - 1) // block_y kernel((grid_x, grid_y), (block_x, block_y), (real_output, texobj, width, height)) del texobj, cuda_array # test outcome assert cupy.allclose(real_output, expected_output) except ModuleNotFoundError: print("Cupy module not found! Test passes nevertheless!") def test_cupy_texture_1channel_normcoord(): try: import cupy with CupyBackend(): source = r""" extern "C"{ __global__ void texture_1channel_normcoord_kernel(float* output, cudaTextureObject_t texObj, int width, int height) { unsigned int x = blockIdx.x * blockDim.x + threadIdx.x; unsigned int y = blockIdx.y * blockDim.y + threadIdx.y; // Read from texture and write to global memory float u = (float(x)+0.5f)/width; float v = (float(y)+0.5f)/height; if (x < width && y < height) { float value = tex2D<float>(texObj, u, v); printf("(%f, %f)=%f\n", u, v, value); output[y * width + x] = value; } } } """ width = 3 height = 5 # allocate input/output arrays tex_data = cupy.arange(width * height, dtype=cupy.float32).reshape(height, width) # set up a texture object texobj, cuda_array = create_cuda_texture( tex_data, num_channels=1, normalised_coords=True, sampling_mode="linear", dtype=cupy.float32 ) real_output = cupy.zeros_like(tex_data) expected_output = tex_data.copy() # get the kernel, which copies from texture memory kernel = cupy.RawKernel(source, "texture_1channel_normcoord_kernel") # launch it block_x = 4 block_y = 4 grid_x = (width + block_x - 1) // block_x grid_y = (height + block_y - 1) // block_y kernel((grid_x, grid_y), (block_x, block_y), (real_output, texobj, width, height)) del texobj, cuda_array # test outcome assert cupy.allclose(real_output, expected_output) except ModuleNotFoundError: print("Cupy module not found! Test passes nevertheless!") def test_cupy_texture_1channel(): try: import cupy with CupyBackend(): source = r""" extern "C"{ __global__ void copyKernel(float* output, cudaTextureObject_t texObj, int width, int height) { unsigned int x = blockIdx.x * blockDim.x + threadIdx.x; unsigned int y = blockIdx.y * blockDim.y + threadIdx.y; // Read from texture and write to global memory float u = x+0.5f; float v = y+0.5f; if (x < width && y < height) { float value = tex2D<float>(texObj, u, v); printf("(%f, %f)=%f\n", u, v, value); output[y * width + x] = value; } } } """ width = 3 height = 5 # allocate input/output arrays tex_data = cupy.arange(width * height, dtype=cupy.float32).reshape(height, width) tex_data[1, 2] = 1 # set up a texture object texobj, cuda_array = create_cuda_texture( tex_data, num_channels=1, sampling_mode="linear", dtype=cupy.float32 ) real_output = cupy.zeros_like(tex_data) expected_output = tex_data.copy() # get the kernel, which copies from texture memory kernel = cupy.RawKernel(source, "copyKernel") # launch it block_x = 4 block_y = 4 grid_x = (width + block_x - 1) // block_x grid_y = (height + block_y - 1) // block_y kernel((grid_x, grid_y), (block_x, block_y), (real_output, texobj, width, height)) del texobj, cuda_array # test outcome assert cupy.allclose(real_output, expected_output) except ModuleNotFoundError: print("Cupy module not found! Test passes nevertheless!") def test_basic_cupy_texture(): try: import cupy with CupyBackend(): source = r""" extern "C"{ __global__ void copyKernel(float* output, cudaTextureObject_t texObj, int width, int height) { unsigned int x = blockIdx.x * blockDim.x + threadIdx.x; unsigned int y = blockIdx.y * blockDim.y + threadIdx.y; // Read from texture and write to global memory float u = x; float v = y; if (x < width && y < height) output[y * width + x] = tex2D<float>(texObj, u, v); } } """ width = 8 height = 16 # set up a texture object ch = cupy.cuda.texture.ChannelFormatDescriptor(32, 0, 0, 0, cupy.cuda.runtime.cudaChannelFormatKindFloat) arr2 = cupy.cuda.texture.CUDAarray(ch, width, height) res = cupy.cuda.texture.ResourceDescriptor(cupy.cuda.runtime.cudaResourceTypeArray, cuArr=arr2) tex = cupy.cuda.texture.TextureDescriptor( (cupy.cuda.runtime.cudaAddressModeClamp, cupy.cuda.runtime.cudaAddressModeClamp), cupy.cuda.runtime.cudaFilterModePoint, cupy.cuda.runtime.cudaReadModeElementType, ) texobj = cupy.cuda.texture.TextureObject(res, tex) # allocate input/output arrays tex_data = cupy.arange(width * height, dtype=cupy.float32).reshape(height, width) real_output = cupy.zeros_like(tex_data) expected_output = cupy.zeros_like(tex_data) arr2.copy_from(tex_data) arr2.copy_to(expected_output) # get the kernel, which copies from texture memory ker = cupy.RawKernel(source, "copyKernel") # launch it block_x = 4 block_y = 4 grid_x = (width + block_x - 1) // block_x grid_y = (height + block_y - 1) // block_y ker((grid_x, grid_y), (block_x, block_y), (real_output, texobj, width, height)) del texobj, arr2 # test outcome assert cupy.allclose(real_output, expected_output) except ModuleNotFoundError: print("Cupy module not found! Test passes nevertheless!") def test_basic_cupy_texture_leak(): try: import cupy with CupyBackend(): # allocate input/output arrays length = 512 tex_data = cupy.arange(length ** 3, dtype=cupy.float32).reshape(length, length, length) with asection("loop"): for i in range(100): aprint(f"i={i}") texobj, cuda_array = create_cuda_texture( tex_data, num_channels=1, sampling_mode="linear", dtype=cupy.float32 ) except ModuleNotFoundError: print("Cupy module not found! Test passes nevertheless!")
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663dffa08d698c3482de75263890caeea5f21bf5
45
py
Python
adaptive/__init__.py
empiricalstateofmind/adaptive
86bc2477309fcb18b3bfc4739888bb9c97b992b3
[ "Apache-2.0" ]
null
null
null
adaptive/__init__.py
empiricalstateofmind/adaptive
86bc2477309fcb18b3bfc4739888bb9c97b992b3
[ "Apache-2.0" ]
null
null
null
adaptive/__init__.py
empiricalstateofmind/adaptive
86bc2477309fcb18b3bfc4739888bb9c97b992b3
[ "Apache-2.0" ]
null
null
null
from .model import * from .functions import *
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6
b09aefedc004f2f99a01fd9056c0363047ad1f37
32
py
Python
syntax.py
franbeep/TWDM-PON-Sim
c34f626c737f03d280bb96fd1dbd4eaa291383e3
[ "MIT" ]
1
2021-11-19T07:20:09.000Z
2021-11-19T07:20:09.000Z
syntax.py
franbeep/TWDM-PON-Sim
c34f626c737f03d280bb96fd1dbd4eaa291383e3
[ "MIT" ]
null
null
null
syntax.py
franbeep/TWDM-PON-Sim
c34f626c737f03d280bb96fd1dbd4eaa291383e3
[ "MIT" ]
null
null
null
import sim print("syntax ok!")
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9fe9eb9ec13e00689fd731255c8929f87227e409
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py
Python
examples/pybind11-project/python-src/pybind11_project/sub_package/sub.py
tttapa/py-build-cmake
29a6970102f567952993ee681cbe0b2d85166adf
[ "MIT" ]
2
2022-02-16T22:37:54.000Z
2022-03-05T19:27:11.000Z
examples/pybind11-project/python-src/pybind11_project/sub_package/sub.py
tttapa/py-build-cmake
29a6970102f567952993ee681cbe0b2d85166adf
[ "MIT" ]
null
null
null
examples/pybind11-project/python-src/pybind11_project/sub_package/sub.py
tttapa/py-build-cmake
29a6970102f567952993ee681cbe0b2d85166adf
[ "MIT" ]
null
null
null
"""Example module that subtracts two integers in Python.""" def sub(a: int, b: int) -> int: """Subtracts two integers""" return a - b
23.833333
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6
b01fa5cf41e52dfa1c60492ed529de3bcf1d7268
2,795
py
Python
tests/factor_max_min_tests.py
petermlm/ProbPy
efb55962283e1c6c2422de812ec8689ffb9dbf16
[ "MIT" ]
16
2015-01-05T19:14:24.000Z
2021-08-19T22:25:04.000Z
tests/factor_max_min_tests.py
petermlm/ProbPy
efb55962283e1c6c2422de812ec8689ffb9dbf16
[ "MIT" ]
null
null
null
tests/factor_max_min_tests.py
petermlm/ProbPy
efb55962283e1c6c2422de812ec8689ffb9dbf16
[ "MIT" ]
7
2015-04-10T18:24:58.000Z
2018-01-26T23:54:59.000Z
from nose.tools import with_setup, nottest from tests.test_base import TestBase from ProbPy import Factor, Event class TestFactorMaxMin(TestBase): def max_test_0(self): """ Maximum with one variable """ for i, domain in enumerate(self.X.domain): fac = Factor(self.X, [1, 2]) fac.values[i] = 10 assert fac.max() == 10 def max_test_1(self): """ Maximum with two variables """ for i, domainx in enumerate(self.X.domain): for j, domainy in enumerate(self.Y.domain): fac = Factor([self.X, self.Y], [1, 2, 3, 4]) fac.values[i + j * 2] = 10 assert fac.max() == 10 def min_test_2(self): """ Minimum with one variable """ for i, domain in enumerate(self.X.domain): fac = Factor(self.X, [1, 2]) fac.values[i] = -10 assert fac.min() == -10 def min_test_3(self): """ Minimum with two variables """ for i, domainx in enumerate(self.X.domain): for j, domainy in enumerate(self.Y.domain): fac = Factor([self.X, self.Y], [1, 2, 3, 4]) fac.values[i + j * 2] = -10 assert fac.min() == -10 def argmax_test_4(self): """ Maximum argument with one variable """ for i, domain in enumerate(self.X.domain): fac = Factor(self.X, [1, 2]) fac.values[i] = 10 event = Event([(self.X, domain)]) assert fac.argmax() == event def argmax_test_5(self): """ Maximum argument with two variables """ for i, domainx in enumerate(self.X.domain): for j, domainy in enumerate(self.Y.domain): fac = Factor([self.X, self.Y], [1, 2, 3, 4]) fac.values[i + j * 2] = 10 event = Event([(self.X, domainx), (self.Y, domainy)]) assert fac.argmax() == event def argmin_test_6(self): """ Minimum argument with one variable """ for i, domain in enumerate(self.X.domain): fac = Factor(self.X, [1, 2]) fac.values[i] = -10 event = Event([(self.X, domain)]) assert fac.argmin() == event def argmin_test_7(self): """ Minimum argument with two variables """ for i, domainx in enumerate(self.X.domain): for j, domainy in enumerate(self.Y.domain): fac = Factor([self.X, self.Y], [1, 2, 3, 4]) fac.values[i + j * 2] = -10 event = Event([(self.X, domainx), (self.Y, domainy)]) assert fac.argmin() == event
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6
b03e94828f73db79f8d3fd228f54b760469f5c15
35
py
Python
lime-transport-websocket/src/lime_transport_websocket/__init__.py
mirlarof/lime-python-transports
992a8cff44e4a3a2156514c5da0077d11653248b
[ "MIT" ]
null
null
null
lime-transport-websocket/src/lime_transport_websocket/__init__.py
mirlarof/lime-python-transports
992a8cff44e4a3a2156514c5da0077d11653248b
[ "MIT" ]
1
2021-06-30T21:47:08.000Z
2021-06-30T21:47:08.000Z
lime-transport-websocket/src/lime_transport_websocket/__init__.py
mirlarof/lime-python-transports
992a8cff44e4a3a2156514c5da0077d11653248b
[ "MIT" ]
1
2021-12-30T12:55:56.000Z
2021-12-30T12:55:56.000Z
from .websocket_transport import *
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6
b04ef9ef1aced43c7ae6fd2c803e7f39278542fe
69
py
Python
main.py
twtg93/tupl
2c1275e0c9af526f06267c7be64df009e3174dc0
[ "MIT" ]
null
null
null
main.py
twtg93/tupl
2c1275e0c9af526f06267c7be64df009e3174dc0
[ "MIT" ]
null
null
null
main.py
twtg93/tupl
2c1275e0c9af526f06267c7be64df009e3174dc0
[ "MIT" ]
null
null
null
import os os.system('python basic.py') os.system('python shell.py')
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6
c68dde390c63fcb58df33a97210dce62214082f0
3,911
py
Python
p011.py
drcsturm/project-euler
07c4e6593f14eed039e580009d5cd5be5f541dfb
[ "MIT" ]
null
null
null
p011.py
drcsturm/project-euler
07c4e6593f14eed039e580009d5cd5be5f541dfb
[ "MIT" ]
null
null
null
p011.py
drcsturm/project-euler
07c4e6593f14eed039e580009d5cd5be5f541dfb
[ "MIT" ]
null
null
null
# In the 20×20 grid below, four numbers along a diagonal line have been marked in red. # 08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08 # 49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00 # 81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65 # 52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91 # 22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80 # 24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50 # 32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70 # 67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21 # 24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72 # 21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95 # 78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92 # 16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57 # 86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58 # 19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40 # 04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66 # 88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69 # 04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36 # 20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16 # 20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54 # 01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48 # The product of these numbers is 26 × 63 × 78 × 14 = 1788696. # What is the greatest product of four adjacent numbers in the same direction (up, down, left, right, or diagonally) in the 20×20 grid? grid = """ 08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08 49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00 81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65 52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91 22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80 24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50 32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70 67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21 24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72 21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95 78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92 16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57 86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58 19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40 04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66 88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69 04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36 20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16 20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54 01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48 """ import re import numpy as np adjacent_nums = 4 data = [int(i) for i in re.findall("[0-9]+", grid)] # make a list of integers arr = np.array(data, dtype=np.int32).reshape(20,20) # put in a matrix of 20 by 20 def max_from_row(vec, n): max_prod = 0 for i in range(len(vec[:-n+1])): prod = 1 for j in range(n): prod *= vec[i + j] # print(prod) max_prod = max(max_prod, prod) return max_prod def max_from_array(arr): max_prod = 0 for row in arr: max_prod = max(max_prod, max_from_row(row, adjacent_nums)) return max_prod def max_from_array_diag(arr): n = len(arr) - adjacent_nums max_prod = 0 for i in range(-n,n+1): # print(arr.diagonal(i)) max_prod = max(max_prod, max_from_row(arr.diagonal(i), adjacent_nums)) return max_prod matrix_maxes = [] # Left or Right matrix_maxes.append(max_from_array(arr)) # Up or Down matrix_maxes.append(max_from_array(np.rot90(arr))) # Down diagonals matrix_maxes.append(max_from_array_diag(arr)) # Up diagonals matrix_maxes.append(max_from_array_diag(np.fliplr(arr))) # print(matrix_maxes) print(max(matrix_maxes))
39.11
135
0.661723
1,060
3,911
2.404717
0.164151
0.032954
0.028246
0.031385
0.77756
0.752452
0.718713
0.681836
0.627697
0.627697
0
0.592219
0.290207
3,911
99
136
39.505051
0.324207
0.419074
0
0.115385
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0.540771
0
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1
0.057692
false
0
0.038462
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0.153846
0.019231
0
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null
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1
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6
c6b527a710ad330e1fb9491a54ac0f7ca624fa63
107
py
Python
python/desc/skycatalogs/__init__.py
LSSTDESC/skyCatalogs
39807b6fb510e45d7db79cf903e2eaa59befa81b
[ "BSD-3-Clause" ]
1
2021-12-20T01:51:00.000Z
2021-12-20T01:51:00.000Z
python/desc/skycatalogs/__init__.py
LSSTDESC/skyCatalogs
39807b6fb510e45d7db79cf903e2eaa59befa81b
[ "BSD-3-Clause" ]
3
2021-11-09T20:20:31.000Z
2022-01-20T20:23:21.000Z
python/desc/skycatalogs/__init__.py
LSSTDESC/skyCatalogs
39807b6fb510e45d7db79cf903e2eaa59befa81b
[ "BSD-3-Clause" ]
null
null
null
from ._version import * from .skyCatalogs import * from .translate import * from .catalog_creator import *
21.4
30
0.775701
13
107
6.230769
0.538462
0.37037
0
0
0
0
0
0
0
0
0
0
0.149533
107
4
31
26.75
0.89011
0
0
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0
0
0
1
0
true
0
1
0
1
0
1
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null
1
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0
0
0
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0
0
1
0
1
0
1
0
0
6
c6cea6970e48448932656800af59cc6740f23e75
26
py
Python
run.py
wiky-avis/journeys
7fc8a7045495e2ab4c16e7ab04681e8f7b9d14f2
[ "MIT" ]
null
null
null
run.py
wiky-avis/journeys
7fc8a7045495e2ab4c16e7ab04681e8f7b9d14f2
[ "MIT" ]
null
null
null
run.py
wiky-avis/journeys
7fc8a7045495e2ab4c16e7ab04681e8f7b9d14f2
[ "MIT" ]
null
null
null
import app app.app.run()
6.5
13
0.692308
5
26
3.6
0.6
0.666667
0
0
0
0
0
0
0
0
0
0
0.153846
26
3
14
8.666667
0.818182
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
0.5
0
0.5
0
1
1
0
null
1
0
0
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0
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0
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1
0
1
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0
0
0
6
c6d82b3e2cea0215b7e2a5b46bde59390dc6a102
21,728
py
Python
lib/datasets/loader/offset_loader.py
shampooma/openseg.pytorch
d1da408a1e870d52c058c359583bc098f7f3d9e2
[ "MIT" ]
1,069
2019-01-21T04:32:05.000Z
2022-03-30T12:07:36.000Z
lib/datasets/loader/offset_loader.py
shampooma/openseg.pytorch
d1da408a1e870d52c058c359583bc098f7f3d9e2
[ "MIT" ]
88
2019-02-13T03:43:09.000Z
2022-03-27T08:23:29.000Z
lib/datasets/loader/offset_loader.py
shampooma/openseg.pytorch
d1da408a1e870d52c058c359583bc098f7f3d9e2
[ "MIT" ]
124
2019-01-23T01:46:00.000Z
2022-03-26T14:07:23.000Z
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ## Created by: JingyiXie, RainbowSecret ## Microsoft Research ## yuyua@microsoft.com ## Copyright (c) 2019 ## ## This source code is licensed under the MIT-style license found in the ## LICENSE file in the root directory of this source tree ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import cv2 import torch import numpy as np import scipy.io as io from torch.utils import data from lib.utils.helpers.image_helper import ImageHelper from lib.extensions.parallel.data_container import DataContainer from lib.utils.tools.logger import Logger as Log from lib.utils.helpers.offset_helper import DTOffsetHelper class DTOffsetLoader(data.Dataset): """ Load [image, label, offset, boundary, name] """ def __init__(self, root_dir, aug_transform=None, dataset=None, img_transform=None, label_transform=None, configer=None): self.configer = configer self.aug_transform = aug_transform self.img_transform = img_transform self.label_transform = label_transform self.img_list, self.label_list, self.offset_list, self.name_list = self.__list_dirs(root_dir, dataset) self.root_dir = root_dir self.dataset = dataset # check whether or not stack the data size_mode = self.configer.get(self.dataset, 'data_transformer')['size_mode'] self.is_stack = size_mode != 'diverse_size' def __len__(self): return len(self.img_list) def _load_maps(self, filename, labelmap): dct = self._load_mat(filename) distance_map = dct['depth'].astype(np.int32) dir_deg = dct['dir_deg'].astype(np.float) # in [0, 360 / deg_reduce] deg_reduce = dct['deg_reduce'][0][0] dir_deg = deg_reduce * dir_deg - 180 # in [-180, 180] return distance_map, dir_deg def load_boundary(self, fn): if fn.endswith('mat'): mat = io.loadmat(fn) if 'depth' in mat: dist_map, _ = self._load_maps(fn, None) boundary_map = DTOffsetHelper.distance_to_mask_label(dist_map, np.zeros_like(dist_map)).astype(np.float32) else: boundary_map = mat['mat'].transpose(1, 2, 0) else: boundary_map = ImageHelper.read_image(fn, tool=self.configer.get('data', 'image_tool'), mode='P') boundary_map = boundary_map.astype(np.float32) / 255 return boundary_map def __getitem__(self, index): img = ImageHelper.read_image(self.img_list[index], tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) img_size = ImageHelper.get_size(img) labelmap = ImageHelper.read_image(self.label_list[index], tool=self.configer.get('data', 'image_tool'), mode='P') if self.configer.exists('data', 'label_list'): labelmap = self._encode_label(labelmap) distance_map, angle_map = self._load_maps(self.offset_list[index], labelmap) if self.configer.exists('data', 'reduce_zero_label') and self.configer.get('data', 'reduce_zero_label') == True: labelmap = self._reduce_zero_label(labelmap) ori_target = ImageHelper.tonp(labelmap).astype(np.int) ori_target[ori_target == 255] = -1 ori_distance_map = np.array(distance_map) ori_angle_map = np.array(angle_map) if self.aug_transform is not None: img, labelmap, distance_map, angle_map = self.aug_transform(img, labelmap=labelmap, distance_map=distance_map, angle_map=angle_map) old_img = img border_size = ImageHelper.get_size(img) if self.img_transform is not None: img = self.img_transform(img) if self.label_transform is not None: labelmap = self.label_transform(labelmap) distance_map = torch.from_numpy(distance_map) angle_map = torch.from_numpy(angle_map) if set(self.configer.get('val_trans', 'trans_seq')) & set(['random_crop', 'crop']): ori_target = labelmap.numpy() ori_distance_map = distance_map.numpy() ori_angle_map = angle_map.numpy() img_size = ori_target.shape[:2][::-1] meta = dict( ori_img_size=img_size, border_size=border_size, ori_target=ori_target, ori_distance_map=ori_distance_map, ori_angle_map=ori_angle_map, basename=os.path.basename(self.label_list[index]) ) return dict( img=DataContainer(img, stack=self.is_stack), labelmap=DataContainer(labelmap, stack=self.is_stack), distance_map=DataContainer(distance_map, stack=self.is_stack), angle_map=DataContainer(angle_map, stack=self.is_stack), meta=DataContainer(meta, stack=False, cpu_only=True), name=DataContainer(self.name_list[index], stack=False, cpu_only=True), ) def _load_mat(self, filename): return io.loadmat(filename) def _replace_ext(self, filename, ext): return '.'.join([filename.rpartition('.')[0], ext]) def _reduce_zero_label(self, labelmap): if not self.configer.get('data', 'reduce_zero_label'): return labelmap labelmap = np.array(labelmap) encoded_labelmap = labelmap - 1 if self.configer.get('data', 'image_tool') == 'pil': encoded_labelmap = ImageHelper.np2img(encoded_labelmap.astype(np.uint8)) return encoded_labelmap def _encode_label(self, labelmap): labelmap = np.array(labelmap) shape = labelmap.shape encoded_labelmap = np.ones(shape=(shape[0], shape[1]), dtype=np.float32) * 255 for i in range(len(self.configer.get('data', 'label_list'))): class_id = self.configer.get('data', 'label_list')[i] encoded_labelmap[labelmap == class_id] = i if self.configer.get('data', 'image_tool') == 'pil': encoded_labelmap = ImageHelper.np2img(encoded_labelmap.astype(np.uint8)) return encoded_labelmap def __list_dirs(self, root_dir, dataset): if os.environ.get('use_cityscapes_style'): if 'GTA5_small' in root_dir: root_dir = root_dir.replace('GTA5_small', 'GTA5_Cityscapes') else: root_dir = root_dir.replace('GTA5', 'GTA5_Cityscapes') Log.info_once('Using Cityscapes style, switch to {}'.format(root_dir)) else: Log.info_once('Using default root dir: {}'.format(root_dir)) img_list = list() label_list = list() offset_list = list() name_list = list() image_subdir = os.environ.get('image_subdir', 'image') label_subdir = os.environ.get('label_dir', 'label') Log.info_once('Using label dir: {}'.format(label_subdir)) offset_subdir = os.environ.get('offset_dir', 'dt_offset') Log.info_once('Using distance transform based offset: {}'.format(offset_subdir)) image_dir = os.path.join(root_dir, dataset, image_subdir) label_dir = os.path.join(root_dir, dataset, label_subdir) offset_dir = os.path.join(root_dir, dataset, offset_subdir) img_extension = os.listdir(image_dir)[0].split('.')[-1] file_list_txt = os.environ.get('use_file_list') if file_list_txt is None: Log.info_once('Using file list: all') files = sorted(os.listdir(label_dir)) else: Log.info_once('Using file list: {}'.format(file_list_txt)) with open(os.path.join(root_dir, dataset, 'file_list', file_list_txt)) as f: files = [x.strip() for x in f] if os.environ.get('chunk'): n, i = map(int, os.environ.get('chunk').split('_')) step = len(files) // n + 4 files = files[step * i: step * (i + 1)] for file_name in files: image_name = '.'.join(file_name.split('.')[:-1]) img_path = os.path.join(image_dir, '{}.{}'.format(image_name, img_extension)) label_path = os.path.join(label_dir, file_name) offset_path = os.path.join(offset_dir, self._replace_ext(file_name, 'mat')) if not os.path.exists(label_path) or not os.path.exists(img_path): Log.error('Label Path: {} not exists.'.format(label_path)) continue img_list.append(img_path) label_list.append(label_path) offset_list.append(offset_path) name_list.append(image_name) if dataset == 'train' and self.configer.get('data', 'include_val'): Log.info_once('Include val set for training ...') image_dir = os.path.join(root_dir, 'val', image_subdir) label_dir = os.path.join(root_dir, 'val', label_subdir) offset_dir = os.path.join(root_dir, 'val', offset_subdir) if file_list_txt is None: files = sorted(os.listdir(label_dir)) else: with open(os.path.join(root_dir, 'val', 'file_list', file_list_txt)) as f: files = [x.strip() for x in f] for file_name in files: image_name = '.'.join(file_name.split('.')[:-1]) img_path = os.path.join(image_dir, '{}.{}'.format(image_name, img_extension)) label_path = os.path.join(label_dir, file_name) offset_path = os.path.join(offset_dir, self._replace_ext(file_name, 'mat')) if not os.path.exists(label_path) or not os.path.exists(img_path): Log.error('Label Path: {} not exists.'.format(label_path)) continue img_list.append(img_path) label_list.append(label_path) offset_list.append(offset_path) name_list.append(image_name) return img_list, label_list, offset_list, name_list class SWOffsetLoader(data.Dataset): def __init__(self, root_dir, aug_transform=None, dataset=None, img_transform=None, label_transform=None, configer=None): self.configer = configer self.aug_transform = aug_transform self.img_transform = img_transform self.label_transform = label_transform self.img_list, self.label_list, self.offset_h_list, self.offset_w_list, self.name_list = self.__list_dirs(root_dir, dataset) self.root_dir = root_dir self.dataset = dataset # check whether or not stack the data size_mode = self.configer.get(dataset, 'data_transformer')['size_mode'] self.is_stack = size_mode != 'diverse_size' def __len__(self): return len(self.img_list) def __getitem__(self, index): img = ImageHelper.read_image(self.img_list[index], tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) img_size = ImageHelper.get_size(img) labelmap = ImageHelper.read_image(self.label_list[index], tool=self.configer.get('data', 'image_tool'), mode='P') offsetmap_h = self._load_mat(self.offset_h_list[index]) offsetmap_w = self._load_mat(self.offset_w_list[index]) if os.environ.get('train_no_offset') and self.dataset == 'train': offsetmap_h = np.zeros_like(offsetmap_h) offsetmap_w = np.zeros_like(offsetmap_w) if self.configer.exists('data', 'label_list'): labelmap = self._encode_label(labelmap) if self.configer.exists('data', 'reduce_zero_label') and self.configer.get('data', 'reduce_zero_label') == True: labelmap = self._reduce_zero_label(labelmap) # Log.info('use dataset {}'.format(self.configer.get('dataset'))) ori_target = ImageHelper.tonp(labelmap).astype(np.int) ori_target[ori_target == 255] = -1 ori_offset_h = np.array(offsetmap_h) ori_offset_w = np.array(offsetmap_w) if self.aug_transform is not None: img, labelmap, offsetmap_h, offsetmap_w = self.aug_transform(img, labelmap=labelmap, offset_h_map=offsetmap_h, offset_w_map=offsetmap_w) border_size = ImageHelper.get_size(img) if self.img_transform is not None: img = self.img_transform(img) if self.label_transform is not None: labelmap = self.label_transform(labelmap) offsetmap_h = torch.from_numpy(np.array(offsetmap_h)).long() offsetmap_w = torch.from_numpy(np.array(offsetmap_w)).long() meta = dict( ori_img_size=img_size, border_size=border_size, ori_target=ori_target, ori_offset_h=ori_offset_h, ori_offset_w=ori_offset_w, ) return dict( img=DataContainer(img, stack=self.is_stack), labelmap=DataContainer(labelmap, stack=self.is_stack), offsetmap_h=DataContainer(offsetmap_h, stack=self.is_stack), offsetmap_w=DataContainer(offsetmap_w, stack=self.is_stack), meta=DataContainer(meta, stack=False, cpu_only=True), name=DataContainer(self.name_list[index], stack=False, cpu_only=True), ) def _load_mat(self, filename): return io.loadmat(filename)['mat'] def _replace_ext(self, filename, ext): return '.'.join([filename.rpartition('.')[0], ext]) def _reduce_zero_label(self, labelmap): if not self.configer.get('data', 'reduce_zero_label'): return labelmap labelmap = np.array(labelmap) encoded_labelmap = labelmap - 1 if self.configer.get('data', 'image_tool') == 'pil': encoded_labelmap = ImageHelper.np2img(encoded_labelmap.astype(np.uint8)) return encoded_labelmap def _encode_label(self, labelmap): labelmap = np.array(labelmap) shape = labelmap.shape encoded_labelmap = np.ones(shape=(shape[0], shape[1]), dtype=np.float32) * 255 for i in range(len(self.configer.get('data', 'label_list'))): class_id = self.configer.get('data', 'label_list')[i] encoded_labelmap[labelmap == class_id] = i if self.configer.get('data', 'image_tool') == 'pil': encoded_labelmap = ImageHelper.np2img(encoded_labelmap.astype(np.uint8)) return encoded_labelmap def __list_dirs(self, root_dir, dataset): img_list = list() label_list = list() offset_h_list = list() offset_w_list = list() name_list = list() image_dir = os.path.join(root_dir, dataset, 'image') label_dir = os.path.join(root_dir, dataset, 'label') offset_h_dir = None offset_w_dir = None subdir = os.environ.get('offset_dir') if subdir is not None: Log.info_once('Using offset dir: {}'.format(subdir)) offset_h_dir = os.path.join(root_dir, dataset, subdir, 'h') offset_w_dir = os.path.join(root_dir, dataset, subdir, 'w') else: offset_type = self.configer.get('data', 'offset_type') assert(offset_type is not None) offset_h_dir = os.path.join(root_dir, dataset, offset_type, 'h') offset_w_dir = os.path.join(root_dir, dataset, offset_type, 'w') img_extension = os.listdir(image_dir)[0].split('.')[-1] for file_name in os.listdir(label_dir): image_name = '.'.join(file_name.split('.')[:-1]) img_path = os.path.join(image_dir, '{}.{}'.format(image_name, img_extension)) label_path = os.path.join(label_dir, file_name) offset_h_path = os.path.join(offset_h_dir, self._replace_ext(file_name, 'mat')) offset_w_path = os.path.join(offset_w_dir, self._replace_ext(file_name, 'mat')) if not os.path.exists(label_path) or not os.path.exists(img_path): Log.error('Label Path: {} not exists.'.format(label_path)) continue img_list.append(img_path) label_list.append(label_path) offset_h_list.append(offset_h_path) offset_w_list.append(offset_w_path) name_list.append(image_name) if dataset == 'train' and self.configer.get('data', 'include_val'): image_dir = os.path.join(root_dir, 'val/image') label_dir = os.path.join(root_dir, 'val/label') subdir = os.environ.get('offset_dir') if subdir is not None: Log.info_once('Using offset dir: {}'.format(subdir)) offset_h_dir = os.path.join(root_dir, 'val', subdir, 'h') offset_w_dir = os.path.join(root_dir, 'val', subdir, 'w') else: offset_type = self.configer.get('data', 'offset_type') assert(offset_type is not None) offset_h_dir = os.path.join(root_dir, 'val', offset_type, 'h') offset_w_dir = os.path.join(root_dir, 'val', offset_type, 'w') for file_name in os.listdir(label_dir): image_name = '.'.join(file_name.split('.')[:-1]) img_path = os.path.join(image_dir, '{}.{}'.format(image_name, img_extension)) label_path = os.path.join(label_dir, file_name) offset_h_path = os.path.join(offset_h_dir, self._replace_ext(file_name, 'mat')) offset_w_path = os.path.join(offset_w_dir, self._replace_ext(file_name, 'mat')) if not os.path.exists(label_path) or not os.path.exists(img_path): Log.error('Label Path: {} not exists.'.format(label_path)) continue img_list.append(img_path) label_list.append(label_path) offset_h_list.append(offset_h_path) offset_w_list.append(offset_w_path) name_list.append(image_name) return img_list, label_list, offset_h_list, offset_w_list, name_list class SWOffsetTestLoader(data.Dataset): def __init__(self, root_dir, dataset='val', img_transform=None, configer=None): self.configer = configer self.img_transform = img_transform self.img_list, self.offset_h_list, self.offset_w_list, self.name_list = self.__list_dirs(root_dir, dataset) size_mode = self.configer.get(dataset, 'data_transformer')['size_mode'] self.is_stack = (size_mode != 'diverse_size') def __len__(self): return len(self.img_list) def __getitem__(self, index): img = ImageHelper.read_image(self.img_list[index], tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) offsetmap_h = self._load_mat(self.offset_h_list[index]) offsetmap_w = self._load_mat(self.offset_w_list[index]) img_size = ImageHelper.get_size(img) if self.img_transform is not None: img = self.img_transform(img) meta = dict( ori_img_size=img_size, border_size=img_size, ) return dict( img=DataContainer(img, stack=self.is_stack), offsetmap_h=DataContainer(offsetmap_h, stack=self.is_stack), offsetmap_w=DataContainer(offsetmap_w, stack=self.is_stack), meta=DataContainer(meta, stack=False, cpu_only=True), name=DataContainer(self.name_list[index], stack=False, cpu_only=True), ) def _load_mat(self, filename): return io.loadmat(filename)['mat'] def _replace_ext(self, filename, ext): return '.'.join([filename.rpartition('.')[0], ext]) def __list_dirs(self, root_dir, dataset): img_list = list() offset_h_list = list() offset_w_list = list() name_list = list() image_dir = os.path.join(root_dir, dataset, 'image') offset_h_dir = None offset_w_dir = None offset_type = self.configer.get('data', 'offset_type') assert(offset_type is not None) offset_h_dir = os.path.join(root_dir, dataset, offset_type, 'h') offset_w_dir = os.path.join(root_dir, dataset, offset_type, 'w') img_extension = os.listdir(image_dir)[0].split('.')[-1] for file_name in os.listdir(label_dir): image_name = '.'.join(file_name.split('.')[:-1]) img_path = os.path.join(image_dir, '{}.{}'.format(image_name, img_extension)) offset_h_path = os.path.join(offset_h_dir, self._replace_ext(file_name, 'mat')) offset_w_path = os.path.join(offset_w_dir, self._replace_ext(file_name, 'mat')) if not os.path.exists(label_path) or not os.path.exists(img_path): Log.error('Label Path: {} not exists.'.format(label_path)) continue img_list.append(img_path) offset_h_list.append(offset_h_path) offset_w_list.append(offset_w_path) name_list.append(image_name) return img_list, offset_h_list, offset_w_list, name_list def load_mat(filename): return io.loadmat(filename)['mat'] def replace_ext(filename, ext): return '.'.join([filename.rpartition('.')[0], ext]) if __name__ == "__main__": pass
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6
af16340c1ed1ba8ab8685932d8db0fa3c3847f4a
47
py
Python
rydprop/hohi/__init__.py
jdrtommey/rydprops
cdc7e14d61ff33929844ee5d779a18fd64f89f4f
[ "MIT" ]
null
null
null
rydprop/hohi/__init__.py
jdrtommey/rydprops
cdc7e14d61ff33929844ee5d779a18fd64f89f4f
[ "MIT" ]
null
null
null
rydprop/hohi/__init__.py
jdrtommey/rydprops
cdc7e14d61ff33929844ee5d779a18fd64f89f4f
[ "MIT" ]
null
null
null
from .adiabatic_solver import Adiabatic_solver
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6
af771e76560ad0e368223c5eacd2d55b98e4ba77
23
py
Python
pyunirpc/__init__.py
aivclab/pyunirpc
1ff8da1a6a8aef0df1aa5486bdf471851ecb4647
[ "MIT" ]
2
2020-11-17T07:43:47.000Z
2020-11-17T08:27:27.000Z
pyunirpc/__init__.py
aivclab/pyunirpc
1ff8da1a6a8aef0df1aa5486bdf471851ecb4647
[ "MIT" ]
null
null
null
pyunirpc/__init__.py
aivclab/pyunirpc
1ff8da1a6a8aef0df1aa5486bdf471851ecb4647
[ "MIT" ]
null
null
null
from .pyunirpc import *
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23
0.782609
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23
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6
afa5da4c2cc0e58a1ddcb101bee077040b12234b
54
py
Python
textsemantics/__init__.py
PrimozGodec/text-semantics
194b0bce7adcc8937a30643959681f0b175927ab
[ "MIT" ]
11
2021-01-27T07:43:33.000Z
2021-12-18T11:58:00.000Z
textsemantics/__init__.py
PrimozGodec/text-semantics
194b0bce7adcc8937a30643959681f0b175927ab
[ "MIT" ]
32
2020-11-24T12:42:46.000Z
2021-12-06T12:01:22.000Z
textsemantics/__init__.py
PrimozGodec/text-semantics
194b0bce7adcc8937a30643959681f0b175927ab
[ "MIT" ]
3
2020-11-10T15:29:16.000Z
2020-11-28T11:42:52.000Z
from .ontology_api import * from .server_api import *
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0
6
afc6af331b84f10911f8624f50bda8a9f290eb70
6,737
py
Python
morse-stf/unittest/test_selectshare.py
alipay/Antchain-MPC
f6916465e1da5722ca7efadc4eeaca13ec229707
[ "Apache-2.0" ]
33
2021-11-23T09:04:03.000Z
2022-03-14T07:56:31.000Z
morse-stf/unittest/test_selectshare.py
qizhi-zhang/Antchain-MPC
f551170f68b0baff328e6594484e9832230fe719
[ "Apache-2.0" ]
null
null
null
morse-stf/unittest/test_selectshare.py
qizhi-zhang/Antchain-MPC
f551170f68b0baff328e6594484e9832230fe719
[ "Apache-2.0" ]
6
2021-11-25T12:38:41.000Z
2022-02-23T03:29:51.000Z
import unittest import numpy as np from stensorflow.basic.basic_class.pair import SharedTensorBase,SharedPairBase from stensorflow.basic.basic_class.private import PrivateTensor import tensorflow as tf from stensorflow.basic.operator.selectshare import native_select, select_share from stensorflow.global_var import StfConfig from stensorflow.random.random import random_init from stensorflow.engine.start_server import start_local_server import os start_local_server(os.path.join(os.environ.get("stf_home", ".."), "conf", "config.json")) class MyTestCase(unittest.TestCase): def setUp(self): self.sess = tf.compat.v1.Session("grpc://0.0.0.0:8887") def tearDown(self): self.sess.close() def test_native_select(self): with tf.device(StfConfig.workerL[0]): tL = np.random.randint(low=0, high=2, size=[32,8]) tL = tf.constant(tL) tL = SharedTensorBase(inner_value=tL, module=2) xL = np.random.randint(low=-(1<<62), high=(1<<62), size=[32,8]) xL = tf.constant(xL, dtype='int64') xL = SharedTensorBase(inner_value=xL) with tf.device(StfConfig.workerR[0]): tR = np.random.randint(low=0, high=2, size=[32,8]) tR = tf.constant(tR) tR = SharedTensorBase(inner_value=tR, module=2) xR = np.random.randint(low=-(1<<62), high=(1<<62), size=[32,8]) xR = tf.constant(xR, dtype='int64') xR = SharedTensorBase(inner_value=xR) t = SharedPairBase(ownerL=StfConfig.workerL[0], ownerR=StfConfig.workerR[0], xL=tL, xR=tR, fixedpoint=0) x = SharedPairBase(ownerL=StfConfig.workerL[0], ownerR=StfConfig.workerR[0], xL=xL, xR=xR, fixedpoint=0) tx = native_select(t, x, prf_flag=True, compress_flag=True) z = tx.to_tf_tensor("R")-t.to_tf_tensor("R")*x.to_tf_tensor("R") self.sess.run(random_init()) self.assertEqual(np.count_nonzero(self.sess.run(z)), 0) def test_select_using_pm1_act(self): with tf.device(StfConfig.workerL[0]): tL = np.random.randint(low=0, high=2, size=[32,8]) tL = tf.constant(tL) tL = SharedTensorBase(inner_value=tL, module=2) xL = np.random.randint(low=-(1<<62), high=(1<<62), size=[32,8]) xL = tf.constant(xL, dtype='int64') xL = SharedTensorBase(inner_value=xL) with tf.device(StfConfig.workerR[0]): tR = np.random.randint(low=0, high=2, size=[32,8]) tR = tf.constant(tR) tR = SharedTensorBase(inner_value=tR, module=2) xR = np.random.randint(low=-(1<<62), high=(1<<62), size=[32,8]) xR = tf.constant(xR, dtype='int64') xR = SharedTensorBase(inner_value=xR) t = SharedPairBase(ownerL=StfConfig.workerL[0], ownerR=StfConfig.workerR[0], xL=tL, xR=tR, fixedpoint=0) x = SharedPairBase(ownerL=StfConfig.workerL[0], ownerR=StfConfig.workerR[0], xL=xL, xR=xR, fixedpoint=0) tx = select_share(t, x, prf_flag=True, compress_flag=True) z = tx.to_tf_tensor("R")-t.to_tf_tensor("R")*x.to_tf_tensor("R") self.sess.run(random_init()) self.assertEqual(np.count_nonzero(self.sess.run(z)), 0) def test_select_Private_Private_share(self): with tf.device(StfConfig.workerL[0]): tL = np.random.randint(low=0, high=2, size=[32,8]) tL = tf.constant(tL) tL = PrivateTensor(inner_value=tL, module=2, fixedpoint=0, owner=StfConfig.workerL[0]) with tf.device(StfConfig.workerR[0]): xR = np.random.randint(low=-(1<<62), high=(1<<62), size=[32,8]) xR = tf.constant(xR, dtype='int64') xR = PrivateTensor(inner_value=xR, owner=StfConfig.workerR[0]) tx = select_share(tL, xR, prf_flag=True, compress_flag=True, ) z = tx.to_tf_tensor("R")-tL.to_tf_tensor("R")*xR.to_tf_tensor("R") self.sess.run(random_init()) self.assertEqual(np.count_nonzero(self.sess.run(z)), 0) def test_select_Private_SharedPair_share(self): with tf.device(StfConfig.workerL[0]): tL = np.random.randint(low=0, high=2, size=[32,8]) tL = tf.constant(tL) tL = PrivateTensor(inner_value=tL, module=2, fixedpoint=0, owner=StfConfig.workerL[0]) xL = np.random.randint(low=-(1<<62), high=(1<<62), size=[32,8]) xL = tf.constant(xL, dtype='int64') xL = SharedTensorBase(inner_value=xL) with tf.device(StfConfig.workerR[0]): # tR = np.random.randint(low=0, high=2, size=[32,8]) # tR = tf.constant(tR) # tR = SharedTensorBase(inner_value=tR, module=2) xR = np.random.randint(low=-(1<<62), high=(1<<62), size=[32,8]) xR = tf.constant(xR, dtype='int64') xR = SharedTensorBase(inner_value=xR) # t = SharedPairBase(ownerL=StfConfig.workerL[0], ownerR=StfConfig.workerR[0], xL=tL, xR=tR, fixedpoint=0) x = SharedPairBase(ownerL=StfConfig.workerL[0], ownerR=StfConfig.workerR[0], xL=xL, xR=xR, fixedpoint=0) tx = select_share(tL, x, prf_flag=True, compress_flag=True, ) z = tx.to_tf_tensor("R")-tL.to_tf_tensor("R")*x.to_tf_tensor("R") self.sess.run(random_init()) self.assertEqual(np.count_nonzero(self.sess.run(z)), 0) def test_select_SharedPair_Private(self): with tf.device(StfConfig.workerL[0]): tL = np.random.randint(low=0, high=2, size=[32,8]) tL = tf.constant(tL) tL = SharedTensorBase(inner_value=tL, module=2) # xL = np.random.randint(low=-(1<<62), high=(1<<62), size=[32,8]) # xL = tf.constant(xL, dtype='int64') # xL = SharedTensorBase(inner_value=xL) with tf.device(StfConfig.workerR[0]): tR = np.random.randint(low=0, high=2, size=[32,8]) tR = tf.constant(tR) tR = SharedTensorBase(inner_value=tR, module=2) xR = np.random.randint(low=-(1<<62), high=(1<<62), size=[32,8]) xR = tf.constant(xR, dtype='int64') xR = PrivateTensor(inner_value=xR, fixedpoint=0, owner=StfConfig.workerR[0]) t = SharedPairBase(ownerL=StfConfig.workerL[0], ownerR=StfConfig.workerR[0], xL=tL, xR=tR, fixedpoint=0) #x = SharedPairBase(ownerL=StfConfig.workerL[0], ownerR=StfConfig.workerR[0], xL=xL, xR=xR, fixedpoint=0) tx = select_share(t, xR, prf_flag=False, compress_flag=False) z = tx.to_tf_tensor("R")-t.to_tf_tensor("R")*xR.to_tf_tensor("R") self.sess.run(random_init()) self.assertEqual(np.count_nonzero(self.sess.run(z)), 0) if __name__ == '__main__': unittest.main()
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6
bb96f0d6f73a39ea52fd9a3e0782e379f8770d43
44
py
Python
je_editor/ui/ui_event/execute/__init__.py
JE-Chen/je_editor
2f18dedb6f0eb27c38668dc53f520739c8d5c6c6
[ "MIT" ]
1
2021-12-10T14:57:15.000Z
2021-12-10T14:57:15.000Z
je_editor/ui/ui_event/execute/__init__.py
JE-Chen/je_editor
2f18dedb6f0eb27c38668dc53f520739c8d5c6c6
[ "MIT" ]
null
null
null
je_editor/ui/ui_event/execute/__init__.py
JE-Chen/je_editor
2f18dedb6f0eb27c38668dc53f520739c8d5c6c6
[ "MIT" ]
null
null
null
from je_editor.ui.ui_event.execute import *
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6
bbbf16874c8477aaa46c3a15a2c21672320619b7
3,481
py
Python
src/bitcoin_tag/models.py
RosaSineSpinis/twitter_bitcon_tag_analyser
3311022b6fd629ce85f0c4fa0516e310bed05d74
[ "bzip2-1.0.6" ]
null
null
null
src/bitcoin_tag/models.py
RosaSineSpinis/twitter_bitcon_tag_analyser
3311022b6fd629ce85f0c4fa0516e310bed05d74
[ "bzip2-1.0.6" ]
null
null
null
src/bitcoin_tag/models.py
RosaSineSpinis/twitter_bitcon_tag_analyser
3311022b6fd629ce85f0c4fa0516e310bed05d74
[ "bzip2-1.0.6" ]
null
null
null
from django.db import models # Create your models here. from picklefield.fields import PickledObjectField from django.utils import timezone # class YearModel(models.Model): # tag_dictionary = PickledObjectField() # tag_date = models.DateField(auto_now_add=False) # date of save # tag_time = models.TimeField(auto_now_add=False) # time of save # beginning_datetime = models.DateTimeField(blank=False) # comes from MonthModel # ending_datetime = models.DateTimeField(blank=False) # comes from MonthModel # # def __str__(self): # return f'{self.tag_date} {self.tag_time}' def default_semantic_analysis_dict(): return {0: 0, 1: 0, -1: 0} class MonthModel(models.Model): tag_dictionary = PickledObjectField() semantic_analysis = PickledObjectField(default=default_semantic_analysis_dict) tag_date = models.DateField(default=timezone.now) # date of save tag_time = models.TimeField(default=timezone.now) # time of save tag_datetime = models.DateTimeField(default=timezone.now) #, blank=True, null=True) beginning_datetime = models.DateTimeField(blank=False) # comes from HourModel the earliest object ending_datetime = models.DateTimeField(blank=False) # comes from HourModel the latest object def __str__(self): return f'{self.tag_date} {self.tag_time}' class DayModel(models.Model): tag_dictionary = PickledObjectField() semantic_analysis = PickledObjectField(default=default_semantic_analysis_dict) tag_date = models.DateField(auto_now_add=True) # date of save tag_time = models.TimeField(auto_now_add=True) # time of save tag_datetime = models.DateTimeField(default=False) #, blank=True, null=True) beginning_datetime = models.DateTimeField(blank=False, null=True) # comes from HourModel the earliest object ending_datetime = models.DateTimeField(blank=False, null=True) # comes from HourModel the latest object def __str__(self): return f'{self.tag_date} {self.tag_time}' class HourModel(models.Model): tag_dictionary = PickledObjectField() # dictionary of tags --> {#tagname: number} semantic_analysis = PickledObjectField(default=default_semantic_analysis_dict) tag_date = models.DateField(auto_now_add=False) # date of tag save tag_time = models.TimeField(auto_now_add=False) # time of tag save tag_datetime = models.DateTimeField(auto_now_add=False) def __str__(self): return f'{self.tag_date} {self.tag_time}' # class MinutesModel(models.Model): # tag_dictionary = PickledObjectField() # tag_date = models.DateField(auto_now=False, auto_now_add=False, blank=False) # tag_time = models.TimeField(auto_now=False, auto_now_add=False) # # def __str__(self): # return f'{self.tag_date} {self.tag_time}' class Test(models.Model): user_name = models.TextField(max_length=200, default="aaa") user_surname = models.CharField(max_length=200, default="missing") def __str__(self): return self.user_name, self.user_surname # class DayModel(models.Model): # dictionary = models.CharField(max_length=30) # date = models.DateField(auto_now=False, auto_now_add=False) # time = models.TimeField(auto_now=False, auto_now_add=False) # # # class MonthModel(models.Model): # dictionary = models.CharField(max_length=30) # date = models.DateField(auto_now=False, auto_now_add=False) # time = models.TimeField(auto_now=False, auto_now_add=False)
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bbc7ecfc8c29f021d9863e8b84c09584d8b3a62f
33
py
Python
Aula Python/Aula 08 ex3.py
ayresmajor/Curso-python
006229cec38ea365bf43b19e3ce93fbd32e1dca6
[ "MIT" ]
null
null
null
Aula Python/Aula 08 ex3.py
ayresmajor/Curso-python
006229cec38ea365bf43b19e3ce93fbd32e1dca6
[ "MIT" ]
null
null
null
Aula Python/Aula 08 ex3.py
ayresmajor/Curso-python
006229cec38ea365bf43b19e3ce93fbd32e1dca6
[ "MIT" ]
null
null
null
import emoji print(emoji.emojize)
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py
Python
tests/macro_liquidMG_UOZrFe.py
niamorelreillet/openiec_with_OC
9e027c7052ca98398bf09758bc05b3daf1aba151
[ "MIT" ]
null
null
null
tests/macro_liquidMG_UOZrFe.py
niamorelreillet/openiec_with_OC
9e027c7052ca98398bf09758bc05b3daf1aba151
[ "MIT" ]
null
null
null
tests/macro_liquidMG_UOZrFe.py
niamorelreillet/openiec_with_OC
9e027c7052ca98398bf09758bc05b3daf1aba151
[ "MIT" ]
null
null
null
import numpy as np import pandas as pd from scipy.optimize import curve_fit import matplotlib.pyplot as plt import matplotlib as mpl from cycler import cycler import os from openiec.property.coherentenergy_OC import CoherentGibbsEnergy_OC from openiec.calculate.calcsigma_OC import SigmaCoherent_OC2 from pyOC import opencalphad as oc from pyOC import GridMinimizerStatus as gmStat from scipy.optimize import minimize, Bounds, LinearConstraint, NonlinearConstraint, BFGS from functools import partial # mass density laws (from Barrachin2004) constituentDensityLaws = { 'U1': lambda T: 17270.0 - 1.358 * (T - 1408), 'ZR1': lambda T: 6844.51 - 0.609898 * T + 2.05008E-4 * T ** 2 - 4.47829E-8 * T ** 3 + 3.26469E-12 * T ** 4, 'O2U1': lambda T: 8860.0 - 9.285E-1 * (T - 3120), 'O2ZR1': lambda T: 5150 - 0.445 * (T - 2983), 'FE1': lambda T: 7030 - 0.88 * (T - 1808), 'NI1': lambda T: 7900 - 1.19 * (T - 1728), 'CR1': lambda T: 6290 - 0.72 * (T - 2178), 'O1': lambda T: 1.141, # set to meaningless value but ok as, no 'free' oxygen in the considered mixtures 'FE1O1': lambda T: 7030 - 0.88 * (T - 1808), # set to Fe value but ok as, almost no such component in the considered mixtures 'FE1O1_5': lambda T: 7030 - 0.88 * (T - 1808), # set to Fe value but ok as, almost no such component in the considered mixtures } constituentDensityLaws['U'] = constituentDensityLaws['U1'] constituentDensityLaws['ZR'] = constituentDensityLaws['ZR1'] constituentDensityLaws['O'] = constituentDensityLaws['O1'] constituentDensityLaws['FE'] = constituentDensityLaws['FE1'] constituentDensityLaws['NI'] = constituentDensityLaws['NI1'] constituentDensityLaws['CR'] = constituentDensityLaws['CR1'] def constituentToEndmembersConverter(constituentMolarFractions, constituentsDescription): endmemberMolarFractions = { 'U1' : constituentMolarFractions['sublattice 0']['U+4']*constituentMolarFractions['sublattice 1']['VA'], 'O2U1' : constituentMolarFractions['sublattice 0']['U+4']*constituentMolarFractions['sublattice 1']['O-2'], 'O1' : constituentMolarFractions['sublattice 1']['O'], 'ZR1' : constituentMolarFractions['sublattice 0']['ZR+4']*constituentMolarFractions['sublattice 1']['VA'], 'FE1' : constituentMolarFractions['sublattice 0']['FE+2']*constituentMolarFractions['sublattice 1']['VA'], 'O2ZR1' : constituentMolarFractions['sublattice 0']['ZR+4']*constituentMolarFractions['sublattice 1']['O-2'], 'FE1O1' : constituentMolarFractions['sublattice 0']['FE+2']*constituentMolarFractions['sublattice 1']['O-2'], 'FE1O1_5' : constituentMolarFractions['sublattice 1']['FEO3/2'], } endmemberMolarMasses = { 'U1' : constituentsDescription['U+4']['mass'], 'O1' : constituentsDescription['O']['mass'], 'O2U1' : constituentsDescription['U+4']['mass']+2.0*constituentsDescription['O']['mass'], 'ZR1' : constituentsDescription['ZR+4']['mass'], 'FE1' : constituentsDescription['FE+2']['mass'], 'O2ZR1' : constituentsDescription['ZR+4']['mass']+2.0*constituentsDescription['O']['mass'], 'FE1O1' : constituentsDescription['FE+2']['mass']+1.0*constituentsDescription['O']['mass'], 'FE1O1_5' : constituentsDescription['FE+2']['mass']+1.5*constituentsDescription['O']['mass'], } endMemberMassFractions = {k : endmemberMolarFractions[k]*endmemberMolarMasses[k] for k in endmemberMolarFractions} factor=1.0/sum(endMemberMassFractions.values()) for k in endMemberMassFractions: endMemberMassFractions[k] = endMemberMassFractions[k]*factor return endMemberMassFractions def ComputeEquilibriumWithConstraints(objectfunction, x0, bulkX, method="trust-constr", tol=1e-16): print("********************") print("starting point: ", x0) print("objective function value at starting point: ", objectfunction(x0)) print("excluded points (bulk composition): ", bulkX) print(bulkX) linearConstraint = LinearConstraint([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 1.0, 1.0]], [0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0], keep_feasible=True) n = len(bulkX) def cons_f(x): f0=np.sqrt((x[0]-bulkX[0][0])**2+(x[1]-bulkX[1][0])**2+(x[2]-bulkX[2][0])**2) f1=np.sqrt((x[0]-bulkX[0][1])**2+(x[1]-bulkX[1][1])**2+(x[2]-bulkX[2][1])**2) return [f0, f1] def cons_J(x): f = cons_f(x) return [ [(x[0]-bulkX[0][0])/f[0], (x[1]-bulkX[1][0])/f[0], (x[2]-bulkX[2][0])/f[0]], [(x[0]-bulkX[0][1])/f[1], (x[1]-bulkX[1][1])/f[1], (x[2]-bulkX[2][1])/f[1]] ] def cons_H(x, v): f = cons_f(x) a11 = 1/f[0]-(x[0]-bulkX[0][0])**2/f[0]**3 a12 = -(x[0]-bulkX[0][0])*(x[1]-bulkX[1][0])/f[0]**3 a13 = -(x[0]-bulkX[0][0])*(x[2]-bulkX[2][0])/f[0]**3 a22 = 1/f[0]-(x[1]-bulkX[1][0])**2/f[0]**3 a23 = -(x[1]-bulkX[1][0])*(x[2]-bulkX[2][0])/f[0]**3 a33 = 1/f[0]-(x[2]-bulkX[2][0])**2/f[0]**3 b11 = 1/f[1]-(x[0]-bulkX[0][1])**2/f[1]**3 b12 = -(x[0]-bulkX[0][1])*(x[1]-bulkX[1][1])/f[1]**3 b13 = -(x[0]-bulkX[0][1])*(x[2]-bulkX[2][1])/f[1]**3 b22 = 1/f[1]-(x[1]-bulkX[1][1])**2/f[1]**3 b23 = -(x[1]-bulkX[1][1])*(x[2]-bulkX[2][1])/f[1]**3 b33 = 1/f[1]-(x[2]-bulkX[2][1])**2/f[1]**3 return v[0]*np.array([[a11, a12, a13], [a12, a22, a23], [a13, a23, a33]]) + v[1]*np.array([[b11, b12, b13], [b12, b22, b23], [b13, b23, b33]]) nonlinearConstraint = NonlinearConstraint(cons_f, 1E-6, np.inf, jac=cons_J, hess=cons_H, keep_feasible=True) res = minimize(objectfunction, x0, method=method, constraints=[linearConstraint, nonlinearConstraint], options={'xtol': tol, 'gtol': tol, 'maxiter': 3000, 'initial_constr_penalty': 0.5, 'verbose': 1}) print(res.x) print(res.fun) #if (res.fun>1E-2): # raise ValueError('misconvergence!') print("********************") return res.x def run(): print('### test U-O-Zr-Fe coherent interface in the liquid miscibility gap ###\n') # tdb filepath tdbFile = os.environ['TDBDATA_PRIVATE']+'/NUCLEA-19_1_mod.TDB' # tdbFile='tests/TAF_uzrofe_V10.TDB' # components comps = ['O', 'U', 'ZR', 'FE'] # phase names phasenames = ['LIQUID', 'LIQUID'] # pressure P = 1E5 # Given initial alloy composition. x0 is the mole fractions of U, Zr, Fe. # # RU/Zr=0.60 CZr=0.3 xSteel=0.1 # x0 = [0.1550142, 0.2583569, 0.1215864] # RU/Zr=1.20 CZr=0.50 xSteel=0.10 x0 = [1.883189e-01, 1.569325e-01, 1.211783e-01] # Composition step for searching initial interfacial equilibrium composition. #dx = 0.5 # Convergence criterion for loop on interfacial composition epsilonX = 1E-5 # temperature range Tmin = 2900.0 Tmax = 4200.0 Trange = np.linspace(Tmin, Tmax, num=11, endpoint=True) results = pd.DataFrame(columns=['temperature', 'n_phase1', 'n_phase2', 'xU_phase1', 'xU_phase2','xZr_phase1', 'xZr_phase2', 'xFe_phase1', 'xFe_phase2','xU_interface','xZr_interface','xFe_interface', 'sigma','VmU','VmZr','VmFe']) x=None for T in Trange: # calculate global equilibrium and retrieve associated chemical potentials CoherentGibbsEnergy_OC.initOC(tdbFile, comps) oc.raw().pytqtgsw(4) # no merging of grid points #oc.raw().pytqtgsw(23) # denser grid model = CoherentGibbsEnergy_OC(T, 1E5, phasenames) mueq = model.chemicalpotential(x0) phasesAtEquilibrium = oc.getPhasesAtEquilibrium() phasesAtEquilibriumMolarAmounts = phasesAtEquilibrium.getPhaseMolarAmounts() if (len(phasesAtEquilibriumMolarAmounts)==1): # it is possible that the miscibility gap has not been detected correctly (can happen when T increases) #print(phasesAtEquilibriumMolarAmounts) # ad hoc strategy: 1) calculate an equilibrium at lower temperature (hopefully finding the two phases) # 2) redo the calculation at the target temperature afterwards without the grid minimizer model = CoherentGibbsEnergy_OC(Tmin, 1E5, phasenames) mueq = model.chemicalpotential(x0) phasesAtEquilibrium = oc.getPhasesAtEquilibrium() phasesAtEquilibriumMolarAmounts = phasesAtEquilibrium.getPhaseMolarAmounts() #print(phasesAtEquilibriumMolarAmounts) oc.setTemperature(T) oc.calculateEquilibrium(gmStat.Off) mueq = model.getChemicalPotentials() phasesAtEquilibrium = oc.getPhasesAtEquilibrium() phasesAtEquilibriumMolarAmounts = phasesAtEquilibrium.getPhaseMolarAmounts() phasesAtEquilibriumElementCompositions = phasesAtEquilibrium.getPhaseElementComposition() print(phasesAtEquilibriumElementCompositions) if (set(phasesAtEquilibriumMolarAmounts)==set(['LIQUID#1', 'LIQUID_AUTO#2'])): # Composition range for searching initial interfacial equilibrium composition # calculated from the actual phase compositions componentsWithLimits = comps[1:] #limit = [ [1.0, 0.0] for each in componentsWithLimits ] #for phase in phasesAtEquilibriumElementCompositions: # for element in phasesAtEquilibriumElementCompositions[phase]: # elementMolarFraction = phasesAtEquilibriumElementCompositions[phase][element] # if element in componentsWithLimits: # limit[componentsWithLimits.index(element)][0] = min(limit[componentsWithLimits.index(element)][0], elementMolarFraction) # limit[componentsWithLimits.index(element)][1] = max(limit[componentsWithLimits.index(element)][1], elementMolarFraction) #limit = [ [each[0]+dx*(each[1]-each[0]), each[1]-dx*(each[1]-each[0])] for each in limit ] bulkX = [ [ phasesAtEquilibriumElementCompositions[phase][element] for phase in phasesAtEquilibriumMolarAmounts ] for element in componentsWithLimits ] notConverged = True if (x==None): x = [ 0.5*(phasesAtEquilibriumElementCompositions['LIQUID#1'][comp] + phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2'][comp]) for comp in componentsWithLimits ] # Iterate on interfacial molar composition while (notConverged): # Molar volumes of pure components evaluated at x CoherentGibbsEnergy_OC.initOC(tdbFile, comps) model = CoherentGibbsEnergy_OC(T, P, phasenames[0], False) if ('TAF' in tdbFile): functions=model.constantPartialMolarVolumeFunctions(x, constituentDensityLaws, 1E-5, constituentToEndmembersConverter) else: functions=model.constantPartialMolarVolumeFunctions(x, constituentDensityLaws, 1E-5) # calculate interfacial energy sigma = SigmaCoherent_OC2( T=T, x0=x0, db=tdbFile, comps=comps, phasenames=phasenames, purevms=functions, guess=x, computeEquilibriumFunction=partial(ComputeEquilibriumWithConstraints, bulkX=bulkX), enforceGridMinimizerForLocalEq=False, mueq=mueq ) print('at T=', T, ' sigma=', sigma.Interfacial_Energy.values, '\n') notConverged = np.linalg.norm(x[:]-sigma.Interfacial_Composition.values[1:], np.inf)>epsilonX print('convergence: ', not notConverged, x[:], sigma.Interfacial_Composition.values[1:]) x[:]=sigma.Interfacial_Composition.values[1:] # store results in pandas dataframe if (np.abs(sigma.Interfacial_Energy.values)>1E-6): print(sigma, "\n") if (abs(np.max(sigma.Partial_Interfacial_Energy.values)-np.min(sigma.Partial_Interfacial_Energy.values))>1E-3): raise ValueError('wrong value discarded') results = results.append({'temperature' : T, 'n_phase1' : phasesAtEquilibriumMolarAmounts['LIQUID#1'], 'n_phase2' : phasesAtEquilibriumMolarAmounts['LIQUID_AUTO#2'], 'xU_phase1' : phasesAtEquilibriumElementCompositions['LIQUID#1']['U'], 'xU_phase2' : phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2']['U'], 'xZr_phase1' : phasesAtEquilibriumElementCompositions['LIQUID#1']['ZR'], 'xZr_phase2' : phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2']['ZR'], 'xFe_phase1' : phasesAtEquilibriumElementCompositions['LIQUID#1']['FE'], 'xFe_phase2' : phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2']['FE'], 'xU_interface' : sigma.Interfacial_Composition.values[1], 'xZr_interface' : sigma.Interfacial_Composition.values[2], 'xFe_interface' : sigma.Interfacial_Composition.values[3], 'sigma' : sigma.Interfacial_Energy.values, 'VmU' : functions[1](T), 'VmZr' : functions[2](T), 'VmFe' : functions[3](T), 'VmO' : functions[0](T), }, ignore_index = True) else: print(sigma, "\n") raise ValueError('wrong value discarded') else: print('at T=', T, ' out of the miscibility gap') print('phases at equilibrium:', phasesAtEquilibriumMolarAmounts) # write csv result file results.to_csv('macro_liquidMG_UOZrFe_NUCLEA19_varyingT.csv') def run2(): print('### test U-O coherent interface in the liquid miscibility gap ###\n') # tdb filepath #tdbFile=os.environ['TDBDATA_PRIVATE']+'/feouzr.tdb' #tdbFile=os.environ['TDBDATA_PRIVATE']+'/NUCLEA-17_1_mod.TDB' #tdbFile=os.environ['TDBDATA_PRIVATE']+'/NUCLEA-19_1_mod.TDB' tdbFile='tests/TAF_uzrofe_V10.TDB' # components comps = ['O', 'U', 'ZR', 'FE'] # mass density laws (from Barrachin2004) constituentDensityLaws = { 'U1' : lambda T: 17270.0-1.358*(T-1408), 'ZR1' : lambda T: 6844.51-0.609898*T+2.05008E-4*T**2-4.47829E-8*T**3+3.26469E-12*T**4, 'O2U1' : lambda T: 8860.0-9.285E-1*(T-3120), 'O2ZR1': lambda T: 5150-0.445*(T-2983), 'FE1' : lambda T: 7030 - 0.88*(T-1808), 'NI1' : lambda T: 7900 - 1.19*(T-1728), 'CR1' : lambda T: 6290 - 0.72*(T-2178), 'O1' : lambda T: 1.141, # set to meaningless value but ok as, no 'free' oxygen in the considered mixtures 'FE1O1' : lambda T: 7030 - 0.88*(T-1808), # set to Fe value but ok as, almost no such component in the considered mixtures 'FE1O1_5' : lambda T: 7030 - 0.88*(T-1808), # set to Fe value but ok as, almost no such component in the considered mixtures } constituentDensityLaws['U'] = constituentDensityLaws['U1'] constituentDensityLaws['ZR'] = constituentDensityLaws['ZR1'] constituentDensityLaws['O'] = constituentDensityLaws['O1'] constituentDensityLaws['FE'] = constituentDensityLaws['FE1'] # phase names phasenames = ['LIQUID', 'LIQUID'] # pressure P = 1E5 # Given initial alloy composition. x0 is the mole fractions of U, Zr, Fe. # # RU/Zr=0.60 CZr=0.3 xSteel=0.1 # x0 = [0.1550142, 0.2583569, 0.1215864] # RU/Zr=1.20 CZr=0.50 xSteel=0.10 x0 = [1.883189e-01, 1.569325e-01, 1.211783e-01] # Composition step for searching initial interfacial equilibrium composition. #dx = 0.5 # Convergence criterion for loop on interfacial composition epsilonX = 1E-5 inputs = pd.read_csv('macro_liquidMG_UOZrFe_run.csv') results = pd.DataFrame(columns=['temperature', 'n_phase1', 'n_phase2', 'xU_phase1', 'xU_phase2','xZr_phase1', 'xZr_phase2', 'xFe_phase1', 'xFe_phase2','xU_interface','xZr_interface','xFe_interface', 'VmU', 'VmZr','VmFe','sigma']) x = None for i,T in enumerate(inputs['temperature']): # calculate global equilibrium and retrieve associated chemical potentials CoherentGibbsEnergy_OC.initOC(tdbFile, comps) oc.raw().pytqtgsw(4) # no merging of grid points #oc.raw().pytqtgsw(23) # denser grid model = CoherentGibbsEnergy_OC(T, 1E5, phasenames) mueq = model.chemicalpotential(x0) phasesAtEquilibrium = oc.getPhasesAtEquilibrium() phasesAtEquilibriumMolarAmounts = phasesAtEquilibrium.getPhaseMolarAmounts() if (len(phasesAtEquilibriumMolarAmounts)==1): # it is possible that the miscibility gap has not been detected correctly (can happen when T increases) #print(phasesAtEquilibriumMolarAmounts) # ad hoc strategy: 1) calculate an equilibrium at lower temperature (hopefully finding the two phases) # 2) redo the calculation at the target temperature afterwards without the grid minimizer model = CoherentGibbsEnergy_OC(2800.0, 1E5, phasenames) mueq = model.chemicalpotential(x0) phasesAtEquilibrium = oc.getPhasesAtEquilibrium() phasesAtEquilibriumMolarAmounts = phasesAtEquilibrium.getPhaseMolarAmounts() #print(phasesAtEquilibriumMolarAmounts) oc.setTemperature(T) oc.calculateEquilibrium(gmStat.Off) mueq = model.getChemicalPotentials() phasesAtEquilibrium = oc.getPhasesAtEquilibrium() phasesAtEquilibriumMolarAmounts = phasesAtEquilibrium.getPhaseMolarAmounts() phasesAtEquilibriumElementCompositions = phasesAtEquilibrium.getPhaseElementComposition() print(phasesAtEquilibriumMolarAmounts) print(phasesAtEquilibriumElementCompositions) if (set(phasesAtEquilibriumMolarAmounts)==set(['LIQUID#1', 'LIQUID_AUTO#2'])): # Composition range for searching initial interfacial equilibrium composition # calculated from the actual phase compositions componentsWithLimits = comps[1:] #limit = [ [1.0, 0.0] for each in componentsWithLimits ] #for phase in phasesAtEquilibriumElementCompositions: # for element in phasesAtEquilibriumElementCompositions[phase]: # elementMolarFraction = phasesAtEquilibriumElementCompositions[phase][element] # if element in componentsWithLimits: # limit[componentsWithLimits.index(element)][0] = min(limit[componentsWithLimits.index(element)][0], elementMolarFraction) # limit[componentsWithLimits.index(element)][1] = max(limit[componentsWithLimits.index(element)][1], elementMolarFraction) #limit = [ [each[0]+dx*(each[1]-each[0]), each[1]-dx*(each[1]-each[0])] for each in limit ] bulkX = [ [ phasesAtEquilibriumElementCompositions[phase][element] for phase in phasesAtEquilibriumMolarAmounts ] for element in componentsWithLimits ] if (x==None): x = [ 0.5*(phasesAtEquilibriumElementCompositions['LIQUID#1'][comp] + phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2'][comp]) for comp in componentsWithLimits ] #x = x0.copy() # Molar volumes of pure components evaluated at x functions = [ lambda _: inputs['VmO'][i], lambda _: inputs['VmU'][i], lambda _: inputs['VmZr'][i], lambda _: inputs['VmFe'][i]] # calculate interfacial energy sigma = SigmaCoherent_OC2( T=T, x0=x0, db=tdbFile, comps=comps, phasenames=phasenames, purevms=functions, guess=x, computeEquilibriumFunction=partial(ComputeEquilibriumWithConstraints, bulkX=bulkX), enforceGridMinimizerForLocalEq=False, mueq=mueq ) print('at T=', T, ' sigma=', sigma.Interfacial_Energy.values, '\n') x[:]=sigma.Interfacial_Composition.values[1:] # Store result if (np.abs(sigma.Interfacial_Energy.values)>1E-6): # store results in pandas dataframe print(sigma, "\n") results = results.append({'temperature' : T, 'n_phase1' : phasesAtEquilibriumMolarAmounts['LIQUID#1'], 'n_phase2' : phasesAtEquilibriumMolarAmounts['LIQUID_AUTO#2'], 'xU_phase1' : phasesAtEquilibriumElementCompositions['LIQUID#1']['U'], 'xU_phase2' : phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2']['U'], 'xZr_phase1' : phasesAtEquilibriumElementCompositions['LIQUID#1']['ZR'], 'xZr_phase2' : phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2']['ZR'], 'xFe_phase1' : phasesAtEquilibriumElementCompositions['LIQUID#1']['FE'], 'xFe_phase2' : phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2']['FE'], 'xU_interface' : sigma.Interfacial_Composition.values[1], 'xZr_interface' : sigma.Interfacial_Composition.values[2], 'xFe_interface' : sigma.Interfacial_Composition.values[3], 'sigma' : sigma.Interfacial_Energy.values, 'VmU' : functions[0](T), 'VmZr' : functions[1](T), 'VmFe' : functions[2](T), }, ignore_index = True) else: print(sigma, "\n") raise ValueError('wrong value discarded') else: print('at T=', T, ' out of the miscibility gap') print('phases at equilibrium:', phasesAtEquilibriumMolarAmounts) # write csv result file results.to_csv('macro_liquidMG_UOZrFe_TAFID_varyingT.csv') def run3(tdbFile, RUZr): print('### test U-O-Zr-Fe coherent interface in the liquid miscibility gap ###\n') # components comps = ['O', 'U', 'ZR', 'FE'] # phase names phasenames = ['LIQUID', 'LIQUID'] # pressure P = 1E5 # initial alloy compositions. x0 is the mole fractions of U, Zr, Fe. read = pd.read_csv('tests/{0:2.1f}RUZr.csv'.format(RUZr), delim_whitespace=True) # Composition step for searching initial interfacial equilibrium composition. #dx = 0.5 # Convergence criterion for loop on interfacial composition epsilonX = 1E-4 # temperature range T = 3000 # Trange = np.linspace(Tmin, Tmax, num=10, endpoint=True) results = pd.DataFrame(columns=['temperature', 'n_phase1', 'n_phase2', 'xU_phase1', 'xU_phase2','xZr_phase1', 'xZr_phase2', 'xFe_phase1', 'xFe_phase2','xU_interface','xZr_interface','xFe_interface', 'sigma','VmU','VmZr','VmFe']) x = None for ii in range(read.shape[0]): x0=[read['xU'][ii],read['xZr'][ii],read['xFe'][ii]] print("*********({0:d}/{1:d})*********".format(ii+1, read.shape[0])) print("x0: ",x0) # calculate global equilibrium and retrieve associated chemical potentials CoherentGibbsEnergy_OC.initOC(tdbFile, comps) oc.raw().pytqtgsw(4) # no merging of grid points #oc.raw().pytqtgsw(23) # denser grid model = CoherentGibbsEnergy_OC(T, 1E5, phasenames) mueq = model.chemicalpotential(x0) phasesAtEquilibrium = oc.getPhasesAtEquilibrium() phasesAtEquilibriumMolarAmounts = phasesAtEquilibrium.getPhaseMolarAmounts() if (len(phasesAtEquilibriumMolarAmounts)==1): # it is possible that the miscibility gap has not been detected correctly (can happen when T increases) #print(phasesAtEquilibriumMolarAmounts) # ad hoc strategy: 1) calculate an equilibrium at lower temperature (hopefully finding the two phases) # 2) redo the calculation at the target temperature afterwards without the grid minimizer model = CoherentGibbsEnergy_OC(2900, 1E5, phasenames) mueq = model.chemicalpotential(x0) phasesAtEquilibrium = oc.getPhasesAtEquilibrium() phasesAtEquilibriumMolarAmounts = phasesAtEquilibrium.getPhaseMolarAmounts() #print(phasesAtEquilibriumMolarAmounts) oc.setTemperature(T) oc.calculateEquilibrium(gmStat.Off) mueq = model.getChemicalPotentials() phasesAtEquilibrium = oc.getPhasesAtEquilibrium() phasesAtEquilibriumMolarAmounts = phasesAtEquilibrium.getPhaseMolarAmounts() phasesAtEquilibriumElementCompositions = phasesAtEquilibrium.getPhaseElementComposition() print(phasesAtEquilibriumMolarAmounts) if (set(phasesAtEquilibriumMolarAmounts)==set(['LIQUID#1', 'LIQUID_AUTO#2'])): # Composition range for searching initial interfacial equilibrium composition # calculated from the actual phase compositions componentsWithLimits = comps[1:] #limit = [ [1.0, 0.0] for each in componentsWithLimits ] #for phase in phasesAtEquilibriumElementCompositions: #for element in phasesAtEquilibriumElementCompositions[phase]: # elementMolarFraction = phasesAtEquilibriumElementCompositions[phase][element] # if element in componentsWithLimits: # limit[componentsWithLimits.index(element)][0] = min(limit[componentsWithLimits.index(element)][0], elementMolarFraction) # limit[componentsWithLimits.index(element)][1] = max(limit[componentsWithLimits.index(element)][1], elementMolarFraction) #limit = [ [each[0]+dx*(each[1]-each[0]), each[1]-dx*(each[1]-each[0])] for each in limit ] bulkX = [ [ phasesAtEquilibriumElementCompositions[phase][element] for phase in phasesAtEquilibriumMolarAmounts ] for element in componentsWithLimits ] notConverged = True if (x==None): x = [ 0.5*(phasesAtEquilibriumElementCompositions['LIQUID#1'][comp] + phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2'][comp]) for comp in componentsWithLimits ] # Iterate on interfacial molar composition while (notConverged): # Molar volumes of pure components evaluated at x CoherentGibbsEnergy_OC.initOC(tdbFile, comps) model = CoherentGibbsEnergy_OC(T, P, phasenames[0], False) if ('TAF' in tdbFile): functions=model.constantPartialMolarVolumeFunctions(x, constituentDensityLaws, 1E-5, constituentToEndmembersConverter) else: functions=model.constantPartialMolarVolumeFunctions(x, constituentDensityLaws, 1E-5) # calculate interfacial energy sigma = SigmaCoherent_OC2( T=T, x0=x0, db=tdbFile, comps=comps, phasenames=phasenames, purevms=functions, guess=x, computeEquilibriumFunction=partial(ComputeEquilibriumWithConstraints, bulkX=bulkX), enforceGridMinimizerForLocalEq=False, mueq=mueq ) print('at T=', T, ' sigma=', sigma.Interfacial_Energy.values, '\n') notConverged = np.linalg.norm(x[:]-sigma.Interfacial_Composition.values[1:], np.inf)>epsilonX print('convergence: ', not notConverged, x[:], sigma.Interfacial_Composition.values[1:]) x[:]=sigma.Interfacial_Composition.values[1:] # store results in pandas dataframe if (np.abs(sigma.Interfacial_Energy.values)>1E-5): print(sigma, "\n") if (abs(np.max(sigma.Partial_Interfacial_Energy.values)-np.min(sigma.Partial_Interfacial_Energy.values))>1E-3): print(np.min(sigma.Partial_Interfacial_Energy.values)) print(np.max(sigma.Partial_Interfacial_Energy.values)) raise ValueError('wrong value discarded') results = results.append({'temperature' : T, 'n_phase1' : phasesAtEquilibriumMolarAmounts['LIQUID#1'], 'n_phase2' : phasesAtEquilibriumMolarAmounts['LIQUID_AUTO#2'], 'xU_phase1' : phasesAtEquilibriumElementCompositions['LIQUID#1']['U'], 'xU_phase2' : phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2']['U'], 'xZr_phase1' : phasesAtEquilibriumElementCompositions['LIQUID#1']['ZR'], 'xZr_phase2' : phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2']['ZR'], 'xFe_phase1' : phasesAtEquilibriumElementCompositions['LIQUID#1']['FE'], 'xFe_phase2' : phasesAtEquilibriumElementCompositions['LIQUID_AUTO#2']['FE'], 'xU_interface' : sigma.Interfacial_Composition.values[1], 'xZr_interface' : sigma.Interfacial_Composition.values[2], 'xFe_interface' : sigma.Interfacial_Composition.values[3], 'sigma' : sigma.Interfacial_Energy.values, 'VmU' : functions[1](T), 'VmZr' : functions[2](T), 'VmFe' : functions[3](T), 'VmO' : functions[0](T), }, ignore_index = True) else: raise ValueError('wrong value discarded') else: print('at T=', T, ' out of the miscibility gap') print('phases at equilibrium:', phasesAtEquilibriumMolarAmounts) # write csv result file if ('TAF' in tdbFile): results.to_csv('macro_liquidMG_UOZrFe_TAFID_RUZR={0:2.1f}.csv'.format(RUZr)) else: results.to_csv('macro_liquidMG_UOZrFe_NUCLEA19_RUZR={0:2.1f}.csv'.format(RUZr)) def fit(): results = pd.read_csv('macro_liquidMG_UOZrFe_NUCLEA_varyingT.csv') # Function to calculate the power-law with constants sigma0, Tc, mu, sigmaC def power_law_plus_const(T, sigma0, Tc, mu, sigmaC): return sigma0*np.power(1.0-T/Tc, mu)+sigmaC def power_law_no_const(T, sigma0, Tc, mu): return sigma0*np.power(1.0-T/Tc, mu) # Fit the power-law data power_law = power_law_no_const print(results['temperature']) print(results['sigma']) pars, cov = curve_fit(f=power_law, xdata=results['temperature'], ydata=results['sigma'], p0=[0.7, results['temperature'][len(results['temperature']) - 1], 1.9], bounds=(-np.inf, np.inf)) # Get the standard deviations of the parameters (square roots of the # diagonal of the covariance) stdevs = np.sqrt(np.diag(cov)) # Calculate the residuals print(power_law(results['temperature'], *pars)) res = results['sigma'] - power_law(results['temperature'], *pars) print(pars, stdevs) plt.rcParams['figure.figsize'] = (12,7) fig,axes=plt.subplots(2,2,constrained_layout=True) # Plots associated with interfacial energy ax = axes[0,0] ax.grid(True) ax.plot(results['temperature'], results['sigma'], marker = 'o', ls='', color='tab:cyan', label='calculated values: $\sigma_{calculated}$') legLabel = 'fit: $\sigma_{fit}='+'{0:4.3f} (1-T/{1:4.1f})^'.format(pars[0], pars[1])+'{'+'{0:4.3f}'.format(pars[2])+'}$' ax.plot(results['temperature'], power_law(results['temperature'], *pars), linestyle='--', linewidth=2, color='black', label=legLabel) ax.set_xlabel('temperature T (K)',fontsize=12) ax.set_ylabel('interfacial energy $\sigma$ (N.m$^{-1}$)',fontsize=12) ax.legend(loc='upper right') # Plots associated with composition ax = axes[0,1] ax.grid(True) ax.plot(results['xU_interface'], results['sigma'], marker = 'o', ls='--', color='tab:cyan') ax.set_ylabel('interfacial energy $\sigma$ (N.m$^{-1}$)',fontsize=12) ax.set_xlabel('interface U molar fraction',fontsize=12) ax = axes[1,0] ax.grid(True) ax.plot(results['xZr_interface'], results['sigma'], marker = 'o', ls='--', color='tab:cyan') ax.set_ylabel('interfacial energy $\sigma$ (N.m$^{-1}$)',fontsize=12) ax.set_xlabel('interface Zr molar fraction',fontsize=12) ax = axes[1,1] ax.grid(True) ax.plot(results['xFe_interface'], results['sigma'], marker = 'o', ls='--', color='tab:cyan') ax.set_ylabel('interfacial energy $\sigma$ (N.m$^{-1}$)',fontsize=12) ax.set_xlabel('interface Fe molar fraction',fontsize=12) plt.savefig('macro_liquidMG_UOZrFe_fit.pdf') plt.show() def plot(tdbFile, RUZr): inputs = pd.read_csv('tests/{0:2.1f}RUZr.csv'.format(RUZr), delim_whitespace=True) CZr=inputs['CZr'] xSteel=inputs['xSteel'] # write csv result file if ('TAF' in tdbFile): results = pd.read_csv('macro_liquidMG_UOZrFe_TAFID_RUZR={0:2.1f}.csv'.format(RUZr)) else: results = pd.read_csv('macro_liquidMG_UOZrFe_NUCLEA19_RUZR={0:2.1f}.csv'.format(RUZr)) # epsilon=1E-4 def calculateSet(array, tol): sortedArray = array.copy() sortedArray.sort() results = [sortedArray.pop(0), ] for value in sortedArray: if abs(results[-1] - value) <= tol: continue results.append(value) return results # colors=['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:cyan', 'tab:brown', 'tab:pink'] markers=['x', '+', 'o', '*', '^', 'v', '<', '>'] prop_cycle = cycler(color=colors) + cycler(marker=markers) + cycler(markevery=[0.1]*len(markers)) prop_cycle2 = prop_cycle * cycler(linestyle=['-', '--', '-.']) # ftSize = 10 plt.rcParams["figure.figsize"] = (12,10) plt.rcParams["legend.fontsize"] = ftSize CZr_set = calculateSet(inputs['CZr'].tolist(), epsilon) x_Fe_set = calculateSet(inputs['xSteel'].tolist(), epsilon) # fig,axes=plt.subplots(2,2,constrained_layout=True) ax1 = axes[0,0] ax1.grid(True) ax1.set_prop_cycle(prop_cycle) ax2 = axes[0,1] ax2.grid(True) ax2.set_prop_cycle(prop_cycle2) ax3 = axes[1,0] ax3.grid(True) ax3.set_prop_cycle(prop_cycle2) ax4 = axes[1,1] ax4.grid(True) ax4.set_prop_cycle(prop_cycle2) for valCZr in CZr_set: indI = [i for i, val in enumerate(CZr) if abs(val - valCZr) < epsilon] legLabel="$C_{Zr}"+"={0:3.2f}$".format(valCZr) csf = ax1.plot(xSteel[indI], results['sigma'][indI], label=legLabel) legLabel="$C_{Zr}"+"={0:3.2f}$".format(valCZr)+" - interfacial liquid" csf = ax2.plot(xSteel[indI], results['xU_interface'][indI], label=legLabel) xmin = [max(results['xU_phase1'][i], results['xU_phase2'][i]) for i in indI] xmax = [min(results['xU_phase1'][i], results['xU_phase2'][i]) for i in indI] legLabel="$C_{Zr}"+"={0:3.2f}$".format(valCZr)+" - bulk metal" csf = ax2.plot(xSteel[indI], xmin, label=legLabel) legLabel="$C_{Zr}"+"={0:3.2f}$".format(valCZr)+" - bulk oxide" csf = ax2.plot(xSteel[indI], xmax, label=legLabel) legLabel="$C_{Zr}"+"={0:3.2f}$".format(valCZr)+" - interfacial liquid" csf = ax3.plot(xSteel[indI], results['xZr_interface'][indI], label=legLabel) xmin = [max(results['xZr_phase1'][i], results['xZr_phase2'][i]) for i in indI] xmax = [min(results['xZr_phase1'][i], results['xZr_phase2'][i]) for i in indI] legLabel="$C_{Zr}"+"={0:3.2f}$".format(valCZr)+" - bulk metal" csf = ax3.plot(xSteel[indI], xmin, label=legLabel) legLabel="$C_{Zr}"+"={0:3.2f}$".format(valCZr)+" - bulk oxide" csf = ax3.plot(xSteel[indI], xmax, label=legLabel) legLabel="$C_{Zr}"+"={0:3.2f}$".format(valCZr)+" - interfacial liquid" csf = ax4.plot(xSteel[indI], results['xFe_interface'][indI], label=legLabel) xmin = [max(results['xFe_phase1'][i], results['xFe_phase2'][i]) for i in indI] xmax = [min(results['xFe_phase1'][i], results['xFe_phase2'][i]) for i in indI] legLabel="$C_{Zr}"+"={0:3.2f}$".format(valCZr)+" - bulk metal" csf = ax4.plot(xSteel[indI], xmax, label=legLabel) legLabel="$C_{Zr}"+"={0:3.2f}$".format(valCZr)+" - bulk oxide" csf = ax4.plot(xSteel[indI], xmin, label=legLabel) ax1.set_xlabel("$x_{steel}$", fontsize=ftSize) ax1.set_ylabel("interfacial energy $\sigma$ (N.m$^{-1}$)", fontsize=ftSize) ax1.set_title("$R_{U/Zr}"+"={0:2.1f}$".format(RUZr), fontsize=ftSize) #ax1.legend(loc="best", ncol=2) ax2.set_xlabel("$x_{steel}$", fontsize=ftSize) ax2.set_ylabel("U molar fraction", fontsize=ftSize) ax2.set_title("$R_{U/Zr}"+"={0:2.1f}$".format(RUZr), fontsize=ftSize) #ax2.legend(loc="best", ncol=2) ax3.set_xlabel("$x_{steel}$", fontsize=ftSize) ax3.set_ylabel("Zr molar fraction", fontsize=ftSize) ax3.set_title("$R_{U/Zr}"+"={0:2.1f}$".format(RUZr), fontsize=ftSize) #ax3.legend(loc="best", ncol=2) ax4.set_xlabel("$x_{steel}$", fontsize=ftSize) ax4.set_ylabel("Fe molar fraction", fontsize=ftSize) ax4.set_title("$R_{U/Zr}"+"={0:2.1f}$".format(RUZr), fontsize=ftSize) #ax4.legend(loc="best", ncol=2) lines, labels = fig.axes[-1].get_legend_handles_labels() fig.legend(lines, labels, loc = 'center') if ('TAF' in tdbFile): figName = 'macro_liquidMG_UOZrFe_TAFID_RUZR={0:2.1f}_plot'.format(RUZr) else: figName = 'macro_liquidMG_UOZrFe_NUCLEA19_RUZR={0:2.1f}_plot'.format(RUZr) plt.savefig(figName+'.pdf') plt.savefig(figName+'.png') plt.show() if __name__ == '__main__': #run() #run2() #fit() # # tdb filepath #tdbFile=os.environ['TDBDATA_PRIVATE']+'/feouzr.tdb' tdbFile=os.environ['TDBDATA_PRIVATE'] + '/NUCLEA-19_1_mod.TDB' #tdbFile='tests/TAF_uzrofe_V10.TDB' RUZr=1.2 #run3(tdbFile, RUZr) plot(tdbFile, RUZr)
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6
a57f29bd0b22567e1079a65486b55435f9aed6e1
86
py
Python
voltagemetricspublisher/core/version.py
SumudithaR/svc.voltage-metrics-publisher
4e0418c855920d3e984acf097681e2fc8c8ec081
[ "Apache-2.0" ]
null
null
null
voltagemetricspublisher/core/version.py
SumudithaR/svc.voltage-metrics-publisher
4e0418c855920d3e984acf097681e2fc8c8ec081
[ "Apache-2.0" ]
null
null
null
voltagemetricspublisher/core/version.py
SumudithaR/svc.voltage-metrics-publisher
4e0418c855920d3e984acf097681e2fc8c8ec081
[ "Apache-2.0" ]
null
null
null
VERSION = (0, 0, 1, 'alpha', 0) def get_version(version=VERSION): return VERSION
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6
3c2db8a51625215900fa135312a2a8971e721b83
6,736
py
Python
src/haizea/pluggable/accounting/models.py
Hamdy/haizea
797e1b0ae19b41887c8970298de3adb9498034f3
[ "Apache-2.0" ]
1
2017-10-31T22:17:31.000Z
2017-10-31T22:17:31.000Z
src/haizea/pluggable/accounting/models.py
Hamdy/haizea
797e1b0ae19b41887c8970298de3adb9498034f3
[ "Apache-2.0" ]
null
null
null
src/haizea/pluggable/accounting/models.py
Hamdy/haizea
797e1b0ae19b41887c8970298de3adb9498034f3
[ "Apache-2.0" ]
null
null
null
from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import Column, Integer, String, ForeignKey, Float from sqlalchemy.orm import relationship, backref Base = declarative_base() class Experiment(Base): __tablename__ = 'experiments' def __init__(self): self.description = "" id = Column(Integer, primary_key=True) description = Column(String) total_accepted_ar = Column(Integer) total_rejected_ar = Column(Integer) total_accepted_im = Column(Integer) total_rejected_im = Column(Integer) total_completed_be = Column(Integer) be_completed_after = Column(Float) def __repr__(self): return self.description class CPU(Base): __tablename__ = 'cpu_utilizations' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('cpu_utilizations', cascade='all, delete, delete-orphan', order_by=id)) time = Column(String) node = Column(String) value = Column(String) avg = Column(String) def __repr__(self): return "Cpu Utilization for experiment %s" % self.experiment_id class CPUPnode(Base): __tablename__ = 'cpu_pnode_load' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('pnodes_cpu_load', cascade='all, delete, delete-orphan', order_by=id)) time = Column(String) node = Column(String) value = Column(Float) def __repr__(self): return "Cpu Utilization for single physical node %s in experiment %s " % (self.node, self.experiment_id) class DiskPnode(Base): __tablename__ = 'disk_pnode_load' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('pnodes_disk_load', cascade='all, delete, delete-orphan', order_by=id)) time = Column(String) node = Column(String) value = Column(Float) def __repr__(self): return "Disk Utilization for single physical node %s in experiment %s " % (self.node, self.experiment_id) class NetInPnode(Base): __tablename__ = 'net_in_pnode_load' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('pnodes_net_in_load', cascade='all, delete, delete-orphan', order_by=id)) time = Column(String) node = Column(String) value = Column(Float) def __repr__(self): return "Net in Utilization for single physical node %s in experiment %s " % (self.node, self.experiment_id) class NetOutPnode(Base): __tablename__ = 'net_out_pnode_load' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('pnodes_net_out_load', cascade='all, delete, delete-orphan', order_by=id)) time = Column(String) node = Column(String) value = Column(Float) class MemoryPnode(Base): __tablename__ = 'memory_pnode_load' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('pnodes_memory_load', cascade='all, delete, delete-orphan', order_by=id)) time = Column(String) node = Column(String) value = Column(Float) def __repr__(self): return "Memory Utilization for single physical node %s in experiment %s " % (self.node, self.experiment_id) class LeaseStatistics(Base): __tablename__ = 'lease_statistics' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('lease_statistics', cascade='all, delete, delete-orphan', order_by=id)) lease_id = Column(Integer) waiting_time = Column(Float) slowdown = Column(Float) class AcceptedAR(Base): __tablename__ = 'accepted_ar' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('accepted_ars', cascade='all, delete, delete-orphan', order_by=id)) time = Column(Float) lease_id = Column(Integer) count = Column(Integer) class AcceptedIM(Base): __tablename__ = 'accepted_im' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('accepted_ims', cascade='all, delete, delete-orphan', order_by=id)) time = Column(Float) lease_id = Column(Integer) count = Column(Integer) class RejectedAR(Base): __tablename__ = 'rejected_ar' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('rejected_ars', cascade='all, delete, delete-orphan', order_by=id)) time = Column(Float) lease_id = Column(Integer) count = Column(Integer) class RejectedIM(Base): __tablename__ = 'rejected_im' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('rejected_ims', cascade='all, delete, delete-orphan', order_by=id)) time = Column(Float) lease_id = Column(Integer) count = Column(Integer) class CompletedBE(Base): __tablename__ = 'completed_be' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('completed_bes', cascade='all, delete, delete-orphan', order_by=id)) time = Column(Float) lease_id = Column(Integer) count = Column(Integer) class QueueSizeBE(Base): __tablename__ = 'queue_size_be' id = Column(Integer, primary_key=True) experiment_id = Column(Integer, ForeignKey('experiments.id', ondelete='CASCADE')) experiment = relationship("Experiment", backref=backref('queue_size_bes', cascade='all, delete, delete-orphan', order_by=id)) time = Column(Float) lease_id = Column(Integer) count = Column(Integer)
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6
3c3352f2f9ab7f9b43d352013b807835e28ecd5d
148
py
Python
src/wai/json/error/_JSONSchemaError.py
waikato-datamining/wai-json
cb013fb16e7c1b8d91e040a387a143d29d4ced96
[ "MIT" ]
null
null
null
src/wai/json/error/_JSONSchemaError.py
waikato-datamining/wai-json
cb013fb16e7c1b8d91e040a387a143d29d4ced96
[ "MIT" ]
2
2020-07-30T22:41:42.000Z
2021-09-21T23:18:06.000Z
src/wai/json/error/_JSONSchemaError.py
waikato-datamining/wai-json
cb013fb16e7c1b8d91e040a387a143d29d4ced96
[ "MIT" ]
null
null
null
from ._JSONError import JSONError class JSONSchemaError(JSONError): """ Base class for all errors involving JSON schema. """ pass
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6
3c7749796a32a2202bb3b4a118381b5a271e4853
247
py
Python
torchsketch/utils/__init__.py
songyzh/torchsketch
42bca1b31ab9699d9b6d77a102b1f46bba82fb33
[ "MIT" ]
182
2020-03-25T01:59:11.000Z
2022-03-29T08:58:47.000Z
torchsketch/utils/__init__.py
songyzh/torchsketch
42bca1b31ab9699d9b6d77a102b1f46bba82fb33
[ "MIT" ]
5
2020-03-25T13:16:50.000Z
2022-02-19T09:51:39.000Z
torchsketch/utils/__init__.py
songyzh/torchsketch
42bca1b31ab9699d9b6d77a102b1f46bba82fb33
[ "MIT" ]
17
2020-03-25T12:40:49.000Z
2022-03-28T06:34:40.000Z
from torchsketch.utils import data_augmentation_utils from torchsketch.utils import general_utils from torchsketch.utils import metric_utils from torchsketch.utils import self_supervised_utils from torchsketch.utils import svg_specific_utils
41.166667
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6.333333
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6
3c7e541ee0be51f8533ccbb0260e6d7438d900de
145
py
Python
NF/Step/__init__.py
AWehenkel/Normalizing-Flows
fe535e25cda32781296557ac5a523a6d2ade1761
[ "BSD-3-Clause" ]
9
2020-11-20T12:36:03.000Z
2022-03-21T03:18:12.000Z
NF/Step/__init__.py
AWehenkel/Normalizing-Flows
fe535e25cda32781296557ac5a523a6d2ade1761
[ "BSD-3-Clause" ]
null
null
null
NF/Step/__init__.py
AWehenkel/Normalizing-Flows
fe535e25cda32781296557ac5a523a6d2ade1761
[ "BSD-3-Clause" ]
null
null
null
from .NormalizingFlow import FCNormalizingFlow, NormalizingFlow, NormalizingFlowStep from .AugmentedFlow import MNISTAugmentedFlow, MNISTBaseline
72.5
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0.896552
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b1e97f9b7c8983f955d23d83925f9dcc3bf049f8
28,787
py
Python
envdsys/envnet/registry/registry.py
NOAA-PMEL/envDataSystem
4db4a3569d2329658799a3eef06ce36dd5c0597d
[ "Unlicense" ]
1
2021-11-06T19:22:53.000Z
2021-11-06T19:22:53.000Z
envdsys/envnet/registry/registry.py
NOAA-PMEL/envDataSystem
4db4a3569d2329658799a3eef06ce36dd5c0597d
[ "Unlicense" ]
25
2019-06-18T20:40:36.000Z
2021-07-23T20:56:48.000Z
envdsys/envnet/registry/registry.py
NOAA-PMEL/envDataSystem
4db4a3569d2329658799a3eef06ce36dd5c0597d
[ "Unlicense" ]
null
null
null
from abc import abstractmethod import asyncio from os import name from shutil import register_archive_format from typing import AsyncIterable # from asgiref.sync import sync_to_async from channels.db import database_sync_to_async from django.core.exceptions import MultipleObjectsReturned from shared.data.status import Status from shared.data.namespace import Namespace # from daq.daq import DAQ # from envdaq import data from envnet.models import Network, ServiceRegistration, DAQRegistration class ServiceRegistry: # number of seconds before daq_server is considered # disconnected disconnected_service_limit = 60 disconnected_daq_limit = 10 # number of seconds before daq_server is removed # from registry auto_clean_limit = 600 # 10 minutes local_network = None run_state = "STOPPED" # @staticmethod # async def start(network="default_network"): # await ServiceRegistry.start_no_wait(network) async def start(network="default_network"): await ServiceRegistry.start_registry(network) # loop=asyncio.get_event_loop() # print(f"registry: {loop}") if ServiceRegistry.run_state != "RUNNING": asyncio.create_task(ServiceRegistry.check_status()) ServiceRegistry.run_state = "RUNNING" # regs = await ServiceRegistry.get_all_registrations() # print(regs) # print(f"all reg: {ServiceRegistry.get_all_registrations()}") @staticmethod @database_sync_to_async def start_registry(network="default_network"): print("starting service registry") # deactivate all networks nets = Network.objects.all() for net in nets: net.deactivate() try: net = Network.objects.get(name=network) # net.activate() except Network.MultipleObjectsReturned: result = Network.objects.filter(name=network) for s in result: s.delete() net = Network(name=network) net.save() except Network.DoesNotExist: net = Network(name=network) # net = Network(name=network) net.save() net.activate() ServiceRegistry.local_network = net # asyncio.create_task(ServiceRegistry.check_status()) # ServiceRegistry.run_state = "RUNNING" # start broadcasting # start housekeeping checks # if ServiceRegistry.run_state == "STOPPED": # ServiceRegistry.start_checks() # ServiceRegistry.run_state = "RUNNING" # return net def start_checks(): # loop = asyncio.get_event_loop() # task = asyncio.ensure_future(ServiceRegistry.check_status()) # print(task) # loop.run_until_complete(task) asyncio.create_task(ServiceRegistry.check_status()) # recieve from other servers # add list of remote services @staticmethod async def register(local=True, config=None): reg = await ServiceRegistry.register_no_wait(local, config) if not reg: reg = await ServiceRegistry.update_registration(local, config) return reg @staticmethod @database_sync_to_async def register_no_wait(local=True, config=None): print(f"register service: {local}, {config}") if config: print(config["host"]) try: # print(f'{config["HOST"]}, {config["PORT"]}') reg = ServiceRegistration.objects.get( host=config["host"], port=config["port"] ) if reg.get_age() > ServiceRegistry.auto_clean_limit: reg.delete() else: return None # registration = ServiceRegistry.update_registration(local, config) # print(f"1:{registration}") # return registration except ServiceRegistration.MultipleObjectsReturned: result = ServiceRegistration.objects.filter( host=config["host"], port=config["port"] ) for s in result: s.delete() except ServiceRegistration.DoesNotExist: pass network = "default" # if local: # network = ServiceRegistry.local_network.name # else: # try: # network = config["network"] # except KeyError: # pass # defaults to "default" # create new Reg reg = ServiceRegistration( local_service=local, host=config["host"], port=config["port"], status="CONNECTED" # service_list = config.service_list ) reg.save() reg.add_services(config["service_list"]) reg.join_network(ServiceRegistry.get_network_name(local, config)) registration = reg.get_registration() return registration @staticmethod async def update_registration(local=True, config=None): reg = await ServiceRegistry.update_registration_no_wait(local, config) return reg @staticmethod @database_sync_to_async def update_registration_no_wait(local=True, config=None): if config: network = "default" if local: network = ServiceRegistry.local_network.name else: try: network = config["network"] except KeyError: pass # defaults to "default" try: # srv = ServiceRegistration.objects.get(regkey=config["regkey"]) reg = ServiceRegistration.objects.get( host=config["host"], port=config["port"] ) if reg.get_age() > ServiceRegistry.auto_clean_limit: reg.delete() elif reg.regkey in config and config["regkey"] != reg.regkey: reg.delete() else: reg.local_service = local reg.host = config["host"] reg.port = config["port"] reg.status = "CONNECTED" # srv.service_list = config.service_list reg.save(do_update=True) reg.add_services(config["service_list"]) # srv.save() # if local: # ServiceRegistry.local_network.add_registration(srv) reg.join_network(ServiceRegistry.get_network_name(local, config)) registration = reg.get_registration() print(f"3:{registration}") return registration except ServiceRegistration.DoesNotExist: pass # create new Reg here don't want to pass back to add ang get caught in loop? reg = ServiceRegistration( local_service=local, host=config["host"], port=config["port"], status="CONNECTED" # service_list = config.service_list ) reg.add_services(config["service_list"]) reg.save(do_update=True) reg.join_network(ServiceRegistry.get_network_name(local, config)) registration = reg.get_registration() print(f"4:{registration}") return registration @staticmethod async def unregister(local=True, config=None): await ServiceRegistry.unregister_no_wait(local, config) @staticmethod @database_sync_to_async def unregister_no_wait(local=True, config=None): if config: try: srv = ServiceRegistration.objects.get( host=config["host"], port=config["port"] ) srv.delete() except ServiceRegistration.DoesNotExist: pass # def ping(local=True, regkey=None, config=None): @staticmethod async def ping(local=True, config=None): await ServiceRegistry.ping_no_wait(local, config) @staticmethod @database_sync_to_async def ping_no_wait(local=True, config=None): # theoretically, we should not be pinging local here # if not regkey and config and (regkey in config): # regkey = config["regkey"] # if regkey: if config: try: reg = ServiceRegistration.objects.get( host=config["host"], port=config["port"] ) reg.status = "CONNECTED" # srv = ServiceRegistration.objects.get(regkey=config["regkey"]) reg.save(do_update=True) # update modified time stamp except ServiceRegistration.DoesNotExist: pass @staticmethod def get_network_name(local=True, config=None): if local: name = ServiceRegistry.local_network.name elif config: name = "default" try: name = config["network"] except KeyError: pass return name @staticmethod @database_sync_to_async def clean_registrations(): # regs = database_sync_to_async(ServiceRegistration.objects.filter)( # network=ServiceRegistry.local_network # ) regs = ServiceRegistration.objects.filter( local_service=False, network=ServiceRegistry.local_network ) # regs = None # print(regs) # return regs for reg in regs: # print(f"status: {reg}, age: {reg.get_age()}") # print(f"check status: {reg}") if reg.get_age() > ServiceRegistry.auto_clean_limit: print(f"removing registration for {reg} due to auto timeout") reg.delete() elif reg.get_age() > ServiceRegistry.disconnected_service_limit: reg.status = "DISCONNECTED" print(reg.status) reg.save() # @sync_to_async @staticmethod async def check_status(): # print(tmp) print("check_status") while True: await ServiceRegistry.clean_registrations() # regs = await database_sync_to_async(ServiceRegistration.objects.filter( # network=ServiceRegistry.local_network # )) # regs = await ServiceRegistry.get_all_registrations() # print(regs) # for reg in regs: # print(f'check status: {reg.name}') # if reg.get_age() > ServiceRegistry.auto_clean_limit: # print(f"removing registration for {reg.name} due to auto timeout") # reg.delete() # elif reg.get_() > ServiceRegistry.disconnected_service_limit: # reg.status = "DISCONNECTED" # print("tick") await asyncio.sleep(2) class DAQRegistry: # number of seconds before daq_server is considered # disconnected # disconnected_service_limit = 60 disconnected_limit = 10 # number of seconds before daq_server is removed # from registry auto_clean_limit = 600 # 10 minutes local_network = None run_state = "STOPPED" # @staticmethod # async def start(network="default_network"): # await ServiceRegistry.start_no_wait(network) @staticmethod async def start(): print("starting daq registry") # await DAQRegistry.start_registry() # loop=asyncio.get_event_loop() # print(f"registry: {loop}") if DAQRegistry.run_state != "RUNNING": while not ServiceRegistry.local_network: print("waiting for service registry to spin up") await asyncio.sleep(0.5) DAQRegistry.local_network = ServiceRegistry.local_network await DAQRegistry.clear() asyncio.create_task(DAQRegistry.check_status()) DAQRegistry.run_state = "RUNNING" # regs = await ServiceRegistry.get_all_registrations() # print(regs) # print(f"all reg: {ServiceRegistry.get_all_registrations()}") @staticmethod async def clear(): await DAQRegistry.clear_no_wait() @staticmethod @database_sync_to_async def clear_no_wait(): regs = DAQRegistration.objects.all() for reg in regs: reg.delete() # @staticmethod # @database_sync_to_async # def start_registry(): # print("starting registry") # try: # net = Network.objects.get(name=network) # net.activate() # except Network.MultipleObjectsReturned: # result = Network.objects.filter(name=network) # for s in result: # s.delete() # except Network.DoesNotExist: # net = Network(name=network) # # net = Network(name=network) # net.save() # net.activate() # ServiceRegistry.local_network = net # # asyncio.create_task(ServiceRegistry.check_status()) # # ServiceRegistry.run_state = "RUNNING" # # start broadcasting # # start housekeeping checks # # if ServiceRegistry.run_state == "STOPPED": # # ServiceRegistry.start_checks() # # ServiceRegistry.run_state = "RUNNING" # # return net # def start_checks(): # # loop = asyncio.get_event_loop() # # task = asyncio.ensure_future(ServiceRegistry.check_status()) # # print(task) # # loop.run_until_complete(task) # asyncio.create_task(DAQRegistry.check_status()) # async def register( # reg_id="default", # reg_id2=None, # namespace={}, # type="DAQServer", # config={}, # config2={}, # ): @staticmethod async def register( reg_id=Namespace().get_namespace_sig(), namespace=Namespace().to_dict(), type=Namespace.DAQSERVER, config=dict(), ): # if not reg_id2: # reg_id2 = Namespace().get_namespace_sig() # registration = await DAQRegistry.register_no_wait( # reg_id=reg_id, # reg_id2=reg_id2, # namespace=namespace, # type=type, # config=config, # config2=config2, # ) registration = await DAQRegistry.register_no_wait( reg_id=reg_id, namespace=namespace, type=type, config=config, ) # if not registration: # registration = await DAQRegistry.update_registration(namespace, type, config) return registration # @database_sync_to_async # def register_no_wait( # reg_id="default", # reg_id2=None, # namespace={}, # type="DAQServer", # config={}, # config2={}, # ): @staticmethod @database_sync_to_async def register_no_wait( reg_id=Namespace().get_namespace_sig(), namespace=Namespace().to_dict(), type=Namespace.DAQSERVER, config=dict(), ): # print(f"register daq: {config}") # if config: # print(config["host"]) # if not reg_id2: # reg_id2 = Namespace().get_namespace_sig() try: # print(f'{config["HOST"]}, {config["PORT"]}') # registration = DAQRegistration.objects.get(reg_id2=reg_id2, daq_type=type) registration = DAQRegistration.objects.get(reg_id=reg_id, daq_type=type) if registration: registration.delete() except DAQRegistration.MultipleObjectsReturned: # result = DAQRegistration.objects.filter(reg_id2=reg_id2, daq_type=type) result = DAQRegistration.objects.filter(reg_id=reg_id, daq_type=type) for s in result: s.delete() except DAQRegistration.DoesNotExist: pass network = "default" # if local: # network = ServiceRegistry.local_network.name # else: # try: # network = config["network"] # except KeyError: # pass # defaults to "default" # create new Reg # registration = DAQRegistration( # reg_id=reg_id, # reg_id2=reg_id2, # namespace=namespace, # daq_type=type, # config=config, # config2=config2, # status="CONNECTED", # ) status2 = Status() status2.set_connection_status(Status.CONNECTED) print(f"register: {reg_id}, {namespace}") registration = DAQRegistration( reg_id=reg_id, namespace=namespace, daq_type=type, config=config, status="CONNECTED", status2=status2.to_dict(), ) registration.save() # TODO: update service definition to include this reg registration = registration.get_registration() return registration # @staticmethod # async def update_registration( # reg_id="default", # reg_id2=Namespace().get_namespace_sig(), # namespace={}, # type="DAQServer", # registration=None, # ): @staticmethod async def update_registration( reg_id=Namespace().get_namespace_sig(), namespace=Namespace().to_dict(), type=Namespace.DAQSERVER, config=dict(), registration=None, ): # reg = await DAQRegistry.update_registration_no_wait( # reg_id=reg_id, # reg_id2=reg_id2, # namespace=namespace, # type=type, # registration=registration, # ) reg = await DAQRegistry.update_registration_no_wait( reg_id=reg_id, namespace=namespace, type=type, registration=registration, ) return reg # @staticmethod # @database_sync_to_async # def update_registration_no_wait( # reg_id="default", # reg_id2=Namespace().get_namespace_sig(), # namespace={}, # type="DAQServer", # registration=None, # ): @staticmethod @database_sync_to_async def update_registration_no_wait( reg_id=Namespace().get_namespace_sig(), namespace=Namespace().to_dict(), type=Namespace.DAQSERVER, config=dict(), registration=None, ): # if config: # network = "default" # if local: # network = ServiceRegistry.local_network.name # else: # try: # network = config["network"] # except KeyError: # pass # defaults to "default" try: # srv = ServiceRegistration.objects.get(regkey=config["regkey"]) # reg = DAQRegistration.objects.get(reg_id2=reg_id2, daq_type=type) reg = DAQRegistration.objects.get(reg_id=reg_id, daq_type=type) except DAQRegistration.DoesNotExist: reg = None # if reg.get_age() > DAQRegistry.auto_clean_limit: # reg.delete() # elif config and reg.regkey in config and config["regkey"] != reg.regkey: # reg.delete() # else: config = {} # regkey = None if registration: config = registration["config"] # config2 = registration["config2"] # regkey = registration["regkey"] if not reg: # reg = DAQRegistration( # reg_id2=reg_id2, # namespace=namespace, # daq_type=type, # config=config, # config2=config2, # ) reg = DAQRegistration( reg_id=reg_id, namespace=namespace, daq_type=type, config=config, ) if reg: # reg.reg_id2 = reg_id2 reg.reg_id = reg_id reg.namespace = namespace reg.daq_type = type reg.config = config # reg.config2 = config2 reg.status = "CONNECTED" status2 = Status() status2.set_connection_status(Status.CONNECTED) reg.status2 = status2.to_dict() # if regkey: # reg.regkey = regkey # srv.service_list = config.service_list reg.save(do_update=True) # TODO: update service # reg.add_services(config["service_list"]) # srv.save() # if local: # ServiceRegistry.local_network.add_registration(srv) # reg.join_network(ServiceRegistry.get_network_name(local, config)) return reg.get_registration() # print(f"3:{registration}") # return registration else: return None # # create new Reg here don't want to pass back to add ang get caught in loop? # reg = DAQRegistration( # namespace=namespace, daq_type=type, config=config, status="CONNECTED" # ) # reg.save() # # TODO: update service # registration = reg.get_registration() # # print(f"4:{registration}") # return registration # @staticmethod # async def unregister( # reg_id="default", reg_id2=Namespace().get_namespace_sig(), type="DAQServer" # ): # await DAQRegistry.unregister_no_wait(reg_id=reg_id, reg_id2=reg_id2, type=type) @staticmethod async def unregister( reg_id=Namespace().get_namespace_sig(), type=Namespace.DAQSERVER ): await DAQRegistry.unregister_no_wait(reg_id=reg_id, type=type) # @staticmethod # @database_sync_to_async # def unregister_no_wait( # reg_id="default", reg_id2=Namespace().get_namespace_sig(), type="DAQServer" # ): @staticmethod @database_sync_to_async def unregister_no_wait( reg_id=Namespace().get_namespace_sig(), type=Namespace.DAQSERVER ): try: # print(f"unregister:{reg_id}") # reg = DAQRegistration.objects.get(reg_id2=reg_id2, daq_type=type) reg = DAQRegistration.objects.get(reg_id=reg_id, daq_type=type) # print(f"Unregistering: {reg}") reg.delete() # print(f"success") except DAQRegistration.DoesNotExist: # print(f"unregister: reg_id does not exist {reg_id}") pass # @staticmethod # async def get_registration(reg_id="default", reg_id2=Namespace().get_namespace_sig(), type="DAQServer"): # registration = await DAQRegistry.get_registration_no_wait(reg_id=reg_id, reg_id2=reg_id2, type=type) # return registration @staticmethod async def get_registration( reg_id=Namespace().get_namespace_sig(), type=Namespace.DAQSERVER ): # registration = await DAQRegistry.get_registration_no_wait( # reg_id=reg_id, reg_id2=reg_id2, type=type # ) registration = await DAQRegistry.get_registration_no_wait( reg_id=reg_id, type=type ) return registration # @staticmethod # @database_sync_to_async # def get_registration_no_wait( # reg_id="default", reg_id2=Namespace().get_namespace_sig(), type="DAQServer" # ): @staticmethod @database_sync_to_async def get_registration_no_wait( reg_id=Namespace().get_namespace_sig(), type=Namespace.DAQSERVER ): # theoretically, we should not be pinging local here # if not regkey and config and (regkey in config): # regkey = config["regkey"] # if regkey: print(f"get_reg: {reg_id}") try: # reg = DAQRegistration.objects.get(reg_id2=reg_id2, daq_type=type) reg = DAQRegistration.objects.get(reg_id=reg_id, daq_type=type) # reg.status = "CONNECTED" # srv = ServiceRegistration.objects.get(regkey=config["regkey"]) # reg.save(do_update=True) # update modified time stamp return reg.get_registration() except DAQRegistration.DoesNotExist: pass return None # def ping(local=True, regkey=None, config=None): # @staticmethod # async def ping( # reg_id="default", reg_id2=Namespace().get_namespace_sig(), type="DAQServer" # ): # await DAQRegistry.ping_no_wait(reg_id, type) @staticmethod async def ping( reg_id=Namespace().get_namespace_sig(), type=Namespace.DAQSERVER ): await DAQRegistry.ping_no_wait(reg_id, type) # @staticmethod # @database_sync_to_async # def ping_no_wait( # reg_id="default", reg_id2=Namespace().get_namespace_sig(), type="DAQServer" # ): @staticmethod @database_sync_to_async def ping_no_wait( reg_id=Namespace().get_namespace_sig(), type=Namespace.DAQSERVER ): # theoretically, we should not be pinging local here # if not regkey and config and (regkey in config): # regkey = config["regkey"] # if regkey: try: # print(f"ping server reg: {reg_id}") # reg = DAQRegistration.objects.get(reg_id2=reg_id2, daq_type=type) reg = DAQRegistration.objects.get(reg_id=reg_id, daq_type=type) reg.status = "CONNECTED" status2 = Status().from_dict(reg.status2) status2.set_connection_status(Status.CONNECTED) reg.status2 = status2.to_dict() # srv = ServiceRegistration.objects.get(regkey=config["regkey"]) reg.save(do_update=True) # update modified time stamp # print(f"ping success") except DAQRegistration.DoesNotExist: pass @staticmethod async def get_registry(type=Namespace.DAQSERVER): return await DAQRegistry.get_registry_no_wait(type=type) @staticmethod @database_sync_to_async def get_registry_no_wait(type=Namespace.DAQSERVER): try: regs = DAQRegistration.objects.filter(daq_type=type) # print(f"regs: {regs}") except DAQRegistration.DoesNotexist: # TODO: return 404 ... lookup how pass regs = [] daq_registration_map = {} if regs: for reg in regs: # id2 = reg.reg_id2 print(f"reg: {reg}") daq_registration_map[f"{reg}"] = reg.get_registration() return daq_registration_map @staticmethod async def check_status(): # print(tmp) print("check_status") while True: await DAQRegistry.clean_registrations() # regs = await database_sync_to_async(ServiceRegistration.objects.filter( # network=ServiceRegistry.local_network # )) # regs = await ServiceRegistry.get_all_registrations() # print(regs) # for reg in regs: # print(f'check status: {reg.name}') # if reg.get_age() > ServiceRegistry.auto_clean_limit: # print(f"removing registration for {reg.name} due to auto timeout") # reg.delete() # elif reg.get_() > ServiceRegistry.disconnected_service_limit: # reg.status = "DISCONNECTED" # print("tick") await asyncio.sleep(2) @staticmethod @database_sync_to_async def clean_registrations(): # regs = database_sync_to_async(ServiceRegistration.objects.filter)( # network=ServiceRegistry.local_network # ) # regs = DAQRegistration.objects.filter( # local_service=False, network=ServiceRegistry.local_network # ) regs = DAQRegistration.objects.all() # regs = None print(f"cleaning regs: {regs}") # return regs for reg in regs: print(f"status: {reg}, age: {reg.get_age()}") # print(f"check status: {reg}") if reg.get_age() > DAQRegistry.auto_clean_limit: print(f"removing registration for {reg} due to auto timeout") reg.delete() elif reg.get_age() > DAQRegistry.disconnected_limit: reg.status = "DISCONNECTED" status2 = Status().from_dict(reg.status2) status2.set_connection_status(Status.NOT_CONNECTED) print(reg.status) reg.save()
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6
3ca7c24956f1449d19480d48467dc05529a44e06
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py
Python
nanome/_internal/_volumetric/_io/__init__.py
rramji/nanome-lib
2806598af31cfb4bb6e16366f0b300d2ddcc9c13
[ "MIT" ]
null
null
null
nanome/_internal/_volumetric/_io/__init__.py
rramji/nanome-lib
2806598af31cfb4bb6e16366f0b300d2ddcc9c13
[ "MIT" ]
null
null
null
nanome/_internal/_volumetric/_io/__init__.py
rramji/nanome-lib
2806598af31cfb4bb6e16366f0b300d2ddcc9c13
[ "MIT" ]
null
null
null
from . import _em_map
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21
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596ff737a198e40790c330402cf8d51db960b0d1
25
py
Python
src/gui/__init__.py
Airthee/LightshotSniffer
466abb28710c9401e6f3fd0e1d61776511f936bc
[ "WTFPL" ]
1
2020-04-30T08:32:26.000Z
2020-04-30T08:32:26.000Z
src/gui/__init__.py
Airthee/LightshotSniffer
466abb28710c9401e6f3fd0e1d61776511f936bc
[ "WTFPL" ]
12
2019-06-18T06:04:58.000Z
2022-01-13T01:20:36.000Z
src/gui/__init__.py
Airthee/LightshotSniffer
466abb28710c9401e6f3fd0e1d61776511f936bc
[ "WTFPL" ]
3
2019-06-18T06:12:53.000Z
2020-12-03T08:46:44.000Z
from .lsswindow import *
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6
59722ce3fb1d4a273cd95ac699ae4274f0b2b163
112
py
Python
orb_simulator/orbsim_language/orbsim_ast/start_sim_node.py
dmguezjaviersnet/IA-Sim-Comp-Project
8165b9546efc45f98091a3774e2dae4f45942048
[ "MIT" ]
1
2022-01-19T22:49:09.000Z
2022-01-19T22:49:09.000Z
orb_simulator/orbsim_language/orbsim_ast/start_sim_node.py
dmguezjaviersnet/IA-Sim-Comp-Project
8165b9546efc45f98091a3774e2dae4f45942048
[ "MIT" ]
15
2021-11-10T14:25:02.000Z
2022-02-12T19:17:11.000Z
orb_simulator/orbsim_language/orbsim_ast/start_sim_node.py
dmguezjaviersnet/IA-Sim-Comp-Project
8165b9546efc45f98091a3774e2dae4f45942048
[ "MIT" ]
null
null
null
from orbsim_language.orbsim_ast.statement_node import StatementNode class StartSimNode(StatementNode): pass
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6
5988d7946471e74d4a8d62bff737f7c847bd909a
4,053
py
Python
spvcm/ops.py
weikang9009/spvcm
00ec35331e0e1a67bcd841a6b3761a23099617f7
[ "MIT" ]
14
2017-06-20T18:39:04.000Z
2021-03-27T02:21:46.000Z
spvcm/ops.py
weikang9009/spvcm
00ec35331e0e1a67bcd841a6b3761a23099617f7
[ "MIT" ]
12
2018-05-11T11:13:21.000Z
2020-02-07T14:23:12.000Z
spvcm/ops.py
weikang9009/spvcm
00ec35331e0e1a67bcd841a6b3761a23099617f7
[ "MIT" ]
8
2017-05-20T00:55:40.000Z
2020-07-02T14:52:49.000Z
import scipy.sparse as spar import scipy.sparse.linalg as spla import theano.tensor as tt import theano.sparse as ts import theano as th from theano.gof import Apply from theano import Op import numpy as np import theano as th # define this as if it's in terms of rho and W, and give the derivatives in # terms of rho, since that's what the graph is expecting class Sparse_LapDet(Op): """Sparse Matrix Determinant of a Laplacian Matrix using Sparse LU Decomposition""" def __init__(self, W): self.W = spar.csc_matrix(W) self.I = spar.identity(W.shape[0]).tocsc() self.Id = self.I.toarray() self.Wd = self.W.toarray() def make_node(self, rho): rho = tt.as_tensor(rho) ld = tt.scalar(dtype=rho.dtype) return Apply(self, [rho], [ld]) def perform(self, node, inputs, outputs): (rho,) = inputs (z, ) = outputs rW = rho * self.W A = self.I - rW Ud = spla.splu(A).U.diagonal() ld = np.asarray(np.sum(np.log(np.abs(Ud)))) z[0] = ld def grad(self, inputs, g_outputs): (rho, ) = inputs (gz,) = g_outputs A = self.Id - tt.mul(rho, self.Wd) dinv = tt.nlinalg.matrix_inverse(A).T out = tt.mul(dinv, - self.Wd) return [tt.as_tensor(tt.sum(tt.mul(out, gz)), ndim=1)] # define this as if it's in terms of rho and W, and give the derivatives in # terms of rho, since that's what the graph is expecting class Sparse_AGrad_LapDet(Op): """Sparse Matrix Determinant of a Laplacian Matrix using Sparse LU Decomposition""" def __init__(self, W): self.W = spar.csc_matrix(W) self.WW = W.dot(W) self.WWW = self.WW.dot(W) self.I = spar.identity(W.shape[0]).tocsc() self.Id = self.I.toarray() self.Wd = self.W.toarray() def make_node(self, rho): rho = tt.as_tensor(rho) ld = tt.scalar(dtype=rho.dtype) return Apply(self, [rho], [ld]) def perform(self, node, inputs, outputs): (rho,) = inputs (z, ) = outputs rW = rho * self.W A = self.I - rW Ud = spla.splu(A).U.diagonal() ld = np.asarray(np.sum(np.log(np.abs(Ud)))) z[0] = ld def grad(self, inputs, g_outputs): (rho, ) = inputs (gz,) = g_outputs A = self.Id - tt.mul(rho, self.Wd) dinv = self.I + ts.mul_s_d(self.W, rho) dinv +=ts.mul_s_d(self.WW, rho**2) dinv +=ts.mul_s_d(self.WWW, rho**3) out = tt.mul(dinv, - self.Wd) return [tt.as_tensor(tt.sum(tt.mul(out, gz)), ndim=1)] class Dense_LULogDet(Op): """Log Determinant of a matrix by sparse LU decomposition, from dense inputs. Use when casting has no significant overhead.""" def make_node(self, A): A = tt.as_tensor(A) ld = tt.scalar(dtype=A.dtype) return Apply(self, [A], [ld]) def perform(self, node, inputs, outputs): (A,) = inputs (z,) = outputs As = spar.csc_matrix(A) Ud = spla.splu(As).U.diagonal() ld = np.sum(np.log(np.abs(Ud))) z[0] = ld def grad(self, inputs, g_outputs): [gz] = g_outputs [A] = inputs dinv = tt.nlinalg.matrix_inverse(A).T dout = tt.dot(gz, dinv) return [dout] class Dense_LogDet(Op): """Log Determinant of a matrix using numpy.linalg.slogdet. Use as a reference implementation""" def make_node(self, A): A = tt.as_tensor(A) ld = tt.scalar(dtype=A.dtype) return Apply(self, [A], [ld]) def perform(self, node, inputs, outputs): (A,) = inputs (z,) = outputs sgn, ld = np.linalg.slogdet(A) if sgn not in [-1,0,1]: raise Exception('Loss of precision in log determinant') ld *= sgn z[0] = ld def grad(self, inputs, g_outputs): [gz] = g_outputs [A] = inputs dinv = tt.nlinalg.matrix_inverse(A).T dout = tt.dot(gz, dinv) return [dout]
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6
59be6ef2e827a13e81112509c8fb433ce1b2cb25
92
py
Python
wpa_project/membership/views/__init__.py
s-amundson/wpa_2p1
43deb859123e5ef2eab3652e403c8d2f53d43b77
[ "MIT" ]
1
2022-01-03T02:46:34.000Z
2022-01-03T02:46:34.000Z
wpa_project/membership/views/__init__.py
s-amundson/wpa_2p1
43deb859123e5ef2eab3652e403c8d2f53d43b77
[ "MIT" ]
31
2021-12-29T17:43:06.000Z
2022-03-25T01:03:17.000Z
wpa_project/membership/views/__init__.py
s-amundson/wpa_2p1
43deb859123e5ef2eab3652e403c8d2f53d43b77
[ "MIT" ]
null
null
null
from .level_view import LevelApiView, LevelView from .membership_view import MembershipView
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6
59cf6604e332531c6c5efa4d6d9ed9bc1d32febe
77
py
Python
optim/__init__.py
jpeg729/pytorch-bits
5d107094042c27472dfb7dee77506b603f5d3e45
[ "MIT" ]
73
2017-12-29T14:43:16.000Z
2021-08-13T02:20:33.000Z
optim/__init__.py
jpeg729/pytorch-bits
5d107094042c27472dfb7dee77506b603f5d3e45
[ "MIT" ]
null
null
null
optim/__init__.py
jpeg729/pytorch-bits
5d107094042c27472dfb7dee77506b603f5d3e45
[ "MIT" ]
5
2017-12-30T14:07:39.000Z
2021-08-13T02:20:34.000Z
from .cocob import COCOB from .adam_hd import Adam_HD, Adam_HD_lr_per_param
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6
59d47b26e3df82f245286d8bcdc55944f48ad58f
39
py
Python
zipf/factories/zipf_from_url/__init__.py
LucaCappelletti94/zipf
956c3a1d56958384a02d5bb4671c6883cd9a25e3
[ "MIT" ]
3
2018-11-07T01:56:09.000Z
2020-05-31T12:24:09.000Z
zipf/factories/zipf_from_url/__init__.py
LucaCappelletti94/zipf
956c3a1d56958384a02d5bb4671c6883cd9a25e3
[ "MIT" ]
1
2018-05-15T15:58:06.000Z
2018-05-15T15:58:06.000Z
zipf/factories/zipf_from_url/__init__.py
LucaCappelletti94/zipf
956c3a1d56958384a02d5bb4671c6883cd9a25e3
[ "MIT" ]
null
null
null
from .zipf_from_url import ZipfFromUrl
19.5
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6
ab6fb41dbd60bf1d4f3293db88733722b676151d
6,894
py
Python
dual_encoder/model_utils_test.py
garyxcheng/federated
ba7133ead6127af71ea9356e26bfd05c02f8324a
[ "Apache-2.0" ]
330
2020-09-14T23:10:16.000Z
2022-03-30T19:49:19.000Z
dual_encoder/model_utils_test.py
garyxcheng/federated
ba7133ead6127af71ea9356e26bfd05c02f8324a
[ "Apache-2.0" ]
52
2020-09-30T06:10:51.000Z
2022-03-31T19:25:16.000Z
dual_encoder/model_utils_test.py
garyxcheng/federated
ba7133ead6127af71ea9356e26bfd05c02f8324a
[ "Apache-2.0" ]
119
2020-09-24T04:54:46.000Z
2022-03-31T21:46:57.000Z
# Copyright 2021, Google LLC. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from absl.testing import absltest import tensorflow as tf from dual_encoder import model_utils as utils class UtilsTest(absltest.TestCase): def test_get_predicted_embeddings_with_l2_normalize(self): y_pred = tf.constant( [[1, 0], [1.0, 1.0], [1, 1], [0, 1], [1, 0], [1, 0]] ) y_true = tf.constant([[2], [3]]) context_embeddings, label_embeddings = utils.get_predicted_embeddings( y_pred, y_true, normalization_fn=utils.l2_normalize_fn) expected_context_embeddings = tf.constant( [[1.0, 0.0], [0.7071067, 0.7071067]] ) expected_label_embeddings = tf.constant( [[0.7071067, 0.7071067], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0]] ) tf.debugging.assert_near(context_embeddings, expected_context_embeddings) tf.debugging.assert_near(label_embeddings, expected_label_embeddings) def test_get_predicted_embeddings_without_normalization(self): y_pred = tf.constant( [[1, 0], [1.0, 1.0], [1, 1], [0, 1], [1, 0], [1, 0]] ) y_true = tf.constant([[2], [3]]) context_embeddings, label_embeddings = utils.get_predicted_embeddings( y_pred, y_true, normalization_fn=None) expected_context_embeddings = tf.constant( [[1.0, 0.0], [1.0, 1.0]] ) expected_label_embeddings = tf.constant( [[1.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0]] ) tf.debugging.assert_near(context_embeddings, expected_context_embeddings) tf.debugging.assert_near(label_embeddings, expected_label_embeddings) def test_get_embeddings_and_similarities(self): y_pred = tf.constant( [[1, 0], [1.0, 1.0], [1, 1], [0, 1], [1, 0], [1, 0]] ) y_true = tf.constant([[2], [3]]) context_embeddings, label_embeddings, similarities = ( utils.get_embeddings_and_similarities(y_pred, y_true)) expected_context_embeddings = tf.constant( [[1.0, 0.0], [0.7071067, 0.7071067]] ) expected_label_embeddings = tf.constant( [[0.7071067, 0.7071067], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0]] ) expected_similarities = tf.constant( [[0.7071067, 0.0, 1.0, 1.0], [1.0, 0.7071067, 0.7071067, 0.7071067]] ) tf.debugging.assert_near(context_embeddings, expected_context_embeddings) tf.debugging.assert_near(label_embeddings, expected_label_embeddings) tf.debugging.assert_near(similarities, expected_similarities) def test_get_embeddings_and_similarities_dot_product(self): y_pred = tf.constant( [[1, 0], [1.0, 1.0], [1, 1], [0, 1], [1, 0], [1, 0]] ) y_true = tf.constant([[2], [3]]) context_embeddings, label_embeddings, similarities = ( utils.get_embeddings_and_similarities( y_pred, y_true, normalization_fn=None)) expected_context_embeddings = tf.constant( [[1.0, 0.0], [1.0, 1.0]] ) expected_label_embeddings = tf.constant( [[1.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0]] ) expected_similarities = tf.constant( [[1.0, 0.0, 1.0, 1.0], [2.0, 1.0, 1.0, 1.0]] ) tf.debugging.assert_near(context_embeddings, expected_context_embeddings) tf.debugging.assert_near(label_embeddings, expected_label_embeddings) tf.debugging.assert_near(similarities, expected_similarities) def test_get_embeddings_and_similarities_similarity(self): y_pred = tf.constant( [[0.7071067, 0.0, 1.0, 1.0], [1.0, 0.7071067, 0.7071067, 0.7071067]] ) y_true = tf.constant([[2], [3]]) context_embeddings, label_embeddings, similarities = ( utils.get_embeddings_and_similarities( y_pred, y_true, expect_embeddings=False)) expected_similarities = tf.constant( [[0.7071067, 0.0, 1.0, 1.0], [1.0, 0.7071067, 0.7071067, 0.7071067]] ) self.assertIsNone(context_embeddings) self.assertIsNone(label_embeddings) tf.debugging.assert_near(similarities, expected_similarities) def test_similarities(self): similarities_layer = utils.Similarities() context_embedding = tf.constant( [[1, 2, 3], [4.0, 5.0, 6.0], [1, 1, 1], [1, 1, 1], [1, 1, 2]]) label_embedding = tf.constant( [[1, 2, 3], [4.0, 5.0, 6.0], [1, 1, 1], [1, 1, 1], [-1, -2, -3]]) similarities = similarities_layer([context_embedding, label_embedding]) expected_similarities = tf.constant( [[0.9999999, 0.97463185, 0.92582005, 0.92582005, -0.9999999], [0.97463185, 1.0000001, 0.98692757, 0.98692757, -0.97463185], [0.92582005, 0.98692757, 0.99999994, 0.99999994, -0.92582005], [0.92582005, 0.98692757, 0.99999994, 0.99999994, -0.92582005], [0.98198044, 0.9770084, 0.942809, 0.942809, -0.98198044]]) tf.debugging.assert_near(expected_similarities, similarities) # Also make sure layer.call works. similarities = similarities_layer.call([context_embedding, label_embedding]) tf.debugging.assert_near(expected_similarities, similarities) def test_similarities_cosine_similarity(self): similarities_layer = utils.Similarities(normalization_fn=None) context_embedding = tf.constant( [[1, 2, 3], [4.0, 5.0, 6.0], [1, 1, 1], [1, 1, 1], [1, 1, 2]]) label_embedding = tf.constant( [[1, 2, 3], [4.0, 5.0, 6.0], [1, 1, 1], [1, 1, 1], [-1, -2, -3]]) similarities = similarities_layer([context_embedding, label_embedding]) expected_similarities = tf.constant( [[14.0, 32, 6, 6, -14], [32, 77, 15, 15, -32], [6, 15, 3, 3, -6], [6, 15, 3, 3, -6], [9, 21, 4, 4, -9]]) tf.debugging.assert_near(expected_similarities, similarities) # Also make sure layer.call works. similarities = similarities_layer.call([context_embedding, label_embedding]) tf.debugging.assert_near(expected_similarities, similarities) if __name__ == '__main__': absltest.main()
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6
ab77eca2be9c4bf1e57da43a77d73a3d3c1d5cc0
16,354
py
Python
circlecitycon-2021/baby-meadows/decrypt.py
onealmond/hacking-lab
631e615944add02db3c2afef47bf1de7171eb065
[ "MIT" ]
9
2021-04-20T15:28:36.000Z
2022-03-08T19:53:48.000Z
circlecitycon-2021/baby-meadows/decrypt.py
onealmond/hacking-lab
631e615944add02db3c2afef47bf1de7171eb065
[ "MIT" ]
null
null
null
circlecitycon-2021/baby-meadows/decrypt.py
onealmond/hacking-lab
631e615944add02db3c2afef47bf1de7171eb065
[ "MIT" ]
6
2021-06-24T03:25:21.000Z
2022-02-20T21:44:52.000Z
#!/usr/bin/env python3 import random g = 2 p = 27364195027981999497713610818487324581721539250673346091482772282510011564291025136146660508795219128557701709138115267357713678480331088419744185203212905091957459339614224615778653860885782170033046504718076905119565522298014609547550378271686461734952043412349624686487216938013960045889798734400260364799100295448466592570911013504023215177896840800637603090083812014709824090261242248660158815056130074711884192533210954160640493418715631256186934347998434364604226848641806710955394434610063365365212141903932895031496716774342948538753169437093579587734530974202008110362571844942019259309141601530779113813093 ciphers = 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20751160439979842255652097809499285200251991412477076489152855717079572748948927570289942045730282203573205674815954444323740133547683712238256772973165241549090504168278296524417790568661947627484033635689159411280391088797261328442029577562671544269438512603607354884878965022005397705781623747274914073316269756564423637090609458599368308738867256773066113453949336341646985985072494007166427167626273944332441055543942445343787561605317471675496529851570942219444230005414173788862832992587299531264185197690068971210276036031608714972569916772582721979547968529968758660275170246580335435366286642450273211339858] random.seed(0x1337) flag = '' for c in ciphers: r = pow(g, random.randrange(2, p-1), p) for a in range(33, 127): if a * r % p == c: flag += chr(a) break print(flag)
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6
abe49a269500ab3759590ff9366ea754fe348207
18,594
py
Python
tensorflow_graphics/projects/gan/losses_test.py
Liang813/graphics
71ab1775228a0a292427551350cbb62bfa8bd01a
[ "Apache-2.0" ]
2,759
2019-01-08T10:40:34.000Z
2022-03-28T13:49:37.000Z
tensorflow_graphics/projects/gan/losses_test.py
Liang813/graphics
71ab1775228a0a292427551350cbb62bfa8bd01a
[ "Apache-2.0" ]
262
2019-04-28T12:25:49.000Z
2022-03-24T19:35:15.000Z
tensorflow_graphics/projects/gan/losses_test.py
Liang813/graphics
71ab1775228a0a292427551350cbb62bfa8bd01a
[ "Apache-2.0" ]
380
2019-05-09T00:14:45.000Z
2022-03-31T12:48:25.000Z
# Copyright 2020 The TensorFlow Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://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. """Tests for gan.losses.""" from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow_graphics.projects.gan import losses from tensorflow_graphics.util import test_case class LossesTest(test_case.TestCase): def test_gradient_penalty_shape_correct(self): discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Reshape((25,))) discriminator.add(tf.keras.layers.Dense(units=1)) real_data = tf.ones(shape=(3, 5, 5)) generated_data = tf.ones(shape=(3, 5, 5)) gradient_penalty = losses.gradient_penalty_loss( real_data=real_data, generated_data=generated_data, discriminator=discriminator) self.assertAllEqual(tf.shape(gradient_penalty), (3,)) def test_gradient_penalty_shape_correct_sequence_input(self): discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Concatenate()) discriminator.add(tf.keras.layers.Reshape((50,))) discriminator.add(tf.keras.layers.Dense(units=1)) real_data = (tf.ones(shape=(3, 5, 5)), tf.ones(shape=(3, 5, 5))) generated_data = (tf.ones(shape=(3, 5, 5)), tf.ones(shape=(3, 5, 5))) gradient_penalty = losses.gradient_penalty_loss( real_data=real_data, generated_data=generated_data, discriminator=discriminator) self.assertAllEqual(tf.shape(gradient_penalty), (3,)) def test_gradient_penalty_loss_positive(self): discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Reshape((25,))) discriminator.add(tf.keras.layers.Dense(units=1)) real_data = tf.ones(shape=(1, 5, 5)) generated_data = tf.ones(shape=(1, 5, 5)) gradient_penalty = losses.gradient_penalty_loss( real_data=real_data, generated_data=generated_data, discriminator=discriminator) self.assertAllGreaterEqual(gradient_penalty, 0.0) def test_gradient_penalty_loss_positive_for_sequence_input(self): discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Concatenate()) discriminator.add(tf.keras.layers.Reshape((50,))) discriminator.add(tf.keras.layers.Dense(units=1)) real_data = (tf.ones(shape=(1, 5, 5)), tf.ones(shape=(1, 5, 5))) generated_data = (tf.ones(shape=(1, 5, 5)), tf.ones(shape=(1, 5, 5))) gradient_penalty = losses.gradient_penalty_loss( real_data=real_data, generated_data=generated_data, discriminator=discriminator) self.assertAllGreaterEqual(gradient_penalty, 0.0) def test_gradient_penalty_loss_jacobian_preset(self): layer_weights = np.zeros(shape=(25, 1), dtype=np.float32) real_data = np.ones(shape=(1, 5, 5), dtype=np.float32) generated_data = np.ones(shape=(1, 5, 5), dtype=np.float32) def gradient_penalty_fn(weights): def multiply(input_tensor): return tf.linalg.matmul(input_tensor, weights) discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Reshape((25,))) # To simulate a dense layer a lambda layer is used, such that we are able # to feed the weights in as numpy array to the assert_jacobian_fn. discriminator.add(tf.keras.layers.Lambda(multiply)) return losses.gradient_penalty_loss( real_data=tf.convert_to_tensor(real_data), generated_data=tf.convert_to_tensor(generated_data), discriminator=discriminator) with self.subTest(name='is_correct'): self.assert_jacobian_is_correct_fn(gradient_penalty_fn, (layer_weights,)) with self.subTest(name='is_finite'): self.assert_jacobian_is_finite_fn(gradient_penalty_fn, (layer_weights,)) def test_gradient_penalty_loss_sequence_input_jacobian_preset(self): layer_weights = np.zeros(shape=(50, 1), dtype=np.float32) real_data = (tf.ones(shape=(1, 5, 5), dtype=tf.float32), tf.ones(shape=(1, 5, 5), dtype=tf.float32)) generated_data = (tf.ones(shape=(1, 5, 5), dtype=tf.float32), tf.ones(shape=(1, 5, 5), dtype=tf.float32)) def gradient_penalty_fn(weights): def multiply(input_tensor): return tf.linalg.matmul(input_tensor, weights) discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Concatenate()) discriminator.add(tf.keras.layers.Reshape((50,))) # To simulate a dense layer a lambda layer is used, such that we are able # to feed the weights in as numpy array to the assert_jacobian_fn. discriminator.add(tf.keras.layers.Lambda(multiply)) return losses.gradient_penalty_loss( real_data=real_data, generated_data=generated_data, discriminator=discriminator) with self.subTest(name='is_correct'): self.assert_jacobian_is_correct_fn(gradient_penalty_fn, (layer_weights,)) with self.subTest(name='is_finite'): self.assert_jacobian_is_finite_fn(gradient_penalty_fn, (layer_weights,)) def test_gradient_penalty_loss_lambda_for_zero_gradient(self): discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Reshape((4,))) # Generates a dense layer that is initialized with all zeros. # This leads to a network that has zero gradient for any input. discriminator.add( tf.keras.layers.Dense( units=1, kernel_initializer='zeros', bias_initializer='zeros')) real_data = tf.ones(shape=(1, 2, 2)) generated_data = tf.ones(shape=(1, 2, 2)) weight = 1.0 gradient_penalty = losses.gradient_penalty_loss( real_data=real_data, generated_data=generated_data, discriminator=discriminator, weight=weight) # Tolerance is large due to eps that is added in the gradient pentaly loss # for numerical stability at 0. self.assertAllClose(gradient_penalty, (weight,), atol=0.001) def test_gradient_penalty_loss_lambda_for_zero_gradient_sequence_input(self): discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Concatenate()) discriminator.add(tf.keras.layers.Reshape((8,))) # Generates a dense layer that is initialized with all zeros. # This leads to a network that has zero gradient for any input. discriminator.add( tf.keras.layers.Dense( units=1, kernel_initializer='zeros', bias_initializer='zeros')) real_data = [tf.ones(shape=(1, 2, 2)), tf.ones(shape=(1, 2, 2))] generated_data = [tf.ones(shape=(1, 2, 2)), tf.ones(shape=(1, 2, 2))] weight = 1.0 gradient_penalty = losses.gradient_penalty_loss( real_data=real_data, generated_data=generated_data, discriminator=discriminator, weight=weight) # Tolerance is large due to eps that is added in the gradient pentaly loss # for numerical stability at 0. self.assertAllClose(gradient_penalty, (weight,), atol=0.001) def test_gradient_penalty_loss_with_wrong_input_types_raises(self): discriminator = tf.keras.Sequential() with self.assertRaisesRegex( TypeError, 'should either both be a tf.Tensor ' 'or both a sequence of tf.Tensor'): losses.gradient_penalty_loss( real_data=(tf.ones((1,)),), generated_data=tf.ones((1,)), discriminator=discriminator) def test_gradient_penalty_loss_with_unequal_number_of_elements_raises(self): discriminator = tf.keras.Sequential() with self.assertRaisesRegex( ValueError, 'number of elements in real_data and generated_data are ' 'expected to be equal'): losses.gradient_penalty_loss( real_data=(tf.ones((1,)),), generated_data=(tf.ones((1,)), tf.ones((1,))), discriminator=discriminator) def test_r1_regularization_shape_correct(self): discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Reshape((25,))) discriminator.add(tf.keras.layers.Dense(units=1)) real_data = tf.ones(shape=(3, 5, 5)) r1_regularization = losses.r1_regularization( real_data=real_data, discriminator=discriminator) r1_regularization_value = self.evaluate(r1_regularization) self.assertSequenceEqual(r1_regularization_value.shape, (3,)) def test_r1_regularization_shape_correct_sequence_input(self): discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Concatenate()) discriminator.add(tf.keras.layers.Reshape((50,))) discriminator.add(tf.keras.layers.Dense(units=1)) real_data = (tf.ones(shape=(3, 5, 5)), tf.ones(shape=(3, 5, 5))) r1_regulatiztion = losses.r1_regularization( real_data=real_data, discriminator=discriminator) r1_regulatiztion_value = self.evaluate(r1_regulatiztion) self.assertSequenceEqual(r1_regulatiztion_value.shape, (3,)) def test_r1_regularization_positive(self): discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Reshape((25,))) discriminator.add(tf.keras.layers.Dense(units=1)) real_data = tf.ones(shape=(1, 5, 5)) r1_regularization = losses.r1_regularization( real_data=real_data, discriminator=discriminator) self.assertAllGreaterEqual(r1_regularization, 0.0) def test_r1_regularization_positive_for_sequence_input(self): discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Concatenate()) discriminator.add(tf.keras.layers.Reshape((50,))) discriminator.add(tf.keras.layers.Dense(units=1)) real_data = (tf.ones(shape=(1, 5, 5)), tf.ones(shape=(1, 5, 5))) r1_regularization = losses.r1_regularization( real_data=real_data, discriminator=discriminator) self.assertAllGreaterEqual(r1_regularization, 0.0) def test_r1_regularization_jacobian_random(self): layer_weights = np.random.uniform(-1, 1, size=(25, 1)).astype(np.float32) real_data = np.ones(shape=(1, 5, 5), dtype=np.float32) def r1_regularization_fn(weights): def multiply(input_tensor): return tf.linalg.matmul(input_tensor, weights) discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Reshape((25,))) # To simulate a dense layer a lambda layer is used, such that we are able # to feed the weights in as numpy array to the assert_jacobian_fn. discriminator.add(tf.keras.layers.Lambda(multiply)) return losses.r1_regularization( real_data=tf.convert_to_tensor(real_data), discriminator=discriminator) with self.subTest(name='is_correct'): self.assert_jacobian_is_correct_fn( r1_regularization_fn, (layer_weights,), delta=0.001, atol=0.01) with self.subTest(name='is_finite'): self.assert_jacobian_is_finite_fn(r1_regularization_fn, (layer_weights,)) def test_r1_regulatization_sequence_input_jacobian_random(self): layer_weights = np.random.uniform(-1, 1, size=(50, 1)).astype(np.float32) real_data = (tf.ones(shape=(1, 5, 5), dtype=tf.float32), tf.ones(shape=(1, 5, 5), dtype=tf.float32)) def r1_regulatization_fn(weights): def multiply(input_tensor): return tf.linalg.matmul(input_tensor, weights) discriminator = tf.keras.Sequential() discriminator.add(tf.keras.layers.Concatenate()) discriminator.add(tf.keras.layers.Reshape((50,))) # To simulate a dense layer a lambda layer is used, such that we are able # to feed the weights in as numpy array to the assert_jacobian_fn. discriminator.add(tf.keras.layers.Lambda(multiply)) return losses.r1_regularization( real_data=real_data, discriminator=discriminator) with self.subTest(name='is_correct'): self.assert_jacobian_is_correct_fn( r1_regulatization_fn, (layer_weights,), delta=0.001, atol=0.01) with self.subTest(name='is_finite'): self.assert_jacobian_is_finite_fn(r1_regulatization_fn, (layer_weights,)) def test_wasserstein_generator_loss_shape_correct(self): loss_input = tf.ones(shape=(2, 1)) loss = self.evaluate(losses.wasserstein_generator_loss(loss_input)) self.assertAllEqual(loss.shape, (2, 1)) @parameterized.parameters((losses.wasserstein_generator_loss, 0.0), (losses.wasserstein_hinge_generator_loss, 0.0), (losses.minimax_generator_loss, 0.0)) def test_generator_loss_jacobian_preset(self, loss_function, loss_input_value): loss_input_init = np.full( shape=(2, 3), fill_value=loss_input_value, dtype=np.float32) loss_input = tf.convert_to_tensor(value=loss_input_init) loss = loss_function(loss_input) with self.subTest(name='is_finite'): self.assert_jacobian_is_finite(loss_input, loss_input_init, loss) with self.subTest(name='is_correct'): self.assert_jacobian_is_correct( loss_input, loss_input_init, loss, delta=1e-4, atol=1e-3) def test_wasserstein_discriminator_loss_shape_correct(self): loss_input = tf.ones(shape=(2, 1)) loss = self.evaluate( losses.wasserstein_discriminator_loss(loss_input, loss_input)) self.assertAllEqual(loss.shape, (2, 1)) def test_wasserstein_discriminator_loss_zero_with_same_input(self): loss_input = tf.ones(shape=(5, 1)) loss = self.evaluate( losses.wasserstein_discriminator_loss(loss_input, loss_input)) self.assertAllClose(tf.reduce_sum(loss), 0.0) @parameterized.parameters( (losses.wasserstein_discriminator_loss, 0.0, 0.0), (losses.wasserstein_discriminator_loss, 0.5, 0.5), (losses.wasserstein_hinge_discriminator_loss, 1.0, -1.0), (losses.wasserstein_hinge_discriminator_loss, 0.0, 0.0), (losses.wasserstein_hinge_discriminator_loss, 2.0, -2.0), (losses.minimax_discriminator_loss, 0.0, 0.0)) def test_discriminator_loss_jacobian_finite_preset( self, loss_function, discriminator_value_real, discriminator_value_generated): discriminator_value_real_init = np.full( shape=(2, 4), fill_value=discriminator_value_real, dtype=np.float32) discriminator_value_generated_init = np.full( shape=(2, 4), fill_value=discriminator_value_generated, dtype=np.float32) discriminator_value_real = tf.convert_to_tensor( value=discriminator_value_real_init) discriminator_value_generated = tf.convert_to_tensor( value=discriminator_value_generated_init) loss = loss_function(discriminator_value_real, discriminator_value_generated) with self.subTest(name='with_respect_to_real'): self.assert_jacobian_is_finite(discriminator_value_real, discriminator_value_real_init, loss) with self.subTest(name='with_respcet_to_generated'): self.assert_jacobian_is_finite(discriminator_value_generated, discriminator_value_generated_init, loss) @parameterized.parameters( (losses.wasserstein_discriminator_loss, 0.0, 0.0), (losses.wasserstein_discriminator_loss, 0.5, 0.5), (losses.wasserstein_hinge_discriminator_loss, 0.0, 0.0), (losses.wasserstein_hinge_discriminator_loss, 2.0, -2.0), (losses.minimax_discriminator_loss, 0.0, 0.0)) def test_discriminator_loss_jacobian_correct_preset( self, loss_function, discriminator_value_real, discriminator_value_generated): discriminator_value_real_init = np.full( shape=(2, 4), fill_value=discriminator_value_real, dtype=np.float32) discriminator_value_generated_init = np.full( shape=(2, 4), fill_value=discriminator_value_generated, dtype=np.float32) discriminator_value_real = tf.convert_to_tensor( value=discriminator_value_real_init) discriminator_value_generated = tf.convert_to_tensor( value=discriminator_value_generated_init) loss = loss_function(discriminator_value_real, discriminator_value_generated) with self.subTest(name='with_respect_to_real'): self.assert_jacobian_is_correct( discriminator_value_real, discriminator_value_real_init, loss, delta=1e-4, atol=1e-3) with self.subTest(name='with_respcet_to_generated'): self.assert_jacobian_is_correct( discriminator_value_generated, discriminator_value_generated_init, loss, delta=1e-4, atol=1e-3) def test_wasserstein_hinge_generator_loss_shape_correct(self): loss_input = tf.ones(shape=(2, 1)) loss = self.evaluate(losses.wasserstein_hinge_generator_loss(loss_input)) self.assertAllEqual(loss.shape, (2, 1)) def test_wasserstein_hinge_discriminator_loss_shape_correct(self): loss_input = tf.ones(shape=(2, 1)) loss = self.evaluate( losses.wasserstein_hinge_discriminator_loss(loss_input, loss_input)) self.assertAllEqual(loss.shape, (2, 1)) @parameterized.parameters((1.0, 2.0, 3.0), (-1.0, 2.0, 5.0), (0.0, 2.0, 4.0), (4.0, 3.0, 4.0), (-4.0, 3.0, 9.0), (4.0, -3.0, 0.0)) def test_wasserstein_hinge_discriminator_loss_correct_value( self, real_data_input, generated_data_input, expected_loss_value): real_data_input = tf.fill(dims=(), value=real_data_input) generated_data_input = tf.fill(dims=(), value=generated_data_input) loss = self.evaluate( losses.wasserstein_hinge_discriminator_loss(real_data_input, generated_data_input)) self.assertAlmostEqual(loss, expected_loss_value) def test_minimax_generator_loss_shape_correct(self): loss_input = tf.ones(shape=(2, 1)) loss = self.evaluate(losses.minimax_generator_loss(loss_input)) self.assertAllEqual(loss.shape, (2, 1)) def test_minimax_discriminator_loss_shape_correct(self): loss_input = tf.ones(shape=(2, 1)) loss = self.evaluate( losses.minimax_discriminator_loss(loss_input, loss_input)) self.assertAllEqual(loss.shape, (2, 1)) if __name__ == '__main__': tf.test.main()
40.421739
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0.710498
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18,594
5.091056
0.085391
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0.032353
0.06399
0.877663
0.865342
0.831558
0.817091
0.783307
0.769634
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0.18065
18,594
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0
6
f9f083da5c331ebd662c578d9ea692584c78527d
205
py
Python
backend/server/models/__init__.py
jessvb/convo
6b8a0d84142a0bfacf94482cebba42d92646be26
[ "MIT" ]
null
null
null
backend/server/models/__init__.py
jessvb/convo
6b8a0d84142a0bfacf94482cebba42d92646be26
[ "MIT" ]
null
null
null
backend/server/models/__init__.py
jessvb/convo
6b8a0d84142a0bfacf94482cebba42d92646be26
[ "MIT" ]
null
null
null
from models.action import * from models.procedure import * from models.condition import * from models.klass import * from models.valueof import * from models.execution import * from models.intent import *
25.625
30
0.795122
28
205
5.821429
0.357143
0.429448
0.588957
0
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0.136585
205
7
31
29.285714
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1
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0
0
6
e61398111c64d5dca991ab1cfa876be9c0ab0071
31
py
Python
src/ctc/toolbox/lending_utils/__init__.py
fei-protocol/checkthechain
ec838f3d0d44af228f45394d9ba8d8eb7f677520
[ "MIT" ]
94
2022-02-15T19:34:49.000Z
2022-03-26T19:26:22.000Z
src/ctc/toolbox/lending_utils/__init__.py
fei-protocol/checkthechain
ec838f3d0d44af228f45394d9ba8d8eb7f677520
[ "MIT" ]
7
2022-03-03T02:58:47.000Z
2022-03-11T18:41:05.000Z
src/ctc/toolbox/lending_utils/__init__.py
fei-protocol/checkthechain
ec838f3d0d44af228f45394d9ba8d8eb7f677520
[ "MIT" ]
7
2022-02-15T17:53:07.000Z
2022-03-17T19:14:17.000Z
from .lending_summary import *
15.5
30
0.806452
4
31
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1
0
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6
e620c211257e1a0bab505dd365e8aa7113c83139
129
py
Python
restaurant/get_env.py
dasky92/django-restaurant
db645868fad1536f6316a78d89a570f374e8b771
[ "MIT" ]
null
null
null
restaurant/get_env.py
dasky92/django-restaurant
db645868fad1536f6316a78d89a570f374e8b771
[ "MIT" ]
null
null
null
restaurant/get_env.py
dasky92/django-restaurant
db645868fad1536f6316a78d89a570f374e8b771
[ "MIT" ]
null
null
null
#! /usr/bin/env python def get_env(): """ Get settings file directory. """ return 'settings.environment.local'
14.333333
39
0.612403
15
129
5.2
0.8
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0.24031
129
8
40
16.125
0.795918
0.387597
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0.412698
0.412698
0
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1
0.5
true
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null
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1
1
0
0
0
1
0
0
6
e64d8444ce097e33b893d6e87ca1bc8dd6f6ae73
1,207
py
Python
apps/covid_19/preprocess/mixing_matrix/funcs.py
malanchak/AuTuMN
0cbd006d1f15da414d02eed44e48bb5c06f0802e
[ "BSD-2-Clause-FreeBSD" ]
null
null
null
apps/covid_19/preprocess/mixing_matrix/funcs.py
malanchak/AuTuMN
0cbd006d1f15da414d02eed44e48bb5c06f0802e
[ "BSD-2-Clause-FreeBSD" ]
null
null
null
apps/covid_19/preprocess/mixing_matrix/funcs.py
malanchak/AuTuMN
0cbd006d1f15da414d02eed44e48bb5c06f0802e
[ "BSD-2-Clause-FreeBSD" ]
null
null
null
""" Functions which can be used to transform dynamic mixing timeseries data """ from typing import List def repeat_prev(prev_vals: List[float]): """ Repeats the previous seen value again """ return prev_vals[-1] def add_to_prev(prev_vals: List[float], increment: float): """ Add increment to previous """ val = prev_vals[-1] + increment if val < 0: return 0 else: return val def add_to_prev_up_to_1(prev_vals: List[float], increment: float): """ Add increment to previous """ val = prev_vals[-1] + increment if val > 1: return 1 elif val < 0: return 0 else: return val def scale_prev(prev_vals: List[float], fraction: float): """ Apply a percentage to the previous value, saturating at zero """ val = prev_vals[-1] * fraction if val < 0: return 0 else: return val def scale_prev_up_to_1(prev_vals: List[float], fraction: float): """ Apply a percentage to the previous value, saturating at one or zero """ val = prev_vals[-1] * fraction if val > 1: return 1 elif val < 0: return 0 else: return val
20.116667
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0
0
0
0
0
0
1
0
0
6
050b48e5c011416a01274bb4adb31656f83cc5e5
24
py
Python
aad2onnx/shape_calculators/__init__.py
matwey/aad2onnx
35f06c22abd433b10b13209ddca9e8eb80717d61
[ "MIT" ]
null
null
null
aad2onnx/shape_calculators/__init__.py
matwey/aad2onnx
35f06c22abd433b10b13209ddca9e8eb80717d61
[ "MIT" ]
null
null
null
aad2onnx/shape_calculators/__init__.py
matwey/aad2onnx
35f06c22abd433b10b13209ddca9e8eb80717d61
[ "MIT" ]
null
null
null
from . import AadForest
12
23
0.791667
3
24
6.333333
1
0
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0
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0.166667
24
1
24
24
0.95
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true
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null
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0
0
0
0
0
1
0
1
0
1
0
0
6
054125394cc38442b03adbd057fa642ee6976d35
190
py
Python
backend/api/infrastcture/sample_router.py
ryutaro-0907/prome
efb8211e6d832e5b1c4b2dffd70b13b696baf45f
[ "MIT" ]
null
null
null
backend/api/infrastcture/sample_router.py
ryutaro-0907/prome
efb8211e6d832e5b1c4b2dffd70b13b696baf45f
[ "MIT" ]
null
null
null
backend/api/infrastcture/sample_router.py
ryutaro-0907/prome
efb8211e6d832e5b1c4b2dffd70b13b696baf45f
[ "MIT" ]
null
null
null
from typing import Dict from fastapi import APIRouter router = APIRouter() @router.get('/sample', tags=["message"]) def fetch_hello_world() -> Dict: return {"message": "Hello world!"}
21.111111
40
0.705263
24
190
5.5
0.666667
0.227273
0
0
0
0
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0
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0.142105
190
8
41
23.75
0.809816
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0.173684
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0.166667
false
0
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0.166667
0.666667
0
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null
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0
0
0
0
0
1
1
1
0
0
6
054aed34d02dc769ead032988c6482eafe60ea05
30,567
py
Python
sdk/python/pulumi_concourse/_inputs.py
brumhard/concourse-pulumi-provider
b94721a64245955b00049f5e2fc176b1178831b7
[ "Apache-2.0" ]
1
2021-09-16T06:15:11.000Z
2021-09-16T06:15:11.000Z
sdk/python/pulumi_concourse/_inputs.py
brumhard/pulumi-concourse
b94721a64245955b00049f5e2fc176b1178831b7
[ "Apache-2.0" ]
null
null
null
sdk/python/pulumi_concourse/_inputs.py
brumhard/pulumi-concourse
b94721a64245955b00049f5e2fc176b1178831b7
[ "Apache-2.0" ]
null
null
null
# coding=utf-8 # *** WARNING: this file was generated by pulumigen. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = [ 'AnonymousResourceArgs', 'DisplayOptionsArgs', 'GetStepArgs', 'GroupArgs', 'JobArgs', 'ResourceTypeArgs', 'ResourceArgs', 'RunArgsArgs', 'TaskConfigArgs', 'TaskStepArgs', ] @pulumi.input_type class AnonymousResourceArgs: def __init__(__self__, *, source: pulumi.Input[Mapping[str, pulumi.Input[str]]], type: pulumi.Input[str], params: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None): pulumi.set(__self__, "source", source) pulumi.set(__self__, "type", type) if params is not None: pulumi.set(__self__, "params", params) @property @pulumi.getter def source(self) -> pulumi.Input[Mapping[str, pulumi.Input[str]]]: return pulumi.get(self, "source") @source.setter def source(self, value: pulumi.Input[Mapping[str, pulumi.Input[str]]]): pulumi.set(self, "source", value) @property @pulumi.getter def type(self) -> pulumi.Input[str]: return pulumi.get(self, "type") @type.setter def type(self, value: pulumi.Input[str]): pulumi.set(self, "type", value) @property @pulumi.getter def params(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: return pulumi.get(self, "params") @params.setter def params(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "params", value) @pulumi.input_type class DisplayOptionsArgs: def __init__(__self__, *, background_image: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] background_image: Allows users to specify a custom background image which is put at 30% opacity, grayscaled and blended into existing background. Must be an http, https, or relative URL. """ if background_image is not None: pulumi.set(__self__, "background_image", background_image) @property @pulumi.getter def background_image(self) -> Optional[pulumi.Input[str]]: """ Allows users to specify a custom background image which is put at 30% opacity, grayscaled and blended into existing background. Must be an http, https, or relative URL. """ return pulumi.get(self, "background_image") @background_image.setter def background_image(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "background_image", value) @pulumi.input_type class GetStepArgs: def __init__(__self__, *, get: pulumi.Input[str], params: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, passed: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, resource: Optional[pulumi.Input[str]] = None, trigger: Optional[pulumi.Input[bool]] = None): pulumi.set(__self__, "get", get) if params is not None: pulumi.set(__self__, "params", params) if passed is not None: pulumi.set(__self__, "passed", passed) if resource is not None: pulumi.set(__self__, "resource", resource) if trigger is not None: pulumi.set(__self__, "trigger", trigger) @property @pulumi.getter def get(self) -> pulumi.Input[str]: return pulumi.get(self, "get") @get.setter def get(self, value: pulumi.Input[str]): pulumi.set(self, "get", value) @property @pulumi.getter def params(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: return pulumi.get(self, "params") @params.setter def params(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "params", value) @property @pulumi.getter def passed(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: return pulumi.get(self, "passed") @passed.setter def passed(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "passed", value) @property @pulumi.getter def resource(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "resource") @resource.setter def resource(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource", value) @property @pulumi.getter def trigger(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "trigger") @trigger.setter def trigger(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "trigger", value) @pulumi.input_type class GroupArgs: def __init__(__self__, *, name: pulumi.Input[str], jobs: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None): """ :param pulumi.Input[str] name: A unique name for the group. This should be short and simple as it will be used as the tab name for navigation. :param pulumi.Input[Sequence[pulumi.Input[str]]] jobs: A list of jobs that should appear in this group. A job may appear in multiple groups. Neighbours of jobs in the current group will also appear on the same page in order to give context of the location of the group in the pipeline. You may also use any valid glob to represent several jobs. """ pulumi.set(__self__, "name", name) if jobs is not None: pulumi.set(__self__, "jobs", jobs) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ A unique name for the group. This should be short and simple as it will be used as the tab name for navigation. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter def jobs(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ A list of jobs that should appear in this group. A job may appear in multiple groups. Neighbours of jobs in the current group will also appear on the same page in order to give context of the location of the group in the pipeline. You may also use any valid glob to represent several jobs. """ return pulumi.get(self, "jobs") @jobs.setter def jobs(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "jobs", value) @pulumi.input_type class JobArgs: def __init__(__self__, *, name: pulumi.Input[str], plan: pulumi.Input[Sequence[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]], ensure: Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]] = None, max_in_flight: Optional[pulumi.Input[float]] = None, on_abort: Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]] = None, on_error: Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]] = None, on_failure: Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]] = None, on_success: Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]] = None, public: Optional[pulumi.Input[bool]] = None, serial: Optional[pulumi.Input[bool]] = None): """ :param pulumi.Input[str] name: The name of the job. This should be short; it will show up in URLs. :param pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']] ensure: Step to execute regardless of whether the job succeeds, fails, errors, or aborts. :param pulumi.Input[float] max_in_flight: If set, specifies a maximum number of builds to run at a time. If serial or serial_groups are set, they take precedence and force this value to be 1. :param pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']] on_abort: Step to execute when the job aborts. :param pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']] on_error: Step to execute when the job errors. :param pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']] on_failure: Step to execute when the job fails. :param pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']] on_success: Step to execute when the job succeeds. :param pulumi.Input[bool] public: Default false. If set to true, the build log of this job will be viewable by unauthenticated users. Unauthenticated users will always be able to see the inputs, outputs, and build status history of a job. This is useful if you would like to expose your pipeline publicly without showing sensitive information in the build log. :param pulumi.Input[bool] serial: Default false. If set to true, builds will queue up and execute one-by-one, rather than executing in parallel. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "plan", plan) if ensure is not None: pulumi.set(__self__, "ensure", ensure) if max_in_flight is not None: pulumi.set(__self__, "max_in_flight", max_in_flight) if on_abort is not None: pulumi.set(__self__, "on_abort", on_abort) if on_error is not None: pulumi.set(__self__, "on_error", on_error) if on_failure is not None: pulumi.set(__self__, "on_failure", on_failure) if on_success is not None: pulumi.set(__self__, "on_success", on_success) if public is not None: pulumi.set(__self__, "public", public) if serial is not None: pulumi.set(__self__, "serial", serial) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ The name of the job. This should be short; it will show up in URLs. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter def plan(self) -> pulumi.Input[Sequence[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]]: return pulumi.get(self, "plan") @plan.setter def plan(self, value: pulumi.Input[Sequence[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]]): pulumi.set(self, "plan", value) @property @pulumi.getter def ensure(self) -> Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]: """ Step to execute regardless of whether the job succeeds, fails, errors, or aborts. """ return pulumi.get(self, "ensure") @ensure.setter def ensure(self, value: Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]): pulumi.set(self, "ensure", value) @property @pulumi.getter def max_in_flight(self) -> Optional[pulumi.Input[float]]: """ If set, specifies a maximum number of builds to run at a time. If serial or serial_groups are set, they take precedence and force this value to be 1. """ return pulumi.get(self, "max_in_flight") @max_in_flight.setter def max_in_flight(self, value: Optional[pulumi.Input[float]]): pulumi.set(self, "max_in_flight", value) @property @pulumi.getter def on_abort(self) -> Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]: """ Step to execute when the job aborts. """ return pulumi.get(self, "on_abort") @on_abort.setter def on_abort(self, value: Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]): pulumi.set(self, "on_abort", value) @property @pulumi.getter def on_error(self) -> Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]: """ Step to execute when the job errors. """ return pulumi.get(self, "on_error") @on_error.setter def on_error(self, value: Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]): pulumi.set(self, "on_error", value) @property @pulumi.getter def on_failure(self) -> Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]: """ Step to execute when the job fails. """ return pulumi.get(self, "on_failure") @on_failure.setter def on_failure(self, value: Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]): pulumi.set(self, "on_failure", value) @property @pulumi.getter def on_success(self) -> Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]: """ Step to execute when the job succeeds. """ return pulumi.get(self, "on_success") @on_success.setter def on_success(self, value: Optional[pulumi.Input[Union['TaskStepArgs', 'GetStepArgs']]]): pulumi.set(self, "on_success", value) @property @pulumi.getter def public(self) -> Optional[pulumi.Input[bool]]: """ Default false. If set to true, the build log of this job will be viewable by unauthenticated users. Unauthenticated users will always be able to see the inputs, outputs, and build status history of a job. This is useful if you would like to expose your pipeline publicly without showing sensitive information in the build log. """ return pulumi.get(self, "public") @public.setter def public(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "public", value) @property @pulumi.getter def serial(self) -> Optional[pulumi.Input[bool]]: """ Default false. If set to true, builds will queue up and execute one-by-one, rather than executing in parallel. """ return pulumi.get(self, "serial") @serial.setter def serial(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "serial", value) @pulumi.input_type class ResourceTypeArgs: def __init__(__self__, *, check_every: Optional[pulumi.Input[str]] = None, defaults: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, name: Optional[pulumi.Input[str]] = None, params: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, privileged: Optional[pulumi.Input[bool]] = None, source: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, type: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] check_every: Default 1m. The interval on which to check for new versions of the resource. Acceptable interval options are defined by the time.ParseDuration function. If set to never the resource will not be automatically checked. The resource can still be checked manually via the web UI, fly, or webhooks. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] defaults: The default configuration for the resource type. This varies by resource type, and is a black box to Concourse; it is merged with (duplicate fields are overwritten by) resource.source and passed to the resource at runtime. :param pulumi.Input[str] name: TThe name of the resource type. This should be short and simple. This name will be referenced by pipeline.resources defined within the same pipeline, and task.image_resources used by tasks running in the pipeline. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] params: Arbitrary config to pass when running the get to fetch the resource type's image. :param pulumi.Input[bool] privileged: Default false. If set to true, the resource's containers will be run with full capabilities, as determined by the worker backend the task runs on. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] source: The configuration for the resource. This varies by resource type, and is a black box to Concourse; it is blindly passed to the resource at runtime. :param pulumi.Input[Sequence[pulumi.Input[str]]] tags: Default []. A list of tags to determine which workers the checks will be performed on. You'll want to specify this if the source is internal to a worker's network, for example. :param pulumi.Input[str] type: The resource type implementing the resource. """ if check_every is not None: pulumi.set(__self__, "check_every", check_every) if defaults is not None: pulumi.set(__self__, "defaults", defaults) if name is not None: pulumi.set(__self__, "name", name) if params is not None: pulumi.set(__self__, "params", params) if privileged is not None: pulumi.set(__self__, "privileged", privileged) if source is not None: pulumi.set(__self__, "source", source) if tags is not None: pulumi.set(__self__, "tags", tags) if type is not None: pulumi.set(__self__, "type", type) @property @pulumi.getter def check_every(self) -> Optional[pulumi.Input[str]]: """ Default 1m. The interval on which to check for new versions of the resource. Acceptable interval options are defined by the time.ParseDuration function. If set to never the resource will not be automatically checked. The resource can still be checked manually via the web UI, fly, or webhooks. """ return pulumi.get(self, "check_every") @check_every.setter def check_every(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "check_every", value) @property @pulumi.getter def defaults(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ The default configuration for the resource type. This varies by resource type, and is a black box to Concourse; it is merged with (duplicate fields are overwritten by) resource.source and passed to the resource at runtime. """ return pulumi.get(self, "defaults") @defaults.setter def defaults(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "defaults", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ TThe name of the resource type. This should be short and simple. This name will be referenced by pipeline.resources defined within the same pipeline, and task.image_resources used by tasks running in the pipeline. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def params(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ Arbitrary config to pass when running the get to fetch the resource type's image. """ return pulumi.get(self, "params") @params.setter def params(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "params", value) @property @pulumi.getter def privileged(self) -> Optional[pulumi.Input[bool]]: """ Default false. If set to true, the resource's containers will be run with full capabilities, as determined by the worker backend the task runs on. """ return pulumi.get(self, "privileged") @privileged.setter def privileged(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "privileged", value) @property @pulumi.getter def source(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ The configuration for the resource. This varies by resource type, and is a black box to Concourse; it is blindly passed to the resource at runtime. """ return pulumi.get(self, "source") @source.setter def source(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "source", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ Default []. A list of tags to determine which workers the checks will be performed on. You'll want to specify this if the source is internal to a worker's network, for example. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @property @pulumi.getter def type(self) -> Optional[pulumi.Input[str]]: """ The resource type implementing the resource. """ return pulumi.get(self, "type") @type.setter def type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "type", value) @pulumi.input_type class ResourceArgs: def __init__(__self__, *, name: pulumi.Input[str], source: pulumi.Input[Mapping[str, pulumi.Input[str]]], type: pulumi.Input[str], check_every: Optional[pulumi.Input[str]] = None, public: Optional[pulumi.Input[bool]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, webhook_token: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] name: The name of the resource. This should be short and simple. This name will be referenced by build plans of jobs in the pipeline. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] source: The configuration for the resource. This varies by resource type, and is a black box to Concourse; it is blindly passed to the resource at runtime. :param pulumi.Input[str] type: The resource type implementing the resource. :param pulumi.Input[str] check_every: Default 1m. The interval on which to check for new versions of the resource. Acceptable interval options are defined by the time.ParseDuration function. If set to never the resource will not be automatically checked. The resource can still be checked manually via the web UI, fly, or webhooks. :param pulumi.Input[bool] public: Default false. If set to true, the metadata for each version of the resource will be viewable by unauthenticated users (assuming the pipeline has been exposed). :param pulumi.Input[Sequence[pulumi.Input[str]]] tags: Default []. A list of tags to determine which workers the checks will be performed on. You'll want to specify this if the source is internal to a worker's network, for example. :param pulumi.Input[str] webhook_token: If specified, web hooks can be sent to trigger an immediate check of the resource, specifying this value as a primitive form of authentication via query params. """ pulumi.set(__self__, "name", name) pulumi.set(__self__, "source", source) pulumi.set(__self__, "type", type) if check_every is not None: pulumi.set(__self__, "check_every", check_every) if public is not None: pulumi.set(__self__, "public", public) if tags is not None: pulumi.set(__self__, "tags", tags) if webhook_token is not None: pulumi.set(__self__, "webhook_token", webhook_token) @property @pulumi.getter def name(self) -> pulumi.Input[str]: """ The name of the resource. This should be short and simple. This name will be referenced by build plans of jobs in the pipeline. """ return pulumi.get(self, "name") @name.setter def name(self, value: pulumi.Input[str]): pulumi.set(self, "name", value) @property @pulumi.getter def source(self) -> pulumi.Input[Mapping[str, pulumi.Input[str]]]: """ The configuration for the resource. This varies by resource type, and is a black box to Concourse; it is blindly passed to the resource at runtime. """ return pulumi.get(self, "source") @source.setter def source(self, value: pulumi.Input[Mapping[str, pulumi.Input[str]]]): pulumi.set(self, "source", value) @property @pulumi.getter def type(self) -> pulumi.Input[str]: """ The resource type implementing the resource. """ return pulumi.get(self, "type") @type.setter def type(self, value: pulumi.Input[str]): pulumi.set(self, "type", value) @property @pulumi.getter def check_every(self) -> Optional[pulumi.Input[str]]: """ Default 1m. The interval on which to check for new versions of the resource. Acceptable interval options are defined by the time.ParseDuration function. If set to never the resource will not be automatically checked. The resource can still be checked manually via the web UI, fly, or webhooks. """ return pulumi.get(self, "check_every") @check_every.setter def check_every(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "check_every", value) @property @pulumi.getter def public(self) -> Optional[pulumi.Input[bool]]: """ Default false. If set to true, the metadata for each version of the resource will be viewable by unauthenticated users (assuming the pipeline has been exposed). """ return pulumi.get(self, "public") @public.setter def public(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "public", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: """ Default []. A list of tags to determine which workers the checks will be performed on. You'll want to specify this if the source is internal to a worker's network, for example. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @property @pulumi.getter def webhook_token(self) -> Optional[pulumi.Input[str]]: """ If specified, web hooks can be sent to trigger an immediate check of the resource, specifying this value as a primitive form of authentication via query params. """ return pulumi.get(self, "webhook_token") @webhook_token.setter def webhook_token(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "webhook_token", value) @pulumi.input_type class RunArgsArgs: def __init__(__self__, *, path: pulumi.Input[str], args: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, dir: Optional[pulumi.Input[str]] = None, user: Optional[pulumi.Input[str]] = None): pulumi.set(__self__, "path", path) if args is not None: pulumi.set(__self__, "args", args) if dir is not None: pulumi.set(__self__, "dir", dir) if user is not None: pulumi.set(__self__, "user", user) @property @pulumi.getter def path(self) -> pulumi.Input[str]: return pulumi.get(self, "path") @path.setter def path(self, value: pulumi.Input[str]): pulumi.set(self, "path", value) @property @pulumi.getter def args(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: return pulumi.get(self, "args") @args.setter def args(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "args", value) @property @pulumi.getter def dir(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "dir") @dir.setter def dir(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "dir", value) @property @pulumi.getter def user(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "user") @user.setter def user(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "user", value) @pulumi.input_type class TaskConfigArgs: def __init__(__self__, *, image_resource: pulumi.Input['AnonymousResourceArgs'], platform: pulumi.Input[str], run: pulumi.Input['RunArgsArgs']): pulumi.set(__self__, "image_resource", image_resource) pulumi.set(__self__, "platform", platform) pulumi.set(__self__, "run", run) @property @pulumi.getter def image_resource(self) -> pulumi.Input['AnonymousResourceArgs']: return pulumi.get(self, "image_resource") @image_resource.setter def image_resource(self, value: pulumi.Input['AnonymousResourceArgs']): pulumi.set(self, "image_resource", value) @property @pulumi.getter def platform(self) -> pulumi.Input[str]: return pulumi.get(self, "platform") @platform.setter def platform(self, value: pulumi.Input[str]): pulumi.set(self, "platform", value) @property @pulumi.getter def run(self) -> pulumi.Input['RunArgsArgs']: return pulumi.get(self, "run") @run.setter def run(self, value: pulumi.Input['RunArgsArgs']): pulumi.set(self, "run", value) @pulumi.input_type class TaskStepArgs: def __init__(__self__, *, task: pulumi.Input[str], config: Optional[pulumi.Input['TaskConfigArgs']] = None, image: Optional[pulumi.Input[str]] = None): pulumi.set(__self__, "task", task) if config is not None: pulumi.set(__self__, "config", config) if image is not None: pulumi.set(__self__, "image", image) @property @pulumi.getter def task(self) -> pulumi.Input[str]: return pulumi.get(self, "task") @task.setter def task(self, value: pulumi.Input[str]): pulumi.set(self, "task", value) @property @pulumi.getter def config(self) -> Optional[pulumi.Input['TaskConfigArgs']]: return pulumi.get(self, "config") @config.setter def config(self, value: Optional[pulumi.Input['TaskConfigArgs']]): pulumi.set(self, "config", value) @property @pulumi.getter def image(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "image") @image.setter def image(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "image", value)
41.872603
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0.648968
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0.646611
0.605317
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6
0553a50b160b57ef05ec2c5a4d844fe204bc8f91
189
py
Python
projectaile/models/layer.py
explabs-ai/projectaile
992cecddb2fa6fdc60661103ac761f5bcd64f82b
[ "MIT" ]
5
2020-10-13T10:17:17.000Z
2021-03-04T08:36:30.000Z
projectaile/models/layer.py
explabs-ai/projectaile
992cecddb2fa6fdc60661103ac761f5bcd64f82b
[ "MIT" ]
2
2020-12-03T06:38:38.000Z
2021-05-08T10:06:55.000Z
projectaile/models/layer.py
explabs-ai/projectaile
992cecddb2fa6fdc60661103ac761f5bcd64f82b
[ "MIT" ]
null
null
null
class LAYER(): def __init__(self): return def call(self, x): return def compute_output_shape(self, input_shape): return def compute_mask(self, input, input_mask=None): return
15.75
48
0.724868
28
189
4.571429
0.5
0.210938
0.25
0
0
0
0
0
0
0
0
0
0.169312
189
12
49
15.75
0.815287
0
0
0.444444
0
0
0
0
0
0
0
0
0
1
0.444444
false
0
0
0.444444
1
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0563ca8ad20e2b3dbf91e0911b60469ab33b1103
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py
Python
registrations/pca/__init__.py
devisperessutti/Python
829098c91234aebe1463dd613af96e1e6bf9fdc1
[ "MIT" ]
1
2020-02-16T15:17:11.000Z
2020-02-16T15:17:11.000Z
registrations/pca/__init__.py
devisperessutti/pca_echo_registration
829098c91234aebe1463dd613af96e1e6bf9fdc1
[ "MIT" ]
null
null
null
registrations/pca/__init__.py
devisperessutti/pca_echo_registration
829098c91234aebe1463dd613af96e1e6bf9fdc1
[ "MIT" ]
1
2016-08-23T08:40:13.000Z
2016-08-23T08:40:13.000Z
# -*- coding: utf-8 -*- """ Created on Sat Mar 1 18:32:28 2014 @author: dp11 """
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057f7e72a55786570b251897fcfe42bb152399f4
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py
Python
template/__init__.py
chucoding/Notion2Github
820aace4e6f52a42adf2587f5c77ef768c4e1586
[ "MIT" ]
null
null
null
template/__init__.py
chucoding/Notion2Github
820aace4e6f52a42adf2587f5c77ef768c4e1586
[ "MIT" ]
null
null
null
template/__init__.py
chucoding/Notion2Github
820aace4e6f52a42adf2587f5c77ef768c4e1586
[ "MIT" ]
null
null
null
from template.calendar import *
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6
5566a80c7351f7cde6cc960cf5520c5bf9e4f317
193
py
Python
gae/src/views.py
Tjorriemorrie/flexitime
b2d8564ba9e0f0c2c16610a8a99d54dd6ea58cae
[ "MIT" ]
null
null
null
gae/src/views.py
Tjorriemorrie/flexitime
b2d8564ba9e0f0c2c16610a8a99d54dd6ea58cae
[ "MIT" ]
null
null
null
gae/src/views.py
Tjorriemorrie/flexitime
b2d8564ba9e0f0c2c16610a8a99d54dd6ea58cae
[ "MIT" ]
null
null
null
from flask import render_template, request, abort from src import app @app.route('/', defaults={'path': ''}) @app.route('/<path:path>') def index(path): return render_template('index.html')
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55750942a0246d19f2f594fbbf9c661c33e282aa
288
py
Python
nlp/Constants.py
Asap7772/DeepCriminalize
c171c6ce6e87e126e6e2b0ed1d9709ee7d0ce667
[ "MIT" ]
1
2019-10-28T02:40:07.000Z
2019-10-28T02:40:07.000Z
nlp/Constants.py
Asap7772/DeepCriminalize
c171c6ce6e87e126e6e2b0ed1d9709ee7d0ce667
[ "MIT" ]
16
2020-01-28T23:05:09.000Z
2022-02-27T03:02:38.000Z
nlp/Constants.py
Asap7772/DeepCriminalize
c171c6ce6e87e126e6e2b0ed1d9709ee7d0ce667
[ "MIT" ]
null
null
null
EXT_ANALYTICS_SUBSCRIPTION_KEY = "f861df55922e4287b9de318f55c1da2c" TEXT_ANALYTICS_ENDPOINT = "https://deepcriminalizenlpnlpnlp.cognitiveservices.azure.com/" TEXT_ANALYTICS_URL = "https://deepcriminalizenlpnlpnlp.cognitiveservices.azure.com/text/analytics/v2.1/keyPhrases?showStats=True"
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6
55858dc8c91d0a5465407b0a3a972318dd419201
3,635
py
Python
pipeline/archivebot/pattern_conversion_test.py
chfoo/ArchiveBot
84e0c1cf9c0a9559fa1370f3570a0837f9a7641f
[ "MIT" ]
null
null
null
pipeline/archivebot/pattern_conversion_test.py
chfoo/ArchiveBot
84e0c1cf9c0a9559fa1370f3570a0837f9a7641f
[ "MIT" ]
null
null
null
pipeline/archivebot/pattern_conversion_test.py
chfoo/ArchiveBot
84e0c1cf9c0a9559fa1370f3570a0837f9a7641f
[ "MIT" ]
null
null
null
import re import unittest from .pattern_conversion import lua_pattern_to_regex class Test(unittest.TestCase): def test_alpha(self): self.assertTrue(re.search(lua_pattern_to_regex('%a'), 'a')) self.assertTrue(re.search(lua_pattern_to_regex('%a'), 'Z')) self.assertFalse(re.search(lua_pattern_to_regex('%a'), '0')) def test_control(self): self.assertTrue(re.search(lua_pattern_to_regex('%c'), '\x01')) self.assertFalse(re.search(lua_pattern_to_regex('%c'), 'a')) def test_graphic(self): self.assertTrue(re.search(lua_pattern_to_regex('%g'), 'P')) self.assertFalse(re.search(lua_pattern_to_regex('%g'), '\t')) def test_lowercase(self): self.assertTrue(re.search(lua_pattern_to_regex('%l'), 'h')) self.assertFalse(re.search(lua_pattern_to_regex('%l'), 'H')) def test_printable(self): self.assertTrue(re.search(lua_pattern_to_regex('%p'), ']')) self.assertTrue(re.search(lua_pattern_to_regex('%p'), '-')) self.assertTrue(re.search(lua_pattern_to_regex('%p'), '#')) self.assertFalse(re.search(lua_pattern_to_regex('%p'), '\x01')) def test_space(self): self.assertTrue(re.search(lua_pattern_to_regex('%s'), ' ')) self.assertTrue(re.search(lua_pattern_to_regex('%s'), '\t')) self.assertFalse(re.search(lua_pattern_to_regex('%s'), 'A')) def test_upper(self): self.assertTrue(re.search(lua_pattern_to_regex('%u'), 'A')) self.assertFalse(re.search(lua_pattern_to_regex('%u'), 'a')) def test_alphanum(self): self.assertTrue(re.search(lua_pattern_to_regex('%w'), 'A')) self.assertFalse(re.search(lua_pattern_to_regex('%w'), '#')) def test_hex(self): self.assertTrue(re.search(lua_pattern_to_regex('%x'), 'A')) self.assertFalse(re.search(lua_pattern_to_regex('%x'), 'z')) def test_complex(self): self.assertTrue(re.search( lua_pattern_to_regex(r'^http://www%.reddit%.com/login%?dest='), 'http://www.reddit.com/login?dest=archiveteam' )) self.assertFalse(re.search( lua_pattern_to_regex(r'^http://www%.reddit%.com/login%?dest='), 'http://www.reddit.com/r/archiveteam' )) self.assertTrue(re.search( lua_pattern_to_regex(r'subscription%.php%?'), 'subscription.php?' )) self.assertFalse(re.search( lua_pattern_to_regex(r'subscription%.php%?'), 'subscriptionXphp?' )) self.assertTrue(re.search( lua_pattern_to_regex(r'^http://.+%.blogspot%.com/search%?'), 'http://archiveteam.blogspot.com/search?q=archives' )) self.assertFalse(re.search( lua_pattern_to_regex(r'^http://.+%.blogspot%.com/search%?'), 'http://.blogspot.com/search?q=archives' )) def test_dash(self): self.assertTrue(re.search( lua_pattern_to_regex('cat%-dog'), 'cat-dog' )) self.assertFalse(re.search( lua_pattern_to_regex('cat%-dog'), 'cattdog' )) self.assertTrue(re.search( lua_pattern_to_regex('cat-dog'), 'cattdog' )) self.assertTrue(re.search( lua_pattern_to_regex('cat-dog'), 'cattttdog' )) def test_nest(self): self.assertTrue(re.search( lua_pattern_to_regex('abc[%a]xyz'), 'abcDxyz' )) self.assertFalse(re.search( lua_pattern_to_regex('abc[%a]xyz'), 'abc xyz' ))
35.990099
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6
55a866d149fd836de36d8bc24985363e886299e7
210
py
Python
mogpe/gating_networks/__init__.py
aidanscannell/mogpe
25a9af473d73d6fa35bd060bee0eb2c372b995e5
[ "Apache-2.0" ]
11
2021-04-01T02:40:21.000Z
2022-01-31T16:14:44.000Z
mogpe/gating_networks/__init__.py
aidanscannell/mogpe
25a9af473d73d6fa35bd060bee0eb2c372b995e5
[ "Apache-2.0" ]
null
null
null
mogpe/gating_networks/__init__.py
aidanscannell/mogpe
25a9af473d73d6fa35bd060bee0eb2c372b995e5
[ "Apache-2.0" ]
3
2021-04-04T02:45:34.000Z
2021-11-22T23:48:28.000Z
#!/usr/bin/env python3 from mogpe.gating_networks.base import GatingNetworkBase from mogpe.gating_networks.svgp import SVGPGatingNetworkBinary, SVGPGatingNetworkMulti, SVGPGatingFunction, SVGPGatingNetworkBase
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6
75a4208309f1604d84b2dec31148c64b1fc5efa1
1,986
py
Python
src/morphometrics/measure/_tests/test_surface.py
haesleinhuepf/morphometrics
c4bdee33a9deaeed6f69a6853bf0787601fa8494
[ "BSD-3-Clause" ]
5
2022-03-17T18:14:18.000Z
2022-03-23T00:48:17.000Z
src/morphometrics/measure/_tests/test_surface.py
haesleinhuepf/morphometrics
c4bdee33a9deaeed6f69a6853bf0787601fa8494
[ "BSD-3-Clause" ]
11
2022-01-27T14:10:43.000Z
2022-03-20T18:22:30.000Z
src/morphometrics/measure/_tests/test_surface.py
haesleinhuepf/morphometrics
c4bdee33a9deaeed6f69a6853bf0787601fa8494
[ "BSD-3-Clause" ]
1
2022-03-17T18:17:21.000Z
2022-03-17T18:17:21.000Z
import numpy as np import pytest import trimesh from morphometrics.measure.surface import distance_between_surfaces @pytest.mark.parametrize("fill_value", [np.nan, 0]) def test_distance_between_surfaces(fill_value): source_vertices = np.array([[0, 0, 0], [0, 10, 0], [0, 10, 10]]) source_faces = np.array([[0, 1, 2]]) vertex_normals = np.array([[1, 0, 0], [1, 0, 0], [1, 0, 0]]) source_mesh = trimesh.Trimesh( vertices=source_vertices, faces=source_faces, vertex_normals=vertex_normals ) destination_vertices = np.array([[5, 5, 10], [5, 15, 5], [5, 15, 15]]) destination_faces = np.array([[0, 1, 2]]) destination_mesh = trimesh.Trimesh( vertices=destination_vertices, faces=destination_faces, vertex_normals=vertex_normals, ) distances = distance_between_surfaces( source_surface=source_mesh, destination_surface=destination_mesh, fill_value=fill_value, ) np.testing.assert_allclose(distances, [fill_value, fill_value, 5]) @pytest.mark.parametrize("fill_value", [np.nan, 0]) def test_distance_between_surfaces_flip_normal(fill_value): source_vertices = np.array([[5, 0, 0], [5, 10, 0], [5, 10, 10]]) source_faces = np.array([[0, 1, 2]]) vertex_normals = np.array([[1, 0, 0], [1, 0, 0], [1, 0, 0]]) source_mesh = trimesh.Trimesh( vertices=source_vertices, faces=source_faces, vertex_normals=vertex_normals ) destination_vertices = np.array([[0, 5, 10], [0, 15, 5], [0, 15, 15]]) destination_faces = np.array([[0, 1, 2]]) destination_mesh = trimesh.Trimesh( vertices=destination_vertices, faces=destination_faces, vertex_normals=vertex_normals, ) distances = distance_between_surfaces( source_surface=source_mesh, destination_surface=destination_mesh, fill_value=fill_value, flip_normals=True, ) np.testing.assert_allclose(distances, [fill_value, fill_value, 5])
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