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string
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int64
ext
string
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string
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string
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string
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string
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list
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int64
max_stars_repo_stars_event_min_datetime
string
max_stars_repo_stars_event_max_datetime
string
max_issues_repo_path
string
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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
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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
761dacd0fe7addbdf010bc1584ca428bda74b670
132
py
Python
satsound/apps.py
saanobhaai/apman
e07452f54fcb895fb6039b6be63abf3861d7b9cb
[ "MIT" ]
null
null
null
satsound/apps.py
saanobhaai/apman
e07452f54fcb895fb6039b6be63abf3861d7b9cb
[ "MIT" ]
null
null
null
satsound/apps.py
saanobhaai/apman
e07452f54fcb895fb6039b6be63abf3861d7b9cb
[ "MIT" ]
null
null
null
from __future__ import unicode_literals from django.apps import AppConfig class SatsoundConfig(AppConfig): name = 'satsound'
16.5
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6.666667
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0.151515
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7
40
18.857143
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0
0
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0.060606
0
0
0
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0
0
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0
1
0
0
null
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0
1
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0
4
522801d076653a6d3b2e171d603a39ea5315eaff
134
py
Python
terafamiliar/individual_therapy/urls.py
Icogerard/WEB-TERAFAMILIAR---DJANGO2
dcf7d6507ef2eda697766ab3b1dd6df4365f0452
[ "MIT" ]
null
null
null
terafamiliar/individual_therapy/urls.py
Icogerard/WEB-TERAFAMILIAR---DJANGO2
dcf7d6507ef2eda697766ab3b1dd6df4365f0452
[ "MIT" ]
null
null
null
terafamiliar/individual_therapy/urls.py
Icogerard/WEB-TERAFAMILIAR---DJANGO2
dcf7d6507ef2eda697766ab3b1dd6df4365f0452
[ "MIT" ]
null
null
null
from django.urls import path from . import views urlpatterns = [ path('', views.individual_therapy, name="individual_therapy"), ]
22.333333
66
0.738806
16
134
6.0625
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5229fdde7056072e9a076b5e38f4df470aab8de2
209
py
Python
handlers/__init__.py
drx/rom-info
0b82d86d4fc9c43f51da6b12b1db820f180a1a34
[ "MIT" ]
6
2016-12-20T00:54:51.000Z
2019-03-22T03:37:56.000Z
handlers/__init__.py
drx/rom-info
0b82d86d4fc9c43f51da6b12b1db820f180a1a34
[ "MIT" ]
30
2016-12-21T20:51:49.000Z
2017-04-03T21:47:25.000Z
handlers/__init__.py
drx/rom-info
0b82d86d4fc9c43f51da6b12b1db820f180a1a34
[ "MIT" ]
null
null
null
from .megadrive import MegadriveHandler from .iso9660 import ISO9660Handler from .dreamcast import DCDataTrackHandler, GDIHandler handlers = [MegadriveHandler, DCDataTrackHandler, ISO9660Handler, GDIHandler]
34.833333
77
0.856459
18
209
9.944444
0.555556
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0.095694
209
5
78
41.8
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4
523829494dd5b01f83d9d33d44fdff1ce2853ab8
227
py
Python
pytpp/attributes/log_application_container.py
Venafi/pytpp
42af655b2403b8c9447c86962abd4aaa0201f646
[ "MIT" ]
4
2022-02-04T23:58:55.000Z
2022-02-15T18:53:08.000Z
pytpp/attributes/log_application_container.py
Venafi/pytpp
42af655b2403b8c9447c86962abd4aaa0201f646
[ "MIT" ]
null
null
null
pytpp/attributes/log_application_container.py
Venafi/pytpp
42af655b2403b8c9447c86962abd4aaa0201f646
[ "MIT" ]
null
null
null
from pytpp.attributes._helper import IterableMeta from pytpp.attributes.top import TopAttributes class LogApplicationContainerAttributes(TopAttributes, metaclass=IterableMeta): __config_class__ = "Log Application Container"
32.428571
79
0.863436
22
227
8.636364
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0.094737
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0.0837
227
6
80
37.833333
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4
523af7db34ae95768e290d847a54ebe9640ec1bb
266
py
Python
trustee/forms.py
Kishan51/Trust
fbc9cc7e790721cd5743941f2f2427e5201eff8a
[ "MIT" ]
null
null
null
trustee/forms.py
Kishan51/Trust
fbc9cc7e790721cd5743941f2f2427e5201eff8a
[ "MIT" ]
2
2020-06-05T21:09:28.000Z
2021-06-01T23:49:53.000Z
trustee/forms.py
Kishan51/Trust
fbc9cc7e790721cd5743941f2f2427e5201eff8a
[ "MIT" ]
1
2019-06-12T16:40:39.000Z
2019-06-12T16:40:39.000Z
from django import forms from .models import ContactUs,Contact class HomeForm(forms.ModelForm): class Meta: model = ContactUs exclude = ('',) class ContactForm1(forms.ModelForm): class Meta: model = Contact exclude = ('',)
19
37
0.635338
27
266
6.259259
0.518519
0.16568
0.224852
0.272189
0.331361
0
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0.005128
0.266917
266
14
38
19
0.861538
0
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0.4
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0.6
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0
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0
1
0
0
4
5243fdb1c9bde1b65c1daebe7f59cf7953801614
1,081
py
Python
web/iot/models.py
rodrigodeleonv/proyectos-ie
ab827fe5d37454399a05c500a5ae20d8481a33e6
[ "Apache-2.0" ]
3
2020-12-09T16:11:23.000Z
2020-12-22T16:39:46.000Z
web/iot/models.py
rodrigodeleonv/proyectos-ie
ab827fe5d37454399a05c500a5ae20d8481a33e6
[ "Apache-2.0" ]
null
null
null
web/iot/models.py
rodrigodeleonv/proyectos-ie
ab827fe5d37454399a05c500a5ae20d8481a33e6
[ "Apache-2.0" ]
2
2020-12-16T01:25:02.000Z
2020-12-23T01:09:51.000Z
from django.db import models class Sensor(models.Model): """ La clase sensor de Python represtan una tabla de la base de datos Atributos de la clase representan los campos de la tabla """ created = models.DateTimeField(auto_now_add=True) updated = models.DateTimeField(auto_now=True) name = models.CharField(max_length=30) tipo = models.CharField(max_length=30) # Humedad, presion, ultrasonico def __str__(self): return f'Sensor: {self.name} tipo={self.tipo}' class Measure(models.Model): created = models.DateTimeField(auto_now_add=True) updated = models.DateTimeField(auto_now=True) value = models.FloatField(default=0) sensor = models.ForeignKey(Sensor, on_delete=models.CASCADE) def __str__(self): return f'Medición: {self.value} | sensor={self.sensor.name}' class MqttLog(models.Model): created = models.DateTimeField(auto_now_add=True) updated = models.DateTimeField(auto_now=True) message = models.CharField(max_length=255) def __str__(self): return f'{self.message}'
30.885714
75
0.712303
143
1,081
5.20979
0.384615
0.15302
0.185235
0.209396
0.477852
0.339597
0.339597
0.339597
0.339597
0.339597
0
0.00904
0.181314
1,081
34
76
31.794118
0.832768
0.141536
0
0.428571
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0.110132
0.027533
0
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1
0.142857
false
0
0.047619
0.142857
1
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null
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1
0
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0
0
0
0
1
1
0
0
4
5255cf28cacc0c1804b10813f37d5f18b4d6b43a
110
py
Python
apps/analisis_factorial/apps.py
nickjesus737/MultivariateAnalysis
d2511ff7e2106f0eea7548adbddd0a37e34c7921
[ "MIT" ]
null
null
null
apps/analisis_factorial/apps.py
nickjesus737/MultivariateAnalysis
d2511ff7e2106f0eea7548adbddd0a37e34c7921
[ "MIT" ]
null
null
null
apps/analisis_factorial/apps.py
nickjesus737/MultivariateAnalysis
d2511ff7e2106f0eea7548adbddd0a37e34c7921
[ "MIT" ]
null
null
null
from django.apps import AppConfig class AnalisisFactorialConfig(AppConfig): name = 'analisis_factorial'
18.333333
41
0.8
11
110
7.909091
0.909091
0
0
0
0
0
0
0
0
0
0
0
0.136364
110
5
42
22
0.915789
0
0
0
0
0
0.163636
0
0
0
0
0
0
1
0
false
0
0.333333
0
1
0
1
0
0
null
0
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0
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0
0
0
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1
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null
0
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0
0
0
0
0
1
0
1
0
0
4
52690417ed3057adf69e4aaa8c3cdac890ed3f7c
160
py
Python
aula5_listas.py
clovisdanielcosta/dio-python
764eaac06320c0531c3038e301eb4c002fe8afce
[ "MIT" ]
null
null
null
aula5_listas.py
clovisdanielcosta/dio-python
764eaac06320c0531c3038e301eb4c002fe8afce
[ "MIT" ]
null
null
null
aula5_listas.py
clovisdanielcosta/dio-python
764eaac06320c0531c3038e301eb4c002fe8afce
[ "MIT" ]
null
null
null
lista = [1, 3, 5, 7] lista_animal = ['cachorro', 'gato', 'elefante'] print(lista) print(lista_animal[1]) #Resultado: gato for x in lista_animal: print(x)
16
47
0.6625
25
160
4.12
0.56
0.320388
0
0
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0
0
0.037313
0.1625
160
9
48
17.777778
0.731343
0.09375
0
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0.13986
0
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0
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0
0
1
0
false
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1
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null
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0
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0
0
1
0
4
5278db83cc62e90a945160d06e0d80dd7ee0557a
1,380
py
Python
tests/v2/test_user_create_data.py
MichaelTROEHLER/datadog-api-client-python
12c46626622fb1277bb1e172753b342c671348bd
[ "Apache-2.0" ]
null
null
null
tests/v2/test_user_create_data.py
MichaelTROEHLER/datadog-api-client-python
12c46626622fb1277bb1e172753b342c671348bd
[ "Apache-2.0" ]
null
null
null
tests/v2/test_user_create_data.py
MichaelTROEHLER/datadog-api-client-python
12c46626622fb1277bb1e172753b342c671348bd
[ "Apache-2.0" ]
null
null
null
# coding: utf-8 # Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License. # This product includes software developed at Datadog (https://www.datadoghq.com/). # Copyright 2019-Present Datadog, Inc. from __future__ import absolute_import import sys import unittest import datadog_api_client.v2 try: from datadog_api_client.v2.model import user_create_attributes except ImportError: user_create_attributes = sys.modules[ 'datadog_api_client.v2.model.user_create_attributes'] try: from datadog_api_client.v2.model import user_relationships except ImportError: user_relationships = sys.modules[ 'datadog_api_client.v2.model.user_relationships'] try: from datadog_api_client.v2.model import users_type except ImportError: users_type = sys.modules[ 'datadog_api_client.v2.model.users_type'] from datadog_api_client.v2.model.user_create_data import UserCreateData class TestUserCreateData(unittest.TestCase): """UserCreateData unit test stubs""" def setUp(self): pass def tearDown(self): pass def testUserCreateData(self): """Test UserCreateData""" # FIXME: construct object with mandatory attributes with example values # model = UserCreateData() # noqa: E501 pass if __name__ == '__main__': unittest.main()
28.163265
108
0.744928
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1,380
5.693642
0.456647
0.081218
0.129949
0.146193
0.270051
0.270051
0.26599
0.192893
0.081218
0
0
0.015887
0.178986
1,380
48
109
28.75
0.853486
0.28913
0
0.310345
0
0
0.147303
0.139004
0
0
0
0.020833
0
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0.103448
false
0.103448
0.37931
0
0.517241
0
0
0
0
null
0
0
0
0
0
0
0
0
0
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null
0
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0
0
0
0
1
1
0
1
0
0
4
5282ad10aa0332fbe48a66cf7b4236cbd0dc33e8
103
py
Python
httpserver/controllersX/css.py
evinlort/http-server
7db0637da7afbdb3736e595c431477c5242320a9
[ "MIT" ]
null
null
null
httpserver/controllersX/css.py
evinlort/http-server
7db0637da7afbdb3736e595c431477c5242320a9
[ "MIT" ]
null
null
null
httpserver/controllersX/css.py
evinlort/http-server
7db0637da7afbdb3736e595c431477c5242320a9
[ "MIT" ]
null
null
null
def index(query): query = query.strip("/") fd = open("css/" + query, "r") return fd.read()
20.6
34
0.533981
14
103
3.928571
0.714286
0.363636
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0.242718
103
4
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25.75
0.705128
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0.25
false
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null
1
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null
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1
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0
0
0
0
0
0
4
bff26d4b7c04348a8e5060fd3a86fabf1d56d307
62
py
Python
bkgames/configuration/__init__.py
michalurbanski/bkgames
69b1d16ae27d3118dd78449ce7deecbd6e1b95e7
[ "MIT" ]
null
null
null
bkgames/configuration/__init__.py
michalurbanski/bkgames
69b1d16ae27d3118dd78449ce7deecbd6e1b95e7
[ "MIT" ]
null
null
null
bkgames/configuration/__init__.py
michalurbanski/bkgames
69b1d16ae27d3118dd78449ce7deecbd6e1b95e7
[ "MIT" ]
null
null
null
from bkgames.configuration.configuration import Configuration
31
61
0.903226
6
62
9.333333
0.666667
0
0
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0.064516
62
1
62
62
0.965517
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true
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0
1
0
1
0
0
0
0
4
872a0b36152db9a207aeb90f2bbb90b0b2d052f0
47
py
Python
20180609/python_cla_02.py
bijitchakraborty12/MyProjects01
503af4cd6e8fa0576add7ac64393f1b4a16456c7
[ "MIT" ]
null
null
null
20180609/python_cla_02.py
bijitchakraborty12/MyProjects01
503af4cd6e8fa0576add7ac64393f1b4a16456c7
[ "MIT" ]
null
null
null
20180609/python_cla_02.py
bijitchakraborty12/MyProjects01
503af4cd6e8fa0576add7ac64393f1b4a16456c7
[ "MIT" ]
null
null
null
from sys import argv for i in argv: print(i)
9.4
20
0.702128
10
47
3.3
0.8
0
0
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0.234043
47
4
21
11.75
0.916667
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false
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0.333333
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0.333333
0.333333
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null
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1
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0
0
0
4
873e785fdbf94a82aa3d92297ae2a518a9671687
883
py
Python
clase2/Tarea/reemplace.py
Tank3-TK3/codigo-basico-Python
580e8d284fa8a4d70b2a264762c91bd64c89ab80
[ "MIT" ]
7
2021-04-19T01:32:49.000Z
2021-06-04T17:38:04.000Z
clase2/Tarea/reemplace.py
Tank3-TK3/codigo-basico-Python
580e8d284fa8a4d70b2a264762c91bd64c89ab80
[ "MIT" ]
null
null
null
clase2/Tarea/reemplace.py
Tank3-TK3/codigo-basico-Python
580e8d284fa8a4d70b2a264762c91bd64c89ab80
[ "MIT" ]
null
null
null
print ("--------------------------------------------------") cad1 = "separar" print (cad1) nuevo_string1 = ",".join(cad1) print (nuevo_string1) print ("--------------------------------------------------") cad2 = "mi archivo de texto.txt" print (cad2) nuevo_string2 = cad2.split() nuevo_string2 = "_".join(nuevo_string2) print (nuevo_string2) print ("--------------------------------------------------") cad3 = "su clave es: 1540" print (cad3) nuevo_string3 = cad3.replace(cad3[13:], "XXXX") print (nuevo_string3) print ("--------------------------------------------------") cad4 = "2552552550" print (cad4) nuevo_string4 = cad4.replace("55", "55.") print (nuevo_string4) nuevo_string4 = nuevo_string4.split(".") print (nuevo_string4) nuevo_string4 = ".".join(nuevo_string4) print (nuevo_string4) print ("--------------------------------------------------")
32.703704
61
0.488109
82
883
5.04878
0.329268
0.231884
0.123188
0.173913
0.140097
0
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0
0.062341
0.109853
883
26
62
33.961538
0.464377
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0.307692
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0.373396
0.291715
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false
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0.576923
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null
1
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0
0
0
0
1
0
4
87423974439c2a75977c377c0729354fe022eae1
70
py
Python
src/fitness_functions/sphere.py
NickDeGroote/Complex_Systems_SS21
7317499dd3ebd8d2b98c15c4006972a1033051ae
[ "MIT" ]
null
null
null
src/fitness_functions/sphere.py
NickDeGroote/Complex_Systems_SS21
7317499dd3ebd8d2b98c15c4006972a1033051ae
[ "MIT" ]
null
null
null
src/fitness_functions/sphere.py
NickDeGroote/Complex_Systems_SS21
7317499dd3ebd8d2b98c15c4006972a1033051ae
[ "MIT" ]
null
null
null
def sphere(x: list) -> float: fitness = sum(x) return fitness
17.5
29
0.614286
10
70
4.3
0.8
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0.257143
70
3
30
23.333333
0.826923
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0.333333
false
0
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0
0
0
1
0
0
4
874b4a77ee5c6518c18bd6036ac66878c5274a83
222
py
Python
auctions/context_processor.py
eld120/CS50-Commerce
6ce6dfd633c0020da609d43311bc6ee70920a7c5
[ "MIT" ]
null
null
null
auctions/context_processor.py
eld120/CS50-Commerce
6ce6dfd633c0020da609d43311bc6ee70920a7c5
[ "MIT" ]
null
null
null
auctions/context_processor.py
eld120/CS50-Commerce
6ce6dfd633c0020da609d43311bc6ee70920a7c5
[ "MIT" ]
null
null
null
from .models import Bid, Comment, Listing def context_processor(request): return { "bid_db": Bid.objects.all(), "listing_db": Listing.objects.all(), "comment_db": Comment.objects.all(), }
22.2
44
0.626126
26
222
5.192308
0.538462
0.222222
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222
9
45
24.666667
0.789474
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0.117117
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1
0.142857
false
0
0.142857
0.142857
0.428571
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null
1
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0
0
1
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0
0
4
8761f060215c10f3d66649f2fd267ceecff714c4
315
py
Python
language_basics/varparameter.py
nebulx29/LearnPython
519a29b04c36bbd1feb2cda5a0c0669e15062ba4
[ "Apache-2.0" ]
null
null
null
language_basics/varparameter.py
nebulx29/LearnPython
519a29b04c36bbd1feb2cda5a0c0669e15062ba4
[ "Apache-2.0" ]
null
null
null
language_basics/varparameter.py
nebulx29/LearnPython
519a29b04c36bbd1feb2cda5a0c0669e15062ba4
[ "Apache-2.0" ]
null
null
null
def multi_print(number = 3, word = "Hallo"): for i in range(0, number): print(str(i) + " " + word) multi_print(1, "Hallo") print("--") multi_print() print("--") multi_print(2) print("--") multi_print(word = "Welt") print("--") multi_print(word = "Welt", number = 5) print("--")
19.6875
45
0.542857
41
315
4.02439
0.414634
0.363636
0.363636
0.230303
0.278788
0
0
0
0
0
0
0.020661
0.231746
315
15
46
21
0.661157
0
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0.384615
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0.09699
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0.076923
false
0
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0.076923
0.923077
0
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null
1
1
1
0
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0
0
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0
0
0
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0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
1
0
4
5e3f6478ad5899bc5ec6b4b7bafac2647004b5a3
192
py
Python
code/api/views.py
alejoortiz/celery-django-multiple-apps
4f168f691f59d79212a93672180153a151261639
[ "MIT" ]
null
null
null
code/api/views.py
alejoortiz/celery-django-multiple-apps
4f168f691f59d79212a93672180153a151261639
[ "MIT" ]
null
null
null
code/api/views.py
alejoortiz/celery-django-multiple-apps
4f168f691f59d79212a93672180153a151261639
[ "MIT" ]
null
null
null
from django.http import HttpResponse # Create your views here. from api.task import async_test_api def main(request): async_test_api.delay() return HttpResponse('Hello, World API!')
21.333333
44
0.760417
28
192
5.071429
0.714286
0.126761
0.169014
0
0
0
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0
0.15625
192
8
45
24
0.876543
0.119792
0
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0.101796
0
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0
0
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1
0.2
false
0
0.4
0
0.8
0
1
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null
0
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0
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0
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null
0
0
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0
0
0
0
0
1
0
1
0
0
4
5e507b9b7f9ec10b506bffaa7dc7f82796b18647
7,400
py
Python
GMAO_etc/unittests/test_check_obsysrc.py
GEOS-ESM/GMAO_Shared
022af23abbc7883891006b57379be96d9a50df23
[ "NASA-1.3", "ECL-2.0", "Apache-2.0" ]
1
2020-02-01T17:36:53.000Z
2020-02-01T17:36:53.000Z
GMAO_etc/unittests/test_check_obsysrc.py
GEOS-ESM/GMAO_Shared
022af23abbc7883891006b57379be96d9a50df23
[ "NASA-1.3", "ECL-2.0", "Apache-2.0" ]
105
2019-07-08T19:27:23.000Z
2022-03-22T02:12:16.000Z
GMAO_etc/unittests/test_check_obsysrc.py
GEOS-ESM/GMAO_Shared
022af23abbc7883891006b57379be96d9a50df23
[ "NASA-1.3", "ECL-2.0", "Apache-2.0" ]
10
2019-07-05T18:00:44.000Z
2022-03-11T16:26:29.000Z
#!/usr/bin/env python import os, sys, time sys.path.append(os.pardir) import check_obsysrc import filecmp import shutil import unittest infile = os.path.join("input","obsys-test-gaas.rc") check_obsysrc = os.path.join(os.pardir, "check_obsysrc.py") class CheckObsysRcTest(unittest.TestCase): #....................................................................... def test_1(self): "Test check_obsysrc without any flags" global check_obsysrc, infile newfil = os.path.join("outdir", "obsys-test1.rc.new") newexp = os.path.join("outexp", "obsys-test1.rc.new") errfil = os.path.join("outdir", "obsys-test1.rc.err") errfil_ = os.path.join("outdir", "obsys-test1.rc.err_") errexp_ = os.path.join("outexp", "obsys-test1.rc.err_") if os.path.isfile(newfil): os.remove(newfil) if os.path.isfile(errfil): os.remove(errfil) cmd = check_obsysrc+" "+infile+" "+newfil+" "+errfil print(cmd) os.system(cmd) with open(errfil, mode="r") as input: with open(errfil_, mode="w") as output: for line in input: if line.find("gap start") == -1: output.write(line) self.assertTrue(filecmp.cmp(newfil, newexp)) self.assertTrue(filecmp.cmp(errfil_, errexp_)) os.remove(newfil) os.remove(errfil) os.remove(errfil_) #....................................................................... def test_2(self): 'Test with "--ignore_gaps mod04_006_flk" flag' global check_obsysrc, infile newfil = os.path.join("outdir", "obsys-test2.rc.new") newexp = os.path.join("outexp", "obsys-test2.rc.new") errfil = os.path.join("outdir", "obsys-test2.rc.err") errfil_ = os.path.join("outdir", "obsys-test2.rc.err_") errexp_ = os.path.join("outexp", "obsys-test2.rc.err_") if os.path.isfile(newfil): os.remove(newfil) if os.path.isfile(errfil): os.remove(errfil) flags = " --ignore_gaps mod04_006_flk " cmd = check_obsysrc+flags+infile+" "+newfil+" "+errfil print(cmd) os.system(cmd) with open(errfil, mode="r") as input: with open(errfil_, mode="w") as output: for line in input: if line.find("gap start") == -1: output.write(line) self.assertTrue(filecmp.cmp(newfil, newexp)) self.assertTrue(filecmp.cmp(errfil_, errexp_)) os.remove(newfil) os.remove(errfil) os.remove(errfil_) #....................................................................... def test_3(self): 'Test with "--obslist mod04_006_flk --ignore_gaps all=2" flags' global check_obsysrc, infile newfil = os.path.join("outdir", "obsys-test3.rc.new") newexp = os.path.join("outexp", "obsys-test3.rc.new") errfil = os.path.join("outdir", "obsys-test3.rc.err") errfil_ = os.path.join("outdir", "obsys-test3.rc.err_") errexp_ = os.path.join("outexp", "obsys-test3.rc.err_") if os.path.isfile(newfil): os.remove(newfil) if os.path.isfile(errfil): os.remove(errfil) flags = " --obslist mod04_006_flk --ignore_gaps all=2 " cmd = check_obsysrc+flags+infile+" "+newfil+" "+errfil print(cmd) os.system(cmd) with open(errfil, mode="r") as input: with open(errfil_, mode="w") as output: for line in input: if line.find("gap start") == -1: output.write(line) self.assertTrue(filecmp.cmp(newfil, newexp)) self.assertTrue(filecmp.cmp(errfil_, errexp_)) os.remove(newfil) os.remove(errfil) os.remove(errfil_) #....................................................................... def test_4(self): 'Test with "--obslist mod04_006_flk --ignore_gaps all=0" flags' global check_obsysrc, infile newfil = os.path.join("outdir", "obsys-test4.rc.new") newexp = os.path.join("outexp", "obsys-test4.rc.new") errfil = os.path.join("outdir", "obsys-test4.rc.err") errfil_ = os.path.join("outdir", "obsys-test4.rc.err_") errexp_ = os.path.join("outexp", "obsys-test4.rc.err_") if os.path.isfile(newfil): os.remove(newfil) if os.path.isfile(errfil): os.remove(errfil) flags = " --obslist mod04_006_flk --ignore_gaps all=0 " cmd = check_obsysrc+flags+infile+" "+newfil+" "+errfil print(cmd) os.system(cmd) with open(errfil, mode="r") as input: with open(errfil_, mode="w") as output: for line in input: if line.find("gap start") == -1: output.write(line) self.assertTrue(filecmp.cmp(newfil, newexp)) self.assertTrue(filecmp.cmp(errfil_, errexp_)) os.remove(newfil) os.remove(errfil) os.remove(errfil_) #....................................................................... def test_5(self): "Test should fail if newfil equals input file" global check_obsysrc, infile errfil = os.path.join("outdir", "obsys-test5.rc.err") self.assertRaises(Exception, check_obsysrc, infile, infile, errfil) #....................................................................... def test_6(self): 'Test with "--obslist nim07_tomseff_nc" flag' global check_obsysrc, infile newfil = os.path.join("outdir", "obsys-test6.rc.new") newexp = os.path.join("outexp", "obsys-test6.rc.new") errfil = os.path.join("outdir", "obsys-test6.rc.err") errfil_ = os.path.join("outdir", "obsys-test6.rc.err_") errexp_ = os.path.join("outexp", "obsys-test6.rc.err_") if os.path.isfile(newfil): os.remove(newfil) if os.path.isfile(errfil): os.remove(errfil) flags = " --obslist nim07_tomseff_nc " cmd = check_obsysrc+flags+infile+" "+newfil+" "+errfil print(cmd) os.system(cmd) with open(errfil, mode="r") as input: with open(errfil_, mode="w") as output: for line in input: if line.find("gap start") == -1: output.write(line) self.assertTrue(filecmp.cmp(newfil, newexp)) self.assertTrue(filecmp.cmp(errfil_, errexp_)) os.remove(newfil) os.remove(errfil) os.remove(errfil_) #....................................................................... if __name__ == "__main__": # setup once for all tests #------------------------- print("running setup") if os.path.isdir("outdir"): print("> removing outdir directory") shutil.rmtree("outdir") print("> making outdir directory") os.mkdir("outdir") if os.path.isdir("archive"): print("> removing archive directory") shutil.rmtree("archive") tarfile = os.path.join("input", "archive.tar") print("> untarring "+tarfile) os.system("tar xf "+tarfile) # run tests #---------- unittest.main(exit=False) # clean up afterwards #-------------------- if os.path.isdir("archive"): print("removing archive directory") shutil.rmtree("archive")
33.944954
76
0.540676
872
7,400
4.482798
0.133028
0.064467
0.074188
0.06549
0.808391
0.797646
0.790739
0.771553
0.688667
0.594014
0
0.013533
0.251081
7,400
217
77
34.101382
0.691808
0.124595
0
0.56
0
0
0.204616
0
0
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0.073333
1
0.04
false
0
0.033333
0
0.08
0.073333
0
0
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null
0
0
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1
1
1
1
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null
0
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0
0
0
0
0
0
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4
5e74e5e0b3c4d6508421d8a36ee04343b9c48333
138
py
Python
Aulas/Aulas-Mundo3/Aula019/Aula019a.py
Sofista23/Aula1_Python
129132d977058ac6f23cc95c7bb8b55d8a1bb429
[ "MIT" ]
null
null
null
Aulas/Aulas-Mundo3/Aula019/Aula019a.py
Sofista23/Aula1_Python
129132d977058ac6f23cc95c7bb8b55d8a1bb429
[ "MIT" ]
null
null
null
Aulas/Aulas-Mundo3/Aula019/Aula019a.py
Sofista23/Aula1_Python
129132d977058ac6f23cc95c7bb8b55d8a1bb429
[ "MIT" ]
null
null
null
a={"nome":"Pedro","idade":18,"sexo":"M"} print(a.values()) print(a.keys()) print(a.items()) for k,v in a.items(): print(f"{k} é {v}.")
23
40
0.557971
27
138
2.851852
0.62963
0.233766
0
0
0
0
0
0
0
0
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0.016129
0.101449
138
6
41
23
0.604839
0
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0.208633
0
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false
0
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0.666667
1
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null
1
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0
0
0
0
0
1
0
4
5e7b3071704b508954bc77ef6d6ee183673eab7a
62
py
Python
kattis/Hissing Microphone.py
jaredliw/python-question-bank
9c8c246623d8d171f875700b57772df0afcbdcdf
[ "MIT" ]
1
2021-04-08T07:49:15.000Z
2021-04-08T07:49:15.000Z
kattis/Hissing Microphone.py
jaredliw/leetcode-solutions
9c8c246623d8d171f875700b57772df0afcbdcdf
[ "MIT" ]
null
null
null
kattis/Hissing Microphone.py
jaredliw/leetcode-solutions
9c8c246623d8d171f875700b57772df0afcbdcdf
[ "MIT" ]
1
2022-01-23T02:12:24.000Z
2022-01-23T02:12:24.000Z
# CPU: 0.05 s print("hiss" if "ss" in input() else "no hiss")
20.666667
47
0.596774
13
62
2.846154
0.923077
0
0
0
0
0
0
0
0
0
0
0.06
0.193548
62
2
48
31
0.68
0.177419
0
0
0
0
0.265306
0
0
0
0
0
0
1
0
true
0
0
0
0
1
1
0
0
null
0
0
0
0
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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
0
1
0
4
5e8601723969ed4b356accfbb81d6e82c3ca71cd
180
py
Python
Timbuchalka/Section-4/contrived.py
Advik-B/Learn-Python
66ac57259764e8f2c3c6513a8de6c106800d8abe
[ "MIT" ]
6
2021-07-26T14:21:25.000Z
2021-07-26T14:32:01.000Z
Timbuchalka/Section-4/contrived.py
Advik-B/Learn-Python
66ac57259764e8f2c3c6513a8de6c106800d8abe
[ "MIT" ]
2
2021-12-10T10:25:19.000Z
2021-12-10T10:27:15.000Z
Timbuchalka/Section-4/contrived.py
Advik-B/Learn-Python
66ac57259764e8f2c3c6513a8de6c106800d8abe
[ "MIT" ]
null
null
null
numbers = [1, 45, 31, 12, 60] for number in numbers: if number % 8 == 0: print ("THe numbers are unacccepetble") break else: print ("The Numbers are good")
22.5
47
0.588889
26
180
4.076923
0.730769
0.150943
0.283019
0.339623
0
0
0
0
0
0
0
0.086614
0.294444
180
8
48
22.5
0.748032
0
0
0
0
0
0.270718
0
0
0
0
0
0
1
0
false
0
0
0
0
0.285714
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
0
0
0
0
0
0
0
4
5ebdc1757c941fb4f6c3694f190a839c7dd58b01
243
py
Python
HW4/VeronyWise/task4.3.py
kolyasalubov/Lv-677.PythonCore
c9f9107c734a61e398154a90b8a3e249276c2704
[ "MIT" ]
null
null
null
HW4/VeronyWise/task4.3.py
kolyasalubov/Lv-677.PythonCore
c9f9107c734a61e398154a90b8a3e249276c2704
[ "MIT" ]
null
null
null
HW4/VeronyWise/task4.3.py
kolyasalubov/Lv-677.PythonCore
c9f9107c734a61e398154a90b8a3e249276c2704
[ "MIT" ]
6
2022-02-22T22:30:49.000Z
2022-03-28T12:51:19.000Z
number_for_fibonacci = int(input("Enter Fibonacci number: ")) first_num = 0 second_num = 1 while second_num <= number_for_fibonacci: print(second_num) first_num, second_num = second_num, first_num + second_num print("Fibonacci number")
34.714286
62
0.769547
36
243
4.833333
0.361111
0.310345
0.206897
0.195402
0.298851
0.298851
0
0
0
0
0
0.009524
0.135802
243
7
63
34.714286
0.819048
0
0
0
0
0
0.163934
0
0
0
0
0
0
1
0
false
0
0
0
0
0.285714
0
0
0
null
1
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
0
0
0
0
0
0
0
4
5ec739fd644d628292b2c5f40f2ace0ca9818d97
274
py
Python
Auction/admin.py
Poornartha/Odonata
71e8dfc4e8d93c6ecc1a3a155459b7e43bd28cdb
[ "MIT" ]
null
null
null
Auction/admin.py
Poornartha/Odonata
71e8dfc4e8d93c6ecc1a3a155459b7e43bd28cdb
[ "MIT" ]
null
null
null
Auction/admin.py
Poornartha/Odonata
71e8dfc4e8d93c6ecc1a3a155459b7e43bd28cdb
[ "MIT" ]
null
null
null
from django.contrib import admin from .models import Task, Taskassigned, Tasksubmission, Tasksubmissionfile # Register your models here. admin.site.register(Task) admin.site.register(Taskassigned) admin.site.register(Tasksubmission) admin.site.register(Tasksubmissionfile)
30.444444
74
0.839416
32
274
7.1875
0.4375
0.156522
0.295652
0
0
0
0
0
0
0
0
0
0.076642
274
8
75
34.25
0.909091
0.094891
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
0.333333
0
0.333333
0
0
0
0
null
0
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
0
0
1
0
1
0
0
0
0
4
0d7f85a81538a8ad33254c5aed1e2dca558082e2
327
py
Python
pybullet-gym/pybulletgym/envs/roboschool/envs/locomotion/half_cheetah_env.py
SmaleZ/vcl_diayn
b2c47a681675b405d2011bc4a43c3914f3af4ecc
[ "MIT" ]
null
null
null
pybullet-gym/pybulletgym/envs/roboschool/envs/locomotion/half_cheetah_env.py
SmaleZ/vcl_diayn
b2c47a681675b405d2011bc4a43c3914f3af4ecc
[ "MIT" ]
null
null
null
pybullet-gym/pybulletgym/envs/roboschool/envs/locomotion/half_cheetah_env.py
SmaleZ/vcl_diayn
b2c47a681675b405d2011bc4a43c3914f3af4ecc
[ "MIT" ]
null
null
null
from pybulletgym.envs.roboschool.envs.locomotion.walker_base_env import WalkerBaseBulletEnv from pybulletgym.envs.roboschool.robots.locomotors import HalfCheetah class HalfCheetahBulletEnv(WalkerBaseBulletEnv): def __init__(self): self.robot = HalfCheetah() WalkerBaseBulletEnv.__init__(self, self.robot)
36.333333
91
0.807339
33
327
7.69697
0.575758
0.11811
0.149606
0.228346
0
0
0
0
0
0
0
0
0.119266
327
8
92
40.875
0.881944
0
0
0
0
0
0
0
0
0
0
0
0
1
0.166667
false
0
0.333333
0
0.666667
0
0
0
0
null
0
0
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
0
0
1
0
1
0
0
4
0da3e655d907d6c720a8161925fc6dd25f10291a
825
py
Python
utils/relevance_info.py
StKlaas/NPRF
9de3b4206aa25e9fe730b84a7b6fdfa5e46ac810
[ "Apache-2.0" ]
36
2018-08-27T15:08:31.000Z
2022-03-07T06:38:55.000Z
utils/relevance_info.py
StKlaas/NPRF
9de3b4206aa25e9fe730b84a7b6fdfa5e46ac810
[ "Apache-2.0" ]
6
2018-11-01T21:45:22.000Z
2021-12-21T09:03:59.000Z
utils/relevance_info.py
StKlaas/NPRF
9de3b4206aa25e9fe730b84a7b6fdfa5e46ac810
[ "Apache-2.0" ]
10
2018-09-25T01:48:18.000Z
2020-12-09T01:29:07.000Z
class Relevance(object): '''Incooperation with result file and qrels file Attributes: qid, int, query id judgement_docid_list: list of list, judged docids in TREC qrels supervised_docid_list: list, top docids from unsupervised models, e.g. BM25, QL. ''' def __init__(self, qid, judged_docid_list, supervised_docid_list, supervised_score_list): self._qid = qid self._judged_docid_list = judged_docid_list self._supervised_docid_list = supervised_docid_list self._supervised_score_list = supervised_score_list def get_qid(self): return self._qid def get_judged_docid_list(self): return self._judged_docid_list def get_supervised_docid_list(self): return self._supervised_docid_list def get_supervised_score_list(self): return self._supervised_score_list
29.464286
91
0.766061
116
825
5.017241
0.318966
0.185567
0.195876
0.092784
0.32646
0
0
0
0
0
0
0.002924
0.170909
825
28
92
29.464286
0.847953
0.283636
0
0
0
0
0
0
0
0
0
0
0
1
0.357143
false
0
0
0.285714
0.714286
0
0
0
0
null
0
1
0
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
0
1
0
0
0
1
1
0
0
4
0dc8854110660d466e6596f38b2f8fce51c55ca7
514
py
Python
tools/fileinfo/features/delphi-timestamp-heuristic/test.py
xbabka01/retdec-regression-tests
1ac40cca5165740364e6f7fb72b20820eac9bc7c
[ "MIT" ]
8
2017-12-14T14:25:17.000Z
2019-03-09T03:29:12.000Z
tools/fileinfo/features/delphi-timestamp-heuristic/test.py
xbabka01/retdec-regression-tests
1ac40cca5165740364e6f7fb72b20820eac9bc7c
[ "MIT" ]
10
2019-06-14T09:12:55.000Z
2021-10-01T12:15:43.000Z
tools/fileinfo/features/delphi-timestamp-heuristic/test.py
xbabka01/retdec-regression-tests
1ac40cca5165740364e6f7fb72b20820eac9bc7c
[ "MIT" ]
8
2019-05-10T14:59:48.000Z
2022-03-07T16:34:23.000Z
from regression_tests import * class Test(Test): settings = TestSettings( tool='fileinfo', input='Abundant-O0-g' ) def test_check_fileinfo_output(self): assert self.fileinfo.succeeded assert self.fileinfo.output.contains('PE') assert self.fileinfo.output.contains('x86') assert self.fileinfo.output.contains('Executable file') assert self.fileinfo.output.contains('Little endian') assert self.fileinfo.output.contains('Borland Delphi')
32.125
63
0.682879
58
514
5.982759
0.5
0.242075
0.311239
0.345821
0.461095
0
0
0
0
0
0
0.007371
0.208171
514
15
64
34.266667
0.845209
0
0
0
0
0
0.132296
0
0
0
0
0
0.461538
1
0.076923
false
0
0.076923
0
0.307692
0
0
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
1
0
0
0
0
0
0
0
0
0
4
0dd02f42e260d56d5dec5e2095b2101e36982ec0
301
py
Python
platypush/plugins/light/__init__.py
shineit/platypush
a67b301cd66235b41bff6bfb3de56b903bf9551d
[ "MIT" ]
1
2020-12-08T06:26:31.000Z
2020-12-08T06:26:31.000Z
platypush/plugins/light/__init__.py
shineit/platypush
a67b301cd66235b41bff6bfb3de56b903bf9551d
[ "MIT" ]
null
null
null
platypush/plugins/light/__init__.py
shineit/platypush
a67b301cd66235b41bff6bfb3de56b903bf9551d
[ "MIT" ]
null
null
null
from .. import Plugin class LightPlugin(Plugin): def on(self): raise NotImplementedError() def off(self): raise NotImplementedError() def toggle(self): raise NotImplementedError() def status(self): raise NotImplementedError() # vim:sw=4:ts=4:et:
15.842105
35
0.631229
32
301
5.9375
0.5625
0.189474
0.589474
0.489474
0
0
0
0
0
0
0
0.009009
0.262458
301
18
36
16.722222
0.846847
0.056478
0
0.4
0
0
0
0
0
0
0
0
0
1
0.4
false
0
0.1
0
0.6
0
0
0
0
null
0
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
0
1
0
0
0
0
1
0
0
4
0def51f2d6c8d71d5fb23adc5538719bca7841b0
72
py
Python
0/1/1085/1085.py
chr0m3/boj-codes
d71d0a22d0a3ae62c225f382442461275f56fe8f
[ "MIT" ]
3
2017-07-08T16:29:06.000Z
2020-07-20T00:17:45.000Z
0/1/1085/1085.py
chr0m3/boj-codes
d71d0a22d0a3ae62c225f382442461275f56fe8f
[ "MIT" ]
null
null
null
0/1/1085/1085.py
chr0m3/boj-codes
d71d0a22d0a3ae62c225f382442461275f56fe8f
[ "MIT" ]
2
2017-11-20T14:06:06.000Z
2020-07-20T00:17:47.000Z
x, y, w, h = map(int, input().split()) print(min([x, y, w - x, h - y]))
24
38
0.472222
16
72
2.125
0.625
0.117647
0.176471
0
0
0
0
0
0
0
0
0
0.208333
72
2
39
36
0.596491
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
0
0
0
0.5
1
0
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
0
0
0
1
0
4
216dc6230121926106741cd40a61dd231d5c83a4
417
py
Python
leonardo_module_redirects/__init__.py
dresl/leonardo-module-redirects
e8d1a04334ee63c36337f01a48923b1d278e5a01
[ "BSD-3-Clause" ]
null
null
null
leonardo_module_redirects/__init__.py
dresl/leonardo-module-redirects
e8d1a04334ee63c36337f01a48923b1d278e5a01
[ "BSD-3-Clause" ]
null
null
null
leonardo_module_redirects/__init__.py
dresl/leonardo-module-redirects
e8d1a04334ee63c36337f01a48923b1d278e5a01
[ "BSD-3-Clause" ]
null
null
null
from django.apps import AppConfig default_app_config = 'leonardo_module_redirects.Config' try: from local_settings import APPS except ImportError: pass if 'leonardo_module_redirects' in APPS: LEONARDO_APPS = [ 'leonardo_module_redirects', 'django.contrib.redirects', ] class Config(AppConfig): name = 'leonardo_module_redirects' verbose_name = "leonardo-module-redirects"
20.85
55
0.736211
47
417
6.255319
0.489362
0.238095
0.391156
0.183673
0
0
0
0
0
0
0
0
0.18705
417
19
56
21.947368
0.867257
0
0
0
0
0
0.375
0.375
0
0
0
0
0
1
0
false
0.071429
0.214286
0
0.428571
0
0
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
0
0
0
1
0
0
0
0
0
4
2173824a52efbb4038be99647ff170a1d3403769
128
py
Python
name_gen_app/admin.py
david-abel/name_generator
3d13fc17552b3a67dd57d87c5c9963d4f33548d0
[ "MIT" ]
1
2019-12-16T04:42:42.000Z
2019-12-16T04:42:42.000Z
name_gen_app/admin.py
david-abel/name_generator
3d13fc17552b3a67dd57d87c5c9963d4f33548d0
[ "MIT" ]
null
null
null
name_gen_app/admin.py
david-abel/name_generator
3d13fc17552b3a67dd57d87c5c9963d4f33548d0
[ "MIT" ]
1
2019-12-16T04:42:44.000Z
2019-12-16T04:42:44.000Z
from django.contrib import admin from name_gen_app import models # Register your models here. admin.site.register(models.NGram)
25.6
33
0.828125
20
128
5.2
0.7
0
0
0
0
0
0
0
0
0
0
0
0.109375
128
5
33
25.6
0.912281
0.203125
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
0.666667
0
0.666667
0
1
0
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
0
0
0
4
21741bdab94df2da4437c9e5316dc923420b6d69
52,090
py
Python
qa_tools/prediction.py
keithgroup/qa-atoms-dimers
fff24f5658b45618de8f2dcb35b3b0b7e87898d0
[ "MIT" ]
null
null
null
qa_tools/prediction.py
keithgroup/qa-atoms-dimers
fff24f5658b45618de8f2dcb35b3b0b7e87898d0
[ "MIT" ]
null
null
null
qa_tools/prediction.py
keithgroup/qa-atoms-dimers
fff24f5658b45618de8f2dcb35b3b0b7e87898d0
[ "MIT" ]
null
null
null
# MIT License # # Copyright (c) 2021, Alex M. Maldonado # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import numpy as np import pandas as pd from qa_tools.utils import * def qa_predictions( df_qc, ref_label, ref_charge, excitation_level=0, lambda_values=[-1, 0, 1], basis_set='aug-cc-pV5Z', bond_length=None, ignore_one_row=True): """Quantum alchemy predictions of a reference system at specified lambda values. Parameters ---------- df_qc : :obj:`pandas.dataframe` A dataframe with quantum chemistry data. ref_label : :obj:`str` The system label of the desired quantum alchemy reference system. For example, `'c'`, `'h'`, etc. ref_charge : :obj:`int` Total system charge of the quantum alchemy reference system. excitation_level : :obj:`int`, optional Electronic state of the system with respect to the ground state. ``0`` represents the ground state, ``1`` the first excited state, etc. Defaults to ground state. lambda_values : :obj:`float`, :obj:`list`, optional Desired nuclear charge perturbations of the quantum alchemy reference system. basis_set : :obj:`str`, optional Desired basis sets the predictions are from. Defaults to ``aug-cc-pV5Z``. bond_length : :obj:`float`, optional Desired bond length for dimers; must be specified. ignore_one_row : :obj:`bool`, optional Used to control errors in ``state_selection`` when there is missing data (i.e., just one state). If ``True``, no errors are raised. Defaults to ``True``. Returns ------- :obj:`numpy.ndarray` Energies at specified lambda values predicted using quantum alchemy. """ if len(df_qc.iloc[0]['atomic_numbers']) == 2: df_qc = df_qc.query('bond_length == @bond_length') if not isinstance(lambda_values, np.ndarray) and not isinstance(lambda_values, list): lambda_values = np.array([lambda_values]) ref_qc = df_qc.query( 'system == @ref_label' '& charge == @ref_charge' '& basis_set == @basis_set' '& lambda_value in @lambda_values' ) # Selects state if required. if len(set(df_qc['multiplicity'].values)) > 1: assert excitation_level is not None sys_mult = get_multiplicity( ref_qc, excitation_level, ignore_one_row=ignore_one_row ) ref_qc = ref_qc.query('multiplicity == @sys_mult') energies = np.zeros(len(lambda_values)) for i in range(len(lambda_values)): lambda_value = lambda_values[i] energies[i] = ref_qc.query( 'lambda_value == @lambda_value' )['electronic_energy'].values[0] return energies def qats_prediction(poly_coeffs, order, lambda_values): """Quantum alchemy predictions using a nth-order Taylor series. Parameters ---------- poly_coeffs : :obj:`numpy.ndarray` Polynomial coefficients in increasing order (e.g., zeroth, first, second, etc.). order :obj:`int` Highest order to include in the Taylor series. lambda_values : :obj:`float`, :obj:`list` Lambda values to make QATS predictions of. Returns ------- :obj:`numpy.ndarray` QATS predictions using a nth-order Taylor series. """ if not isinstance(lambda_values, np.ndarray) \ or not isinstance(lambda_values, list): lambda_values = np.array([lambda_values]) return np.polyval(poly_coeffs[:order+1][::-1], lambda_values) def energy_change_charge_qc_atom( df_qc, target_label, delta_charge, target_initial_charge=0, change_signs=False, basis_set='aug-cc-pV5Z', ignore_one_row=True): """Calculate the energy difference to change the charge of an atom using quantum chemistry. Quantum chemistry here implies no nuclear charge perturbations (i.e., a lambda of 0). This represents what the direct quantum chemical prediction. Parameters ---------- df_qc : :obj:`pandas.DataFrame` A pandas dataframe with quantum chemistry data. It should have the following columns (from `get_qc_dframe`): system, atomic_numbers, charge, multiplicity, n_electrons, qc_method, basis_set, lambda_range, finite_diff_delta, finite_diff_acc, poly_coeff. target_label : :obj:`str` Atoms in the system. For example, ``'c'``, ``'si'``, or ``'f.h'``. delta_charge : :obj:`int` Overall change in the initial target system. target_initial_charge : :obj:`int` Specifies the initial charge state of the target system. For example, the first ionization energy is the energy difference going from charge ``0 -> 1``, so ``target_initial_charge`` must equal ``0``. change_signs : :obj:`bool`, optional Multiply all energies by -1 (used to predict electron affinities). Defaults to ``False``. basis_set : :obj:`str`, optional Desired basis sets the predictions are from. Defaults to ``aug-cc-pV5Z``. ignore_one_row : :obj:`bool`, optional Used to control errors in ``state_selection`` when there is missing data (i.e., just one state). If ``True``, no errors are raised. Defaults to ``True``. Returns ------- :obj:`float` Energy required to change the charge of an atom from quantum chemistry in Hartrees. """ # Checks. assert len(df_qc.iloc[0]['atomic_numbers']) != 2 assert delta_charge != 0 if delta_charge < 0: assert change_signs == True ### QC DATA FOR INITIAL STATE OF TARGET ### target_initial_qc = df_qc.query( 'system == @target_label' '& charge == @target_initial_charge' '& lambda_value == 0' '& basis_set == @basis_set' ) if len(target_initial_qc) == 0: # Often we do not have the data to make the prediction. # So we return NaN. return np.nan else: target_initial_qc = select_state( target_initial_qc, 0, ignore_one_row=ignore_one_row ) assert len(target_initial_qc) == 1 # Should only have one row. ### QC DATA FOR FINAL STATE OF TARGET ### target_final_n_electrons = target_initial_qc.n_electrons.values[0] - delta_charge target_final_qc = df_qc.query( 'system == @target_label' '& lambda_value == 0' '& n_electrons == @target_final_n_electrons' '& basis_set == @basis_set' ) if len(target_final_qc) == 0: # Often we do not have the data to make the prediction. # So we return NaN. return np.nan else: target_final_qc = select_state( target_final_qc, 0, ignore_one_row=ignore_one_row ) assert len(target_final_qc) == 1 # Should only have one row. ### COMPUTES ENERGY DIFFERENCE ### e_diff = target_final_qc.iloc[0]['electronic_energy'] - target_initial_qc.iloc[0]['electronic_energy'] if change_signs: e_diff *= -1 return e_diff def energy_change_charge_qa_atom( df_qc, df_qats, target_label, delta_charge, target_initial_charge=0, change_signs=False, basis_set='aug-cc-pV5Z', use_ts=True, ignore_one_row=True, considered_lambdas=None, return_qats_vs_qa=False): """Calculate the energy difference to change the charge of a target atom using quantum alchemy with or without a Taylor series. Parameters ---------- df_qc : :obj:`pandas.DataFrame` A pandas dataframe with quantum chemistry data. It should have the following columns (from `get_qc_dframe`): system, atomic_numbers, charge, multiplicity, n_electrons, qc_method, basis_set, lambda_range, finite_diff_delta, finite_diff_acc, poly_coeff. df_qats : :obj:`pandas.DataFrame` A pandas dataframe with QATS data. It should have the following columns (from `get_qats_dframe`): system, atomic_numbers, charge, multiplicity, n_electrons, qc_method, basis_set, lambda, electronic_energy, hf_energy, and correlation_energy. target_label : :obj:`str` Atoms in the system. For example, ``'c'``, ``'si'``, or ``'f.h'``. delta_charge : :obj:`str` Overall change in the initial target system. target_initial_charge : :obj:`int` Specifies the initial charge state of the target system. For example, the first ionization energy is the energy difference going from charge ``0 -> 1``, so ``target_initial_charge`` must equal ``0``. change_signs : :obj:`bool`, optional Multiply all predictions by -1. Used to correct the sign for computing electron affinities. Defaults to ``False``. basis_set : :obj:`str`, optional Specifies the basis set to use for predictions. Defaults to ``'aug-cc-pV5Z'``. use_ts : :obj:`bool`, optional Use a Taylor series approximation (with finite differences) to make QATS-n predictions (where n is the order). Defaults to ``True``. ignore_one_row : :obj:`bool`, optional Used to control errors in ``state_selection`` when there is missing data (i.e., just one state). If ``True``, no errors are raised. Defaults to ``True``. considered_lambdas : :obj:`list`, optional Allows specification of lambda values that will be considered. ``None`` will allow all lambdas to be valid, ``[1, -1]`` would only report predictions using references using a lambda of ``1`` or ``-1``. Defaults to ``None``. return_qats_vs_qa : :obj:`bool`, optional Return the difference of QATS-n - QA predictions; i.e., the error of using a Taylor series with repsect to the alchemical PES. Defaults to ``False``. Returns ------- :obj:`dict` Quantum alchemy predictions with or without a Taylor series for the energy required to change the charge of an atom. Keys are system labels of the references and values are :obj:`numpy.ndarray` of energy predictions in Hartrees. """ if return_qats_vs_qa: assert use_ts == True assert len(df_qc.iloc[0]['atomic_numbers']) != 2 assert delta_charge != 0 if delta_charge < 0: assert change_signs == True ### GETS INFORMATION ON TARGET SYSTEM ### # Selects initial target ground state QC data. target_initial_qc = df_qc[ (df_qc.system == target_label) & (df_qc.charge == target_initial_charge) & (df_qc.lambda_value == 0.0) & (df_qc.basis_set == basis_set) ] target_initial_qc = select_state( target_initial_qc, 0, ignore_one_row=ignore_one_row ) assert len(target_initial_qc) == 1 target_initial_n_electrons = target_initial_qc.n_electrons.values[0] target_atomic_numbers = target_initial_qc.iloc[0]['atomic_numbers'] # Selects final target ground state QC data. target_final_n_electrons = target_initial_n_electrons - delta_charge ### GETS QUANTUM ALCHEMY REFERENCES ### # Get all available references for the initial target based on ground state # energies. avail_ref_final_sys = set( df_qats[ (df_qats.system != target_label) & (df_qats.n_electrons == target_final_n_electrons) & (df_qats.basis_set == basis_set) ].system.values ) ref_initial_qats = get_qa_refs( df_qc, df_qats, target_label, target_initial_n_electrons, basis_set=basis_set ) ref_initial_qats = ref_initial_qats[ ref_initial_qats['system'].isin(avail_ref_final_sys) ] ref_initial_qats = select_state( ref_initial_qats, 0, ignore_one_row=ignore_one_row ) # Get all available references for the final target based on ground state # energies. ref_final_qats = get_qa_refs( df_qc, df_qats, target_label, target_final_n_electrons, basis_set=basis_set ) ref_final_qats = ref_final_qats[ ref_final_qats['system'].isin(ref_initial_qats.system) ] ref_final_qats = select_state( ref_final_qats, 0, ignore_one_row=ignore_one_row ) # Checks that the size of initial and final dataframe is the same assert len(ref_initial_qats) == len(ref_final_qats) ### MAKE PREDICTIONS ### predictions = {} for system in ref_initial_qats.system: # Gets lambda value to go from reference to target. ref_initial = ref_initial_qats.query('system == @system') ref_final = ref_final_qats.query('system == @system') lambda_initial = get_lambda_value( ref_initial.iloc[0]['atomic_numbers'], target_atomic_numbers, specific_atom=None, direction=None ) lambda_final = get_lambda_value( ref_final.iloc[0]['atomic_numbers'], target_atomic_numbers, specific_atom=None, direction=None ) assert lambda_initial == lambda_final if considered_lambdas is not None: if lambda_initial not in considered_lambdas: continue # Predictions with a Taylor series. if use_ts or return_qats_vs_qa == True: order_preds = [] for order in range(len(ref_initial.iloc[0]['poly_coeffs'])): e_target_initial = qats_prediction( ref_initial.iloc[0]['poly_coeffs'], order, lambda_initial ) e_target_final = qats_prediction( ref_final.iloc[0]['poly_coeffs'], order, lambda_final ) e_diff = (e_target_final - e_target_initial)[0] if change_signs: e_diff *= -1 order_preds.append(e_diff) predictions[system] = np.array(order_preds) # Predictions without a Taylor series or compute the difference. if not use_ts or return_qats_vs_qa == True: chrg_ref_initial = ref_initial.iloc[0]['charge'] mult_ref_initial = ref_initial.iloc[0]['multiplicity'] ref_initial_qc = df_qc.query( 'system == @system & lambda_value == @lambda_initial' '& charge == @chrg_ref_initial' '& multiplicity == @mult_ref_initial' '& basis_set == @basis_set' ) assert len(ref_initial_qc) == 1 e_target_initial = ref_initial_qc.iloc[0]['electronic_energy'] chrg_ref_final = ref_final.iloc[0]['charge'] mult_ref_final = ref_final.iloc[0]['multiplicity'] ref_final_qc = df_qc.query( 'system == @system & lambda_value == @lambda_initial' '& charge == @chrg_ref_final' '& multiplicity == @mult_ref_final' '& basis_set == @basis_set' ) e_target_final = ref_final_qc.iloc[0]['electronic_energy'] e_diff = e_target_final - e_target_initial if change_signs: e_diff *= -1 if return_qats_vs_qa: pred_diff = [i - e_diff for i in predictions[system]] predictions[system] = np.array(pred_diff) else: predictions[system] = np.array([e_diff]) return predictions def dimer_minimum(bond_lengths, energies, n_points=2, poly_order=4, remove_outliers=False, zscore_cutoff=3.0): """Interpolate the minimum energy of a dimer with respect to bond length using a fitted polynomial to the lowest energies. Parameters ---------- bond_lengths : :obj:`numpy.ndarray` All bond lengths considered. energies : :obj:`numpy.ndarray` Corresponding electronic energies. n_points : :obj:`int`, optional The number of surrounding points on either side of the minimum bond length. Defaults to ``2``. poly_order : :obj:`int`, optional Maximum order of the fitted polynomial. Defaults to ``2``. remove_outliers : :obj:`bool`, optional Do not include bond lengths that are marked as outliers by their z score. Useful if there are cases where one quantum alchemy prediction is significantly off (i.e., errors on the order of hundreds of eV). Defaults to ``False``. zscore_cutoff : :obj:`float`, optional Bond length energies that have a z score higher than this are considered outliers. Defaults to ``3.0``. Returns ------- :obj:`float` Equilibrium bond length in Angstroms. :obj:`float` Electronic energy (Hartrees) corresponding to the equilibrium bond length. """ bond_lengths_fit, poly_coeffs = fit_dimer_poly( bond_lengths, energies, n_points=n_points, poly_order=poly_order, remove_outliers=remove_outliers, zscore_cutoff=zscore_cutoff ) eq_bond_length, eq_energy = find_poly_min( bond_lengths_fit, poly_coeffs ) return eq_bond_length, eq_energy def _dimer_curve(df, lambda_value=None, use_ts=False, qats_order=None): """Bond lengths and their respective electronic energies using quantum chemistry or alchemy. Dataframe should only have one system present. Parameters ---------- df : :obj:`pandas.dataframe` A quantum chemistry or QATS dataframe. lambda_value : :obj:`float`, optional Nuclear charge perturbation to get from reference to target; required if quantum alchemy predictions are desired. use_ts : :obj:`bool`, optional Make quantum alchemy predictions using a Taylor series with energy derivatives from finite differences. Defaults to ``False``. qats_order : :obj:`int`, optional Taylor series order to be used in QATS predictions. Returns ------- :obj:`numpy.ndarray` All considered bond lengths available for a system in increasing length. :obj:`numpy.ndarray` Respective energies predicted from either quantum chemistry or alchemy. """ assert len(set(df['system'].values)) == 1 \ and len(set(df['charge'].values)) == 1 \ and len(set(df['multiplicity'].values)) == 1 assert len(df.iloc[0]['atomic_numbers']) == 2 if use_ts: assert 'poly_coeffs' in df.columns assert qats_order is not None bond_length_order = np.argsort(df['bond_length'].values) bond_lengths = [] energies = [] for idx in bond_length_order: bond_lengths.append( df.iloc[idx]['bond_length'] ) poly_coeffs = df.iloc[idx]['poly_coeffs'] energies.append( qats_prediction(poly_coeffs, qats_order, lambda_value)[0] ) return np.array(bond_lengths), np.array(energies) else: assert 'electronic_energy' in df.columns assert qats_order is None if lambda_value is not None: df = df.query('lambda_value == @lambda_value') bond_length_order = np.argsort(df['bond_length'].values) bond_lengths = df['bond_length'].values[bond_length_order] energies = df['electronic_energy'].values[bond_length_order] return np.array(bond_lengths), np.array(energies) def dimer_bonding_curve( df_qc, target_label, target_charge, excitation_level=0, calc_type='qc', use_ts=False, df_qats=None, specific_atom=0, direction=None, basis_set='cc-pV5Z', n_points=2, poly_order=4, remove_outliers=False, zscore_cutoff=3.0, considered_lambdas=None): """Compute the equilibrium bond length and energy using a polynomial fit. Parameters ---------- df_qc : :obj:`pandas.DataFrame` Quantum chemistry dataframe. target_label : :obj:`str` Atoms in the system. For example, ``'f.h'``. target_charge : :obj:`str` Overall change in the system. excitation_level : :obj:`int`, optional Specifies the desired electronic state. ``0`` for ground state and ``1`` for first excited state. calc_type : :obj:`str`, optional Specifies the method of the calculation. Can either be ``'qc'`` or ``'alchemy'``. Defaults to ``'qc'``. df_qats : :obj:`pandas.DataFrame`, optional QATS dataframe. Needs to be specified if ``calc_type == 'alchemy'``. qats_order : :obj:`int`, optional Taylor series order used for QATS predictions. Defaults to ``2``. basis_set : :obj:`str`, optional Specifies the basis set to use for predictions. Defaults to ``'cc-pV5Z'``. ignore_one_row : :obj:`bool`, optional Used to control errors in ``state_selection`` when there is missing data (i.e., just one state). If ``True``, no errors are raised. Defaults to ``True``. n_points : :obj:`int`, optional The number of surrounding points on either side of the minimum bond length. Defaults to ``2``. poly_order : :obj:`int`, optional Maximum order of the fitted polynomial. Defaults to ``2``. remove_outliers : :obj:`bool`, optional Do not include bond lengths that are marked as outliers by their z score. Defaults to ``False``. zscore_cutoff : :obj:`float`, optional Bond length energies that have a z score higher than this are considered outliers. Defaults to ``3.0``. considered_lambdas : :obj:`list`, optional Allows specification of lambda values that will be considered. ``None`` will allow all lambdas to be valid, ``[1, -1]`` would only report predictions using references using a lambda of ``1`` or ``-1``. Returns ------- :obj:`dict` The equilibrium bond length from all possible references. For the qc method the reference is the same as the system. :obj:`dict` The equilibrium energy from all possible references. """ assert calc_type in ['qc', 'alchemy'] df_sys = df_qc.query( 'system == @target_label' '& charge == @target_charge' ) multiplicity_sys = get_multiplicity( df_sys.query('lambda_value == 0'), excitation_level ) df_sys = df_sys.query('multiplicity == @multiplicity_sys') if calc_type == 'qc': df_sys = df_sys.query('lambda_value == 0') bl_sys, e_sys = _dimer_curve(df_sys, lambda_value=0) bl_sys = np.array(bl_sys) e_sys = np.array(e_sys) bl_dict = {target_label: bl_sys} e_dict = {target_label: e_sys} return bl_dict, e_dict elif calc_type == 'alchemy': sys_n_electron = df_sys.iloc[0]['n_electrons'] sys_atomic_numbers = df_sys.iloc[0]['atomic_numbers'] if use_ts: assert df_qats is not None df_selection = 'qats' else: df_selection = 'qc' df_refs = get_qa_refs( df_qc, df_qats, target_label, sys_n_electron, basis_set=basis_set, df_selection=df_selection, excitation_level=excitation_level, specific_atom=specific_atom, direction=direction ) ref_system_labels = tuple(set(df_refs['system'])) bl_dict = {} e_dict = {} for i in range(len(ref_system_labels)): ref_label = ref_system_labels[i] df_ref = df_refs.query('system == @ref_label') ref_atomic_numbers = df_ref.iloc[0]['atomic_numbers'] ref_lambda_value = get_lambda_value( ref_atomic_numbers, sys_atomic_numbers, specific_atom=specific_atom ) if considered_lambdas is not None: if ref_lambda_value not in considered_lambdas: continue if not use_ts: bl_ref, e_ref = _dimer_curve( df_ref, lambda_value=ref_lambda_value, use_ts=use_ts, qats_order=None ) else: bl_ref = [] e_ref = [] max_qats_order = len(df_ref.iloc[0]['poly_coeffs']) for qats_order in range(max_qats_order): bl_ref_order, e_ref_order = _dimer_curve( df_ref, lambda_value=ref_lambda_value, use_ts=use_ts, qats_order=qats_order ) bl_ref.append(bl_ref_order) e_ref.append(e_ref_order) bl_ref = np.array(bl_ref) e_ref = np.array(e_ref) bl_dict[ref_label] = bl_ref e_dict[ref_label] = e_ref return bl_dict, e_dict def dimer_eq( df_qc, target_label, target_charge, excitation_level=0, calc_type='qc', use_ts=False, df_qats=None, specific_atom=0, direction=None, basis_set='cc-pV5Z', n_points=2, poly_order=4, remove_outliers=False, zscore_cutoff=3.0, considered_lambdas=None): """Compute the equilibrium bond length from quantum chemistry or alchemy. Parameters ---------- df_qc : :obj:`pandas.DataFrame` Quantum chemistry dataframe. target_label : :obj:`str` Atoms in the system. For example, ``'f.h'``. target_charge : :obj:`str` Overall change in the system. excitation_level : :obj:`int`, optional Specifies the desired electronic state. ``0`` for ground state and ``1`` for first excited state. calc_type : :obj:`str`, optional Specifies the method of the calculation. Can either be ``'qc'`` or ``'alchemy'``. Defaults to ``'qc'``. df_qats : :obj:`pandas.DataFrame`, optional QATS dataframe. Needs to be specified if ``calc_type == 'alchemy'``. qats_order : :obj:`int`, optional Taylor series order used for QATS predictions. Defaults to ``2``. basis_set : :obj:`str`, optional Specifies the basis set to use for predictions. Defaults to ``'cc-pV5Z'``. ignore_one_row : :obj:`bool`, optional Used to control errors in ``state_selection`` when there is missing data (i.e., just one state). If ``True``, no errors are raised. Defaults to ``True``. n_points : :obj:`int`, optional The number of surrounding points on either side of the minimum bond length. Defaults to ``2``. poly_order : :obj:`int`, optional Maximum order of the fitted polynomial. Defaults to ``2``. remove_outliers : :obj:`bool`, optional Do not include bond lengths that are marked as outliers by their z score. Defaults to ``False``. zscore_cutoff : :obj:`float`, optional Bond length energies that have a z score higher than this are considered outliers. Defaults to ``3.0``. considered_lambdas : :obj:`list`, optional Allows specification of lambda values that will be considered. ``None`` will allow all lambdas to be valid, ``[1, -1]`` would only report predictions using references using a lambda of ``1`` or ``-1``. Returns ------- :obj:`dict` The equilibrium bond length from all possible references. For the qc method the reference is the same as the system. :obj:`dict` The equilibrium energy from all possible references. """ assert calc_type in ['qc', 'alchemy'] bl_dict, e_dict = dimer_bonding_curve( df_qc, target_label, target_charge, excitation_level=excitation_level, calc_type=calc_type, use_ts=use_ts, df_qats=df_qats, specific_atom=specific_atom, direction=direction, basis_set=basis_set, n_points=n_points, poly_order=poly_order, remove_outliers=remove_outliers, zscore_cutoff=zscore_cutoff, considered_lambdas=considered_lambdas ) bl_eq_dict = {} e_eq_dict ={} for sys_label in bl_dict.keys(): bl_sys = bl_dict[sys_label] e_sys = e_dict[sys_label] if len(bl_sys.shape) == 1: bl_sys = np.array([bl_sys]) e_sys = np.array([e_sys]) bl_eq = [] e_eq = [] for i in range(len(bl_sys)): bl_eq_i, e_eq_i = dimer_minimum( bl_sys[i], e_sys[i], n_points=n_points, poly_order=poly_order, remove_outliers=remove_outliers, zscore_cutoff=zscore_cutoff ) bl_eq.append(bl_eq_i) e_eq.append(e_eq_i) bl_eq_dict[sys_label] = np.array(bl_eq) e_eq_dict[sys_label] = np.array(e_eq) return bl_eq_dict, e_eq_dict def energy_change_charge_qc_dimer( df_qc, target_label, delta_charge, target_initial_charge=0, change_signs=False, basis_set='cc-pV5Z', ignore_one_row=True, n_points=2, poly_order=4, remove_outliers=False, zscore_cutoff=3.0): """ Parameters ---------- df_qc : :obj:`pandas.DataFrame` A pandas dataframe with quantum chemistry data. target_label : :obj:`str` Atoms in the system. For example, ``'f.h'``. delta_charge : :obj:`str` Overall change in the initial target system. target_initial_charge : :obj:`int` Specifies the initial charge state of the target system. For example, the first ionization energy is the energy difference going from charge ``0 -> 1``, so ``target_initial_charge`` must equal ``0``. change_signs : :obj:`bool`, optional Multiply all predictions by -1. Used to correct the sign for computing electron affinities. Defaults to ``False``. basis_set : :obj:`str`, optional Specifies the basis set to use for predictions. Defaults to ``'cc-pV5Z'``. ignore_one_row : :obj:`bool`, optional Used to control errors in ``state_selection`` when there is missing data (i.e., just one state). If ``True``, no errors are raised. Defaults to ``True``. n_points : :obj:`int`, optional The number of surrounding points on either side of the minimum bond length. Defaults to ``2``. poly_order : :obj:`int`, optional Maximum order of the fitted polynomial. Defaults to ``2``. remove_outliers : :obj:`bool`, optional Do not include bond lengths that are marked as outliers by their z score. Defaults to ``False``. zscore_cutoff : :obj:`float`, optional Bond length energies that have a z score higher than this are considered outliers. Defaults to ``3.0``. Returns ------- :obj:`float` Quantum chemistry predicted energy change due to changing the charge of the system. """ # Checks that a bond_length is provided for dimers. assert len(df_qc.iloc[0]['atomic_numbers']) == 2 # Selects initial target ground state QC data. target_initial_qc = df_qc[ (df_qc.system == target_label) & (df_qc.charge == target_initial_charge) & (df_qc.lambda_value == 0.0) & (df_qc.basis_set == basis_set) ] ground_multiplicity_initial = get_multiplicity(target_initial_qc, 0) target_initial_qc = target_initial_qc.query( 'multiplicity == @ground_multiplicity_initial' ) target_initial_n_electrons = target_initial_qc.n_electrons.values[0] target_initial_bond_lengths, target_initial_energies = _dimer_curve( target_initial_qc, lambda_value=None, use_ts=False, qats_order=None ) _, target_initial_energy = dimer_minimum( target_initial_bond_lengths, target_initial_energies, n_points=n_points, poly_order=poly_order, remove_outliers=remove_outliers, zscore_cutoff=zscore_cutoff ) # Selects final target ground state QC data. target_final_n_electrons = target_initial_n_electrons - delta_charge target_final_qc = df_qc[ (df_qc.system == target_label) & (df_qc.lambda_value == 0.0) & (df_qc.n_electrons == target_final_n_electrons) & (df_qc.basis_set == basis_set) ] ground_multiplicity_final = get_multiplicity(target_final_qc, 0) target_final_qc = target_final_qc.query( 'multiplicity == @ground_multiplicity_final' ) target_final_bond_lengths, target_final_energies = _dimer_curve( target_final_qc, lambda_value=None, use_ts=False, qats_order=None ) _, target_final_energy = dimer_minimum( target_final_bond_lengths, target_final_energies, n_points=n_points, poly_order=poly_order, remove_outliers=remove_outliers, zscore_cutoff=zscore_cutoff ) e_diff = target_final_energy - target_initial_energy if change_signs: e_diff *= -1 return e_diff def energy_change_charge_qa_dimer( df_qc, df_qats, target_label, delta_charge, target_initial_charge=0, change_signs=False, basis_set='cc-pV5Z', use_ts=True, lambda_specific_atom=None, lambda_direction=None, ignore_one_row=True, poly_order=4, n_points=2, remove_outliers=False, considered_lambdas=None, return_qats_vs_qa=False): """Use an QATS reference to predict the energy change due to adding or removing an electron to dimers. The minimum energy from a fitted parabola is used for each state. Parameters ---------- df_qc : :obj:`pandas.DataFrame` A pandas dataframe with quantum chemistry data. It should have the following columns (from `get_qc_dframe`): system, atomic_numbers, charge, multiplicity, n_electrons, qc_method, basis_set, lambda_range, finite_diff_delta, finite_diff_acc, poly_coeff. df_qats : :obj:`pandas.DataFrame` A pandas dataframe with QATS data. It should have the following columns (from `get_qats_dframe`): system, atomic_numbers, charge, multiplicity, n_electrons, qc_method, basis_set, lambda, electronic_energy, hf_energy, and correlation_energy. target_label : :obj:`str` Atoms in the system. For example, ``'c'``, ``'si'``, or ``'f.h'``. delta_charge : :obj:`str` Overall change in the initial target system. target_initial_charge : :obj:`int`, optional Specifies the initial charge state of the target system. For example, the first ionization energy is the energy difference going from charge ``0 -> 1``, so ``target_initial_charge`` must equal ``0``. Defaults to ``0``. change_signs : :obj:`bool`, optional Multiply all predictions by -1. Used to correct the sign for computing electron affinities. Defaults to ``False``. basis_set : :obj:`str`, optional Specifies the basis set to use for predictions. Defaults to ``'cc-pV5Z'``. use_ts : :obj:`bool`, optional Use a Taylor series approximation to make QATS-n predictions (where n is the order). Defaults to ``True``. lambda_specific_atom : :obj:`int`, optional Applies the entire lambda change to a single atom in dimers. For example, OH -> FH+ would be a lambda change of +1 only on the first atom. lambda_direction : :obj:`str`, optional Defines the direction of lambda changes for dimers. ``'counter'`` is is where one atom increases and the other decreases their nuclear charge (e.g., CO -> BF). If the atomic numbers of the reference are the same, the first atom's nuclear charge is decreased and the second is increased. IF they are different, the atom with the largest atomic number increases by lambda. ignore_one_row : :obj:`bool`, optional Used to control errors in ``state_selection`` when there is missing data (i.e., just one state). If ``True``, no errors are raised. Defaults to ``True``. poly_order : :obj:`int`, optional Maximum order of the fitted polynomial. Defaults to ``2``. n_points : :obj:`int`, optional The number of surrounding points on either side of the minimum bond length. Defaults to ``2``. remove_outliers : :obj:`bool`, optional Do not include bond lengths that are marked as outliers by their z score. Defaults to ``False``. considered_lambdas : :obj:`list`, optional Allows specification of lambda values that will be considered. ``None`` will allow all lambdas to be valid, ``[1, -1]`` would only report predictions using references using a lambda of ``1`` or ``-1``. return_qats_vs_qa : :obj:`bool`, optional Return the difference of QATS-n - QATS predictions; i.e., the error of using a Taylor series approximation with repsect to the alchemical potential energy surface. Defaults to ``False``. """ assert delta_charge != 0 assert len(df_qc.iloc[0]['atomic_numbers']) == 2 if return_qats_vs_qa: assert use_ts == True # Selects initial target ground state QC data. target_initial_qc = df_qc[ (df_qc.system == target_label) & (df_qc.charge == target_initial_charge) & (df_qc.lambda_value == 0.0) & (df_qc.basis_set == basis_set) ] ground_multiplicity_initial = get_multiplicity(target_initial_qc, 0) target_initial_qc = target_initial_qc.query( 'multiplicity == @ground_multiplicity_initial' ) assert len(target_initial_qc) > 1 target_initial_n_electrons = target_initial_qc.iloc[0]['n_electrons'] target_atomic_numbers = target_initial_qc.iloc[0]['atomic_numbers'] # Performs checks on lambda selections. assert lambda_specific_atom is not None or lambda_direction is not None # Selects final target ground state QC data. target_final_n_electrons = target_initial_n_electrons - delta_charge target_final_qc = df_qc[ (df_qc.system == target_label) & (df_qc.charge == target_initial_charge + delta_charge) & (df_qc.lambda_value == 0.0) & (df_qc.basis_set == basis_set) ] ground_multiplicity_final = get_multiplicity(target_final_qc, 0) target_final_qc = target_final_qc.query( 'multiplicity == @ground_multiplicity_final' ) # Get all available references for the initial target based on ground state # energies. avail_ref_final_sys = set( df_qats[ (df_qats.system != target_label) & (df_qats.n_electrons == target_final_n_electrons) & (df_qats.basis_set == basis_set) ].system.values ) ref_initial_qats = df_qats.query( 'n_electrons == @target_initial_n_electrons' '& basis_set == @basis_set' '& multiplicity == @ground_multiplicity_initial' ) ref_initial_qats = ref_initial_qats[ ref_initial_qats['system'].isin(avail_ref_final_sys) ] # Get all available references for the final target based on ground state # energies. ref_final_qats = df_qats.query( 'n_electrons == @target_final_n_electrons' '& basis_set == @basis_set' '& multiplicity == @ground_multiplicity_final' ) ref_final_qats = ref_final_qats[ ref_final_qats['system'].isin(ref_initial_qats.system) ] # Checks that the size of initial and final dataframe is the same assert len(ref_initial_qats) == len(ref_final_qats) predictions = {} for system in set(ref_initial_qats.system): ref_initial = ref_initial_qats.query('system == @system') ref_final = ref_final_qats.query('system == @system') lambda_initial = get_lambda_value( ref_initial.iloc[0]['atomic_numbers'], target_atomic_numbers, specific_atom=lambda_specific_atom, direction=lambda_direction ) lambda_final = get_lambda_value( ref_final.iloc[0]['atomic_numbers'], target_atomic_numbers, specific_atom=lambda_specific_atom, direction=lambda_direction ) assert lambda_initial == lambda_final if considered_lambdas is not None: if lambda_initial not in considered_lambdas: continue bond_length_order_initial = np.argsort(ref_initial['bond_length'].values) bond_length_order_final = np.argsort(ref_final['bond_length'].values) if use_ts or return_qats_vs_qa == True: order_preds = [] for order in range(len(ref_initial.iloc[0]['poly_coeffs'])): bond_lengths_initial, energies_initial = _dimer_curve( ref_initial, lambda_value=lambda_initial, use_ts=True, qats_order=order ) _, e_target_initial = dimer_minimum( bond_lengths_initial, energies_initial, n_points=n_points, remove_outliers=remove_outliers ) bond_lengths_final, energies_final = _dimer_curve( ref_final, lambda_value=lambda_final, use_ts=True, qats_order=order ) _, e_target_final = dimer_minimum( bond_lengths_final, energies_final, n_points=n_points, remove_outliers=remove_outliers ) e_diff = e_target_final - e_target_initial if change_signs: e_diff *= -1 order_preds.append(e_diff) predictions[system] = np.array(order_preds) if not use_ts or return_qats_vs_qa == True: chrg_ref_initial = ref_initial.iloc[0]['charge'] mult_ref_initial = ref_initial.iloc[0]['multiplicity'] ref_initial_qc = df_qc.query( 'system == @system & lambda_value == @lambda_initial' '& charge == @chrg_ref_initial' '& multiplicity == @mult_ref_initial' '& basis_set == @basis_set' ) bond_lengths_initial, energies_initial = _dimer_curve( ref_initial_qc, lambda_value=lambda_initial, use_ts=False, qats_order=None ) _, e_target_initial = dimer_minimum( bond_lengths_initial, energies_initial, n_points=n_points, remove_outliers=remove_outliers ) chrg_ref_final = ref_final.iloc[0]['charge'] mult_ref_final = ref_final.iloc[0]['multiplicity'] ref_final_qc = df_qc.query( 'system == @system & lambda_value == @lambda_initial' '& charge == @chrg_ref_final' '& multiplicity == @mult_ref_final' '& basis_set == @basis_set' ) bond_lengths_final, energies_final = _dimer_curve( ref_final_qc, lambda_value=lambda_final, use_ts=False, qats_order=None ) _, e_target_final = dimer_minimum( bond_lengths_final, energies_final, n_points=n_points, remove_outliers=remove_outliers ) e_diff = e_target_final - e_target_initial if change_signs: e_diff *= -1 if return_qats_vs_qa: pred_diff = [i - e_diff for i in predictions[system]] predictions[system] = np.array(pred_diff) else: predictions[system] = np.array([e_diff]) return predictions def mult_gap_qc_atom( df_qc, target_label, target_charge=0, basis_set='aug-cc-pV5Z', ignore_one_row=True): """Multiplicity gap predictions of atoms using quantum chemistry. Parameters ---------- df_qc : :obj:`pandas.dataframe` Quantum chemistry dataframe. target_label : :obj:`str` Atoms in the system. For example, ``'c'``, ``'si'``, or ``'f.h'``. target_charge : :obj:`int` The system charge. basis_set : :obj:`str`, optional Specifies the basis set to use for predictions. Defaults to ``'aug-cc-pV5Z'``. ignore_one_row : :obj:`bool`, optional Used to control errors in ``state_selection`` when there is missing data (i.e., just one state). If ``True``, no errors are raised. Defaults to ``True``. Returns ------- :obj:`numpy.float64` Difference in energy between ground and excited state in Hartrees. """ # Selects initial target ground state QC data. target_qc = df_qc[ (df_qc.system == target_label) & (df_qc.charge == target_charge) & (df_qc.lambda_value == 0.0) & (df_qc.basis_set == basis_set) ] if len(target_qc) == 0 or len(target_qc) == 1: # Often we do not have the data to make the prediction. # So we return NaN. return np.nan elif len(target_qc) > 1: target_initial_qc = select_state( target_qc, 0, ignore_one_row=ignore_one_row ) assert len(target_initial_qc) == 1 # Should only have one row. target_final_qc = select_state( target_qc, 1, ignore_one_row=ignore_one_row ) assert len(target_final_qc) == 1 # Should only have one row. e_diff = target_final_qc.iloc[0]['electronic_energy'] \ - target_initial_qc.iloc[0]['electronic_energy'] return e_diff def mult_gap_qa_atom( df_qc, df_qats, target_label, target_charge=0, basis_set='aug-cc-pV5Z', use_ts=True, ignore_one_row=True, considered_lambdas=None, return_qats_vs_qa=False ): """Multiplicity gap predictions of atoms using quantum alchemy. Parameters ---------- df_qc : :obj:`pandas.dataframe` Quantum chemistry dataframe. df_qats : :obj:`pandas.DataFrame` A pandas dataframe with QATS data. target_label : :obj:`str` Atoms in the system. For example, ``'c'``, ``'si'``, or ``'f.h'``. target_charge : :obj:`int`, optional The system charge. Defaults to ``0``. basis_set : :obj:`str`, optional Specifies the basis set to use for predictions. Defaults to ``'aug-cc-pV5Z'``. ignore_one_row : :obj:`bool`, optional Used to control errors in ``state_selection`` when there is missing data (i.e., just one state). If ``True``, no errors are raised. Defaults to ``True``. considered_lambdas : :obj:`list`, optional Allows specification of lambda values that will be considered. ``None`` will allow all lambdas to be valid, ``[1, -1]`` would only report predictions using references using a lambda of ``1`` or ``-1``. return_qats_vs_qa : :obj:`bool`, optional Return the difference of QATS-n - QATS predictions; i.e., the error of using a Taylor series approximation with repsect to the alchemical potential energy surface. Defaults to ``False``. Returns ------- :obj:`dict` Difference in energy between ground and excited state in Hartrees (values) for each quantum alchemy reference (keys). """ if return_qats_vs_qa: assert use_ts == True # Selects initial target ground state QC data. target_qc = df_qc[ (df_qc.system == target_label) & (df_qc.charge == target_charge) & (df_qc.lambda_value == 0.0) & (df_qc.basis_set == basis_set) ] if len(target_qc) == 0 or len(target_qc) == 1: # Often we do not have the data to make the prediction. # So we return nothing. return {} elif len(target_qc) > 1: n_electrons = list(set(target_qc.n_electrons.values)) assert len(n_electrons) == 1 n_electrons = n_electrons[0] target_initial_qc = select_state( target_qc, 0, ignore_one_row=ignore_one_row ) assert len(target_initial_qc) == 1 # Should only have one row. target_final_qc = select_state( target_qc, 1, ignore_one_row=ignore_one_row ) assert len(target_final_qc) == 1 # Should only have one row. target_atomic_numbers = target_initial_qc.iloc[0]['atomic_numbers'] ref_qats = get_qa_refs( df_qc, df_qats, target_label, n_electrons, basis_set=basis_set ) if len(ref_qats) == 0 or len(ref_qats) == 1: # Often we do not have the data to make the prediction. # So we return nothing. return {} elif len(ref_qats) > 1: ref_initial_qats = select_state( ref_qats, 0, ignore_one_row=ignore_one_row ) ref_final_qats = select_state( ref_qats, 1, ignore_one_row=ignore_one_row ) # Checks that the size of initial and final dataframe is the same assert len(ref_initial_qats) == len(ref_final_qats) predictions = {} for system in ref_initial_qats.system: ref_initial = ref_initial_qats.query('system == @system') lambda_initial = get_lambda_value( ref_initial.iloc[0]['atomic_numbers'], target_atomic_numbers ) ref_final = ref_final_qats.query('system == @system') lambda_final = get_lambda_value( ref_final.iloc[0]['atomic_numbers'], target_atomic_numbers ) assert lambda_initial == lambda_final if considered_lambdas is not None: if lambda_initial not in considered_lambdas: continue if use_ts or return_qats_vs_qa == True: order_preds = [] for order in range(len(ref_initial.iloc[0]['poly_coeffs'])): e_target_initial = qats_prediction( ref_initial.iloc[0]['poly_coeffs'], order, lambda_initial ) e_target_final = qats_prediction( ref_final.iloc[0]['poly_coeffs'], order, lambda_final ) e_diff = (e_target_final - e_target_initial)[0] order_preds.append(e_diff) predictions[system] = np.array(order_preds) if not use_ts or return_qats_vs_qa == True: chrg_ref_initial = ref_initial.iloc[0]['charge'] mult_ref_initial = ref_initial.iloc[0]['multiplicity'] ref_initial_qc = df_qc.query( 'system == @system & lambda_value == @lambda_initial' '& charge == @chrg_ref_initial' '& multiplicity == @mult_ref_initial' '& basis_set == @basis_set' ) assert len(ref_initial_qc) == 1 e_target_initial = ref_initial_qc.iloc[0]['electronic_energy'] chrg_ref_final = ref_final.iloc[0]['charge'] mult_ref_final = ref_final.iloc[0]['multiplicity'] ref_final_qc = df_qc.query( 'system == @system & lambda_value == @lambda_initial' '& charge == @chrg_ref_final' '& multiplicity == @mult_ref_final' '& basis_set == @basis_set' ) e_target_final = ref_final_qc.iloc[0]['electronic_energy'] e_diff = e_target_final - e_target_initial if return_qats_vs_qa: pred_diff = [i - e_diff for i in predictions[system]] predictions[system] = np.array(pred_diff) else: predictions[system] = np.array([e_diff]) return predictions
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4
218e26245f753f8fa8f7e9e3cb508a4273f664b5
85
py
Python
Album/apps.py
alstn2468/Likelion_DRF_Project
35a359a05185f551ed2e999ab17e0108a69d6b57
[ "MIT" ]
28
2019-10-15T13:15:26.000Z
2021-11-08T08:23:45.000Z
15_LikeLionDRFProject/Album/apps.py
jhleed/LikeLion_Django_Study_Summary
c788182af5bcfd16bdd4b57235a48659758e494b
[ "MIT" ]
null
null
null
15_LikeLionDRFProject/Album/apps.py
jhleed/LikeLion_Django_Study_Summary
c788182af5bcfd16bdd4b57235a48659758e494b
[ "MIT" ]
17
2019-09-09T00:15:36.000Z
2021-01-28T13:08:51.000Z
from django.apps import AppConfig class AlbumConfig(AppConfig): name = 'Album'
14.166667
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1d08b9313e1fbbbedb902b663180ae924e90c8af
579
py
Python
tests/integration_tests/utils/sample_projects/wrong_hexa_arch_project/hexagonal_config.py
rfrezino/hexagonal-sanity-check
78c8711d9be6ec173abead4ab344f7ac57d5d4ac
[ "MIT" ]
1
2022-03-14T10:17:38.000Z
2022-03-14T10:17:38.000Z
tests/integration_tests/utils/sample_projects/wrong_hexa_arch_project/hexagonal_config.py
rfrezino/hexagonal-sanity-check
78c8711d9be6ec173abead4ab344f7ac57d5d4ac
[ "MIT" ]
null
null
null
tests/integration_tests/utils/sample_projects/wrong_hexa_arch_project/hexagonal_config.py
rfrezino/hexagonal-sanity-check
78c8711d9be6ec173abead4ab344f7ac57d5d4ac
[ "MIT" ]
2
2021-12-14T10:35:24.000Z
2022-01-31T14:17:36.000Z
from hexagonal.domain.hexagonal_layer import HexagonalLayer from hexagonal.hexagonal_config import hexagonal_config hexagonal_config.add_inner_layer(HexagonalLayer(name='adapters', directories_groups=[['/adapters/new_printer'], ['/adapters/old_printer']])) hexagonal_config.add_inner_layer(HexagonalLayer(name='port', directories_groups=[['/port']])) hexagonal_config.add_inner_layer(HexagonalLayer(name='domain', directories_groups=[['/domain']])) hexagonal_config.excluded_dirs = ['*/test']
64.333333
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4
df14af408cfee194dde4f956e94d321cf58ee579
179
py
Python
PyMOTW/source/zipfile/zipfile_namelist.py
axetang/AxePython
3b517fa3123ce2e939680ad1ae14f7e602d446a6
[ "Apache-2.0" ]
1
2019-01-04T05:47:50.000Z
2019-01-04T05:47:50.000Z
PyMOTW/source/zipfile/zipfile_namelist.py
axetang/AxePython
3b517fa3123ce2e939680ad1ae14f7e602d446a6
[ "Apache-2.0" ]
1
2020-07-18T03:52:03.000Z
2020-07-18T04:18:01.000Z
PyMOTW/source/zipfile/zipfile_namelist.py
axetang/AxePython
3b517fa3123ce2e939680ad1ae14f7e602d446a6
[ "Apache-2.0" ]
2
2021-03-06T04:28:32.000Z
2021-03-06T04:59:17.000Z
#!/usr/bin/env python3 """Reading the names out of a ZIP archive. """ #end_pymotw_header import zipfile with zipfile.ZipFile('example.zip', 'r') as zf: print(zf.namelist())
17.9
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4.392857
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179
9
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0
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1
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0
4
df40fd279f4621587dbc01e8782b1a279c64af23
11,689
py
Python
lfs/customer/migrations/0004_auto__chg_field_customer_dsa_object_id__chg_field_customer_dia_object_.py
restless/django-lfs
4058f9d45b416ef2e8c28a87856ea0f1550b523d
[ "BSD-3-Clause" ]
1
2020-02-26T03:07:39.000Z
2020-02-26T03:07:39.000Z
lfs/customer/migrations/0004_auto__chg_field_customer_dsa_object_id__chg_field_customer_dia_object_.py
mxins/django-lfs
bf42ed80ce0e1ec96db6ab985adcc614ea79dfc8
[ "BSD-3-Clause" ]
null
null
null
lfs/customer/migrations/0004_auto__chg_field_customer_dsa_object_id__chg_field_customer_dia_object_.py
mxins/django-lfs
bf42ed80ce0e1ec96db6ab985adcc614ea79dfc8
[ "BSD-3-Clause" ]
null
null
null
# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Changing field 'Customer.dsa_object_id' db.alter_column('customer_customer', 'dsa_object_id', self.gf('django.db.models.fields.PositiveIntegerField')(default=1)) # Changing field 'Customer.dia_object_id' db.alter_column('customer_customer', 'dia_object_id', self.gf('django.db.models.fields.PositiveIntegerField')(default=1)) def backwards(self, orm): # Changing field 'Customer.dsa_object_id' db.alter_column('customer_customer', 'dsa_object_id', self.gf('django.db.models.fields.PositiveIntegerField')(null=True)) # Changing field 'Customer.dia_object_id' db.alter_column('customer_customer', 'dia_object_id', self.gf('django.db.models.fields.PositiveIntegerField')(null=True)) models = { 'auth.group': { 'Meta': {'object_name': 'Group'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, 'auth.permission': { 'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, 'catalog.deliverytime': { 'Meta': {'ordering': "('min',)", 'object_name': 'DeliveryTime'}, 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'max': ('django.db.models.fields.FloatField', [], {}), 'min': ('django.db.models.fields.FloatField', [], {}), 'unit': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '2'}) }, 'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, 'core.country': { 'Meta': {'ordering': "('name',)", 'object_name': 'Country'}, 'code': ('django.db.models.fields.CharField', [], {'max_length': '2'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, 'customer.bankaccount': { 'Meta': {'object_name': 'BankAccount'}, 'account_number': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'bank_identification_code': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'bank_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}), 'customer': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'bank_accounts'", 'null': 'True', 'to': "orm['customer.Customer']"}), 'depositor': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, 'customer.creditcard': { 'Meta': {'object_name': 'CreditCard'}, 'customer': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'credit_cards'", 'null': 'True', 'to': "orm['customer.Customer']"}), 'expiration_date_month': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'expiration_date_year': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'number': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'owner': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}), 'type': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}) }, 'customer.customer': { 'Meta': {'object_name': 'Customer'}, 'dia_object_id': ('django.db.models.fields.PositiveIntegerField', [], {}), 'dsa_object_id': ('django.db.models.fields.PositiveIntegerField', [], {}), 'ia_content_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'ia_content_type'", 'to': "orm['contenttypes.ContentType']"}), 'ia_object_id': ('django.db.models.fields.PositiveIntegerField', [], {}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'sa_content_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'sa_content_type'", 'to': "orm['contenttypes.ContentType']"}), 'sa_object_id': ('django.db.models.fields.PositiveIntegerField', [], {}), 'selected_bank_account': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'selected_bank_account'", 'null': 'True', 'to': "orm['customer.BankAccount']"}), 'selected_country': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['core.Country']", 'null': 'True', 'blank': 'True'}), 'selected_credit_card': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'selected_credit_card'", 'null': 'True', 'to': "orm['customer.CreditCard']"}), 'selected_payment_method': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'selected_payment_method'", 'null': 'True', 'to': "orm['payment.PaymentMethod']"}), 'selected_shipping_method': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'selected_shipping_method'", 'null': 'True', 'to': "orm['shipping.ShippingMethod']"}), 'session': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']", 'null': 'True', 'blank': 'True'}) }, 'payment.paymentmethod': { 'Meta': {'ordering': "('priority',)", 'object_name': 'PaymentMethod'}, 'active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'deletable': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'module': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'note': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'price': ('django.db.models.fields.FloatField', [], {'default': '0.0'}), 'priority': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'tax': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tax.Tax']", 'null': 'True', 'blank': 'True'}), 'type': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}) }, 'shipping.shippingmethod': { 'Meta': {'ordering': "('priority',)", 'object_name': 'ShippingMethod'}, 'active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'delivery_time': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['catalog.DeliveryTime']", 'null': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'note': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'price': ('django.db.models.fields.FloatField', [], {'default': '0.0'}), 'price_calculator': ('django.db.models.fields.CharField', [], {'default': "'lfs.shipping.GrossShippingMethodPriceCalculator'", 'max_length': '200'}), 'priority': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'tax': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tax.Tax']", 'null': 'True', 'blank': 'True'}) }, 'tax.tax': { 'Meta': {'object_name': 'Tax'}, 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'rate': ('django.db.models.fields.FloatField', [], {'default': '0'}) } } complete_apps = ['customer']
77.926667
212
0.573958
1,178
11,689
5.565365
0.123939
0.109823
0.190055
0.271507
0.771965
0.737035
0.703478
0.631483
0.563148
0.491916
0
0.008376
0.182907
11,689
150
213
77.926667
0.678044
0.015485
0
0.214815
0
0
0.59428
0.35156
0
0
0
0
0
1
0.014815
false
0.007407
0.02963
0
0.066667
0
0
0
0
null
0
1
1
0
1
1
0
0
0
0
0
0
0
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0
0
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0
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null
0
0
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0
0
0
0
0
0
0
0
0
4
df495b9677370b299b72a7f2f44fe8b0be6fa108
5,232
py
Python
plugins/disapproval.py
tommai78101/IRCBot
93a8b53eff81a8bf00fa69aa847a4e5febb336fa
[ "MIT" ]
null
null
null
plugins/disapproval.py
tommai78101/IRCBot
93a8b53eff81a8bf00fa69aa847a4e5febb336fa
[ "MIT" ]
null
null
null
plugins/disapproval.py
tommai78101/IRCBot
93a8b53eff81a8bf00fa69aa847a4e5febb336fa
[ "MIT" ]
null
null
null
from PluginBot import PRIVMSG from PluginBot import getMessage def version(): return "Look of Disapproval v1.0" def plugin_main(parent, tokens): if (len(tokens) > 3): if (tokens[3] == ".ugh" or tokens[3] == ".sigh"): if (len(tokens) > 4): parent.s.send(PRIVMSG(tokens[2], " -- \u0ca0_\u0ca0 at %s." % tokens[4], 0)) try: if (parent.guiParent != None): parent.guiParent.print(" -- %s ಠ_ಠ" % tokens[4]) else: print(" -- %s ಠ_ಠ" % tokens[4]) except: if (parent.guiParent != None): parent.guiParent.print("Giving the look of disapproval towards %s." % tokens[4]) else: print("Giving the look of disapproval towards %s." % tokens[4]) else: parent.s.send(PRIVMSG(tokens[2], " -- \u0ca0_\u0ca0", 0)) try: if (parent.guiParent != None): parent.guiParent.print("\u0ca0_\u0ca0") else: print("Giving the look of disapproval.") except: if (parent.guiParent != None): parent.guiParent.print("Giving the look of disapproval.") else: print("Giving the look of disapproval.") elif (tokens[3] == ".flip"): if (len(tokens) > 4): message = getMessage(tokens, 4) parent.s.send(PRIVMSG(tokens[2], " -- (\u256f\u25e6\u25ab\u25e6)\u256f \u2312 \u2538\u2014\u2538 - %s" % message, 0)) if (parent.guiParent != None): parent.guiParent.print("Flips table for %s!" % message) else: print("Flips table!") else: parent.s.send(PRIVMSG(tokens[2], " -- (\u256f\u25e6\u25ab\u25e6)\u256f \u2312 \u2538\u2014\u2538", 0)) if (parent.guiParent != None): parent.guiParent.print("Flips table!") else: print("Flips table!") elif (tokens[3] == ".cm" or tokens[3] == ".checkmate"): #☚(°ヮ°)☚ if (len(tokens) > 4): message = getMessage(tokens, 4) parent.s.send(PRIVMSG(tokens[2], " -- ☜(\u25e6\u30ee\u25e6)☜ - %s" % message, 0)) if (parent.guiParent != None): parent.guiParent.print("Checkmate! ☜ with message") else: print("Checkmate! ☜ with message") else: parent.s.send(PRIVMSG(tokens[2], " -- ☜(\u25e6\u30ee\u25e6)☜", 0)) if (parent.guiParent != None): parent.guiParent.print("Checkmate! ☜ ") else: print("Checkmate! ☜ ") elif (tokens[3] == ".facepalm" or tokens[3] == ".fp"): if (len(tokens) > 4): parent.s.send(PRIVMSG(tokens[2], " -- (-\u032d \u10da) at %s" % tokens[4], 0)) else: parent.s.send(PRIVMSG(tokens[2], " -- (-\u032d \u10da)", 0)) if (parent.guiParent != None): parent.guiParent.print("Facepalm!") else: print("Facepalm!") elif (tokens[3] == ".le"): #´_>` if (len(tokens) > 4): message = getMessage(tokens, 4) parent.s.send(PRIVMSG(tokens[2], " -- %s ´_>`" % message, 0)) if (parent.guiParent != None): parent.guiParent.print("´_>` with message") else: print("´_>` with message") else: parent.s.send(PRIVMSG(tokens[2], " -- ´_>`", 0)) if (parent.guiParent != None): parent.guiParent.print("´_>`") else: print("´_>`") elif (tokens[3] == ".wnk"): #(ง ͡~ ͜ʖ͡°)ᕤ ✧ parent.s.send(PRIVMSG(tokens[2], " -- \u0028\u00E07\u0020\u0361\u007E\u0020\u035C\u0296\u0361\u00B0\u0029\u1564\u0020\u2727", 0)) try: if (parent.guiParent != None): parent.guiParent.print("\u0028\u00E07\u0020\u0361\u007E\u0020\u035C\u0296\u0361\u00B0\u0029\u1564\u0020\u2727") else: print("Giving the wink.") except: if (parent.guiParent != None): parent.guiParent.print("Giving the wink.") else: print("Giving the wink.") elif (tokens[3] == ".mf"): #ಠ︵ಠ 凸 parent.s.send(PRIVMSG(tokens[2], " -- \u0CA0\uFE35\u0CA0\u0020\u51F8", 0)) try: if (parent.guiParent != None): parent.guiParent.print("\u0CA0\uFE35\u0CA0\u0020\u51F8") else: print("Giving the finger.") except: if (parent.guiParent != None): parent.guiParent.print("Giving the finger.") else: print("Giving the finger.") elif (tokens[3] == ".lenny"): #( ͡° ͜ʖ ͡° ) parent.s.send(PRIVMSG(tokens[2], " -- \u0028\u0020\u0361\u00B0\u0020\u035C\u0296\u0020\u0361\u00B0\u0020\u0029", 0)) try: if (parent.guiParent != None): parent.guiParent.print("\u0028\u0020\u0361\u00B0\u0020\u035C\u0296\u0020\u0361\u00B0\u0020\u0029") else: print("Lenny.") except: if (parent.guiParent != None): parent.guiParent.print("Lenny.") else: print("Lenny.") elif (tokens[3] == ".help"): parent.s.send(PRIVMSG(tokens[0], "USAGE: .ugh / .sigh / .facepalm - Gives a look of disapproval.", 1)) parent.s.send(PRIVMSG(tokens[0], "USAGE: .ugh / .sigh / .facepalm [user] - Gives a look of disapproval to the user.", 1)) parent.s.send(PRIVMSG(tokens[0], "USAGE: .checkmate [message] / .cm [message] - Gives a sarcastic look with the optional following message.", 1)) parent.s.send(PRIVMSG(tokens[0], "USAGE: .le [message] - Gives a sarcastic look with the optional following message.", 1)) parent.s.send(PRIVMSG(tokens[0], "USAGE: .wnk - Gives a confident wink!", 1)) parent.s.send(PRIVMSG(tokens[0], "USAGE: .mf - Gives THE FINGER.", 1)) parent.s.send(PRIVMSG(tokens[0], "USAGE: .flip [message] - Flips table with the optional following message.", 1))
39.044776
148
0.609327
717
5,232
4.471409
0.145049
0.159077
0.068621
0.112289
0.820649
0.752027
0.724891
0.675608
0.590455
0.530256
0
0.092509
0.196292
5,232
133
149
39.338346
0.662545
0.008219
0
0.531746
0
0.055556
0.341887
0.092996
0
0
0
0
0
0
null
null
0
0.015873
null
null
0.269841
0
0
0
null
0
0
0
1
1
1
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
0
0
4
df6b6aa0914ba5bc8773fd64a50bf74e255f5b70
134
py
Python
Beginner/1044.py
pedrodanieljardim/DesafiosURI-feitos-em-JAVA
4e727e1b08e01f527d0b7b884c268643f1472ded
[ "MIT" ]
1
2022-03-19T18:06:25.000Z
2022-03-19T18:06:25.000Z
Beginner/1044.py
pedrodanieljardim/beecrowd
4e727e1b08e01f527d0b7b884c268643f1472ded
[ "MIT" ]
null
null
null
Beginner/1044.py
pedrodanieljardim/beecrowd
4e727e1b08e01f527d0b7b884c268643f1472ded
[ "MIT" ]
null
null
null
A = int(input()) B = int(input()) if B % A == 0 or A % B == 0: print("São Múltiplos \n") else: print("Não são Múltiplos \n")
16.75
33
0.529851
24
134
2.958333
0.541667
0.225352
0.366197
0
0
0
0
0
0
0
0
0.020202
0.261194
134
7
34
19.142857
0.69697
0
0
0
0
0
0.268657
0
0
0
0
0
0
1
0
false
0
0
0
0
0.333333
1
0
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
0
0
0
0
0
0
0
0
0
4
df77b8b8930489ffd3bae381a6bdaa60a25cfd06
86
py
Python
hijack/signals.py
glizer/django-hijack
023cf4f6bc8f1f01bcf01756c2c103521f98ebc5
[ "MIT" ]
null
null
null
hijack/signals.py
glizer/django-hijack
023cf4f6bc8f1f01bcf01756c2c103521f98ebc5
[ "MIT" ]
null
null
null
hijack/signals.py
glizer/django-hijack
023cf4f6bc8f1f01bcf01756c2c103521f98ebc5
[ "MIT" ]
null
null
null
from django.dispatch import Signal hijack_started = Signal() hijack_ended = Signal()
17.2
34
0.790698
11
86
6
0.727273
0.363636
0
0
0
0
0
0
0
0
0
0
0.127907
86
4
35
21.5
0.88
0
0
0
0
0
0
0
0
0
0
0
0
1
0
false
0
0.333333
0
0.333333
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
0
0
1
0
0
0
0
4
df826f1eb9fa477b2303b55e45a401e519b14632
554
py
Python
aulas_secao_17_metodos_magicos.py
Romuloro/Curso_udemy_geek_academy_python
10d514874d913b7c2e3e4c30bd8a610973ffff3a
[ "MIT" ]
null
null
null
aulas_secao_17_metodos_magicos.py
Romuloro/Curso_udemy_geek_academy_python
10d514874d913b7c2e3e4c30bd8a610973ffff3a
[ "MIT" ]
null
null
null
aulas_secao_17_metodos_magicos.py
Romuloro/Curso_udemy_geek_academy_python
10d514874d913b7c2e3e4c30bd8a610973ffff3a
[ "MIT" ]
null
null
null
""" Métodos mágicos são todos os métodos que utilizam o dunder __method__ """ # Construtor (__init__) class Livro: def __init__(self, titulo, autor, paginas): self.__titulo = titulo self.__autor = autor self.__pagina = paginas def __repr__(self): #__repr__ É a representasão do objeto. return self.__titulo def __str__(self): #__str__ É a string que é a representação inicial para usuário. return self.__titulo def __len__(self): #__len__ o tamanho do objeto. return self.__paginas
23.083333
86
0.6787
70
554
4.685714
0.5
0.121951
0.085366
0.109756
0
0
0
0
0
0
0
0
0.249097
554
23
87
24.086957
0.788462
0.395307
0
0.181818
0
0
0
0
0
0
0
0.043478
0
1
0.363636
false
0
0
0.272727
0.727273
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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df927cfa1da48e0834ef7d11b919e85e79f612ef
362
py
Python
examples/0_boot.py/minimal/boot.py
fasteddy516/CircuitPython_GamepadXL
63626ff04cbe205510620801fe492d73c63e703d
[ "MIT" ]
1
2021-12-23T18:40:18.000Z
2021-12-23T18:40:18.000Z
examples/0_boot.py/minimal/boot.py
fasteddy516/CircuitPython_GamepadXL
63626ff04cbe205510620801fe492d73c63e703d
[ "MIT" ]
7
2021-08-18T16:34:12.000Z
2021-12-23T08:35:28.000Z
examples/0_boot.py/minimal/boot.py
fasteddy516/CircuitPython_JoystickXL
63626ff04cbe205510620801fe492d73c63e703d
[ "MIT" ]
null
null
null
"""JoystickXL minimal boot.py.""" import usb_hid # type: ignore (this is a CircuitPython built-in) from joystick_xl.hid import create_joystick # This will enable a joystick USB HID device. All other standard CircuitPython USB HID # devices (keyboard, mouse, consumer control) will be disabled. usb_hid.enable((create_joystick(axes=8, buttons=128, hats=4),))
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10d678a3ab60786ad15ae7b08d7a223f4c51cde2
154
py
Python
awtg/dispatcher/dispatcher.py
kvxmmu/aw
134603864f624075bc7b06876b2df3386bbeef2d
[ "BSD-3-Clause" ]
2
2020-05-21T11:45:16.000Z
2020-07-28T16:35:38.000Z
awtg/dispatcher/dispatcher.py
kvxmmu/aw
134603864f624075bc7b06876b2df3386bbeef2d
[ "BSD-3-Clause" ]
1
2020-05-20T09:35:19.000Z
2020-05-20T09:35:19.000Z
awtg/dispatcher/dispatcher.py
kvxmmu/aw
134603864f624075bc7b06876b2df3386bbeef2d
[ "BSD-3-Clause" ]
2
2020-05-19T17:39:25.000Z
2020-07-01T15:05:06.000Z
from ..abstract.dispatcher import AbstractDispatcher class Dispatcher(AbstractDispatcher): def process_message(self): raise NotImplemented
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py
Python
kubetools/dev/backend.py
EDITD/kubetools
3a87ed3b233aed152af9e52a4c1d5844a9c26437
[ "MIT" ]
5
2020-04-20T21:24:14.000Z
2022-01-29T15:35:02.000Z
kubetools/dev/backend.py
EDITD/kubetools
3a87ed3b233aed152af9e52a4c1d5844a9c26437
[ "MIT" ]
69
2019-12-17T16:16:21.000Z
2022-03-23T11:19:49.000Z
kubetools/dev/backend.py
EDITD/kubetools
3a87ed3b233aed152af9e52a4c1d5844a9c26437
[ "MIT" ]
1
2022-01-14T04:12:15.000Z
2022-01-14T04:12:15.000Z
# dummy file - see ./backends/__init__.py for actual module
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10e4b146691da7d25d7a3b572b70484922c30028
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py
Python
src/insideblog/apps.py
abhishek18620/django-forum
7d4354645cdd5b2af5dfe3ee4a07fb6674f94a84
[ "MIT" ]
null
null
null
src/insideblog/apps.py
abhishek18620/django-forum
7d4354645cdd5b2af5dfe3ee4a07fb6674f94a84
[ "MIT" ]
null
null
null
src/insideblog/apps.py
abhishek18620/django-forum
7d4354645cdd5b2af5dfe3ee4a07fb6674f94a84
[ "MIT" ]
null
null
null
from django.apps import AppConfig class InsideblogConfig(AppConfig): name = 'insideblog'
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10ea15ebe189f4358d9d280d07014fb1a4e899a1
320
py
Python
components/gpio_control/GPIODevices/__init__.py
jorgemiar/RPi-Jukebox-RFID
e0c445cf3d1115915d03a56deb5d9242abfd74ae
[ "MIT" ]
null
null
null
components/gpio_control/GPIODevices/__init__.py
jorgemiar/RPi-Jukebox-RFID
e0c445cf3d1115915d03a56deb5d9242abfd74ae
[ "MIT" ]
null
null
null
components/gpio_control/GPIODevices/__init__.py
jorgemiar/RPi-Jukebox-RFID
e0c445cf3d1115915d03a56deb5d9242abfd74ae
[ "MIT" ]
null
null
null
from .rotary_encoder import RotaryEncoder from .two_button_control import TwoButtonControl from .shutdown_button import ShutdownButton from .simple_button import SimpleButton from .two_button_control import TwoButtonControl from .VolumeControl import VolumeControl from .long_button import LongButton from .led import *
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292
py
Python
src/daipecore/lineage/GenericOutputDecorator.py
daipe-ai/daipe-core
aa205495fa6b464fa6078d17e439c60345ac99ea
[ "MIT" ]
1
2021-09-17T09:07:09.000Z
2021-09-17T09:07:09.000Z
src/daipecore/lineage/GenericOutputDecorator.py
daipe-ai/daipe-core
aa205495fa6b464fa6078d17e439c60345ac99ea
[ "MIT" ]
2
2021-12-20T07:46:33.000Z
2022-02-24T07:02:05.000Z
src/daipecore/lineage/GenericOutputDecorator.py
daipe-ai/daipe-core
aa205495fa6b464fa6078d17e439c60345ac99ea
[ "MIT" ]
null
null
null
from daipecore.lineage.OutputDecoratorInterface import OutputDecoratorInterface class GenericOutputDecorator(OutputDecoratorInterface): def __init__(self, identifier: str): self.__identifier = identifier @property def identifier(self): return self.__identifier
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py
Python
AnalyzeMaxima/__init__.py
Brow71189/AnalyzeMaxima
9d91a3448e3d76216e683834b1fe66e88b2cddbf
[ "MIT" ]
null
null
null
AnalyzeMaxima/__init__.py
Brow71189/AnalyzeMaxima
9d91a3448e3d76216e683834b1fe66e88b2cddbf
[ "MIT" ]
null
null
null
AnalyzeMaxima/__init__.py
Brow71189/AnalyzeMaxima
9d91a3448e3d76216e683834b1fe66e88b2cddbf
[ "MIT" ]
null
null
null
""" Package AnalyzeMaxima """
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801433f3173a620a628c4d9b2842b2f7bb547619
283
py
Python
sample/urls.py
ivissani/django-bootstrap3-datetimepicker
fc916c72a78bdafb54f09d8a30add516cbfe7689
[ "Apache-2.0" ]
86
2015-01-04T06:57:12.000Z
2021-03-28T03:35:45.000Z
sample/urls.py
ivissani/django-bootstrap3-datetimepicker
fc916c72a78bdafb54f09d8a30add516cbfe7689
[ "Apache-2.0" ]
38
2015-01-27T15:22:14.000Z
2021-11-08T21:24:09.000Z
sample/urls.py
ivissani/django-bootstrap3-datetimepicker
fc916c72a78bdafb54f09d8a30add516cbfe7689
[ "Apache-2.0" ]
128
2015-01-27T15:24:22.000Z
2021-11-28T14:52:32.000Z
from django.conf import settings from django.conf.urls.defaults import patterns, url urlpatterns = patterns('', url(r'.*', 'sample.todo_app.views.edit'), ) from django.conf.urls.static import static urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)
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33985ef0b67e128539f5887a48f77dfb10b76cc7
10,216
py
Python
analysis.py
dmitrijsg123/Programming_and_Scripting_Project
3035f42cca794b5c4bc520a8c3bfaac289d0e35f
[ "MIT" ]
null
null
null
analysis.py
dmitrijsg123/Programming_and_Scripting_Project
3035f42cca794b5c4bc520a8c3bfaac289d0e35f
[ "MIT" ]
null
null
null
analysis.py
dmitrijsg123/Programming_and_Scripting_Project
3035f42cca794b5c4bc520a8c3bfaac289d0e35f
[ "MIT" ]
null
null
null
#Import pandas library that allows to use its in-built functions, like max(), mnin(), mean() and describe() import pandas as pd df=pd.read_csv('Iris.csv') # reads the csv file output_textfile = "Variables_Summary.txt" # writing to a text file in Python reference: https://www.geeksforgeeks.org/writing-to-file-in-python/ file = open(output_textfile, "w") file.close() #FINDING SEPAL LENGTH VALUES FOR ALL IRIS TYPES - MAX, MIN, MEAN AS WELL AS MORE DETAILED INFO WITH DESCRIBE FUNCTION maximum = df['Sepal_length'].max() minimum = df['Sepal_length'].min() average = df['Sepal_length'].mean() description = df['Sepal_length'].describe() print('\nMaximum sepal length of all Iris species is ', maximum, file=open(output_textfile,"a")) # \n - new line reference: https://wtmatter.com/python-new-line/ print('Minimum sepal length of all Iris species is ', minimum, file=open(output_textfile,"a")) print('Average sepal length of all Iris species is ', round(average,1), file=open(output_textfile,"a")) # rounding to 1 decimal function - round(my_float,1) reference: https://tutorialdeep.com/knowhow/round-float-to-2-decimal-places-python/ print('Some more detailed summarized information:', file=open(output_textfile,"a")) print(round(description,1), file=open(output_textfile,"a")) #FINDING SEPAL WIDTH VALUES FOR ALL IRIS TYPES - MAX, MIN, MEAN AS WELL AS MORE DETAILED INFO WITH DESCRIBE FUNCTION maximum = df['Sepal_width'].max() minimum = df['Sepal_width'].min() average = df['Sepal_width'].mean() description = df['Sepal_width'].describe() print('\nMaximum sepal width of all Iris species is ', maximum, file=open(output_textfile,"a")) print('Minimum sepal width of all Iris species is ', minimum, file=open(output_textfile,"a")) print('Average sepal width of all Iris species is ', round(average,1), file=open(output_textfile,"a")) print('Some more detailed summarized information:', file=open(output_textfile,"a")) print(round(description,1), file=open(output_textfile,"a")) #FINDING PETAL LENGTH VALUES FOR ALL IRIS TYPES - MAX, MIN, MEAN AS WELL AS MORE DETAILED INFO WITH DESCRIBE FUNCTION maximum = df['Petal_length'].max() minimum = df['Petal_length'].min() average = df['Petal_length'].mean() description = df['Petal_length'].describe() print('\nMaximum petal length of all Iris species is ', maximum, file=open(output_textfile,"a")) print('Minimum petal length of all Iris species is ', minimum, file=open(output_textfile,"a")) print('Average petal length of all Iris species is ', round(average,1), file=open(output_textfile,"a")) print('Some more detailed summarized information:', file=open(output_textfile,"a")) print(round(description,1), file=open(output_textfile,"a")) #FINDING PETAL WIDTH VALUES FOR ALL IRIS TYPES - MAX, MIN, MEAN AS WELL AS MORE DETAILED INFO WITH DESCRIBE FUNCTION maximum = df['Petal_width'].max() minimum = df['Petal_width'].min() average = df['Petal_width'].mean() description = df['Petal_width'].describe() print('\nMaximum petal width of all Iris species is ', maximum, file=open(output_textfile,"a")) print('Minimum petal width of all Iris species is ', minimum, file=open(output_textfile,"a")) print('Average petal width of all Iris species is ', round(average,1), file=open(output_textfile,"a")) print('Some more detailed summarized information:', file=open(output_textfile,"a")) print(round(description,1), file=open(output_textfile,"a")) #.................................................................................................................... #Iris Setosa only - maximums for all four parameters df=pd.read_csv('Iris.csv') # reads the csv file Setosa_maximum = df[df.Species=='setosa'] # df[df.Column_Name==''] function allows to narrow down only to the needed rows (reference: https://youtube.com/watch?V=xvpNA7bC8cs) print('\nMaximum values for Iris Setosa flowers are: ', file=open(output_textfile,"a")) print(Setosa_maximum.max(), file=open(output_textfile,"a")) #Iris Versicolor only - maximums for all four parameters df=pd.read_csv('Iris.csv') # reads the csv file Versicolor_maximum = df[df.Species=='versicolor'] print('\nMaximum values for Iris Versicolor flowers are: ', file=open(output_textfile,"a")) print(Versicolor_maximum.max(), file=open(output_textfile,"a")) #Iris Virginica only - maximums for all four parameters df=pd.read_csv('Iris.csv') # reads the csv file Virginica_maximum = df[df.Species=='virginica'] print('\nMaximum values for Iris Virginica flowers are: ', file=open(output_textfile,"a")) print(Virginica_maximum.max(), file=open(output_textfile,"a")) #................................................................................................... #Iris Setosa only - minimums for all four parameters df=pd.read_csv('Iris.csv') # reads the csv file Setosa_minimum = df[df.Species=='setosa'] print('\nMinimum values for Iris Setosa flowers are: ', file=open(output_textfile,"a")) print(Setosa_minimum.min(), file=open(output_textfile,"a")) #Iris Versicolor only - minimums for all four parameters df=pd.read_csv('Iris.csv') # reads the csv file Versicolor_minimum = df[df.Species=='versicolor'] print('\nMinimum values for Iris Versicolor flowers are: ', file=open(output_textfile,"a")) print(Versicolor_minimum.min(), file=open(output_textfile,"a")) #Iris Virginica only - minimums for all four parameters df=pd.read_csv('Iris.csv') # reads the csv file Virginica_minimum = df[df.Species=='virginica'] print('\nMinimum values for Iris Virginica flowers are: ', file=open(output_textfile,"a")) print(Virginica_minimum.min(), file=open(output_textfile,"a")) #................................................................................................... #Iris Setosa only - average values for all four parameters df=pd.read_csv('Iris.csv') # reads the csv file Setosa_average = df[df.Species=='setosa'] print('\nAverage values for Iris Setosa flowers are: ', file=open(output_textfile,"a")) print(round(Setosa_average.mean(),2), file=open(output_textfile,"a")) # will round to 2 decimals here #Iris Versicolor only - average values for all four parameters df=pd.read_csv('Iris.csv') # reads the csv file Versicolor_average = df[df.Species=='versicolor'] print('\nAverage values for Iris Versicolor flowers are: ', file=open(output_textfile,"a")) print(round(Versicolor_average.mean(),2), file=open(output_textfile,"a")) #Iris Virginica only - average values for all four parameters df=pd.read_csv('Iris.csv') # reads the csv file Virginica_average = df[df.Species=='virginica'] print('\nAverage values for Iris Virginica flowers are: ', file=open(output_textfile,"a")) print(round(Virginica_average.mean(),2), file=open(output_textfile,"a")) # SCATTERPLOTS # References https://www.youtube.com/watch?v=GcXcSZ0gQps # https://matplotlib.org/3.1.1/tutorials/text/text_intro.html # https://web.microsoftstream.com/video/025ef713-d7c8-492f-97f4-5590015da029?referrer=https:%2F%2Flearnonline.gmit.ie%2Fcourse%2Fview.php%3Fid%3D1598 import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np df = pd.read_csv("Iris.csv") sns.set(style = 'whitegrid') # colour of the background grid sns.set_palette(None) # option of changing colour palette (default one left here) sns.scatterplot(x = 'Sepal_length',y = 'Sepal_width', data = df,hue = 'Species') plt.title('Comparison of sepal length against sepal width',fontsize=15, fontweight='bold',color='red',fontstyle='italic') # option of adjusting font size, weight, colour and style plt.legend(loc='lower right') # legend positioning plt.show() df = pd.read_csv("Iris.csv") sns.set(style = 'whitegrid') sns.scatterplot(x = 'Petal_length', y = 'Petal_width', data = df,hue = 'Species') plt.title('Comparison of petal length against petal width',fontsize=15, fontweight='bold',color='red',fontstyle='italic') plt.legend(loc='lower right') plt.show() df = pd.read_csv("Iris.csv") sns.set(style = 'whitegrid') sns.scatterplot(x = 'Sepal_length', y = 'Petal_length', data = df,hue = 'Species') plt.title('Comparison of sepal length against petal length',fontsize=15, fontweight='bold',color='red',fontstyle='italic') plt.legend(loc='lower right') plt.show() df = pd.read_csv("Iris.csv") sns.set(style = 'whitegrid') sns.scatterplot(x = 'Sepal_length', y = 'Petal_width', data = df,hue = 'Species') plt.title('Comparison of sepal length against petal width',fontsize=15, fontweight='bold',color='red',fontstyle='italic') plt.legend(loc='lower right') plt.show() df = pd.read_csv("Iris.csv") sns.set(style = 'whitegrid') sns.scatterplot(x = 'Sepal_width', y = 'Petal_length', data = df,hue = 'Species') plt.title('Comparison of sepal width against petal length',fontsize=15, fontweight='bold',color='red',fontstyle='italic') plt.legend(loc='upper right') plt.show() df = pd.read_csv("Iris.csv") sns.set(style = 'whitegrid') sns.scatterplot(x = 'Sepal_width', y = 'Petal_width', data = df,hue = 'Species') plt.title('Comparison of sepal width against petal width',fontsize=15, fontweight='bold',color='red',fontstyle='italic') plt.legend(loc='upper right') plt.show() sns.pairplot(df, hue='Species') plt.show()
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4
33e47325e95795d792dde3a01a437a11ea8bbe0e
486
py
Python
boto3_type_annotations/boto3_type_annotations/opsworkscm/paginator.py
cowboygneox/boto3_type_annotations
450dce1de4e066b939de7eac2ec560ed1a7ddaa2
[ "MIT" ]
119
2018-12-01T18:20:57.000Z
2022-02-02T10:31:29.000Z
boto3_type_annotations/boto3_type_annotations/opsworkscm/paginator.py
cowboygneox/boto3_type_annotations
450dce1de4e066b939de7eac2ec560ed1a7ddaa2
[ "MIT" ]
15
2018-11-16T00:16:44.000Z
2021-11-13T03:44:18.000Z
boto3_type_annotations/boto3_type_annotations/opsworkscm/paginator.py
cowboygneox/boto3_type_annotations
450dce1de4e066b939de7eac2ec560ed1a7ddaa2
[ "MIT" ]
11
2019-05-06T05:26:51.000Z
2021-09-28T15:27:59.000Z
from typing import Dict from botocore.paginate import Paginator class DescribeBackups(Paginator): def paginate(self, BackupId: str = None, ServerName: str = None, PaginationConfig: Dict = None) -> Dict: pass class DescribeEvents(Paginator): def paginate(self, ServerName: str, PaginationConfig: Dict = None) -> Dict: pass class DescribeServers(Paginator): def paginate(self, ServerName: str = None, PaginationConfig: Dict = None) -> Dict: pass
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4
d5084da4b988566707b1b7de151427985d1d8594
174
py
Python
core/__init__.py
iColdPlayer/kasir
5c83c201a6e5e3bc0ca402ed4bb824ea85e02e38
[ "MIT" ]
29
2019-12-04T16:21:14.000Z
2022-03-02T23:27:59.000Z
core/__init__.py
mejeng/kasir
cc6f9158b61c0cb45078ddf798af9588c8771311
[ "MIT" ]
13
2019-11-29T18:12:41.000Z
2021-06-27T02:01:07.000Z
core/__init__.py
mejeng/kasir
cc6f9158b61c0cb45078ddf798af9588c8771311
[ "MIT" ]
14
2019-12-04T16:21:15.000Z
2022-02-24T07:05:12.000Z
from __future__ import absolute_import, unicode_literals from .celery import app as celery_app # to ensure celery keep start everytime django started __all__ = ('celery_app')
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1d45c276ce3d8322b934fa1dfbb7ce2b9dcc1d38
668
py
Python
tests/test_Level2_Pipeline.py
jchiang87/Level2_Pipeline
d7f4d49b8f28a112c1a20e6812e9d810d2562e45
[ "BSD-3-Clause" ]
1
2016-12-20T21:47:39.000Z
2016-12-20T21:47:39.000Z
tests/test_Level2_Pipeline.py
jchiang87/Level2_Pipeline
d7f4d49b8f28a112c1a20e6812e9d810d2562e45
[ "BSD-3-Clause" ]
null
null
null
tests/test_Level2_Pipeline.py
jchiang87/Level2_Pipeline
d7f4d49b8f28a112c1a20e6812e9d810d2562e45
[ "BSD-3-Clause" ]
null
null
null
""" Example unit tests for Level2_Pipeline package """ import unittest import desc.level2_pipeline class Level2_PipelineTestCase(unittest.TestCase): def setUp(self): self.message = 'Hello, world' def tearDown(self): pass def test_run(self): foo = desc.level2_pipeline.Level2_Pipeline(self.message) self.assertEquals(foo.run(), self.message) def test_failure(self): self.assertRaises(TypeError, desc.level2_pipeline.Level2_Pipeline) foo = desc.level2_pipeline.Level2_Pipeline(self.message) self.assertRaises(RuntimeError, foo.run, True) if __name__ == '__main__': unittest.main()
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4
1d51f7d7b7c6029074d8a6b0681a61564626dac3
61
py
Python
src/genie/libs/parser/arubaos/__init__.py
ykoehler/genieparser
b62cf622c3d8eab77c7b69e932c214ed04a2565a
[ "Apache-2.0" ]
null
null
null
src/genie/libs/parser/arubaos/__init__.py
ykoehler/genieparser
b62cf622c3d8eab77c7b69e932c214ed04a2565a
[ "Apache-2.0" ]
null
null
null
src/genie/libs/parser/arubaos/__init__.py
ykoehler/genieparser
b62cf622c3d8eab77c7b69e932c214ed04a2565a
[ "Apache-2.0" ]
null
null
null
from genie import abstract abstract.declare_token(__name__)
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4
1d6c9dbf7a2f8f559af1aebb978666643dd41566
42
py
Python
fem/continuous/__init__.py
joepatmckenna/fem
18b3e3cb0b83f7a4eb464c84f09f00673ea2fcb3
[ "MIT" ]
null
null
null
fem/continuous/__init__.py
joepatmckenna/fem
18b3e3cb0b83f7a4eb464c84f09f00673ea2fcb3
[ "MIT" ]
null
null
null
fem/continuous/__init__.py
joepatmckenna/fem
18b3e3cb0b83f7a4eb464c84f09f00673ea2fcb3
[ "MIT" ]
null
null
null
name = 'continuous' import fit, simulate
10.5
20
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1d6cd1793ea2d62f7ac0111b29aa68a521054da4
593
py
Python
backend/events/schema.py
DDoS000/Event_Registers
84a77d0914333ee830a72e2d31fa5374f70dea35
[ "MIT" ]
null
null
null
backend/events/schema.py
DDoS000/Event_Registers
84a77d0914333ee830a72e2d31fa5374f70dea35
[ "MIT" ]
null
null
null
backend/events/schema.py
DDoS000/Event_Registers
84a77d0914333ee830a72e2d31fa5374f70dea35
[ "MIT" ]
null
null
null
import datetime from pydantic import BaseModel class EventBase(BaseModel): id: int name: str description: str location: str start_date: datetime.datetime end_date: datetime.datetime created_by: int created_on: datetime.datetime status: str image: str class EventCreate(BaseModel): name: str description: str location: str start_date: datetime.datetime end_date: datetime.datetime created_on: datetime.datetime status: str image: str class EventRegister(BaseModel): events_id: int code: str token: str
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0.647643
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4
1d752560244e76cf8a47c924cea23be76dd6af94
185
py
Python
centerpoints/__main__.py
grundrauschen/center-points
5a12f68ac012a0a2bf52d8a8381d0272e309ac18
[ "MIT" ]
null
null
null
centerpoints/__main__.py
grundrauschen/center-points
5a12f68ac012a0a2bf52d8a8381d0272e309ac18
[ "MIT" ]
2
2015-06-03T10:57:13.000Z
2015-09-15T12:43:22.000Z
centerpoints/__main__.py
fu-berlin-swp-2014/center-points
0fa523314a3168d4d229b6f61d0d05d314a8b35a
[ "MIT" ]
null
null
null
if __name__ == "__main__": # To make relative paths work, the module has to be invoked with the # -m argument like: python -m centerpoints from .cli import main main()
26.428571
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4
1daa5c4e8d587ca76aee1aab4b7cca6d675c15e8
185
py
Python
open_python_console.py
hugoburiel/OpenPythonConsole
8a0f0e40e9187bcfbdb246bad013b5c6dae72b95
[ "MIT" ]
null
null
null
open_python_console.py
hugoburiel/OpenPythonConsole
8a0f0e40e9187bcfbdb246bad013b5c6dae72b95
[ "MIT" ]
null
null
null
open_python_console.py
hugoburiel/OpenPythonConsole
8a0f0e40e9187bcfbdb246bad013b5c6dae72b95
[ "MIT" ]
null
null
null
import sublime, sublime_plugin # TODO: add comments def plugin_loaded(): window = sublime.active_window() window.run_command("show_panel", {"panel": "console", "toggle": True})
30.833333
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6
74
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4
d52b03be55248e5a96e5d11828e66d4838a6bd34
27
py
Python
aevum_auth.example.py
kaylynn234/AevumBot
63a99dd20b2fbc62ee0a57a3679aff0ced129992
[ "MIT" ]
null
null
null
aevum_auth.example.py
kaylynn234/AevumBot
63a99dd20b2fbc62ee0a57a3679aff0ced129992
[ "MIT" ]
null
null
null
aevum_auth.example.py
kaylynn234/AevumBot
63a99dd20b2fbc62ee0a57a3679aff0ced129992
[ "MIT" ]
null
null
null
TOKEN = "(your token here)"
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4.5
0.75
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1
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0.782609
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4
d53fa1040a42f59e80a470139ceed065b7aadf52
555
py
Python
custom/champ/utils.py
dimagilg/commcare-hq
ea1786238eae556bb7f1cbd8d2460171af1b619c
[ "BSD-3-Clause" ]
471
2015-01-10T02:55:01.000Z
2022-03-29T18:07:18.000Z
custom/champ/utils.py
dimagilg/commcare-hq
ea1786238eae556bb7f1cbd8d2460171af1b619c
[ "BSD-3-Clause" ]
14,354
2015-01-01T07:38:23.000Z
2022-03-31T20:55:14.000Z
custom/champ/utils.py
dimagilg/commcare-hq
ea1786238eae556bb7f1cbd8d2460171af1b619c
[ "BSD-3-Clause" ]
175
2015-01-06T07:16:47.000Z
2022-03-29T13:27:01.000Z
ENHANCED_PEER_MOBILIZATION = 'enhanced_peer_mobilization' CHAMP_CAMEROON = 'champ_client_forms' TARGET_XMLNS = 'http://openrosa.org/formdesigner/A79467FD-4CDE-47B6-8218-4394699A5C95' PREVENTION_XMLNS = 'http://openrosa.org/formdesigner/DF2FBEEA-31DE-4537-9913-07D57591502C' POST_TEST_XMLNS = 'http://openrosa.org/formdesigner/E2B4FD32-9A62-4AE8-AAB0-0CE4B8C28AA1' ACCOMPAGNEMENT_XMLNS = 'http://openrosa.org/formdesigner/027DEB76-422B-434C-9F53-ECBBE21F890F' SUIVI_MEDICAL_XMLNS = 'http://openrosa.org/formdesigner/7C8BC256-0E79-4A96-9ECB-D6D8C50CD69E'
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4
d5747e16ce619b2b977803951d6943a7136a6ae0
374
py
Python
src/python/20ValidParentheses/solution_test.py
foomango/leetcode
a9d509db4b0902216b13449afe59b4ef22a416ca
[ "MIT" ]
null
null
null
src/python/20ValidParentheses/solution_test.py
foomango/leetcode
a9d509db4b0902216b13449afe59b4ef22a416ca
[ "MIT" ]
null
null
null
src/python/20ValidParentheses/solution_test.py
foomango/leetcode
a9d509db4b0902216b13449afe59b4ef22a416ca
[ "MIT" ]
null
null
null
import unittest from solution import Solution class TestSolution(unittest.TestCase): def test_solution(self): inst = Solution() self.assertTrue(inst.isValid("()")) self.assertTrue(inst.isValid("()[]{}")) self.assertTrue(inst.isValid("{[]}")) self.assertFalse(inst.isValid("(]")) self.assertFalse(inst.isValid("([)]"))
24.933333
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4
d58def71a94759edd1d0de3744a21e3ab5322d2d
286
py
Python
src/__init__.py
veechand/swPractise
91d2941c7e8372567e95ed9871c92174a144c368
[ "Apache-2.0" ]
null
null
null
src/__init__.py
veechand/swPractise
91d2941c7e8372567e95ed9871c92174a144c368
[ "Apache-2.0" ]
null
null
null
src/__init__.py
veechand/swPractise
91d2941c7e8372567e95ed9871c92174a144c368
[ "Apache-2.0" ]
null
null
null
from flask import Flask from flask import render_template, make_response app = Flask(__name__) @app.route('/hello/') def hello_world(): return render_template('hello.html', name="") @app.route('/hello/<name>') def hello_name(name): return render_template('hello.html', name=name)
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d59f53305b68ec40f6e06c5141f7ca97658ae2cf
93
py
Python
protestId/apps.py
gurleen-kaur1313/Oneness-Backend
90c19d6c82c1d447f878575c132a7087d5347c85
[ "MIT" ]
1
2020-12-25T12:21:17.000Z
2020-12-25T12:21:17.000Z
protestId/apps.py
gurleen-kaur1313/Oneness-Backend
90c19d6c82c1d447f878575c132a7087d5347c85
[ "MIT" ]
null
null
null
protestId/apps.py
gurleen-kaur1313/Oneness-Backend
90c19d6c82c1d447f878575c132a7087d5347c85
[ "MIT" ]
null
null
null
from django.apps import AppConfig class ProtestidConfig(AppConfig): name = 'protestId'
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634139e233f9799538f322255b69764d01818509
684
py
Python
llvm/utils/lit/tests/test-data-micro.py
medismailben/llvm-project
e334a839032fe500c3bba22bf976ab7af13ce1c1
[ "Apache-2.0" ]
4,812
2015-01-02T19:38:10.000Z
2022-03-27T12:42:24.000Z
llvm/utils/lit/tests/test-data-micro.py
medismailben/llvm-project
e334a839032fe500c3bba22bf976ab7af13ce1c1
[ "Apache-2.0" ]
11,789
2015-01-05T04:50:15.000Z
2022-03-31T23:39:19.000Z
llvm/utils/lit/tests/test-data-micro.py
medismailben/llvm-project
e334a839032fe500c3bba22bf976ab7af13ce1c1
[ "Apache-2.0" ]
2,543
2015-01-01T11:18:36.000Z
2022-03-22T21:32:36.000Z
# Test features related to formats which support reporting additional test data. # and multiple test results. # RUN: %{lit} -j 1 -v %{inputs}/test-data-micro | FileCheck %s # CHECK: -- Testing: # CHECK: PASS: test-data-micro :: micro-tests.ini # CHECK-NEXT: *** TEST 'test-data-micro :: micro-tests.ini' RESULTS *** # CHECK-NEXT: value0: 1 # CHECK-NEXT: value1: 2.3456 # CHECK-NEXT: *** # CHECK-NEXT: *** MICRO-TEST: test0 # CHECK-NEXT: micro_value0: 4 # CHECK-NEXT: micro_value1: 1.3 # CHECK-NEXT: *** MICRO-TEST: test1 # CHECK-NEXT: micro_value0: 4 # CHECK-NEXT: micro_value1: 1.3 # CHECK-NEXT: *** MICRO-TEST: test2 # CHECK-NEXT: micro_value0: 4 # CHECK-NEXT: micro_value1: 1.3
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6370722419a2586fe2ce2890f1f62b268d2a2a82
107
py
Python
deferredCounters/apps.py
federicoemartinez/django-deferred-counter
871e52f1adcdb8bdc88c85b02e4a34fde26ac097
[ "BSD-3-Clause" ]
1
2019-12-15T00:13:20.000Z
2019-12-15T00:13:20.000Z
deferredCounters/apps.py
federicoemartinez/django-deferred-counter
871e52f1adcdb8bdc88c85b02e4a34fde26ac097
[ "BSD-3-Clause" ]
null
null
null
deferredCounters/apps.py
federicoemartinez/django-deferred-counter
871e52f1adcdb8bdc88c85b02e4a34fde26ac097
[ "BSD-3-Clause" ]
null
null
null
from django.apps import AppConfig class DeferredcountersConfig(AppConfig): name = 'deferredCounters'
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638bc0990e7baa601f3d41ecc0718235b239c077
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py
Python
Tutorials/01. 10 Days of Statistics/016. Day 6 - The Central Limit Theorem III.py
stonehengee/HackerrankPractice
ec052e7447391e40d1919cf0b641ff5023da3da3
[ "MIT" ]
null
null
null
Tutorials/01. 10 Days of Statistics/016. Day 6 - The Central Limit Theorem III.py
stonehengee/HackerrankPractice
ec052e7447391e40d1919cf0b641ff5023da3da3
[ "MIT" ]
null
null
null
Tutorials/01. 10 Days of Statistics/016. Day 6 - The Central Limit Theorem III.py
stonehengee/HackerrankPractice
ec052e7447391e40d1919cf0b641ff5023da3da3
[ "MIT" ]
null
null
null
# Problem: https://www.hackerrank.com/challenges/s10-the-central-limit-theorem-3/problem # Score: 30
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0.772277
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4
63bba6bc144329d88f8529fc7892e26dbded74c6
1,151
py
Python
apis_core/apis_tei/views.py
acdh-oeaw/apis-core
f7ece05eec46c820321fd28d3e947653dcb98ae7
[ "MIT" ]
11
2018-07-11T18:11:40.000Z
2022-03-25T11:07:12.000Z
apis_core/apis_tei/views.py
acdh-oeaw/apis-core
f7ece05eec46c820321fd28d3e947653dcb98ae7
[ "MIT" ]
309
2018-06-11T08:38:50.000Z
2022-03-31T13:45:22.000Z
apis_core/apis_tei/views.py
acdh-oeaw/apis-core
f7ece05eec46c820321fd28d3e947653dcb98ae7
[ "MIT" ]
5
2017-08-21T10:37:07.000Z
2021-09-27T19:08:47.000Z
import lxml.etree as ET from django.http import HttpResponse from django.shortcuts import get_object_or_404 from apis_core.apis_entities.models import Person, Place, Institution from .tei_utils import get_node_from_template def person_as_tei(request, pk): full = request.GET.get('full') res = get_object_or_404(Person, pk=pk) doc = get_node_from_template('apis_tei/person.xml', res, full=full) tei = ET.tostring(doc, pretty_print=True, encoding='UTF-8') return HttpResponse(tei, content_type="application/xml") def place_as_tei(request, pk): full = request.GET.get('full') res = get_object_or_404(Place, pk=pk) doc = get_node_from_template('apis_tei/place.xml', res, full=full) tei = ET.tostring(doc, pretty_print=True, encoding='UTF-8') return HttpResponse(tei, content_type="application/xml") def org_as_tei(request, pk): full = request.GET.get('full') res = get_object_or_404(Institution, pk=pk) doc = get_node_from_template('apis_tei/org.xml', res, full=full) tei = ET.tostring(doc, pretty_print=True, encoding='UTF-8') return HttpResponse(tei, content_type="application/xml")
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892138b9b158cfb0d22cbfe2399dee0a3fbac88b
139
py
Python
4. Estrutura de Repeticao While/Lista 01/programa 05.py
enosteteo/Introducao-a-Programacao-P1
bd8f90c96fd16c88e7dc7136942fe29a828f86fa
[ "MIT" ]
null
null
null
4. Estrutura de Repeticao While/Lista 01/programa 05.py
enosteteo/Introducao-a-Programacao-P1
bd8f90c96fd16c88e7dc7136942fe29a828f86fa
[ "MIT" ]
null
null
null
4. Estrutura de Repeticao While/Lista 01/programa 05.py
enosteteo/Introducao-a-Programacao-P1
bd8f90c96fd16c88e7dc7136942fe29a828f86fa
[ "MIT" ]
null
null
null
a = 10 b = 22 while b - a > 4: c = b - (2 * a) if c > 0: d = c + 1 else: d = c + 5 a = a + 3 b = b - 2
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89243bf1315e9a3d44088e0289d4de4644eff531
329
py
Python
mudminnow/mudminnow.py
DJHostetler/mudminnow
e7785e615ef04a11c6d091d58593ea2e2bf93c9d
[ "MIT" ]
1
2019-10-15T01:27:46.000Z
2019-10-15T01:27:46.000Z
mudminnow/mudminnow.py
DJHostetler/mudminnow
e7785e615ef04a11c6d091d58593ea2e2bf93c9d
[ "MIT" ]
null
null
null
mudminnow/mudminnow.py
DJHostetler/mudminnow
e7785e615ef04a11c6d091d58593ea2e2bf93c9d
[ "MIT" ]
null
null
null
import shapely from dem import DEM from geometry_v2 import Geometry class Mapper: '''Mapper class for mapping cross section elevation data''' def __init__(self, xs_file, reach_file, dem_file): self.geometry = Geometry(xs_file, reach_file) self.dem = DEM(dem_file) def map(self): pass
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4
892b1fd4c18ad8ae357f80f9f1fd509380c1107f
224
py
Python
corpustools/corpus/classes/__init__.py
akki2825/CorpusTools
ba236f55266eceb7e719fa36545985d8453953d3
[ "BSD-3-Clause" ]
97
2015-07-06T18:58:43.000Z
2022-03-10T23:00:07.000Z
corpustools/corpus/classes/__init__.py
akki2825/CorpusTools
ba236f55266eceb7e719fa36545985d8453953d3
[ "BSD-3-Clause" ]
443
2015-03-10T21:24:39.000Z
2022-03-22T22:20:13.000Z
corpustools/corpus/classes/__init__.py
akki2825/CorpusTools
ba236f55266eceb7e719fa36545985d8453953d3
[ "BSD-3-Clause" ]
22
2015-07-19T18:56:24.000Z
2020-09-17T17:58:12.000Z
from .lexicon import (Corpus, Word, Environment, EnvironmentFilter, FeatureMatrix, Segment, Transcription, Attribute, ) from .spontaneous import Speaker, WordToken, Discourse, SpontaneousSpeechCorpus
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89346dc74e8bfcb719fb5788fd85e4b9fbb9c4b6
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py
Python
enthought/enable/qt4/base_window.py
enthought/etsproxy
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
[ "BSD-3-Clause" ]
3
2016-12-09T06:05:18.000Z
2018-03-01T13:00:29.000Z
enthought/enable/qt4/base_window.py
enthought/etsproxy
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
[ "BSD-3-Clause" ]
1
2020-12-02T00:51:32.000Z
2020-12-02T08:48:55.000Z
enthought/enable/qt4/base_window.py
enthought/etsproxy
4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347
[ "BSD-3-Clause" ]
null
null
null
# proxy module from __future__ import absolute_import from enable.qt4.base_window import *
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8962e467ae54be542b4ed08170f0da5cd00bb0ce
87
py
Python
super_recetas/receta/apps.py
Fahrek/test_project
2e7f41f82c43c8b2ff9b49885afcca573883afaa
[ "MIT" ]
null
null
null
super_recetas/receta/apps.py
Fahrek/test_project
2e7f41f82c43c8b2ff9b49885afcca573883afaa
[ "MIT" ]
null
null
null
super_recetas/receta/apps.py
Fahrek/test_project
2e7f41f82c43c8b2ff9b49885afcca573883afaa
[ "MIT" ]
null
null
null
from django.apps import AppConfig class RecetaConfig(AppConfig): name = 'receta'
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4
89969590ce097ec5594e3d4d48bf574a0e330a25
50
py
Python
tests/__init__.py
sahulkrishan/python-masterthermconnect
7610056a5c387ccfeb7a01f3b6e0f8d42f63ddbf
[ "MIT" ]
null
null
null
tests/__init__.py
sahulkrishan/python-masterthermconnect
7610056a5c387ccfeb7a01f3b6e0f8d42f63ddbf
[ "MIT" ]
null
null
null
tests/__init__.py
sahulkrishan/python-masterthermconnect
7610056a5c387ccfeb7a01f3b6e0f8d42f63ddbf
[ "MIT" ]
1
2021-11-27T15:07:27.000Z
2021-11-27T15:07:27.000Z
"""Python API wrapper for Mastertherm Connect."""
25
49
0.74
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6.166667
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982f348c7ea0ddbcf48063aece26f1a3c3615b68
78
py
Python
Python-Fundamentals-June-2019/03_lists/04_rotate_list_of_strings.py
marinakolova/Python-Courses
eb95c782307be561b5026c5adafaa001b04caf4f
[ "MIT" ]
null
null
null
Python-Fundamentals-June-2019/03_lists/04_rotate_list_of_strings.py
marinakolova/Python-Courses
eb95c782307be561b5026c5adafaa001b04caf4f
[ "MIT" ]
null
null
null
Python-Fundamentals-June-2019/03_lists/04_rotate_list_of_strings.py
marinakolova/Python-Courses
eb95c782307be561b5026c5adafaa001b04caf4f
[ "MIT" ]
null
null
null
strings = input().split(' ') print(strings[-1] + ' ' + ' '.join(strings[:-1]))
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4
9848a892b4159a6813f1acf134e232e2743263cb
1,502
py
Python
tests/logging_/test_record_templates.py
UOC/dlkit
a9d265db67e81b9e0f405457464e762e2c03f769
[ "MIT" ]
2
2018-02-23T12:16:11.000Z
2020-10-08T17:54:24.000Z
tests/logging_/test_record_templates.py
UOC/dlkit
a9d265db67e81b9e0f405457464e762e2c03f769
[ "MIT" ]
87
2017-04-21T18:57:15.000Z
2021-12-13T19:43:57.000Z
tests/logging_/test_record_templates.py
UOC/dlkit
a9d265db67e81b9e0f405457464e762e2c03f769
[ "MIT" ]
1
2018-03-01T16:44:25.000Z
2018-03-01T16:44:25.000Z
"""Unit tests of logging records.""" import pytest from ..utilities.general import is_never_authz, is_no_authz, uses_cataloging, uses_filesystem_only @pytest.mark.usefixtures("log_entry_record_class_fixture", "log_entry_record_test_fixture") class TestLogEntryRecord(object): """Tests for LogEntryRecord""" @pytest.mark.usefixtures("log_entry_query_record_class_fixture", "log_entry_query_record_test_fixture") class TestLogEntryQueryRecord(object): """Tests for LogEntryQueryRecord""" @pytest.mark.usefixtures("log_entry_form_record_class_fixture", "log_entry_form_record_test_fixture") class TestLogEntryFormRecord(object): """Tests for LogEntryFormRecord""" @pytest.mark.usefixtures("log_entry_search_record_class_fixture", "log_entry_search_record_test_fixture") class TestLogEntrySearchRecord(object): """Tests for LogEntrySearchRecord""" @pytest.mark.usefixtures("log_record_class_fixture", "log_record_test_fixture") class TestLogRecord(object): """Tests for LogRecord""" @pytest.mark.usefixtures("log_query_record_class_fixture", "log_query_record_test_fixture") class TestLogQueryRecord(object): """Tests for LogQueryRecord""" @pytest.mark.usefixtures("log_form_record_class_fixture", "log_form_record_test_fixture") class TestLogFormRecord(object): """Tests for LogFormRecord""" @pytest.mark.usefixtures("log_search_record_class_fixture", "log_search_record_test_fixture") class TestLogSearchRecord(object): """Tests for LogSearchRecord"""
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py
Python
guild-file-intro/train.py
lambdaofgod/examples
26f7dd0ab58aba2f251e307982c34c9bd67d4797
[ "Apache-2.0" ]
9
2017-12-14T01:20:07.000Z
2020-04-30T09:33:55.000Z
guild-file-intro/train.py
lambdaofgod/examples
26f7dd0ab58aba2f251e307982c34c9bd67d4797
[ "Apache-2.0" ]
2
2019-08-15T16:32:00.000Z
2020-03-18T18:22:38.000Z
guild-file-intro/train.py
lambdaofgod/examples
26f7dd0ab58aba2f251e307982c34c9bd67d4797
[ "Apache-2.0" ]
4
2019-08-22T07:25:11.000Z
2020-06-08T07:07:50.000Z
f1 = 1 f2 = 2 not_a_flag = 3 print("step: 1") print("stat_1: 0.5") print("stat_2: 0.6") print("step: 2") print("stat_1: 0.7") print("stat_2: 0.8")
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4
98abdf7a7e0af400b1e608fa4cb89763c7571991
1,401
py
Python
tests/test_markdownify_notion.py
chekos/markdownify-notion
d9a4b8140902e0039f0de88aa6364b33199508fa
[ "Apache-2.0" ]
1
2022-01-25T22:46:17.000Z
2022-01-25T22:46:17.000Z
tests/test_markdownify_notion.py
chekos/markdownify-notion
d9a4b8140902e0039f0de88aa6364b33199508fa
[ "Apache-2.0" ]
8
2022-01-26T22:07:59.000Z
2022-02-01T04:48:06.000Z
tests/test_markdownify_notion.py
chekos/markdownify-notion
d9a4b8140902e0039f0de88aa6364b33199508fa
[ "Apache-2.0" ]
null
null
null
from markdownify_notion import markdownify_block from blocks import PARAGRAPH, BOOKMARK, CODE, HEADING_1, HEADING_2, HEADING_3, DIVIDER from tests.blocks import IMAGE def test_markdownify_block_paragraph(): assert ( markdownify_block(PARAGRAPH) == "I wonder if the _markdown_ works - or **how** it looks like in the json returned." ) def test_markdownify_block_bookmark(): assert ( markdownify_block(BOOKMARK) == "[github.com/stedolan/jq/issues/124](https://github.com/stedolan/jq/issues/124#issuecomment-17875972)\n" ) def test_markdownify_block_code(): assert ( markdownify_block(CODE) == '```json\n{\n\tid: "123",\n\ttitle: "page 1",\n\tcreated: "2022-01-25T23:15:00.000Z"\n}\n{\n\tid: "124",\n\ttitle: "page 2",\n\tcreated: "2022-01-26T13:18:15.000Z"\n}\n{\n\tid: "125",\n\ttitle: "page 3",\n\tcreated: "2022-01-27T18:37:05.000Z"\n}\n```' ) def test_markdownify_block_headings(): assert markdownify_block(HEADING_1) == "# what i learned" assert markdownify_block(HEADING_2) == "## what i learned" assert markdownify_block(HEADING_3) == "### what i learned" def test_markdownify_block_divider(): assert markdownify_block(DIVIDER) == "\n***\n" def test_markdownify_block_image(): assert ( markdownify_block(IMAGE) == "\n![image](https://website.domain/images/image.png)\n" )
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1,401
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98aefe29eb57a2caf73ea44b7878db0246f47a40
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py
Python
Projects in Python/projeto_1.py
gusta-ino/Projects-in-Python
4cc8e636f1e940b345070cf0a4ab153bc0d50044
[ "MIT" ]
1
2022-03-17T17:14:31.000Z
2022-03-17T17:14:31.000Z
Projects in Python/projeto_1.py
gusta-ino/Projects-in-Python
4cc8e636f1e940b345070cf0a4ab153bc0d50044
[ "MIT" ]
null
null
null
Projects in Python/projeto_1.py
gusta-ino/Projects-in-Python
4cc8e636f1e940b345070cf0a4ab153bc0d50044
[ "MIT" ]
null
null
null
#Primeiro código em python - 11-03-22 print('Hello World!!!')
20.666667
37
0.693548
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62
4.3
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7f3e6c3f62ce0dca785e4f6b449b863bb6fcf82d
1,735
py
Python
abp/configs/tests/reinforce_config_tests.py
amritas02/abp-lets-work-together
c061e1ad92c4a807e39b681a29d7e28265ddf664
[ "MIT" ]
null
null
null
abp/configs/tests/reinforce_config_tests.py
amritas02/abp-lets-work-together
c061e1ad92c4a807e39b681a29d7e28265ddf664
[ "MIT" ]
9
2018-11-14T23:35:05.000Z
2019-05-22T18:31:30.000Z
abp/configs/tests/reinforce_config_tests.py
Zaerei/abp
2459c1b4d77606c1d70715ce8378d738ba102f37
[ "MIT" ]
1
2018-11-14T22:34:09.000Z
2018-11-14T22:34:09.000Z
import unittest from abp.configs import ReinforceConfig import os file_path = os.path.dirname(os.path.abspath(__file__)) filename = "assets/reinforce.yml" test_file = os.path.join(file_path, filename) class ReinforceConfigTests(unittest.TestCase): """Unit tests for config objects""" def test_should_create_reinforce_config_object_from_file(self): reinforce_config = ReinforceConfig.load_from_yaml(test_file) self.assertEqual(reinforce_config.decay_steps, 500) self.assertEqual(reinforce_config.starting_epsilon, 0.9) self.assertEqual(reinforce_config.decay_rate, 0.80) self.assertEqual(reinforce_config.discount_factor, 1) self.assertEqual(reinforce_config.batch_size, 10) self.assertEqual(reinforce_config.memory_size, 20000) self.assertEqual(reinforce_config.summaries_path, "path/to/reinforcement/summaries.ckpt") def test_should_have_default_values(self): reinforce_config = ReinforceConfig() self.assertEqual(reinforce_config.decay_steps, 250) self.assertEqual(reinforce_config.starting_epsilon, 1.0) self.assertEqual(reinforce_config.decay_rate, 0.96) self.assertEqual(reinforce_config.discount_factor, 0.95) self.assertEqual(reinforce_config.batch_size, 35) self.assertEqual(reinforce_config.memory_size, 10000) self.assertEqual(reinforce_config.summaries_path, None) def test_should_be_able_to_set_property(self): reinforce_config = ReinforceConfig() self.assertEqual(reinforce_config.decay_steps, 250) reinforce_config.decay_steps = 100 self.assertEqual(reinforce_config.decay_steps, 100) if __name__ == '__main__': unittest.main()
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5.9
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4
7f420a31321ae5b0039c559e4ff575c4b2fe2c6d
1,057
py
Python
.upython/stubs/network.py
stalyatech/upython-samples
1a28e09bef278532eda7a5ab1a80b3f80f525e63
[ "MIT" ]
null
null
null
.upython/stubs/network.py
stalyatech/upython-samples
1a28e09bef278532eda7a5ab1a80b3f80f525e63
[ "MIT" ]
null
null
null
.upython/stubs/network.py
stalyatech/upython-samples
1a28e09bef278532eda7a5ab1a80b3f80f525e63
[ "MIT" ]
1
2020-11-15T09:00:59.000Z
2020-11-15T09:00:59.000Z
""" Module: 'network' on pySBC 1.13.0 with FW1.5.12-264 """ # MCU: (sysname='pySBC', nodename='pySBC', release='1.13.0 with FW1.5.12', version='v1.13-264-gb0c6ec5bb-dirty on 2020-11-25', machine='simpleRTK-SBC with STM32F745') # Stubber: 1.3.4 class SMS: '' def callback(): pass def send(): pass def isbusy(): pass def iserror(): pass class GSM: '' AUTHTYPE_CHAP = 2 AUTHTYPE_EAP = 5 AUTHTYPE_MSCHAP = 3 AUTHTYPE_MSCHAP_V2 = 4 AUTHTYPE_NONE = 0 AUTHTYPE_PAP = 1 SMS = None def active(): pass def config(): pass def connect(): pass def disconnect(): pass def ifconfig(): pass def imei(): pass def imsi(): pass def isconnected(): pass def qos(): pass def status(): pass class LAN: '' def active(): pass def config(): pass def ifconfig(): pass def isconnected(): pass def status(): pass def route(): pass
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1
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0
1
0
0
4
7f6f80e935a7dad9192f285c63912a9d52b5e2f1
20,492
py
Python
apps/graph/purpleserver/graph/tests/test_templates.py
rcknr/purplship-server
f8ec35af3da870fada0e989c20a8349c958c637c
[ "ECL-2.0", "Apache-2.0" ]
12
2020-02-03T08:11:21.000Z
2021-04-13T02:00:38.000Z
apps/graph/purpleserver/graph/tests/test_templates.py
rcknr/purplship-server
f8ec35af3da870fada0e989c20a8349c958c637c
[ "ECL-2.0", "Apache-2.0" ]
9
2020-02-12T00:25:08.000Z
2021-04-20T10:31:59.000Z
apps/graph/purpleserver/graph/tests/test_templates.py
rcknr/purplship-server
f8ec35af3da870fada0e989c20a8349c958c637c
[ "ECL-2.0", "Apache-2.0" ]
7
2020-02-03T08:10:50.000Z
2021-04-13T15:17:12.000Z
import json from unittest.mock import ANY from purpleserver.graph.tests.base import GraphTestCase import purpleserver.manager.models as manager import purpleserver.graph.models as graph class TestAddressTemplate(GraphTestCase): def _create_address_template(self): return self.query( """ mutation create_template($data: CreateTemplateInput!) { create_template(input: $data) { id is_default label address { company_name person_name address_line1 address_line2 postal_code residential city state_code country_code email phone_number validation validate_location } errors { field messages } } } """, op_name="create_template", variables=ADDRESS_TEMPLATE_DATA, ) def test_create_address_template(self): response = self._create_address_template() response_data = json.loads(response.content) self.assertResponseNoErrors(response) self.assertDictEqual(response_data, ADDRESS_TEMPLATE_RESPONSE) def test_update_address_template(self): template = json.loads(self._create_address_template().content) response = self.query( """ mutation update_template($data: UpdateTemplateInput!) { update_template(input: $data) { id is_default label address { company_name person_name address_line1 address_line2 postal_code residential city state_code country_code email phone_number validation validate_location } errors { field messages } } } """, op_name="update_template", variables={ **ADDRESS_TEMPLATE_UPDATE_DATA, "data": { "id": template["data"]["create_template"]["id"], **ADDRESS_TEMPLATE_UPDATE_DATA["data"], }, }, ) response_data = json.loads(response.content) self.assertResponseNoErrors(response) self.assertDictEqual(response_data, ADDRESS_TEMPLATE_UPDATE_RESPONSE) def test_delete_address_template(self): template = json.loads(self._create_address_template().content) template_id = template["data"]["create_template"]["id"] delete_template_data = {"data": {"id": template_id}} response = self.query( """ mutation delete_template($data: DeleteTemplateInput!) { delete_template(input: $data) { id } } """, op_name="delete_template", variables=delete_template_data, ) response_data = json.loads(response.content) self.assertResponseNoErrors(response) self.assertEqual(response_data["data"]["delete_template"]["id"], template_id) self.assertEqual(len(graph.Template.objects.all()), 0) self.assertEqual(len(manager.Address.objects.all()), 0) class TestParcelTemplate(GraphTestCase): def _create_parcel_template(self): return self.query( """ mutation create_template($data: CreateTemplateInput!) { create_template(input: $data) { id is_default label parcel { width height length dimension_unit weight weight_unit packaging_type package_preset } errors { field messages } } } """, op_name="create_template", variables=PARCEL_TEMPLATE_DATA, ) def test_create_parcel_template(self): response = self._create_parcel_template() response_data = json.loads(response.content) self.assertResponseNoErrors(response) self.assertDictEqual(response_data, PARCEL_TEMPLATE_RESPONSE) def test_update_parcel_template(self): template = json.loads(self._create_parcel_template().content) response = self.query( """ mutation update_template($data: UpdateTemplateInput!) { update_template(input: $data) { id is_default label parcel { width height length dimension_unit weight weight_unit packaging_type package_preset } errors { field messages } } } """, op_name="update_template", variables={ **PARCEL_TEMPLATE_UPDATE_DATA, "data": { "id": template["data"]["create_template"]["id"], **PARCEL_TEMPLATE_UPDATE_DATA["data"], }, }, ) response_data = json.loads(response.content) self.assertResponseNoErrors(response) self.assertDictEqual(response_data, PARCEL_TEMPLATE_UPDATE_RESPONSE) def test_delete_parcel_template(self): template = json.loads(self._create_parcel_template().content) template_id = template["data"]["create_template"]["id"] delete_template_data = {"data": {"id": template_id}} response = self.query( """ mutation delete_template($data: DeleteTemplateInput!) { delete_template(input: $data) { id } } """, op_name="delete_template", variables=delete_template_data, ) response_data = json.loads(response.content) self.assertResponseNoErrors(response) self.assertEqual(response_data["data"]["delete_template"]["id"], template_id) self.assertEqual(len(graph.Template.objects.all()), 0) self.assertEqual(len(manager.Parcel.objects.all()), 0) class TestCustomsTemplate(GraphTestCase): def _create_customs_info_template(self): return self.query( """ mutation create_template($data: CreateTemplateInput!) { create_template(input: $data) { id label is_default customs { aes eel_pfc incoterm content_type commercial_invoice certificate_number content_description duty { paid_by currency account_number declared_value bill_to { company_name person_name address_line1 address_line2 postal_code residential city state_code country_code email phone_number validation validate_location } } invoice signer certify commodities { id sku weight quantity weight_unit description value_amount value_currency origin_country } } errors { field messages } } } """, op_name="create_template", variables=CUSTOMS_TEMPLATE_DATA, ) def test_create_customs_info_template(self): response = self._create_customs_info_template() response_data = json.loads(response.content) self.assertResponseNoErrors(response) self.assertDictEqual(response_data, CUSTOMS_TEMPLATE_RESPONSE) def test_update_customs_info_template(self): template = json.loads(self._create_customs_info_template().content) CUSTOMS_TEMPLATE_UPDATE_DATA["data"]["id"] = template["data"][ "create_template" ]["id"] CUSTOMS_TEMPLATE_UPDATE_DATA["data"]["customs"]["commodities"][0]["id"] = next( c["id"] for c in template["data"]["create_template"]["customs"]["commodities"] if c["sku"] == template["data"]["create_template"]["customs"]["commodities"][0]["sku"] ) response = self.query( """ mutation update_template($data: UpdateTemplateInput!) { update_template(input: $data) { id is_default label customs { aes eel_pfc incoterm content_type commercial_invoice certificate_number content_description duty { paid_by currency account_number declared_value bill_to { company_name person_name address_line1 address_line2 postal_code residential city state_code country_code email phone_number validation validate_location } } invoice signer certify commodities { id sku weight quantity weight_unit description value_amount value_currency origin_country } } errors { field messages } } } """, op_name="update_template", variables=CUSTOMS_TEMPLATE_UPDATE_DATA, ) response_data = json.loads(response.content) self.assertResponseNoErrors(response) self.assertDictEqual(response_data, CUSTOMS_TEMPLATE_UPDATE_RESPONSE) def test_delete_customs_info(self): template = json.loads(self._create_customs_info_template().content) template_id = template["data"]["create_template"]["id"] delete_template_data = {"data": {"id": template_id}} response = self.query( """ mutation delete_template($data: DeleteTemplateInput!) { delete_template(input: $data) { id } } """, op_name="delete_template", variables=delete_template_data, ) response_data = json.loads(response.content) self.assertResponseNoErrors(response) self.assertEqual(response_data["data"]["delete_template"]["id"], template_id) self.assertEqual(len(graph.Template.objects.all()), 0) self.assertEqual(len(manager.Customs.objects.all()), 0) ADDRESS_TEMPLATE_DATA = { "data": { "label": "Warehouse", "address": { "address_line1": "125 Church St", "person_name": "John Doe", "company_name": "A corp.", "phone_number": "514 000 0000", "city": "Moncton", "country_code": "CA", "postal_code": "E1C4Z8", "residential": False, "state_code": "NB", }, } } ADDRESS_TEMPLATE_RESPONSE = { "data": { "create_template": { "address": { "address_line1": "125 Church St", "address_line2": None, "city": "Moncton", "company_name": "A corp.", "country_code": "CA", "email": None, "person_name": "John Doe", "phone_number": "+1 514-000-0000", "postal_code": "E1C4Z8", "residential": False, "state_code": "NB", "validate_location": None, "validation": None, }, 'errors': None, 'id': ANY, 'is_default': False, 'label': 'Warehouse' } } } ADDRESS_TEMPLATE_UPDATE_DATA = { "data": { "label": "Warehouse Update", "address": { "city": "Moncton", "email": "test@gmail.com", "person_name": "John Moe", }, } } ADDRESS_TEMPLATE_UPDATE_RESPONSE = { "data": { "update_template": { "address": { "address_line1": "125 Church St", "address_line2": None, "city": "Moncton", "company_name": "A corp.", "country_code": "CA", "email": "test@gmail.com", "person_name": "John Moe", "phone_number": "+1 514-000-0000", "postal_code": "E1C4Z8", "residential": False, "state_code": "NB", "validate_location": None, "validation": None, }, 'id': ANY, 'is_default': False, 'label': 'Warehouse Update', 'errors': None, } } } PARCEL_TEMPLATE_DATA = { "data": { "label": "Purple Pack", "parcel": { "weight": 1, "weight_unit": "KG", "package_preset": "canadapost_corrugated_small_box", }, } } PARCEL_TEMPLATE_RESPONSE = { 'data': { 'create_template': { 'errors': None, 'id': ANY, 'is_default': False, 'label': 'Purple Pack', 'parcel': { 'dimension_unit': 'CM', 'height': 32.0, 'length': 32.0, 'package_preset': 'canadapost_corrugated_small_box', 'packaging_type': None, 'weight': 1.0, 'weight_unit': 'KG', 'width': 42.0 } } } } PARCEL_TEMPLATE_UPDATE_DATA = { "data": { "parcel": { "weight": 0.45, "weight_unit": "LB", } } } PARCEL_TEMPLATE_UPDATE_RESPONSE = { "data": { "update_template": { "errors": None, "id": ANY, "is_default": False, "label": "Purple Pack", "parcel": { "dimension_unit": "CM", "height": 32.0, "length": 32.0, "package_preset": "canadapost_corrugated_small_box", "packaging_type": None, "weight": 0.45, "weight_unit": "LB", "width": 42.0, }, } } } CUSTOMS_TEMPLATE_DATA = { "data": { "label": "Customs info template", "customs": { "content_type": "documents", "incoterm": "DDU", "commodities": [ {"weight": 1.15, "weight_unit": "KG", "sku": "6787L8K7J8L7J8L7K8"}, { "weight": 0.75, "weight_unit": "KG", "sku": "3PO4I5J4PO5I4HI5OH4O5IH4IO5", "quantity": 4, }, ], }, } } CUSTOMS_TEMPLATE_RESPONSE = { "data": { "create_template": { "customs": { "aes": None, "certificate_number": None, "certify": None, "commercial_invoice": None, "commodities": [ { "description": None, "id": ANY, "origin_country": None, "quantity": 4, "sku": "3PO4I5J4PO5I4HI5OH4O5IH4IO5", "value_amount": None, "value_currency": None, "weight": 0.75, "weight_unit": "KG", }, { "description": None, "id": ANY, "origin_country": None, "quantity": None, "sku": "6787L8K7J8L7J8L7K8", "value_amount": None, "value_currency": None, "weight": 1.15, "weight_unit": "KG", }, ], "content_description": None, "content_type": "documents", "duty": None, "eel_pfc": None, "incoterm": "DDU", "invoice": None, "signer": None, }, 'id': ANY, 'is_default': False, 'label': 'Customs info template', 'errors': None, } } } CUSTOMS_TEMPLATE_UPDATE_DATA = { "data": { "customs": { "commodities": [ { "weight": 1, "weight_unit": "LB", } ], "duty": '{"paid_by": "SENDER"}', } } } CUSTOMS_TEMPLATE_UPDATE_RESPONSE = { "data": { "update_template": { "id": ANY, "is_default": False, "label": "Customs info template", "customs": { "aes": None, "eel_pfc": None, "incoterm": "DDU", "content_type": "documents", "commercial_invoice": None, "certificate_number": None, "content_description": None, "duty": { "account_number": None, "bill_to": None, "currency": None, "declared_value": None, "paid_by": "SENDER", }, "invoice": None, "signer": None, "certify": None, "commodities": [ { "id": ANY, "sku": "3PO4I5J4PO5I4HI5OH4O5IH4IO5", "weight": 1.0, "quantity": 4, "weight_unit": "LB", "description": None, "value_amount": None, "value_currency": None, "origin_country": None, }, { "id": ANY, "sku": "6787L8K7J8L7J8L7K8", "weight": 1.15, "quantity": None, "weight_unit": "KG", "description": None, "value_amount": None, "value_currency": None, "origin_country": None, }, ], }, "errors": None, } } }
30.768769
87
0.432901
1,454
20,492
5.828748
0.105227
0.041062
0.023363
0.020177
0.822537
0.764366
0.705133
0.683658
0.63528
0.625133
0
0.017155
0.473746
20,492
665
88
30.815038
0.768731
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0.568922
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0.204878
0.012482
0
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0.06015
1
0.030075
false
0
0.012531
0.007519
0.057644
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null
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null
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0
0
0
0
0
0
0
0
0
0
4
7f782af94eecb324c73402d1693faf09bee1f77c
137
py
Python
problem/01000~09999/05347/5347.py3.py
njw1204/BOJ-AC
1de41685725ae4657a7ff94e413febd97a888567
[ "MIT" ]
1
2019-04-19T16:37:44.000Z
2019-04-19T16:37:44.000Z
problem/01000~09999/05347/5347.py3.py
njw1204/BOJ-AC
1de41685725ae4657a7ff94e413febd97a888567
[ "MIT" ]
1
2019-04-20T11:42:44.000Z
2019-04-20T11:42:44.000Z
problem/01000~09999/05347/5347.py3.py
njw1204/BOJ-AC
1de41685725ae4657a7ff94e413febd97a888567
[ "MIT" ]
3
2019-04-19T16:37:47.000Z
2021-10-25T00:45:00.000Z
def G(a,b): return G(b,a%b) if b else a def L(a,b): return a*b//G(a,b) for i in range(int(input())): print(L(*map(int,input().split())))
45.666667
65
0.59854
35
137
2.342857
0.485714
0.121951
0.073171
0
0
0
0
0
0
0
0
0
0.124088
137
3
65
45.666667
0.683333
0
0
0
0
0
0
0
0
0
0
0
0
1
0.666667
false
0
0
0.666667
0.666667
0.333333
0
0
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
1
0
0
0
1
1
0
0
4
7f8fc533a0c00287f0b70a9a6f0fc86d7b90806a
55
py
Python
pdml2flow/__init__.py
Enteee/pdml2flow
2e5da6f03bc799f0e8ef77dd987031b969d4a5df
[ "Apache-2.0" ]
12
2016-04-01T10:59:14.000Z
2022-01-27T04:05:43.000Z
pdml2flow/__init__.py
Enteee/pdml2flow
2e5da6f03bc799f0e8ef77dd987031b969d4a5df
[ "Apache-2.0" ]
16
2016-03-18T10:44:00.000Z
2019-08-12T05:52:24.000Z
pdml2flow/__init__.py
Enteee/pdml2flow
2e5da6f03bc799f0e8ef77dd987031b969d4a5df
[ "Apache-2.0" ]
2
2016-09-08T11:49:39.000Z
2020-09-09T04:39:15.000Z
# vim: set fenc=utf8 ts=4 sw=4 et : from .cli import *
18.333333
35
0.636364
12
55
2.916667
0.916667
0
0
0
0
0
0
0
0
0
0
0.069767
0.218182
55
2
36
27.5
0.744186
0.6
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
0
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0
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0
0
0
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1
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null
0
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0
0
0
1
0
1
0
0
0
0
4
7f9df8388891590a596839bbda988c34ec742bfd
492
py
Python
tests/conftest.py
skspoon/cascade
00534bd7e2558b880dfeb2e8bb2248a104ba6083
[ "MIT" ]
null
null
null
tests/conftest.py
skspoon/cascade
00534bd7e2558b880dfeb2e8bb2248a104ba6083
[ "MIT" ]
null
null
null
tests/conftest.py
skspoon/cascade
00534bd7e2558b880dfeb2e8bb2248a104ba6083
[ "MIT" ]
null
null
null
import pytest @pytest.fixture def mock_execution_context(mocker): mock_context = mocker.Mock() mock_context.parameters.database = "test_database" mock_context.parameters.model_version_id = 12345 return mock_context @pytest.fixture def mock_ezfuncs(mocker): return mocker.patch("cascade.core.db.ezfuncs") @pytest.fixture def mock_database_access(mock_ezfuncs): return {"cursor": mock_ezfuncs.get_connection().cursor(), "connection": mock_ezfuncs.get_connetion()}
24.6
105
0.772358
62
492
5.854839
0.419355
0.121212
0.132231
0.165289
0
0
0
0
0
0
0
0.011547
0.119919
492
19
106
25.894737
0.82679
0
0
0.230769
0
0
0.105691
0.046748
0
0
0
0
0
1
0.230769
false
0
0.076923
0.153846
0.538462
0
0
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0
null
0
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1
0
0
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0
0
0
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0
0
0
0
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0
0
0
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null
0
0
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0
0
1
0
0
0
1
1
0
0
4
7f9fad8ad41b5faa0832fed342552cb8503fb54f
326
py
Python
Lessons-Learned/nasa/websockets.py
defionscode/Ansible-at-NASA
caaa626ed5d88863e011ee6014d9dd0b6ab633d1
[ "MIT" ]
11
2015-02-23T23:33:00.000Z
2019-03-29T10:31:02.000Z
Lessons-Learned/nasa/websockets.py
nousdefions/Ansible-at-NASA
caaa626ed5d88863e011ee6014d9dd0b6ab633d1
[ "MIT" ]
null
null
null
Lessons-Learned/nasa/websockets.py
nousdefions/Ansible-at-NASA
caaa626ed5d88863e011ee6014d9dd0b6ab633d1
[ "MIT" ]
4
2015-02-23T18:06:35.000Z
2019-03-08T07:33:51.000Z
# Copyright (c) 2014, Matt Makai # All rights reserved. # Full License can be read here: http://bit.ly/1qBgqzn from flask.ext.socketio import emit from . import socketio @socketio.on('connect', namespace='/nasa') def test_connect(): pass @socketio.on('disconnect', namespace='/nasa') def test_disconnect(): pass
18.111111
54
0.711656
45
326
5.111111
0.711111
0.086957
0.13913
0.173913
0
0
0
0
0
0
0
0.018116
0.153374
326
17
55
19.176471
0.815217
0.319018
0
0.25
0
0
0.124424
0
0
0
0
0
0
1
0.25
true
0.25
0.25
0
0.5
0
0
0
0
null
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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0
0
0
0
0
0
null
0
0
0
0
0
1
1
1
0
0
0
0
0
4
7fa06af38b3b9e8bb58097976147c3bbdc50ac5d
670
py
Python
application/bills/bill_forms.py
akelshareif/fiscally
ca44ca00537d2b9ef1bca8a3a67b66427394dc72
[ "MIT" ]
1
2020-09-18T04:18:58.000Z
2020-09-18T04:18:58.000Z
application/bills/bill_forms.py
akelshareif/fiscally
ca44ca00537d2b9ef1bca8a3a67b66427394dc72
[ "MIT" ]
null
null
null
application/bills/bill_forms.py
akelshareif/fiscally
ca44ca00537d2b9ef1bca8a3a67b66427394dc72
[ "MIT" ]
null
null
null
from flask_wtf import FlaskForm from wtforms import StringField, DecimalField from wtforms.fields.html5 import DateField from wtforms.validators import InputRequired class BillForm(FlaskForm): """ Add bill form """ bill_name = StringField('Bill Name', validators=[ InputRequired(message="You must add a bill name.")]) bill_due_date = DateField('Bill Date', format='%Y-%m-%d', validators=[InputRequired(message="You must select a bill due date.")]) bill_amount = DecimalField('Bill Amount', validators=[ InputRequired(message="You must enter a bill amount.")])
37.222222
101
0.649254
75
670
5.733333
0.426667
0.076744
0.209302
0.230233
0.25814
0
0
0
0
0
0
0.001992
0.250746
670
17
102
39.411765
0.854582
0.019403
0
0
0
0
0.189522
0
0
0
0
0
0
1
0
false
0
0.363636
0
0.727273
0
0
0
0
null
0
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
0
0
0
0
1
0
1
0
0
4
7fbb5f7d1f6cc405dadecbec98d91c441f6e9900
82
py
Python
EulerProject/p1.py
cuihaoleo/exercises
401dc2f19b6474e84e357a79a53754684fc93784
[ "WTFPL" ]
1
2015-08-30T10:06:41.000Z
2015-08-30T10:06:41.000Z
EulerProject/p1.py
cuihaoleo/exercises
401dc2f19b6474e84e357a79a53754684fc93784
[ "WTFPL" ]
null
null
null
EulerProject/p1.py
cuihaoleo/exercises
401dc2f19b6474e84e357a79a53754684fc93784
[ "WTFPL" ]
null
null
null
#!/usr/bin/env python3 print(sum([i for i in range(1,1000) if i%3==0 or i%5==0]))
27.333333
58
0.621951
21
82
2.428571
0.809524
0
0
0
0
0
0
0
0
0
0
0.140845
0.134146
82
2
59
41
0.577465
0.256098
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
0
0
0
1
1
0
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
0
0
0
1
0
4
f680e221c7545bf1356d2c2caab48fc19c51fbe2
420
py
Python
rastervision/v2/core/pipeline_config.py
carderne/raster-vision
915fbcd3263d8f2193e65c2cd0eb53e050a47a01
[ "Apache-2.0" ]
1
2019-11-07T10:02:23.000Z
2019-11-07T10:02:23.000Z
rastervision/v2/core/pipeline_config.py
carderne/raster-vision
915fbcd3263d8f2193e65c2cd0eb53e050a47a01
[ "Apache-2.0" ]
null
null
null
rastervision/v2/core/pipeline_config.py
carderne/raster-vision
915fbcd3263d8f2193e65c2cd0eb53e050a47a01
[ "Apache-2.0" ]
null
null
null
from rastervision.v2.core.config import Config from rastervision.v2.core.config import register_config from rastervision.v2.core.pipeline import Pipeline @register_config('pipeline') class PipelineConfig(Config): root_uri: str def build(self, tmp_dir): return Pipeline(self, tmp_dir) def get_config(runner): return PipelineConfig(root_uri='/opt/data/') def register_plugin(registry): pass
21
55
0.761905
56
420
5.571429
0.464286
0.153846
0.173077
0.211538
0.326923
0.217949
0
0
0
0
0
0.008357
0.145238
420
19
56
22.105263
0.860724
0
0
0
0
0
0.042857
0
0
0
0
0
0
1
0.25
false
0.083333
0.25
0.166667
0.833333
0
0
0
0
null
0
0
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
0
1
0
1
0
1
0
0
0
4
f6862211a2a19a57b79c1b4ecd18bc47845dce6d
2,076
py
Python
tests/api/judge_protocol_tests.py
ericbrandwein/CodeforcesAPI
12ae641910a3308033584dc518bb2fc0173e56f3
[ "MIT" ]
26
2015-06-21T16:19:44.000Z
2021-11-15T12:32:25.000Z
tests/api/judge_protocol_tests.py
ericbrandwein/CodeforcesAPI
12ae641910a3308033584dc518bb2fc0173e56f3
[ "MIT" ]
5
2015-03-10T06:00:52.000Z
2020-01-18T12:59:25.000Z
tests/api/judge_protocol_tests.py
ericbrandwein/CodeforcesAPI
12ae641910a3308033584dc518bb2fc0173e56f3
[ "MIT" ]
12
2015-04-24T17:16:50.000Z
2022-01-04T14:21:25.000Z
""" This module provides classes for testing JudgeProtocol object """ import unittest from codeforces import JudgeProtocol class JudgeProtocolTests(unittest.TestCase): def setUp(self): self.protocol = JudgeProtocol() def test_load_from_dict(self): protocol = (b"Solution verdict:\nWRONG_ANSWER\n\nChecker:\nwrong answer 1st words differ - " b"expected: \u0027Poor\u0027, found: \u00272\u0027\r\n\n\nInput:\n1 5 1 3 1 1\r\n\n\n" b"Output:\n2\r\n\n\nAnswer:\nPoor Inna and pony!\r\n\n\nTime:\n62\n\nMemory:\n0\n") protocol = protocol.decode('utf-8') d = { "manual": "true", "protocol": protocol, "verdict": "Successful hacking attempt" } self.protocol.load_from_dict(d) self.assertEqual(True, self.protocol.manual) self.assertEqual(protocol, self.protocol.protocol) self.assertEqual('Successful hacking attempt', self.protocol.verdict) def test_load_from_json(self): protocol = ("Solution verdict:\nWRONG_ANSWER\n\nChecker:\nwrong answer 1st words differ - " "expected: \u0027Poor\u0027, found: \u00272\u0027\r\n\n\nInput:\n1 5 1 3 1 1\r\n\n\n" "Output:\n2\r\n\n\nAnswer:\nPoor Inna and pony!\r\n\n\nTime:\n62\n\nMemory:\n0\n") json = r''' { "manual":"true", "protocol":"Solution verdict:\nWRONG_ANSWER\n\nChecker:\nwrong answer 1st words differ - ''' \ r'''expected: \u0027Poor\u0027, found: \u00272\u0027\r\n\n\nInput:\n1 5 1 3 1 1\r\n\n\n''' \ r'''Output:\n2\r\n\n\nAnswer:\nPoor Inna and pony!\r\n\n\nTime:\n62\n\nMemory:\n0\n", "verdict":"Successful hacking attempt" }''' self.protocol.load_from_json(json) self.assertEqual(True, self.protocol.manual) self.assertEqual(protocol, self.protocol.protocol) self.assertEqual('Successful hacking attempt', self.protocol.verdict) if __name__ == '__main__': unittest.main()
37.745455
115
0.617534
273
2,076
4.619048
0.249084
0.023791
0.028549
0.088818
0.731166
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f68fc9be7c7c4c5eb2df2ecf739c4801eb3923df
375,017
py
Python
icons_rc.py
LOC1207/Starting_with_QT-Creator
2f7401a40dc00e508ac3b3736e76f66b202010d2
[ "MIT" ]
1
2019-03-17T10:03:11.000Z
2019-03-17T10:03:11.000Z
icons_rc.py
gaurav3210/QT_creater
2f7401a40dc00e508ac3b3736e76f66b202010d2
[ "MIT" ]
null
null
null
icons_rc.py
gaurav3210/QT_creater
2f7401a40dc00e508ac3b3736e76f66b202010d2
[ "MIT" ]
null
null
null
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\x10\xce\xe6\xff\x0f\x00\x2d\x6b\x5d\x72\x4c\x12\x57\xc9\x00\x00\ \x00\x00\x49\x45\x4e\x44\xae\x42\x60\x82\ " qt_resource_name = b"\ \x00\x0c\ \x0b\xd5\x60\x67\ \x00\x66\ \x00\x61\x00\x63\x00\x65\x00\x62\x00\x6f\x00\x6f\x00\x6b\x00\x2e\x00\x70\x00\x6e\x00\x67\ \x00\x0e\ \x0f\x7a\x14\x27\ \x00\x67\ \x00\x6f\x00\x6f\x00\x67\x00\x6c\x00\x65\x00\x70\x00\x6c\x00\x75\x00\x73\x00\x2e\x00\x70\x00\x6e\x00\x67\ \x00\x0b\ \x0a\x32\x96\x27\ \x00\x74\ \x00\x77\x00\x69\x00\x74\x00\x74\x00\x65\x00\x72\x00\x2e\x00\x70\x00\x6e\x00\x67\ " qt_resource_struct_v1 = b"\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x01\ \x00\x00\x00\x40\x00\x00\x00\x00\x00\x01\x00\x01\x36\x8b\ \x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\ \x00\x00\x00\x1e\x00\x00\x00\x00\x00\x01\x00\x00\x33\x9d\ " qt_resource_struct_v2 = b"\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x01\ \x00\x00\x00\x00\x00\x00\x00\x00\ \x00\x00\x00\x40\x00\x00\x00\x00\x00\x01\x00\x01\x36\x8b\ \x00\x00\x01\x64\xbc\xbd\x81\x64\ \x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\ \x00\x00\x01\x64\xbc\xbc\xd1\x54\ \x00\x00\x00\x1e\x00\x00\x00\x00\x00\x01\x00\x00\x33\x9d\ \x00\x00\x01\x64\xbc\xbd\xe7\x9e\ " qt_version = [int(v) for v in QtCore.qVersion().split('.')] if qt_version < [5, 8, 0]: rcc_version = 1 qt_resource_struct = qt_resource_struct_v1 else: rcc_version = 2 qt_resource_struct = qt_resource_struct_v2 def qInitResources(): QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data) def qCleanupResources(): QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data) qInitResources()
65.528045
105
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90,827
375,017
3.002323
0.00338
0.008779
0.003961
0.005193
0.036811
0.029957
0.025762
0.025025
0.024078
0.023561
0
0.309661
0.015493
375,017
5,722
106
65.539497
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0.000003
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4
f69038ee8586a57244e6b8341ffb825502dd0a48
20,169
py
Python
src/genie/libs/parser/iosxe/tests/ShowIpOspfDatabase/cli/equal/golden_output2_expected.py
balmasea/genieparser
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
[ "Apache-2.0" ]
204
2018-06-27T00:55:27.000Z
2022-03-06T21:12:18.000Z
src/genie/libs/parser/iosxe/tests/ShowIpOspfDatabase/cli/equal/golden_output2_expected.py
balmasea/genieparser
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
[ "Apache-2.0" ]
468
2018-06-19T00:33:18.000Z
2022-03-31T23:23:35.000Z
src/genie/libs/parser/iosxe/tests/ShowIpOspfDatabase/cli/equal/golden_output2_expected.py
balmasea/genieparser
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
[ "Apache-2.0" ]
309
2019-01-16T20:21:07.000Z
2022-03-30T12:56:41.000Z
expected_output = { "vrf": { "default": { "address_family": { "ipv4": { "instance": { "65109": { "areas": { "0.0.0.8": { "database": { "lsa_types": { 1: { "lsa_type": 1, "lsas": { "192.168.165.220": { "adv_router": "192.168.165.220", "lsa_id": "192.168.165.220", "ospfv2": { "header": { "adv_router": "192.168.165.220", "age": 113, "checksum": "0x007C93", "link_count": 2, "lsa_id": "192.168.165.220", "seq_num": "0x800006E3", } }, }, "192.168.255.0": { "adv_router": "192.168.255.0", "lsa_id": "192.168.255.0", "ospfv2": { "header": { "adv_router": "192.168.255.0", "age": 1407, "checksum": "0x00ADD6", "link_count": 501, "lsa_id": "192.168.255.0", "seq_num": "0x800007BC", } }, }, "10.22.102.64": { "adv_router": "10.22.102.64", "lsa_id": "10.22.102.64", "ospfv2": { "header": { "adv_router": "10.22.102.64", "age": 2220, "checksum": "0x008BD8", "link_count": 3, "lsa_id": "10.22.102.64", "seq_num": "0x800003EC", } }, }, "172.31.197.252": { "adv_router": "172.31.197.252", "lsa_id": "172.31.197.252", "ospfv2": { "header": { "adv_router": "172.31.197.252", "age": 1272, "checksum": "0x00B9E5", "link_count": 6, "lsa_id": "172.31.197.252", "seq_num": "0x80000DBD", } }, }, "172.31.197.253": { "adv_router": "172.31.197.253", "lsa_id": "172.31.197.253", "ospfv2": { "header": { "adv_router": "172.31.197.253", "age": 663, "checksum": "0x00FFD8", "link_count": 4, "lsa_id": "172.31.197.253", "seq_num": "0x8000009D", } }, }, "172.31.197.254": { "adv_router": "172.31.197.254", "lsa_id": "172.31.197.254", "ospfv2": { "header": { "adv_router": "172.31.197.254", "age": 1900, "checksum": "0x00D029", "link_count": 3, "lsa_id": "172.31.197.254", "seq_num": "0x800000D9", } }, }, }, }, 2: { "lsa_type": 2, "lsas": { "192.168.255.0": { "adv_router": "172.31.197.252", "lsa_id": "192.168.255.0", "ospfv2": { "header": { "adv_router": "172.31.197.252", "age": 26, "checksum": "0x009E8D", "lsa_id": "192.168.255.0", "seq_num": "0x800000D1", } }, }, "10.22.102.50": { "adv_router": "10.22.102.64", "lsa_id": "10.22.102.50", "ospfv2": { "header": { "adv_router": "10.22.102.64", "age": 220, "checksum": "0x003A0A", "lsa_id": "10.22.102.50", "seq_num": "0x800000AD", } }, }, "10.22.102.58": { "adv_router": "10.22.102.64", "lsa_id": "10.22.102.58", "ospfv2": { "header": { "adv_router": "10.22.102.64", "age": 1220, "checksum": "0x00E2CD", "lsa_id": "10.22.102.58", "seq_num": "0x80000038", } }, }, "172.31.197.102": { "adv_router": "192.168.165.220", "lsa_id": "172.31.197.102", "ospfv2": { "header": { "adv_router": "192.168.165.220", "age": 113, "checksum": "0x009ACA", "lsa_id": "172.31.197.102", "seq_num": "0x80000055", } }, }, "172.31.197.94": { "adv_router": "172.31.197.254", "lsa_id": "172.31.197.94", "ospfv2": { "header": { "adv_router": "172.31.197.254", "age": 911, "checksum": "0x007ACC", "lsa_id": "172.31.197.94", "seq_num": "0x80000052", } }, }, "172.31.197.97": { "adv_router": "172.31.197.253", "lsa_id": "172.31.197.97", "ospfv2": { "header": { "adv_router": "172.31.197.253", "age": 663, "checksum": "0x00AAB4", "lsa_id": "172.31.197.97", "seq_num": "0x80000037", } }, }, }, }, 3: { "lsa_type": 3, "lsas": { "192.168.165.119": { "adv_router": "172.31.197.252", "lsa_id": "192.168.165.119", "ospfv2": { "header": { "adv_router": "172.31.197.252", "age": 1030, "checksum": "0x007847", "lsa_id": "192.168.165.119", "seq_num": "0x800000D4", } }, }, "192.168.165.120": { "adv_router": "172.31.197.252", "lsa_id": "192.168.165.120", "ospfv2": { "header": { "adv_router": "172.31.197.252", "age": 26, "checksum": "0x005160", "lsa_id": "192.168.165.120", "seq_num": "0x800003DE", } }, }, "192.168.165.48": { "adv_router": "172.31.197.252", "lsa_id": "192.168.165.48", "ospfv2": { "header": { "adv_router": "172.31.197.252", "age": 26, "checksum": "0x0006F6", "lsa_id": "192.168.165.48", "seq_num": "0x800003DF", } }, }, "192.168.165.56": { "adv_router": "172.31.197.252", "lsa_id": "192.168.165.56", "ospfv2": { "header": { "adv_router": "172.31.197.252", "age": 1779, "checksum": "0x00D42E", "lsa_id": "192.168.165.56", "seq_num": "0x800000D4", } }, }, }, }, 4: { "lsa_type": 4, "lsas": { "192.168.165.119": { "adv_router": "172.31.197.252", "lsa_id": "192.168.165.119", "ospfv2": { "header": { "adv_router": "172.31.197.252", "age": 1030, "checksum": "0x00605F", "lsa_id": "192.168.165.119", "seq_num": "0x800000D4", } }, }, "192.168.165.120": { "adv_router": "172.31.197.252", "lsa_id": "192.168.165.120", "ospfv2": { "header": { "adv_router": "172.31.197.252", "age": 26, "checksum": "0x003978", "lsa_id": "192.168.165.120", "seq_num": "0x800003DE", } }, }, }, }, } } } } } } } } } } }
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4
f6ca21d245e8a5cd437e9c60eac782b48a96bf95
217
py
Python
sparcli/capture/platform_base.py
z0u/sparcli
976e857c3802f61dead386f67404fc44e97b6672
[ "MIT" ]
3
2020-04-13T23:58:23.000Z
2020-04-25T08:08:27.000Z
sparcli/capture/platform_base.py
z0u/sparcli
976e857c3802f61dead386f67404fc44e97b6672
[ "MIT" ]
null
null
null
sparcli/capture/platform_base.py
z0u/sparcli
976e857c3802f61dead386f67404fc44e97b6672
[ "MIT" ]
null
null
null
class Platform: def set_nonblocking(self, read_fd): raise NotImplementedError( "Non-blocking capture not impelemented on this platform" ) def apply_workarounds(self): pass
24.111111
68
0.64977
23
217
6
0.869565
0.15942
0
0
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0.290323
217
8
69
27.125
0.896104
0
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0.248848
0
0
0
0
0
0
1
0.285714
false
0.142857
0
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0.428571
0
1
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null
0
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0
1
0
0
0
0
0
4
f6d9be41cc369863429673efc485ea9840bfbbd0
109
py
Python
src/config.py
JonathanWillnow/european_factor_stockpicking_screener
4bb9396490a2171954ecf7aff16656d4225ae251
[ "MIT" ]
67
2015-01-19T17:41:02.000Z
2019-10-17T19:12:49.000Z
src/config.py
JonathanWillnow/european_factor_stockpicking_screener
4bb9396490a2171954ecf7aff16656d4225ae251
[ "MIT" ]
64
2015-02-23T15:18:24.000Z
2019-11-20T19:58:59.000Z
src/config.py
JonathanWillnow/european_factor_stockpicking_screener
4bb9396490a2171954ecf7aff16656d4225ae251
[ "MIT" ]
51
2015-01-15T16:10:33.000Z
2019-10-28T21:14:03.000Z
from pathlib import Path ROOT = Path(__file__).parent.parent SRC = Path(__file__).parent BLD = ROOT / "bld"
18.166667
35
0.733945
16
109
4.5
0.5625
0.222222
0.388889
0
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0.146789
109
5
36
21.8
0.774194
0
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0.027523
0
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false
0
0.25
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0.25
0
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null
1
1
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0
0
0
0
0
0
0
0
0
4
f6e44d3d7dc334264ce073ad82023ed36af9e205
255
py
Python
fossbotpy/gateway/dms/request.py
fosscord/fossbotpy
c10c57c42f403acc93197d524d254ab3215c1737
[ "MIT" ]
2
2021-12-04T15:16:30.000Z
2021-12-21T04:37:17.000Z
fossbotpy/gateway/dms/request.py
fosscord/fossbotpy
c10c57c42f403acc93197d524d254ab3215c1737
[ "MIT" ]
null
null
null
fossbotpy/gateway/dms/request.py
fosscord/fossbotpy
c10c57c42f403acc93197d524d254ab3215c1737
[ "MIT" ]
null
null
null
#request class DmRequest(object): __slots__ = ['gatewayobj'] def __init__(self, gatewayobj): self.gatewayobj = gatewayobj def DMchannel(self, channel_id): self.gatewayobj.send({'op':self.gatewayobj.OPCODE.DM_UPDATE,'d':{'channel_id':channel_id}})
28.333333
93
0.74902
32
255
5.59375
0.5625
0.312849
0
0
0
0
0
0
0
0
0
0
0.094118
255
9
93
28.333333
0.774892
0.027451
0
0
0
0
0.092742
0
0
0
0
0
0
1
0.333333
false
0
0
0
0.666667
0
0
0
0
null
1
0
0
0
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0
0
0
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0
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1
0
0
0
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0
0
0
0
0
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null
0
0
0
0
0
1
0
0
0
0
1
0
0
4
101030963f3e4fbf033859d7e7b6ea68b8b51a27
3,611
py
Python
agents/maze_agents/modules/value_function.py
victorcampos7/edl
ffdf23d4e102ca7d69a1408bafa267b0c7d8bfa0
[ "MIT" ]
30
2020-02-16T15:52:59.000Z
2022-03-22T10:54:54.000Z
agents/maze_agents/modules/value_function.py
imatge-upc/edl
ffdf23d4e102ca7d69a1408bafa267b0c7d8bfa0
[ "MIT" ]
null
null
null
agents/maze_agents/modules/value_function.py
imatge-upc/edl
ffdf23d4e102ca7d69a1408bafa267b0c7d8bfa0
[ "MIT" ]
7
2020-02-16T15:53:05.000Z
2022-01-18T03:41:03.000Z
# Copyright (c) 2019, salesforce.com, inc. # All rights reserved. # SPDX-License-Identifier: MIT # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/MIT import torch import torch.nn as nn from dist_train.utils.helpers import create_nn from base.modules.normalization import Normalizer class Critic(nn.Module): def __init__(self, env, hidden_size, use_antigoal=False, a_range=None, state_size=None, goal_size=None, action_size=None, num_layers=4, normalize_inputs=False, hidden_init_fn=None, b_init_value=None, last_fc_init_w=None): super().__init__() self.use_antigoal = use_antigoal self.a_range = env.action_range if a_range is None else a_range self.state_size = env.state_size if state_size is None else state_size self.goal_size = env.goal_size if goal_size is None else goal_size self.action_size = env.action_size if action_size is None else action_size assert num_layers >= 2 self.num_layers = int(num_layers) input_size = self.state_size + self.goal_size + self.action_size if self.use_antigoal: input_size += self.goal_size input_normalizer = Normalizer(input_size) if normalize_inputs else nn.Sequential() self.layers = create_nn(input_size=input_size, output_size=1, hidden_size=hidden_size, num_layers=self.num_layers, input_normalizer=input_normalizer, hidden_init_fn=hidden_init_fn, b_init_value=b_init_value, last_fc_init_w=last_fc_init_w) def q_no_grad(self, s, a, g, ag=None): for p in self.parameters(): p.requires_grad = False q = self(s, a, g, ag) for p in self.parameters(): p.requires_grad = True return q def forward(self, s, a, g, ag=None): """Produce an action""" if self.use_antigoal: return self.layers(torch.cat([s, a, g, ag], dim=1)).view(-1) else: return self.layers(torch.cat([s, a, g], dim=1)).view(-1) class Value(nn.Module): def __init__(self, env, hidden_size, use_antigoal=False, a_range=None, state_size=None, goal_size=None, num_layers=4, normalize_inputs=False, hidden_init_fn=None, b_init_value=None, last_fc_init_w=None, antigoal_size=None): super().__init__() self.use_antigoal = use_antigoal self.a_range = env.action_range if a_range is None else a_range self.state_size = env.state_size if state_size is None else state_size self.goal_size = env.goal_size if goal_size is None else goal_size self.antigoal_size = env.goal_size if antigoal_size is None else antigoal_size assert num_layers >= 2 self.num_layers = int(num_layers) input_size = self.state_size + self.goal_size if self.use_antigoal: input_size += self.antigoal_size input_normalizer = Normalizer(input_size) if normalize_inputs else nn.Sequential() self.layers = create_nn(input_size=input_size, output_size=1, hidden_size=hidden_size, num_layers=self.num_layers, input_normalizer=input_normalizer, hidden_init_fn=hidden_init_fn, b_init_value=b_init_value, last_fc_init_w=last_fc_init_w) def forward(self, s, g, ag=None): if self.use_antigoal: return self.layers(torch.cat([s, g, ag], dim=1)).view(-1) else: return self.layers(torch.cat([s, g], dim=1)).view(-1)
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101180e060687c6c9b6b5fbf3e494ee1cb47e291
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py
Python
employee_management_backend/users/utils.py
SamwelOpiyo/simple_company_employee_management_backend
75723dd6aed002a1bc30f2d78d077e498eb25226
[ "MIT" ]
null
null
null
employee_management_backend/users/utils.py
SamwelOpiyo/simple_company_employee_management_backend
75723dd6aed002a1bc30f2d78d077e498eb25226
[ "MIT" ]
null
null
null
employee_management_backend/users/utils.py
SamwelOpiyo/simple_company_employee_management_backend
75723dd6aed002a1bc30f2d78d077e498eb25226
[ "MIT" ]
null
null
null
from datetime import datetime import os import time import pytz def get_extension(filename): return os.path.splitext(filename)[1] def user_avatar_path(instance, filename): """ Path for storing the profile image of the user """ # file will be uploaded to MEDIA_ROOT/avatars/<username>/<time>.<extension> new_filename = str(time.time()).replace(".", "_") + get_extension(filename) return "avatars/{0}/{1}".format(instance.username, new_filename)
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63e9751fe18c84204aad03d6694488df775f0b5a
73
py
Python
Olympiad Solutions/DMOJ/shortest1.py
Ashwanigupta9125/code-DS-ALGO
49f6cf7d0c682da669db23619aef3f80697b352b
[ "MIT" ]
36
2019-12-27T08:23:08.000Z
2022-01-24T20:35:47.000Z
Olympiad Solutions/DMOJ/shortest1.py
Ashwanigupta9125/code-DS-ALGO
49f6cf7d0c682da669db23619aef3f80697b352b
[ "MIT" ]
10
2019-11-13T02:55:18.000Z
2021-10-13T23:28:09.000Z
Olympiad Solutions/DMOJ/shortest1.py
Ashwanigupta9125/code-DS-ALGO
49f6cf7d0c682da669db23619aef3f80697b352b
[ "MIT" ]
53
2020-08-15T11:08:40.000Z
2021-10-09T15:51:38.000Z
# Ivan Carvalho # Solution to https://dmoj.ca/problem/shortest1 while 1:0
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120bb757fc8bc7f2ffc5cb286759a06ea5097d04
338
py
Python
Enums/cooling_type.py
clean-code-craft-tcq-2/coverage-in-py-Shivam6033
a44d54826b9abbf16b463c1de974cb4a95ee46e9
[ "MIT" ]
null
null
null
Enums/cooling_type.py
clean-code-craft-tcq-2/coverage-in-py-Shivam6033
a44d54826b9abbf16b463c1de974cb4a95ee46e9
[ "MIT" ]
null
null
null
Enums/cooling_type.py
clean-code-craft-tcq-2/coverage-in-py-Shivam6033
a44d54826b9abbf16b463c1de974cb4a95ee46e9
[ "MIT" ]
null
null
null
from enum import Enum from concrete_class.hi_active_cooling import HighActiveCooling from concrete_class.med_active_cooling import MedActiveCooling from concrete_class.passive_cooling import PassiveCooling class CoolingType(Enum): PASSIVE = PassiveCooling() HIGH_ACTIVE = HighActiveCooling() MED_ACTIVE = MedActiveCooling()
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121bf53d2459dd354f8b16b435a9acc394866fd4
163
py
Python
posts/forms.py
inziani/myInsta
3f564f8a0240fc02665b06b4032214046771d3ef
[ "Unlicense" ]
null
null
null
posts/forms.py
inziani/myInsta
3f564f8a0240fc02665b06b4032214046771d3ef
[ "Unlicense" ]
null
null
null
posts/forms.py
inziani/myInsta
3f564f8a0240fc02665b06b4032214046771d3ef
[ "Unlicense" ]
null
null
null
from django import forms from .models import Post, Comment # class CommentForm(forms.ModelForm): # class meta: # model = Comment # fields = ('comment')
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12275067cf76b824609fdb87476e1b79b56d6104
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py
Python
allauth/socialaccount/providers/meetup_provider/urls.py
Fuzzwah/django-allauth
071cbef1388bb61a563d3e41197bd5b7c26664d2
[ "MIT" ]
null
null
null
allauth/socialaccount/providers/meetup_provider/urls.py
Fuzzwah/django-allauth
071cbef1388bb61a563d3e41197bd5b7c26664d2
[ "MIT" ]
null
null
null
allauth/socialaccount/providers/meetup_provider/urls.py
Fuzzwah/django-allauth
071cbef1388bb61a563d3e41197bd5b7c26664d2
[ "MIT" ]
null
null
null
from allauth.socialaccount.providers.oauth2_provider.urls import default_urlpatterns from .provider import MeetupProvider urlpatterns = default_urlpatterns(MeetupProvider)
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1252db5e921c08fc596303de2dd87b4b5332e90c
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py
Python
cynetworkx/algorithms/approximation/tests/test_independent_set.py
Viech/cynetworkx
01a37859c67b752392e9e783c949084964eef2cf
[ "BSD-3-Clause" ]
12
2019-07-23T08:07:53.000Z
2022-03-09T06:13:16.000Z
cynetworkx/algorithms/approximation/tests/test_independent_set.py
Viech/cynetworkx
01a37859c67b752392e9e783c949084964eef2cf
[ "BSD-3-Clause" ]
7
2019-08-30T07:00:00.000Z
2021-12-30T08:02:56.000Z
cynetworkx/algorithms/approximation/tests/test_independent_set.py
Viech/cynetworkx
01a37859c67b752392e9e783c949084964eef2cf
[ "BSD-3-Clause" ]
5
2020-10-10T03:40:32.000Z
2021-11-23T12:28:53.000Z
from nose.tools import * import cynetworkx as nx import cynetworkx.algorithms.approximation as a def test_independent_set(): # smoke test G = nx.Graph() assert_equal(len(a.maximum_independent_set(G)), 0)
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4
89ceec934560d0dfc394a85b9d6c812847858ef7
130
py
Python
Day14/persistance.py
ananthramas/pythonEducation
7abd0341ee6cf46aaf3b337ba5870e3e3ea72e83
[ "MIT" ]
null
null
null
Day14/persistance.py
ananthramas/pythonEducation
7abd0341ee6cf46aaf3b337ba5870e3e3ea72e83
[ "MIT" ]
null
null
null
Day14/persistance.py
ananthramas/pythonEducation
7abd0341ee6cf46aaf3b337ba5870e3e3ea72e83
[ "MIT" ]
1
2021-03-16T00:04:42.000Z
2021-03-16T00:04:42.000Z
userinput = "" print("Before User input ", userinput) userinput = input("EnterSomething :") print("After User input ", userinput)
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