hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d0ec67e18fcced834b36dc99b5855ab17c2cf278
| 1,291
|
py
|
Python
|
Fast_style_transfer/drawLoss.py
|
HunterJ-Lin/Lightweight-Image-Style-Transfer-Model
|
4afb49b7d3d3d9c75d265e9e997b32b5747bd4fa
|
[
"MIT"
] | 1
|
2022-03-08T06:37:24.000Z
|
2022-03-08T06:37:24.000Z
|
Fast_style_transfer/drawLoss.py
|
HunterJ-Lin/Lightweight-Image-Style-Transfer-Model
|
4afb49b7d3d3d9c75d265e9e997b32b5747bd4fa
|
[
"MIT"
] | null | null | null |
Fast_style_transfer/drawLoss.py
|
HunterJ-Lin/Lightweight-Image-Style-Transfer-Model
|
4afb49b7d3d3d9c75d265e9e997b32b5747bd4fa
|
[
"MIT"
] | 1
|
2021-06-10T06:43:57.000Z
|
2021-06-10T06:43:57.000Z
|
import matplotlib.pyplot as plt
import numpy as np
file = open("c:/users/hunterj/desktop/实验数据/starry_night/styleloss.txt", "r")
x = []
for line in file.readlines():
x.append(float(line.strip('\n')))
print(min(x))
plt.title("Loss")
plt.xlabel("update times/100")
plt.ylabel("variance updated every 100")
line1, = plt.plot(range(1, len(x) + 1), x, color='r', linestyle='--')
file.close()
file = open("c:/users/hunterj/desktop/实验数据/Fire/starry_night/styleloss.txt", "r")
x = []
for line in file.readlines():
x.append(float(line.strip('\n')))
print(min(x))
line2, = plt.plot(range(1, len(x) + 1), x, color='b')
file.close()
plt.legend([line1, line2], ["Normal", "Fire"], loc='upper left')#添加图例
# file = open("c:/users/hunterj/desktop/实验数据/starry_night/styleloss.txt", "r")
# x = []
# for line in file.readlines():
# x.append(float(line.strip('\n')))
# print(min(x))
# plt.figure(3)
# plt.subplot(312)
# plt.title("styleloss")
# plt.plot(range(1, len(x) + 1), x)
# file.close()
#
# file = open("c:/users/hunterj/desktop/实验数据/starry_night/totalloss.txt", "r")
# x = []
# for line in file.readlines():
# x.append(float(line.strip('\n')))
# print(min(x))
# plt.figure(3)
# plt.subplot(313)
# plt.title("totalloss")
# plt.plot(range(1, len(x) + 1), x)
# file.close()
plt.show()
| 26.346939
| 81
| 0.644462
| 209
| 1,291
| 3.961722
| 0.30622
| 0.038647
| 0.043478
| 0.067633
| 0.708937
| 0.708937
| 0.708937
| 0.708937
| 0.708937
| 0.549517
| 0
| 0.022867
| 0.119287
| 1,291
| 48
| 82
| 26.895833
| 0.705365
| 0.416731
| 0
| 0.47619
| 0
| 0
| 0.264022
| 0.160055
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.095238
| 0
| 0.095238
| 0.095238
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 0
| 0
| 0
| 0
|
0
| 4
|
efc32ed5f957b0274df10b0d0a33af23d40c8ec1
| 242
|
py
|
Python
|
car_sim_gen/quantities.py
|
svenlr/car-physics-pacejka
|
bef64a7c3c813419a76f55c2b0553b5fe82f0808
|
[
"BSD-2-Clause"
] | null | null | null |
car_sim_gen/quantities.py
|
svenlr/car-physics-pacejka
|
bef64a7c3c813419a76f55c2b0553b5fe82f0808
|
[
"BSD-2-Clause"
] | null | null | null |
car_sim_gen/quantities.py
|
svenlr/car-physics-pacejka
|
bef64a7c3c813419a76f55c2b0553b5fe82f0808
|
[
"BSD-2-Clause"
] | null | null | null |
from casadi import MX
class CarPhysicalQuantities:
def __init__(self, n_wheels):
self.wheel_quantities = [WheelPhysicalQuantities() for _ in range(n_wheels)]
class WheelPhysicalQuantities:
def __init__(self):
pass
| 20.166667
| 84
| 0.727273
| 26
| 242
| 6.307692
| 0.692308
| 0.085366
| 0.134146
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.202479
| 242
| 11
| 85
| 22
| 0.849741
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0.142857
| 0.142857
| 0
| 0.714286
| 0
| 1
| 0
| 0
| null | 0
| 0
| 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
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
efcd801d140d15270f4c1cec683ccf428d4a8c74
| 1,640
|
py
|
Python
|
tikzqlmap.py
|
shipcod3/tikzqlmap
|
6377e76b702975ea5183a4bde948f4d55c796ac9
|
[
"Apache-2.0"
] | 1
|
2022-02-18T02:06:08.000Z
|
2022-02-18T02:06:08.000Z
|
tikzqlmap.py
|
shipcod3/tikzqlmap
|
6377e76b702975ea5183a4bde948f4d55c796ac9
|
[
"Apache-2.0"
] | null | null | null |
tikzqlmap.py
|
shipcod3/tikzqlmap
|
6377e76b702975ea5183a4bde948f4d55c796ac9
|
[
"Apache-2.0"
] | null | null | null |
tikz = '''
/~\
|oo )
_\=/_
___ / _ \
/() \ //|/.\|\\
_|_____|_ \\ \_/ ||
| | === | | \|\ /|||
|_| O |_| # _ _/#
|| O || | | |
||__*__|| | | |
|~ \___/ ~| []|[]
/=\ /=\ /=\ | | |
________________[_]_[_]_[_]________/_]_[_\_________________________
_ _ _ _
| | (_) | | |
| |_ _| | __ ______ _| |_ __ ___ __ _ _ __
| __| | |/ /|_ / _` | | '_ ` _ \ / _` | '_ \
| |_| | < / / (_| | | | | | | | (_| | |_) |
\__|_|_|\_\/___\__, |_|_| |_| |_|\__,_| .__/
| | | |
|_| |_|
-= automatic pwet grabber exploitation and fingerprinting tool =-
'''
arguments = '''
-h show help
-wafnuke try to bypass web application firewall
--grab-pwet downloads ass pics
-u URL of the website
-finger fingerprints the website
--crawl crawls the website recursively based on the hyperlinks
'''
print tikz
print arguments
| 44.324324
| 70
| 0.246951
| 49
| 1,640
| 5.346939
| 0.795918
| 0.114504
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.625
| 1,640
| 36
| 71
| 45.555556
| 0.426016
| 0
| 0
| 0.058824
| 0
| 0.117647
| 0.962195
| 0.040854
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.029412
| 0
| null | null | 0.117647
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
ef0d0922136f9c74db39d2857149365c6b91c7b5
| 18
|
py
|
Python
|
python/testData/formatter/spaceAroundKeywords.py
|
truthiswill/intellij-community
|
fff88cfb0dc168eea18ecb745d3e5b93f57b0b95
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/formatter/spaceAroundKeywords.py
|
truthiswill/intellij-community
|
fff88cfb0dc168eea18ecb745d3e5b93f57b0b95
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/formatter/spaceAroundKeywords.py
|
truthiswill/intellij-community
|
fff88cfb0dc168eea18ecb745d3e5b93f57b0b95
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
True and False
| 9
| 17
| 0.666667
| 3
| 18
| 4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 18
| 1
| 18
| 18
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
ef2deb86eed2b2677efad7846572bc7f5ccbc42a
| 121
|
py
|
Python
|
dissect/formats/pdf.py
|
AKOU0/dissect
|
b521153d86fe94dddc04846eb7ba3b6196917ee7
|
[
"Apache-2.0"
] | 19
|
2015-07-08T18:51:40.000Z
|
2020-03-08T16:06:16.000Z
|
dissect/formats/pdf.py
|
AKOU0/dissect
|
b521153d86fe94dddc04846eb7ba3b6196917ee7
|
[
"Apache-2.0"
] | 5
|
2016-02-24T15:23:13.000Z
|
2019-11-09T11:23:47.000Z
|
dissect/formats/pdf.py
|
AKOU0/dissect
|
b521153d86fe94dddc04846eb7ba3b6196917ee7
|
[
"Apache-2.0"
] | 11
|
2015-10-22T00:32:20.000Z
|
2017-07-14T01:45:14.000Z
|
import os
import sys
from binascii import unhexlify as xeh
from vstruct2.types import *
from dissect.filelab import *
| 13.444444
| 37
| 0.793388
| 18
| 121
| 5.333333
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.01
| 0.173554
| 121
| 8
| 38
| 15.125
| 0.95
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
|
ef314a18194cf2e00ea5c4f4b193afea4b8dc75a
| 4,589
|
py
|
Python
|
app/migrations/0001_initial.py
|
raptor419/SIH2020_AN314_AMRTrack
|
8bbdfce967ff8f52a6ee2aae5664047ce953d8a8
|
[
"MIT"
] | null | null | null |
app/migrations/0001_initial.py
|
raptor419/SIH2020_AN314_AMRTrack
|
8bbdfce967ff8f52a6ee2aae5664047ce953d8a8
|
[
"MIT"
] | null | null | null |
app/migrations/0001_initial.py
|
raptor419/SIH2020_AN314_AMRTrack
|
8bbdfce967ff8f52a6ee2aae5664047ce953d8a8
|
[
"MIT"
] | null | null | null |
# Generated by Django 2.1.1 on 2020-08-02 23:20
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Hospital',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('hospitalid', models.CharField(max_length=25)),
('name', models.CharField(max_length=50)),
('state', models.CharField(max_length=50)),
('district', models.CharField(max_length=50)),
('hospital', models.CharField(max_length=50)),
('address', models.CharField(max_length=50)),
],
),
migrations.CreateModel(
name='PathTest',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('testid', models.CharField(max_length=25)),
('patientid', models.CharField(max_length=25)),
('date', models.DateField(null=True)),
('year', models.IntegerField()),
('month', models.IntegerField()),
('sampletype', models.CharField(max_length=50)),
('organism', models.CharField(max_length=50)),
('hospital', models.CharField(max_length=50)),
('collsite', models.CharField(max_length=50)),
('amikacin', models.IntegerField(default=-1)),
('amoxicillin_clavulanicacid', models.IntegerField(default=-1)),
('ampicillin', models.IntegerField(default=-1)),
('ampicillin_sulbactum', models.IntegerField(default=-1)),
('cefaperazone_sulbactum', models.IntegerField(default=-1)),
('cefexime', models.IntegerField(default=-1)),
('cefotaxime', models.IntegerField(default=-1)),
('cefoxitin', models.IntegerField(default=-1)),
('ceftazidime', models.IntegerField(default=-1)),
('ceftazidime_clavalunicacid', models.IntegerField(default=-1)),
('ceftriaxone', models.IntegerField(default=-1)),
('chloramphenicol', models.IntegerField(default=-1)),
('ciprofloxacin', models.IntegerField(default=-1)),
('colistin', models.IntegerField(default=-1)),
('cotrimoxazole', models.IntegerField(default=-1)),
('ertapenem', models.IntegerField(default=-1)),
('erythromycin', models.IntegerField(default=-1)),
('gentamicin_highlevel', models.IntegerField(default=-1)),
('imipenem', models.IntegerField(default=-1)),
('levofloxacin', models.IntegerField(default=-1)),
('linezolid', models.IntegerField(default=-1)),
('meropenem', models.IntegerField(default=-1)),
('netilmicin', models.IntegerField(default=-1)),
('nitrofurantoin', models.IntegerField(default=-1)),
('penicillin', models.IntegerField(default=-1)),
('piperacillin_tazobactum', models.IntegerField(default=-1)),
('rifampicin', models.IntegerField(default=-1)),
('teicoplanin', models.IntegerField(default=-1)),
('tetracycline', models.IntegerField(default=-1)),
('ticarcillin_clavulanicacid', models.IntegerField(default=-1)),
('tigecycline', models.IntegerField(default=-1)),
('vancomycin', models.IntegerField(default=-1)),
],
),
migrations.CreateModel(
name='Patient',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('patientid', models.CharField(max_length=25)),
('testid', models.CharField(max_length=25)),
('state', models.CharField(max_length=50)),
('district', models.CharField(max_length=50)),
('hospital', models.CharField(max_length=50)),
('symptoms', models.CharField(max_length=50)),
('diagnosis', models.CharField(max_length=50)),
('test', models.CharField(max_length=50)),
('prescription', models.CharField(max_length=50)),
('allergy', models.IntegerField(default=0)),
],
),
]
| 50.988889
| 114
| 0.559599
| 392
| 4,589
| 6.456633
| 0.252551
| 0.248913
| 0.325958
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| 0.248518
| 0.195575
| 0.195575
| 0.195575
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| 115
| 51.561798
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|
0
| 4
|
ef3c1ce12ecd236763b55074ef1090bf71ffdbe7
| 51
|
py
|
Python
|
inst/python/install.py
|
BDSI-Utwente/ExtractAnonymizeChop
|
1379a7a6770190540cdf0a881f5d0c9968e19bd5
|
[
"MIT"
] | null | null | null |
inst/python/install.py
|
BDSI-Utwente/ExtractAnonymizeChop
|
1379a7a6770190540cdf0a881f5d0c9968e19bd5
|
[
"MIT"
] | null | null | null |
inst/python/install.py
|
BDSI-Utwente/ExtractAnonymizeChop
|
1379a7a6770190540cdf0a881f5d0c9968e19bd5
|
[
"MIT"
] | 1
|
2021-09-22T20:49:48.000Z
|
2021-09-22T20:49:48.000Z
|
import spacy
spacy.cli.download("en_core_web_md")
| 12.75
| 36
| 0.803922
| 9
| 51
| 4.222222
| 0.888889
| 0
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|
0
| 4
|
ef41a7c152b636de5e791672af62e8b5f0ceb7b3
| 127
|
py
|
Python
|
examples/docs_snippets/docs_snippets/guides/dagster/versioning_memoization/memoization_enabled_job.py
|
rpatil524/dagster
|
6f918d94cbd543ab752ab484a65e3a40fd441716
|
[
"Apache-2.0"
] | 1
|
2021-01-31T19:16:29.000Z
|
2021-01-31T19:16:29.000Z
|
examples/docs_snippets/docs_snippets/guides/dagster/versioning_memoization/memoization_enabled_job.py
|
rpatil524/dagster
|
6f918d94cbd543ab752ab484a65e3a40fd441716
|
[
"Apache-2.0"
] | null | null | null |
examples/docs_snippets/docs_snippets/guides/dagster/versioning_memoization/memoization_enabled_job.py
|
rpatil524/dagster
|
6f918d94cbd543ab752ab484a65e3a40fd441716
|
[
"Apache-2.0"
] | 1
|
2019-09-11T03:02:27.000Z
|
2019-09-11T03:02:27.000Z
|
from dagster import SourceHashVersionStrategy, job
@job(version_strategy=SourceHashVersionStrategy())
def the_job():
...
| 18.142857
| 50
| 0.779528
| 12
| 127
| 8.083333
| 0.75
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| 0
| 0
| 0.11811
| 127
| 6
| 51
| 21.166667
| 0.866071
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| 0.25
| true
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| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
3234f9432a4e1d95b3440b26f5f530a38c481117
| 109
|
py
|
Python
|
src/airflow_docker/views/__init__.py
|
Jwan622/airflow-docker
|
55310bc730f94bc1a293ba6e27ecf5bb663052ba
|
[
"Apache-2.0"
] | 17
|
2019-11-16T13:25:59.000Z
|
2022-03-31T02:50:59.000Z
|
src/airflow_docker/views/__init__.py
|
Jwan622/airflow-docker
|
55310bc730f94bc1a293ba6e27ecf5bb663052ba
|
[
"Apache-2.0"
] | 14
|
2019-09-13T20:02:15.000Z
|
2022-03-16T19:23:13.000Z
|
src/airflow_docker/views/__init__.py
|
Jwan622/airflow-docker
|
55310bc730f94bc1a293ba6e27ecf5bb663052ba
|
[
"Apache-2.0"
] | 2
|
2020-02-16T10:46:51.000Z
|
2022-03-14T18:52:04.000Z
|
import pkg_resources
template_folder = pkg_resources.resource_filename("airflow_docker", "views/templates")
| 27.25
| 86
| 0.844037
| 13
| 109
| 6.692308
| 0.846154
| 0.275862
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| 0
| 0.06422
| 109
| 3
| 87
| 36.333333
| 0.852941
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| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
324c3d75b6d3f9125f663c5da34186cea470b784
| 99
|
py
|
Python
|
pyshader/_coreutils.py
|
pygfx/pyshader
|
804f2a63221b40434ebcbeb4a01eeebe0d361a90
|
[
"BSD-2-Clause"
] | 48
|
2020-07-19T15:55:08.000Z
|
2022-03-21T15:02:45.000Z
|
pyshader/_coreutils.py
|
almarklein/python-shader
|
804f2a63221b40434ebcbeb4a01eeebe0d361a90
|
[
"BSD-2-Clause"
] | 22
|
2019-12-31T16:01:28.000Z
|
2020-06-15T20:03:58.000Z
|
pyshader/_coreutils.py
|
almarklein/spirv-py
|
804f2a63221b40434ebcbeb4a01eeebe0d361a90
|
[
"BSD-2-Clause"
] | 2
|
2020-10-12T09:42:28.000Z
|
2021-03-04T08:20:19.000Z
|
class ShaderError(Exception):
"""Error raised when the user shader code cannot be compiled."""
| 33
| 68
| 0.737374
| 13
| 99
| 5.615385
| 1
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0.161616
| 99
| 2
| 69
| 49.5
| 0.879518
| 0.585859
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| true
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| null | 0
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| 0
| 0
| 1
| 0
|
0
| 4
|
3264c13b328b7fbf7bfcb024a7be66d4da37afe3
| 58
|
py
|
Python
|
flask_value_checker/restrictions/parsing/__init__.py
|
cbcoutinho/flask-value-checker
|
cbdff51c401486dea7d49eda30eae1211d392ee7
|
[
"MIT"
] | null | null | null |
flask_value_checker/restrictions/parsing/__init__.py
|
cbcoutinho/flask-value-checker
|
cbdff51c401486dea7d49eda30eae1211d392ee7
|
[
"MIT"
] | null | null | null |
flask_value_checker/restrictions/parsing/__init__.py
|
cbcoutinho/flask-value-checker
|
cbdff51c401486dea7d49eda30eae1211d392ee7
|
[
"MIT"
] | null | null | null |
from .parsing import make_restrictions, RestrictionParser
| 29
| 57
| 0.87931
| 6
| 58
| 8.333333
| 1
| 0
| 0
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| 0
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| 0.086207
| 58
| 1
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| 58
| 0.943396
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| 1
| 0
| 0
| 0
|
0
| 4
|
3277671f2a9aea4c149b705c8cad9510b4b5b14c
| 156
|
py
|
Python
|
logic/emails/send_blast.py
|
q82cap/company-website
|
72ec56e1d7965c6ffbba61f567599224a68e4533
|
[
"MIT"
] | null | null | null |
logic/emails/send_blast.py
|
q82cap/company-website
|
72ec56e1d7965c6ffbba61f567599224a68e4533
|
[
"MIT"
] | null | null | null |
logic/emails/send_blast.py
|
q82cap/company-website
|
72ec56e1d7965c6ffbba61f567599224a68e4533
|
[
"MIT"
] | null | null | null |
from logic.emails import mailing_list
from tools import db_utils
with db_utils.request_context():
mailing_list.send_one_off("may_2018_newsletter")
| 26
| 52
| 0.807692
| 24
| 156
| 4.875
| 0.75
| 0.188034
| 0
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| 0
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| 0
| 0.029412
| 0.128205
| 156
| 6
| 53
| 26
| 0.830882
| 0
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| null | 0
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| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
328c3c8b8d7133f481c99d1ad6bc62ef4f9fd60b
| 89
|
py
|
Python
|
bridges/apps.py
|
vitale232/InspectionPlanner
|
4d9c9b494e6b3587eb182e9c34ea3d6aee5546e8
|
[
"MIT"
] | 1
|
2020-01-30T12:32:38.000Z
|
2020-01-30T12:32:38.000Z
|
bridges/apps.py
|
vitale232/InspectionPlanner
|
4d9c9b494e6b3587eb182e9c34ea3d6aee5546e8
|
[
"MIT"
] | 45
|
2019-07-27T02:12:11.000Z
|
2022-03-02T04:59:15.000Z
|
bridges/apps.py
|
vitale232/InspectionPlanner
|
4d9c9b494e6b3587eb182e9c34ea3d6aee5546e8
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class BridgesConfig(AppConfig):
name = 'bridges'
| 14.833333
| 33
| 0.752809
| 10
| 89
| 6.7
| 0.9
| 0
| 0
| 0
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| 0
| 0
| 0.168539
| 89
| 5
| 34
| 17.8
| 0.905405
| 0
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| 0
| 0.078652
| 0
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| false
| 0
| 0.333333
| 0
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| null | 0
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| 0
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| 1
| 0
| 1
| 0
|
0
| 4
|
329175f4e32f38f66cb295a6b73fad817389e4c1
| 783
|
py
|
Python
|
boundaries/tests/test_titlecase.py
|
MinnPost/represent-boundaries
|
17f65d34a6ed761e72dbdf13ea78b64fdeaa356d
|
[
"MIT"
] | 20
|
2015-03-17T09:10:39.000Z
|
2020-06-30T06:08:08.000Z
|
boundaries/tests/test_titlecase.py
|
rhymeswithcycle/represent-boundaries
|
f487f9b18d6c1b8fe3e7f47171fc7741c14be4b3
|
[
"MIT"
] | 14
|
2015-04-24T17:22:00.000Z
|
2021-06-22T16:50:24.000Z
|
boundaries/tests/test_titlecase.py
|
rhymeswithcycle/represent-boundaries
|
f487f9b18d6c1b8fe3e7f47171fc7741c14be4b3
|
[
"MIT"
] | 16
|
2015-04-27T23:32:46.000Z
|
2020-07-05T11:18:04.000Z
|
# coding: utf-8
from __future__ import unicode_literals
from django.test import TestCase
from boundaries.titlecase import titlecase
class TitlecaseTestCase(TestCase):
def test_uc_initials(self):
self.assertEqual(titlecase('X.Y.Z. INC.'), 'X.Y.Z. Inc.')
def test_apos_second(self):
self.assertEqual(titlecase("duck à l'orange"), "Duck à L'Orange")
def test_inline_period(self):
self.assertEqual(titlecase('example.com'), 'example.com')
def test_small_words(self):
self.assertEqual(titlecase('FOR WHOM THE BELL TOLLS'), 'For Whom the Bell Tolls')
def test_mac_mc(self):
self.assertEqual(titlecase('MACDONALD'), 'MacDonald')
def test_slash(self):
self.assertEqual(titlecase('foo/bar/baz'), 'Foo/Bar/Baz')
| 29
| 89
| 0.696041
| 106
| 783
| 4.990566
| 0.45283
| 0.079395
| 0.215501
| 0.31758
| 0.071834
| 0
| 0
| 0
| 0
| 0
| 0
| 0.001546
| 0.173691
| 783
| 26
| 90
| 30.115385
| 0.816074
| 0.016603
| 0
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| 0
| 0.208333
| 0
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| 0
| 0.375
| 1
| 0.375
| false
| 0
| 0.1875
| 0
| 0.625
| 0
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| 1
| 1
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
329c21839027d705c850e0bf8df70524cc1456e4
| 35
|
py
|
Python
|
workflows/arlexecute/simulation/__init__.py
|
ska-telescope/algorithm-reference-library
|
1b2c8d6079249202864abf8c60cdea40f0f123cb
|
[
"Apache-2.0"
] | 22
|
2016-12-14T11:20:07.000Z
|
2021-08-13T15:23:41.000Z
|
workflows/arlexecute/simulation/__init__.py
|
ska-telescope/algorithm-reference-library
|
1b2c8d6079249202864abf8c60cdea40f0f123cb
|
[
"Apache-2.0"
] | 30
|
2017-06-27T09:15:38.000Z
|
2020-09-11T18:16:37.000Z
|
workflows/arlexecute/simulation/__init__.py
|
SKA-ScienceDataProcessor/algorithm-reference-library
|
1b2c8d6079249202864abf8c60cdea40f0f123cb
|
[
"Apache-2.0"
] | 20
|
2017-07-02T03:45:49.000Z
|
2019-12-11T17:19:01.000Z
|
__all__ = ['simulation_arlexecute']
| 35
| 35
| 0.8
| 3
| 35
| 7.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0.057143
| 35
| 1
| 35
| 35
| 0.69697
| 0
| 0
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| 0
| 0.583333
| 0.583333
| 0
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| null | 0
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| 0
| 0
| 0
| 0
|
0
| 4
|
32a2a86b28fae6eb32687687a9cbda5afcbb4e80
| 34
|
py
|
Python
|
homeassistant/components/hikvisioncam/__init__.py
|
domwillcode/home-assistant
|
f170c80bea70c939c098b5c88320a1c789858958
|
[
"Apache-2.0"
] | 30,023
|
2016-04-13T10:17:53.000Z
|
2020-03-02T12:56:31.000Z
|
homeassistant/components/hikvisioncam/__init__.py
|
jagadeeshvenkatesh/core
|
1bd982668449815fee2105478569f8e4b5670add
|
[
"Apache-2.0"
] | 31,101
|
2020-03-02T13:00:16.000Z
|
2022-03-31T23:57:36.000Z
|
homeassistant/components/hikvisioncam/__init__.py
|
jagadeeshvenkatesh/core
|
1bd982668449815fee2105478569f8e4b5670add
|
[
"Apache-2.0"
] | 11,956
|
2016-04-13T18:42:31.000Z
|
2020-03-02T09:32:12.000Z
|
"""The hikvisioncam component."""
| 17
| 33
| 0.705882
| 3
| 34
| 8
| 1
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| 0
| 0
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| 0.088235
| 34
| 1
| 34
| 34
| 0.774194
| 0.794118
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
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| null | 0
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| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
32c33de1ece537d30014c207b4d3008f0d237ab2
| 305
|
py
|
Python
|
cwProject/cwApp/views.py
|
cs-fullstack-2019-spring/django-bootstrap-grid-cw-autumn-ragland
|
d85d0ae326f55f9adab4c963241e54d516fc36b2
|
[
"Apache-2.0"
] | null | null | null |
cwProject/cwApp/views.py
|
cs-fullstack-2019-spring/django-bootstrap-grid-cw-autumn-ragland
|
d85d0ae326f55f9adab4c963241e54d516fc36b2
|
[
"Apache-2.0"
] | null | null | null |
cwProject/cwApp/views.py
|
cs-fullstack-2019-spring/django-bootstrap-grid-cw-autumn-ragland
|
d85d0ae326f55f9adab4c963241e54d516fc36b2
|
[
"Apache-2.0"
] | null | null | null |
from django.shortcuts import render
# render page 1
def index(request):
return render(request, 'cwApp/index.html')
# render page 2
def page_two(request):
return render(request, 'cwApp/pageTwo.html')
# render page 3
def page_three(request):
return render(request, 'cwApp/pageThree.html')
| 17.941176
| 50
| 0.727869
| 43
| 305
| 5.116279
| 0.44186
| 0.136364
| 0.259091
| 0.354545
| 0.422727
| 0
| 0
| 0
| 0
| 0
| 0
| 0.011719
| 0.160656
| 305
| 16
| 51
| 19.0625
| 0.847656
| 0.134426
| 0
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| 0.207692
| 0
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| 1
| 0.428571
| false
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| 0.142857
| 0.428571
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| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 4
|
08713962a8cff65770d654593ba556dc5f9b3472
| 137
|
py
|
Python
|
reddit2telegram/channels/~inactive/cahaf_avir/app.py
|
mainyordle/reddit2telegram
|
1163e15aed3b6ff0fba65b222d3d9798f644c386
|
[
"MIT"
] | 187
|
2016-09-20T09:15:54.000Z
|
2022-03-29T12:22:33.000Z
|
reddit2telegram/channels/~inactive/cahaf_avir/app.py
|
mainyordle/reddit2telegram
|
1163e15aed3b6ff0fba65b222d3d9798f644c386
|
[
"MIT"
] | 84
|
2016-09-22T14:25:07.000Z
|
2022-03-19T01:26:17.000Z
|
reddit2telegram/channels/~inactive/cahaf_avir/app.py
|
mainyordle/reddit2telegram
|
1163e15aed3b6ff0fba65b222d3d9798f644c386
|
[
"MIT"
] | 172
|
2016-09-21T15:39:39.000Z
|
2022-03-16T15:15:58.000Z
|
#encoding:utf-8
subreddit = 'ani_bm'
t_channel = '@cahaf_avir'
def send_post(submission, r2t):
return r2t.send_simple(submission)
| 15.222222
| 38
| 0.737226
| 20
| 137
| 4.8
| 0.85
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025424
| 0.138686
| 137
| 8
| 39
| 17.125
| 0.788136
| 0.10219
| 0
| 0
| 0
| 0
| 0.139344
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0.25
| 0.5
| 0
| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 4
|
087ea32939f31a431d80f8969192e6beb737d794
| 72
|
py
|
Python
|
planning_poker_jira/__init__.py
|
rheinwerk-verlag/planning-poker-jira
|
23a4f4d5cc33a148d648c341416723216f338397
|
[
"BSD-3-Clause"
] | 1
|
2021-08-24T08:12:17.000Z
|
2021-08-24T08:12:17.000Z
|
planning_poker_jira/__init__.py
|
rheinwerk-verlag/planning-poker-jira
|
23a4f4d5cc33a148d648c341416723216f338397
|
[
"BSD-3-Clause"
] | 1
|
2021-09-13T07:18:46.000Z
|
2021-09-13T07:18:46.000Z
|
planning_poker_jira/__init__.py
|
rheinwerk-verlag/planning-poker-jira
|
23a4f4d5cc33a148d648c341416723216f338397
|
[
"BSD-3-Clause"
] | null | null | null |
default_app_config = 'planning_poker_jira.apps.PlanningPokerJiraConfig'
| 36
| 71
| 0.888889
| 8
| 72
| 7.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.041667
| 72
| 1
| 72
| 72
| 0.869565
| 0
| 0
| 0
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| 0
| 0.666667
| 0.666667
| 0
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| 0
| 0
| 0
| 1
| 0
| false
| 0
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| 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
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0882bf0632cfccae643916359cf99a3312615318
| 1,673
|
py
|
Python
|
EasyDeep/base/base_experiment.py
|
strawsyz/straw
|
db313c78c2e3c0355cd10c70ac25a15bb5632d41
|
[
"MIT"
] | 2
|
2020-04-06T09:09:19.000Z
|
2020-07-24T03:59:55.000Z
|
EasyDeep/base/base_experiment.py
|
strawsyz/straw
|
db313c78c2e3c0355cd10c70ac25a15bb5632d41
|
[
"MIT"
] | null | null | null |
EasyDeep/base/base_experiment.py
|
strawsyz/straw
|
db313c78c2e3c0355cd10c70ac25a15bb5632d41
|
[
"MIT"
] | null | null | null |
from configs.experiment_config import BaseExperimentConfig
class BaseExperiment(BaseExperimentConfig):
def __init__(self):
super(BaseExperiment, self).__init__()
# self.config_instance = config_instance
# self.load_config()
# def load_config(self):
# if self.config_instance is not None:
# copy_attr(self.config_instance, self)
# else:
# self.logger.error("need a eperiment config file!")
# raise NotImplementedError
# def show_config(self):
# """list all configure in a experiment"""
# # if self.config_instance is None:
# # self.logger.warning("please set a configure file for the experiment")
# # raise RuntimeError("please set a configure file for the experiment")
# # else:
# config_instance_str = str(self.config_instance)
# self.logger.info(config_instance_str)
# return config_instance_str
def prepare_net(self):
raise NotImplementedError
def prepare_dataset(self):
raise NotImplementedError
def train(self):
raise NotImplementedError
def before_test(self):
raise NotImplementedError
def estimate(self):
raise NotImplementedError
def save(self):
raise NotImplementedError
def load(self):
raise NotImplementedError
def estimate_history(self):
raise NotImplementedError
def check(self):
# used to check config file and something else
raise NotImplementedError
if __name__ == '__main__':
expriment = BaseExperiment()
expriment.train()
expriment.estimate_history()
| 27.42623
| 85
| 0.656904
| 174
| 1,673
| 6.097701
| 0.344828
| 0.226202
| 0.229029
| 0.233742
| 0.188501
| 0.073516
| 0.073516
| 0.073516
| 0
| 0
| 0
| 0
| 0.266587
| 1,673
| 60
| 86
| 27.883333
| 0.864711
| 0.426778
| 0
| 0.346154
| 0
| 0
| 0.008547
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.384615
| false
| 0
| 0.038462
| 0
| 0.461538
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0884adcc5c0d637f5645e931117cb42cd5f102fd
| 324
|
py
|
Python
|
Dataset/Leetcode/valid/3/602.py
|
kkcookies99/UAST
|
fff81885aa07901786141a71e5600a08d7cb4868
|
[
"MIT"
] | null | null | null |
Dataset/Leetcode/valid/3/602.py
|
kkcookies99/UAST
|
fff81885aa07901786141a71e5600a08d7cb4868
|
[
"MIT"
] | null | null | null |
Dataset/Leetcode/valid/3/602.py
|
kkcookies99/UAST
|
fff81885aa07901786141a71e5600a08d7cb4868
|
[
"MIT"
] | null | null | null |
class Solution:
def XXX(self, s: str) -> int:
max_sub_s = ''
len_sub_s = 0
for i_s in s:
if i_s in max_sub_s:
max_sub_s = max_sub_s[max_sub_s.index(i_s)+1:]
max_sub_s += i_s
len_sub_s = max(len_sub_s, len(max_sub_s))
return len_sub_s
| 27
| 62
| 0.524691
| 60
| 324
| 2.4
| 0.316667
| 0.305556
| 0.340278
| 0.208333
| 0.194444
| 0.194444
| 0.194444
| 0.194444
| 0.194444
| 0
| 0
| 0.01
| 0.382716
| 324
| 11
| 63
| 29.454545
| 0.71
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
089907f3d9f8bcc2abd0bcd73b6358fc421e90ed
| 105
|
py
|
Python
|
niftivis/__init__.py
|
jstutters/niftivis
|
65b440d6c82e6ed87b824208f0b8d1c4dac083fa
|
[
"MIT"
] | null | null | null |
niftivis/__init__.py
|
jstutters/niftivis
|
65b440d6c82e6ed87b824208f0b8d1c4dac083fa
|
[
"MIT"
] | null | null | null |
niftivis/__init__.py
|
jstutters/niftivis
|
65b440d6c82e6ed87b824208f0b8d1c4dac083fa
|
[
"MIT"
] | null | null | null |
from niftivis.niftivis import make_thumbnails
__version__ = "2021.04.13"
__all__ = ["make_thumbnails"]
| 17.5
| 45
| 0.780952
| 13
| 105
| 5.538462
| 0.769231
| 0.388889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.086022
| 0.114286
| 105
| 5
| 46
| 21
| 0.688172
| 0
| 0
| 0
| 0
| 0
| 0.238095
| 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
|
08afabb347256359a4c52576b478792751976be2
| 332
|
py
|
Python
|
photo_location_plotter/file_structure_helper.py
|
Wesley-Fisher/photo-location-plotter
|
169b68a0bd44fd128c4fb364b5ea0a5c8bc99386
|
[
"MIT"
] | null | null | null |
photo_location_plotter/file_structure_helper.py
|
Wesley-Fisher/photo-location-plotter
|
169b68a0bd44fd128c4fb364b5ea0a5c8bc99386
|
[
"MIT"
] | null | null | null |
photo_location_plotter/file_structure_helper.py
|
Wesley-Fisher/photo-location-plotter
|
169b68a0bd44fd128c4fb364b5ea0a5c8bc99386
|
[
"MIT"
] | null | null | null |
class FileStructureHelper:
def __init__(self, run_settings):
self.run_settings = run_settings
def get_project_directory(self):
return self.run_settings.app_directory + '/projects/' + self.run_settings.project
def get_config_file_path(self):
return self.get_project_directory() + "/config.yaml"
| 33.2
| 89
| 0.725904
| 41
| 332
| 5.463415
| 0.414634
| 0.245536
| 0.267857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.180723
| 332
| 10
| 90
| 33.2
| 0.823529
| 0
| 0
| 0
| 0
| 0
| 0.066265
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.428571
| false
| 0
| 0
| 0.285714
| 0.857143
| 0
| 0
| 0
| 0
| null | 1
| 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
| 0
| 0
|
0
| 4
|
08b2bb78c4d351b874f83d9446eed8f2e0e0d6bc
| 94
|
py
|
Python
|
subjects/apps.py
|
encrypted-fox/students_performance_monitoring
|
1a6c80ff70f3738496809586ae3fc204a156ca3b
|
[
"MIT"
] | null | null | null |
subjects/apps.py
|
encrypted-fox/students_performance_monitoring
|
1a6c80ff70f3738496809586ae3fc204a156ca3b
|
[
"MIT"
] | 22
|
2020-01-23T17:41:36.000Z
|
2021-07-02T14:00:00.000Z
|
subjects/apps.py
|
encrypted-fox/students_performance_monitoring
|
1a6c80ff70f3738496809586ae3fc204a156ca3b
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class DepartmentsConfig(AppConfig):
name = 'subjects'
| 15.666667
| 35
| 0.765957
| 10
| 94
| 7.2
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.159574
| 94
| 5
| 36
| 18.8
| 0.911392
| 0
| 0
| 0
| 0
| 0
| 0.085106
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
08beafa1af35ca9dbbe9eae07311e3e6d2c8601e
| 120
|
py
|
Python
|
decred/decred/__init__.py
|
JoeGruffins/tinydecred
|
9c62378d04139446391f675f0e6646c5c882deb1
|
[
"ISC"
] | null | null | null |
decred/decred/__init__.py
|
JoeGruffins/tinydecred
|
9c62378d04139446391f675f0e6646c5c882deb1
|
[
"ISC"
] | 2
|
2021-06-02T03:28:57.000Z
|
2021-06-02T03:36:44.000Z
|
decred/decred/__init__.py
|
JoeGruffins/tinydecred
|
9c62378d04139446391f675f0e6646c5c882deb1
|
[
"ISC"
] | null | null | null |
"""
Copyright (c) 2019-2020, the Decred developers
See LICENSE for details
"""
class DecredError(Exception):
pass
| 13.333333
| 46
| 0.716667
| 15
| 120
| 5.733333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.080808
| 0.175
| 120
| 8
| 47
| 15
| 0.787879
| 0.583333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
08dbad8ef499caea590cfebbce6ecfc3d9c666a3
| 332
|
py
|
Python
|
10. Functions Advanced - Exercise/05_function_executor.py
|
elenaborisova/Python-Advanced
|
4c266d81f294372c3599741e8ba53f59fdc834c5
|
[
"MIT"
] | 2
|
2021-04-04T06:26:13.000Z
|
2022-02-18T22:21:49.000Z
|
10. Functions Advanced - Exercise/05_function_executor.py
|
elenaborisova/Python-Advanced
|
4c266d81f294372c3599741e8ba53f59fdc834c5
|
[
"MIT"
] | null | null | null |
10. Functions Advanced - Exercise/05_function_executor.py
|
elenaborisova/Python-Advanced
|
4c266d81f294372c3599741e8ba53f59fdc834c5
|
[
"MIT"
] | 3
|
2021-02-01T12:32:03.000Z
|
2021-04-12T13:45:20.000Z
|
def sum_numbers(num1, num2):
return num1 + num2
def multiply_numbers(num1, num2):
return num1 * num2
def func_executor(*args):
results = []
for arg in args:
func, nums = arg
results.append(func(*nums))
return results
print(func_executor((sum_numbers, (1, 2)), (multiply_numbers, (2, 4))))
| 17.473684
| 71
| 0.63253
| 45
| 332
| 4.533333
| 0.444444
| 0.156863
| 0.147059
| 0.205882
| 0.313725
| 0.313725
| 0.313725
| 0
| 0
| 0
| 0
| 0.047431
| 0.237952
| 332
| 19
| 71
| 17.473684
| 0.758893
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.272727
| false
| 0
| 0
| 0.181818
| 0.545455
| 0.090909
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
08e875dfd1b08ef238c3ff6f6ffaf0995cebec61
| 96
|
py
|
Python
|
tests/__init__.py
|
hsolbrig/definednamespace
|
f1178ba9c36a94bbd422844f4ddc71de67521d7b
|
[
"CC0-1.0"
] | null | null | null |
tests/__init__.py
|
hsolbrig/definednamespace
|
f1178ba9c36a94bbd422844f4ddc71de67521d7b
|
[
"CC0-1.0"
] | null | null | null |
tests/__init__.py
|
hsolbrig/definednamespace
|
f1178ba9c36a94bbd422844f4ddc71de67521d7b
|
[
"CC0-1.0"
] | 1
|
2021-09-02T09:03:07.000Z
|
2021-09-02T09:03:07.000Z
|
import os
cwd = os.path.abspath(os.path.dirname(__file__))
test_dir = os.path.join(cwd, 'data')
| 24
| 48
| 0.729167
| 17
| 96
| 3.823529
| 0.647059
| 0.276923
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09375
| 96
| 4
| 49
| 24
| 0.747126
| 0
| 0
| 0
| 0
| 0
| 0.041237
| 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
|
08f1f581807b6c318f30c2f8071ca32b679f49f3
| 132
|
py
|
Python
|
examples/print_for_function.py
|
dominicj-nylas/traceback_with_variables
|
899565d52f89587a29f14745ef5820b05bda8187
|
[
"MIT"
] | null | null | null |
examples/print_for_function.py
|
dominicj-nylas/traceback_with_variables
|
899565d52f89587a29f14745ef5820b05bda8187
|
[
"MIT"
] | null | null | null |
examples/print_for_function.py
|
dominicj-nylas/traceback_with_variables
|
899565d52f89587a29f14745ef5820b05bda8187
|
[
"MIT"
] | null | null | null |
from traceback_with_variables import prints_tb
@prints_tb
def f(n):
print(1 / n)
def main():
f(0)
main()
| 9.428571
| 47
| 0.583333
| 20
| 132
| 3.65
| 0.7
| 0.219178
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021978
| 0.310606
| 132
| 13
| 48
| 10.153846
| 0.78022
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0
| 0.142857
| 0
| 0.428571
| 0.428571
| 1
| 0
| 0
| null | 1
| 0
| 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
| 0
| 0
| 1
|
0
| 4
|
08f215a3bf979fde2e638a9ab0de680f225db804
| 428
|
py
|
Python
|
numpy/random_fn.py
|
dnootana/Python
|
2881bafe8bc378fa3cae50a747fcea1a55630c63
|
[
"MIT"
] | 1
|
2021-02-19T11:00:11.000Z
|
2021-02-19T11:00:11.000Z
|
numpy/random_fn.py
|
dnootana/Python
|
2881bafe8bc378fa3cae50a747fcea1a55630c63
|
[
"MIT"
] | null | null | null |
numpy/random_fn.py
|
dnootana/Python
|
2881bafe8bc378fa3cae50a747fcea1a55630c63
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3.8
from numpy import random
a = random.randint(100)
print(a)
a = random.rand()
print(a)
a = random.randint(100, size=(5))
print(a)
a = random.randint(100, size=(3, 5))
print(a)
a = random.rand(5)
print(a)
a = random.rand(5,3)
print(a)
a = random.choice([1,2,3,4,5])
print(a)
a = random.choice(["dfasdf", "dsfas", "sdfsdf", "asdfasd"])
print(a)
a = random.choice([1,2,3,4,5], size=(2,2))
print(a)
| 13.806452
| 59
| 0.628505
| 82
| 428
| 3.280488
| 0.304878
| 0.234201
| 0.208178
| 0.386617
| 0.654275
| 0.516729
| 0.516729
| 0.178439
| 0.178439
| 0.178439
| 0
| 0.078591
| 0.13785
| 428
| 31
| 60
| 13.806452
| 0.650407
| 0.053738
| 0
| 0.473684
| 0
| 0
| 0.059259
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.052632
| 0
| 0.052632
| 0.473684
| 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
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
3ea5683a9b712d868cdacf5d12db38e5e5993d95
| 90
|
py
|
Python
|
tests/__init__.py
|
Mischback/django-calingen
|
3354c751e29d301609ec44e64d69a8729ec36de4
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
Mischback/django-calingen
|
3354c751e29d301609ec44e64d69a8729ec36de4
|
[
"MIT"
] | 51
|
2021-11-15T20:44:19.000Z
|
2022-02-10T08:33:08.000Z
|
tests/__init__.py
|
Mischback/django-calingen
|
3354c751e29d301609ec44e64d69a8729ec36de4
|
[
"MIT"
] | null | null | null |
# SPDX-License-Identifier: MIT
"""Contains the app's tests and utility configuration."""
| 22.5
| 57
| 0.744444
| 12
| 90
| 5.583333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122222
| 90
| 3
| 58
| 30
| 0.848101
| 0.9
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
3ecead7ac0c6586a36a616dc6c2d38dabe00e091
| 77
|
py
|
Python
|
B/apps.py
|
subhrangshu/django-backend-demo
|
43ac5dbe1566e95eaf0fa5f5562b88bfb3036820
|
[
"Apache-2.0"
] | null | null | null |
B/apps.py
|
subhrangshu/django-backend-demo
|
43ac5dbe1566e95eaf0fa5f5562b88bfb3036820
|
[
"Apache-2.0"
] | null | null | null |
B/apps.py
|
subhrangshu/django-backend-demo
|
43ac5dbe1566e95eaf0fa5f5562b88bfb3036820
|
[
"Apache-2.0"
] | 1
|
2020-11-24T08:47:37.000Z
|
2020-11-24T08:47:37.000Z
|
from django.apps import AppConfig
class BConfig(AppConfig):
name = 'B'
| 12.833333
| 33
| 0.714286
| 10
| 77
| 5.5
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.194805
| 77
| 5
| 34
| 15.4
| 0.887097
| 0
| 0
| 0
| 0
| 0
| 0.012987
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
3ecf44dba3ad409b29b4bc1ec8dde98a3f75d30c
| 48
|
py
|
Python
|
Language Skills/Python/Unit 01 Python Syntax/01 Python Syntax/Whitespace and Statements/whiteSpace.py
|
rhyep/Python_tutorials
|
f5c8a64b91802b005dfe7dd9035f8d8daae8c3e3
|
[
"MIT"
] | 346
|
2016-02-22T20:21:10.000Z
|
2022-01-27T20:55:53.000Z
|
Language Skills/Python/Unit 1/1-Python Syntax/Whitespace and Statements/whiteSpace.py
|
vpstudios/Codecademy-Exercise-Answers
|
ebd0ee8197a8001465636f52c69592ea6745aa0c
|
[
"MIT"
] | 55
|
2016-04-07T13:58:44.000Z
|
2020-06-25T12:20:24.000Z
|
Language Skills/Python/Unit 1/1-Python Syntax/Whitespace and Statements/whiteSpace.py
|
vpstudios/Codecademy-Exercise-Answers
|
ebd0ee8197a8001465636f52c69592ea6745aa0c
|
[
"MIT"
] | 477
|
2016-02-21T06:17:02.000Z
|
2021-12-22T10:08:01.000Z
|
def spam():
eggs = 12
return eggs
print spam()
| 8
| 12
| 0.666667
| 8
| 48
| 4
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.052632
| 0.208333
| 48
| 5
| 13
| 9.6
| 0.789474
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.25
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
3eda5769b22c30c7a2068c471089851cd3055efb
| 107
|
py
|
Python
|
web/transiq/restapi/search.py
|
manibhushan05/transiq
|
763fafb271ce07d13ac8ce575f2fee653cf39343
|
[
"Apache-2.0"
] | 5
|
2019-01-31T10:41:24.000Z
|
2019-09-22T12:38:53.000Z
|
web/transiq/restapi/search.py
|
manibhushan05/transiq
|
763fafb271ce07d13ac8ce575f2fee653cf39343
|
[
"Apache-2.0"
] | 14
|
2020-06-05T23:06:45.000Z
|
2022-03-12T00:00:18.000Z
|
web/transiq/restapi/search.py
|
manibhushan05/transiq
|
763fafb271ce07d13ac8ce575f2fee653cf39343
|
[
"Apache-2.0"
] | 1
|
2019-09-14T11:39:49.000Z
|
2019-09-14T11:39:49.000Z
|
from rest_framework import filters
class CustomSearch(filters.SearchFilter):
search_param = "search"
| 17.833333
| 41
| 0.794393
| 12
| 107
| 6.916667
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.140187
| 107
| 5
| 42
| 21.4
| 0.902174
| 0
| 0
| 0
| 0
| 0
| 0.056075
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
3ef85def06a38ce108c741808e7814259543f838
| 251
|
py
|
Python
|
examples/materials/testing_materials.py
|
JorgeDeLosSantos/nusa
|
05623a72b892330e4b0e059a03ac4614da934ce9
|
[
"MIT"
] | 92
|
2016-11-14T01:39:55.000Z
|
2022-03-27T17:23:41.000Z
|
examples/materials/testing_materials.py
|
JorgeDeLosSantos/nusa
|
05623a72b892330e4b0e059a03ac4614da934ce9
|
[
"MIT"
] | 1
|
2017-11-30T05:04:02.000Z
|
2018-08-29T04:31:39.000Z
|
examples/materials/testing_materials.py
|
JorgeDeLosSantos/nusa
|
05623a72b892330e4b0e059a03ac4614da934ce9
|
[
"MIT"
] | 31
|
2017-05-17T18:50:18.000Z
|
2022-03-12T03:08:00.000Z
|
# -*- coding: utf-8 -*-
# ***********************************
# Author: Pedro Jorge De Los Santos
# E-mail: delossantosmfq@gmail.com
# License: MIT License
# ***********************************
from nusa.lib import *
print(dir(STEEL_1018))
| 22.818182
| 41
| 0.462151
| 25
| 251
| 4.6
| 0.96
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.023585
| 0.155378
| 251
| 10
| 42
| 25.1
| 0.518868
| 0.756972
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 1
| 0
| 0
| null | 0
| 0
| 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
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 4
|
4108c571fa013aef935edc19bd403ecff79c30ad
| 250
|
py
|
Python
|
config.py
|
LemmyMwaura/Personal-log
|
cec2186497a7c4829ba07fb5522ac06a9192b5b3
|
[
"MIT"
] | null | null | null |
config.py
|
LemmyMwaura/Personal-log
|
cec2186497a7c4829ba07fb5522ac06a9192b5b3
|
[
"MIT"
] | null | null | null |
config.py
|
LemmyMwaura/Personal-log
|
cec2186497a7c4829ba07fb5522ac06a9192b5b3
|
[
"MIT"
] | 1
|
2022-03-15T07:50:08.000Z
|
2022-03-15T07:50:08.000Z
|
# class Config():
# app.config['SECRET_KEY'] = os.environ.get('SECRET_KEY')
# app.config['SQLALCHEMY_DATABASE_URI'] = f'postgresql+psycopg2://{DB_USER}:{DB_PASS}@localhost/pitchesapp'
# app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
| 62.5
| 111
| 0.72
| 31
| 250
| 5.548387
| 0.709677
| 0.156977
| 0.22093
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004464
| 0.104
| 250
| 4
| 112
| 62.5
| 0.763393
| 0.968
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
412baa593f442058aa6c5b3927fbc08e74fa47a0
| 95
|
py
|
Python
|
enif_app/apps.py
|
aejsi5/Enif
|
60c82cfe5272ee748d2c10dbd21b6c52cd674d5b
|
[
"MIT"
] | null | null | null |
enif_app/apps.py
|
aejsi5/Enif
|
60c82cfe5272ee748d2c10dbd21b6c52cd674d5b
|
[
"MIT"
] | null | null | null |
enif_app/apps.py
|
aejsi5/Enif
|
60c82cfe5272ee748d2c10dbd21b6c52cd674d5b
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class EnifAppConfig(AppConfig):
name = 'enif_app'
| 15.833333
| 34
| 0.715789
| 11
| 95
| 6.090909
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.210526
| 95
| 5
| 35
| 19
| 0.893333
| 0
| 0
| 0
| 0
| 0
| 0.088889
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
f5cded9f45f4dc3a14ba4c47145b431ea8a2a5fa
| 66
|
py
|
Python
|
src/main.py
|
strah19/Contacts
|
9eda372c77ce965208549c2a6dc07484311149aa
|
[
"MIT"
] | null | null | null |
src/main.py
|
strah19/Contacts
|
9eda372c77ce965208549c2a6dc07484311149aa
|
[
"MIT"
] | null | null | null |
src/main.py
|
strah19/Contacts
|
9eda372c77ce965208549c2a6dc07484311149aa
|
[
"MIT"
] | null | null | null |
import app
application = app.Application()
application.update()
| 11
| 31
| 0.772727
| 7
| 66
| 7.285714
| 0.571429
| 0.54902
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 66
| 5
| 32
| 13.2
| 0.87931
| 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
|
eb2a8cba6f899bbd6964f98a78ab026eb5d68a94
| 88
|
py
|
Python
|
ex6.py
|
arunkumarang/python
|
1960e285dfe2ef54d2e3ab37584bfef8b24ecca9
|
[
"Apache-2.0"
] | null | null | null |
ex6.py
|
arunkumarang/python
|
1960e285dfe2ef54d2e3ab37584bfef8b24ecca9
|
[
"Apache-2.0"
] | null | null | null |
ex6.py
|
arunkumarang/python
|
1960e285dfe2ef54d2e3ab37584bfef8b24ecca9
|
[
"Apache-2.0"
] | null | null | null |
month_year = input("Please enter the current month and the year: ")
print(month_year)
| 29.333333
| 68
| 0.75
| 14
| 88
| 4.571429
| 0.642857
| 0.28125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.159091
| 88
| 3
| 69
| 29.333333
| 0.864865
| 0
| 0
| 0
| 0
| 0
| 0.523256
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 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
| 0
| 0
| 0
| 1
|
0
| 4
|
de1794969b6610640fca85505c9f95f733361c13
| 182
|
py
|
Python
|
geral/curso_devmedia_api_django_rest/api_vagas/api/serializer.py
|
flaviogf/Cursos
|
2b120dbcd24a907121f58482fdcdfa01b164872c
|
[
"MIT"
] | 2
|
2021-02-20T23:50:07.000Z
|
2021-08-15T03:04:35.000Z
|
geral/curso_devmedia_api_django_rest/api_vagas/api/serializer.py
|
flaviogf/Cursos
|
2b120dbcd24a907121f58482fdcdfa01b164872c
|
[
"MIT"
] | 18
|
2019-08-07T02:33:00.000Z
|
2021-03-18T22:52:38.000Z
|
geral/curso_devmedia_api_django_rest/api_vagas/api/serializer.py
|
flaviogf/Cursos
|
2b120dbcd24a907121f58482fdcdfa01b164872c
|
[
"MIT"
] | 2
|
2020-09-28T13:00:09.000Z
|
2021-12-30T12:21:08.000Z
|
from rest_framework import serializers
from .models import Vaga
class VagaSerializer(serializers.ModelSerializer):
class Meta:
model = Vaga
fields = '__all__'
| 18.2
| 50
| 0.71978
| 19
| 182
| 6.631579
| 0.736842
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.225275
| 182
| 9
| 51
| 20.222222
| 0.893617
| 0
| 0
| 0
| 0
| 0
| 0.038462
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
de599521042a18c885a7bfc5328650f37e7281eb
| 4,113
|
py
|
Python
|
plots/2020-plots/prepare-workers-vs-data-data.py
|
etesami/MOE-FL
|
b2bc45334d4df2f47959fba1f7771486793c8010
|
[
"Apache-2.0"
] | null | null | null |
plots/2020-plots/prepare-workers-vs-data-data.py
|
etesami/MOE-FL
|
b2bc45334d4df2f47959fba1f7771486793c8010
|
[
"Apache-2.0"
] | null | null | null |
plots/2020-plots/prepare-workers-vs-data-data.py
|
etesami/MOE-FL
|
b2bc45334d4df2f47959fba1f7771486793c8010
|
[
"Apache-2.0"
] | 1
|
2021-06-08T22:20:46.000Z
|
2021-06-08T22:20:46.000Z
|
#!/usr/bin/python
DIR = "data_tmp/"
FILES = {}
FILES['20_avg'] = []
FILES['40_avg'] = []
FILES['50_avg'] = []
FILES['60_avg'] = []
FILES['80_avg'] = []
FILES['20_opt'] = []
FILES['40_opt'] = []
FILES['50_opt'] = []
FILES['60_opt'] = []
FILES['80_opt'] = []
FILES['20_avg'].append("04_attk2_avg20_20_test")
FILES['20_avg'].append("04_attk2_avg20_40_test")
FILES['20_avg'].append("04_attk2_avg20_60_test")
FILES['20_avg'].append("04_attk2_avg20_80_test")
FILES['40_avg'].append("04_attk2_avg40_20_test")
FILES['40_avg'].append("04_attk2_avg40_40_test")
FILES['40_avg'].append("04_attk2_avg40_60_test")
FILES['40_avg'].append("04_attk2_avg40_80_test")
FILES['50_avg'].append("04_attk2_avg50_20_test")
FILES['50_avg'].append("04_attk2_avg50_40_test")
FILES['50_avg'].append("04_attk2_avg50_60_test")
FILES['50_avg'].append("04_attk2_avg50_80_test")
FILES['60_avg'].append("04_attk2_avg60_20_test")
FILES['60_avg'].append("04_attk2_avg60_40_test")
FILES['60_avg'].append("04_attk2_avg60_60_test")
FILES['60_avg'].append("04_attk2_avg60_80_test")
FILES['80_avg'].append("04_attk2_avg80_20_test")
FILES['80_avg'].append("04_attk2_avg80_40_test")
FILES['80_avg'].append("04_attk2_avg80_60_test")
FILES['80_avg'].append("04_attk2_avg80_80_test")
FILES['20_opt'].append("05_attk2_opt20_20_test")
FILES['20_opt'].append("05_attk2_opt20_40_test")
FILES['20_opt'].append("05_attk2_opt20_60_test")
FILES['20_opt'].append("05_attk2_opt20_80_test")
FILES['40_opt'].append("05_attk2_opt40_20_test")
FILES['40_opt'].append("05_attk2_opt40_40_test")
FILES['40_opt'].append("05_attk2_opt40_60_test")
FILES['40_opt'].append("05_attk2_opt40_80_test")
FILES['50_opt'].append("05_attk2_opt50_20_test")
FILES['50_opt'].append("05_attk2_opt50_40_test")
FILES['50_opt'].append("05_attk2_opt50_60_test")
FILES['50_opt'].append("05_attk2_opt50_80_test")
FILES['60_opt'].append("05_attk2_opt60_20_test")
FILES['60_opt'].append("05_attk2_opt60_40_test")
FILES['60_opt'].append("05_attk2_opt60_60_test")
FILES['60_opt'].append("05_attk2_opt60_80_test")
FILES['80_opt'].append("05_attk2_opt80_20_test")
FILES['80_opt'].append("05_attk2_opt80_40_test")
FILES['80_opt'].append("05_attk2_opt80_60_test")
FILES['80_opt'].append("05_attk2_opt80_80_test")
# AVG
nums = []
for id in ['20_avg', '40_avg', '50_avg', '60_avg', '80_avg']:
for ff in FILES[id]:
with open(DIR + ff, 'r') as f:
print("Working on " + ff)
lines = f.readlines()
nums.append(lines[4].split()[2])
f.close()
with open(DIR + "09-workers-data-avg.txt", 'w') as f:
f.write('- - "20% Data Alteration" - "40% Data Alteration" - "50% Data Alteration" - "60% Data Alteration" - "80% Data Alteration "\n')
f.write("20 20% " + nums[0] + " " + nums[1] + " " + nums[2] + " " + nums[3] + "\n")
f.write("40 40% " + nums[4] + " " + nums[5] + " " + nums[6] + " " + nums[7] + "\n")
f.write("50 50% " + nums[8] + " " + nums[9] + " " + nums[10] + " " + nums[11] + "\n")
f.write("60 60% " + nums[12] + " " + nums[13] + " " + nums[14] + " " + nums[15] + "\n")
f.write("80 80% " + nums[16] + " " + nums[17] + " " + nums[17] + " " + nums[19] + "\n")
f.close()
# OPT
nums = []
for id in ['20_opt', '40_opt', '50_opt', '60_opt', '80_opt']:
for ff in FILES[id]:
with open(DIR + ff, 'r') as f:
print("Working on " + ff)
lines = f.readlines()
nums.append(lines[4].split()[2])
f.close()
with open(DIR + "09-workers-data-opt.txt", 'w') as f:
f.write('- - "20% Data Alteration" - "40% Data Alteration" - "50% Data Alteration" - "60% Data Alteration" - "80% Data Alteration "\n')
f.write("20 20% " + nums[0] + " " + nums[1] + " " + nums[2] + " " + nums[3] + "\n")
f.write("40 40% " + nums[4] + " " + nums[5] + " " + nums[6] + " " + nums[7] + "\n")
f.write("50 50% " + nums[8] + " " + nums[9] + " " + nums[10] + " " + nums[11] + "\n")
f.write("60 60% " + nums[12] + " " + nums[13] + " " + nums[14] + " " + nums[15] + "\n")
f.write("80 80% " + nums[16] + " " + nums[17] + " " + nums[17] + " " + nums[19] + "\n")
f.close()
| 38.801887
| 139
| 0.619985
| 673
| 4,113
| 3.460624
| 0.106984
| 0.150708
| 0.094461
| 0.137398
| 0.8845
| 0.873336
| 0.873336
| 0.861314
| 0.325462
| 0.325462
| 0
| 0.151116
| 0.150498
| 4,113
| 106
| 140
| 38.801887
| 0.515455
| 0.005835
| 0
| 0.333333
| 0
| 0.02381
| 0.413262
| 0.226572
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.02381
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 0
| 0
| 0
| 0
|
0
| 4
|
de66f3469e1aaa32c62e49257ca4cc56b01c0357
| 68
|
py
|
Python
|
recognizer/__main__.py
|
janaSunrise/OpenCV-Image-Color-Recognizer
|
51d94f080a4893ee2ff2e04e6ddbc22e201eb47f
|
[
"MIT"
] | 3
|
2021-05-08T05:48:49.000Z
|
2021-05-08T11:24:04.000Z
|
recognizer/__main__.py
|
janaSunrise/OpenCV-Image-Color-Recognizer
|
51d94f080a4893ee2ff2e04e6ddbc22e201eb47f
|
[
"MIT"
] | null | null | null |
recognizer/__main__.py
|
janaSunrise/OpenCV-Image-Color-Recognizer
|
51d94f080a4893ee2ff2e04e6ddbc22e201eb47f
|
[
"MIT"
] | null | null | null |
from . import recognize
if __name__ == "__main__":
recognize()
| 13.6
| 26
| 0.676471
| 7
| 68
| 5.428571
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205882
| 68
| 4
| 27
| 17
| 0.703704
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 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
|
de6991aea501859c14a432e679accc5a1653c1eb
| 186
|
py
|
Python
|
src/dataprocess/transform/__init__.py
|
jiangtaoo2333/StaticGestureRecognition
|
9d554b137f217f3bcb046b2c6978b9487685de2a
|
[
"MIT"
] | null | null | null |
src/dataprocess/transform/__init__.py
|
jiangtaoo2333/StaticGestureRecognition
|
9d554b137f217f3bcb046b2c6978b9487685de2a
|
[
"MIT"
] | null | null | null |
src/dataprocess/transform/__init__.py
|
jiangtaoo2333/StaticGestureRecognition
|
9d554b137f217f3bcb046b2c6978b9487685de2a
|
[
"MIT"
] | null | null | null |
#
# Lightnet data transforms
# Copyright EAVISE
#
from .dataAug_box import *
from .dataAug_pts import *
from .mixup import mixup_data,mixup_criterion
from .cutmix import cutmix_data
| 20.666667
| 45
| 0.784946
| 25
| 186
| 5.64
| 0.52
| 0.156028
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.155914
| 186
| 9
| 46
| 20.666667
| 0.898089
| 0.231183
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
|
de6e03420bdc590b582884cd97eff82b497ee4df
| 130
|
py
|
Python
|
netspot/apps.py
|
MaxIV-KitsControls/netspot
|
42f505d004bcadcfb32b6ca0511572d38641c23a
|
[
"MIT"
] | null | null | null |
netspot/apps.py
|
MaxIV-KitsControls/netspot
|
42f505d004bcadcfb32b6ca0511572d38641c23a
|
[
"MIT"
] | null | null | null |
netspot/apps.py
|
MaxIV-KitsControls/netspot
|
42f505d004bcadcfb32b6ca0511572d38641c23a
|
[
"MIT"
] | null | null | null |
from __future__ import unicode_literals
from django.apps import AppConfig
class NetspotConfig(AppConfig):
name = 'netspot'
| 16.25
| 39
| 0.792308
| 15
| 130
| 6.533333
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 130
| 7
| 40
| 18.571429
| 0.890909
| 0
| 0
| 0
| 0
| 0
| 0.053846
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 1
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
debc6848ff46f86e7849ab616b9dfafde5e1ce35
| 848
|
py
|
Python
|
blog/views.py
|
OleMissSquad/MyBlog
|
72902a00d134e662834ab76f75f240a0616265cf
|
[
"MIT"
] | null | null | null |
blog/views.py
|
OleMissSquad/MyBlog
|
72902a00d134e662834ab76f75f240a0616265cf
|
[
"MIT"
] | 11
|
2017-09-17T16:53:23.000Z
|
2017-10-06T13:47:14.000Z
|
blog/views.py
|
khoa102/MyBlog
|
72902a00d134e662834ab76f75f240a0616265cf
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render, get_object_or_404
from django.http import HttpResponse
from .models import Post
# Create your views here.
def index(request):
latest_post_list = Post.objects.order_by("-date_published")[:5]
context = {'latest_post_list': latest_post_list}
return render(request, 'blog/index.html', context)
def post(request):
return None
def detail(request, post_id):
post = get_object_or_404(Post, pk=post_id)
context = {'post': post}
return render(request, 'blog/detail.html', context)
def archive(request):
return HttpResponse("This is the archive page")
def dashboard(request):
return HttpResponse("This is the dashboard page")
def signup(request):
return HttpResponse("This is the singup page")
def signin(request):
return HttpResponse("This is the signin page")
| 21.74359
| 67
| 0.728774
| 118
| 848
| 5.101695
| 0.381356
| 0.107973
| 0.166113
| 0.192691
| 0.225914
| 0.225914
| 0
| 0
| 0
| 0
| 0
| 0.009915
| 0.167453
| 848
| 38
| 68
| 22.315789
| 0.842776
| 0.027123
| 0
| 0
| 0
| 0
| 0.19732
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.142857
| 0.238095
| 0.809524
| 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
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
def08788d9bb9f800fdb0ca4974608734125b149
| 4,218
|
py
|
Python
|
tests/test_utils.py
|
certego/django-group-role
|
264b8e80578bed53b119cc1d141a1fcb8f21efcc
|
[
"Apache-2.0"
] | 3
|
2021-11-29T08:23:03.000Z
|
2021-12-01T20:29:20.000Z
|
tests/test_utils.py
|
certego/django-group-role
|
264b8e80578bed53b119cc1d141a1fcb8f21efcc
|
[
"Apache-2.0"
] | null | null | null |
tests/test_utils.py
|
certego/django-group-role
|
264b8e80578bed53b119cc1d141a1fcb8f21efcc
|
[
"Apache-2.0"
] | null | null | null |
from django.test import SimpleTestCase, override_settings
from django_group_role.utils import map_permissions
class UtilsSimpleTestCase(SimpleTestCase):
def test_map_permissions_list_wrong_code(self):
with self.assertRaisesMessage(
ValueError,
"Permissions, should be defined in the format: 'app_label.codename' (is view_user)",
):
map_permissions(["view_user"])
def test_map_permissions_list(self):
perm_map = map_permissions(
["auth.view_user", "auth.view_group", "myapp.view_mymodel"]
)
self.assertEqual(
perm_map,
{
"auth": {"_codenames": {"view_user", "view_group"}},
"myapp": {"_codenames": {"view_mymodel"}},
},
)
def test_map_permissions_dict(self):
perm_map = map_permissions(
{
"auth.user": ["view_user", "change_user"],
"auth": {"group": ["view_group"]},
"myapp.mymodel": ["view_mymodel", "change_mymodel"],
}
)
self.assertEqual(
perm_map,
{
"auth": {"user": {"view_user", "change_user"}, "group": {"view_group"}},
"myapp": {
"mymodel": {"view_mymodel", "change_mymodel"},
},
},
)
def test_map_permissions_dict_plus_list(self):
perm_map = map_permissions(
{
"auth.user": ["view_user", "change_user"],
"auth": {"group": ["view_group"]},
"myapp.mymodel": ["view_mymodel", "change_mymodel"],
},
["auth.view_user", "myapp.delete_mymodel", "otherapp.view_element"],
)
self.assertEqual(
perm_map,
{
"auth": {
"_codenames": {"view_user"},
"user": {"view_user", "change_user"},
"group": {"view_group"},
},
"myapp": {
"_codenames": {"delete_mymodel"},
"mymodel": {"view_mymodel", "change_mymodel"},
},
"otherapp": {
"_codenames": {"view_element"},
},
},
)
def test_map_permissions_list_plus_dict(self):
perm_map = map_permissions(
["auth.view_user", "myapp.delete_mymodel", "otherapp.view_element"],
{
"auth.user": ["view_user", "change_user"],
"auth": {"group": ["view_group"]},
"myapp.mymodel": ["view_mymodel", "change_mymodel"],
},
)
self.assertEqual(
perm_map,
{
"auth": {
"_codenames": {"view_user"},
"user": {"view_user", "change_user"},
"group": {"view_group"},
},
"myapp": {
"_codenames": {"delete_mymodel"},
"mymodel": {"view_mymodel", "change_mymodel"},
},
"otherapp": {
"_codenames": {"view_element"},
},
},
)
def test_map_permissions_dict_plus_dict(self):
perm_map = map_permissions(
{
"auth": {"user": ["view_user"]},
"myapp": {"mymodel": ["delete_mymodel"]},
"otherapp.element": ["view_element"],
},
{
"auth.user": ["view_user", "change_user"],
"auth": {"group": ["view_group"]},
"myapp.mymodel": ["view_mymodel", "change_mymodel"],
},
)
self.assertEqual(
perm_map,
{
"auth": {
"user": {"view_user", "change_user"},
"group": {"view_group"},
},
"myapp": {
"mymodel": {"view_mymodel", "change_mymodel", "delete_mymodel"},
},
"otherapp": {
"element": {"view_element"},
},
},
)
| 33.744
| 96
| 0.439545
| 322
| 4,218
| 5.385093
| 0.142857
| 0.078431
| 0.080738
| 0.083045
| 0.814879
| 0.786044
| 0.750865
| 0.703576
| 0.6609
| 0.581892
| 0
| 0
| 0.416785
| 4,218
| 124
| 97
| 34.016129
| 0.704878
| 0
| 0
| 0.461538
| 0
| 0
| 0.293267
| 0.009957
| 0
| 0
| 0
| 0
| 0.051282
| 1
| 0.051282
| false
| 0
| 0.017094
| 0
| 0.076923
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 0
| 0
| 0
| 0
|
0
| 4
|
defd2af25ef58312169d6b60464e4d8fe2a4140c
| 97
|
py
|
Python
|
list/migrations/__init__.py
|
juli212/kitchenin
|
aefd0e493442c36b2044753fb19e19ae789eae67
|
[
"MIT"
] | null | null | null |
list/migrations/__init__.py
|
juli212/kitchenin
|
aefd0e493442c36b2044753fb19e19ae789eae67
|
[
"MIT"
] | null | null | null |
list/migrations/__init__.py
|
juli212/kitchenin
|
aefd0e493442c36b2044753fb19e19ae789eae67
|
[
"MIT"
] | null | null | null |
from django.contrib.auth.models import User, models
User._meta.get_field('email')._unique = True
| 32.333333
| 51
| 0.793814
| 15
| 97
| 4.933333
| 0.866667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.082474
| 97
| 3
| 52
| 32.333333
| 0.831461
| 0
| 0
| 0
| 0
| 0
| 0.05102
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 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
|
a0cb2903adb55c3f8734eb3936f02862dc8e6457
| 101
|
py
|
Python
|
snipsmanager/utils/object_from_dict.py
|
beevesuw/snipsmanager
|
9eb45c076db4ed90e1a5a7cdeadfda253affaadb
|
[
"MIT"
] | 1
|
2019-02-14T08:13:04.000Z
|
2019-02-14T08:13:04.000Z
|
snipsmanager/utils/object_from_dict.py
|
beevesuw/snipsmanager
|
9eb45c076db4ed90e1a5a7cdeadfda253affaadb
|
[
"MIT"
] | null | null | null |
snipsmanager/utils/object_from_dict.py
|
beevesuw/snipsmanager
|
9eb45c076db4ed90e1a5a7cdeadfda253affaadb
|
[
"MIT"
] | 1
|
2019-02-14T08:13:18.000Z
|
2019-02-14T08:13:18.000Z
|
class ObjectFromDict(object):
def __init__(self, dictionary):
self.__dict__ = dictionary
| 25.25
| 35
| 0.712871
| 10
| 101
| 6.4
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.19802
| 101
| 3
| 36
| 33.666667
| 0.790123
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
a0e3b8e3fbf4c0ebeeb0ac9f242b2185ba872eae
| 839
|
py
|
Python
|
notebook/numpy_broadcasting_error.py
|
vhn0912/python-snippets
|
80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038
|
[
"MIT"
] | 174
|
2018-05-30T21:14:50.000Z
|
2022-03-25T07:59:37.000Z
|
notebook/numpy_broadcasting_error.py
|
vhn0912/python-snippets
|
80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038
|
[
"MIT"
] | 5
|
2019-08-10T03:22:02.000Z
|
2021-07-12T20:31:17.000Z
|
notebook/numpy_broadcasting_error.py
|
vhn0912/python-snippets
|
80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038
|
[
"MIT"
] | 53
|
2018-04-27T05:26:35.000Z
|
2022-03-25T07:59:37.000Z
|
import numpy as np
a = np.zeros((4, 3), dtype=np.int)
print(a)
# [[0 0 0]
# [0 0 0]
# [0 0 0]
# [0 0 0]]
print(a.shape)
# (4, 3)
b = np.arange(6).reshape(2, 3)
print(b)
# [[0 1 2]
# [3 4 5]]
print(b.shape)
# (2, 3)
# print(a + b)
# ValueError: operands could not be broadcast together with shapes (4,3) (2,3)
a = np.zeros((2, 3, 4), dtype=np.int)
print(a)
# [[[0 0 0 0]
# [0 0 0 0]
# [0 0 0 0]]
#
# [[0 0 0 0]
# [0 0 0 0]
# [0 0 0 0]]]
print(a.shape)
# (2, 3, 4)
b = np.arange(3)
print(b)
# [0 1 2]
print(b.shape)
# (3,)
# print(a + b)
# ValueError: operands could not be broadcast together with shapes (2,3,4) (3,)
b_3_1 = b.reshape(3, 1)
print(b_3_1)
# [[0]
# [1]
# [2]]
print(b_3_1.shape)
# (3, 1)
print(a + b_3_1)
# [[[0 0 0 0]
# [1 1 1 1]
# [2 2 2 2]]
#
# [[0 0 0 0]
# [1 1 1 1]
# [2 2 2 2]]]
| 13.109375
| 80
| 0.498212
| 187
| 839
| 2.192513
| 0.15508
| 0.195122
| 0.263415
| 0.312195
| 0.670732
| 0.634146
| 0.585366
| 0.585366
| 0.585366
| 0.585366
| 0
| 0.176948
| 0.265793
| 839
| 63
| 81
| 13.31746
| 0.488636
| 0.536353
| 0
| 0.470588
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.058824
| 0
| 0.058824
| 0.647059
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
9d2b134528efd1519790f90bdca15b1f067664aa
| 46
|
py
|
Python
|
beginner/chapter_1/exam_1_2.py
|
Bokji24Dev/CodeStudy
|
4c0fc852e6f472d082e9836c59ad22d229f74d87
|
[
"MIT"
] | null | null | null |
beginner/chapter_1/exam_1_2.py
|
Bokji24Dev/CodeStudy
|
4c0fc852e6f472d082e9836c59ad22d229f74d87
|
[
"MIT"
] | null | null | null |
beginner/chapter_1/exam_1_2.py
|
Bokji24Dev/CodeStudy
|
4c0fc852e6f472d082e9836c59ad22d229f74d87
|
[
"MIT"
] | null | null | null |
# -*- coding:utf-8 -*-
Print("Hello World!")
| 11.5
| 22
| 0.543478
| 6
| 46
| 4.166667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025641
| 0.152174
| 46
| 3
| 23
| 15.333333
| 0.615385
| 0.434783
| 0
| 0
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
9d2bb262434b62b3e8edffc6f00e37354f3d80c0
| 114
|
py
|
Python
|
app/articles/__init__.py
|
AlexRAV/flask-blog
|
df8036e01794914ca0e88856ed93f8a91cc1d47a
|
[
"BSD-3-Clause"
] | null | null | null |
app/articles/__init__.py
|
AlexRAV/flask-blog
|
df8036e01794914ca0e88856ed93f8a91cc1d47a
|
[
"BSD-3-Clause"
] | null | null | null |
app/articles/__init__.py
|
AlexRAV/flask-blog
|
df8036e01794914ca0e88856ed93f8a91cc1d47a
|
[
"BSD-3-Clause"
] | null | null | null |
# -*- coding: utf-8 -*-
"""The articles module, including operations with articles."""
from . import views # noqa
| 38
| 62
| 0.675439
| 14
| 114
| 5.5
| 0.928571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010417
| 0.157895
| 114
| 3
| 63
| 38
| 0.791667
| 0.736842
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
|
9d364cddb663a76cf46c67bd36066ef710af4952
| 777
|
py
|
Python
|
fdk_client/platform/models/EventSubscription.py
|
kavish-d/fdk-client-python
|
a1023eb530473322cb52e095fc4ceb226c1e6037
|
[
"MIT"
] | null | null | null |
fdk_client/platform/models/EventSubscription.py
|
kavish-d/fdk-client-python
|
a1023eb530473322cb52e095fc4ceb226c1e6037
|
[
"MIT"
] | null | null | null |
fdk_client/platform/models/EventSubscription.py
|
kavish-d/fdk-client-python
|
a1023eb530473322cb52e095fc4ceb226c1e6037
|
[
"MIT"
] | null | null | null |
"""Platform Models."""
from marshmallow import fields, Schema
from marshmallow.validate import OneOf
from ..enums import *
from ..models.BaseSchema import BaseSchema
from .EventSubscriptionTemplate import EventSubscriptionTemplate
class EventSubscription(BaseSchema):
# Communication swagger.json
template = fields.Nested(EventSubscriptionTemplate, required=False)
is_default = fields.Boolean(required=False)
_id = fields.Str(required=False)
application = fields.Str(required=False)
event = fields.Str(required=False)
slug = fields.Str(required=False)
created_at = fields.Str(required=False)
updated_at = fields.Str(required=False)
__v = fields.Int(required=False)
| 15.54
| 71
| 0.697555
| 79
| 777
| 6.78481
| 0.43038
| 0.218284
| 0.190299
| 0.246269
| 0.089552
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.216216
| 777
| 49
| 72
| 15.857143
| 0.880131
| 0.056628
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 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
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
c236a82d9fbe06bc0d88fbb431bd687b87e99c5b
| 50
|
py
|
Python
|
run.py
|
thautwarm/voicecontrol
|
dc5565d114fe80f0f06a0e7c541ee447fb7712f3
|
[
"MIT"
] | 2
|
2021-06-05T08:27:44.000Z
|
2021-06-05T13:46:27.000Z
|
run.py
|
thautwarm/voicecontrol
|
dc5565d114fe80f0f06a0e7c541ee447fb7712f3
|
[
"MIT"
] | null | null | null |
run.py
|
thautwarm/voicecontrol
|
dc5565d114fe80f0f06a0e7c541ee447fb7712f3
|
[
"MIT"
] | null | null | null |
from voicecontrol.pinyin_typing import main
main()
| 25
| 43
| 0.86
| 7
| 50
| 6
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08
| 50
| 2
| 44
| 25
| 0.913043
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 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
|
dfa5b5298217bec2ae65c111e2e3c14f4fc907c4
| 91
|
py
|
Python
|
python/learn/base/module/l1/pack/p1.py
|
qrsforever/workspace
|
53c7ce7ca7da62c9fbb3d991ae9e4e34d07ece5f
|
[
"MIT"
] | 2
|
2017-06-07T03:20:42.000Z
|
2020-01-07T09:14:26.000Z
|
python/learn/base/module/l1/pack/p1.py
|
qrsforever/workspace
|
53c7ce7ca7da62c9fbb3d991ae9e4e34d07ece5f
|
[
"MIT"
] | null | null | null |
python/learn/base/module/l1/pack/p1.py
|
qrsforever/workspace
|
53c7ce7ca7da62c9fbb3d991ae9e4e34d07ece5f
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python2.7
print "run here pack/p1"
def p1_fun(): print "function pack/p1_fun"
| 15.166667
| 42
| 0.703297
| 17
| 91
| 3.647059
| 0.705882
| 0.193548
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.063291
| 0.131868
| 91
| 5
| 43
| 18.2
| 0.721519
| 0.208791
| 0
| 0
| 0
| 0
| 0.507042
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
dfa74e9db5d8c1d6b3ac0016f3c11dfb850e59bd
| 11,936
|
py
|
Python
|
scripts/venv/lib/python2.7/site-packages/cogent/app/gctmpca.py
|
sauloal/cnidaria
|
fe6f8c8dfed86d39c80f2804a753c05bb2e485b4
|
[
"MIT"
] | 3
|
2015-11-20T08:44:42.000Z
|
2016-12-14T01:40:03.000Z
|
scripts/venv/lib/python2.7/site-packages/cogent/app/gctmpca.py
|
sauloal/cnidaria
|
fe6f8c8dfed86d39c80f2804a753c05bb2e485b4
|
[
"MIT"
] | 1
|
2017-09-04T14:04:32.000Z
|
2020-05-26T19:04:00.000Z
|
scripts/venv/lib/python2.7/site-packages/cogent/app/gctmpca.py
|
sauloal/cnidaria
|
fe6f8c8dfed86d39c80f2804a753c05bb2e485b4
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# Author: Greg Caporaso (gregcaporaso@gmail.com)
# gctmpca.py
"""Application controller for the Generalized Continuous-Time Markov
Process Coevolutionary Algorithm (GCTMPCA). GCTMPCA is presented in:
Detecting coevolution in and among protein domains.
Yeang CH, Haussler D., PLoS Comput Biol. 2007 Nov;3(11):e211.
Detecting the coevolution of biosequences--an example
of RNA interaction prediction. Yeang CH, Darot JF, Noller HF,
Haussler D. Mol Biol Evol. 2007 Sep;24(9):2119-31.
This code requires the GCTMPCA package to be installed. As of Nov. 2008,
that software is available at:
http://www.sns.ias.edu/~chyeang/coevolution_download.zip
Note that the authors did not name their algorithm or software when they
published it. GCTMPCA was suggested as a name by the first author via e-mail.
"""
from __future__ import division
from cogent.app.util import CommandLineApplication, ResultPath,\
ApplicationError
from cogent.app.parameters import FilePath
from cogent.evolve.models import DSO78_freqs, DSO78_matrix
__author__ = "Greg Caporaso"
__copyright__ = "Copyright 2007-2012, The Cogent Project"
__credits__ = ["Greg Caporaso"]
__license__ = "GPL"
__version__ = "1.5.3"
__maintainer__ = "Greg Caporaso"
__email__ = "gregcaporaso@gmail.com"
__status__ = "Beta"
# Are these values in PyCogent somewhere?
gctmpca_base_order = 'ACGU'
default_gctmpca_rna_priors = {'A':0.2528,'C':0.2372,'G':0.3099,'U':0.2001}
default_gctmpca_rna_sub_matrix = """-1.4150\t0.2372\t0.9777\t0.2001
0.2528\t-1.1940\t0.3099\t0.6313
0.7976\t0.2372\t-1.2349\t0.2001
0.2528\t0.7484\t0.3099\t-1.3111"""
gctmpca_aa_order = 'ARNDCQEGHILKMFPSTWYV'
# By default, the Gctmpca method used the Dayhoff 78 frequencies and rate matrix
default_gctmpca_aa_priors = DSO78_freqs
default_gctmpca_aa_sub_matrix = """-133.941451\t1.104408\t3.962336\t5.624640\t1.205064\t3.404695\t9.806940\t21.266880\t0.773214\t2.397590\t3.499637\t2.092532\t1.062216\t0.715896\t12.670000\t28.456993\t21.719082\t0.000000\t0.717984\t13.461344
2.352429\t-86.970372\t1.293824\t0.000000\t0.769902\t9.410730\t0.049530\t0.797508\t8.068320\t2.360704\t1.280355\t37.343648\t1.327770\t0.556808\t5.220040\t10.714858\t1.522092\t2.109294\t0.239328\t1.553232
8.538446\t1.308928\t-179.776579\t42.419160\t0.000000\t3.940265\t7.330440\t12.317068\t17.985630\t2.840222\t2.902138\t25.593276\t0.014753\t0.556808\t2.128560\t34.440615\t13.406118\t0.241362\t2.842020\t0.970770
10.455240\t0.000000\t36.590960\t-142.144945\t0.000000\t5.126170\t57.108090\t11.076500\t2.891148\t0.885264\t0.000000\t5.714222\t0.000000\t0.000000\t0.658840\t6.609815\t3.863772\t0.000000\t0.000000\t1.164924
3.136572\t0.940792\t0.000000\t0.000000\t-26.760991\t0.000000\t0.000000\t0.974732\t0.941304\t1.622984\t0.000000\t0.000000\t0.000000\t0.000000\t0.962920\t11.201897\t0.936672\t0.000000\t2.871936\t3.171182
7.754303\t10.062384\t4.164496\t6.280848\t0.000000\t-124.487960\t35.463480\t2.481136\t20.372508\t0.663948\t6.231061\t12.313746\t1.681842\t0.000000\t7.754040\t3.896312\t3.102726\t0.000000\t0.000000\t2.265130
17.251146\t0.040904\t5.983936\t54.043416\t0.000000\t27.390580\t-136.769106\t7.177572\t1.445574\t2.250046\t0.938927\t6.680006\t0.442590\t0.000000\t2.584680\t5.496583\t1.990428\t0.000000\t0.658152\t2.394566
20.910480\t0.368136\t5.620048\t5.859000\t0.368214\t1.071140\t4.011930\t-65.418192\t0.336180\t0.000000\t0.597499\t2.173014\t0.250801\t0.596580\t1.723120\t16.281018\t1.756260\t0.000000\t0.000000\t3.494772
2.003921\t9.816960\t21.631120\t4.030992\t0.937272\t23.182530\t2.129790\t0.886120\t-88.051504\t0.258202\t3.755708\t2.092532\t0.000000\t1.909056\t4.763920\t2.435195\t1.287924\t0.283338\t3.799332\t2.847592
5.663255\t2.617856\t3.113264\t1.124928\t1.472856\t0.688590\t3.021330\t0.000000\t0.235326\t-128.487912\t21.936749\t3.702172\t4.957008\t7.795312\t0.608160\t1.669848\t11.240064\t0.000000\t1.106892\t57.534302
3.572207\t0.613560\t1.374688\t0.000000\t0.000000\t2.792615\t0.544830\t0.620284\t1.479192\t9.479702\t-53.327266\t1.448676\t7.774831\t6.244204\t1.621760\t1.182809\t1.931886\t0.482724\t0.837648\t11.325650
2.265302\t18.979456\t12.857376\t3.327912\t0.000000\t5.853015\t4.110990\t2.392524\t0.874068\t1.696756\t1.536426\t-74.828436\t3.584979\t0.000000\t1.672440\t6.679392\t7.961712\t0.000000\t0.388908\t0.647180
6.273144\t3.681360\t0.040432\t0.000000\t0.000000\t4.361070\t1.485900\t1.506404\t0.000000\t12.393696\t44.983139\t19.557126\t-125.902241\t3.659024\t0.861560\t4.313774\t6.088368\t0.000000\t0.000000\t16.697244
1.568286\t0.572656\t0.566048\t0.000000\t0.000000\t0.000000\t0.000000\t1.329180\t1.613664\t7.229656\t13.401049\t0.000000\t1.357276\t-54.612411\t0.557480\t3.200542\t0.761046\t0.797544\t20.881368\t0.776616
21.781750\t4.213112\t1.698144\t0.609336\t0.636006\t5.853015\t2.526030\t3.012808\t3.160092\t0.442632\t2.731424\t2.655906\t0.250801\t0.437492\t-74.727653\t17.046365\t4.566276\t0.000000\t0.000000\t3.106464
35.634943\t6.299216\t20.013840\t4.452840\t5.389314\t2.142280\t3.912870\t20.735208\t1.176630\t0.885264\t1.451069\t7.726272\t0.914686\t1.829512\t12.416600\t-160.924378\t32.198100\t0.787050\t1.017144\t1.941540
32.324117\t1.063504\t9.258928\t3.093552\t0.535584\t2.027515\t1.684020\t2.658360\t0.739596\t7.082112\t2.816781\t10.945552\t1.534312\t0.517036\t3.953040\t38.267350\t-129.918557\t0.000000\t1.256472\t10.160726
0.000000\t8.221704\t0.929936\t0.000000\t0.000000\t0.000000\t0.000000\t0.000000\t0.907686\t0.000000\t3.926422\t0.000000\t0.000000\t3.022672\t0.000000\t5.218275\t0.000000\t-24.051571\t1.824876\t0.000000
2.091048\t0.327232\t3.841040\t0.000000\t3.213504\t0.000000\t1.089660\t0.000000\t4.269486\t1.364782\t2.389996\t1.046266\t0.000000\t27.760856\t0.000000\t2.365618\t2.458764\t0.640134\t-54.670490\t1.812104
18.122416\t0.981696\t0.606480\t0.843696\t1.640226\t1.338925\t1.832610\t4.785048\t1.479192\t32.791654\t14.937475\t0.804820\t3.806274\t0.477264\t2.432640\t2.087310\t9.191094\t0.000000\t0.837648\t-98.996468"""
class Gctmpca(CommandLineApplication):
""" App controller for the GCTMPCA algorithm for detecting sequence coevolution
The Generalized Continuous-Time Markov Process Coevolutionary
Algorithm (GCTMPCA) is presented in:
Detecting coevolution in and among protein domains.
Yeang CH, Haussler D., PLoS Comput Biol. 2007 Nov;3(11):e211.
Detecting the coevolution of biosequences--an example
of RNA interaction prediction. Yeang CH, Darot JF, Noller HF,
Haussler D. Mol Biol Evol. 2007 Sep;24(9):2119-31.
This code requires the GCTMPCA package to be installed. As of 11/08,
that software is available at:
http://www.sns.ias.edu/~chyeang/coevolution_download.zip
"""
_command = 'calculate_likelihood'
_input_handler = '_gctmpca_cl_input'
_data = {'mol_type':None,'comparison_type':0,'seqs1':None,\
'seqs2':'-','tree1':None,'tree2':'-',\
'seq_names':None,'species_tree':None,\
'seq_to_species1':None,'seq_to_species2':'-',\
'char_priors':None,'sub_matrix':None,'epsilon':0.7,\
'max_gap_threshold':1.0,'max_seq_distance':1.0,\
'covariation_threshold':0.0,'likelihood_threshold':0.0,\
'output_path':None,'single_pair_only':0,'family_reps':'-',\
'pos1':'','pos2':''}
_parameter_order = ['mol_type','comparison_type','seqs1','seqs2',\
'tree1','tree2','seq_names','species_tree',\
'seq_to_species1','seq_to_species2','char_priors',\
'sub_matrix','epsilon','max_gap_threshold','max_seq_distance',\
'covariation_threshold','likelihood_threshold','output_path',\
'single_pair_only','family_reps','pos1','pos2']
_potential_paths = ['seqs1','tree1','seq_names',\
'species_tree','seq_to_species1']
_mol_type_lookup = {'rna':0,'0':0,'protein':1,'1':1}
_default_priors = {0:default_gctmpca_rna_priors, 1:default_gctmpca_aa_priors}
_default_sub_matrix = {0:default_gctmpca_rna_sub_matrix, 1:default_gctmpca_aa_sub_matrix}
_char_order = {0:gctmpca_base_order,1:gctmpca_aa_order}
_required_parameters = {}.fromkeys(['mol_type','seqs1','tree1',\
'seq_names','species_tree','seq_to_species1'])
def _set_command_line_parameters(self,data):
""" Get the right setting for each command line parameter """
# This function could be cleaned up.
# for each command line parameter, set it to the value passed in or
# the default value.
for p in self._parameter_order:
if p not in data:
if p in self._required_parameters:
raise ApplicationError,\
"Required parameter %s missing." % p
else: data[p] = self._data[p]
# Write necessary files to disk -- need to modify this so paths
# to existing files can be passed in.
if p in self._potential_paths:
try:
data[p] = self._input_as_lines(data[p])
except TypeError:
pass
if data['single_pair_only'] == 1 and \
not (data['pos1'] and data['pos2']):
raise ApplicationError,\
"Must specify pos1 and pos2 if single_pair_only == 1."
# Make sure the MolType is in the correct format (i.e., 1 or 0)
data['mol_type'] = mol_type = \
self._mol_type_lookup[str(data['mol_type']).lower()]
char_order = self._char_order[mol_type]
# If we didn't get several values as parameters, set the defaults.
# These are done outside of the above loop b/c they require special
# handling.
if not data['char_priors']:
data['char_priors'] = self._default_priors[mol_type]
data['char_priors'] = \
self._input_as_lines(\
self._input_as_gctmpca_char_priors(\
data['char_priors'],char_order))
if not data['sub_matrix']:
data['sub_matrix'] = \
self._input_as_multiline_string(\
self._default_sub_matrix[mol_type])
else:
data['sub_matrix'] = \
self._input_as_lines(\
self._input_as_gctmpca_rate_matrix(\
data['sub_matrix'],char_order))
if not data['output_path']:
data['output_path'] = \
self._input_as_path(self.getTmpFilename())
return data
def _gctmpca_cl_input(self,data):
""" Write the list of 22 command line parameters to a string
"""
# Get the right setting for each parameter
data = self._set_command_line_parameters(data)
# Explicitly disallow intermolecular experiments (I do this here to
# make sure I'm looking at the final version of data)
if data['comparison_type'] == 1:
raise NotImplementedError,\
"Intermolecular experiments currently supported only via coevolve_alignments."
# Create the command line parameter string and return it
return ' '.join([str(data[p]) for p in self._parameter_order]).strip()
def _input_as_gctmpca_char_priors(self,priors,char_order):
"""convert dict of priors to string and write it to tmp file
"""
# priors t be followed by a newline
return ['\t'.join([str(priors[c]) for c in char_order]),'']
def _input_as_gctmpca_rate_matrix(self,matrix,char_order):
"""convert 2D dict rate matrix to string and write it to tmp file
"""
matrix_rows = []
for c in char_order:
matrix_rows.append('\t'.join([str(matrix[c][col_c]) \
for col_c in char_order]))
return matrix_rows
def _get_result_paths(self,data):
"""A single file is written, w/ name specified in command line input
"""
return {'output':ResultPath(Path=data['output_path'],IsWritten=True)}
| 59.089109
| 241
| 0.70258
| 1,864
| 11,936
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0
| 4
|
dfc85cb397c45ef64a1d11124832fefedde08680
| 12,235
|
py
|
Python
|
rivendell/splunk/defaultNav.py
|
ezaspy/elrond
|
3e358f20112be403b895d873a7e3892ce4181d8b
|
[
"MIT"
] | 1
|
2021-03-29T08:05:31.000Z
|
2021-03-29T08:05:31.000Z
|
rivendell/splunk/defaultNav.py
|
ezaspy/elrond
|
3e358f20112be403b895d873a7e3892ce4181d8b
|
[
"MIT"
] | 17
|
2020-11-24T11:00:38.000Z
|
2021-05-18T18:20:21.000Z
|
rivendell/splunk/defaultNav.py
|
ezaspy/elrond
|
3e358f20112be403b895d873a7e3892ce4181d8b
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3 -tt
def doNav(defaultxml):
defaultxml.write("<collection label=\"MITRE\">\n ")
defaultxml.write("<view name=\"mitre\" default=\"true\" />\n ")
defaultxml.write("<a href=\"http://localhost/attack-navigator/index.html\" target=\"_blank\">ATT&CK® Navigator Mapping</a>\n ")
defaultxml.write("<view name=\"info\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"ATT&CK® Techniques\">\n ")
defaultxml.write("<collection label=\"Initial Access\">\n ")
defaultxml.write("<view name=\"t1189\" />\n ")
defaultxml.write("<view name=\"t1190\" />\n ")
defaultxml.write("<view name=\"t1133\" />\n ")
defaultxml.write("<view name=\"t1200\" />\n ")
defaultxml.write("<view name=\"t1566\" />\n ")
defaultxml.write("<view name=\"t1091\" />\n ")
defaultxml.write("<view name=\"t1195\" />\n ")
defaultxml.write("<view name=\"t1199\" />\n ")
defaultxml.write("<view name=\"t1078\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Execution\">\n ")
defaultxml.write("<view name=\"t1059\" />\n ")
defaultxml.write("<view name=\"t1609\" />\n ")
defaultxml.write("<view name=\"t1610\" />\n ")
defaultxml.write("<view name=\"t1203\" />\n ")
defaultxml.write("<view name=\"t1559\" />\n ")
defaultxml.write("<view name=\"t1106\" />\n ")
defaultxml.write("<view name=\"t1053\" />\n ")
defaultxml.write("<view name=\"t1129\" />\n ")
defaultxml.write("<view name=\"t1072\" />\n ")
defaultxml.write("<view name=\"t1569\" />\n ")
defaultxml.write("<view name=\"t1204\" />\n ")
defaultxml.write("<view name=\"t1047\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Persistence\">\n ")
defaultxml.write("<view name=\"t1098\" />\n ")
defaultxml.write("<view name=\"t1197\" />\n ")
defaultxml.write("<view name=\"t1547\" />\n ")
defaultxml.write("<view name=\"t1037\" />\n ")
defaultxml.write("<view name=\"t1176\" />\n ")
defaultxml.write("<view name=\"t1554\" />\n ")
defaultxml.write("<view name=\"t1136\" />\n ")
defaultxml.write("<view name=\"t1543\" />\n ")
defaultxml.write("<view name=\"t1546\" />\n ")
defaultxml.write("<view name=\"t1133\" />\n ")
defaultxml.write("<view name=\"t1574\" />\n ")
defaultxml.write("<view name=\"t1525\" />\n ")
defaultxml.write("<view name=\"t1556\" />\n ")
defaultxml.write("<view name=\"t1137\" />\n ")
defaultxml.write("<view name=\"t1542\" />\n ")
defaultxml.write("<view name=\"t1053\" />\n ")
defaultxml.write("<view name=\"t1505\" />\n ")
defaultxml.write("<view name=\"t1205\" />\n ")
defaultxml.write("<view name=\"t1078\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Privilege Escalation\">\n ")
defaultxml.write("<view name=\"t1548\" />\n ")
defaultxml.write("<view name=\"t1134\" />\n ")
defaultxml.write("<view name=\"t1547\" />\n ")
defaultxml.write("<view name=\"t1037\" />\n ")
defaultxml.write("<view name=\"t1543\" />\n ")
defaultxml.write("<view name=\"t1484\" />\n ")
defaultxml.write("<view name=\"t1611\" />\n ")
defaultxml.write("<view name=\"t1546\" />\n ")
defaultxml.write("<view name=\"t1068\" />\n ")
defaultxml.write("<view name=\"t1574\" />\n ")
defaultxml.write("<view name=\"t1055\" />\n ")
defaultxml.write("<view name=\"t1053\" />\n ")
defaultxml.write("<view name=\"t1078\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Defense Evasion\">\n ")
defaultxml.write("<view name=\"t1548\" />\n ")
defaultxml.write("<view name=\"t1134\" />\n ")
defaultxml.write("<view name=\"t1197\" />\n ")
defaultxml.write("<view name=\"t1612\" />\n ")
defaultxml.write("<view name=\"t1140\" />\n ")
defaultxml.write("<view name=\"t1610\" />\n ")
defaultxml.write("<view name=\"t1006\" />\n ")
defaultxml.write("<view name=\"t1480\" />\n ")
defaultxml.write("<view name=\"t1211\" />\n ")
defaultxml.write("<view name=\"t1222\" />\n ")
defaultxml.write("<view name=\"t1484\" />\n ")
defaultxml.write("<view name=\"t1564\" />\n ")
defaultxml.write("<view name=\"t1574\" />\n ")
defaultxml.write("<view name=\"t1562\" />\n ")
defaultxml.write("<view name=\"t1070\" />\n ")
defaultxml.write("<view name=\"t1202\" />\n ")
defaultxml.write("<view name=\"t1036\" />\n ")
defaultxml.write("<view name=\"t1556\" />\n ")
defaultxml.write("<view name=\"t1578\" />\n ")
defaultxml.write("<view name=\"t1112\" />\n ")
defaultxml.write("<view name=\"t1601\" />\n ")
defaultxml.write("<view name=\"t1599\" />\n ")
defaultxml.write("<view name=\"t1027\" />\n ")
defaultxml.write("<view name=\"t1542\" />\n ")
defaultxml.write("<view name=\"t1055\" />\n ")
defaultxml.write("<view name=\"t1207\" />\n ")
defaultxml.write("<view name=\"t1014\" />\n ")
defaultxml.write("<view name=\"t1218\" />\n ")
defaultxml.write("<view name=\"t1216\" />\n ")
defaultxml.write("<view name=\"t1553\" />\n ")
defaultxml.write("<view name=\"t1221\" />\n ")
defaultxml.write("<view name=\"t1205\" />\n ")
defaultxml.write("<view name=\"t1127\" />\n ")
defaultxml.write("<view name=\"t1535\" />\n ")
defaultxml.write("<view name=\"t1550\" />\n ")
defaultxml.write("<view name=\"t1078\" />\n ")
defaultxml.write("<view name=\"t1497\" />\n ")
defaultxml.write("<view name=\"t1600\" />\n ")
defaultxml.write("<view name=\"t1220\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Credential Access\">\n ")
defaultxml.write("<view name=\"t1110\" />\n ")
defaultxml.write("<view name=\"t1555\" />\n ")
defaultxml.write("<view name=\"t1212\" />\n ")
defaultxml.write("<view name=\"t1187\" />\n ")
defaultxml.write("<view name=\"t1606\" />\n ")
defaultxml.write("<view name=\"t1056\" />\n ")
defaultxml.write("<view name=\"t1557\" />\n ")
defaultxml.write("<view name=\"t1556\" />\n ")
defaultxml.write("<view name=\"t1040\" />\n ")
defaultxml.write("<view name=\"t1003\" />\n ")
defaultxml.write("<view name=\"t1528\" />\n ")
defaultxml.write("<view name=\"t1539\" />\n ")
defaultxml.write("<view name=\"t1111\" />\n ")
defaultxml.write("<view name=\"t1552\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Discovery\">\n ")
defaultxml.write("<view name=\"t1087\" />\n ")
defaultxml.write("<view name=\"t1010\" />\n ")
defaultxml.write("<view name=\"t1217\" />\n ")
defaultxml.write("<view name=\"t1580\" />\n ")
defaultxml.write("<view name=\"t1538\" />\n ")
defaultxml.write("<view name=\"t1526\" />\n ")
defaultxml.write("<view name=\"t1613\" />\n ")
defaultxml.write("<view name=\"t1482\" />\n ")
defaultxml.write("<view name=\"t1083\" />\n ")
defaultxml.write("<view name=\"t1046\" />\n ")
defaultxml.write("<view name=\"t1135\" />\n ")
defaultxml.write("<view name=\"t1040\" />\n ")
defaultxml.write("<view name=\"t1201\" />\n ")
defaultxml.write("<view name=\"t1120\" />\n ")
defaultxml.write("<view name=\"t1069\" />\n ")
defaultxml.write("<view name=\"t1057\" />\n ")
defaultxml.write("<view name=\"t1012\" />\n ")
defaultxml.write("<view name=\"t1018\" />\n ")
defaultxml.write("<view name=\"t1518\" />\n ")
defaultxml.write("<view name=\"t1082\" />\n ")
defaultxml.write("<view name=\"t1614\" />\n ")
defaultxml.write("<view name=\"t1016\" />\n ")
defaultxml.write("<view name=\"t1049\" />\n ")
defaultxml.write("<view name=\"t1033\" />\n ")
defaultxml.write("<view name=\"t1007\" />\n ")
defaultxml.write("<view name=\"t1124\" />\n ")
defaultxml.write("<view name=\"t1497\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Lateral Movement\">\n ")
defaultxml.write("<view name=\"t1210\" />\n ")
defaultxml.write("<view name=\"t1534\" />\n ")
defaultxml.write("<view name=\"t1570\" />\n ")
defaultxml.write("<view name=\"t1563\" />\n ")
defaultxml.write("<view name=\"t1021\" />\n ")
defaultxml.write("<view name=\"t1091\" />\n ")
defaultxml.write("<view name=\"t1072\" />\n ")
defaultxml.write("<view name=\"t1080\" />\n ")
defaultxml.write("<view name=\"t1550\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Collection\">\n ")
defaultxml.write("<view name=\"t1560\" />\n ")
defaultxml.write("<view name=\"t1123\" />\n ")
defaultxml.write("<view name=\"t1119\" />\n ")
defaultxml.write("<view name=\"t1115\" />\n ")
defaultxml.write("<view name=\"t1530\" />\n ")
defaultxml.write("<view name=\"t1602\" />\n ")
defaultxml.write("<view name=\"t1213\" />\n ")
defaultxml.write("<view name=\"t1005\" />\n ")
defaultxml.write("<view name=\"t1039\" />\n ")
defaultxml.write("<view name=\"t1025\" />\n ")
defaultxml.write("<view name=\"t1074\" />\n ")
defaultxml.write("<view name=\"t1114\" />\n ")
defaultxml.write("<view name=\"t1056\" />\n ")
defaultxml.write("<view name=\"t1185\" />\n ")
defaultxml.write("<view name=\"t1557\" />\n ")
defaultxml.write("<view name=\"t1113\" />\n ")
defaultxml.write("<view name=\"t1125\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Command & Control\">\n ")
defaultxml.write("<view name=\"t1071\" />\n ")
defaultxml.write("<view name=\"t1092\" />\n ")
defaultxml.write("<view name=\"t1132\" />\n ")
defaultxml.write("<view name=\"t1001\" />\n ")
defaultxml.write("<view name=\"t1568\" />\n ")
defaultxml.write("<view name=\"t1573\" />\n ")
defaultxml.write("<view name=\"t1008\" />\n ")
defaultxml.write("<view name=\"t1105\" />\n ")
defaultxml.write("<view name=\"t1104\" />\n ")
defaultxml.write("<view name=\"t1095\" />\n ")
defaultxml.write("<view name=\"t1571\" />\n ")
defaultxml.write("<view name=\"t1572\" />\n ")
defaultxml.write("<view name=\"t1090\" />\n ")
defaultxml.write("<view name=\"t1219\" />\n ")
defaultxml.write("<view name=\"t1205\" />\n ")
defaultxml.write("<view name=\"t1102\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Exfiltration\">\n ")
defaultxml.write("<view name=\"t1020\" />\n ")
defaultxml.write("<view name=\"t1030\" />\n ")
defaultxml.write("<view name=\"t1048\" />\n ")
defaultxml.write("<view name=\"t1041\" />\n ")
defaultxml.write("<view name=\"t1011\" />\n ")
defaultxml.write("<view name=\"t1052\" />\n ")
defaultxml.write("<view name=\"t1567\" />\n ")
defaultxml.write("<view name=\"t1029\" />\n ")
defaultxml.write("<view name=\"t1537\" />\n ")
defaultxml.write("</collection>\n ")
defaultxml.write("<collection label=\"Impact\">\n ")
defaultxml.write("<view name=\"t1531\" />\n ")
defaultxml.write("<view name=\"t1485\" />\n ")
defaultxml.write("<view name=\"t1486\" />\n ")
defaultxml.write("<view name=\"t1565\" />\n ")
defaultxml.write("<view name=\"t1491\" />\n ")
defaultxml.write("<view name=\"t1561\" />\n ")
defaultxml.write("<view name=\"t1499\" />\n ")
defaultxml.write("<view name=\"t1495\" />\n ")
defaultxml.write("<view name=\"t1490\" />\n ")
defaultxml.write("<view name=\"t1498\" />\n ")
defaultxml.write("<view name=\"t1496\" />\n ")
defaultxml.write("<view name=\"t1489\" />\n ")
defaultxml.write("<view name=\"t1529\" />\n ")
defaultxml.write("</collection>\n </collection>\n ")
| 53.195652
| 136
| 0.561422
| 1,373
| 12,235
| 5.003642
| 0.158048
| 0.495633
| 0.526346
| 0.57933
| 0.84425
| 0.422416
| 0.413683
| 0.413683
| 0.413683
| 0.315575
| 0
| 0.078837
| 0.182019
| 12,235
| 229
| 137
| 53.427948
| 0.607414
| 0.002043
| 0
| 0.289474
| 0
| 0
| 0.378983
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.004386
| false
| 0
| 0
| 0
| 0.004386
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 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
| 0
| 0
| 0
| 0
|
0
| 4
|
dff2396e618936b8330f4bd520daa9f14998d954
| 233
|
py
|
Python
|
06-programing-languages/01-pythons/01-lang-features/01-introspection-codes/01_id.py
|
jameszhan/notes-ml
|
c633d04e5443eab71bc3b27fff89d57b89d1786c
|
[
"Apache-2.0"
] | null | null | null |
06-programing-languages/01-pythons/01-lang-features/01-introspection-codes/01_id.py
|
jameszhan/notes-ml
|
c633d04e5443eab71bc3b27fff89d57b89d1786c
|
[
"Apache-2.0"
] | null | null | null |
06-programing-languages/01-pythons/01-lang-features/01-introspection-codes/01_id.py
|
jameszhan/notes-ml
|
c633d04e5443eab71bc3b27fff89d57b89d1786c
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
a = []
b = a
c = []
print("id(a) = ", id(a))
print("id(b) = ", id(b))
print("id(c) = ", id(c))
a.append(1)
b.append(2)
c.append(3)
print("id(a) = ", id(a))
print("id(b) = ", id(b))
print("id(c) = ", id(c))
| 13.705882
| 24
| 0.446352
| 46
| 233
| 2.26087
| 0.26087
| 0.403846
| 0.153846
| 0.192308
| 0.634615
| 0.634615
| 0.634615
| 0.634615
| 0.634615
| 0.634615
| 0
| 0.021164
| 0.188841
| 233
| 16
| 25
| 14.5625
| 0.529101
| 0.090129
| 0
| 0.5
| 0
| 0
| 0.228571
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
5f0fdc875fbc92072f12a7e82fddf978d28d52f5
| 14,524
|
py
|
Python
|
assets/View/keyboards.py
|
HoolyPanda/HorNet
|
b9d811ac74fdd3f9b01a4a886c566c90090abab3
|
[
"MIT"
] | null | null | null |
assets/View/keyboards.py
|
HoolyPanda/HorNet
|
b9d811ac74fdd3f9b01a4a886c566c90090abab3
|
[
"MIT"
] | null | null | null |
assets/View/keyboards.py
|
HoolyPanda/HorNet
|
b9d811ac74fdd3f9b01a4a886c566c90090abab3
|
[
"MIT"
] | null | null | null |
import json
mKB = {
'one_time': True,
'buttons':
[
[
{
"action":
{
"type":"text",
"payload": "{\"mainMenu\":\"wallet\"}",
"label": "Украсть деньги со счета"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"mainMenu\":\"profile\"}",
"label": "Получить доступ к профилю"
},
"color": "secondary"
}
]
]
}
hCKB = {
'one_time': True,
'buttons':
[
[
{
"action":
{
"type":"text",
"payload": "{\"button\":\"name\"}",
"label": "Имя"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"button\":\"work\"}",
"label": "Работа"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"button\":\"color\"}",
"label": "Любимый Цвет"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"button\":\"eyes\"}",
"label": "Цвет Глаз"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"button\":\"hair\"}",
"label": "Цвет волос"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"button\":\"music\"}",
"label": "Любимая музыка"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"button\":\"end\"}",
"label": "Завершить"
},
"color": "negative"
},
{
"action":
{
"type":"text",
"payload": "{\"button\":\"confirm\"}",
"label": "Подтвердить"
},
"color": "positive"
}
]
]
}
dKB = {
'one_time': True,
'buttons':
[
[
{
"action":
{
"type":"text",
"payload": "{\"district\":\"Фавеллы\"}",
"label": "Фавеллы"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"district\":\"Ист-Енд\"}",
"label": "Ист-Енд"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"district\":\"Коулун\"}",
"label": "Коулун"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"district\":\"Тобэй\"}",
"label": "Тобэй"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"district\":\"Доминго\"}",
"label": "Доминго"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"district\":\"Дурбан\"}",
"label": "Дурбан"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"district\":\"Кангване\"}",
"label": "Кангване"
},
"color": "secondary"
}
]
]
}
heKB = {
'one_time': True,
'buttons':
[
[
{
"action":
{
"type":"text",
"payload": "{\"height\":\"Низкий\"}",
"label": "Низкий"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"height\":\"Средний\"}",
"label": "Средний"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"height\":\"Высокий\"}",
"label": "Высокий"
},
"color": "secondary"
}
]
]
}
nKB = {
'one_time': True,
'buttons':[]
}
wKB = {
'one_time': True,
'buttons':
[
[
{
"action":
{
"type":"text",
"payload": "{\"work\":\"shogun\"}",
"label": "ShoGun"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"work\":\"sintech\"}",
"label": "SinTech"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"work\":\"cybersteel\"}",
"label": "CyberSteel"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"work\":\"c-corp\"}",
"label": "C-Corp"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"work\":\"dell\"}",
"label": "Dell"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"work\":\"obs news\"}",
"label": "OBS News"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"work\":\"безработный\"}",
"label": "Безработный"
},
"color": "secondary"
}
]
]
}
eKB = {
'one_time': True,
'buttons':
[
[
{
"action":
{
"type":"text",
"payload": "{\"eyeColor\":\"зеленые\"}",
"label": "Зеленый"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"eyeColor\":\"Синие\"}",
"label": "Синий"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"eyeColor\":\"коричневые\"}",
"label": "Карий"
},
"color": "secondary"
}
]
]
}
hairKB = {
'one_time': True,
'buttons':
[
[
{
"action":
{
"type":"text",
"payload": "{\"hairColor\":\"Русые\"}",
"label": "Русые"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"hairColor\":\"Шатен\"}",
"label": "Шатен"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"hairColor\":\"Рыжие\"}",
"label": "Рыжие"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"hairColor\":\"Брюнет\"}",
"label": "Брюнет"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"hairColor\":\"Цветные\"}",
"label": "Цветные"
},
"color": "secondary"
}
]
]
}
hCKB = {
'one_time': True,
'buttons':
[
[
{
"action":
{
"type":"text",
"payload": "{\"button\":\"name\"}",
"label": "Имя"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"button\":\"work\"}",
"label": "Работа"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"button\":\"district\"}",
"label": "Район"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"button\":\"eyes\"}",
"label": "Цвет Глаз"
},
"color": "secondary"
},
{
"action":
{
"type":"text",
"payload": "{\"button\":\"hair\"}",
"label": "Цвет волос"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"button\":\"height\"}",
"label": "Рост"
},
"color": "secondary"
}
],
[
{
"action":
{
"type":"text",
"payload": "{\"button\":\"end\"}",
"label": "Завершить"
},
"color": "negative"
},
{
"action":
{
"type":"text",
"payload": "{\"button\":\"confirm\"}",
"label": "Подтвердить"
},
"color": "positive"
}
]
]
}
districtKB = json.dumps(dKB)
nullKB = json.dumps(nKB)
heightKB = json.dumps(heKB)
worksKB = json.dumps(wKB)
hairKB = json.dumps(hairKB)
humanCreatorKB = json.dumps(hCKB)
eyeColorKB = json.dumps(eKB)
humanCreatorKB = json.dumps(hCKB)
# eyeColorKB = json.dumps(eKB)
mainKB = json.dumps(mKB)
| 28.534381
| 69
| 0.207312
| 535
| 14,524
| 5.611215
| 0.181308
| 0.143238
| 0.200533
| 0.300799
| 0.726849
| 0.70986
| 0.698201
| 0.41972
| 0.309127
| 0.309127
| 0
| 0
| 0.655605
| 14,524
| 509
| 70
| 28.534381
| 0.60016
| 0.001928
| 0
| 0.380368
| 0
| 0
| 0.166126
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.002045
| 0
| 0.002045
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 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
|
5f1c35bc86c1194855e8c04264be264563566b39
| 271
|
py
|
Python
|
iex/market/__init__.py
|
udrea/iex
|
5db5e84575a999b41f7ddf74aa356018941c9ee6
|
[
"Apache-2.0"
] | 2
|
2018-04-14T17:52:34.000Z
|
2021-03-12T09:16:49.000Z
|
iex/market/__init__.py
|
udrea/iex
|
5db5e84575a999b41f7ddf74aa356018941c9ee6
|
[
"Apache-2.0"
] | null | null | null |
iex/market/__init__.py
|
udrea/iex
|
5db5e84575a999b41f7ddf74aa356018941c9ee6
|
[
"Apache-2.0"
] | null | null | null |
# Filename: market/__init__.py
"""
Data provided for free by IEX (https://iextrading.com/developer/).
See https://iextrading.com/api-exhibit-a/ for more information.
"""
from iex.market.tops import TOPS
from iex.market.hist import HIST
from iex.market.deep import DEEP
| 24.636364
| 66
| 0.760148
| 42
| 271
| 4.809524
| 0.595238
| 0.10396
| 0.193069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114391
| 271
| 10
| 67
| 27.1
| 0.841667
| 0.590406
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
a029982ab95e58adcfde2bbea943451b433de0ed
| 321
|
py
|
Python
|
backend/drugs/admin.py
|
hippocampus13/IndianMedicineDB
|
f8d96c0a96c622937d12d3eaf7257f68a2e488c5
|
[
"MIT"
] | null | null | null |
backend/drugs/admin.py
|
hippocampus13/IndianMedicineDB
|
f8d96c0a96c622937d12d3eaf7257f68a2e488c5
|
[
"MIT"
] | null | null | null |
backend/drugs/admin.py
|
hippocampus13/IndianMedicineDB
|
f8d96c0a96c622937d12d3eaf7257f68a2e488c5
|
[
"MIT"
] | 1
|
2022-03-28T08:27:57.000Z
|
2022-03-28T08:27:57.000Z
|
from django.contrib import admin
from .models import DrugType, Manufacturer, DrugComposition, Drug, DataSource, PackSizeLabel
admin.site.register(Manufacturer)
admin.site.register(PackSizeLabel)
admin.site.register(DrugComposition)
admin.site.register(DataSource)
admin.site.register(Drug)
admin.site.register(DrugType)
| 32.1
| 92
| 0.841121
| 38
| 321
| 7.105263
| 0.368421
| 0.2
| 0.377778
| 0.222222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.062305
| 321
| 9
| 93
| 35.666667
| 0.89701
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.25
| 0
| 0.25
| 0
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| 0
| 0
| null | 0
| 1
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
a03c6c227b161fd955d9d9b97c7e2848c5e30bab
| 122
|
py
|
Python
|
ptpop/__main__.py
|
andrewburnheimer/ptpop
|
4801ae38169f1fac738969f9d05b0811fff5e47e
|
[
"Apache-2.0"
] | 2
|
2018-04-18T05:29:13.000Z
|
2020-11-24T01:36:05.000Z
|
ptpop/__main__.py
|
andrewburnheimer/ptpop
|
4801ae38169f1fac738969f9d05b0811fff5e47e
|
[
"Apache-2.0"
] | 3
|
2016-10-23T18:12:36.000Z
|
2016-11-01T21:39:39.000Z
|
ptpop/__main__.py
|
andrewburnheimer/ptpop
|
4801ae38169f1fac738969f9d05b0811fff5e47e
|
[
"Apache-2.0"
] | null | null | null |
# __main__.py is executed when the package is instantiated
import Console
if __name__ == '__main__':
Console.main()
| 17.428571
| 58
| 0.737705
| 16
| 122
| 4.875
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.180328
| 122
| 6
| 59
| 20.333333
| 0.78
| 0.459016
| 0
| 0
| 0
| 0
| 0.125
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 0
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| 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
|
a04cb1e91ecba363731bfd3edce8f91ab9a3c574
| 52
|
py
|
Python
|
venv/lib/python2.7/site-packages/way2sms.py
|
theabstractguy/Fall-detection-using-piezo-electrice-sensors
|
f26d7c0979f0a519cc2c416862af83acea633bf7
|
[
"Apache-2.0"
] | null | null | null |
venv/lib/python2.7/site-packages/way2sms.py
|
theabstractguy/Fall-detection-using-piezo-electrice-sensors
|
f26d7c0979f0a519cc2c416862af83acea633bf7
|
[
"Apache-2.0"
] | null | null | null |
venv/lib/python2.7/site-packages/way2sms.py
|
theabstractguy/Fall-detection-using-piezo-electrice-sensors
|
f26d7c0979f0a519cc2c416862af83acea633bf7
|
[
"Apache-2.0"
] | null | null | null |
from lib import gui
start = gui.Gui()
start.main()
| 17.333333
| 20
| 0.692308
| 9
| 52
| 4
| 0.666667
| 0.444444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173077
| 52
| 3
| 21
| 17.333333
| 0.837209
| 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
|
a05ba8a02019d6dd0886a7ccee15fe961bc34d9e
| 43
|
py
|
Python
|
pysnc/__version__.py
|
ServiceNow/PySNC
|
4be8fa19e1a15fa2d0cc1b1e5d4c96ed4857b735
|
[
"MIT"
] | 22
|
2020-10-22T23:44:50.000Z
|
2022-03-26T11:21:39.000Z
|
pysnc/__version__.py
|
ServiceNow/PySNC
|
4be8fa19e1a15fa2d0cc1b1e5d4c96ed4857b735
|
[
"MIT"
] | 15
|
2020-11-09T23:27:05.000Z
|
2021-05-20T02:47:55.000Z
|
pysnc/__version__.py
|
ServiceNow/PySNC
|
4be8fa19e1a15fa2d0cc1b1e5d4c96ed4857b735
|
[
"MIT"
] | 9
|
2020-10-23T01:58:13.000Z
|
2022-03-22T22:32:03.000Z
|
__title__ = 'pysnc'
__version__ = '1.0.4'
| 10.75
| 21
| 0.651163
| 6
| 43
| 3.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 0.162791
| 43
| 3
| 22
| 14.333333
| 0.472222
| 0
| 0
| 0
| 0
| 0
| 0.238095
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
a07988d8b615fd5a1eba1800190f50437708c41e
| 421
|
py
|
Python
|
online_pharmacy/items/models.py
|
geekyJock8/online_pharmacy
|
892852857786ec17259b71f2a178896cd6d12e60
|
[
"Apache-2.0"
] | 5
|
2020-09-09T13:59:17.000Z
|
2021-09-30T07:20:55.000Z
|
online_pharmacy/items/models.py
|
geekyJock8/online_pharmacy
|
892852857786ec17259b71f2a178896cd6d12e60
|
[
"Apache-2.0"
] | 10
|
2017-09-03T06:13:31.000Z
|
2017-10-10T15:22:30.000Z
|
online_pharmacy/items/models.py
|
geekyJock8/Online-Pharmacy
|
892852857786ec17259b71f2a178896cd6d12e60
|
[
"Apache-2.0"
] | 9
|
2017-09-03T04:59:18.000Z
|
2019-10-17T11:33:18.000Z
|
from django.db import models
class item(models.Model):
item_id = models.CharField(max_length=20,primary_key = True)
item_name = models.CharField(max_length=50)
image = models.ImageField()
otc_or_not = models.BooleanField()
brand_name = models.CharField(max_length=50)
salts = models.TextField()
specifications = models.CharField(max_length=100)
category = models.CharField(max_length=30)
| 35.083333
| 64
| 0.741093
| 56
| 421
| 5.375
| 0.553571
| 0.249169
| 0.299003
| 0.398671
| 0.199336
| 0.199336
| 0
| 0
| 0
| 0
| 0
| 0.030899
| 0.154394
| 421
| 11
| 65
| 38.272727
| 0.814607
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.1
| 0
| 1
| 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
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
a08d7944fdb739dda28ac833c848f3aed8cac63b
| 508
|
py
|
Python
|
awe/tasks/views.py
|
Awesomebug95/aweum
|
618fe27e1792722e6a622c0801c97529195f76a6
|
[
"BSD-3-Clause"
] | null | null | null |
awe/tasks/views.py
|
Awesomebug95/aweum
|
618fe27e1792722e6a622c0801c97529195f76a6
|
[
"BSD-3-Clause"
] | null | null | null |
awe/tasks/views.py
|
Awesomebug95/aweum
|
618fe27e1792722e6a622c0801c97529195f76a6
|
[
"BSD-3-Clause"
] | null | null | null |
from django.shortcuts import render
from tasks.forms import TaskForm
def index(request):
return render(request, 'tasks/index.html')
def profile(request):
return render(request, 'tasks/profile.html')
def create_task(request):
form = TaskForm(request.POST or None)
if not form.is_valid():
return render(request, 'tasks/create_task.html', {'form': form})
form.instance.author = request.user
form.save()
return render(request, 'tasks/create_task.html', {'form': form})
| 24.190476
| 72
| 0.702756
| 68
| 508
| 5.191176
| 0.411765
| 0.135977
| 0.215297
| 0.271955
| 0.436261
| 0.260623
| 0.260623
| 0.260623
| 0.260623
| 0
| 0
| 0
| 0.167323
| 508
| 20
| 73
| 25.4
| 0.834515
| 0
| 0
| 0.153846
| 0
| 0
| 0.169291
| 0.086614
| 0
| 0
| 0
| 0
| 0
| 1
| 0.230769
| false
| 0
| 0.153846
| 0.153846
| 0.692308
| 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
| 1
| 0
| 0
|
0
| 4
|
a0946cf0268b1dae5b2e48f45e274f9d3252cf5e
| 881
|
py
|
Python
|
core/embeds.py
|
Pug234/BytesBump
|
d5ff3130bffae92e1c5c671db4ed8904c403e9dc
|
[
"MIT"
] | 11
|
2020-11-14T17:28:50.000Z
|
2021-05-19T18:21:07.000Z
|
core/embeds.py
|
AnimeDyno/BytesBump
|
a0cf0bfc4c13592c7b10ad46faa46a2a98dc1443
|
[
"MIT"
] | 3
|
2021-01-22T15:48:41.000Z
|
2021-06-22T17:16:50.000Z
|
core/embeds.py
|
zImPinguin/Bump-Bot
|
3f449a4e5581a35a5cff998e94a13ae33dbe2b04
|
[
"MIT"
] | 13
|
2020-11-18T05:20:31.000Z
|
2021-06-19T16:31:30.000Z
|
import random
from discord import Embed, Color
class Embeds:
def __init__(self, message):
self.message = message
def success(self, **kwargs):
embed = Embed(
description=self.message,
color=Color.green()
)
for i in kwargs:
embed.add_field(name=i.replace("_", " "), value=kwargs[i])
return embed
def error(self, **kwargs):
embed = Embed(
description=self.message,
color=Color.red()
)
for i in kwargs:
embed.add_field(name=i.replace("_", " "), value=kwargs[i])
return embed
def warn(self, **kwargs):
embed = Embed(
description=self.message,
color=Color.orange()
)
for i in kwargs:
embed.add_field(name=i.replace("_", " "), value=kwargs[i])
return embed
| 26.69697
| 70
| 0.53462
| 97
| 881
| 4.752577
| 0.298969
| 0.143167
| 0.097614
| 0.130152
| 0.741866
| 0.741866
| 0.741866
| 0.741866
| 0.741866
| 0.403471
| 0
| 0
| 0.343927
| 881
| 33
| 71
| 26.69697
| 0.797578
| 0
| 0
| 0.517241
| 0
| 0
| 0.006803
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.137931
| false
| 0
| 0.068966
| 0
| 0.344828
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 0
| 0
| 0
| 0
|
0
| 4
|
a0b624137782bd6b6d6896194a83419c30323ebb
| 225
|
py
|
Python
|
src/pyscoresaber/models/player_info.py
|
Kiyomi-Parents/PyScoreSaber
|
5dbfbfad04bf53ac3fc2fc803acf0374db5c2552
|
[
"MIT"
] | null | null | null |
src/pyscoresaber/models/player_info.py
|
Kiyomi-Parents/PyScoreSaber
|
5dbfbfad04bf53ac3fc2fc803acf0374db5c2552
|
[
"MIT"
] | 6
|
2022-02-10T08:59:01.000Z
|
2022-03-01T08:06:57.000Z
|
src/pyscoresaber/models/player_info.py
|
Kiyomi-Parents/PyScoreSaber
|
5dbfbfad04bf53ac3fc2fc803acf0374db5c2552
|
[
"MIT"
] | null | null | null |
from dataclasses import dataclass
from dataclasses_json import dataclass_json
from .fields import default
from .player import Player
@dataclass_json
@dataclass
class PlayerInfo:
player: Player = default("playerInfo")
| 17.307692
| 43
| 0.808889
| 27
| 225
| 6.62963
| 0.37037
| 0.167598
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142222
| 225
| 12
| 44
| 18.75
| 0.927461
| 0
| 0
| 0
| 0
| 0
| 0.044444
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 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
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
267c20816c379981340c001e13271911022efec6
| 213
|
py
|
Python
|
my/stackexchange.py
|
jhermann/HPI
|
a60c30868b285f233caf65a59d3496082fb9d5c2
|
[
"MIT"
] | null | null | null |
my/stackexchange.py
|
jhermann/HPI
|
a60c30868b285f233caf65a59d3496082fb9d5c2
|
[
"MIT"
] | null | null | null |
my/stackexchange.py
|
jhermann/HPI
|
a60c30868b285f233caf65a59d3496082fb9d5c2
|
[
"MIT"
] | null | null | null |
import mycfg.repos.stexport.model as stexport
from mycfg import paths
def get_data():
sources = [max(paths.stexport.export_dir.glob('*.json'))]
return stexport.Model(sources).site_model('stackoverflow')
| 26.625
| 62
| 0.751174
| 29
| 213
| 5.413793
| 0.689655
| 0.165605
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117371
| 213
| 7
| 63
| 30.428571
| 0.835106
| 0
| 0
| 0
| 0
| 0
| 0.089202
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0
| 0.8
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
13fc27e9793e76ffc420b061ce7bf793662d8fb0
| 122
|
py
|
Python
|
config.py
|
mesmesgit/SCNN_Pytorch
|
6b4ec6af124bac66b58624dc03943a510a397007
|
[
"MIT"
] | 199
|
2019-03-23T07:28:25.000Z
|
2022-03-09T05:32:25.000Z
|
config.py
|
mesmesgit/SCNN_Pytorch
|
6b4ec6af124bac66b58624dc03943a510a397007
|
[
"MIT"
] | 65
|
2019-04-05T02:10:23.000Z
|
2022-02-17T08:49:31.000Z
|
config.py
|
mesmesgit/SCNN_Pytorch
|
6b4ec6af124bac66b58624dc03943a510a397007
|
[
"MIT"
] | 69
|
2019-03-24T03:12:45.000Z
|
2022-03-28T04:14:59.000Z
|
Dataset_Path = dict(
CULane = "/home/lion/Dataset/CULane/data/CULane",
Tusimple = "/home/lion/Dataset/tusimple"
)
| 24.4
| 53
| 0.688525
| 15
| 122
| 5.533333
| 0.533333
| 0.192771
| 0.361446
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147541
| 122
| 4
| 54
| 30.5
| 0.798077
| 0
| 0
| 0
| 0
| 0
| 0.52459
| 0.52459
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 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
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
cd825a774635cbe7d44ed5f08f03161b16113c71
| 321
|
py
|
Python
|
welut/settings.py
|
agusmakmun/welut
|
ee176b51d4637691a616a17eb0491c933211799a
|
[
"MIT"
] | 6
|
2017-11-17T10:34:27.000Z
|
2022-01-06T14:17:23.000Z
|
welut/settings.py
|
agusmakmun/welut
|
ee176b51d4637691a616a17eb0491c933211799a
|
[
"MIT"
] | 2
|
2017-12-06T14:40:31.000Z
|
2020-08-15T10:41:11.000Z
|
welut/settings.py
|
agusmakmun/welut
|
ee176b51d4637691a616a17eb0491c933211799a
|
[
"MIT"
] | 7
|
2017-11-13T20:47:05.000Z
|
2020-06-04T03:20:13.000Z
|
# -*- coding: utf-8 -*-
from django.conf import settings
WELUT_EXTENSIONS = getattr(settings, 'WELUT_EXTENSIONS', ['.pdf', '.epub', '.mobi'])
WELUT_REMOVED_EXTENSIONS = getattr(settings, 'WELUT_REMOVED_EXTENSIONS', ['.pdf', '.epub', '.mobi'])
WELUT_IMAGES_EXTENSION = getattr(settings, 'WELUT_IMAGE_EXTENSION', '.png')
| 40.125
| 100
| 0.719626
| 37
| 321
| 5.972973
| 0.513514
| 0.235294
| 0.271493
| 0.271493
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003436
| 0.093458
| 321
| 7
| 101
| 45.857143
| 0.756014
| 0.065421
| 0
| 0
| 0
| 0
| 0.312081
| 0.151007
| 0
| 0
| 0
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| 1
| 0
| false
| 0
| 0.25
| 0
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
cd9214ae5c09797aea4b3c581123b349f3199538
| 238
|
py
|
Python
|
models/faster_rcnn.py
|
wk910930/mask_rcnn_pytorch
|
21dc137f4dd75384b39a384437b5fbb18f111d9e
|
[
"MIT"
] | 5
|
2017-08-17T02:53:02.000Z
|
2021-10-19T01:44:45.000Z
|
models/faster_rcnn.py
|
wk910930/mask_rcnn_pytorch
|
21dc137f4dd75384b39a384437b5fbb18f111d9e
|
[
"MIT"
] | null | null | null |
models/faster_rcnn.py
|
wk910930/mask_rcnn_pytorch
|
21dc137f4dd75384b39a384437b5fbb18f111d9e
|
[
"MIT"
] | 4
|
2017-08-22T14:19:58.000Z
|
2021-03-09T02:04:23.000Z
|
from .modules.mask_rcnn import FasterRCNN
def create_model(data, config_of_data, num_classes=80, backbone='resnet-50-c4',
share_features=True, **kwargs):
return FasterRCNN(backbone=backbone, num_classes=num_classes)
| 39.666667
| 79
| 0.747899
| 32
| 238
| 5.3125
| 0.75
| 0.176471
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.024752
| 0.151261
| 238
| 5
| 80
| 47.6
| 0.816832
| 0
| 0
| 0
| 0
| 0
| 0.05042
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0.25
| 0.75
| 0
| 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
|
cd9a22f99f90d02aa1d56d319d23ea25c9dc05cc
| 141
|
py
|
Python
|
other_rank_computers.py
|
touqir14/LUP-rank-computer
|
e790f0a2954ec57190d4314a9cec48c309e7ff8b
|
[
"MIT"
] | 5
|
2020-07-18T10:59:03.000Z
|
2021-12-06T13:30:56.000Z
|
other_rank_computers.py
|
touqir14/LUP-rank-computer
|
e790f0a2954ec57190d4314a9cec48c309e7ff8b
|
[
"MIT"
] | null | null | null |
other_rank_computers.py
|
touqir14/LUP-rank-computer
|
e790f0a2954ec57190d4314a9cec48c309e7ff8b
|
[
"MIT"
] | null | null | null |
import torch
def rank_torch(A, args=None):
A_tensor = torch.from_numpy(A).cuda()
return torch.matrix_rank(A_tensor).cpu().item()
| 28.2
| 51
| 0.695035
| 23
| 141
| 4.043478
| 0.652174
| 0.150538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.156028
| 141
| 5
| 51
| 28.2
| 0.781513
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.75
| 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
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
269976e74bd9467621016a4a3883427d7f697ec0
| 284
|
py
|
Python
|
topCoder/srms/500s/srm524/div2/shipping_cubes.py
|
ferhatelmas/algo
|
a7149c7a605708bc01a5cd30bf5455644cefd04d
|
[
"WTFPL"
] | 25
|
2015-01-21T16:39:18.000Z
|
2021-05-24T07:01:24.000Z
|
topCoder/srms/500s/srm524/div2/shipping_cubes.py
|
ferhatelmas/algo
|
a7149c7a605708bc01a5cd30bf5455644cefd04d
|
[
"WTFPL"
] | 2
|
2020-09-30T19:39:36.000Z
|
2020-10-01T17:15:16.000Z
|
topCoder/srms/500s/srm524/div2/shipping_cubes.py
|
ferhatelmas/algo
|
a7149c7a605708bc01a5cd30bf5455644cefd04d
|
[
"WTFPL"
] | 15
|
2015-01-21T16:39:27.000Z
|
2020-10-01T17:00:22.000Z
|
class ShippingCubes:
def minimalCost(self, N):
m = 600
for i in xrange(1, 200):
for j in xrange(1, i + 1):
for k in xrange(1, j + 1):
if i * j * k == N:
m = min(m, i + j + k)
return m
| 28.4
| 45
| 0.380282
| 41
| 284
| 2.634146
| 0.463415
| 0.222222
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.079137
| 0.510563
| 284
| 9
| 46
| 31.555556
| 0.697842
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.111111
| false
| 0
| 0
| 0
| 0.333333
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 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
|
26b0ea5b5385591b1c3491de693ff6f69b050aee
| 147
|
py
|
Python
|
toggl/__main__.py
|
Bass-03/toggl-cli
|
ba1bb0409bdd85dab5cf10fba9fc37b6b533eb38
|
[
"MIT"
] | 178
|
2018-12-03T08:45:43.000Z
|
2022-03-24T21:44:49.000Z
|
toggl/__main__.py
|
Bass-03/toggl-cli
|
ba1bb0409bdd85dab5cf10fba9fc37b6b533eb38
|
[
"MIT"
] | 123
|
2018-02-04T10:03:49.000Z
|
2022-03-30T18:30:31.000Z
|
toggl/__main__.py
|
beauraines/toggl-cli
|
d79af4f48518725a80db1fddf3e5c180aecfdf20
|
[
"MIT"
] | 44
|
2015-02-12T20:30:39.000Z
|
2018-10-29T22:53:12.000Z
|
"""toggl.__main__: executed when bootstrap directory is called as script."""
from toggl.toggl import main
if __name__ == '__main__':
main()
| 18.375
| 76
| 0.714286
| 19
| 147
| 4.894737
| 0.736842
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.170068
| 147
| 7
| 77
| 21
| 0.762295
| 0.47619
| 0
| 0
| 0
| 0
| 0.114286
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 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
|
26c538ca6f64f44af0f642f4fc6774dcf32916eb
| 42
|
py
|
Python
|
vic/drivers/python/vic/__init__.py
|
lingyunan0510/VIC
|
dbc00a813b5df5a88027d1dc57a7805e9a464436
|
[
"MIT"
] | 1
|
2022-01-18T01:23:47.000Z
|
2022-01-18T01:23:47.000Z
|
vic/drivers/python/vic/__init__.py
|
yusheng-wang/VIC
|
8f6cc0661bdc67c4f6caabdd4dcd0b8782517435
|
[
"MIT"
] | null | null | null |
vic/drivers/python/vic/__init__.py
|
yusheng-wang/VIC
|
8f6cc0661bdc67c4f6caabdd4dcd0b8782517435
|
[
"MIT"
] | null | null | null |
from .vic import *
VIC_DRIVER = b'Python'
| 14
| 22
| 0.714286
| 7
| 42
| 4.142857
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 42
| 2
| 23
| 21
| 0.828571
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
26c9d013d21a7b87454a5f0891c90f03e5e150f2
| 151
|
py
|
Python
|
slackd/common/__init__.py
|
dmwesterhoff/slackd
|
ec87abc693d65fcedb2233b97f84b604c37b5930
|
[
"MIT"
] | 1
|
2016-03-18T21:35:54.000Z
|
2016-03-18T21:35:54.000Z
|
slackd/common/__init__.py
|
dmwesterhoff/slackd
|
ec87abc693d65fcedb2233b97f84b604c37b5930
|
[
"MIT"
] | null | null | null |
slackd/common/__init__.py
|
dmwesterhoff/slackd
|
ec87abc693d65fcedb2233b97f84b604c37b5930
|
[
"MIT"
] | null | null | null |
"""
slackd.common
~~~~~~~~~~~~~
Provides application level utility functions and classes
:copyright: (c) 2016 Pinn
:license: All rights reserved
"""
| 15.1
| 56
| 0.688742
| 17
| 151
| 6.117647
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.030769
| 0.139073
| 151
| 9
| 57
| 16.777778
| 0.769231
| 0.933775
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f809d9c0f524641a254177bb85f519109563bcac
| 184
|
py
|
Python
|
challenge_1/python/wobboz/src/reverse.py
|
rchicoli/2017-challenges
|
44f0b672e5dea34de1dde131b6df837d462f8e29
|
[
"Apache-2.0"
] | 271
|
2017-01-01T22:58:36.000Z
|
2021-11-28T23:05:29.000Z
|
challenge_1/python/wobboz/src/reverse.py
|
AakashOfficial/2017Challenges
|
a8f556f1d5b43c099a0394384c8bc2d826f9d287
|
[
"Apache-2.0"
] | 283
|
2017-01-01T23:26:05.000Z
|
2018-03-23T00:48:55.000Z
|
challenge_1/python/wobboz/src/reverse.py
|
AakashOfficial/2017Challenges
|
a8f556f1d5b43c099a0394384c8bc2d826f9d287
|
[
"Apache-2.0"
] | 311
|
2017-01-01T22:59:23.000Z
|
2021-09-23T00:29:12.000Z
|
from sys import argv
if len(argv) == 1:
print("[usage] reverse needs one argument")
elif len(argv) == 2:
print(argv[1][::-1])
else:
print("[usage] reverse only takes one argument")
| 23
| 49
| 0.673913
| 30
| 184
| 4.133333
| 0.6
| 0.112903
| 0.274194
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025641
| 0.152174
| 184
| 8
| 49
| 23
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0.394595
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.142857
| 0
| 0.142857
| 0.428571
| 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
| 0
| 0
| 0
| 1
|
0
| 4
|
f80d7fcc447d776157c66367c1a886befd5836b9
| 1,320
|
py
|
Python
|
setup.py
|
jayclassless/coverage_python_version
|
a5fc967455beb666d0cc2be23f59df230bc82f4c
|
[
"MIT"
] | null | null | null |
setup.py
|
jayclassless/coverage_python_version
|
a5fc967455beb666d0cc2be23f59df230bc82f4c
|
[
"MIT"
] | 2
|
2020-11-25T10:27:49.000Z
|
2022-01-25T07:18:41.000Z
|
setup.py
|
jayclassless/coverage_python_version
|
a5fc967455beb666d0cc2be23f59df230bc82f4c
|
[
"MIT"
] | null | null | null |
from setuptools import setup, find_packages
setup(
name='coverage_python_version',
version='0.2.0',
description='A coverage.py plugin to facilitate exclusions based on'
' Python version',
long_description=open('README.rst', 'r').read(),
keywords='coverage plugin version exclude',
author='Jason Simeone',
author_email='jay@classless.net',
license='MIT',
classifiers=[
'Intended Audience :: Developers',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: Implementation :: CPython',
'Programming Language :: Python :: Implementation :: PyPy',
'Topic :: Software Development',
'Topic :: Software Development :: Testing',
],
url='https://github.com/jayclassless/coverage_python_version',
package_dir={'': 'src'},
packages=find_packages('src'),
zip_safe=True,
include_package_data=True,
install_requires=[
'coverage>=4.5,<6',
],
)
| 33.846154
| 72
| 0.624242
| 136
| 1,320
| 5.963235
| 0.544118
| 0.210851
| 0.277435
| 0.160296
| 0.066584
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017734
| 0.231061
| 1,320
| 38
| 73
| 34.736842
| 0.781281
| 0
| 0
| 0.057143
| 0
| 0
| 0.571645
| 0.017437
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.028571
| 0
| 0.028571
| 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
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f872a017476740479ee2c7f7ad5a39b95832f940
| 122
|
py
|
Python
|
apps/reversedns/forms.py
|
jawr/kontrolvm
|
74bfd8af3f2da173ddf2c8f77e79ff8d6b83e032
|
[
"MIT"
] | 2
|
2016-09-24T17:38:29.000Z
|
2016-12-31T13:35:31.000Z
|
apps/reversedns/forms.py
|
jawr/kontrolvm
|
74bfd8af3f2da173ddf2c8f77e79ff8d6b83e032
|
[
"MIT"
] | 2
|
2020-04-10T02:09:18.000Z
|
2020-04-10T02:09:24.000Z
|
apps/reversedns/forms.py
|
jawr/kontrolvm
|
74bfd8af3f2da173ddf2c8f77e79ff8d6b83e032
|
[
"MIT"
] | null | null | null |
from django import forms
class ReverseDNSRequestForm(forms.Form):
rdns = forms.CharField(max_length=255, label="rDNS")
| 24.4
| 54
| 0.786885
| 16
| 122
| 5.9375
| 0.8125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027523
| 0.106557
| 122
| 4
| 55
| 30.5
| 0.844037
| 0
| 0
| 0
| 0
| 0
| 0.032787
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
f8a266cbbb0bf8252c9bd86761132de780c2bc65
| 541
|
py
|
Python
|
meeting_scheduler/scheduler_api/models.py
|
HulewiczKamil/kpz-2021-meeting-scheduler
|
f17227ff8f3b8450cbbb6a8b285972054f577b94
|
[
"MIT"
] | 3
|
2021-03-15T16:14:12.000Z
|
2021-03-15T16:15:48.000Z
|
meeting_scheduler/scheduler_api/models.py
|
HulewiczKamil/kpz-2021-meeting-scheduler
|
f17227ff8f3b8450cbbb6a8b285972054f577b94
|
[
"MIT"
] | 8
|
2021-03-24T23:51:23.000Z
|
2021-04-15T18:22:41.000Z
|
meeting_scheduler/scheduler_api/models.py
|
HulewiczKamil/kpz-2021-meeting-scheduler
|
f17227ff8f3b8450cbbb6a8b285972054f577b94
|
[
"MIT"
] | 1
|
2021-09-07T17:59:48.000Z
|
2021-09-07T17:59:48.000Z
|
import json
from django.db import models
# Create your models here.
class Calendar(models.Model):
id = models.CharField(max_length=100, primary_key=True)
etag = models.CharField(max_length=100)
summary = models.CharField(max_length=1000)
accessRole = models.CharField(max_length=100)
timeZone = models.CharField(max_length=100)
description = models.CharField(max_length=1000)
def toJSON(self):
return json.dumps(self, default=lambda o: o.__dict__,
sort_keys=True, indent=4)
| 28.473684
| 61
| 0.702403
| 71
| 541
| 5.183099
| 0.549296
| 0.244565
| 0.293478
| 0.391304
| 0.445652
| 0
| 0
| 0
| 0
| 0
| 0
| 0.048499
| 0.19963
| 541
| 18
| 62
| 30.055556
| 0.801386
| 0.044362
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.083333
| false
| 0
| 0.166667
| 0.083333
| 0.916667
| 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
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
f8b12f3bfb7ce05402fea34272e50bdcc1a63a12
| 151
|
py
|
Python
|
moto/timestreamwrite/__init__.py
|
symroe/moto
|
4e106995af6f2820273528fca8a4e9ee288690a5
|
[
"Apache-2.0"
] | 5,460
|
2015-01-01T01:11:17.000Z
|
2022-03-31T23:45:38.000Z
|
moto/timestreamwrite/__init__.py
|
symroe/moto
|
4e106995af6f2820273528fca8a4e9ee288690a5
|
[
"Apache-2.0"
] | 4,475
|
2015-01-05T19:37:30.000Z
|
2022-03-31T13:55:12.000Z
|
moto/timestreamwrite/__init__.py
|
symroe/moto
|
4e106995af6f2820273528fca8a4e9ee288690a5
|
[
"Apache-2.0"
] | 1,831
|
2015-01-14T00:00:44.000Z
|
2022-03-31T20:30:04.000Z
|
from .models import timestreamwrite_backends
from ..core.models import base_decorator
mock_timestreamwrite = base_decorator(timestreamwrite_backends)
| 30.2
| 63
| 0.874172
| 17
| 151
| 7.470588
| 0.529412
| 0.188976
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.07947
| 151
| 4
| 64
| 37.75
| 0.913669
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
3e2585b5a4a0add556169780e41819053890efc5
| 180
|
py
|
Python
|
Recognition/transformer/test.py
|
MengLcool/Ac-OCR
|
370152cc33995f41ee79374b3f5d62e94fea09d3
|
[
"MIT"
] | 1
|
2021-07-11T10:24:58.000Z
|
2021-07-11T10:24:58.000Z
|
Recognition/transformer/test.py
|
MengLcool/Oc-OCR
|
370152cc33995f41ee79374b3f5d62e94fea09d3
|
[
"MIT"
] | null | null | null |
Recognition/transformer/test.py
|
MengLcool/Oc-OCR
|
370152cc33995f41ee79374b3f5d62e94fea09d3
|
[
"MIT"
] | null | null | null |
import sys
import os
name = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
print(name)
sys.path.append(name)
import HyperParameters as hp
print(hp.EPOCH)
| 16.363636
| 67
| 0.727778
| 28
| 180
| 4.535714
| 0.5
| 0.141732
| 0.204724
| 0.23622
| 0.251969
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15
| 180
| 10
| 68
| 18
| 0.830065
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.428571
| 0
| 0.428571
| 0.285714
| 1
| 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
| 0
| 0
|
0
| 4
|
3e3546e7a8813ffa85fe71914c97ab13d0e321c0
| 53
|
py
|
Python
|
mnmt/encoder/__init__.py
|
Lawhy/Multi-task-NMT
|
d8e6a957f3d6e870172f6aa92e9871769d863244
|
[
"MIT"
] | 5
|
2020-12-05T14:53:33.000Z
|
2022-01-12T02:04:10.000Z
|
mnmt/encoder/__init__.py
|
Lawhy/Multi-task-NMT
|
d8e6a957f3d6e870172f6aa92e9871769d863244
|
[
"MIT"
] | null | null | null |
mnmt/encoder/__init__.py
|
Lawhy/Multi-task-NMT
|
d8e6a957f3d6e870172f6aa92e9871769d863244
|
[
"MIT"
] | 2
|
2021-01-15T02:37:55.000Z
|
2022-01-12T02:04:14.000Z
|
from mnmt.encoder.basic_encoder import BasicEncoder
| 26.5
| 52
| 0.867925
| 7
| 53
| 6.428571
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09434
| 53
| 1
| 53
| 53
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
|
3e456eacec4796619077c19329eac993a396ba6d
| 154
|
py
|
Python
|
aws_lambda_powertools/utilities/batch/exceptions.py
|
JRetza/aws-lambda-powertools-python
|
f0ae5a1dce0b54793da5a61c45e9ad2d1394bfe3
|
[
"MIT-0"
] | 1
|
2021-07-11T07:14:25.000Z
|
2021-07-11T07:14:25.000Z
|
aws_lambda_powertools/utilities/batch/exceptions.py
|
JRetza/aws-lambda-powertools-python
|
f0ae5a1dce0b54793da5a61c45e9ad2d1394bfe3
|
[
"MIT-0"
] | null | null | null |
aws_lambda_powertools/utilities/batch/exceptions.py
|
JRetza/aws-lambda-powertools-python
|
f0ae5a1dce0b54793da5a61c45e9ad2d1394bfe3
|
[
"MIT-0"
] | null | null | null |
"""
Batch processing exceptions
"""
class SQSBatchProcessingError(Exception):
"""When at least one message within a batch could not be processed"""
| 19.25
| 73
| 0.74026
| 18
| 154
| 6.333333
| 0.944444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.162338
| 154
| 7
| 74
| 22
| 0.883721
| 0.590909
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 1
| 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
| 0
| 0
| 1
| 0
|
0
| 4
|
3e472db0c3da293b4f63e31ba9a50096eb3ec3c7
| 837
|
py
|
Python
|
lib/database/database.py
|
LogicJake/Proxy_IP
|
11adc2a98a5e6e65b6e3a24abc5377040af41c04
|
[
"MIT"
] | 3
|
2018-03-23T09:26:59.000Z
|
2019-01-10T04:13:03.000Z
|
lib/database/database.py
|
LogicJake/proxy_pool
|
11adc2a98a5e6e65b6e3a24abc5377040af41c04
|
[
"MIT"
] | null | null | null |
lib/database/database.py
|
LogicJake/proxy_pool
|
11adc2a98a5e6e65b6e3a24abc5377040af41c04
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# @Author: LogicJake
# @Date: 2019-01-16 13:21:21
# @Last Modified time: 2019-01-22 19:55:42
from abc import ABCMeta, abstractmethod
class DataBase(metaclass=ABCMeta):
@abstractmethod
def connect(self):
pass
@abstractmethod
def close(self):
pass
@abstractmethod
def update(self, table_name, key_values, where):
pass
@abstractmethod
def insert(self, table_name, key_values):
pass
@abstractmethod
def is_table_exist(self, table_name):
pass
@abstractmethod
def select(self, table_name, column_name, where=None, limit=None, order_by=None, order='desc'):
pass
@abstractmethod
def delete(self, table_name, where=None):
pass
@abstractmethod
def create_required_tables(self):
pass
| 20.414634
| 99
| 0.647551
| 102
| 837
| 5.186275
| 0.5
| 0.257089
| 0.277883
| 0.094518
| 0.083176
| 0
| 0
| 0
| 0
| 0
| 0
| 0.046326
| 0.252091
| 837
| 40
| 100
| 20.925
| 0.798722
| 0.131422
| 0
| 0.615385
| 0
| 0
| 0.00554
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.307692
| false
| 0.307692
| 0.038462
| 0
| 0.384615
| 0
| 0
| 0
| 0
| null | 1
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
3e69e3a22f84436bfb90e8f8d4c349b29e56379e
| 343
|
py
|
Python
|
disk/exceptions.py
|
idin/revelio
|
3c67a05cb71c27af36469205e88d73ba72097da9
|
[
"MIT"
] | 3
|
2019-05-04T07:34:24.000Z
|
2020-01-02T06:13:38.000Z
|
disk/exceptions.py
|
idin/revelio
|
3c67a05cb71c27af36469205e88d73ba72097da9
|
[
"MIT"
] | 1
|
2020-03-02T21:27:22.000Z
|
2020-03-02T21:27:22.000Z
|
disk/exceptions.py
|
idin/revelio
|
3c67a05cb71c27af36469205e88d73ba72097da9
|
[
"MIT"
] | 2
|
2019-11-01T03:23:10.000Z
|
2020-08-20T05:06:47.000Z
|
class DiskError(RuntimeError):
pass
class SaveError(DiskError):
pass
class LoadError(DiskError):
pass
class RenameError(DiskError):
pass
class PathDoesNotExistError(DiskError):
pass
class PathExistsError(DiskError):
pass
class NotAFileError(FileNotFoundError):
pass
class DirectoryNotFoundError(NotADirectoryError):
pass
| 11.064516
| 49
| 0.795918
| 32
| 343
| 8.53125
| 0.40625
| 0.230769
| 0.32967
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.134111
| 343
| 30
| 50
| 11.433333
| 0.919192
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 1
| 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
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
3e73c05f4fe4ab98d6c104b536cc9fd9e7dfb098
| 802
|
py
|
Python
|
apps/auth/models/trackuser.py
|
rainydaygit/testtcloudserver
|
8037603efe4502726a4d794fb1fc0a3f3cc80137
|
[
"MIT"
] | 349
|
2020-08-04T10:21:01.000Z
|
2022-03-23T08:31:29.000Z
|
apps/auth/models/trackuser.py
|
rainydaygit/testtcloudserver
|
8037603efe4502726a4d794fb1fc0a3f3cc80137
|
[
"MIT"
] | 2
|
2021-01-07T06:17:05.000Z
|
2021-04-01T06:01:30.000Z
|
apps/auth/models/trackuser.py
|
rainydaygit/testtcloudserver
|
8037603efe4502726a4d794fb1fc0a3f3cc80137
|
[
"MIT"
] | 70
|
2020-08-24T06:46:14.000Z
|
2022-03-25T13:23:27.000Z
|
from library.api.db import db, EntityModel
class TrackUser(EntityModel):
ACTIVE = 0
DISABLE = 1
nickname = db.Column(db.String(100))
wx_userid = db.Column(db.String(200))
status = db.Column(db.Integer, default=ACTIVE)
email = db.Column(db.String(100))
telephone = db.Column(db.String(30))
weight = db.Column(db.Integer, default=1)
track_token = db.Column(db.Text())
name = db.Column(db.String(100))
user_id = db.Column(db.Integer)
class TrackUpload(EntityModel):
ACTIVE = 0
DISABLE = 1
project_id = db.Column(db.Integer)
user_id = db.Column(db.Integer)
device_type = db.Column(db.Integer)
device_typename = db.Column(db.String(200))
device_number = db.Column(db.String(500))
status = db.Column(db.Integer, default=ACTIVE)
| 29.703704
| 50
| 0.677057
| 117
| 802
| 4.57265
| 0.333333
| 0.224299
| 0.280374
| 0.209346
| 0.629907
| 0.220561
| 0.134579
| 0
| 0
| 0
| 0
| 0.038285
| 0.185786
| 802
| 26
| 51
| 30.846154
| 0.781011
| 0
| 0
| 0.363636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.045455
| 0
| 1
| 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
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
e42cfa73397bd683e0f52120bccd8b51c69d56b6
| 38
|
py
|
Python
|
ctrl-hyper/ctrl-v.py
|
MTfirst/cmd-ctrl_onLinux
|
38a6db67796bdc8d438ca63171d9fea03e84f5f7
|
[
"MIT"
] | 1
|
2020-05-02T03:46:10.000Z
|
2020-05-02T03:46:10.000Z
|
ctrl-hyper/ctrl-v.py
|
MTfirst/cmd-ctrl_onLinux
|
38a6db67796bdc8d438ca63171d9fea03e84f5f7
|
[
"MIT"
] | null | null | null |
ctrl-hyper/ctrl-v.py
|
MTfirst/cmd-ctrl_onLinux
|
38a6db67796bdc8d438ca63171d9fea03e84f5f7
|
[
"MIT"
] | null | null | null |
keyboard.send_keys("<ctrl>+<shift>+v")
| 38
| 38
| 0.710526
| 6
| 38
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 38
| 1
| 38
| 38
| 0.684211
| 0
| 0
| 0
| 0
| 0
| 0.410256
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
e43ddcafbcfe5e580e5ca9df83cae47cdab797f5
| 117
|
py
|
Python
|
Chapter__7/unittest/cap.py
|
nil1729/python__noob
|
d82d951dc511eafa9f4315e1fdfdc749f484abf1
|
[
"MIT"
] | null | null | null |
Chapter__7/unittest/cap.py
|
nil1729/python__noob
|
d82d951dc511eafa9f4315e1fdfdc749f484abf1
|
[
"MIT"
] | null | null | null |
Chapter__7/unittest/cap.py
|
nil1729/python__noob
|
d82d951dc511eafa9f4315e1fdfdc749f484abf1
|
[
"MIT"
] | null | null | null |
def cap_text(text):
"""
Input a String
Output the capitalized String
"""
return text.title()
| 19.5
| 34
| 0.581197
| 14
| 117
| 4.785714
| 0.785714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.316239
| 117
| 6
| 35
| 19.5
| 0.8375
| 0.376068
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
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0
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e448bd123598af9e0ecb5fcd1dbe365539555c32
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py
|
Python
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recommender/server/views.py
|
abhishekpathak/recommendation-system
|
b91961f2baa2ab70626aaadad2f90f609c92a449
|
[
"MIT"
] | null | null | null |
recommender/server/views.py
|
abhishekpathak/recommendation-system
|
b91961f2baa2ab70626aaadad2f90f609c92a449
|
[
"MIT"
] | null | null | null |
recommender/server/views.py
|
abhishekpathak/recommendation-system
|
b91961f2baa2ab70626aaadad2f90f609c92a449
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from server import api
from server.resources import EngineResource, EnginesResource, TaskResource
api.add_resource(EngineResource, '/engines/<engine_id>')
api.add_resource(EnginesResource, '/engines/')
api.add_resource(TaskResource, '/tasks/<task_id>')
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0
| 4
|
e47b3f30006b1125fe3fb166d95cf04ee299ccdb
| 38,969
|
py
|
Python
|
resource.py
|
amandashack/QDmapping
|
ee93dc693ebc8e6cfd378d5b69367c5293d232be
|
[
"MIT"
] | null | null | null |
resource.py
|
amandashack/QDmapping
|
ee93dc693ebc8e6cfd378d5b69367c5293d232be
|
[
"MIT"
] | null | null | null |
resource.py
|
amandashack/QDmapping
|
ee93dc693ebc8e6cfd378d5b69367c5293d232be
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.12.1)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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qt_resource_name = b"\
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qt_resource_struct_v1 = b"\
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qt_resource_struct_v2 = b"\
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\x00\x00\x00\x26\x00\x00\x00\x00\x00\x01\x00\x00\x1a\xff\
\x00\x00\x01\x69\x02\x23\x12\x90\
"
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()
| 62.752013
| 130
| 0.715492
| 9,211
| 38,969
| 3.022907
| 0.03333
| 0.026289
| 0.023919
| 0.01767
| 0.037926
| 0.035771
| 0.033364
| 0.033364
| 0.031641
| 0.029701
| 0
| 0.310752
| 0.034078
| 38,969
| 620
| 131
| 62.853226
| 0.428974
| 0.003901
| 0
| 0.029801
| 0
| 0.932119
| 0.000026
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0.003311
| false
| 0
| 0.001656
| 0
| 0.004967
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
e4a886f422924beccae48ec98f62dc22d04556a1
| 1,007
|
py
|
Python
|
jcasts/podcasts/migrations/0088_auto_20211112_2045.py
|
danjac/jcasts
|
04f5ef1f536d51962c0433d092817c0153acb6af
|
[
"MIT"
] | 13
|
2021-09-17T07:41:00.000Z
|
2022-02-10T10:00:48.000Z
|
jcasts/podcasts/migrations/0088_auto_20211112_2045.py
|
danjac/jcasts
|
04f5ef1f536d51962c0433d092817c0153acb6af
|
[
"MIT"
] | 167
|
2021-07-17T09:41:38.000Z
|
2021-08-31T06:03:34.000Z
|
jcasts/podcasts/migrations/0088_auto_20211112_2045.py
|
danjac/jcasts
|
04f5ef1f536d51962c0433d092817c0153acb6af
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.2.9 on 2021-11-12 20:45
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
("podcasts", "0087_podcast_subscribe_ping"),
]
operations = [
migrations.RemoveField(
model_name="podcast",
name="hub",
),
migrations.RemoveField(
model_name="podcast",
name="hub_exception",
),
migrations.RemoveField(
model_name="podcast",
name="subscribe_ping",
),
migrations.RemoveField(
model_name="podcast",
name="subscribe_requested",
),
migrations.RemoveField(
model_name="podcast",
name="subscribe_secret",
),
migrations.RemoveField(
model_name="podcast",
name="subscribe_status",
),
migrations.RemoveField(
model_name="podcast",
name="subscribed",
),
]
| 23.97619
| 52
| 0.533267
| 82
| 1,007
| 6.365854
| 0.414634
| 0.281609
| 0.348659
| 0.402299
| 0.630268
| 0.630268
| 0.551724
| 0
| 0
| 0
| 0
| 0.029412
| 0.358491
| 1,007
| 41
| 53
| 24.560976
| 0.778638
| 0.044687
| 0
| 0.6
| 1
| 0
| 0.182292
| 0.028125
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.028571
| 0
| 0.114286
| 0
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| null | 1
| 1
| 1
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| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
e4c97e09b4c144c572e32d52775cdacde48aa96c
| 59
|
py
|
Python
|
geoindex/wsgi.py
|
openregister/geoindex
|
7acd4ad69f5ad868775d06007f6a46b8640f6a92
|
[
"MIT"
] | null | null | null |
geoindex/wsgi.py
|
openregister/geoindex
|
7acd4ad69f5ad868775d06007f6a46b8640f6a92
|
[
"MIT"
] | null | null | null |
geoindex/wsgi.py
|
openregister/geoindex
|
7acd4ad69f5ad868775d06007f6a46b8640f6a92
|
[
"MIT"
] | 1
|
2021-04-11T08:30:56.000Z
|
2021-04-11T08:30:56.000Z
|
from geoindex.factory import create_app
app = create_app()
| 19.666667
| 39
| 0.813559
| 9
| 59
| 5.111111
| 0.666667
| 0.391304
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.118644
| 59
| 2
| 40
| 29.5
| 0.884615
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
e4e257d79edb3b1e620f09b5b01da63fd7f02ec1
| 96
|
py
|
Python
|
doctor_dash/apps.py
|
surajsjain/universal_medical_history
|
e6d38830af53133f2d3306438778128eaa2926bb
|
[
"MIT"
] | 3
|
2021-07-14T15:32:41.000Z
|
2022-02-08T08:34:34.000Z
|
doctor_dash/apps.py
|
surajsjain/universal-medical-history
|
e6d38830af53133f2d3306438778128eaa2926bb
|
[
"MIT"
] | null | null | null |
doctor_dash/apps.py
|
surajsjain/universal-medical-history
|
e6d38830af53133f2d3306438778128eaa2926bb
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class DoctorDashConfig(AppConfig):
name = 'doctor_dash'
| 16
| 34
| 0.770833
| 11
| 96
| 6.636364
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15625
| 96
| 5
| 35
| 19.2
| 0.901235
| 0
| 0
| 0
| 0
| 0
| 0.114583
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
e4f02ac4d597421566aec6f917c3bd4c26360eba
| 76,728
|
py
|
Python
|
src/experiments/experiments.py
|
Mrpatekful/Pytorch-MT
|
65c53245a9ab0bf4f5d933a239de1bfd7be79c3f
|
[
"MIT"
] | 7
|
2018-02-15T10:54:57.000Z
|
2018-03-07T16:53:35.000Z
|
src/experiments/experiments.py
|
Mrpatekful/nmt-BMEVIAUAL01
|
65c53245a9ab0bf4f5d933a239de1bfd7be79c3f
|
[
"MIT"
] | null | null | null |
src/experiments/experiments.py
|
Mrpatekful/nmt-BMEVIAUAL01
|
65c53245a9ab0bf4f5d933a239de1bfd7be79c3f
|
[
"MIT"
] | null | null | null |
"""@package experiments
"""
__author__ = "Patrik Purgai"
__copyright__ = "Copyright 2018, Patrik Purgai"
__date__ = "23 Apr 2018"
__version__ = "0.1"
import numpy
import logging
import torch
import torch.autograd
import copy
import tqdm
from src.components.utils.utils import Classifier, Layer
from src.models.models import Model
from src.modules.modules import AutoEncoder, Translator, Discriminator, WordTranslator, NoiseModel
from src.utils.analysis import DataLog, TextData, ScalarData
from src.utils.reader import Language
from src.utils.utils import Component, ModelWrapper, Policy, call, format_outputs, sentence_from_ids, UNMTPolicy, \
Interface
class Experiment(Component):
"""
Abstract base class for the experiments.
"""
def train(self, epoch):
raise NotImplementedError
def validate(self):
raise NotImplementedError
def test(self):
raise NotImplementedError
def evaluate(self):
raise NotImplementedError
@property
def state(self):
raise NotImplementedError
@state.setter
def state(self, value):
raise NotImplementedError
class UnsupervisedTranslation(Experiment):
"""
Translation experiment, without parallel corpus. The method follows the
main principles described
in this article:
https://arxiv.org/abs/1711.00043
The main goal of this experiment is to train a denoising auto-encoder, that
learns to map sentences to
sentences in two ways. The first way is to transform a noisy version of the
source sentence to it's
original form, and the second way is to transform a translated version of a
sentence to it's original form.
There is an additional factor during training, which is an adversarial
reguralization, that learns to
discriminate the hidden representations of the source and target languages.
"""
interface = Interface(**{
'policy': (0, Policy),
'language_identifiers': (1, None),
'languages': (2, Language),
'model': (3, Model),
'initial_translator': (4, WordTranslator),
'reguralizer': (5, Classifier)
})
abstract = False
@staticmethod
def clear_optimizers(optimizers: list):
"""
Convenience function for the execution of the clear function on the
provided optimizer.
Clear will reset the gradients of the optimized parameters.
Args:
optimizers: A list, containing the optimizer type objects.
"""
call('clear', optimizers)
@staticmethod
def step_optimizers(optimizers: list):
"""
Convenience function for the execution of the step function on the
provided optimizer.
Step will modify the values of the required parameters.
Args:
optimizers: A list, containing the optimizer type objects.
"""
call('step', optimizers)
@staticmethod
def freeze(task_components: list):
"""
Convenience function for the freezing the given components of the task.
The frozen
components won't receive any updates by the optimizers.
Args:
task_components: A list, containing Modules.
"""
call('freeze', task_components)
@staticmethod
def unfreeze(task_components: list):
"""
Convenience function for unfreezing the weights of the provided
components. The
optimizers will be able to modify the weights of these components.
Args:
task_components: A list, containing Modules.
"""
call('unfreeze', task_components)
def __init__(self,
model: Model,
policy: UNMTPolicy,
language_identifiers: list,
languages: list,
initial_translator: WordTranslator,
reguralizer: Classifier = None):
"""
Initialization of an unsupervised translation task. The embedding and
output layers for the model
are created in this function as well. These modules have to be
changeable during training,
so their references are kept in a list, where each index corresponds
to the index of language.
:param model:
A Model type instance, that will be used during the experiment.
:param languages:
:param policy:
An UNMTPolicy object, that contains specific information about this
particular task.
The information is divided into three segments, the train,
validation and test policy.
The data contained in the segments are the following.
tf_ratio:
A float scalar, that determines the rate of teacher forcing
during training phase.
A value of 0 will prevent teacher forcing, so the model
will use predictive decoding.
A value of 1 will force the model to use the targets as
previous outputs and a value of
0.5 will create a 50% chance of using either techniques.
Default value is 1.
noise:
A boolean value signaling the presence of noise in the
input data. The characteristics
of the noise function is ...
:param reguralizer:
Reguralization, that will be used as an adversarial reguralizer
during training. Default
value is None, meaning there won't be any reguralization used
during training.
:raises ValueError:
If the corpora was not created with a Monolingual type Corpora
object an
exception is raised.
"""
def initialize_embeddings() -> list:
"""
Initializer function for the embeddings of different languages.
Each language uses a different
embedding layer, which have to be switched during training and
evaluation.
"""
nonlocal languages
embeddings = []
for language in languages:
embeddings.append(language.vocabulary.embedding)
return embeddings
def initialize_loss_functions() -> list:
"""
Initializer function for the loss functions of different languages.
Each loss is a negative loss
likelihood function. The difference is the padding value, that
differs for the languages.
"""
nonlocal languages
loss_functions = []
for language in languages:
loss_functions.append(torch.nn.NLLLoss(
ignore_index=language.vocabulary.tokens['<PAD>'],
reduce=False))
return loss_functions
def initialize_output_layers() -> list:
"""
Initializer function for the output layers of different languages.
Each language uses a different
output layer, which have to be switched during training and
evaluation.
"""
nonlocal languages
nonlocal self
output_layers = []
for language in languages:
output_layers.append(Layer(
input_size=self._model.output_size,
output_size=language.vocabulary.vocab_size,
use_cuda=self._policy.cuda))
return output_layers
def initialize_tokens() -> list:
"""
Initializer function for the tokens of the different languages.
These tokens are the <EOS>, <SOS>
and <UNK> tokens. The returned 'tokens' list contains their ID
representation, that is retrieved
from the vocabulary of the corresponding language.
"""
nonlocal languages
tokens = []
for language in languages:
tokens.append(language.vocabulary.tokens)
return tokens
def initialize_input_pipelines() -> tuple:
"""
Initializer function for the tokens of the different languages.
These tokens are the <EOS>, <SOS>
and <UNK> tokens. The returned 'tokens' list contains their ID
representation, that is retrieved
from the vocabulary of the corresponding language.
"""
nonlocal languages
train_pipelines = []
validation_pipelines = []
test_pipelines = []
for language in languages:
train_pipelines.append(language.input_pipelines['train'])
validation_pipelines.append(language.input_pipelines['dev'])
test_pipelines.append(language.input_pipelines['test'])
assert all(list(map(
lambda x: x.batch_size == train_pipelines[0].batch_size,
train_pipelines))), \
'Invalid batch size'
return train_pipelines, validation_pipelines, test_pipelines
self._policy = policy
self._model = model
self._reguralizer = reguralizer
self.reguralize = False
self._noise_model = NoiseModel(use_cuda=self._policy.cuda)
self._language_identifiers = language_identifiers
self._add_language_token = self._policy.add_language_token
self._vocabularies = [l.vocabulary for l in languages]
self._initial_translator = initial_translator
self._initial_translator.vocabs = self._vocabularies
self._initial_translator.cuda = self._policy.cuda
self._initial_translator.language_tokens_required = \
self._add_language_token
self._previous_translator = self._initial_translator
# Initialization of the parameters, which will be different
# for each language used in the experiment.
self._tokens = initialize_tokens()
self._embeddings = initialize_embeddings()
self._loss_functions = initialize_loss_functions()
self._output_layers = initialize_output_layers()
self._train_input, self._dev_input, self._test_input = \
initialize_input_pipelines()
self._discriminator_loss_function = torch.nn.CrossEntropyLoss(
reduce=False)
# Initialization of the model wrapper object, that will be used by
# the modules, defined below.
# The modules do not have full control over the model, so they use
# this interface, to set the
# correct look up tables for the given input.
self._model_wrapper = ModelWrapper(self._model, self._tokens)
self._model_wrapper.init_table({
'encoder_inputs': self._embeddings,
'decoder_inputs': self._embeddings,
'decoder_outputs': self._output_layers
})
self._num_languages = len(languages)
# Initialization of the modules, which will be used during the
# experiment. These objects (modules)
# are at a higher abstraction level than the model, their
# responsibility is to iterate the given
# batch through the model, with the correct configuration of the
# model look up tables.
self._auto_encoder = AutoEncoder(
# --OPTIONAL PARAMS--
cuda=self._policy.cuda,
noise_model=self._noise_model,
add_language_token=self._add_language_token,
language_identifiers=self._language_identifiers,
# --REQUIRED PARAMS--
model=self._model_wrapper,
tokens=self._tokens,
loss_functions=self._loss_functions,
vocabularies=self._vocabularies
)
self._translator = Translator(
# --OPTIONAL PARAMS--
cuda=self._policy.cuda,
add_language_token=self._add_language_token,
language_identifiers=self._language_identifiers,
# --REQUIRED PARAMS--
model=self._model_wrapper,
tokens=self._tokens,
loss_functions=self._loss_functions,
vocabularies=self._vocabularies
)
self._discriminator = Discriminator(
# --OPTIONAL PARAMS--
cuda=self._policy.cuda,
# --REQUIRED PARAMS--
model=self._reguralizer,
loss_function=self._discriminator_loss_function,
)
self._iteration = 0
self._batch_size = self._train_input[0].batch_size
self._total_length = min(list(map(lambda x: x.total_length,
self._train_input)))
# Convenience attributes, that will help freezing and unfreezing
# the parameters of the model
# or the discriminator during specific phases of the training or
# evaluation.
self._auto_encoder_outputs = dict(
zip(list(map(lambda x: f'auto_encoding_{str(x)}',
self._model.output_types.keys())),
self._model.output_types.values())
)
self._translator_outputs = dict(
zip(list(map(lambda x: f'translation_{str(x)}',
self._model.output_types.keys())),
self._model.output_types.values())
)
self._model_optimizers = [
*self._model.optimizers,
*[embedding.optimizer for embedding in self._embeddings],
*[layer.optimizer for layer in self._output_layers]
]
self._model_components = [
self._model,
*self._embeddings,
*self._output_layers
]
def _format_auto_encoder_batch(self, batch: dict) -> dict:
"""
The special batch format, that is required by the task. This function
is passed to the input_pipeline,
and will be used to produce batches and targets, in a way, that is
convenient for this particular task.
:param batch:
An unprocessed batch, that contains an <SOS> at the 0. index,
<LNG> at 1. index
and an <EOS> token at the -2. index. The element at the last index
is the length of
the sequence.
:return Formatted batch:
A dictionary, that contains different types of formatted inputs for
the model.
The batch is created from a monolingual corpora, so the only
difference between
the inputs and targets, are the shifting, and the tokens.
inputs:
A torch Variable, that is the input of the model. The
<SOS> and <EOS> tokens are
cut from the original input.
targets:
A torch Variable, which will be the target of the model.
The <LNG> token is removed
from the original batch.
lengths:
A NumPy Array, the lengths of the inputs provided to the
encoder. These are required
by the PaddedSequence PyTorch utility.
"""
formatted_batch = {
'inputs': torch.from_numpy(batch[:, 1: -2]),
'targets': torch.from_numpy(batch[:, : -1]),
'input_lengths': batch[:, -1]
}
if self._add_language_token:
formatted_batch['input_lengths'] = \
formatted_batch['input_lengths'] - 1
else:
formatted_batch['input_lengths'] = \
formatted_batch['input_lengths'] - 2
if self._policy.cuda:
formatted_batch['targets'] = formatted_batch['targets'].cuda()
formatted_batch['targets'] = torch.autograd.Variable(
formatted_batch['targets'])
return formatted_batch
def train(self, epoch: int):
raise NotImplementedError
def validate(self):
raise NotImplementedError
def test(self):
raise NotImplementedError
def evaluate(self):
raise NotImplementedError
def _train_discriminator(self, batches, logs):
"""
:param batches:
:param logs:
"""
discriminator_loss = 0
discriminator_inputs = self._create_discriminator_inputs(batches)
for batch in discriminator_inputs:
inputs = self._numpy_to_variable(batch[:, 0])
loss = self._discriminator(inputs=inputs, targets=batch[:, 1])
discriminator_loss += loss
discriminator_loss.backward()
discriminator_loss /= len(discriminator_inputs)
logs.add(DataLog.TRAIN_DATA_ID, 'discriminator_loss',
discriminator_loss.data)
def _eval_discriminator(self, batches, logs, identifier):
"""
:param batches:
:param logs:
:param identifier:
"""
discriminator_loss = 0
discriminator_inputs = self._create_discriminator_inputs(batches)
for index, batch in enumerate(discriminator_inputs):
inputs = self._numpy_to_variable(batch[:, 0])
loss = self._discriminator(inputs=inputs, targets=batch[:, 1])
discriminator_loss += loss
discriminator_loss /= len(discriminator_inputs)
logs.add(identifier, 'discriminator_loss', discriminator_loss.data)
def _train_auto_encoder(self, batches, logs, forced_targets=True):
"""
Implementation of a step of auto-encoding. The look up tables of the model are fitted to the
provided inputs, and the <LNG> are substituted with the appropriate token. In this case the token
is the source language token. The inputs are then transformed by a noise function, and then fed
through the model. If reguralization is applied, the encoder outputs are fetched from the output
of the model, which is used by the discriminator to apply an adversarial reguralization on these
outputs.
:param batches:
A list, containing the batches from the input pipelines.
:return loss:
A scalar loss value, indicating the average loss of the auto encoder.
:return outputs:
A dictionary, that contains the outputs of the model. The types (keys) contained
by this dictionary depends on the model specifications.
"""
auto_encoding_loss = 0
reguralization_loss = 0
for language_index, batch in enumerate(batches):
loss, outputs, _ = self._auto_encoder(batch=batch,
lang_index=language_index,
forced_targets=forced_targets,
denoising=self._policy.train_noise)
auto_encoding_loss += loss
logs[language_index].add(DataLog.TRAIN_DATA_ID, 'auto_encoding_loss', loss.data)
if self._reguralizer is not None and self.reguralize:
for _language_index in range(self._num_languages):
if _language_index != language_index:
reguralization_loss += self._reguralize(outputs['encoder_outputs'], _language_index)
return auto_encoding_loss, reguralization_loss
def _validate_auto_encoder(self, batches, logs, identifier,
forced_targets=True):
"""
Implementation of a step of auto-encoding. The look up tables of the
model are fitted to the
provided inputs, and the <LNG> are substituted with the appropriate
token. In this case the token
is the source language token. The inputs are then transformed by a
noise function, and then fed
through the model. If reguralization is applied, the encoder outputs
are fetched from the output
of the model, which is used by the discriminator to apply an
adversarial reguralization on these
outputs.
:param batches:
A list, containing the batches from the input pipelines.
:return loss:
A scalar loss value, indicating the average loss of the auto
encoder.
:return outputs:
A dictionary, that contains the outputs of the model. The types
(keys) contained
by this dictionary depends on the model specifications.
"""
auto_encoding_loss = 0
reguralization_loss = 0
for language_index, batch in enumerate(batches):
loss, outputs, inputs = self._auto_encoder(
batch=batch,
lang_index=language_index,
forced_targets=forced_targets,
denoising=self._policy.validation_noise)
vocabulary = self._vocabularies[language_index]
outputs['input_text'] = sentence_from_ids(vocabulary=vocabulary,
ids=inputs)
outputs['output_text'] = sentence_from_ids(vocabulary=vocabulary,
ids=outputs['symbols'][0])
auto_encoding_loss += loss
logs[language_index].add(identifier, 'auto_encoding_loss',
loss.data)
logs[language_index].add(identifier, 'auto_encoding_text', {
'input_text': outputs['input_text'],
'target_text': sentence_from_ids(
vocabulary=vocabulary,
ids=batches[language_index]['targets']
.data.cpu().squeeze(0)[1:].numpy()),
'output_text': outputs['output_text']
})
for key in self._auto_encoder_outputs.keys():
logs[language_index].add(
identifier, key,
{key: outputs[key] for key in
logs[language_index].get_required_keys(key)})
if self._reguralizer is not None and self.reguralize:
for _language_index in range(self._num_languages):
if _language_index != language_index:
reguralization_loss += self._reguralize(
outputs['encoder_outputs'], _language_index)
return auto_encoding_loss, reguralization_loss
def _reguralize(self, encoder_outputs, lang_index):
"""
This function implements the reguralization mechanism. The inputs are
fed into the discriminator and evaluated based on the cross entropy
loss, that is defined in the
init function. The targets are either one-hot coded vectors, or their
inverse. This depends on
whether the loss is calculated for the discriminator or model loss.
:param lang_index:
An int value, that represents the index of the target language.
This value will serve as
the index of the substitution token for the input batch.
:param encoder_outputs:
PyTorch Variable, containing the outputs of the encoder.
:return loss:
A scalar loss value, indicating the average loss of the
discriminator for either the
inverse or normal target vector.
"""
targets = numpy.array([lang_index]*encoder_outputs.size(0))
loss = self._discriminator(inputs=encoder_outputs[:, -1, :],
targets=targets)
return loss
def _create_discriminator_inputs(self, batches):
"""
"""
batch_size = batches[0]['inputs'].size(0)
concat_input = self._create_encoder_output(batches[0], lang_index=0)
for index in range(1, self._num_languages):
concat_input = [
*concat_input,
*self._create_encoder_output(batches[index], lang_index=index)
]
concat_input = numpy.array(concat_input)
numpy.random.shuffle(concat_input)
return numpy.array([
concat_input[index * batch_size:index * batch_size + batch_size]
for index in range(len(batches))])
def _create_encoder_output(self, batch, lang_index):
"""
"""
self._model_wrapper.set_lookup({'source': lang_index})
if self._language_identifiers is not None and self._add_language_token:
inputs = self._add_random_language_token(batch['inputs'],
lang_index)
else:
inputs = batch['inputs']
if self._policy.cuda:
inputs = inputs.cuda()
inputs = torch.autograd.Variable(inputs)
outputs = self._model.encoder(
inputs=inputs,
lengths=batch['input_lengths'])['encoder_outputs']\
.data.cpu().numpy()
return [(outputs[index, -1, :], lang_index) for index in range(len(
outputs))]
def _add_random_language_token(self, batch, lang_index):
"""
"""
lang_tokens = numpy.random.uniform(0, self._num_languages,
size=(batch.size(0)))
tokens = torch.from_numpy(numpy.array([
self._vocabularies[lang_index](self._language_identifiers[int(
lang_tokens[token])])
for token in range(len(lang_tokens))
])).view(-1, 1)
return torch.cat((tokens, batch), dim=1)
def _numpy_to_variable(self, inputs):
"""
"""
shaped_inputs = numpy.zeros((inputs.shape[0], inputs[0].shape[0]))
for index in range(shaped_inputs.shape[0]):
shaped_inputs[index, :] = inputs[index]
shaped_inputs = torch.from_numpy(shaped_inputs).float()
if self._policy.cuda:
shaped_inputs = shaped_inputs.cuda()
shaped_inputs = torch.autograd.Variable(shaped_inputs)
return shaped_inputs
@property
def state(self):
raise NotImplementedError
@state.setter
def state(self, value):
raise NotImplementedError
class DividedCurriculumTranslation(UnsupervisedTranslation): # TODO
"""
"""
interface = UnsupervisedTranslation.interface
abstract = False
def __init__(self,
policy: UNMTPolicy,
model: Model,
language_identifiers: list,
languages: list,
initial_translator: WordTranslator,
reguralizer: Classifier = None):
"""
:param policy:
:param model:
:param language_identifiers:
:param languages:
:param initial_translator:
:param reguralizer:
"""
super().__init__(model=model,
policy=policy,
language_identifiers=language_identifiers,
languages=languages,
initial_translator=initial_translator,
reguralizer=reguralizer)
def initialize_input_pipelines() -> tuple:
"""
Initializer function for the tokens of the different languages.
These tokens are the <EOS>, <SOS>
and <UNK> tokens. The returned 'tokens' list contains their ID
representation, that is retrieved
from the vocabulary of the corresponding language.
"""
nonlocal languages
translated_train_pipelines = []
translated_dev_pipelines = []
for language in languages:
translated_train_pipelines.append(
language.input_pipelines['translated_train'])
translated_dev_pipelines.append(
language.input_pipelines['translated_dev'])
return translated_train_pipelines, translated_dev_pipelines
assert 'translated' in languages[0].input_pipelines, 'InputPipeline dictionary of the ' \
'languages must contain \'translated\' key'
self._translated_train_input, self._translated_dev_input = initialize_input_pipelines()
def _format_translator_batch(self, batch: dict) -> dict:
"""
The special batch format, that is required by the task. This function is passed to the input_pipeline,
and will be used to produce batches and targets, in a way, that is convenient for this particular task.
Args:
batch:
An unprocessed batch, that contains an <SOS> at the 0. index, <LNG> at 1. index
and an <EOS> token at the -2. index. The element at the last index is the length of
the sequence.
Returns:
Formatted batch:
A dictionary, that contains different types of formatted inputs for the model.
The batch is created from a monolingual corpora, so the only difference between
the inputs and targets, are the shifting, and the tokens.
inputs:
A torch Variable, that is the input of the model. The <SOS> and <EOS> tokens are
cut from the original input.
targets:
A torch Variable, which will be the target of the model. The <LNG> token is removed
from the original batch.
lengths:
A NumPy Array, the lengths of the inputs provided to the encoder. These are required
by the PaddedSequence PyTorch utility.
"""
formatted_batch = {
'inputs': torch.from_numpy(batch[:, 1: -2]),
'targets': torch.from_numpy(batch[:, : -1]),
'input_lengths': batch[:, -1] - 1
}
if self._policy.cuda:
formatted_batch['targets'] = formatted_batch['targets'].cuda()
formatted_batch['targets'] = torch.autograd.Variable(formatted_batch['targets'])
return formatted_batch
def train(self, epoch: int) -> dict:
"""
A single training iteration/epoch of the task. The method iterates through the training
corpora once and updates the parameters of the model, based on the generated loss. The iteration
has 2 main steps, the model and the discriminator training. During the model training, the inputs
are propagated through an auto encoding, translation, and reguralization phase. The losses are
calculated after each step, and summed with a specific weight. The weights are tuneable hyper
parameters. The sum of the losses are minimized, where auto encoding and translation losses are
calculated by a negative log likelihood loss, and the reguralization is calculated by a cross
entropy loss.
:raise RuntimeError:
In case of an occurrence of NaN values a runtime exception is raised.
:return total_iteration_loss:
Loss of the model, including the auto encoding, translation and reguralization loss.
The value is normalized, so this value represents the sum of average loss of a word
after translation,
:return tr_loss:
Average loss of the translation phase of the model for an iteration. This value is
a NumPy Array, with a dimension of (num_languages). A value at a given index
corresponds to the average loss of a word prediction for the language of that index.
:return ae_loss:
Average loss of the auto encoding phase of the model.
:return reg_loss:
Average loss, created by the reguralization term, that contributes to the total model loss.
:return dsc_loss:
Average loss that is created by the discriminator, during its training phase.
"""
language_logs = [DataLog({
'translation_loss': ScalarData,
'auto_encoding_loss': ScalarData,
}) for _ in range(self._num_languages)]
mutual_logs = DataLog({
'total_loss': ScalarData,
'discriminator_loss': ScalarData,
'reguralization_loss': ScalarData
})
self.reguralize = (epoch + 1) % 2 == 0
with tqdm.tqdm() as p_bar:
p_bar.set_description('Translating corpora')
for batches in zip(*list(map(lambda x: x.batch_generator(), self._train_input))):
p_bar.update()
with tqdm.tqdm() as p_bar:
p_bar.set_description(f'Processing epoch {epoch}')
for batches in zip(*list(map(lambda x: x.batch_generator(),
[*self._train_input, *self._translated_train_input]))):
p_bar.update()
# Batches are generated from the InputPipeline object. In this experiment each language
# has its own pipeline, with its vocabulary. The number of languages, however, may differ.
# The generated 'batches' object contains the input, target, and length data for the model.
auto_encoder_batches = list(map(self._format_auto_encoder_batch, batches[:len(batches) // 2]))
translator_batches = list(map(self._format_translator_batch, batches[:len(batches) // 2]))
iteration_loss = 0
total_reguralization_loss = 0
# Discriminator training or reguralization is not used by default, only if it has been explicitly
# defined for the experiment.
if self._reguralizer is not None and not self.reguralize:
self.freeze(self._model_components)
self.unfreeze([self._reguralizer])
self.clear_optimizers([self._reguralizer.optimizer])
self._train_discriminator(logs=mutual_logs, batches=auto_encoder_batches)
self.step_optimizers([self._reguralizer.optimizer])
self.unfreeze(self._model_components)
if self._reguralizer is not None:
self.freeze([self._reguralizer])
self.clear_optimizers(self._model_optimizers)
# Choosing the mode of decoding for the iteration. During predictive decoding (when teacher
# forcing is not used), the embeddings of the model must be set to frozen state.
forced_targets = numpy.random.random() < self._policy.train_tf_ratio
if not forced_targets:
self.freeze(self._embeddings)
auto_encoding_loss, reguralization_loss = self._train_auto_encoder(logs=language_logs,
batches=batches,
forced_targets=forced_targets)
iteration_loss += auto_encoding_loss
iteration_loss += reguralization_loss
if self._reguralizer is not None and self.reguralize:
total_reguralization_loss += reguralization_loss.data
translation_loss, reguralization_loss = self._train_translator(logs=language_logs,
batches=batches,
forced_targets=forced_targets)
iteration_loss += translation_loss
iteration_loss += reguralization_loss
if self._reguralizer is not None and self.reguralize:
total_reguralization_loss += reguralization_loss.data
mutual_logs.add(DataLog.TRAIN_DATA_ID, 'total_loss', iteration_loss.data)
mutual_logs.add(DataLog.TRAIN_DATA_ID, 'reguralization_loss', total_reguralization_loss)
iteration_loss.backward()
self.step_optimizers(self._model_optimizers)
if not forced_targets:
self.unfreeze(self._embeddings)
return {**dict(zip(self._language_identifiers, language_logs)), DataLog.MUTUAL_TOKEN_ID: mutual_logs}
def validate(self) -> dict:
"""
This function evaluates the model. Input data is propagated forward, and then the loss calculated
based on the same loss function which was used during training. The weights however, are not modified
in this function.
:return logs:
A list of DataLog type objects, that contain the logging data for the languages. The number of
data logs equal to the number of languages, and each data log contains information about the
produced output for the whole data set of a language.
total_loss:
The total loss of the iteration, which is the same as the model loss during training.
The value contains the loss of translation, auto-encoding and reguralization loss. The
individual error of the discriminator is not included.
translation_loss:
The error, that is produced by the model, when translating a sentence.
auto_encoding_loss:
The error, that is produced by the model,
when restoring (auto-encoding) a sentence.
reguralization_loss:
The reguralization loss, that is produced by the discriminator.
discriminator_loss:
The error of the discriminator, which is the loss that is produced, when the
discriminator identifies a given latent vector.
translation_text:
The textual representation of the input, target and output symbols at the
translation phase. These texts are produced by the format outputs
utility function.
auto_encoding_text:
The textual representation of the input, target and output symbols at the
auto encoding phase. These texts are produced by the format outputs
utility function.
Additional outputs depend on the chosen model.
"""
language_logs = [DataLog({
'translation_loss': ScalarData,
'auto_encoding_loss': ScalarData,
'translation_text': TextData,
'auto_encoding_text': TextData,
**self._auto_encoder_outputs,
**self._translator_outputs
}) for _ in range(self._num_languages)]
mutual_logs = DataLog({
'total_loss': ScalarData,
'discriminator_loss': ScalarData,
'reguralization_loss': ScalarData,
})
with tqdm.tqdm() as p_bar:
p_bar.set_description('Translating corpora')
for batches in zip(*list(map(lambda x: x.batch_generator(), self._train_input))):
p_bar.update()
with tqdm.tqdm() as p_bar:
p_bar.set_description('Validating')
for identifier, batches in enumerate(zip(*list(
map(lambda x: x.batch_generator(), self._dev_input)))):
p_bar.update()
batches = list(map(self._format_auto_encoder_batch, batches))
iteration_loss = 0
full_reguralization_loss = 0
self.freeze(self._model_components)
if self._reguralizer is not None:
self.freeze([self._reguralizer])
self._eval_discriminator(logs=mutual_logs,
batches=batches,
identifier=identifier)
auto_encoding_loss, reguralization_loss = self._validate_auto_encoder(logs=language_logs,
batches=batches,
identifier=identifier)
iteration_loss += auto_encoding_loss
iteration_loss += reguralization_loss
if self._reguralizer is not None and self.reguralize:
full_reguralization_loss += reguralization_loss.data
translation_loss, reguralization_loss = self._validate_translator(logs=language_logs,
batches=batches,
identifier=identifier)
iteration_loss += auto_encoding_loss
iteration_loss += reguralization_loss
mutual_logs.add(identifier, 'total_loss', iteration_loss.data)
mutual_logs.add(identifier, 'reguralization_loss', full_reguralization_loss)
self.unfreeze(self._model_components)
if self._reguralizer is not None and self.reguralize:
self.unfreeze([self._reguralizer])
return {**dict(zip(self._language_identifiers, language_logs)), DataLog.MUTUAL_TOKEN_ID: mutual_logs}
def test(self):
pass
def evaluate(self):
pass
def _train_translator(self, batches, logs, forced_targets=True):
"""
:param batches:
:param logs:
:param forced_targets:
:return total_translation_loss:
:return total_reguralization_loss:
"""
total_translation_loss = 0
total_reguralization_loss = 0
translation_loss, reguralization_loss, outputs, _ = self._translate(
batch=batches[0],
logs=logs,
input_lang_index=0,
target_lang_index=1,
identifier=DataLog.TRAIN_DATA_ID,
forced_targets=forced_targets)
total_translation_loss += translation_loss
total_reguralization_loss += reguralization_loss
translation_loss, reguralization_loss, outputs, _ = self._translate(
batch=batches[1],
logs=logs,
input_lang_index=1,
target_lang_index=0,
identifier=DataLog.TRAIN_DATA_ID,
forced_targets=forced_targets)
total_translation_loss += translation_loss
total_reguralization_loss += reguralization_loss
return total_translation_loss, total_reguralization_loss
def _validate_translator(self, batches, logs, identifier, forced_targets=False):
"""
:param batches:
:param logs:
:param identifier:
:param forced_targets:
:return translation_loss:
:return reguralization_loss:
"""
total_translation_loss = 0
total_reguralization_loss = 0
translation_loss, reguralization_loss, outputs, translated_symbols = self._translate(
batch=batches[0],
logs=logs,
input_lang_index=0,
target_lang_index=1,
identifier=identifier,
forced_targets=forced_targets)
total_translation_loss += translation_loss
total_reguralization_loss += reguralization_loss
source_vocabulary = self._vocabularies[0]
target_vocabulary = self._vocabularies[1]
outputs['input_text'] = sentence_from_ids(vocabulary=source_vocabulary, ids=translated_symbols)
outputs['output_text'] = sentence_from_ids(vocabulary=target_vocabulary, ids=outputs['symbols'][0])
logs[0].add(identifier, 'translation_text', format_outputs(
(source_vocabulary, translated_symbols),
(target_vocabulary, batches[1]['inputs']),
(target_vocabulary, outputs['symbols'][0])
)
)
logs[0].add(identifier, 'translation_text', {
'input_text': outputs['input_text'],
'target_text': sentence_from_ids(vocabulary=target_vocabulary, ids=batches[1]['inputs']
.data.cpu().squeeze(0)[1:].numpy()),
'output_text': outputs['output_text']
})
for key in self._translator_outputs.keys():
logs[0].add(identifier, key, {key: outputs[key] for key in logs[0].get_required_keys(key)})
translation_loss, reguralization_loss, outputs, translated_symbols = self._translate(
batch=batches[1],
logs=logs,
input_lang_index=1,
target_lang_index=0,
identifier=identifier,
forced_targets=forced_targets)
total_translation_loss += translation_loss
total_reguralization_loss += reguralization_loss
source_vocabulary = self._vocabularies[1]
target_vocabulary = self._vocabularies[0]
outputs['input_text'] = sentence_from_ids(vocabulary=source_vocabulary, ids=translated_symbols)
outputs['output_text'] = sentence_from_ids(vocabulary=target_vocabulary, ids=outputs['symbols'][0])
logs[1].add(identifier, 'translation_text', format_outputs(
(source_vocabulary, translated_symbols),
(target_vocabulary, batches[0]['inputs']),
(target_vocabulary, outputs['symbols'][0])
)
)
for key in self._translator_outputs.keys():
logs[1].add(identifier, key, {key: outputs[key] for key in logs[1].get_required_keys(key)})
return translation_loss, reguralization_loss
def _translate(self, batch, logs, input_lang_index, target_lang_index, identifier, forced_targets):
"""
:param batch:
:param logs:
:param input_lang_index:
:param target_lang_index:
:param identifier:
:param forced_targets:
:return translation_loss:
:return reguralization_loss:
:return outputs:
:return translated_symbols:
"""
reguralization_loss = 0
# Loss will only be calculated by the translator, if targets, and targets_lengths are both provided.
# During this step, the lengths of the targets are not provided, so loss will not be calculated.
translation_loss, _, outputs, _, _, = self._translator(
input_lang_index=input_lang_index,
target_lang_index=target_lang_index,
batch=batch,
forced_targets=forced_targets)
if self._reguralizer is not None and self.reguralize:
for _language_index in range(self._num_languages):
if _language_index != target_lang_index:
reguralization_loss += self._reguralize(outputs['encoder_outputs'], _language_index)
if logs is not None:
logs[input_lang_index].add(identifier, 'translation_loss', translation_loss.data)
return translation_loss, reguralization_loss, outputs, batch['inputs']
@property
def state(self):
"""
Property for the state of the task.
"""
return {
'model': self._model.state,
'embeddings': [embedding.state for embedding in self._embeddings],
'output_layers': [layer.state for layer in self._output_layers],
}
# noinspection PyMethodOverriding
@state.setter
def state(self, state):
"""
Setter function for the state of the task, and the embeddings.
"""
self._model.state = state['model']
for index, embedding_state in enumerate(state['embeddings']):
self._embeddings[index].state = embedding_state
for index, layer_state in enumerate(state['output_layers']):
self._output_layers[index].state = layer_state
class MergedCurriculumTranslation(UnsupervisedTranslation):
"""
"""
interface = UnsupervisedTranslation.interface
abstract = False
def __init__(self,
model: Model,
policy: UNMTPolicy,
language_identifiers: list,
languages: list,
initial_translator: WordTranslator,
reguralizer: Classifier = None):
"""
:param model:
:param policy:
:param language_identifiers:
:param languages:
:param initial_translator:
:param reguralizer:
"""
super().__init__(model=model,
policy=policy,
language_identifiers=language_identifiers,
languages=languages,
initial_translator=initial_translator,
reguralizer=reguralizer)
self._previous_model = copy.deepcopy(self._model)
self._previous_embeddings = copy.deepcopy(self._embeddings)
self._previous_output_layers = copy.deepcopy(self._output_layers)
self._previous_model_wrapper = ModelWrapper(self._previous_model, self._tokens)
self._previous_model_wrapper.init_table({
'encoder_inputs': self._previous_embeddings,
'decoder_inputs': self._previous_embeddings,
'decoder_outputs': self._previous_output_layers
})
self._previous_model_components = [
self._previous_model,
*self._previous_embeddings,
*self._previous_output_layers
]
def train(self, epoch: int) -> dict:
"""
A single training iteration/epoch of the task. The method iterates through the training
corpora once and updates the parameters of the model, based on the generated loss. The iteration
has 2 main steps, the model and the discriminator training. During the model training, the inputs
are propagated through an auto encoding, translation, and reguralization phase. The losses are
calculated after each step, and summed with a specific weight. The weights are tuneable hyper
parameters. The sum of the losses are minimized, where auto encoding and translation losses are
calculated by a negative log likelihood loss, and the reguralization is calculated by a cross
entropy loss.
:raises RuntimeError:
In case of an occurrence of NaN values a runtime exception is raised.
:return total_iteration_loss:
Loss of the model, including the auto encoding, translation and reguralization loss.
The value is normalized, so this value represents the sum of average loss of a word
after translation,
:return tr_loss:
Average loss of the translation phase of the model for an iteration. This value is
a NumPy Array, with a dimension of (num_languages). A value at a given index
corresponds to the average loss of a word prediction for the language of that index.
:return ae_loss:
Average loss of the auto encoding phase of the model.
:return reg_loss:
Average loss, created by the reguralization term, that contributes to the total model loss.
:return dsc_loss:
Average loss that is created by the discriminator, during its training phase.
"""
language_logs = [DataLog({
'translation_loss': ScalarData,
'auto_encoding_loss': ScalarData,
}) for _ in range(self._num_languages)]
mutual_logs = DataLog({
'total_loss': ScalarData,
'discriminator_loss': ScalarData,
'reguralization_loss': ScalarData
})
self._previous_model.eval()
self.freeze(self._previous_model_components)
self.reguralize = True
with tqdm.tqdm(total=self._total_length) as p_bar:
p_bar.set_description(f'Processing epoch {epoch}')
for iteration, batches in enumerate(zip(*list(map(lambda x: x.batch_generator(), self._train_input)))):
p_bar.update()
if iteration*self._batch_size < self._iteration:
continue
else:
self._iteration = iteration*self._batch_size
# Batches are generated from the InputPipeline object. In this experiment each language
# has its own pipeline, with its vocabulary. The number of languages, however, may differ.
# The generated 'batches' object contains the input, target, and length data for the model.
batches = list(map(self._format_auto_encoder_batch, batches))
iteration_loss = 0
total_reguralization_loss = 0
# Discriminator training or reguralization is not used by default, only if it has been explicitly
# defined for the experiment.
if self._reguralizer is not None:
self._model.eval()
self._reguralizer.train()
self.freeze(self._model_components)
self.unfreeze([self._reguralizer])
self.clear_optimizers([self._reguralizer.optimizer])
self._train_discriminator(logs=mutual_logs, batches=batches)
self.step_optimizers([self._reguralizer.optimizer])
self.unfreeze(self._model_components)
if self._reguralizer is not None:
self.freeze([self._reguralizer])
self._reguralizer.eval()
self.clear_optimizers(self._model_optimizers)
# Choosing the mode of decoding for the iteration. During predictive decoding (when teacher
# forcing is not used), the embeddings of the model must be set to frozen state.
forced_targets = numpy.random.random() < self._policy.train_tf_ratio
self._model.train()
if not forced_targets:
self.freeze(self._embeddings)
auto_encoding_loss, reguralization_loss = self._train_auto_encoder(logs=language_logs,
batches=batches,
forced_targets=forced_targets)
iteration_loss += auto_encoding_loss
iteration_loss += reguralization_loss
del auto_encoding_loss
if self._reguralizer is not None and self.reguralize:
total_reguralization_loss += reguralization_loss.data
del reguralization_loss
translation_loss, reguralization_loss = self._train_translator(logs=language_logs,
batches=batches,
forced_targets=forced_targets)
iteration_loss += translation_loss
iteration_loss += reguralization_loss
del translation_loss
if self._reguralizer is not None and self.reguralize:
total_reguralization_loss += reguralization_loss.data
del reguralization_loss
mutual_logs.add(DataLog.TRAIN_DATA_ID, 'total_loss', iteration_loss.data)
mutual_logs.add(DataLog.TRAIN_DATA_ID, 'reguralization_loss', total_reguralization_loss)
iteration_loss.backward()
del iteration_loss
del total_reguralization_loss
self.step_optimizers(self._model_optimizers)
if not forced_targets:
self.unfreeze(self._embeddings)
self._iteration = 0
return {**dict(zip(self._language_identifiers, language_logs)), DataLog.MUTUAL_TOKEN_ID: mutual_logs}
def validate(self) -> dict:
"""
This function evaluates the model. Input data is propagated forward, and then the loss calculated
based on the same loss function which was used during training. The weights however, are not modified
in this function.
:return logs:
A list of DataLog type objects, that contain the logging data for the languages. The number of
data logs equal to the number of languages, and each data log contains information about the
produced output for the whole data set of a language.
total_loss:
The total loss of the iteration, which is the same as the model loss during training.
The value contains the loss of translation, auto-encoding and reguralization loss. The
individual error of the discriminator is not included.
translation_loss:
The error, that is produced by the model, when translating a sentence.
auto_encoding_loss:
The error, that is produced by the model,
when restoring (auto-encoding) a sentence.
reguralization_loss:
The reguralization loss, that is produced by the discriminator.
discriminator_loss:
The error of the discriminator, which is the loss that is produced, when the
discriminator identifies a given latent vector.
translation_text:
The textual representation of the input, target and output symbols at the
translation phase. These texts are produced by the format outputs
utility function.
auto_encoding_text:
The textual representation of the input, target and output symbols at the
auto encoding phase. These texts are produced by the format outputs
utility function.
Additional outputs depend on the chosen model.
"""
language_logs = [DataLog({
'translation_loss': ScalarData,
'auto_encoding_loss': ScalarData,
'translation_text': TextData,
'auto_encoding_text': TextData,
**self._auto_encoder_outputs,
**self._translator_outputs
}) for _ in range(self._num_languages)]
mutual_logs = DataLog({
'total_loss': ScalarData,
'discriminator_loss': ScalarData,
'reguralization_loss': ScalarData,
})
self._model.eval()
self._previous_model.eval()
if self._reguralizer is not None:
self._reguralizer.eval()
self.reguralize = True
with tqdm.tqdm() as p_bar:
p_bar.set_description('Validating')
for identifier, batches in enumerate(zip(*list(map(lambda x: x.batch_generator(), self._dev_input)))):
p_bar.update()
batches = list(map(self._format_auto_encoder_batch, batches))
iteration_loss = 0
full_reguralization_loss = 0
self.freeze(self._model_components)
if self._reguralizer is not None:
self.freeze([self._reguralizer])
self._eval_discriminator(logs=mutual_logs,
batches=batches,
identifier=identifier)
auto_encoding_loss, reguralization_loss = self._validate_auto_encoder(logs=language_logs,
batches=batches,
identifier=identifier)
iteration_loss += auto_encoding_loss
iteration_loss += reguralization_loss
if self._reguralizer is not None and self.reguralize:
full_reguralization_loss += reguralization_loss.data
translation_loss, reguralization_loss = self._validate_translator(logs=language_logs,
batches=batches,
identifier=identifier)
iteration_loss += auto_encoding_loss
iteration_loss += reguralization_loss
mutual_logs.add(identifier, 'total_loss', iteration_loss.data)
mutual_logs.add(identifier, 'reguralization_loss', full_reguralization_loss)
self.unfreeze(self._model_components)
if self._reguralizer is not None and self.reguralize:
self.unfreeze([self._reguralizer])
self._previous_model.state = self._model.state
for index, embedding_state in enumerate(self._embeddings):
self._previous_embeddings[index].state = embedding_state.state
for index, layer_state in enumerate(self._output_layers):
self._previous_output_layers[index].state = layer_state.state
self._previous_translator = Translator(
# --OPTIONAL PARAMS--
cuda=self._policy.cuda,
language_identifiers=self._language_identifiers,
# --REQUIRED PARAMS--
model=self._previous_model_wrapper,
tokens=self._tokens,
add_language_token=self._add_language_token,
loss_functions=self._loss_functions,
vocabularies=self._vocabularies
)
return {**dict(zip(self._language_identifiers, language_logs)), DataLog.MUTUAL_TOKEN_ID: mutual_logs}
def test(self) -> dict:
"""
This function evaluates the model. Input data is propagated forward, and then the loss calculated
based on the same loss function which was used during training. The weights however, are not modified
in this function.
:return logs:
A list of DataLog type objects, that contain the logging data for the languages. The number of
data logs equal to the number of languages, and each data log contains information about the
produced output for the whole data set of a language.
Additional outputs depend on the chosen model.
"""
language_logs = [DataLog({
'translation_loss': ScalarData,
'translation_text': TextData,
**self._translator_outputs
}) for _ in range(self._num_languages)]
mutual_logs = DataLog({
'discriminator_loss': ScalarData,
})
self._model.eval()
self._previous_model.eval()
self._reguralizer.eval()
self.freeze(self._model_components)
self.freeze(self._previous_model_components)
self.reguralize = False
with tqdm.tqdm() as p_bar:
p_bar.set_description('Testing')
for identifier, batches in enumerate(zip(*list(map(lambda x: x.batch_generator(), self._test_input)))):
p_bar.update()
batches = list(map(self._format_auto_encoder_batch, batches))
if self._reguralizer is not None:
self.freeze([self._reguralizer])
self._eval_discriminator(logs=mutual_logs,
batches=batches,
identifier=identifier)
self._validate_translator(logs=language_logs,
batches=batches,
identifier=identifier)
self.reguralize = True
return {**dict(zip(self._language_identifiers, language_logs)), DataLog.MUTUAL_TOKEN_ID: mutual_logs}
def evaluate(self) -> dict:
"""
This function evaluates the model. Input data is propagated forward, and then the loss calculated
based on the same loss function which was used during training. The weights however, are not modified
in this function.
:return logs:
A list of DataLog type objects, that contain the logging data for the languages. The number of
data logs equal to the number of languages, and each data log contains information about the
produced output for the whole data set of a language.
Additional outputs depend on the chosen model.
"""
language_logs = [DataLog({
'translation_text': TextData,
**self._translator_outputs
}) for _ in range(self._num_languages)]
self._model.eval()
self._previous_model.eval()
self.freeze(self._model_components)
with tqdm.tqdm() as p_bar:
p_bar.set_description('Inference')
outputs = []
for identifier, batch in enumerate(self._test_input[0].batch_generator()):
p_bar.update()
batch = self._format_auto_encoder_batch(batch)
input_text, output_text = self._eval_translator(batch=batch,
input_lang_index=0,
target_lang_index=1,
logs=language_logs,
identifier=identifier)
outputs.append((input_text, output_text))
for identifier, batch in enumerate(self._test_input[1].batch_generator()):
p_bar.update()
batch = self._format_auto_encoder_batch(batch)
input_text, output_text = self._eval_translator(batch=batch,
input_lang_index=1,
target_lang_index=0,
logs=language_logs,
identifier=identifier)
outputs.append((input_text, output_text))
logging.info('\n\n'.join(list(map(lambda x: f'Input: {x[0]}\nOutput: {x[1]}', outputs))))
return dict(zip(self._language_identifiers, language_logs))
def _train_translator(self, batches, logs, forced_targets=True):
"""
:param batches:
:param logs:
:param forced_targets:
:return total_translation_loss:
:return total_reguralization_loss:
"""
total_translation_loss = 0
total_reguralization_loss = 0
translation_loss, reguralization_loss, outputs, _ = self._translate(
batch=batches[0],
logs=logs,
input_lang_index=0,
target_lang_index=1,
identifier=DataLog.TRAIN_DATA_ID,
forced_targets=forced_targets)
total_translation_loss += translation_loss
total_reguralization_loss += reguralization_loss
translation_loss, reguralization_loss, outputs, _ = self._translate(
batch=batches[1],
logs=logs,
input_lang_index=1,
target_lang_index=0,
identifier=DataLog.TRAIN_DATA_ID,
forced_targets=forced_targets)
total_translation_loss += translation_loss
total_reguralization_loss += reguralization_loss
return total_translation_loss, total_reguralization_loss
def _validate_translator(self, batches, logs, identifier, forced_targets=False):
"""
:param batches:
:param logs:
:param identifier:
:param forced_targets:
:return translation_loss:
:return reguralization_loss:
"""
total_translation_loss = 0
total_reguralization_loss = 0
translation_loss, reguralization_loss, outputs, translated_symbols = self._translate(
batch=batches[0],
logs=logs,
input_lang_index=0,
target_lang_index=1,
identifier=identifier,
forced_targets=forced_targets)
total_translation_loss += translation_loss
total_reguralization_loss += reguralization_loss
source_vocabulary = self._vocabularies[1]
target_vocabulary = self._vocabularies[0]
outputs['input_text'] = sentence_from_ids(vocabulary=source_vocabulary, ids=translated_symbols)
outputs['output_text'] = sentence_from_ids(vocabulary=target_vocabulary, ids=outputs['symbols'][0])
targets = batches[0]['inputs'].cpu().squeeze(0)[1:].numpy()
targets = sentence_from_ids(vocabulary=target_vocabulary, ids=targets)
logs[1].add(identifier, 'translation_text', {
'input_text': outputs['input_text'],
'target_text': targets,
'output_text': outputs['output_text']
})
for key in self._translator_outputs.keys():
logs[1].add(identifier, key, {key: outputs[key] for key in logs[1].get_required_keys(key)})
translation_loss, reguralization_loss, outputs, translated_symbols = self._translate(
batch=batches[1],
logs=logs,
input_lang_index=1,
target_lang_index=0,
identifier=identifier,
forced_targets=forced_targets)
total_translation_loss += translation_loss
total_reguralization_loss += reguralization_loss
source_vocabulary = self._vocabularies[0]
target_vocabulary = self._vocabularies[1]
outputs['input_text'] = sentence_from_ids(vocabulary=source_vocabulary, ids=translated_symbols)
outputs['output_text'] = sentence_from_ids(vocabulary=target_vocabulary, ids=outputs['symbols'][0])
targets = batches[1]['inputs'].cpu().squeeze(0)[1:].numpy()
targets = sentence_from_ids(vocabulary=target_vocabulary, ids=targets)
logs[0].add(identifier, 'translation_text', {
'input_text': outputs['input_text'],
'target_text': targets,
'output_text': outputs['output_text']
})
for key in self._translator_outputs.keys():
logs[0].add(identifier, key, {key: outputs[key] for key in logs[0].get_required_keys(key)})
return translation_loss, reguralization_loss
def _eval_translator(self, batch, input_lang_index, target_lang_index, logs, identifier):
"""
:param batch:
:param logs:
:param identifier:
:return translation_loss:
:return reguralization_loss:
"""
_, translated_symbols, outputs, inputs, _, = self._translator(
input_lang_index=input_lang_index,
target_lang_index=target_lang_index,
batch=batch,
forced_targets=False)
source_vocabulary = self._vocabularies[input_lang_index]
target_vocabulary = self._vocabularies[target_lang_index]
outputs['input_text'] = sentence_from_ids(vocabulary=source_vocabulary, ids=inputs.data.cpu().squeeze(0))
outputs['output_text'] = sentence_from_ids(vocabulary=target_vocabulary, ids=translated_symbols.squeeze(0))
logs[input_lang_index].add(identifier, 'translation_text', {
'input_text': outputs['input_text'],
'output_text': outputs['output_text']
})
for key in self._translator_outputs.keys():
logs[input_lang_index].add(identifier, key, {key: outputs[key] for key in
logs[input_lang_index].get_required_keys(key)})
return ' '.join(outputs['input_text']), ' '.join(outputs['output_text'])
def _translate(self, batch, logs, input_lang_index, target_lang_index, identifier, forced_targets):
"""
:param batch:
:param logs:
:param input_lang_index:
:param target_lang_index:
:param identifier:
:param forced_targets:
:return translation_loss:
:return reguralization_loss:
:return outputs:
:return translated_symbols:
"""
reguralization_loss = 0
# Loss will only be calculated by the translator, if targets, and targets_lengths are both provided.
# During this step, the lengths of the targets are not provided, so loss will not be calculated.
_, translated_symbols, _, inputs, translated_lengths = self._previous_translator(
input_lang_index=input_lang_index,
target_lang_index=target_lang_index,
batch=batch,
forced_targets=False)
# During 'back translation' loss can be calculated, because the lengths of the targets are known.
translated_batch = {
'inputs': translated_symbols,
'input_lengths': translated_lengths,
'targets': batch['inputs'],
'target_lengths': batch['input_lengths']
}
translation_loss, _, outputs, _, _, = self._translator(
input_lang_index=target_lang_index,
target_lang_index=input_lang_index,
batch=translated_batch,
forced_targets=forced_targets)
if self._reguralizer is not None and self.reguralize:
for _language_index in range(self._num_languages):
if _language_index != target_lang_index:
reguralization_loss += self._reguralize(outputs['encoder_outputs'], _language_index)
logs[target_lang_index].add(identifier, 'translation_loss', translation_loss.data)
translated_symbols = translated_symbols.squeeze(0).cpu().numpy()
return translation_loss, reguralization_loss, outputs, translated_symbols
@property
def state(self):
"""
Property for the state of the task.
"""
return {
'model': self._model.state,
'iteration': self._iteration,
'previous_model': self._previous_model.state,
'previous_translator': type(self._previous_translator),
'embeddings': [embedding.state for embedding in self._embeddings],
'output_layers': [layer.state for layer in self._output_layers],
'previous_embeddings': [embedding.state for embedding in self._previous_embeddings],
'previous_output_layers': [layer.state for layer in self._previous_output_layers],
}
@state.setter
def state(self, state):
"""
Setter function for the state of the task, and the embeddings.
"""
self._model.state = state['model']
self._previous_model.state = state['previous_model']
self._iteration = state['iteration']
for index, embedding_state in enumerate(state['embeddings']):
self._embeddings[index].state = embedding_state
for index, layer_state in enumerate(state['output_layers']):
self._output_layers[index].state = layer_state
for index, embedding_state in enumerate(state['previous_embeddings']):
self._previous_embeddings[index].state = embedding_state
for index, layer_state in enumerate(state['previous_output_layers']):
self._previous_output_layers[index].state = layer_state
if isinstance(state['previous_translator'], WordTranslator):
self._previous_translator = self._initial_translator
else:
self._previous_translator = Translator(
# --OPTIONAL PARAMS--
cuda=self._policy.cuda,
language_identifiers=self._language_identifiers,
# --REQUIRED PARAMS--
model=self._previous_model_wrapper,
tokens=self._tokens,
add_language_token=self._add_language_token,
loss_functions=self._loss_functions,
vocabularies=self._vocabularies
)
| 37.355404
| 115
| 0.597774
| 7,970
| 76,728
| 5.533752
| 0.066374
| 0.043261
| 0.021948
| 0.008616
| 0.786527
| 0.759183
| 0.73202
| 0.716647
| 0.690686
| 0.68275
| 0
| 0.003602
| 0.334233
| 76,728
| 2,053
| 116
| 37.3736
| 0.859777
| 0.27707
| 0
| 0.65249
| 0
| 0
| 0.047735
| 0.00128
| 0
| 0
| 0
| 0.000487
| 0.002075
| 1
| 0.057054
| false
| 0.002075
| 0.012448
| 0
| 0.110996
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
9033ea91788734f683a9cf767fe94cdf5b79e80e
| 393
|
py
|
Python
|
StreamApp/models.py
|
felixfaisal/StreamApp
|
9ba93f7af389eef3d4334f2a04dca5ab84aa59e8
|
[
"MIT"
] | null | null | null |
StreamApp/models.py
|
felixfaisal/StreamApp
|
9ba93f7af389eef3d4334f2a04dca5ab84aa59e8
|
[
"MIT"
] | null | null | null |
StreamApp/models.py
|
felixfaisal/StreamApp
|
9ba93f7af389eef3d4334f2a04dca5ab84aa59e8
|
[
"MIT"
] | null | null | null |
from django.db import models
# Create your models here.
class VideoInfo(models.Model):
Video_id = models.IntegerField()
Video_Name = models.CharField(max_length=200)
Video_Description = models.CharField(max_length=200)
Video_Link = models.CharField(max_length=200)
pub_date = models.DateTimeField('date published')
def __str__(self):
return self.Video_Name
| 26.2
| 56
| 0.737913
| 51
| 393
| 5.431373
| 0.568627
| 0.162455
| 0.194946
| 0.259928
| 0.32852
| 0.231047
| 0
| 0
| 0
| 0
| 0
| 0.027607
| 0.170483
| 393
| 14
| 57
| 28.071429
| 0.822086
| 0.061069
| 0
| 0
| 0
| 0
| 0.038147
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.111111
| false
| 0
| 0.111111
| 0.111111
| 1
| 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
| 0
| 1
| 1
| 0
|
0
| 4
|
903654a995e9073029c31e7c3ba37662c5024318
| 76
|
py
|
Python
|
lathspell/__init__.py
|
tmoerman/lathspell
|
7bccc304cf30b535ce5e783831b71e603d2234ff
|
[
"BSD-3-Clause"
] | null | null | null |
lathspell/__init__.py
|
tmoerman/lathspell
|
7bccc304cf30b535ce5e783831b71e603d2234ff
|
[
"BSD-3-Clause"
] | null | null | null |
lathspell/__init__.py
|
tmoerman/lathspell
|
7bccc304cf30b535ce5e783831b71e603d2234ff
|
[
"BSD-3-Clause"
] | null | null | null |
"""
References:
* Learning Topic Models -- Provably and Efficiently.
"""
| 19
| 56
| 0.671053
| 7
| 76
| 7.285714
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.184211
| 76
| 4
| 57
| 19
| 0.822581
| 0.894737
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
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| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
5f4246dd1935a2ff5aedacef8a42dd9af62aa8b7
| 2,392
|
py
|
Python
|
qiskit_dynamics/solvers/__init__.py
|
dekelmeirom/qiskit-dynamics
|
9ed616c0715d1ba6189ea2bc57330cecb21ef181
|
[
"Apache-2.0"
] | 32
|
2021-06-15T17:59:35.000Z
|
2022-03-16T09:43:50.000Z
|
qiskit_dynamics/solvers/__init__.py
|
dekelmeirom/qiskit-dynamics
|
9ed616c0715d1ba6189ea2bc57330cecb21ef181
|
[
"Apache-2.0"
] | 46
|
2021-07-22T10:58:49.000Z
|
2022-03-15T13:04:29.000Z
|
qiskit_dynamics/solvers/__init__.py
|
dekelmeirom/qiskit-dynamics
|
9ed616c0715d1ba6189ea2bc57330cecb21ef181
|
[
"Apache-2.0"
] | 19
|
2021-06-21T12:23:28.000Z
|
2022-02-11T21:32:47.000Z
|
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
r"""
========================================
Solvers (:mod:`qiskit_dynamics.solvers`)
========================================
.. currentmodule:: qiskit_dynamics.solvers
This module provides classes and functions for solving differential equations.
The following table summarizes the solver interfaces exposed in this module.
Broadly, the *solver functions* are low-level interfaces exposing numerical methods for
solving particular classes of differential equations, while the *solver classes*
provide high level interfaces for solving models of quantum systems.
.. list-table:: Solver interfaces
:widths: 10 50
:header-rows: 1
* - Object
- Description
* - :class:`~qiskit_dynamics.solvers.Solver`
- High level solver class for both Hamiltonian and Lindblad dynamics.
Automatically constructs the relevant model type based on system details, and
the :meth:`~qiskit_dynamics.solvers.Solver.solve` method automatically handles
``qiskit.quantum_info`` input types.
* - :func:`~qiskit_dynamics.solvers.solve_ode`
- Low level solver function for ordinary differential equations:
.. math::
\dot{y}(t) = f(t, y(t)),
for :math:`y(t)` arrays of arbitrary shape.
* - :func:`~qiskit_dynamics.solvers.solve_lmde`
- Low level solver function for linear matrix differential equations in *standard form*:
.. math::
\dot{y}(t) = G(t)y(t),
where :math:`G(t)` is either a callable or a ``qiskit_dynamics``
model type, and :math:`y(t)` arrays of suitable shape for the matrix multiplication above.
Solver classes
==============
.. autosummary::
:toctree: ../stubs/
Solver
Solver functions
================
.. autosummary::
:toctree: ../stubs/
solve_ode
solve_lmde
"""
from .solver_functions import solve_ode, solve_lmde
from .solver_classes import Solver
| 30.666667
| 97
| 0.68102
| 312
| 2,392
| 5.169872
| 0.480769
| 0.060756
| 0.078115
| 0.033478
| 0.119033
| 0.033478
| 0
| 0
| 0
| 0
| 0
| 0.007183
| 0.185201
| 2,392
| 77
| 98
| 31.064935
| 0.820421
| 0.948579
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
5fb8cef323d07dcccb6247984347768a5c2609c6
| 91
|
py
|
Python
|
bugtests/test244.py
|
jeff5/jython-whinchat
|
65d8e5268189f8197295ff2d91be3decb1ee0081
|
[
"CNRI-Jython"
] | 577
|
2020-06-04T16:34:44.000Z
|
2022-03-31T11:46:07.000Z
|
bugtests/test244.py
|
jeff5/jython-whinchat
|
65d8e5268189f8197295ff2d91be3decb1ee0081
|
[
"CNRI-Jython"
] | 174
|
2015-01-08T20:37:09.000Z
|
2020-06-03T16:48:59.000Z
|
bugtests/test244.py
|
jeff5/jython-whinchat
|
65d8e5268189f8197295ff2d91be3decb1ee0081
|
[
"CNRI-Jython"
] | 162
|
2015-02-07T02:14:38.000Z
|
2020-05-30T16:42:03.000Z
|
import support
support.compileJava("test244p/A.java")
import test244p.A
a=test244p.A()
| 10.111111
| 38
| 0.758242
| 13
| 91
| 5.307692
| 0.461538
| 0.391304
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 0.10989
| 91
| 8
| 39
| 11.375
| 0.740741
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 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
|
5fc2d19b7979e6a003cecb7a735f21ddeedaac8f
| 46
|
py
|
Python
|
seq2seq/__init__.py
|
mtran14/pytorch-seq2seq
|
738059377eee9be07863e33f21c7d255139c44d6
|
[
"Apache-2.0"
] | 1,491
|
2017-06-30T16:15:40.000Z
|
2022-03-22T02:05:16.000Z
|
seq2seq/__init__.py
|
mtran14/pytorch-seq2seq
|
738059377eee9be07863e33f21c7d255139c44d6
|
[
"Apache-2.0"
] | 128
|
2017-07-07T21:41:03.000Z
|
2021-06-30T13:18:23.000Z
|
seq2seq/__init__.py
|
mtran14/pytorch-seq2seq
|
738059377eee9be07863e33f21c7d255139c44d6
|
[
"Apache-2.0"
] | 434
|
2017-07-08T12:35:15.000Z
|
2022-03-25T06:28:13.000Z
|
src_field_name = 'src'
tgt_field_name = 'tgt'
| 15.333333
| 22
| 0.73913
| 8
| 46
| 3.75
| 0.5
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 46
| 2
| 23
| 23
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 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
| 0
| 0
| 0
| 0
|
0
| 4
|
395905244d3c06f86e6dd5ff30c595374ea33e6e
| 338
|
py
|
Python
|
python/BubbleSort/unitTest.py
|
Catfish1210/python-examples
|
0c2e1574478ae9e1634274eb2df5b8ff0202f9e3
|
[
"MIT"
] | null | null | null |
python/BubbleSort/unitTest.py
|
Catfish1210/python-examples
|
0c2e1574478ae9e1634274eb2df5b8ff0202f9e3
|
[
"MIT"
] | null | null | null |
python/BubbleSort/unitTest.py
|
Catfish1210/python-examples
|
0c2e1574478ae9e1634274eb2df5b8ff0202f9e3
|
[
"MIT"
] | 1
|
2021-06-16T14:23:45.000Z
|
2021-06-16T14:23:45.000Z
|
import unittest
from main import BubleSort
class TestBubleSort(unittest.TestCase):
def test_success(self):
self.assertEqual(BubleSort([12, 3, 1, 7]), [1, 3, 7, 12]) #success
def test_failed(self):
self.assertEqual(BubleSort([12, 3, 1, 7]), [1, 3, 7, 13]) #failed
if __name__ == '__main__':
unittest.main()
| 24.142857
| 74
| 0.647929
| 47
| 338
| 4.446809
| 0.446809
| 0.066986
| 0.181818
| 0.267943
| 0.344498
| 0.344498
| 0.344498
| 0.344498
| 0.344498
| 0.344498
| 0
| 0.073801
| 0.198225
| 338
| 13
| 75
| 26
| 0.697417
| 0.038462
| 0
| 0
| 0
| 0
| 0.024768
| 0
| 0
| 0
| 0
| 0
| 0.222222
| 1
| 0.222222
| false
| 0
| 0.222222
| 0
| 0.555556
| 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
|
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