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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bb64100ba3fb0158060234ff152cec1164f363c5
| 1,363
|
py
|
Python
|
forms/migrations/0013_auto_20150324_0700.py
|
digideskio/gmmp
|
d82a4be0787c3a3a9e27dc590d7974f9f884fbb6
|
[
"Apache-2.0"
] | 4
|
2020-01-05T09:14:19.000Z
|
2022-02-17T03:22:09.000Z
|
forms/migrations/0013_auto_20150324_0700.py
|
digideskio/gmmp
|
d82a4be0787c3a3a9e27dc590d7974f9f884fbb6
|
[
"Apache-2.0"
] | 68
|
2019-12-23T02:19:55.000Z
|
2021-04-23T06:13:36.000Z
|
forms/migrations/0013_auto_20150324_0700.py
|
OpenUpSA/gmmp
|
d82a4be0787c3a3a9e27dc590d7974f9f884fbb6
|
[
"Apache-2.0"
] | 2
|
2019-07-25T11:53:10.000Z
|
2020-06-22T02:07:40.000Z
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('forms', '0012_auto_20150312_1400'),
]
operations = [
migrations.RemoveField(
model_name='internetnewssheet',
name='country',
),
migrations.RemoveField(
model_name='internetnewssheet',
name='monitor',
),
migrations.RemoveField(
model_name='newspapersheet',
name='country',
),
migrations.RemoveField(
model_name='newspapersheet',
name='monitor',
),
migrations.RemoveField(
model_name='radiosheet',
name='country',
),
migrations.RemoveField(
model_name='radiosheet',
name='monitor',
),
migrations.RemoveField(
model_name='televisionsheet',
name='country',
),
migrations.RemoveField(
model_name='televisionsheet',
name='monitor',
),
migrations.RemoveField(
model_name='twittersheet',
name='country',
),
migrations.RemoveField(
model_name='twittersheet',
name='monitor',
),
]
| 24.781818
| 45
| 0.527513
| 95
| 1,363
| 7.378947
| 0.326316
| 0.299572
| 0.370899
| 0.42796
| 0.768902
| 0.768902
| 0
| 0
| 0
| 0
| 0
| 0.019585
| 0.363169
| 1,363
| 54
| 46
| 25.240741
| 0.788018
| 0.015407
| 0
| 0.833333
| 0
| 0
| 0.174627
| 0.017164
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.041667
| 0
| 0.104167
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
bb6ef1076b71f8893e365962bfed5784ff422f78
| 32
|
py
|
Python
|
XMLtoJSON/__init__.py
|
Pretzel-Bytes/XMLtoJSON
|
77de59c9cbcb793b00baa1de15a227a38ee52878
|
[
"MIT"
] | null | null | null |
XMLtoJSON/__init__.py
|
Pretzel-Bytes/XMLtoJSON
|
77de59c9cbcb793b00baa1de15a227a38ee52878
|
[
"MIT"
] | null | null | null |
XMLtoJSON/__init__.py
|
Pretzel-Bytes/XMLtoJSON
|
77de59c9cbcb793b00baa1de15a227a38ee52878
|
[
"MIT"
] | null | null | null |
from .XMLtoJSON import convert
| 16
| 31
| 0.8125
| 4
| 32
| 6.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15625
| 32
| 1
| 32
| 32
| 0.962963
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
bb8f0622081ac4282fd1033d8d5e2665bcaf1ae3
| 122
|
py
|
Python
|
services/JSONEncoderService.py
|
johnyenter-briars/Grove
|
a1a3e784b3ae22113d2596ecea019b52aa2c138d
|
[
"MIT"
] | null | null | null |
services/JSONEncoderService.py
|
johnyenter-briars/Grove
|
a1a3e784b3ae22113d2596ecea019b52aa2c138d
|
[
"MIT"
] | null | null | null |
services/JSONEncoderService.py
|
johnyenter-briars/Grove
|
a1a3e784b3ae22113d2596ecea019b52aa2c138d
|
[
"MIT"
] | null | null | null |
from json import JSONEncoder
class ClassEncoder(JSONEncoder):
def default(self, o):
return o.__dict__
| 30.5
| 32
| 0.680328
| 14
| 122
| 5.642857
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.254098
| 122
| 4
| 33
| 30.5
| 0.868132
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0.25
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
bbe9369c40f8ef960dc1e65b6aae97ade91eb5c1
| 256
|
py
|
Python
|
tests/fakeapi/hello.py
|
adalekin/connexion-buzz
|
c96beebb26ef84858b5131ba2f3c1a0b9152b137
|
[
"BSD-3-Clause"
] | null | null | null |
tests/fakeapi/hello.py
|
adalekin/connexion-buzz
|
c96beebb26ef84858b5131ba2f3c1a0b9152b137
|
[
"BSD-3-Clause"
] | null | null | null |
tests/fakeapi/hello.py
|
adalekin/connexion-buzz
|
c96beebb26ef84858b5131ba2f3c1a0b9152b137
|
[
"BSD-3-Clause"
] | null | null | null |
import http
import connexion_buzz
class OverloadBuzz(connexion_buzz.ConnexionBuzz):
status_code = http.HTTPStatus.UNAUTHORIZED
def index():
raise connexion_buzz.ConnexionBuzz('basic test')
def status():
raise OverloadBuzz('status test')
| 16
| 52
| 0.765625
| 29
| 256
| 6.62069
| 0.551724
| 0.203125
| 0.270833
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148438
| 256
| 15
| 53
| 17.066667
| 0.880734
| 0
| 0
| 0
| 0
| 0
| 0.082031
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.75
| 0
| 1
| 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
| 0
| 1
| 0
|
0
| 5
|
bbf19994db30feaf39380fd20af556c8f391cdf0
| 213
|
py
|
Python
|
frictionless/validate/__init__.py
|
kant/frictionless-py
|
09cc98e1966d6f97f4eecb47757f45f8a946c5e7
|
[
"MIT"
] | null | null | null |
frictionless/validate/__init__.py
|
kant/frictionless-py
|
09cc98e1966d6f97f4eecb47757f45f8a946c5e7
|
[
"MIT"
] | null | null | null |
frictionless/validate/__init__.py
|
kant/frictionless-py
|
09cc98e1966d6f97f4eecb47757f45f8a946c5e7
|
[
"MIT"
] | null | null | null |
from .main import validate
from .inquiry import validate_inquiry
from .package import validate_package
from .resource import validate_resource
from .schema import validate_schema
from .table import validate_table
| 30.428571
| 39
| 0.859155
| 29
| 213
| 6.137931
| 0.310345
| 0.47191
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.112676
| 213
| 6
| 40
| 35.5
| 0.941799
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
bbf52cd6988e576d398f19eaf32ce0f5004c4d17
| 157
|
py
|
Python
|
cum/version.py
|
theowhy/cum
|
fe91124705fa8e93a1a7be7b547227f6064ca736
|
[
"Apache-2.0"
] | 163
|
2015-07-14T09:46:24.000Z
|
2022-03-20T10:20:21.000Z
|
cum/version.py
|
theowhy/cum
|
fe91124705fa8e93a1a7be7b547227f6064ca736
|
[
"Apache-2.0"
] | 63
|
2015-08-01T16:14:20.000Z
|
2021-07-05T07:24:58.000Z
|
cum/version.py
|
theowhy/cum
|
fe91124705fa8e93a1a7be7b547227f6064ca736
|
[
"Apache-2.0"
] | 22
|
2015-07-26T13:00:59.000Z
|
2022-03-08T22:37:32.000Z
|
__version__ = '0.9.1'
__version_name__ = 'Morino Kirin-chan'
def version_string():
return '%(prog)s version %(version)s "{}"'.format(__version_name__)
| 22.428571
| 71
| 0.700637
| 21
| 157
| 4.52381
| 0.666667
| 0.231579
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021898
| 0.127389
| 157
| 6
| 72
| 26.166667
| 0.671533
| 0
| 0
| 0
| 0
| 0
| 0.350318
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0.25
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 5
|
a5484c5e58501a5d784acc21007aca03fe1b7abc
| 429
|
py
|
Python
|
Source_Code/Python/labinstrument/interfaces/na_interface.py
|
fenglwh/instruments
|
7886158d1ed97fe6bfe372a55f4fca107e834311
|
[
"MIT"
] | null | null | null |
Source_Code/Python/labinstrument/interfaces/na_interface.py
|
fenglwh/instruments
|
7886158d1ed97fe6bfe372a55f4fca107e834311
|
[
"MIT"
] | 3
|
2018-09-21T00:57:21.000Z
|
2018-09-21T01:49:40.000Z
|
Source_Code/Python/labinstrument/interfaces/na_interface.py
|
fenglwh/instruments
|
7886158d1ed97fe6bfe372a55f4fca107e834311
|
[
"MIT"
] | null | null | null |
import abc
class NAInterface:
@abc.abstractmethod
def peak_search(self):
pass
@abc.abstractmethod
def get_linear_response(self,freq):
pass
@abc.abstractmethod
def get_log_response(self,freq):
pass
@abc.abstractmethod
def get_range_linear_response(self,start,stop):
pass
@abc.abstractmethod
def get_range_log_response(self,start,stop):
pass
| 15.888889
| 51
| 0.662005
| 51
| 429
| 5.352941
| 0.352941
| 0.311355
| 0.3663
| 0.351648
| 0.717949
| 0.450549
| 0.315018
| 0.315018
| 0
| 0
| 0
| 0
| 0.263403
| 429
| 26
| 52
| 16.5
| 0.863924
| 0
| 0
| 0.588235
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.294118
| false
| 0.294118
| 0.058824
| 0
| 0.411765
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
a5535c5867dd885133985ee991f405fa4375d760
| 45
|
py
|
Python
|
helpers/ipynb_py_convert/__init__.py
|
luk400/vim-jukit
|
72f37faebc58efdd69f0e85478faf9350176aec8
|
[
"MIT"
] | 25
|
2021-04-03T01:33:12.000Z
|
2022-03-28T01:45:12.000Z
|
helpers/ipynb_py_convert/__init__.py
|
luk400/vim-jukit
|
72f37faebc58efdd69f0e85478faf9350176aec8
|
[
"MIT"
] | 2
|
2022-03-22T05:41:55.000Z
|
2022-03-30T16:35:39.000Z
|
helpers/ipynb_py_convert/__init__.py
|
luk400/vim-jukit
|
72f37faebc58efdd69f0e85478faf9350176aec8
|
[
"MIT"
] | null | null | null |
from .__main__ import nb2py, py2nb, convert
| 22.5
| 44
| 0.777778
| 6
| 45
| 5.166667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.052632
| 0.155556
| 45
| 1
| 45
| 45
| 0.763158
| 0
| 0
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| 0
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| 0
| 0
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| 0
| 1
| 0
| true
| 0
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| 0
| 1
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| 0
| null | 0
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| 1
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| null | 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
a5784645e9894b192f24ba86f549cab48e1b0808
| 370
|
py
|
Python
|
SpiderX/core/coreutils.py
|
OldDriverPickMeUp/SpiderX
|
819cb9ab05b05feca561d45baada70af7a359f9a
|
[
"MIT"
] | 3
|
2017-10-04T04:13:35.000Z
|
2017-11-20T00:18:57.000Z
|
SpiderX/core/coreutils.py
|
OldDriverPickMeUp/SpiderX
|
819cb9ab05b05feca561d45baada70af7a359f9a
|
[
"MIT"
] | null | null | null |
SpiderX/core/coreutils.py
|
OldDriverPickMeUp/SpiderX
|
819cb9ab05b05feca561d45baada70af7a359f9a
|
[
"MIT"
] | null | null | null |
#coding=utf-8
import platform
def get_current_platform():
return platform.system()
def is_windows():
if platform.system() == 'Windows':
return True
return False
def is_linux():
if platform.system() == 'Linux':
return True
return False
def is_mac():
if platform.system() == 'Darwin':
return True
return False
| 13.703704
| 38
| 0.618919
| 45
| 370
| 4.977778
| 0.4
| 0.25
| 0.214286
| 0.28125
| 0.232143
| 0.232143
| 0
| 0
| 0
| 0
| 0
| 0.00369
| 0.267568
| 370
| 26
| 39
| 14.230769
| 0.822878
| 0.032432
| 0
| 0.4
| 0
| 0
| 0.050562
| 0
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| 0
| 0
| 0
| 0
| 1
| 0.266667
| true
| 0
| 0.066667
| 0.066667
| 0.8
| 0
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| 0
| null | 1
| 1
| 1
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| null | 0
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| 1
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| 0
| 0
| 0
| 0
|
0
| 5
|
a58fac78792dd565e0b1c32070317f25a82a34ad
| 9,857
|
py
|
Python
|
salika/views/django_session_views.py
|
BarisSari/django_crud
|
ce9586c10da2f865d29d9a18e9ff5582abe5e3a0
|
[
"MIT"
] | null | null | null |
salika/views/django_session_views.py
|
BarisSari/django_crud
|
ce9586c10da2f865d29d9a18e9ff5582abe5e3a0
|
[
"MIT"
] | null | null | null |
salika/views/django_session_views.py
|
BarisSari/django_crud
|
ce9586c10da2f865d29d9a18e9ff5582abe5e3a0
|
[
"MIT"
] | null | null | null |
from django.views.generic.detail import DetailView
from django.views.generic.edit import CreateView, UpdateView, DeleteView
from django.views.generic.list import ListView
from ..models import DjangoSession
from ..forms import DjangoSessionForm
from django.urls import reverse_lazy
from django.urls import reverse
from django.http import Http404
class DjangoSessionListView(ListView):
model = DjangoSession
template_name = "salika/django_session_list.html"
paginate_by = 20
context_object_name = "django_session_list"
allow_empty = True
page_kwarg = 'page'
paginate_orphans = 0
def __init__(self, **kwargs):
return super(DjangoSessionListView, self).__init__(**kwargs)
def dispatch(self, *args, **kwargs):
return super(DjangoSessionListView, self).dispatch(*args, **kwargs)
def get(self, request, *args, **kwargs):
return super(DjangoSessionListView, self).get(request, *args, **kwargs)
def get_queryset(self):
return super(DjangoSessionListView, self).get_queryset()
def get_allow_empty(self):
return super(DjangoSessionListView, self).get_allow_empty()
def get_context_data(self, *args, **kwargs):
ret = super(DjangoSessionListView, self).get_context_data(*args, **kwargs)
return ret
def get_paginate_by(self, queryset):
return super(DjangoSessionListView, self).get_paginate_by(queryset)
def get_context_object_name(self, object_list):
return super(DjangoSessionListView, self).get_context_object_name(object_list)
def paginate_queryset(self, queryset, page_size):
return super(DjangoSessionListView, self).paginate_queryset(queryset, page_size)
def get_paginator(self, queryset, per_page, orphans=0, allow_empty_first_page=True):
return super(DjangoSessionListView, self).get_paginator(queryset, per_page, orphans=0, allow_empty_first_page=True)
def render_to_response(self, context, **response_kwargs):
return super(DjangoSessionListView, self).render_to_response(context, **response_kwargs)
def get_template_names(self):
return super(DjangoSessionListView, self).get_template_names()
class DjangoSessionDetailView(DetailView):
model = DjangoSession
template_name = "salika/django_session_detail.html"
context_object_name = "django_session"
slug_field = 'slug'
slug_url_kwarg = 'slug'
pk_url_kwarg = 'pk'
def __init__(self, **kwargs):
return super(DjangoSessionDetailView, self).__init__(**kwargs)
def dispatch(self, *args, **kwargs):
return super(DjangoSessionDetailView, self).dispatch(*args, **kwargs)
def get(self, request, *args, **kwargs):
return super(DjangoSessionDetailView, self).get(request, *args, **kwargs)
def get_object(self, queryset=None):
return super(DjangoSessionDetailView, self).get_object(queryset)
def get_queryset(self):
return super(DjangoSessionDetailView, self).get_queryset()
def get_slug_field(self):
return super(DjangoSessionDetailView, self).get_slug_field()
def get_context_data(self, **kwargs):
ret = super(DjangoSessionDetailView, self).get_context_data(**kwargs)
return ret
def get_context_object_name(self, obj):
return super(DjangoSessionDetailView, self).get_context_object_name(obj)
def render_to_response(self, context, **response_kwargs):
return super(DjangoSessionDetailView, self).render_to_response(context, **response_kwargs)
def get_template_names(self):
return super(DjangoSessionDetailView, self).get_template_names()
class DjangoSessionCreateView(CreateView):
model = DjangoSession
form_class = DjangoSessionForm
# fields = ['session_key', 'session_data', 'expire_date']
template_name = "salika/django_session_create.html"
success_url = reverse_lazy("django_session_list")
def __init__(self, **kwargs):
return super(DjangoSessionCreateView, self).__init__(**kwargs)
def dispatch(self, request, *args, **kwargs):
return super(DjangoSessionCreateView, self).dispatch(request, *args, **kwargs)
def get(self, request, *args, **kwargs):
return super(DjangoSessionCreateView, self).get(request, *args, **kwargs)
def post(self, request, *args, **kwargs):
return super(DjangoSessionCreateView, self).post(request, *args, **kwargs)
def get_form_class(self):
return super(DjangoSessionCreateView, self).get_form_class()
def get_form(self, form_class=None):
return super(DjangoSessionCreateView, self).get_form(form_class)
def get_form_kwargs(self, **kwargs):
return super(DjangoSessionCreateView, self).get_form_kwargs(**kwargs)
def get_initial(self):
return super(DjangoSessionCreateView, self).get_initial()
def form_invalid(self, form):
return super(DjangoSessionCreateView, self).form_invalid(form)
def form_valid(self, form):
obj = form.save(commit=False)
obj.save()
return super(DjangoSessionCreateView, self).form_valid(form)
def get_context_data(self, **kwargs):
ret = super(DjangoSessionCreateView, self).get_context_data(**kwargs)
return ret
def render_to_response(self, context, **response_kwargs):
return super(DjangoSessionCreateView, self).render_to_response(context, **response_kwargs)
def get_template_names(self):
return super(DjangoSessionCreateView, self).get_template_names()
def get_success_url(self):
return reverse("salika:django_session_detail", args=(self.object.pk,))
class DjangoSessionUpdateView(UpdateView):
model = DjangoSession
form_class = DjangoSessionForm
# fields = ['session_key', 'session_data', 'expire_date']
template_name = "salika/django_session_update.html"
initial = {}
slug_field = 'slug'
slug_url_kwarg = 'slug'
pk_url_kwarg = 'pk'
context_object_name = "django_session"
def __init__(self, **kwargs):
return super(DjangoSessionUpdateView, self).__init__(**kwargs)
def dispatch(self, *args, **kwargs):
return super(DjangoSessionUpdateView, self).dispatch(*args, **kwargs)
def get(self, request, *args, **kwargs):
return super(DjangoSessionUpdateView, self).get(request, *args, **kwargs)
def post(self, request, *args, **kwargs):
return super(DjangoSessionUpdateView, self).post(request, *args, **kwargs)
def get_object(self, queryset=None):
return super(DjangoSessionUpdateView, self).get_object(queryset)
def get_queryset(self):
return super(DjangoSessionUpdateView, self).get_queryset()
def get_slug_field(self):
return super(DjangoSessionUpdateView, self).get_slug_field()
def get_form_class(self):
return super(DjangoSessionUpdateView, self).get_form_class()
def get_form(self, form_class=None):
return super(DjangoSessionUpdateView, self).get_form(form_class)
def get_form_kwargs(self, **kwargs):
return super(DjangoSessionUpdateView, self).get_form_kwargs(**kwargs)
def get_initial(self):
return super(DjangoSessionUpdateView, self).get_initial()
def form_invalid(self, form):
return super(DjangoSessionUpdateView, self).form_invalid(form)
def form_valid(self, form):
obj = form.save(commit=False)
obj.save()
return super(DjangoSessionUpdateView, self).form_valid(form)
def get_context_data(self, **kwargs):
ret = super(DjangoSessionUpdateView, self).get_context_data(**kwargs)
return ret
def get_context_object_name(self, obj):
return super(DjangoSessionUpdateView, self).get_context_object_name(obj)
def render_to_response(self, context, **response_kwargs):
return super(DjangoSessionUpdateView, self).render_to_response(context, **response_kwargs)
def get_template_names(self):
return super(DjangoSessionUpdateView, self).get_template_names()
def get_success_url(self):
return reverse("salika:django_session_detail", args=(self.object.pk,))
class DjangoSessionDeleteView(DeleteView):
model = DjangoSession
template_name = "salika/django_session_delete.html"
slug_field = 'slug'
slug_url_kwarg = 'slug'
pk_url_kwarg = 'pk'
context_object_name = "django_session"
def __init__(self, **kwargs):
return super(DjangoSessionDeleteView, self).__init__(**kwargs)
def dispatch(self, *args, **kwargs):
return super(DjangoSessionDeleteView, self).dispatch(*args, **kwargs)
def get(self, request, *args, **kwargs):
raise Http404
def post(self, request, *args, **kwargs):
return super(DjangoSessionDeleteView, self).post(request, *args, **kwargs)
def delete(self, request, *args, **kwargs):
return super(DjangoSessionDeleteView, self).delete(request, *args, **kwargs)
def get_object(self, queryset=None):
return super(DjangoSessionDeleteView, self).get_object(queryset)
def get_queryset(self):
return super(DjangoSessionDeleteView, self).get_queryset()
def get_slug_field(self):
return super(DjangoSessionDeleteView, self).get_slug_field()
def get_context_data(self, **kwargs):
ret = super(DjangoSessionDeleteView, self).get_context_data(**kwargs)
return ret
def get_context_object_name(self, obj):
return super(DjangoSessionDeleteView, self).get_context_object_name(obj)
def render_to_response(self, context, **response_kwargs):
return super(DjangoSessionDeleteView, self).render_to_response(context, **response_kwargs)
def get_template_names(self):
return super(DjangoSessionDeleteView, self).get_template_names()
def get_success_url(self):
return reverse("salika:django_session_list")
| 36.917603
| 123
| 0.721315
| 1,123
| 9,857
| 6.071238
| 0.080142
| 0.093576
| 0.062335
| 0.089176
| 0.875183
| 0.75154
| 0.61631
| 0.576855
| 0.530361
| 0.530361
| 0
| 0.001349
| 0.172771
| 9,857
| 266
| 124
| 37.056391
| 0.834805
| 0.011261
| 0
| 0.475936
| 0
| 0
| 0.036847
| 0.025146
| 0
| 0
| 0
| 0
| 0
| 1
| 0.358289
| false
| 0
| 0.042781
| 0.315508
| 0.946524
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
a59e3aeec74ca4ff4934ed5213d044c9fe3fd6b3
| 20
|
py
|
Python
|
app/__init__.py
|
calio/taski
|
c06346d7e3600f41b1347c6d9f73616f17b226e4
|
[
"MIT"
] | null | null | null |
app/__init__.py
|
calio/taski
|
c06346d7e3600f41b1347c6d9f73616f17b226e4
|
[
"MIT"
] | 1
|
2021-06-01T22:24:59.000Z
|
2021-06-01T22:24:59.000Z
|
app/__init__.py
|
calio/taski
|
c06346d7e3600f41b1347c6d9f73616f17b226e4
|
[
"MIT"
] | null | null | null |
VERSION = "0.1.20"
| 6.666667
| 18
| 0.55
| 4
| 20
| 2.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 0.2
| 20
| 2
| 19
| 10
| 0.4375
| 0
| 0
| 0
| 0
| 0
| 0.315789
| 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
| 1
| 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
| 5
|
3c05645456a3cbdbe4ec9470710519db56e7927e
| 110
|
py
|
Python
|
src/labster/ldap/__init__.py
|
jean3108/labandco
|
4317e7d3875f10d76076ad5fc68c1ba3c12badba
|
[
"Apache-2.0"
] | 2
|
2019-11-11T22:09:58.000Z
|
2020-01-20T19:44:30.000Z
|
src/labster/ldap/__init__.py
|
jean3108/labandco
|
4317e7d3875f10d76076ad5fc68c1ba3c12badba
|
[
"Apache-2.0"
] | 15
|
2020-03-31T10:58:37.000Z
|
2022-01-22T09:14:49.000Z
|
src/labster/ldap/__init__.py
|
jean3108/labandco
|
4317e7d3875f10d76076ad5fc68c1ba3c12badba
|
[
"Apache-2.0"
] | 2
|
2021-05-28T12:20:24.000Z
|
2021-09-08T11:27:57.000Z
|
"""Nouvel importeur LDAP (pour l'annuaire chapeau Sorbonne Université)."""
from __future__ import annotations
| 36.666667
| 74
| 0.8
| 13
| 110
| 6.461538
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.109091
| 110
| 2
| 75
| 55
| 0.857143
| 0.618182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 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
| 1
| 0
|
0
| 5
|
3c2927b6be0cdf6e55278176658ada2f0a5eb7b6
| 14,232
|
py
|
Python
|
apps/challenge/migrations/0001_initial.py
|
mehrbodjavadi79/AIC21-Backend
|
9f4342781f0722804a2eb704b43b52984c81b40a
|
[
"MIT"
] | 3
|
2021-03-12T18:32:39.000Z
|
2021-11-08T10:21:04.000Z
|
apps/challenge/migrations/0001_initial.py
|
mehrbodjavadi79/AIC21-Backend
|
9f4342781f0722804a2eb704b43b52984c81b40a
|
[
"MIT"
] | null | null | null |
apps/challenge/migrations/0001_initial.py
|
mehrbodjavadi79/AIC21-Backend
|
9f4342781f0722804a2eb704b43b52984c81b40a
|
[
"MIT"
] | 2
|
2021-01-29T14:52:53.000Z
|
2022-03-05T10:24:24.000Z
|
# Generated by Django 3.1.5 on 2021-03-23 10:35
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
import model_utils.fields
import uuid
class Migration(migrations.Migration):
initial = True
dependencies = [
('team', '0006_team_level_one_payed'),
]
operations = [
migrations.CreateModel(
name='Clan',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('name', models.CharField(max_length=256, unique=True)),
('image', models.ImageField(blank=True, null=True, upload_to='clan/images/')),
('score', models.PositiveIntegerField(default=0)),
('wins', models.PositiveIntegerField(default=0)),
('losses', models.PositiveIntegerField(default=0)),
('draws', models.PositiveIntegerField(default=0)),
('leader', models.OneToOneField(on_delete=django.db.models.deletion.RESTRICT, related_name='owned_clan', to='team.team')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='Level',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('number', models.PositiveSmallIntegerField(default=1)),
],
),
migrations.CreateModel(
name='Map',
fields=[
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('id', model_utils.fields.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)),
('name', models.CharField(max_length=256, unique=True)),
('file', models.FileField(upload_to='maps/')),
('active', models.BooleanField(default=True)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='Match',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('status', models.CharField(choices=[('failed', 'Failed'), ('successful', 'Successful'), ('running', 'Running'), ('freeze', 'Freeze')], default='running', max_length=50)),
('log', models.FileField(blank=True, null=True, upload_to='match/logs/')),
('team1', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='matches_first', to='team.team')),
('team2', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='matches_second', to='team.team')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='Scoreboard',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('freeze', models.BooleanField(default=False)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='Tournament',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('name', models.CharField(max_length=512)),
('type', models.CharField(choices=[('successful', 'Normal'), ('friendly', 'Friendly'), ('clanwar', 'Clanwar')], default='successful', max_length=50)),
('start_time', models.DateTimeField(blank=True, null=True)),
('end_time', models.DateTimeField(blank=True, null=True)),
('is_hidden', models.BooleanField(default=False)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='ScoreboardRow',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('score', models.PositiveIntegerField(default=0)),
('wins', models.PositiveIntegerField(default=0)),
('losses', models.PositiveIntegerField(default=0)),
('draws', models.PositiveIntegerField(default=0)),
('scoreboard', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='rows', to='challenge.scoreboard')),
('team', models.ForeignKey(on_delete=django.db.models.deletion.RESTRICT, related_name='scoreboard_rows', to='team.team')),
],
options={
'abstract': False,
},
),
migrations.AddField(
model_name='scoreboard',
name='tournament',
field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='scoreboard', to='challenge.tournament'),
),
migrations.CreateModel(
name='Request',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('status', models.CharField(choices=[('pending', 'Pending'), ('rejected', 'Rejected'), ('accepted', 'Accepted')], default='pending', max_length=50)),
('type', models.CharField(choices=[('friendly_match', 'Friendly match'), ('clan_invite', 'Clan invite'), ('clanwar', 'Clanwar')], max_length=50)),
('source_team', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='sent_requests', to='team.team')),
('target_team', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='received_request', to='team.team')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='MatchInfo',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('team1_score', models.PositiveIntegerField(blank=True, null=True)),
('team2_score', models.PositiveIntegerField(blank=True, null=True)),
('match_duration', models.PositiveSmallIntegerField(blank=True, null=True)),
('map', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='match_info', to='challenge.map')),
('match', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='match_info', to='challenge.match')),
('team1_code', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='matches_first', to='team.submission')),
('team2_code', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='matches_second', to='team.submission')),
],
options={
'abstract': False,
},
),
migrations.AddField(
model_name='match',
name='tournament',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='matches', to='challenge.tournament'),
),
migrations.AddField(
model_name='match',
name='winner',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='won_matches', to='team.team'),
),
migrations.CreateModel(
name='LobbyQueue',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('game_type', models.CharField(choices=[('friendly_match', 'Friendly match'), ('level_based_tournament', 'Level based tournament')], max_length=50)),
('team', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='lobby_queues', to='team.team')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='LevelMatch',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('level', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='level_matches', to='challenge.level')),
('match', models.OneToOneField(on_delete=django.db.models.deletion.RESTRICT, related_name='level_match', to='challenge.match')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='LevelBasedTournament',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('size', models.PositiveSmallIntegerField(default=8)),
('tournament', models.OneToOneField(on_delete=django.db.models.deletion.RESTRICT, related_name='level_based_tournament', to='challenge.tournament')),
],
options={
'abstract': False,
},
),
migrations.AddField(
model_name='level',
name='level_based_tournament',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='levels', to='challenge.levelbasedtournament'),
),
migrations.CreateModel(
name='ClanWar',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')),
('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')),
('clan1', models.ForeignKey(on_delete=django.db.models.deletion.RESTRICT, related_name='clanwars1', to='challenge.clan')),
('clan2', models.ForeignKey(on_delete=django.db.models.deletion.RESTRICT, related_name='clanwars2', to='challenge.clan')),
('tournament', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='clanwar', to='challenge.tournament')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='ClanTeam',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('clan', models.OneToOneField(on_delete=django.db.models.deletion.RESTRICT, to='team.team')),
('teams', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='challenge.clan')),
],
),
]
| 61.081545
| 187
| 0.616217
| 1,386
| 14,232
| 6.189755
| 0.113276
| 0.051754
| 0.069006
| 0.06411
| 0.795547
| 0.792283
| 0.764308
| 0.717799
| 0.69845
| 0.650425
| 0
| 0.005436
| 0.237423
| 14,232
| 232
| 188
| 61.344828
| 0.785036
| 0.003162
| 0
| 0.617778
| 1
| 0
| 0.13902
| 0.00853
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.022222
| 0
| 0.04
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
3c35df45a820cd2b31f81d888dd7eea3e07c3247
| 46
|
py
|
Python
|
app/lti_app/launch/exceptions.py
|
oss6/scriba
|
104fb6718891fb57da42b5b175826cd5f0f0ec9b
|
[
"MIT"
] | null | null | null |
app/lti_app/launch/exceptions.py
|
oss6/scriba
|
104fb6718891fb57da42b5b175826cd5f0f0ec9b
|
[
"MIT"
] | 40
|
2018-06-21T22:17:14.000Z
|
2018-08-29T16:02:29.000Z
|
app/lti_app/launch/exceptions.py
|
birmingham-international-academy/scriba
|
104fb6718891fb57da42b5b175826cd5f0f0ec9b
|
[
"MIT"
] | null | null | null |
class InvalidLaunchError(Exception):
pass
| 15.333333
| 36
| 0.782609
| 4
| 46
| 9
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.152174
| 46
| 2
| 37
| 23
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 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
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
3c3f78ae39c2c1cd8a65704bf8e971b2ac00dfd9
| 178
|
py
|
Python
|
search_space/fbnet/__init__.py
|
eric8607242/OSNASLib
|
43908ab454fb78f835f8a015935205179b9acec4
|
[
"MIT"
] | 3
|
2021-06-14T11:00:21.000Z
|
2021-10-18T02:59:54.000Z
|
search_space/fbnet/__init__.py
|
eric8607242/OneShot_NAS_example
|
2e758a9e5d9e03eecb9c4cc0e2e6a8ec38cf7052
|
[
"MIT"
] | 1
|
2021-12-04T07:42:25.000Z
|
2021-12-04T15:14:12.000Z
|
search_space/fbnet/__init__.py
|
eric8607242/OneShot_NAS_example
|
2e758a9e5d9e03eecb9c4cc0e2e6a8ec38cf7052
|
[
"MIT"
] | null | null | null |
from .fbnet_supernet import FBNetSSupernet, FBNetLSupernet
from .fbnet_lookup_table import FBNetSLookUpTable, FBNetLLookUpTable
from .fbnet_model import FBNetSModel, FBNetLModel
| 44.5
| 68
| 0.882022
| 19
| 178
| 8.052632
| 0.684211
| 0.176471
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08427
| 178
| 3
| 69
| 59.333333
| 0.93865
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3c40390f00bd47ab8c3c8619ecf0fe5063c5f53b
| 1,584
|
py
|
Python
|
utils/train.py
|
e-hulten/maf
|
2c0604ac8573ab14a6bc83dd51827d47a4266a96
|
[
"MIT"
] | 12
|
2020-02-29T11:42:27.000Z
|
2021-12-08T04:09:21.000Z
|
utils/train.py
|
e-hulten/maf
|
2c0604ac8573ab14a6bc83dd51827d47a4266a96
|
[
"MIT"
] | 1
|
2021-01-22T07:02:22.000Z
|
2021-01-22T07:02:22.000Z
|
utils/train.py
|
e-hulten/maf
|
2c0604ac8573ab14a6bc83dd51827d47a4266a96
|
[
"MIT"
] | null | null | null |
import os
import math
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.distributions import MultivariateNormal
def train_one_epoch_maf(model, epoch, optimizer, train_loader):
model.train()
train_loss = 0
for batch in train_loader:
u, log_det = model.forward(batch.float())
negloglik_loss = 0.5 * (u ** 2).sum(dim=1)
negloglik_loss += 0.5 * batch.shape[1] * np.log(2 * math.pi)
negloglik_loss -= log_det
negloglik_loss = torch.mean(negloglik_loss)
negloglik_loss.backward()
train_loss += negloglik_loss.item()
optimizer.step()
optimizer.zero_grad()
avg_loss = np.sum(train_loss) / len(train_loader)
print("Epoch: {} Average loss: {:.5f}".format(epoch, avg_loss))
return avg_loss
def train_one_epoch_made(model, epoch, optimizer, train_loader):
model.train()
train_loss = 0
for batch in train_loader:
out = model.forward(batch.float())
mu, logp = torch.chunk(out, 2, dim=1)
u = (batch - mu) * torch.exp(0.5 * logp)
negloglik_loss = 0.5 * (u ** 2).sum(dim=1)
negloglik_loss += 0.5 * batch.shape[1] * np.log(2 * math.pi)
negloglik_loss -= 0.5 * torch.sum(logp, dim=1)
negloglik_loss = torch.mean(negloglik_loss)
negloglik_loss.backward()
train_loss += negloglik_loss.item()
optimizer.step()
optimizer.zero_grad()
avg_loss = np.sum(train_loss) / len(train_loader)
print("Epoch: {} Average loss: {:.5f}".format(epoch, avg_loss))
return avg_loss
| 30.461538
| 68
| 0.641414
| 223
| 1,584
| 4.367713
| 0.26009
| 0.186858
| 0.071869
| 0.077002
| 0.712526
| 0.712526
| 0.712526
| 0.712526
| 0.712526
| 0.712526
| 0
| 0.022241
| 0.233586
| 1,584
| 51
| 69
| 31.058824
| 0.780066
| 0
| 0
| 0.65
| 0
| 0
| 0.037879
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.05
| false
| 0
| 0.15
| 0
| 0.25
| 0.05
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 0
|
0
| 5
|
3c4828cce4743cddd3fd5fb20f132a2e5788d7ea
| 17,225
|
py
|
Python
|
models.py
|
xavierzw/ogb-geniepath-bs
|
d4da66595b17490f63ea74f620642c99d8159008
|
[
"MIT"
] | 4
|
2020-09-23T13:37:19.000Z
|
2022-01-18T08:04:19.000Z
|
models.py
|
xavierzw/ogb-geniepath-bs
|
d4da66595b17490f63ea74f620642c99d8159008
|
[
"MIT"
] | 2
|
2020-09-24T08:16:22.000Z
|
2021-05-30T10:52:05.000Z
|
models.py
|
xavierzw/gnn-bs
|
a2415a9085cd1d4d589a3ff057a2762013b5b5ae
|
[
"MIT"
] | null | null | null |
import numpy as np
import tensorflow as tf
flags = tf.app.flags
FLAGS = flags.FLAGS
def attention_mechanism(name, v, W_s, W_d, V, cur_embed, left, right, n2n):
# a_{i,j} \propto v^\top tanh (W_s (\mu_i + \mu_j))
if name == 'linear':
t = tf.sparse_tensor_dense_matmul(sp_a=edge, b=cur_embed) # edge \in \R^{m, n}
t = tf.matmul(t, W_s) # m by 16
t = tf.nn.tanh(t)
t = tf.matmul(t, tf.reshape(v, [-1,1])) # m by 1
sparse_attention = tf.SparseTensor(n2n.indices, tf.reshape(t, [-1]), n2n.dense_shape)
sparse_attention = tf.sparse_softmax(sparse_attention)
# a_{i,j} \propto v^\top tanh (W_s |\mu_i - \mu_j|)
elif name == 'abs':
t = tf.sparse_tensor_dense_matmul(sp_a=edge, b=cur_embed) # edge \in \R^{m, n}
t = tf.abs(t)
t = tf.matmul(t, W_s) # m by 16
t = tf.nn.tanh(t)
t = tf.matmul(t, tf.reshape(v, [-1,1])) # m by 1
sparse_attention = tf.SparseTensor(n2n.indices, tf.reshape(t, [-1]), n2n.dense_shape)
sparse_attention = tf.sparse_softmax(sparse_attention)
# a_{i,j} \propto leakyrelu (\mu_i V \mu_j)
elif name == 'bilinear':
tl = tf.sparse_tensor_dense_matmul(sp_a=left, b=cur_embed) # m by k
tl = tf.matmul(tl, V)
tr = tf.sparse_tensor_dense_matmul(sp_a=right, b=cur_embed)
t = tf.reduce_sum(tf.multiply(tl, tr), 1, keep_dims=True)
t = tf.keras.layers.LeakyReLU(t)
sparse_attention = tf.SparseTensor(n2n.indices, tf.reshape(t, [-1]), n2n.dense_shape)
sparse_attention = tf.sparse_softmax(sparse_attention)
# a_{i,j} \propto v^\top tanh (W_s \mu_i + W_d \mu_j)
if name == 'generalized_linear':
tl = tf.sparse_tensor_dense_matmul(sp_a=left, b=cur_embed) # m by k
tl = tf.matmul(tl, W_s)
tr = tf.sparse_tensor_dense_matmul(sp_a=right, b=cur_embed)
tr = tf.matmul(tr, W_d)
t = tf.nn.tanh(tf.add(tl,tr))
t = tf.matmul(t, tf.reshape(v, [-1,1]))
sparse_attention = tf.SparseTensor(n2n.indices, tf.reshape(t, [-1]), n2n.dense_shape)
sparse_attention = tf.sparse_softmax(sparse_attention)
else:
sys.exit(-1)
return sparse_attention
def glorot(shape, name=None):
"""Glorot & Bengio (AISTATS 2010) init."""
if len(shape)==2:
init_range = np.sqrt(6.0/(shape[0]+shape[1]))
elif len(shape)==1:
init_range = np.sqrt(6.0/shape[0])
initial = tf.random_uniform(shape, minval=-init_range, maxval=init_range, dtype=tf.float32)
return tf.Variable(initial, name=name)
class Model(object):
def __init__(self, **kwargs):
allowed_kwargs = {'name', 'logging'}
for kwarg in kwargs.keys():
assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg
name = kwargs.get('name')
if not name:
name = self.__class__.__name__.lower()
self.name = name
logging = kwargs.get('logging', False)
self.logging = logging
self.task_type = None
self.vars = {}
self.placeholders = {}
self.label_dim = None
self.inputs = None
self.layers = []
self.activations = []
self.outputs = None
self.sparse_attention_l0 = None
self.loss = 0
self.accuracy = 0
self.optimizer = None
self.opt_op = None
def _build(self):
raise NotImplementedError
def build(self):
""" Wrapper for _build() """
with tf.variable_scope(self.name):
self._build()
# Store model variables for easy access
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name)
self.vars = {var.name: var for var in variables}
# Build metrics
self._loss()
grads_and_vars = self.optimizer.compute_gradients(self.loss)
clipped_grads_and_vars = [(tf.clip_by_value(grad, -5.0, 5.0) if grad is not None else None, var)
for grad, var in grads_and_vars]
self.grad, _ = clipped_grads_and_vars[0]
self.opt_op = self.optimizer.apply_gradients(clipped_grads_and_vars)
#self.opt_op = self.optimizer.minimize(self.loss)
def _loss(self):
raise NotImplementedError
def save(self, sess=None):
if not sess:
raise AttributeError("TensorFlow session not provided.")
saver = tf.train.Saver(self.vars)
save_path = saver.save(sess, "tmp/%s.ckpt" % self.name)
print("Model saved in file: %s" % save_path)
def load(self, sess=None):
if not sess:
raise AttributeError("TensorFlow session not provided.")
saver = tf.train.Saver(self.vars)
save_path = "tmp/%s.ckpt" % self.name
saver.restore(sess, save_path)
print("Model restored from file: %s" % save_path)
class GeniePath(Model):
def __init__(self, task_type, placeholders, input_dim, label_dim, **kwargs):
super(GeniePath, self).__init__(**kwargs)
self.inputs = placeholders['features']
assert task_type in ["exclusive-label", "multi-label"], "Unknown task type!"
self.task_type = task_type
self.input_dim = input_dim
self.label_dim = label_dim
self.placeholders = placeholders
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
self.build()
def _loss(self):
# Weight decay loss
#for i in range(len(self.layers)):
# for var in self.layers[i].vars.values():
# self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
l2_loss = 0
for i in range(2):
l2_loss += tf.nn.l2_loss(self.vars_wn[i])
l2_loss += tf.nn.l2_loss(self.vars_bn[i])
l2_loss += tf.nn.l2_loss(self.vars_ws[i])
l2_loss += tf.nn.l2_loss(self.vars_wd[i])
l2_loss += tf.nn.l2_loss(self.vars_v[i])
l2_loss += tf.nn.l2_loss(self.vars_V[i])
l2_loss += tf.nn.l2_loss(self.vars_wi[i])
l2_loss += tf.nn.l2_loss(self.vars_wf[i])
l2_loss += tf.nn.l2_loss(self.vars_wo[i])
l2_loss += tf.nn.l2_loss(self.vars_wc[i])
l2_loss += tf.nn.l2_loss(self.vars_bc[i])
l2_loss += tf.nn.l2_loss(self.vars_bo[i])
l2_loss += tf.nn.l2_loss(self.vars_bf[i])
l2_loss += tf.nn.l2_loss(self.vars_bi[i])
l2_loss += tf.nn.l2_loss(self.W_x)
l2_loss += tf.nn.l2_loss(self.b_x)
l2_loss += tf.nn.l2_loss(self.v_o)
l2_loss += tf.nn.l2_loss(self.ws_o)
l2_loss += tf.nn.l2_loss(self.wd_o)
l2_loss += tf.nn.l2_loss(self.V_o)
l2_loss += tf.nn.l2_loss(self.wn_o)
l2_loss += tf.nn.l2_loss(self.b_o)
self.loss += FLAGS.weight_decay * l2_loss
# Cross entropy error
if self.task_type == "exclusive-label":
self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
labels=self.placeholders['labels'],
logits=self.outputs))
else: # multi-label
self.loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
labels=self.placeholders['labels'],
logits=self.outputs))
def _build(self):
# placeholder
self.n2n=self.placeholders['support']
self.node_feat=self.placeholders['features']
self.node_select=self.placeholders['node_select']
self.left=self.placeholders['left']
self.right=self.placeholders['right']
self.n_nd=self.placeholders['n_nd']
# parameters
hidden_dim = FLAGS.hidden1
input_dim=self.input_dim
label_dim = self.label_dim
self.vars_wn=[]
self.vars_bn=[]
self.vars_ws=[]
self.vars_wd=[]
self.vars_v=[]
self.vars_V=[]
self.vars_wi=[]
self.vars_wf=[]
self.vars_wo=[]
self.vars_wc=[]
self.vars_bc=[]
self.vars_bo=[]
self.vars_bf=[]
self.vars_bi=[]
collector=[]
for i in range(2):
self.vars_wn.append(glorot([hidden_dim, hidden_dim], 'W_n_%d'%i))
self.vars_bn.append(tf.Variable(tf.zeros([hidden_dim], dtype=tf.float32), name='b_n_%d'%i))
self.vars_ws.append(glorot([hidden_dim, hidden_dim], 'W_s_%d'%i))
self.vars_wd.append(glorot([hidden_dim, hidden_dim], 'W_d_%d'%i))
self.vars_v.append(glorot([hidden_dim], 'v_%d'%i))
self.vars_V.append(glorot([hidden_dim, hidden_dim], 'V_%d'%i))
self.vars_wi.append(glorot([hidden_dim*2, hidden_dim], 'W_i_%d'%i))
self.vars_wf.append(glorot([hidden_dim*2, hidden_dim], 'W_f_%d'%i))
self.vars_wo.append(glorot([hidden_dim*2, hidden_dim], 'W_o_%d'%i))
self.vars_wc.append(glorot([hidden_dim*2, hidden_dim], 'W_c_%d'%i))
self.vars_bc.append(tf.Variable(tf.zeros([hidden_dim], dtype=tf.float32), name='b_c_%d'%i))
self.vars_bo.append(tf.Variable(tf.zeros([hidden_dim], dtype=tf.float32), name='b_o_%d'%i))
self.vars_bf.append(tf.Variable(tf.zeros([hidden_dim], dtype=tf.float32), name='b_f_%d'%i))
self.vars_bi.append(tf.Variable(tf.zeros([hidden_dim], dtype=tf.float32), name='b_i_%d'%i))
self.W_x = glorot([input_dim, hidden_dim], 'W_x')
self.b_x = tf.Variable(tf.zeros([hidden_dim], dtype=tf.float32), name='b_x')
self.v_o = glorot([hidden_dim], 'v_o')
self.ws_o = glorot([hidden_dim, hidden_dim], 'W_s_o')
self.wd_o = glorot([hidden_dim, hidden_dim], 'W_d_o')
self.V_o = glorot([hidden_dim, hidden_dim], 'V_o')
self.wn_o = glorot([hidden_dim, label_dim], 'W_n_o')
self.b_o = tf.Variable(tf.zeros([label_dim], dtype=tf.float32), name='b_o')
# inference
#self.node_feat = tf.nn.dropout(self.node_feat, rate=1-self.keep_prob)
node_embed = tf.matmul(self.node_feat, self.W_x) + self.b_x
cur_embed = node_embed
C = tf.zeros([self.n_nd, hidden_dim], tf.float32)
for i in range(2):
cur_embed = tf.nn.dropout(cur_embed, rate=FLAGS.dropout)
# build sparse attention matrix a_{i,j}
sparse_attention = attention_mechanism(
"generalized_linear", self.vars_v[i], self.vars_ws[i], self.vars_wd[i],
self.vars_V[i], cur_embed, self.left, self.right, self.n2n)
if i == 0:
self.sparse_attention_l0 = sparse_attention.values
# propagation
n2npool = tf.sparse_tensor_dense_matmul(sp_a=sparse_attention, b=cur_embed)
node_linear = tf.matmul(n2npool, self.vars_wn[i]) + self.vars_bn[i]
if FLAGS.residual == 1:
merged_linear = tf.add(node_linear, node_embed)
else:
merged_linear = node_linear
cur_embed = tf.nn.tanh(merged_linear)
#cur_embed = tf.nn.dropout(cur_embed, rate=1-self.keep_prob)
collector.append(cur_embed)
for i in range(len(collector)):
input_gate = tf.nn.sigmoid(tf.matmul(tf.concat([collector[i], node_embed], 1), self.vars_wi[i])+self.vars_bi[i])
forget_gate = tf.nn.sigmoid(tf.matmul(tf.concat([collector[i], node_embed], 1), self.vars_wf[i])+self.vars_bf[i])
output_gate = tf.nn.sigmoid(tf.matmul(tf.concat([collector[i], node_embed], 1), self.vars_wo[i])+self.vars_bo[i])
C_update = tf.nn.tanh(tf.matmul(tf.concat([collector[i], node_embed], 1), self.vars_wc[i])+self.vars_bc[i])
C = tf.add(tf.multiply(forget_gate, C), tf.multiply(input_gate, C_update))
node_embed = tf.multiply(output_gate, tf.nn.tanh(C))
node_embed = tf.matmul(node_embed, self.wn_o)+self.b_o
self.outputs = tf.sparse_tensor_dense_matmul(sp_a=self.node_select, b=node_embed)
class GAT(Model):
def __init__(self, task_type, placeholders, input_dim, label_dim, **kwargs):
super(GAT, self).__init__(**kwargs)
self.inputs = placeholders['features']
assert task_type in ["exclusive-label", "multi-label"], "Unknown task type!"
self.task_type = task_type
self.input_dim = input_dim
self.label_dim = label_dim
self.placeholders = placeholders
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
self.build()
def _loss(self):
# Weight decay loss
#for i in range(len(self.layers)):
# for var in self.layers[i].vars.values():
# self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
for i in range(2):
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(self.vars_wn[i])
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(self.vars_bn[i])
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(self.vars_ws[i])
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(self.vars_wd[i])
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(self.vars_v[i])
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(self.vars_V[i])
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(self.W_x)
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(self.b_x)
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(self.wn_o)
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(self.b_o)
# Cross entropy error
if self.task_type == "exclusive-label":
self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
labels=self.placeholders['labels'],
logits=self.outputs))
else: # multi-label
self.loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
labels=self.placeholders['labels'],
logits=self.outputs))
def _build(self):
# placeholder
self.n2n=self.placeholders['support']
self.node_feat=self.placeholders['features']
self.node_select=self.placeholders['node_select']
self.left=self.placeholders['left']
self.right=self.placeholders['right']
self.n_nd=self.placeholders['n_nd']
# parameters
hidden_dim = FLAGS.hidden1
input_dim=self.input_dim
label_dim = self.label_dim
self.vars_wn=[]
self.vars_bn=[]
self.vars_ws=[]
self.vars_wd=[]
self.vars_v=[]
self.vars_V=[]
for i in range(2):
if i == 0:
self.vars_wn.append(glorot([hidden_dim, hidden_dim], 'W_n_%d'%i))
self.vars_bn.append(tf.Variable(tf.zeros([hidden_dim], dtype=tf.float32), name='b_n_%d'%i))
self.vars_ws.append(glorot([hidden_dim, hidden_dim], 'W_s_%d'%i))
self.vars_wd.append(glorot([hidden_dim, hidden_dim], 'W_d_%d'%i))
self.vars_v.append(glorot([hidden_dim], 'v_%d'%i))
self.vars_V.append(glorot([hidden_dim, hidden_dim], 'V_%d'%i))
else:
self.vars_wn.append(glorot([hidden_dim, hidden_dim], 'W_n_%d'%i))
self.vars_bn.append(tf.Variable(tf.zeros([hidden_dim], dtype=tf.float32), name='b_n_%d'%i))
self.vars_ws.append(glorot([hidden_dim, hidden_dim], 'W_s_%d'%i))
self.vars_wd.append(glorot([hidden_dim, hidden_dim], 'W_d_%d'%i))
self.vars_v.append(glorot([hidden_dim], 'v_%d'%i))
self.vars_V.append(glorot([hidden_dim, hidden_dim], 'V_%d'%i))
self.W_x = glorot([input_dim, hidden_dim], 'W_x')
self.b_x = tf.Variable(tf.zeros([hidden_dim], dtype=tf.float32), name='b_x')
self.wn_o = glorot([hidden_dim, label_dim], 'W_n_o')
self.b_o = tf.Variable(tf.zeros([label_dim], dtype=tf.float32), name='b_o')
# inference
#self.node_feat = tf.nn.dropout(self.node_feat, rate=1-self.keep_prob)
node_embed = tf.matmul(self.node_feat, self.W_x)+self.b_x
#node_embed = self.node_feat
cur_embed = node_embed
bp = 2
for i in range(bp):
cur_embed = tf.nn.dropout(cur_embed, rate=FLAGS.dropout)
# build sparse attention matrix a_{i,j}
sparse_attention = attention_mechanism(
"generalized_linear", self.vars_v[i], self.vars_ws[i], self.vars_wd[i],
self.vars_V[i], cur_embed, self.left, self.right, self.n2n)
if i == 0:
self.sparse_attention_l0 = sparse_attention.values
# propagation
n2npool = tf.sparse_tensor_dense_matmul(sp_a=sparse_attention, b=cur_embed)
node_linear = tf.matmul(n2npool, self.vars_wn[i])+self.vars_bn[i]
if FLAGS.residual == 1:
merged_linear = tf.add(node_linear, node_embed)
else:
merged_linear = node_linear
cur_embed = tf.nn.tanh(merged_linear)
#cur_embed = tf.nn.dropout(cur_embed, rate=1-self.keep_prob)
node_embed = tf.matmul(cur_embed, self.wn_o)+self.b_o
self.outputs = tf.sparse_tensor_dense_matmul(sp_a=self.node_select, b=node_embed)
| 43.497475
| 125
| 0.605283
| 2,561
| 17,225
| 3.823116
| 0.09098
| 0.073537
| 0.032172
| 0.034726
| 0.786947
| 0.765805
| 0.756
| 0.749055
| 0.718211
| 0.688796
| 0
| 0.013111
| 0.256081
| 17,225
| 395
| 126
| 43.607595
| 0.750976
| 0.073033
| 0
| 0.542763
| 0
| 0
| 0.04273
| 0
| 0
| 0
| 0
| 0
| 0.009868
| 1
| 0.046053
| false
| 0
| 0.006579
| 0
| 0.069079
| 0.006579
| 0
| 0
| 0
| null | 0
| 0
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| 1
| 1
| 1
| 1
| 1
| 0
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| null | 0
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| 0
| 0
|
0
| 5
|
3c5ad137248656d098c911417ce8a217b92134f6
| 416
|
py
|
Python
|
src/onegov/election_day/formats/vote/__init__.py
|
politbuero-kampagnen/onegov-cloud
|
20148bf321b71f617b64376fe7249b2b9b9c4aa9
|
[
"MIT"
] | null | null | null |
src/onegov/election_day/formats/vote/__init__.py
|
politbuero-kampagnen/onegov-cloud
|
20148bf321b71f617b64376fe7249b2b9b9c4aa9
|
[
"MIT"
] | null | null | null |
src/onegov/election_day/formats/vote/__init__.py
|
politbuero-kampagnen/onegov-cloud
|
20148bf321b71f617b64376fe7249b2b9b9c4aa9
|
[
"MIT"
] | null | null | null |
from onegov.election_day.formats.vote.default import import_vote_default
from onegov.election_day.formats.vote.internal import import_vote_internal
from onegov.election_day.formats.vote.wabsti import import_vote_wabsti
from onegov.election_day.formats.vote.wabstic import import_vote_wabstic
__all__ = [
'import_vote_default',
'import_vote_internal',
'import_vote_wabsti',
'import_vote_wabstic',
]
| 32
| 74
| 0.824519
| 57
| 416
| 5.596491
| 0.210526
| 0.250784
| 0.225705
| 0.263323
| 0.401254
| 0.401254
| 0
| 0
| 0
| 0
| 0
| 0
| 0.100962
| 416
| 12
| 75
| 34.666667
| 0.852941
| 0
| 0
| 0
| 0
| 0
| 0.182692
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.8
| 0
| 0.8
| 0
| 0
| 0
| 0
| null | 1
| 1
| 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
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3c5de8fa9d6827a18c78e36b7733986083eaae7d
| 139
|
py
|
Python
|
lbrynet/blob/__init__.py
|
vyaspranjal33/lbry
|
e03e41ad3105ccc0d8d8891b0e9fa63f9bbfce34
|
[
"MIT"
] | null | null | null |
lbrynet/blob/__init__.py
|
vyaspranjal33/lbry
|
e03e41ad3105ccc0d8d8891b0e9fa63f9bbfce34
|
[
"MIT"
] | 110
|
2018-11-26T05:41:35.000Z
|
2021-08-03T15:37:20.000Z
|
lbrynet/blob/__init__.py
|
vyaspranjal33/lbry
|
e03e41ad3105ccc0d8d8891b0e9fa63f9bbfce34
|
[
"MIT"
] | 1
|
2018-09-20T22:15:59.000Z
|
2018-09-20T22:15:59.000Z
|
from .blob_file import BlobFile
from .creator import BlobFileCreator
from .writer import HashBlobWriter
from .reader import HashBlobReader
| 27.8
| 36
| 0.856115
| 17
| 139
| 6.941176
| 0.647059
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115108
| 139
| 4
| 37
| 34.75
| 0.95935
| 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
| 1
| 0
|
0
| 5
|
b1b6f2757049b6ad4318211cbab962c81f257979
| 173
|
py
|
Python
|
tests/test_default.py
|
katiebreivik/showyourwork
|
77a15de6778e14c3a3936e86e181539cc31cc693
|
[
"MIT"
] | null | null | null |
tests/test_default.py
|
katiebreivik/showyourwork
|
77a15de6778e14c3a3936e86e181539cc31cc693
|
[
"MIT"
] | null | null | null |
tests/test_default.py
|
katiebreivik/showyourwork
|
77a15de6778e14c3a3936e86e181539cc31cc693
|
[
"MIT"
] | null | null | null |
from helpers import TemporaryShowyourworkRepository
class TestDefault(TemporaryShowyourworkRepository):
"""Test setting up and building the default repo."""
pass
| 21.625
| 56
| 0.791908
| 16
| 173
| 8.5625
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.150289
| 173
| 8
| 57
| 21.625
| 0.931973
| 0.265896
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 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
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 5
|
b1d4c92cd547d554324dece42bcd9ccfec2fc9fb
| 142
|
py
|
Python
|
mitm/__init__.py
|
cchinnasamy/mitm
|
28366606e3622a86fc3aa10c66272d5d42934f5b
|
[
"MIT"
] | null | null | null |
mitm/__init__.py
|
cchinnasamy/mitm
|
28366606e3622a86fc3aa10c66272d5d42934f5b
|
[
"MIT"
] | null | null | null |
mitm/__init__.py
|
cchinnasamy/mitm
|
28366606e3622a86fc3aa10c66272d5d42934f5b
|
[
"MIT"
] | null | null | null |
from .API import ManInTheMiddle
from .utils import RSA, color
from .client import EmulatedClient
from .server import HTTP, HTTPS, Interceptor
| 28.4
| 44
| 0.816901
| 19
| 142
| 6.105263
| 0.684211
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133803
| 142
| 4
| 45
| 35.5
| 0.943089
| 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
| 1
| 0
|
0
| 5
|
b1f725ed782d3a6675697bd1262cd8d37b524f80
| 293
|
py
|
Python
|
torecsys/layers/emb/__init__.py
|
p768lwy3/torecsys
|
2251366268b4fbe6f8c3ab1628fa72a0db043dcd
|
[
"MIT"
] | 92
|
2019-08-15T11:03:50.000Z
|
2022-03-12T01:21:05.000Z
|
torecsys/layers/emb/__init__.py
|
p768lwy3/torecsys
|
2251366268b4fbe6f8c3ab1628fa72a0db043dcd
|
[
"MIT"
] | 3
|
2020-03-11T08:57:50.000Z
|
2021-01-06T01:39:47.000Z
|
torecsys/layers/emb/__init__.py
|
p768lwy3/torecsys
|
2251366268b4fbe6f8c3ab1628fa72a0db043dcd
|
[
"MIT"
] | 16
|
2019-10-12T11:28:53.000Z
|
2022-03-28T14:04:12.000Z
|
""""
torecsys.layers.emb is a sub model of implementation of layers in embedding.
"""
from torecsys.layers.emb.generalized_matrix_factorization import GeneralizedMatrixFactorizationLayer
from torecsys.layers.emb.starspace import StarSpaceLayer
GMFLayer = GeneralizedMatrixFactorizationLayer
| 32.555556
| 100
| 0.853242
| 31
| 293
| 8
| 0.645161
| 0.169355
| 0.205645
| 0.169355
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088737
| 293
| 8
| 101
| 36.625
| 0.928839
| 0.266212
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
590dec1407e617a51cf5985bff2870844ae7cf71
| 162
|
py
|
Python
|
text_ckeditor/text_ckeditor_links/admin.py
|
rouxcode/django-text-ckeditor
|
dd8f86a6ffcbde8d1b85fc6e70d2653dd65b2737
|
[
"MIT"
] | null | null | null |
text_ckeditor/text_ckeditor_links/admin.py
|
rouxcode/django-text-ckeditor
|
dd8f86a6ffcbde8d1b85fc6e70d2653dd65b2737
|
[
"MIT"
] | null | null | null |
text_ckeditor/text_ckeditor_links/admin.py
|
rouxcode/django-text-ckeditor
|
dd8f86a6ffcbde8d1b85fc6e70d2653dd65b2737
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from ..admin import DjangoLinkAdmin
from .models import Link
@admin.register(Link)
class LinkAdmin(DjangoLinkAdmin):
pass
| 16.2
| 35
| 0.790123
| 20
| 162
| 6.4
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141975
| 162
| 9
| 36
| 18
| 0.920863
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.166667
| 0.5
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 5
|
591bd0ae083e720d7184c11d29529341dbcacd45
| 8,165
|
py
|
Python
|
pyflowline/formats/read_flowline.py
|
changliao1025/pyflowline
|
fb8677c5ebb3d0db8638f7fcc495ffb97376e00f
|
[
"Unlicense"
] | 4
|
2022-03-23T12:10:20.000Z
|
2022-03-29T13:41:16.000Z
|
pyflowline/formats/read_flowline.py
|
changliao1025/pyflowline
|
fb8677c5ebb3d0db8638f7fcc495ffb97376e00f
|
[
"Unlicense"
] | 1
|
2022-03-24T16:08:35.000Z
|
2022-03-24T16:08:35.000Z
|
pyflowline/formats/read_flowline.py
|
changliao1025/pyflowline
|
fb8677c5ebb3d0db8638f7fcc495ffb97376e00f
|
[
"Unlicense"
] | null | null | null |
import os
import numpy as np
from osgeo import ogr, osr, gdal
from shapely.wkt import loads
from pyflowline.formats.convert_coordinates import convert_gcs_coordinates_to_flowline
def read_flowline_shapefile(sFilename_shapefile_in):
"""
convert a shpefile to json format.
This function should be used for stream flowline only.
"""
iReturn_code = 1
if os.path.isfile(sFilename_shapefile_in):
pass
else:
print('This shapefile does not exist: ', sFilename_shapefile_in )
iReturn_code = 0
return iReturn_code
aFlowline=list()
pDriver_shapefile = ogr.GetDriverByName('ESRI Shapefile')
pDataset_shapefile = pDriver_shapefile.Open(sFilename_shapefile_in, gdal.GA_ReadOnly)
pLayer_shapefile = pDataset_shapefile.GetLayer(0)
pSpatialRef_shapefile = pLayer_shapefile.GetSpatialRef()
pSpatial_reference_gcs = osr.SpatialReference()
pSpatial_reference_gcs.ImportFromEPSG(4326)
pSpatial_reference_gcs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)
comparison = pSpatialRef_shapefile.IsSame(pSpatial_reference_gcs)
if(comparison != 1):
iFlag_transform =1
pTransform = osr.CoordinateTransformation(pSpatialRef_shapefile, pSpatial_reference_gcs)
else:
iFlag_transform =0
lID = 0
for pFeature_shapefile in pLayer_shapefile:
pGeometry_in = pFeature_shapefile.GetGeometryRef()
sGeometry_type = pGeometry_in.GetGeometryName()
lNHDPlusID = int(pFeature_shapefile.GetField("NHDPlusID"))
if (iFlag_transform ==1): #projections are different
pGeometry_in.Transform(pTransform)
if (pGeometry_in.IsValid()):
pass
else:
print('Geometry issue')
if(sGeometry_type == 'MULTILINESTRING'):
aLine = ogr.ForceToLineString(pGeometry_in)
for Line in aLine:
dummy = loads( Line.ExportToWkt() )
aCoords = dummy.coords
dummy1= np.array(aCoords)
pLine = convert_gcs_coordinates_to_flowline(dummy1)
pLine.lIndex = lID
pLine.lNHDPlusID= lNHDPlusID
aFlowline.append(pLine)
lID = lID + 1
else:
if sGeometry_type =='LINESTRING':
dummy = loads( pGeometry_in.ExportToWkt() )
aCoords = dummy.coords
dummy1= np.array(aCoords)
pLine = convert_gcs_coordinates_to_flowline(dummy1)
pLine.lIndex = lID
pLine.lNHDPlusID= lNHDPlusID
aFlowline.append(pLine)
lID = lID + 1
else:
print(sGeometry_type)
pass
#we also need to spatial reference
return aFlowline, pSpatialRef_shapefile
def read_flowline_shapefile_swat(sFilename_shapefile_in):
"""
convert a shpefile to json format.
This function should be used for stream flowline only.
"""
aFlowline=list()
pDriver_shapefile = ogr.GetDriverByName('ESRI Shapefile')
pDataset_shapefile = pDriver_shapefile.Open(sFilename_shapefile_in, gdal.GA_ReadOnly)
pLayer_shapefile = pDataset_shapefile.GetLayer(0)
pSpatialRef_shapefile = pLayer_shapefile.GetSpatialRef()
pSpatial_reference_gcs = osr.SpatialReference()
pSpatial_reference_gcs.ImportFromEPSG(4326)
pSpatial_reference_gcs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)
comparison = pSpatialRef_shapefile.IsSame(pSpatial_reference_gcs)
if(comparison != 1):
iFlag_transform =1
pTransform = osr.CoordinateTransformation(pSpatialRef_shapefile, pSpatial_reference_gcs)
else:
iFlag_transform =0
lID = 0
for pFeature_shapefile in pLayer_shapefile:
pGeometry_in = pFeature_shapefile.GetGeometryRef()
sGeometry_type = pGeometry_in.GetGeometryName()
if (iFlag_transform ==1): #projections are different
pGeometry_in.Transform(pTransform)
if (pGeometry_in.IsValid()):
pass
else:
print('Geometry issue')
if(sGeometry_type == 'MULTILINESTRING'):
aLine = ogr.ForceToLineString(pGeometry_in)
for Line in aLine:
dummy = loads( Line.ExportToWkt() )
aCoords = dummy.coords
dummy1= np.array(aCoords)
pLine = convert_gcs_coordinates_to_flowline(dummy1)
pLine.lIndex = lID
aFlowline.append(pLine)
lID = lID + 1
else:
if sGeometry_type =='LINESTRING':
dummy = loads( pGeometry_in.ExportToWkt() )
aCoords = dummy.coords
dummy1= np.array(aCoords)
pLine = convert_gcs_coordinates_to_flowline(dummy1)
pLine.lIndex = lID
aFlowline.append(pLine)
lID = lID + 1
else:
print(sGeometry_type)
pass
return aFlowline, pSpatialRef_shapefile
def read_flowline_geojson(sFilename_geojson_in):
"""
read a geojson flowline
This function should be used for stream flowline only.
"""
aFlowline=list()
pDriver_geojson = ogr.GetDriverByName('GeoJSON')
if os.path.isfile(sFilename_geojson_in):
print(sFilename_geojson_in)
else:
print('This geojson file does not exist: ', sFilename_geojson_in )
exit()
pDataset_geojson = pDriver_geojson.Open(sFilename_geojson_in, gdal.GA_ReadOnly)
pLayer_geojson = pDataset_geojson.GetLayer(0)
pSpatialRef_geojson = pLayer_geojson.GetSpatialRef()
ldefn = pLayer_geojson.GetLayerDefn()
schema =list()
for n in range(ldefn.GetFieldCount()):
fdefn = ldefn.GetFieldDefn(n)
schema.append(fdefn.name)
if 'iseg' in schema:
iFlag_segment = 1
else:
iFlag_segment = 0
if 'id' in schema:
iFlag_id = 1
else:
iFlag_id = 0
if 'NHDPlusID' in schema:
iFlag_NHDPlusID = 1
else:
iFlag_NHDPlusID = 0
lID = 0
for pFeature_geojson in pLayer_geojson:
pGeometry_geojson = pFeature_geojson.GetGeometryRef()
pGeometry_in = pFeature_geojson.GetGeometryRef()
sGeometry_type = pGeometry_in.GetGeometryName()
if iFlag_segment ==1:
iStream_segment = pFeature_geojson.GetField("iseg")
else:
iStream_segment = -1
if iFlag_id ==1:
lFlowlineID = pFeature_geojson.GetField("id")
else:
lFlowlineID = -1
if iFlag_NHDPlusID ==1:
lNHDPlusID = pFeature_geojson.GetField("NHDPlusID")
else:
lNHDPlusID = -1
if(sGeometry_type == 'MULTILINESTRING'):
aLine = ogr.ForceToLineString(pGeometry_in)
for Line in aLine:
dummy = loads( Line.ExportToWkt() )
aCoords = dummy.coords
dummy1= np.array(aCoords)
pLine = convert_gcs_coordinates_to_flowline(dummy1)
pLine.lIndex = lID
pLine.lFlowlineID = lFlowlineID
pLine.lNHDPlusID= lNHDPlusID
aFlowline.append(pLine)
lID = lID + 1
else:
if sGeometry_type =='LINESTRING':
dummy = loads( pGeometry_in.ExportToWkt() )
aCoords = dummy.coords
dummy1= np.array(aCoords)
pLine = convert_gcs_coordinates_to_flowline(dummy1)
pLine.lIndex = lID
pLine.iStream_segment = iStream_segment
pLine.lFlowlineID = lFlowlineID
pLine.lNHDPlusID = lNHDPlusID
aFlowline.append(pLine)
lID = lID + 1
else:
print(sGeometry_type)
pass
return aFlowline, pSpatialRef_geojson
| 35.811404
| 96
| 0.607961
| 791
| 8,165
| 6.046776
| 0.168142
| 0.036797
| 0.041815
| 0.033661
| 0.748275
| 0.724232
| 0.724232
| 0.70876
| 0.696216
| 0.696216
| 0
| 0.009772
| 0.323209
| 8,165
| 228
| 97
| 35.811404
| 0.855773
| 0.041886
| 0
| 0.728261
| 0
| 0
| 0.03117
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.016304
| false
| 0.032609
| 0.038043
| 0
| 0.076087
| 0.043478
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
59295a94ead8f3a8c92edd6247bf52ed4c3043dc
| 3,346
|
py
|
Python
|
valloes.py
|
HarleyEDU/pythoneduwork
|
6c5a28217c96fac394cb7ad0fb8d186b5080f1de
|
[
"bzip2-1.0.6"
] | null | null | null |
valloes.py
|
HarleyEDU/pythoneduwork
|
6c5a28217c96fac394cb7ad0fb8d186b5080f1de
|
[
"bzip2-1.0.6"
] | null | null | null |
valloes.py
|
HarleyEDU/pythoneduwork
|
6c5a28217c96fac394cb7ad0fb8d186b5080f1de
|
[
"bzip2-1.0.6"
] | null | null | null |
import time
time.sleep(1)
print("Yo kid, gimme you test papier, mans out here, needin dat ting for copiying, got me g, asnee, skrr, bam, weewee-woowoo!")
time.sleep(4)
science=input(("- Choose YES or NO: "))
print("")
time.sleep(2)
yes=input(("- You chose NO right: "))
time.sleep(2)
print("- Ok so now we have confirmed you chose NO, lets see how it plays out...")
print("")
time.sleep(2)
print("-Bully kills you.exe-")
time.sleep(3)
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
time.sleep(3)
print("- OK TRY YES THIS TIME!")
print("")
time.sleep(2)
science1=input("- Choose YES or NO: ")
time.sleep(2)
yeet=input("- So you chose NO right...?: ")
time.sleep(3)
print("- I am just kidding, you would never be that stupid... ;)")
time.sleep(1)
print("- probably")
time.sleep(3)
print("You give the bully your test paper...")
print("")
analysis=input("Whats your score on the analysis test (he asks)? Remember, its out of 50: ")
design=input("+ what was your score on the design test? REMEMBER, its out of 35: ")
implementation=input("NERD WHATS YOUR IMPLEMENTATION SCORE, TELL ME! ITS OUT OF 80 REMEMBER!: ")
evaluation=input("AND FINALLY, WHATS YOUR F______ EVALUATION SCORE ON THE TEST??? ITS OUT OF 50 IDIOT!")
print("")
time.sleep(2)
print("So your analysis score was", int(analysis)/50 *100, "%", " right")
print("")
time.sleep(3)
print("and your design score was", int(design)/35 *100, "%", " right")
print("")
time.sleep(3)
print("and yo' goddamn implementation score was", int(implementation)/80 *100, "%", " right")
print("")
time.sleep(3)
print("HAHAHAHA! Finally, your evaluation score was", int(evaluation)/50*100, "%", " right!")
time.sleep(3)
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
time.sleep(2)
print("and now...")
time.sleep(2)
print("its time....")
time.sleep(2)
print("for you......")
time.sleep(3)
print("to die.........")
time.sleep(3)
print("Thanks for playing!")
time.sleep(4)
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
print("")
time.sleep(2)
import PIL
import Image
from PIL import Image
#...
img = Image.open('picture.png')
img.show()
| 9.478754
| 128
| 0.521817
| 404
| 3,346
| 4.306931
| 0.269802
| 0.482759
| 0.698276
| 0.896552
| 0.413793
| 0.357471
| 0.329885
| 0.313793
| 0.278161
| 0.278161
| 0
| 0.02158
| 0.265989
| 3,346
| 352
| 129
| 9.505682
| 0.686889
| 0.000897
| 0
| 0.798658
| 0
| 0.006711
| 0.34767
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.026846
| 0
| 0.026846
| 0.744966
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
3ca3a169112ca36514de8e92dd6acd4f13f14931
| 216
|
py
|
Python
|
L1Trigger/TrackerDTC/python/AnalyzerDAQ_cff.py
|
Jingyan95/cmssw
|
f78d843f0837f269ee6811b0e0f4c0432928c190
|
[
"Apache-2.0"
] | 5
|
2020-07-02T19:05:26.000Z
|
2022-02-25T14:37:09.000Z
|
L1Trigger/TrackerDTC/python/AnalyzerDAQ_cff.py
|
Jingyan95/cmssw
|
f78d843f0837f269ee6811b0e0f4c0432928c190
|
[
"Apache-2.0"
] | 61
|
2020-07-14T17:22:52.000Z
|
2022-03-16T11:11:12.000Z
|
L1Trigger/TrackerDTC/python/AnalyzerDAQ_cff.py
|
dally96/cmssw
|
c37b9bfa391850cb349c71190b0bbb2d04224cc8
|
[
"Apache-2.0"
] | 8
|
2020-06-08T16:28:54.000Z
|
2021-11-16T14:40:00.000Z
|
import FWCore.ParameterSet.Config as cms
from L1Trigger.TrackerDTC.AnalyzerDAQ_cfi import TrackerDTCAnalyzerDAQ_params
TrackerDTCAnalyzerDAQ = cms.EDAnalyzer('trackerDTC::AnalyzerDAQ', TrackerDTCAnalyzerDAQ_params)
| 43.2
| 95
| 0.87963
| 21
| 216
| 8.904762
| 0.666667
| 0.224599
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004926
| 0.060185
| 216
| 5
| 95
| 43.2
| 0.916256
| 0
| 0
| 0
| 0
| 0
| 0.105991
| 0.105991
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 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
| 1
| 0
|
0
| 5
|
3cacb6bca871ba9bfab249f6e9ad299dac830b54
| 426
|
py
|
Python
|
addons/calendar/models/__init__.py
|
SHIVJITH/Odoo_Machine_Test
|
310497a9872db7844b521e6dab5f7a9f61d365a4
|
[
"Apache-2.0"
] | null | null | null |
addons/calendar/models/__init__.py
|
SHIVJITH/Odoo_Machine_Test
|
310497a9872db7844b521e6dab5f7a9f61d365a4
|
[
"Apache-2.0"
] | null | null | null |
addons/calendar/models/__init__.py
|
SHIVJITH/Odoo_Machine_Test
|
310497a9872db7844b521e6dab5f7a9f61d365a4
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
from . import ir_http
from . import res_partner
from . import calendar_event
from . import calendar_alarm
from . import calendar_alarm_manager
from . import calendar_attendee
from . import calendar_contact
from . import calendar_event_type
from . import calendar_recurrence
from . import mail_activity
from . import res_users
| 28.4
| 74
| 0.798122
| 61
| 426
| 5.360656
| 0.52459
| 0.336391
| 0.385321
| 0.140673
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.002755
| 0.147887
| 426
| 14
| 75
| 30.428571
| 0.898072
| 0.220657
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3cadff189d6659ade586919540ab548b96d3a259
| 122
|
py
|
Python
|
ReadingNotes/CythonDocumentation/prime/setup_cpp.py
|
agent1894/Quant-Practice-Workspace
|
f102e136389e2247bbbfb36ef78c16807a0ba7d2
|
[
"MIT"
] | 1
|
2021-03-17T01:25:05.000Z
|
2021-03-17T01:25:05.000Z
|
ReadingNotes/CythonDocumentation/prime/setup_cpp.py
|
agent1894/Quant-Practice-Workspace
|
f102e136389e2247bbbfb36ef78c16807a0ba7d2
|
[
"MIT"
] | null | null | null |
ReadingNotes/CythonDocumentation/prime/setup_cpp.py
|
agent1894/Quant-Practice-Workspace
|
f102e136389e2247bbbfb36ef78c16807a0ba7d2
|
[
"MIT"
] | 2
|
2020-06-29T15:31:10.000Z
|
2021-03-24T14:20:15.000Z
|
from distutils.core import setup
from Cython.Build import cythonize
setup(ext_modules=cythonize("prime_cython_cpp.pyx"))
| 24.4
| 52
| 0.836066
| 18
| 122
| 5.5
| 0.722222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.081967
| 122
| 4
| 53
| 30.5
| 0.883929
| 0
| 0
| 0
| 0
| 0
| 0.163934
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3cae0443470051b39519af610077f59c4458ba33
| 780
|
py
|
Python
|
cw_wrapper/utils/test_vectors.py
|
BeneciaLee/cw_wrapper
|
a1562aa04e11acf9c1646777e2edc52981df9d2e
|
[
"MIT"
] | 3
|
2021-06-30T05:36:48.000Z
|
2021-07-01T10:24:59.000Z
|
cw_wrapper/utils/test_vectors.py
|
BeneciaLee/cw_wrapper
|
a1562aa04e11acf9c1646777e2edc52981df9d2e
|
[
"MIT"
] | 1
|
2021-07-12T12:11:35.000Z
|
2021-07-12T12:11:35.000Z
|
cw_wrapper/utils/test_vectors.py
|
BeneciaLee/cw_wrapper
|
a1562aa04e11acf9c1646777e2edc52981df9d2e
|
[
"MIT"
] | 2
|
2021-06-30T08:13:41.000Z
|
2021-07-01T09:18:04.000Z
|
AES_128_ECB_test_vectors = (
{"key": "911500915E8514174402A13118EA362C",
"plain": "4163F3BEABA14D6C1E406BD5646CAC9A",
"cipher": "39610A1E8F66501D952C27AB52C4DC9A"},
{"key": "BCCF986A4D74B719EEB1D93CDABE96D5",
"plain": "A6325414DDE2E367AABA669766316976",
"cipher": "666C5668ECAAD6E66C7FB569E52AA928"},
{"key": "5AC9583DCAC4CB19A451820A909FAFEC",
"plain": "8C45132DFC87959BF89396844FA1A2F2",
"cipher": "0965016DBE90009C75B4D31C460AC94C"},
{"key": "3880E49151EE2E0BDCBA8C73E0FC84A0",
"plain": "FB7F2920028338CDD37CB0A440E6E337",
"cipher": "B1873D3B12FE1F83F7D7B03104D5F878"},
{"key": "7EC254E4A483777D23A5086858133D15",
"plain": "AD03D4516D30F30C15E5591E0ED6D324",
"cipher": "ED1DCBC75D76E2BD35666BC56939ADDD"}
)
| 43.333333
| 51
| 0.735897
| 35
| 780
| 16.285714
| 0.657143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.455621
| 0.133333
| 780
| 17
| 52
| 45.882353
| 0.387574
| 0
| 0
| 0
| 0
| 0
| 0.705128
| 0.615385
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
3cb9ce9cc8841b819db03679cec3aaee32a8248e
| 66
|
py
|
Python
|
MotunrayoKoyejo/Phase 1/Python Basic 1/Day6/Q46.py
|
CodedLadiesInnovateTech/-python-challenge-solutions
|
430cd3eb84a2905a286819eef384ee484d8eb9e7
|
[
"MIT"
] | 6
|
2020-05-23T19:53:25.000Z
|
2021-05-08T20:21:30.000Z
|
MotunrayoKoyejo/Phase 1/Python Basic 1/Day6/Q46.py
|
CodedLadiesInnovateTech/-python-challenge-solutions
|
430cd3eb84a2905a286819eef384ee484d8eb9e7
|
[
"MIT"
] | 8
|
2020-05-14T18:53:12.000Z
|
2020-07-03T00:06:20.000Z
|
MotunrayoKoyejo/Phase 1/Python Basic 1/Day6/Q46.py
|
CodedLadiesInnovateTech/-python-challenge-solutions
|
430cd3eb84a2905a286819eef384ee484d8eb9e7
|
[
"MIT"
] | 39
|
2020-05-10T20:55:02.000Z
|
2020-09-12T17:40:59.000Z
|
import os
print('Current file name: ', os.path.realpath(__file__))
| 33
| 56
| 0.757576
| 10
| 66
| 4.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 66
| 2
| 56
| 33
| 0.766667
| 0
| 0
| 0
| 0
| 0
| 0.283582
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 5
|
3ccc1cd2b4c7b885f2392e5ad1be6c71f0a11251
| 9,882
|
py
|
Python
|
core/migrations/0002_auto_20201214_1553.py
|
cumanachao/utopia-crm
|
6d648971c427ca9f380b15ed0ceaf5767b88e8b9
|
[
"BSD-3-Clause"
] | 13
|
2020-12-14T19:56:04.000Z
|
2021-11-06T13:24:48.000Z
|
core/migrations/0002_auto_20201214_1553.py
|
cumanachao/utopia-crm
|
6d648971c427ca9f380b15ed0ceaf5767b88e8b9
|
[
"BSD-3-Clause"
] | 5
|
2020-12-14T19:56:30.000Z
|
2021-09-22T22:09:39.000Z
|
core/migrations/0002_auto_20201214_1553.py
|
cumanachao/utopia-crm
|
6d648971c427ca9f380b15ed0ceaf5767b88e8b9
|
[
"BSD-3-Clause"
] | 3
|
2021-03-24T03:55:08.000Z
|
2022-01-13T15:22:34.000Z
|
# -*- coding: utf-8 -*-
# Generated by Django 1.11.29 on 2020-12-14 15:53
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import taggit.managers
class Migration(migrations.Migration):
initial = True
dependencies = [
('support', '0001_initial'),
('taggit', '0002_auto_20150616_2121'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('logistics', '0001_initial'),
('core', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='subscriptionproduct',
name='route',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='route', to='logistics.Route', verbose_name='Route'),
),
migrations.AddField(
model_name='subscriptionproduct',
name='subscription',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Subscription'),
),
migrations.AddField(
model_name='subscriptionnewsletter',
name='contact',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Contact'),
),
migrations.AddField(
model_name='subscriptionnewsletter',
name='product',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Product'),
),
migrations.AddField(
model_name='subscription',
name='billing_address',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='billing_contacts', to='core.Address', verbose_name='Billing address'),
),
migrations.AddField(
model_name='subscription',
name='campaign',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.Campaign', verbose_name='Campaign'),
),
migrations.AddField(
model_name='subscription',
name='contact',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='subscriptions', to='core.Contact', verbose_name='Contact'),
),
migrations.AddField(
model_name='subscription',
name='pickup_point',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='logistics.PickupPoint', verbose_name='Pickup point'),
),
migrations.AddField(
model_name='subscription',
name='products',
field=models.ManyToManyField(through='core.SubscriptionProduct', to='core.Product'),
),
migrations.AddField(
model_name='subscription',
name='seller',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='support.Seller', verbose_name='Seller'),
),
migrations.AddField(
model_name='subscription',
name='unsubscription_manager',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='Unsubscription manager'),
),
migrations.AddField(
model_name='pricerule',
name='choose_one_product',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='chosen_product', to='core.Product'),
),
migrations.AddField(
model_name='pricerule',
name='products_not_pool',
field=models.ManyToManyField(blank=True, related_name='not_pool', to='core.Product'),
),
migrations.AddField(
model_name='pricerule',
name='products_pool',
field=models.ManyToManyField(related_name='pool', to='core.Product'),
),
migrations.AddField(
model_name='pricerule',
name='resulting_product',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='resulting_product', to='core.Product'),
),
migrations.AddField(
model_name='dynamiccontactfilter',
name='newsletters',
field=models.ManyToManyField(blank=True, related_name='newsletters', to='core.Product'),
),
migrations.AddField(
model_name='dynamiccontactfilter',
name='products',
field=models.ManyToManyField(blank=True, related_name='products', to='core.Product'),
),
migrations.AddField(
model_name='contactproducthistory',
name='campaign',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.Campaign'),
),
migrations.AddField(
model_name='contactproducthistory',
name='contact',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Contact'),
),
migrations.AddField(
model_name='contactproducthistory',
name='product',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Product'),
),
migrations.AddField(
model_name='contactproducthistory',
name='subscription',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.Subscription'),
),
migrations.AddField(
model_name='contactcampaignstatus',
name='campaign',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Campaign'),
),
migrations.AddField(
model_name='contactcampaignstatus',
name='contact',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Contact'),
),
migrations.AddField(
model_name='contactcampaignstatus',
name='seller_resolution',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='support.Seller'),
),
migrations.AddField(
model_name='contact',
name='institution',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.Institution', verbose_name='Institution'),
),
migrations.AddField(
model_name='contact',
name='ocupation',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.Ocupation', verbose_name='Ocupation'),
),
migrations.AddField(
model_name='contact',
name='referrer',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='referred', to='core.Contact', verbose_name='Referrer'),
),
migrations.AddField(
model_name='contact',
name='seller',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='support.Seller'),
),
migrations.AddField(
model_name='contact',
name='subtype',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.Subtype', verbose_name='Subtype'),
),
migrations.AddField(
model_name='contact',
name='tags',
field=taggit.managers.TaggableManager(blank=True, help_text='A comma-separated list of tags.', through='taggit.TaggedItem', to='taggit.Tag', verbose_name='Tags'),
),
migrations.AddField(
model_name='campaign',
name='product',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.Product'),
),
migrations.AddField(
model_name='address',
name='contact',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='addresses', to='core.Contact', verbose_name='Contact'),
),
migrations.AddField(
model_name='address',
name='geo_ref_address',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='logistics.GeorefAddress', verbose_name='GeorefAddress'),
),
migrations.AddField(
model_name='activity',
name='campaign',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.Campaign'),
),
migrations.AddField(
model_name='activity',
name='contact',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.Contact'),
),
migrations.AddField(
model_name='activity',
name='issue',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='support.Issue', verbose_name='Issue'),
),
migrations.AddField(
model_name='activity',
name='product',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.Product'),
),
migrations.AddField(
model_name='activity',
name='seller',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='support.Seller', verbose_name='Seller'),
),
]
| 45.962791
| 188
| 0.626391
| 1,008
| 9,882
| 6.010913
| 0.112103
| 0.11289
| 0.144248
| 0.169335
| 0.804588
| 0.787094
| 0.664796
| 0.64202
| 0.63971
| 0.591352
| 0
| 0.006152
| 0.243372
| 9,882
| 214
| 189
| 46.17757
| 0.8042
| 0.006982
| 0
| 0.713592
| 1
| 0
| 0.180224
| 0.030989
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.024272
| 0
| 0.043689
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
3ceff14a7e4a7eb1b5f85131e3a3bb59b36b2942
| 138
|
py
|
Python
|
tests/test_iauc.py
|
klintan/dc-multiple-myeloma-metrics
|
6999a8d165f2f30da80408d099bbd96067924c66
|
[
"MIT"
] | 1
|
2020-09-17T03:53:25.000Z
|
2020-09-17T03:53:25.000Z
|
tests/test_iauc.py
|
klintan/dc-multiple-myeloma-metrics
|
6999a8d165f2f30da80408d099bbd96067924c66
|
[
"MIT"
] | null | null | null |
tests/test_iauc.py
|
klintan/dc-multiple-myeloma-metrics
|
6999a8d165f2f30da80408d099bbd96067924c66
|
[
"MIT"
] | 1
|
2022-01-06T17:08:26.000Z
|
2022-01-06T17:08:26.000Z
|
import unittest
from metrics.iauc import integrateAUC
class IntegratedAUCTests(unittest.TestCase):
def test_iauc(self):
pass
| 19.714286
| 44
| 0.768116
| 16
| 138
| 6.5625
| 0.8125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 138
| 6
| 45
| 23
| 0.921053
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0.2
| 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
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
a7240e594d0f77cdb252d4401e25c0b80766973b
| 1,380
|
py
|
Python
|
_notebooks/snippets.py
|
slamer59/energies-domestique
|
a3432ac82e952f5c5e4ac3fb3daa2ea3e790ce37
|
[
"Apache-2.0"
] | null | null | null |
_notebooks/snippets.py
|
slamer59/energies-domestique
|
a3432ac82e952f5c5e4ac3fb3daa2ea3e790ce37
|
[
"Apache-2.0"
] | null | null | null |
_notebooks/snippets.py
|
slamer59/energies-domestique
|
a3432ac82e952f5c5e4ac3fb3daa2ea3e790ce37
|
[
"Apache-2.0"
] | null | null | null |
import pandas as pd
import hvplot
def export_plot_fastpages(plot, filename):
hvplot.save(plot, filename)
import fileinput
with open(filename, 'r') as original: data = original.read()
with open(filename, 'w') as modified: modified.write("{% raw %}\n" + data + "\n{% endraw %}")
for text_to_search in ["<!DOCTYPE html>", '<html lang="en">','</html>', '<head>', '</head>', '<body>','</body>']:
with fileinput.FileInput(filename, inplace=True) as file:
for line in file:
replacement_text = ""
print(line.replace(text_to_search, replacement_text), end='')
def export_plot_fastpages_panel(plot, filename, options=None):
if options:
plot.save(filename, **options)
else:
plot.save(filename)
import fileinput
with open(filename, 'r') as original: data = original.read()
with open(filename, 'w') as modified: modified.write("{% raw %}\n" + data + "\n{% endraw %}")
for text_to_search in ["<!DOCTYPE html>", '<html lang="en">','</html>', '<head>', '</head>', '<body>','</body>']:
with fileinput.FileInput(filename, inplace=True) as file:
for line in file:
replacement_text = ""
print(line.replace(text_to_search, replacement_text), end='')
| 36.315789
| 125
| 0.57029
| 159
| 1,380
| 4.842767
| 0.301887
| 0.041558
| 0.083117
| 0.057143
| 0.779221
| 0.779221
| 0.779221
| 0.779221
| 0.779221
| 0.779221
| 0
| 0
| 0.268116
| 1,380
| 37
| 126
| 37.297297
| 0.762376
| 0
| 0
| 0.64
| 0
| 0
| 0.13198
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.08
| false
| 0
| 0.16
| 0
| 0.24
| 0.08
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 0
|
0
| 5
|
595e49ac80606ebc32edf96ee782e5541b42d42f
| 46
|
py
|
Python
|
tests/__init__.py
|
SpaceWhale/doddlebot
|
973f41d62126eb458167ab56b67a84066be8e560
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
SpaceWhale/doddlebot
|
973f41d62126eb458167ab56b67a84066be8e560
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
SpaceWhale/doddlebot
|
973f41d62126eb458167ab56b67a84066be8e560
|
[
"MIT"
] | null | null | null |
"""
:author: john.sosoka
:date: 5/10/2018
"""
| 9.2
| 20
| 0.586957
| 7
| 46
| 3.857143
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175
| 0.130435
| 46
| 5
| 21
| 9.2
| 0.5
| 0.804348
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 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
| 5
|
597c97bf92460ece4eca38beaf148b584c9935c1
| 142
|
py
|
Python
|
app/settings.py
|
mizuho1998/co2_monitor
|
00592ec183ef5199ce08acc0c20bc85d7fdf8a8e
|
[
"MIT"
] | null | null | null |
app/settings.py
|
mizuho1998/co2_monitor
|
00592ec183ef5199ce08acc0c20bc85d7fdf8a8e
|
[
"MIT"
] | null | null | null |
app/settings.py
|
mizuho1998/co2_monitor
|
00592ec183ef5199ce08acc0c20bc85d7fdf8a8e
|
[
"MIT"
] | null | null | null |
from dotenv import load_dotenv
import os
def init():
cur_dir = os.path.dirname(__file__)
load_dotenv(os.path.join(cur_dir, '.env'))
| 17.75
| 46
| 0.711268
| 23
| 142
| 4.043478
| 0.608696
| 0.258065
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.161972
| 142
| 7
| 47
| 20.285714
| 0.781513
| 0
| 0
| 0
| 0
| 0
| 0.028169
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0
| 0.6
| 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
| 1
| 0
|
0
| 5
|
598869ef499eb55c8adbb3ba9b234bd8697a65fa
| 159
|
py
|
Python
|
pygraph/__init__.py
|
mavnt/pygraph
|
e3f73fc853b37247946763bbd80d1f11e915b229
|
[
"MIT"
] | null | null | null |
pygraph/__init__.py
|
mavnt/pygraph
|
e3f73fc853b37247946763bbd80d1f11e915b229
|
[
"MIT"
] | null | null | null |
pygraph/__init__.py
|
mavnt/pygraph
|
e3f73fc853b37247946763bbd80d1f11e915b229
|
[
"MIT"
] | null | null | null |
import shutil
from .logging_utils import logging
from .pygraph import Graph
if shutil.which("dot") is None:
logging.critical("dot binary not found !!!")
| 19.875
| 48
| 0.742138
| 23
| 159
| 5.086957
| 0.695652
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157233
| 159
| 7
| 49
| 22.714286
| 0.873134
| 0
| 0
| 0
| 0
| 0
| 0.169811
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.6
| 0
| 0.6
| 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
| 1
| 0
|
0
| 5
|
599ebe90eec5c088acec555d525793bc109f5580
| 173
|
py
|
Python
|
Learning/CodeWars/Python/6kyu_Consecutive_strings.py
|
aliasfoxkde/snippets
|
bb6dcc6597316ef9c88611f526935059451c3b5a
|
[
"MIT"
] | null | null | null |
Learning/CodeWars/Python/6kyu_Consecutive_strings.py
|
aliasfoxkde/snippets
|
bb6dcc6597316ef9c88611f526935059451c3b5a
|
[
"MIT"
] | null | null | null |
Learning/CodeWars/Python/6kyu_Consecutive_strings.py
|
aliasfoxkde/snippets
|
bb6dcc6597316ef9c88611f526935059451c3b5a
|
[
"MIT"
] | null | null | null |
# See: https://www.codewars.com/kata/56a5d994ac971f1ac500003e
def longest_consec(s, k):
return max([''.join(i) for i in zip(*[s[i:] for i in range(k)])]+[''], key=len)
| 34.6
| 83
| 0.647399
| 29
| 173
| 3.827586
| 0.758621
| 0.072072
| 0.09009
| 0.126126
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.10596
| 0.127168
| 173
| 4
| 84
| 43.25
| 0.629139
| 0.34104
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
59afcf921ee3c891c26a68dd1e4d2116e70ba07a
| 88
|
py
|
Python
|
test/conftest.py
|
jhrmnn/torchcubicspline
|
3869a90d8120df8067b1ab790fefb86806604a85
|
[
"Apache-2.0"
] | null | null | null |
test/conftest.py
|
jhrmnn/torchcubicspline
|
3869a90d8120df8067b1ab790fefb86806604a85
|
[
"Apache-2.0"
] | null | null | null |
test/conftest.py
|
jhrmnn/torchcubicspline
|
3869a90d8120df8067b1ab790fefb86806604a85
|
[
"Apache-2.0"
] | null | null | null |
import os
import torch
torch.manual_seed(int(os.environ.get('SPLINE_MANUAL_SEED', 7)))
| 17.6
| 63
| 0.784091
| 15
| 88
| 4.4
| 0.666667
| 0.30303
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012346
| 0.079545
| 88
| 4
| 64
| 22
| 0.802469
| 0
| 0
| 0
| 0
| 0
| 0.204545
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
59d2d74cd4cadb7bb39053a8480aba15d347b046
| 45
|
py
|
Python
|
python/testData/psi/PositionalOnlyParameters.py
|
tgodzik/intellij-community
|
f5ef4191fc30b69db945633951fb160c1cfb7b6f
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/psi/PositionalOnlyParameters.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2022-02-19T09:45:05.000Z
|
2022-02-27T20:32:55.000Z
|
python/testData/psi/PositionalOnlyParameters.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
def f(pos1, /, pos_or_kwd, *, kwd1):
pass
| 22.5
| 36
| 0.577778
| 8
| 45
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057143
| 0.222222
| 45
| 2
| 37
| 22.5
| 0.628571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0.5
| 0
| 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
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
aba877132ac0150a492208f3eb7958fe0d77fa36
| 50
|
py
|
Python
|
KafkaHandler/__init__.py
|
ThalesMR/KafkaHandler
|
1ccb15d3e85df69e9dbe9e8013d9b7b2792eff59
|
[
"MIT"
] | null | null | null |
KafkaHandler/__init__.py
|
ThalesMR/KafkaHandler
|
1ccb15d3e85df69e9dbe9e8013d9b7b2792eff59
|
[
"MIT"
] | null | null | null |
KafkaHandler/__init__.py
|
ThalesMR/KafkaHandler
|
1ccb15d3e85df69e9dbe9e8013d9b7b2792eff59
|
[
"MIT"
] | null | null | null |
from KafkaHandler.kafkaHandler import KafkaHandler
| 50
| 50
| 0.92
| 5
| 50
| 9.2
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.06
| 50
| 1
| 50
| 50
| 0.978723
| 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
| 1
| 0
|
0
| 5
|
abb15be6233c5a305aff2246914341044f6423bf
| 3,679
|
py
|
Python
|
dataworkspace/dataworkspace/tests/explorer/test_schema.py
|
uktrade/jupyterhub-data-auth-admin
|
91544f376209a201531f4dbfb8faad1b8ada18c9
|
[
"MIT"
] | 1
|
2019-06-10T08:22:56.000Z
|
2019-06-10T08:22:56.000Z
|
dataworkspace/dataworkspace/tests/explorer/test_schema.py
|
uktrade/jupyterhub-data-auth-admin
|
91544f376209a201531f4dbfb8faad1b8ada18c9
|
[
"MIT"
] | 2
|
2019-05-17T13:10:42.000Z
|
2019-06-17T10:48:46.000Z
|
dataworkspace/dataworkspace/tests/explorer/test_schema.py
|
uktrade/jupyterhub-data-auth-admin
|
91544f376209a201531f4dbfb8faad1b8ada18c9
|
[
"MIT"
] | null | null | null |
from unittest.mock import patch
import pytest
from django.conf import settings
from django.core.cache import cache
from dataworkspace.apps.explorer import schema
class TestSchemaInfo:
@pytest.fixture(scope="function", autouse=True)
def _clear_cache(self):
cache.clear()
@staticmethod
def _get_connection_data():
connection_info = settings.DATABASES_DATA["my_database"]
return {
"db_host": connection_info["HOST"],
"db_port": connection_info["PORT"],
"db_user": connection_info["USER"],
"db_password": connection_info["PASSWORD"],
"db_name": connection_info["NAME"],
}
@patch("dataworkspace.apps.explorer.schema.get_user_explorer_connection_settings")
@patch("dataworkspace.apps.explorer.schema._get_includes")
@patch("dataworkspace.apps.explorer.schema._get_excludes")
def test_schema_info_returns_valid_data(
self, mocked_excludes, mocked_includes, mock_connection_settings, staff_user
):
mocked_includes.return_value = None
mocked_excludes.return_value = []
mock_connection_settings.return_value = self._get_connection_data()
res = schema.schema_info(staff_user, settings.EXPLORER_CONNECTIONS["Postgres"])
assert mocked_includes.called # sanity check: ensure patch worked
tables = [x.name.name for x in res]
assert "explorer_query" in tables
schemas = [x.name.schema for x in res]
assert "public" in schemas
@patch("dataworkspace.apps.explorer.schema.get_user_explorer_connection_settings")
@patch("dataworkspace.apps.explorer.schema._get_includes")
@patch("dataworkspace.apps.explorer.schema._get_excludes")
def test_table_exclusion_list(
self, mocked_excludes, mocked_includes, mock_connection_settings, staff_user
):
mocked_includes.return_value = None
mocked_excludes.return_value = ("explorer_",)
mock_connection_settings.return_value = self._get_connection_data()
res = schema.schema_info(staff_user, settings.EXPLORER_CONNECTIONS["Postgres"])
tables = [x.name.name for x in res]
assert "explorer_query" not in tables
@patch("dataworkspace.apps.explorer.schema.get_user_explorer_connection_settings")
@patch("dataworkspace.apps.explorer.schema._get_includes")
@patch("dataworkspace.apps.explorer.schema._get_excludes")
def test_app_inclusion_list(
self, mocked_excludes, mocked_includes, mock_connection_settings, staff_user
):
mocked_includes.return_value = ("auth_",)
mocked_excludes.return_value = []
mock_connection_settings.return_value = self._get_connection_data()
res = schema.schema_info(staff_user, settings.EXPLORER_CONNECTIONS["Postgres"])
tables = [x.name.name for x in res]
assert "explorer_query" not in tables
assert "auth_user" in tables
@patch("dataworkspace.apps.explorer.schema.get_user_explorer_connection_settings")
@patch("dataworkspace.apps.explorer.schema._get_includes")
@patch("dataworkspace.apps.explorer.schema._get_excludes")
def test_app_inclusion_list_excluded(
self, mocked_excludes, mocked_includes, mock_connection_settings, staff_user
):
# Inclusion list "wins"
mocked_includes.return_value = ("explorer_",)
mocked_excludes.return_value = ("explorer_",)
mock_connection_settings.return_value = self._get_connection_data()
res = schema.schema_info(staff_user, settings.EXPLORER_CONNECTIONS["Postgres"])
tables = [x.name.name for x in res]
assert "explorer_query" in tables
| 44.325301
| 87
| 0.720304
| 432
| 3,679
| 5.793981
| 0.171296
| 0.088294
| 0.129844
| 0.143827
| 0.746304
| 0.740312
| 0.740312
| 0.740312
| 0.740312
| 0.740312
| 0
| 0
| 0.183746
| 3,679
| 82
| 88
| 44.865854
| 0.8335
| 0.01495
| 0
| 0.591549
| 0
| 0
| 0.245512
| 0.185584
| 0
| 0
| 0
| 0
| 0.098592
| 1
| 0.084507
| false
| 0.014085
| 0.070423
| 0
| 0.183099
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 0
|
0
| 5
|
abc986fa9c0c04d72ca19590537c695bd33e32b4
| 228
|
py
|
Python
|
python-structures-presentation/code/exercise.py
|
iz4vve-talks/misc-training
|
4f676080e54539cbaf283e611278fdd5d7ef93c4
|
[
"Apache-2.0"
] | null | null | null |
python-structures-presentation/code/exercise.py
|
iz4vve-talks/misc-training
|
4f676080e54539cbaf283e611278fdd5d7ef93c4
|
[
"Apache-2.0"
] | null | null | null |
python-structures-presentation/code/exercise.py
|
iz4vve-talks/misc-training
|
4f676080e54539cbaf283e611278fdd5d7ef93c4
|
[
"Apache-2.0"
] | null | null | null |
from this import s
# 1) count occurrence of words s and represent it in a dictionary
# 2) count occurrences of characters in s that are not (1, h, e, /, \, −)
# 3) print a sorted version of 1) based on the number of occurrences
| 45.6
| 73
| 0.714912
| 43
| 228
| 3.813953
| 0.744186
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027778
| 0.210526
| 228
| 5
| 74
| 45.6
| 0.877778
| 0.885965
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 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
| 1
| 0
| 1
| 0
|
0
| 5
|
abf97c9d782a3033cc9723f88ee38ec6654effdd
| 171
|
py
|
Python
|
1890.py
|
ShawonBarman/URI-Online-judge-Ad-Hoc-level-problem-solution-in-python
|
9a0f0ad5efd4a9e73589c357ab4b34b7c73a11da
|
[
"MIT"
] | 1
|
2022-01-14T08:45:32.000Z
|
2022-01-14T08:45:32.000Z
|
1890.py
|
ShawonBarman/URI-Online-judge-Ad-Hoc-level-problem-solution-in-python
|
9a0f0ad5efd4a9e73589c357ab4b34b7c73a11da
|
[
"MIT"
] | null | null | null |
1890.py
|
ShawonBarman/URI-Online-judge-Ad-Hoc-level-problem-solution-in-python
|
9a0f0ad5efd4a9e73589c357ab4b34b7c73a11da
|
[
"MIT"
] | null | null | null |
t = int(input())
while t:
t -= 1
c, d = map(int, input().split())
if (26**c) * (10**d) == 1:
print(0)
else:
print((26**c) * (10**d))
| 21.375
| 37
| 0.380117
| 27
| 171
| 2.407407
| 0.555556
| 0.246154
| 0.153846
| 0.184615
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.100917
| 0.362573
| 171
| 8
| 38
| 21.375
| 0.495413
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.25
| 1
| 0
| 0
| null | 1
| 0
| 1
| 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
| 5
|
f9f60dfcb141f54dde21d8eee2c49035f1135c90
| 68
|
py
|
Python
|
torch/csrc/deploy/unity/example2.py
|
abishekvashok/pytorch
|
d4ae7896554d156732de34c3d3600050f9cb18ec
|
[
"Intel"
] | 173
|
2017-05-12T08:54:16.000Z
|
2022-01-17T14:13:27.000Z
|
torch/csrc/deploy/unity/example2.py
|
abishekvashok/pytorch
|
d4ae7896554d156732de34c3d3600050f9cb18ec
|
[
"Intel"
] | 1
|
2017-05-01T07:44:57.000Z
|
2017-05-01T07:57:08.000Z
|
torch/csrc/deploy/unity/example2.py
|
abishekvashok/pytorch
|
d4ae7896554d156732de34c3d3600050f9cb18ec
|
[
"Intel"
] | 23
|
2017-05-15T10:47:38.000Z
|
2019-12-23T01:07:21.000Z
|
print("Hello, this is the second example for torch::deploy unity!")
| 34
| 67
| 0.75
| 11
| 68
| 4.636364
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.132353
| 68
| 1
| 68
| 68
| 0.864407
| 0
| 0
| 0
| 0
| 0
| 0.852941
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
e6269d3eff85b93adceeea9d1adf45570f8df046
| 200
|
py
|
Python
|
Backend/gige/store/admin.py
|
tanmayb104/Gige
|
481754eda8b3679b1a2ddfe04fc3ef994ba9e1fe
|
[
"MIT"
] | 2
|
2021-08-19T14:50:40.000Z
|
2021-10-06T21:28:02.000Z
|
Backend/gige/store/admin.py
|
tanmayb104/Gige
|
481754eda8b3679b1a2ddfe04fc3ef994ba9e1fe
|
[
"MIT"
] | null | null | null |
Backend/gige/store/admin.py
|
tanmayb104/Gige
|
481754eda8b3679b1a2ddfe04fc3ef994ba9e1fe
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Item, Transaction, Todoitem
# Register your models here.
admin.site.register(Item)
admin.site.register(Transaction)
admin.site.register(Todoitem)
| 25
| 47
| 0.815
| 27
| 200
| 6.037037
| 0.481481
| 0.165644
| 0.312883
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095
| 200
| 8
| 48
| 25
| 0.900552
| 0.13
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 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
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
05172b6454b3ddbe8468471cb39c50b3022eb34a
| 291
|
py
|
Python
|
SUAVE/SUAVE-2.5.0/trunk/SUAVE/Methods/Flight_Dynamics/Dynamic_Stability/Approximations/__init__.py
|
Vinicius-Tanigawa/Undergraduate-Research-Project
|
e92372f07882484b127d7affe305eeec2238b8a9
|
[
"MIT"
] | null | null | null |
SUAVE/SUAVE-2.5.0/trunk/SUAVE/Methods/Flight_Dynamics/Dynamic_Stability/Approximations/__init__.py
|
Vinicius-Tanigawa/Undergraduate-Research-Project
|
e92372f07882484b127d7affe305eeec2238b8a9
|
[
"MIT"
] | null | null | null |
SUAVE/SUAVE-2.5.0/trunk/SUAVE/Methods/Flight_Dynamics/Dynamic_Stability/Approximations/__init__.py
|
Vinicius-Tanigawa/Undergraduate-Research-Project
|
e92372f07882484b127d7affe305eeec2238b8a9
|
[
"MIT"
] | null | null | null |
## @defgroup Methods-Flight_Dynamics-Dynamic_Stability-Approximations Approximations
# @ingroup Methods-Flight_Dynamics-Dynamic_Stability
from .phugoid import phugoid
from .short_period import short_period
from .dutch_roll import dutch_roll
from .spiral import spiral
from .roll import roll
| 36.375
| 84
| 0.85567
| 38
| 291
| 6.342105
| 0.421053
| 0.107884
| 0.174274
| 0.232365
| 0.307054
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.092784
| 291
| 8
| 85
| 36.375
| 0.912879
| 0.453608
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
051d70fc3364fbe8dc621a382324cde40764931c
| 120
|
py
|
Python
|
Chapter 6/06/PaxHeader/cart.py
|
robert0714/Python-Testing-Cookbook-Second-Edition
|
c7c5d59e42e9ca2874faf12a6dd201736a45ca83
|
[
"MIT"
] | null | null | null |
Chapter 6/06/PaxHeader/cart.py
|
robert0714/Python-Testing-Cookbook-Second-Edition
|
c7c5d59e42e9ca2874faf12a6dd201736a45ca83
|
[
"MIT"
] | null | null | null |
Chapter 6/06/PaxHeader/cart.py
|
robert0714/Python-Testing-Cookbook-Second-Edition
|
c7c5d59e42e9ca2874faf12a6dd201736a45ca83
|
[
"MIT"
] | null | null | null |
15 uid=2057284
20 ctime=1291669506
20 atime=1302270632
24 SCHILY.dev=234881026
23 SCHILY.ino=26571004
18 SCHILY.nlink=1
| 17.142857
| 23
| 0.825
| 21
| 120
| 4.714286
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.527778
| 0.1
| 120
| 6
| 24
| 20
| 0.388889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
0521955bc290ea2c992a7cdeeced5e262e244a93
| 25
|
py
|
Python
|
on.py
|
PabloEckardt/flask-raspberrypi-servo
|
e7f5eaf6e5a2be033d7e5442b126d98bb3fcf0f9
|
[
"MIT"
] | 1
|
2019-10-12T10:37:09.000Z
|
2019-10-12T10:37:09.000Z
|
on.py
|
PabloEckardt/flask-raspberrypi-servo
|
e7f5eaf6e5a2be033d7e5442b126d98bb3fcf0f9
|
[
"MIT"
] | null | null | null |
on.py
|
PabloEckardt/flask-raspberrypi-servo
|
e7f5eaf6e5a2be033d7e5442b126d98bb3fcf0f9
|
[
"MIT"
] | null | null | null |
import servo
servo.on()
| 6.25
| 12
| 0.72
| 4
| 25
| 4.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 25
| 3
| 13
| 8.333333
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 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
| 5
|
054e06c496991b99041c294aeb8382b8c9f97bf8
| 247
|
py
|
Python
|
Server/Python/src/dbs/dao/MySQL/OutputModuleConfig/GetID.py
|
vkuznet/DBS
|
14df8bbe8ee8f874fe423399b18afef911fe78c7
|
[
"Apache-2.0"
] | 8
|
2015-08-14T04:01:32.000Z
|
2021-06-03T00:56:42.000Z
|
Server/Python/src/dbs/dao/MySQL/OutputModuleConfig/GetID.py
|
yuyiguo/DBS
|
14df8bbe8ee8f874fe423399b18afef911fe78c7
|
[
"Apache-2.0"
] | 162
|
2015-01-07T21:34:47.000Z
|
2021-10-13T09:42:41.000Z
|
Server/Python/src/dbs/dao/MySQL/OutputModuleConfig/GetID.py
|
yuyiguo/DBS
|
14df8bbe8ee8f874fe423399b18afef911fe78c7
|
[
"Apache-2.0"
] | 16
|
2015-01-22T15:27:29.000Z
|
2021-04-28T09:23:28.000Z
|
#!/usr/bin/env python
"""
This module provides ApplicationExecutable.GetID data access object.
"""
from dbs.dao.Oracle.OutputModuleConfig.GetID import GetID as OraOutputModuleConfigGetID
class GetID(OraOutputModuleConfigGetID):
pass
| 24.7
| 87
| 0.781377
| 26
| 247
| 7.423077
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137652
| 247
| 9
| 88
| 27.444444
| 0.906103
| 0.360324
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 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
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 5
|
055d9048a435248a9fcb38d8a5cf722c6fdd1893
| 295
|
py
|
Python
|
backend/src/baserow/contrib/database/management/commands/clear_model_cache.py
|
ashishdhngr/baserow
|
b098678d2165eb7c42930ee24dc6753a3cb520c3
|
[
"MIT"
] | null | null | null |
backend/src/baserow/contrib/database/management/commands/clear_model_cache.py
|
ashishdhngr/baserow
|
b098678d2165eb7c42930ee24dc6753a3cb520c3
|
[
"MIT"
] | null | null | null |
backend/src/baserow/contrib/database/management/commands/clear_model_cache.py
|
ashishdhngr/baserow
|
b098678d2165eb7c42930ee24dc6753a3cb520c3
|
[
"MIT"
] | null | null | null |
from django.core.management import BaseCommand
from baserow.contrib.database.table.cache import clear_generated_model_cache
class Command(BaseCommand):
help = "Clears Baserow's internal generated model cache"
def handle(self, *args, **options):
clear_generated_model_cache()
| 26.818182
| 76
| 0.776271
| 37
| 295
| 6.027027
| 0.675676
| 0.188341
| 0.255605
| 0.215247
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.145763
| 295
| 10
| 77
| 29.5
| 0.884921
| 0
| 0
| 0
| 1
| 0
| 0.159322
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.333333
| 0
| 0.833333
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
0593ca6079c50f1590faecb8f958ba00f5b40178
| 70
|
py
|
Python
|
src/advanced python/executatble_dirs/package2/__init__.py
|
sudeep0901/python
|
7a50af12e72d21ca4cad7f2afa4c6f929552043f
|
[
"MIT"
] | null | null | null |
src/advanced python/executatble_dirs/package2/__init__.py
|
sudeep0901/python
|
7a50af12e72d21ca4cad7f2afa4c6f929552043f
|
[
"MIT"
] | 3
|
2019-12-26T05:13:55.000Z
|
2020-03-07T06:59:56.000Z
|
src/advanced python/executatble_dirs/package2/__init__.py
|
sudeep0901/python
|
7a50af12e72d21ca4cad7f2afa4c6f929552043f
|
[
"MIT"
] | null | null | null |
from pprint import pprint
pprint(locals())
pprint("I am in package 2")
| 23.333333
| 27
| 0.757143
| 12
| 70
| 4.416667
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.016393
| 0.128571
| 70
| 3
| 27
| 23.333333
| 0.852459
| 0
| 0
| 0
| 0
| 0
| 0.239437
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 1
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 5
|
552ebc73efbd7542d86241b90bd7da693c02dcdb
| 371
|
py
|
Python
|
EasyNN/model/__init__.py
|
danielwilczak101/EasyNN
|
89319e974c324dda228c6ecff7c39d723eda3ca2
|
[
"MIT"
] | 5
|
2021-01-28T21:19:02.000Z
|
2022-02-03T05:47:47.000Z
|
EasyNN/model/__init__.py
|
danielwilczak101/EasyNN
|
89319e974c324dda228c6ecff7c39d723eda3ca2
|
[
"MIT"
] | 1
|
2021-02-04T20:57:45.000Z
|
2021-03-03T14:49:44.000Z
|
EasyNN/model/__init__.py
|
danielwilczak101/EasyNN
|
89319e974c324dda228c6ecff7c39d723eda3ca2
|
[
"MIT"
] | 2
|
2021-02-12T04:27:40.000Z
|
2021-12-19T20:11:20.000Z
|
import EasyNN.model.activation as activation
from EasyNN.model.abc import Model
from EasyNN.model.activation import *
from EasyNN.model.bias import Bias
from EasyNN.model.dense_layer import DenseLayer
from EasyNN.model.network import Network
from EasyNN.model.normalize import Normalize
from EasyNN.model.randomize import Randomize
from EasyNN.model.weight import Weight
| 37.1
| 47
| 0.851752
| 54
| 371
| 5.833333
| 0.277778
| 0.314286
| 0.380952
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097035
| 371
| 9
| 48
| 41.222222
| 0.940299
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
556fa9a9595506c188b8e4080766c65ac3de8210
| 661
|
py
|
Python
|
art/defences/detector/poisoning/__init__.py
|
meghana-sesetti/adversarial-robustness-toolbox
|
6a5ce9e4142734ad9004e5c093ef8fa754ea6b39
|
[
"MIT"
] | null | null | null |
art/defences/detector/poisoning/__init__.py
|
meghana-sesetti/adversarial-robustness-toolbox
|
6a5ce9e4142734ad9004e5c093ef8fa754ea6b39
|
[
"MIT"
] | null | null | null |
art/defences/detector/poisoning/__init__.py
|
meghana-sesetti/adversarial-robustness-toolbox
|
6a5ce9e4142734ad9004e5c093ef8fa754ea6b39
|
[
"MIT"
] | 1
|
2020-09-28T12:58:01.000Z
|
2020-09-28T12:58:01.000Z
|
"""
Module implementing detector-based defences against poisoning attacks.
"""
from art.defences.detector.poisoning.poison_filtering_defence import PoisonFilteringDefence
from art.defences.detector.poisoning.ground_truth_evaluator import GroundTruthEvaluator
from art.defences.detector.poisoning.activation_defence import ActivationDefence
from art.defences.detector.poisoning.clustering_analyzer import ClusteringAnalyzer
from art.defences.detector.poisoning.provenance_defense import ProvenanceDefense
from art.defences.detector.poisoning.roni import RONIDefense
from art.defences.detector.poisoning.spectral_signature_defense import SpectralSignatureDefense
| 60.090909
| 95
| 0.889561
| 73
| 661
| 7.931507
| 0.438356
| 0.084629
| 0.181347
| 0.278066
| 0.386874
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.055976
| 661
| 10
| 96
| 66.1
| 0.927885
| 0.1059
| 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
| 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
| 1
| 0
| 1
| 0
|
0
| 5
|
557071d387c64c39b3a16559f3ae4523eed8a4a3
| 352
|
py
|
Python
|
dreamerv2/__init__.py
|
Tiamat-Tech/dreamerv2
|
9bc2a315c8abc9caadaa247d458658c4d168fadb
|
[
"MIT"
] | null | null | null |
dreamerv2/__init__.py
|
Tiamat-Tech/dreamerv2
|
9bc2a315c8abc9caadaa247d458658c4d168fadb
|
[
"MIT"
] | null | null | null |
dreamerv2/__init__.py
|
Tiamat-Tech/dreamerv2
|
9bc2a315c8abc9caadaa247d458658c4d168fadb
|
[
"MIT"
] | null | null | null |
import pathlib
import sys
sys.path.append(str(pathlib.Path(__file__).parent))
from .common import Config
from .common import DictSpaces
from .common import Flags
from .common import ResizeImage
from .common import TerminalOutput
from .common import JSONLOutput
from .common import TensorBoardOutput
from .train import configs
from .train import run
| 22
| 51
| 0.821023
| 48
| 352
| 5.9375
| 0.416667
| 0.245614
| 0.392982
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| 352
| 15
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| true
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| 0.916667
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| null | 0
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| 1
| 0
| 1
| 0
|
0
| 5
|
e95666351502e66aad620a456381469bd18cd56d
| 3,571
|
py
|
Python
|
quick_extract.py
|
YujieLu10/tslam
|
1341dbecdf02ee6b1b6cdd1a538272fffdea6ffd
|
[
"Apache-2.0"
] | null | null | null |
quick_extract.py
|
YujieLu10/tslam
|
1341dbecdf02ee6b1b6cdd1a538272fffdea6ffd
|
[
"Apache-2.0"
] | null | null | null |
quick_extract.py
|
YujieLu10/tslam
|
1341dbecdf02ee6b1b6cdd1a538272fffdea6ffd
|
[
"Apache-2.0"
] | null | null | null |
obj_map = [["glass", 0.015, [0, 0, 0], [0, 0, 0.05510244]], # 0.06
["donut", 0.01, [0, 0, 0], [0, 0, 0.01466367]],
["heart", 0.0006, [0.70738827, -0.70682518, 0], [0, 0, 0.8]],
["airplane", 1, [0, 0, 0], [0, 0, 2.58596408e-02]],
["alarmclock", 1, [0.70738827, -0.70682518, 0], [0, 0, 2.47049890e-02]],
["apple", 1, [0, 0, 0], [0, 0, 0.04999409]],
["banana", 1, [0, 0, 0], [0, 0, 0.02365614]],
["binoculars", 1, [0.70738827, -0.70682518, 0], [0, 0, 0.07999943]],
["body", 0.1, [0, 0, 0], [0, 0, 0.0145278]],
["bowl", 1, [0, 0, 0], [0, 0, 0.03995771]],
["camera", 1, [0, 0, 0], [0, 0, 0.03483407]],
["coffeemug", 1, [0, 0, 0], [0, 0, 0.05387171]],
["cubelarge", 1, [0, 0, 0], [0, 0, 0.06039196]],
["cubemedium", 1, [0, 0, 0], [0, 0, 0.04103902]],
["cubemiddle", 1, [0, 0, 0], [0, 0, 0.04103902]],
["cubesmall", 1, [0, 0, 0], [0, 0, 0.02072159]],
["cup", 1, [0, 0, 0], [0, 0, 0.05127277]],
["cylinderlarge", 1, [0, 0, 0], [0, 0, 0.06135697]],
["cylindermedium", 1, [0, 0, 0], [0, 0, 0.04103905]],
["cylindersmall", 1, [0, 0, 0], [0, 0, 0.02072279]],
["doorknob", 1, [0, 0, 0], [0, 0, 0.0379012]],
["duck", 1, [0, 0, 0], [0, 0, 0.04917608]],
["elephant", 1, [0, 0, 0], [0, 0, 0.05097572]],
["eyeglasses", 1, [0, 0, 0], [0, 0, 0.02300015]],
["flashlight", 1, [0, 0, 0], [0, 0, 0.07037258]],
["flute", 1, [0, 0, 0], [0, 0, 0.0092959]],
["fryingpan", 0.8, [0, 0, 0], [0, 0, 0.01514528]],
["gamecontroller", 1, [0, 0, 0], [0, 0, 0.02604568]],
["hammer", 1, [0, 0, 0], [0, 0, 0.01267463]],
["hand", 1, [0, 0, 0], [0, 0, 0.07001909]],
["headphones", 1, [0, 0, 0], [0, 0, 0.02992321]],
["knife", 1, [0, 0, 0], [0, 0, 0.00824503]],
["lightbulb", 1, [0, 0, 0], [0, 0, 0.03202522]],
["mouse", 1, [0, 0, 0], [0, 0, 0.0201307]],
["mug", 1, [0, 0, 0], [0, 0, 0.05387171]],
["phone", 1, [0, 0, 0], [0, 0, 0.02552063]],
["piggybank", 1, [0, 0, 0], [0, 0, 0.06923257]],
["pyramidlarge", 1, [0, 0, 0], [0, 0, 0.05123203]],
["pyramidmedium", 1, [0, 0, 0], [0, 0, 0.04103812]],
["pyramidsmall", 1, [0, 0, 0], [0, 0, 0.02072198]],
["rubberduck", 1, [0, 0, 0], [0, 0, 0.04917608]],
["scissors", 1, [0, 0, 0], [0, 0, 0.00802606]],
["spherelarge", 1, [0, 0, 0], [0, 0, 0.05382598]],
["spheremedium", 1, [0, 0, 0], [0, 0, 0.03729011]],
["spheresmall", 1, [0, 0, 0], [0, 0, 0.01897534]],
["stamp", 1, [0, 0, 0], [0, 0, 0.0379012]],
["stanfordbunny", 1, [0, 0, 0], [0, 0, 0.06453102]],
["stapler", 1, [0, 0, 0], [0, 0, 0.02116039]],
["table", 5, [0, 0, 0], [0, 0, 0.01403165]],
["teapot", 1, [0, 0, 0], [0, 0, 0.05761634]],
["toothbrush", 1, [0, 0, 0], [0, 0, 0.00701304]],
["toothpaste", 1, [0.50039816, -0.49999984, -0.49960184], [0, 0, 0.02]],
["toruslarge", 1, [0, 0, 0], [0, 0, 0.02080752]],
["torusmedium", 1, [0, 0, 0], [0, 0, 0.01394647]],
["torussmall", 1, [0, 0, 0], [0, 0, 0.00734874]],
["train", 1, [0, 0, 0], [0, 0, 0.04335064]],
["watch", 1, [0, 0, 0], [0, 0, 0.0424445]],
["waterbottle", 1, [0, 0, 0], [0, 0, 0.08697578]],
["wineglass", 1, [0, 0, 0], [0, 0, 0.0424445]],
["wristwatch", 1, [0, 0, 0], [0, 0, 0.06880109]]]
output_map = {}
for obj in obj_map:
output_map[obj[0]] = obj[2]
print(output_map)
| 55.796875
| 80
| 0.429852
| 576
| 3,571
| 2.65625
| 0.232639
| 0.377778
| 0.44902
| 0.44183
| 0.346405
| 0.346405
| 0.326797
| 0.139216
| 0
| 0
| 0
| 0.374855
| 0.273873
| 3,571
| 64
| 81
| 55.796875
| 0.215195
| 0.00112
| 0
| 0
| 0
| 0
| 0.139652
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.015625
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
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| null | 0
| 0
| 0
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| 0
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| 0
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| 0
| 0
| 0
| 0
|
0
| 5
|
e97113b9367af5677fd3839f5c1ac2656452ef27
| 5,278
|
py
|
Python
|
prob_unet/ConvGaussian.py
|
FrankWJW/Probabilistic-Unet-Pytorch
|
f4f57383b55a72bb6c6bca849a9c83b313cb3968
|
[
"Apache-2.0"
] | null | null | null |
prob_unet/ConvGaussian.py
|
FrankWJW/Probabilistic-Unet-Pytorch
|
f4f57383b55a72bb6c6bca849a9c83b313cb3968
|
[
"Apache-2.0"
] | null | null | null |
prob_unet/ConvGaussian.py
|
FrankWJW/Probabilistic-Unet-Pytorch
|
f4f57383b55a72bb6c6bca849a9c83b313cb3968
|
[
"Apache-2.0"
] | null | null | null |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal, Independent, kl
from prob_unet.Encoders import Encoder
from utils.utils import init_weights,init_weights_orthogonal_normal
import numpy as np
class IsotropicGaussian(nn.Module):
"""
A convolutional net that parametrizes a Gaussian distribution with axis aligned covariance matrix.
"""
def __init__(self, input_channels, num_filters, no_convs_per_block, latent_dim, initializers, posterior=False, isotropic=False):
super(IsotropicGaussian, self).__init__()
self.input_channels = input_channels
self.channel_axis = 1
self.num_filters = num_filters
self.no_convs_per_block = no_convs_per_block
self.latent_dim = latent_dim
self.posterior = posterior
if self.posterior:
self.name = 'Posterior'
else:
self.name = 'Prior'
self.encoder = Encoder(self.input_channels, self.num_filters, self.no_convs_per_block, initializers, posterior=self.posterior)
self.conv_layer = nn.Conv2d(num_filters[-1], 2 * self.latent_dim, (1,1), stride=1)
self.show_img = 0
self.show_seg = 0
self.show_concat = 0
self.show_enc = 0
self.sum_input = 0
self.isotropic = isotropic
if initializers['w'] == 'orthogonal':
self.conv_layer.apply(init_weights_orthogonal_normal)
else:
self.conv_layer.apply(init_weights)
def forward(self, input, segm=None):
if segm is not None:
self.show_img = input
self.show_seg = segm
input = torch.cat((input, segm), dim=1)
self.show_concat = input
self.sum_input = torch.sum(input)
encoding = self.encoder(input)
self.show_enc = encoding
encoding = torch.mean(encoding, dim=2, keepdim=True)
encoding = torch.mean(encoding, dim=3, keepdim=True)
mu_log_sigma = self.conv_layer(encoding)
mu_log_sigma = torch.squeeze(mu_log_sigma, dim=2)
mu_log_sigma = torch.squeeze(mu_log_sigma, dim=2)
# separating mu and log_sigma
mu = mu_log_sigma[:,:self.latent_dim]
log_sigma = mu_log_sigma[:, self.latent_dim:]
# do the reparameterization trick here
std = torch.exp(0.5*log_sigma)
eps = torch.rand_like(std).cuda()
# sampling
z = eps * std + mu
return z
class AxisAlignedGaussian(nn.Module):
"""
A convolutional net that parametrizes a Gaussian distribution with axis aligned covariance matrix.
"""
def __init__(self, input_channels, num_filters, no_convs_per_block, latent_dim, initializers, posterior=False,
isotropic=False):
super(AxisAlignedGaussian, self).__init__()
self.input_channels = input_channels
self.channel_axis = 1
self.num_filters = num_filters
self.no_convs_per_block = no_convs_per_block
self.latent_dim = latent_dim
self.posterior = posterior
if self.posterior:
self.name = 'Posterior'
else:
self.name = 'Prior'
self.encoder = Encoder(self.input_channels, self.num_filters, self.no_convs_per_block, initializers,
posterior=self.posterior)
self.conv_layer = nn.Conv2d(num_filters[-1], 2 * self.latent_dim, (1, 1), stride=1)
self.show_img = 0
self.show_seg = 0
self.show_concat = 0
self.show_enc = 0
self.sum_input = 0
self.isotropic = isotropic
if initializers['w'] == 'orthogonal':
self.conv_layer.apply(init_weights_orthogonal_normal)
else:
self.conv_layer.apply(init_weights)
def forward(self, input, segm=None):
# If segmentation is not none, concatenate the mask to the channel axis of the input
if segm is not None:
self.show_img = input
self.show_seg = segm
input = torch.cat((input, segm), dim=1)
self.show_concat = input
self.sum_input = torch.sum(input)
encoding = self.encoder(input)
self.show_enc = encoding
# We only want the mean of the resulting hxw image
encoding = torch.mean(encoding, dim=2, keepdim=True)
encoding = torch.mean(encoding, dim=3, keepdim=True)
# Convert encoding to 2 x latent dim and split up for mu and log_sigma
mu_log_sigma = self.conv_layer(encoding)
# We squeeze the second dimension twice, since otherwise it won't work when batch size is equal to 1
mu_log_sigma = torch.squeeze(mu_log_sigma, dim=2)
mu_log_sigma = torch.squeeze(mu_log_sigma, dim=2)
mu = mu_log_sigma[:, :self.latent_dim]
if not self.isotropic:
log_sigma = mu_log_sigma[:, self.latent_dim:]
else:
log_sigma = mu_log_sigma[:, self.latent_dim:]
log_sigma = torch.mean(log_sigma, dim=1).view(-1, 1).repeat(1, 6)
# This is a multivariate normal with diagonal covariance matrix sigma
# https://github.com/pytorch/pytorch/pull/11178
dist = Independent(Normal(loc=mu, scale=torch.exp(log_sigma)), 1)
return dist
| 38.246377
| 134
| 0.643804
| 702
| 5,278
| 4.621083
| 0.213675
| 0.059186
| 0.046239
| 0.036991
| 0.743218
| 0.73767
| 0.735203
| 0.708385
| 0.67016
| 0.67016
| 0
| 0.012688
| 0.268283
| 5,278
| 138
| 135
| 38.246377
| 0.827292
| 0.129973
| 0
| 0.752475
| 0
| 0
| 0.010979
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.039604
| false
| 0
| 0.069307
| 0
| 0.148515
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
e989e113ce5a6473abcb3ca29ca749605efb1295
| 10,782
|
py
|
Python
|
dlmb/activations.py
|
Jonathan-Andrews/dlmb
|
552148bcac2ffb4308c8db24599c458652684ed2
|
[
"MIT"
] | 5
|
2019-11-23T13:32:21.000Z
|
2022-01-01T16:32:48.000Z
|
dlmb/activations.py
|
Jonathan-Andrews/dlmb
|
552148bcac2ffb4308c8db24599c458652684ed2
|
[
"MIT"
] | null | null | null |
dlmb/activations.py
|
Jonathan-Andrews/dlmb
|
552148bcac2ffb4308c8db24599c458652684ed2
|
[
"MIT"
] | null | null | null |
from abc import ABCMeta, abstractmethod
import numpy as np
from utils.function_helpers import *
class Base_Activation(metaclass=ABCMeta):
@abstractmethod
def __init__(self) -> None:
"""
The Base_Activation class is an abstract class for all activation functions.
All activation functions must inherit from Base_Activation.
"""
self.name = "Base_Activation"
@abstractmethod
def map_data(self, data) -> np.ndarray:
"""
map_data() takes some data and applies a mathematical mapping to it.
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the function will be mapping to an output.
Return:
output : np.ndarray : An n dimensional numpy array of the mapped data.
"""
return output
@abstractmethod
def calculate_gradients(self, data) -> np.ndarray:
"""
Calculates the derivative of the activation function.
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the derivative will be calculated W.R.T.
Return:
output : np.ndarray : An n dimensional numpy array of the calculated derivative.
"""
return output
class Linear(Base_Activation):
def __init__(self) -> None:
"""
The Linear class is the default activation function and generally isn't actually used.
"""
self.name = "linear"
@accepts(self="any", data=np.ndarray)
def map_data(self, data) -> np.ndarray:
"""
Maps some data to an output with the form of f(x) = x.
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the function will be mapping to an output.
Return:
output : np.ndarray : An n dimensional numpy array of the mapped data.
"""
return data
@accepts(self="any", data=np.ndarray)
def calculate_gradients(self, data) -> np.ndarray:
"""
Calculates the derivative of the activation function.
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the derivative will be calculated W.R.T.
Return:
output : np.ndarray : An n dimensional numpy array of the calculated derivative.
"""
return np.ones_like(data)
class Softmax(Base_Activation):
def __init__(self) -> None:
"""
The Softmax class takes an array and normalizes it into a probability distribution with the same size.
Generally used for the output layer.
"""
self.name = "softmax"
@accepts(self="any", data=np.ndarray)
def map_data(self, data) -> np.ndarray:
"""
Maps some data to an output with the form of f(x) = e^x_k / sum(e^x_i).
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the function will be mapping to an output.
Return:
output : np.ndarray : An n dimensional numpy array of the mapped data.
"""
e_x = np.exp(data-np.max(data))
return division_check(e_x, np.sum(e_x, axis=1, keepdims=True))
@accepts(self="any", data=np.ndarray)
def calculate_gradients(self, data) -> np.ndarray:
"""
Calculates the derivative of the activation function.
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the derivative will be calculated W.R.T.
Return:
output : np.ndarray : An n dimensional numpy array of the calculated derivative.
"""
a = np.reshape(self.map_data(data), (data.shape[0], data.shape[1], 1))
e = np.ones((a.shape[1], 1))
i = np.identity(a.shape[1])
return (a*e.T) * (i - e*a.reshape((a.shape[0], a.shape[2], a.shape[1])))
class Sigmoid(Base_Activation):
def __init__(self) -> None:
"""
The Sigmoid class squashes some data between a range of 0 and 1.
Good for probabilities and generally used for any layer.
"""
self.name = "sigmoid"
@accepts(self="any", data=np.ndarray)
def map_data(self, data) -> np.ndarray:
"""
Maps some data to an output with the form of f(x) = 1/(1+e^-x).
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the function will be mapping to an output.
Return:
output : np.ndarray : An n dimensional numpy array of the mapped data.
"""
return division_check(1, 1+np.exp(-data))
@accepts(self="any", data=np.ndarray)
def calculate_gradients(self, data) -> np.ndarray:
"""
Calculates the derivative of the activation function.
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the derivative will be calculated W.R.T.
Return:
output : np.ndarray : An n dimensional numpy array of the calculated derivative.
"""
return self.map_data(data) * (1-self.map_data(data))
class Tanh(Base_Activation):
def __init__(self) -> None:
"""
The Tanh class is the hyperbolic tangent function.
Squashes some data between a range of -1 and 1.
"""
self.name = "tanh"
@accepts(self="any", data=np.ndarray)
def map_data(self, data) -> np.ndarray:
"""
Maps some data to an output with the form of f(x) = sinh(x)/cosh(x).
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the function will be mapping to an output.
Return:
output : np.ndarray : An n dimensional numpy array of the mapped data.
"""
return np.tanh(data) # Numpy already has a tahn function.
@accepts(self="any", data=np.ndarray)
def calculate_gradients(self, data) -> np.ndarray:
"""
Calculates the derivative of the activation function.
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the derivative will be calculated W.R.T.
Return:
output : np.ndarray : An n dimensional numpy array of the calculated derivative.
"""
return 1-self.map_data(data)**2
class ReLU(Base_Activation):
def __init__(self) -> None:
"""
The ReLU class is the Rectified Linear Unit function.
Commonly used for hidden layers.
"""
self.name = "relu"
@accepts(self="any", data=np.ndarray)
def map_data(self, data) -> np.ndarray:
"""
Maps some data to an output with the form of f(x) = max(x, 0).
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the function will be mapping to an output.
Return:
output : np.ndarray : An n dimensional numpy array of the mapped data.
"""
return np.where(data>=0, data, 0)
@accepts(self="any", data=np.ndarray)
def calculate_gradients(self, data) -> np.ndarray:
"""
Calculates the derivative of the activation function.
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the derivative will be calculated W.R.T.
Return:
output : np.ndarray : An n dimensional numpy array of the calculated derivative.
"""
return np.where(data>=0, 1, 0)
class Leaky_ReLU(Base_Activation):
@accepts(self="any", alpha=float)
def __init__(self, alpha=1.0e-1) -> None:
"""
The Leaky_ReLU class is a suggested improvement of the ReLU function.
Commonly used for hidden layers.
Arguments:
alpha : float : Allows for flow of the data if it's less than zero, fixes the dying ReLU problem.
"""
self.alpha = alpha
self.name = "leaky_relu"
@accepts(self="any", data=np.ndarray)
def map_data(self, data) -> np.ndarray:
"""
Maps some data to an output with the form of f(x) = max(x, alpha*x).
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the function will be mapping to an output.
Return:
output : np.ndarray : An n dimensional numpy array of the mapped data.
"""
return np.where(data>=0, data, self.alpha*data)
@accepts(self="any", data=np.ndarray)
def calculate_gradients(self, data) -> np.ndarray:
"""
Calculates the derivative of the activation function.
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the derivative will be calculated W.R.T.
Return:
output : np.ndarray : An n dimensional numpy array of the calculated derivative.
"""
return np.where(data>=0, 1, self.alpha)
class ELU(Base_Activation):
@accepts(self="any", alpha=float)
def __init__(self, alpha=1.0e-1) -> None:
"""
The ELU class is a suggested improvement of the ReLU function.
Commonly used for hidden layers.
Arguments:
alpha : float : Allows for flow of the data if it's less than zero, fixes the dying ReLU problem.
"""
self.alpha = alpha
self.name = "elu"
@accepts(self="any", data=np.ndarray)
def map_data(self, data) -> np.ndarray:
"""
Maps some data to an output with the form of f(x) = max(x, alpha*(e^x - 1)).
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the function will be mapping to an output.
Return:
output : np.ndarray : An n dimensional numpy array of the mapped data.
"""
return np.where(data>=0, data, self.alpha*(np.exp(data)-1))
@accepts(self="any", data=np.ndarray)
def calculate_gradients(self, data) -> np.ndarray:
"""
Calculates the derivative of the activation function.
Arguments:
data : np.ndarray : An n dimensional numpy array of data that the derivative will be calculated W.R.T.
Return:
output : np.ndarray : An n dimensional numpy array of the calculated derivative.
"""
return np.where(data>=0, 1, self.alpha*np.exp(data))
@accepts(activation=(Base_Activation, str))
def get(activation) -> Base_Activation:
"""
Finds and returns the correct activation function.
Arguments:
activation : Base_Activation/str : The activation function the user wants to use.
Returns:
activation : Base_Activation : The correct optimization function.
"""
if type(activation) == str:
if activation.lower() in ("linear"):
return Linear()
elif activation.lower() in ("softmax"):
return Softmax()
elif activation.lower() in ("sigmoid"):
return Sigmoid()
elif activation.lower() in ("tanh"):
return Tanh()
elif activation.lower() in ("relu"):
return ReLU()
elif activation.lower() in ("leaky_relu", "lrelu"):
return Leaky_ReLU()
elif activation.lower() in ("elu"):
return ELU()
else:
print("At activations.get(): '%s' is not an available activation function. Has been set to 'Linear' by default" % activation)
return Linear()
elif isinstance(activation, Base_Activation):
return activation
else:
raise ValueError("At activations.get(): Expected 'class inheriting from Base_Activation' or 'str' for the argument 'activation', recieved '%s'" % type(activation))
| 25.309859
| 166
| 0.657948
| 1,532
| 10,782
| 4.575718
| 0.110313
| 0.079601
| 0.085307
| 0.054779
| 0.72796
| 0.711127
| 0.706134
| 0.670613
| 0.670613
| 0.670613
| 0
| 0.005
| 0.239473
| 10,782
| 425
| 167
| 25.369412
| 0.849878
| 0.540438
| 0
| 0.472222
| 0
| 0.018519
| 0.089762
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.231481
| false
| 0
| 0.027778
| 0
| 0.564815
| 0.009259
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 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
| 5
|
e99128b6f26524f642e9f254e622b7311178d6cf
| 175
|
py
|
Python
|
setup.py
|
dpford/sheer
|
105b60f5c96a6130699561393e8c9ca5ccfb36f5
|
[
"CC0-1.0"
] | null | null | null |
setup.py
|
dpford/sheer
|
105b60f5c96a6130699561393e8c9ca5ccfb36f5
|
[
"CC0-1.0"
] | null | null | null |
setup.py
|
dpford/sheer
|
105b60f5c96a6130699561393e8c9ca5ccfb36f5
|
[
"CC0-1.0"
] | null | null | null |
from setuptools import setup
setup(name='sheer',
version='1.0',
py_modules=['sheer'],
scripts =['sheer/scripts/sheer'],
test_suite = 'tests',
)
| 19.444444
| 39
| 0.582857
| 20
| 175
| 5
| 0.75
| 0.24
| 0.34
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.015267
| 0.251429
| 175
| 8
| 40
| 21.875
| 0.748092
| 0
| 0
| 0
| 0
| 0
| 0.211429
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.142857
| 0
| 0.142857
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
e9b63ac504050ff860eca5d04b21db0a2ace90a7
| 14
|
py
|
Python
|
modul/test.py
|
onselaydin/pytry
|
314aa50b6f8535e275dc8a2edd0c21637fb5a745
|
[
"Apache-2.0"
] | null | null | null |
modul/test.py
|
onselaydin/pytry
|
314aa50b6f8535e275dc8a2edd0c21637fb5a745
|
[
"Apache-2.0"
] | null | null | null |
modul/test.py
|
onselaydin/pytry
|
314aa50b6f8535e275dc8a2edd0c21637fb5a745
|
[
"Apache-2.0"
] | null | null | null |
print('onsel')
| 14
| 14
| 0.714286
| 2
| 14
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 14
| 1
| 14
| 14
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
e9d3368c2e7ee4210444d51c1cd7a257d219d1c0
| 209
|
py
|
Python
|
users/admin.py
|
onizuka341/mumbleapi
|
ddcf3ae35c8ef6590afe2e34a1fc278a3be0e148
|
[
"Apache-2.0"
] | null | null | null |
users/admin.py
|
onizuka341/mumbleapi
|
ddcf3ae35c8ef6590afe2e34a1fc278a3be0e148
|
[
"Apache-2.0"
] | null | null | null |
users/admin.py
|
onizuka341/mumbleapi
|
ddcf3ae35c8ef6590afe2e34a1fc278a3be0e148
|
[
"Apache-2.0"
] | null | null | null |
from django.contrib import admin
# Register your models here.
from .models import TopicTag, SkillTag, UserProfile
admin.site.register(TopicTag)
admin.site.register(SkillTag)
admin.site.register(UserProfile)
| 23.222222
| 51
| 0.818182
| 27
| 209
| 6.333333
| 0.481481
| 0.157895
| 0.298246
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095694
| 209
| 8
| 52
| 26.125
| 0.904762
| 0.124402
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 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
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
7575477595746c7efe8b711e7af4095e47326893
| 55
|
py
|
Python
|
packages/server/invites/src/app/infra/middlewares/__init__.py
|
gbartoczevicz/moosic
|
003ff5cff628505093cc08ad0fbd273272172a51
|
[
"MIT"
] | 3
|
2021-09-30T00:41:31.000Z
|
2022-03-15T00:14:23.000Z
|
packages/server/invites/src/app/infra/middlewares/__init__.py
|
gbartoczevicz/moosic
|
003ff5cff628505093cc08ad0fbd273272172a51
|
[
"MIT"
] | 13
|
2021-09-20T22:29:52.000Z
|
2021-12-05T01:59:34.000Z
|
packages/server/invites/src/app/infra/middlewares/__init__.py
|
gabrielbartoczevicz/moosic
|
003ff5cff628505093cc08ad0fbd273272172a51
|
[
"MIT"
] | 1
|
2021-11-10T22:11:55.000Z
|
2021-11-10T22:11:55.000Z
|
from .ensure_authenticated import ensure_authenticated
| 27.5
| 54
| 0.909091
| 6
| 55
| 8
| 0.666667
| 0.791667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.072727
| 55
| 1
| 55
| 55
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
7588aeb0eb91ec07115ecc85b6996dc9abd279ec
| 40
|
py
|
Python
|
hublabbot/github/__init__.py
|
Potpourri/HubLabBot
|
791ff834f56e4d1635737dd2e084db3c5585188d
|
[
"MIT"
] | null | null | null |
hublabbot/github/__init__.py
|
Potpourri/HubLabBot
|
791ff834f56e4d1635737dd2e084db3c5585188d
|
[
"MIT"
] | 5
|
2020-02-24T15:33:04.000Z
|
2020-06-13T11:16:02.000Z
|
hublabbot/github/__init__.py
|
Potpourri/HubLabBot
|
791ff834f56e4d1635737dd2e084db3c5585188d
|
[
"MIT"
] | null | null | null |
"""Package for GitHub-specific code."""
| 20
| 39
| 0.7
| 5
| 40
| 5.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 40
| 1
| 40
| 40
| 0.777778
| 0.825
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 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
| 5
|
75dabe200003356e2a5c17a799e05353beae0874
| 162
|
py
|
Python
|
hi_nbdev/core.py
|
deutschmn/hi_nbdev
|
a29f245113c6ee7bed9a46073a4457160df1bf55
|
[
"Apache-2.0"
] | null | null | null |
hi_nbdev/core.py
|
deutschmn/hi_nbdev
|
a29f245113c6ee7bed9a46073a4457160df1bf55
|
[
"Apache-2.0"
] | null | null | null |
hi_nbdev/core.py
|
deutschmn/hi_nbdev
|
a29f245113c6ee7bed9a46073a4457160df1bf55
|
[
"Apache-2.0"
] | null | null | null |
# AUTOGENERATED! DO NOT EDIT! File to edit: 00_core.ipynb (unless otherwise specified).
__all__ = ['do_something']
# Cell
def do_something():
print('brztt')
| 23.142857
| 87
| 0.716049
| 22
| 162
| 4.954545
| 0.818182
| 0.201835
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.014599
| 0.154321
| 162
| 7
| 88
| 23.142857
| 0.781022
| 0.555556
| 0
| 0
| 1
| 0
| 0.242857
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.333333
| 0.333333
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
75e60091ec0c2894825137d0c64ff93ad21662c6
| 713
|
py
|
Python
|
Services/DataService/IDataService.py
|
carlCarlson6/NERwithBERT
|
109733c3816e39b0eff201a3e69acddf8a121844
|
[
"MIT"
] | 1
|
2020-10-11T08:47:43.000Z
|
2020-10-11T08:47:43.000Z
|
Services/DataService/IDataService.py
|
carlCarlson6/NERwithBERT
|
109733c3816e39b0eff201a3e69acddf8a121844
|
[
"MIT"
] | null | null | null |
Services/DataService/IDataService.py
|
carlCarlson6/NERwithBERT
|
109733c3816e39b0eff201a3e69acddf8a121844
|
[
"MIT"
] | null | null | null |
from abc import ABC, abstractmethod
import pandas as pd
class IDataService():
"""
DataService interface
"""
@abstractmethod
def __init__(self):
self.DataFrame: pd.DataFrame
pass
@abstractmethod
def LoadCsv(self, DataPath, CsvSeparator, Encoding):
pass
@abstractmethod
def GetDocIds(self):
pass
@abstractmethod
def GetSentences(self):
pass
@abstractmethod
def GetLabels(self):
pass
@abstractmethod
def GetTags(self):
pass
@abstractmethod
def GetNumLabels(self):
pass
@abstractmethod
def PutDataIntoTorch(self, Set, Inputs, Masks, Tags, BatchSize):
pass
| 17.390244
| 68
| 0.619916
| 65
| 713
| 6.738462
| 0.476923
| 0.310502
| 0.335616
| 0.285388
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.30575
| 713
| 41
| 69
| 17.390244
| 0.884848
| 0.029453
| 0
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0.285714
| 0.071429
| 0
| 0.392857
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
f9363dafcf3b31c7fd988c1331b38a1fef237dc9
| 207
|
py
|
Python
|
temas/tema1/codigo/t2e03b.py
|
GabJL/FP2021
|
9c2c80c3bd0b7e112f66475c48ecdcf20b611338
|
[
"MIT"
] | 1
|
2021-11-29T12:12:48.000Z
|
2021-11-29T12:12:48.000Z
|
temas/tema1/codigo/t2e03b.py
|
GabJL/FP2021
|
9c2c80c3bd0b7e112f66475c48ecdcf20b611338
|
[
"MIT"
] | null | null | null |
temas/tema1/codigo/t2e03b.py
|
GabJL/FP2021
|
9c2c80c3bd0b7e112f66475c48ecdcf20b611338
|
[
"MIT"
] | null | null | null |
from turtle import *
print("Dibujando un pentágono")
lado = 5
angulo = 360 / 5
forward(80)
left(angulo)
forward(80)
left(angulo)
forward(80)
left(angulo)
forward(80)
left(angulo)
forward(80)
left(angulo)
| 11.5
| 31
| 0.729469
| 32
| 207
| 4.71875
| 0.4375
| 0.298013
| 0.430464
| 0.629139
| 0.629139
| 0.629139
| 0.629139
| 0.629139
| 0.629139
| 0.629139
| 0
| 0.083799
| 0.135266
| 207
| 17
| 32
| 12.176471
| 0.759777
| 0
| 0
| 0.714286
| 0
| 0
| 0.10628
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.071429
| 0
| 0.071429
| 0.071429
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
f9a7b660603821a1185d1de9b48d8d0d4875eeb0
| 372
|
py
|
Python
|
Python/src/bqs/linkedlist.py
|
chatterjeem-nu/INFO6206_Assignment3
|
d2d065a11a9a3f384500c15b7430f1343e47e42b
|
[
"Apache-2.0"
] | 3
|
2021-04-20T05:06:16.000Z
|
2022-03-26T23:55:11.000Z
|
Python/src/bqs/linkedlist.py
|
chatterjeem-nu/INFO6206_Assignment3
|
d2d065a11a9a3f384500c15b7430f1343e47e42b
|
[
"Apache-2.0"
] | null | null | null |
Python/src/bqs/linkedlist.py
|
chatterjeem-nu/INFO6206_Assignment3
|
d2d065a11a9a3f384500c15b7430f1343e47e42b
|
[
"Apache-2.0"
] | 1
|
2021-03-02T01:19:42.000Z
|
2021-03-02T01:19:42.000Z
|
from abc import abstractmethod, ABC
from typing import Generic
from util.generic_type import T
class LinkedList(ABC, Generic[T]):
@abstractmethod
def add(self, item):
pass
@abstractmethod
def remove(self):
pass
@abstractmethod
def get_head(self):
pass
@abstractmethod
def is_empty(self) -> bool:
pass
| 15.5
| 35
| 0.642473
| 44
| 372
| 5.363636
| 0.5
| 0.288136
| 0.266949
| 0.211864
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.284946
| 372
| 23
| 36
| 16.173913
| 0.887218
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0.25
| 0.1875
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
f9b109c9aca2a138fbb5000d1181efddd1d5a476
| 129
|
py
|
Python
|
Ejercicios/Palabra/Ejercicio2.py
|
Dharian/pythonProject
|
262d2b58d99befe668d29198bb28c98b75597a34
|
[
"MIT"
] | null | null | null |
Ejercicios/Palabra/Ejercicio2.py
|
Dharian/pythonProject
|
262d2b58d99befe668d29198bb28c98b75597a34
|
[
"MIT"
] | null | null | null |
Ejercicios/Palabra/Ejercicio2.py
|
Dharian/pythonProject
|
262d2b58d99befe668d29198bb28c98b75597a34
|
[
"MIT"
] | null | null | null |
nombre= str(input("Cual es tu nombre?"))
edad=int(input("Cual es tu edad?"))
print("Tu nombre es: ", nombre, "y tu edad: ", edad)
| 43
| 52
| 0.651163
| 23
| 129
| 3.652174
| 0.434783
| 0.214286
| 0.261905
| 0.309524
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.139535
| 129
| 3
| 52
| 43
| 0.756757
| 0
| 0
| 0
| 0
| 0
| 0.453846
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
f9cf6daeb6f2c855873c1a060b15672323aa46b8
| 63
|
py
|
Python
|
src/test/integration/__init__.py
|
HenrikPilz/BMEcatConverter
|
28c6840fc70a3f04e3eae5fc7be32c7bc779c1da
|
[
"BSD-3-Clause"
] | 1
|
2021-03-14T08:20:51.000Z
|
2021-03-14T08:20:51.000Z
|
src/test/integration/__init__.py
|
HenrikPilz/BMEcatConverter
|
28c6840fc70a3f04e3eae5fc7be32c7bc779c1da
|
[
"BSD-3-Clause"
] | 1
|
2021-11-29T09:56:18.000Z
|
2021-12-01T22:01:13.000Z
|
src/test/integration/__init__.py
|
HenrikPilz/BMEcatConverter
|
28c6840fc70a3f04e3eae5fc7be32c7bc779c1da
|
[
"BSD-3-Clause"
] | 2
|
2021-08-30T08:14:34.000Z
|
2021-09-28T15:10:23.000Z
|
from test.integration.testConverting import TestMainConverter
| 31.5
| 62
| 0.888889
| 6
| 63
| 9.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.079365
| 63
| 1
| 63
| 63
| 0.965517
| 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
| 1
| 0
|
0
| 5
|
ddcd3910070f0b170d271d7c2a1d18745f6c2956
| 218
|
py
|
Python
|
core/file_manager/__init__.py
|
anthill-arch/platform
|
ff45dc71b2f3141bbd95baaf4da7ff1d2ac24ca0
|
[
"MIT"
] | null | null | null |
core/file_manager/__init__.py
|
anthill-arch/platform
|
ff45dc71b2f3141bbd95baaf4da7ff1d2ac24ca0
|
[
"MIT"
] | null | null | null |
core/file_manager/__init__.py
|
anthill-arch/platform
|
ff45dc71b2f3141bbd95baaf4da7ff1d2ac24ca0
|
[
"MIT"
] | null | null | null |
from anthill.framework.core.files.storage import default_storage
class FileManager:
def __init__(self, storage=None):
self.storage = storage or default_storage
# TODO: provide file system operations
| 24.222222
| 64
| 0.756881
| 27
| 218
| 5.888889
| 0.740741
| 0.176101
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.178899
| 218
| 8
| 65
| 27.25
| 0.888268
| 0.165138
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 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
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
fb0bf5528c2ab452f1a995b2bc2d14925fbe95c3
| 134
|
py
|
Python
|
ursa/__init__.py
|
adgirish/ursa
|
c14fccacb81efd33e86453f979cb4ec799aa8a3a
|
[
"Apache-2.0"
] | null | null | null |
ursa/__init__.py
|
adgirish/ursa
|
c14fccacb81efd33e86453f979cb4ec799aa8a3a
|
[
"Apache-2.0"
] | null | null | null |
ursa/__init__.py
|
adgirish/ursa
|
c14fccacb81efd33e86453f979cb4ec799aa8a3a
|
[
"Apache-2.0"
] | null | null | null |
from . import database
from .local_manager import Graph_manager
from . import graph
__all__ = ["database", "Graph_manager", "graph"]
| 22.333333
| 48
| 0.761194
| 17
| 134
| 5.588235
| 0.411765
| 0.210526
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.134328
| 134
| 5
| 49
| 26.8
| 0.818966
| 0
| 0
| 0
| 0
| 0
| 0.19403
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.75
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
fb2bf2c47ae88809e01c1c98586563b8863ee02c
| 1,434
|
py
|
Python
|
tests/function/test_activations.py
|
gustavgransbo/gustavgrad
|
61fdf3c763edf1660c789248184a73ee0a748881
|
[
"MIT"
] | null | null | null |
tests/function/test_activations.py
|
gustavgransbo/gustavgrad
|
61fdf3c763edf1660c789248184a73ee0a748881
|
[
"MIT"
] | 15
|
2020-07-08T18:15:36.000Z
|
2021-04-21T20:42:04.000Z
|
tests/function/test_activations.py
|
gustavgransbo/gustavgrad
|
61fdf3c763edf1660c789248184a73ee0a748881
|
[
"MIT"
] | null | null | null |
import numpy as np
from gustavgrad import Tensor
from gustavgrad.function import sigmoid, tanh
class TestActivation:
def test_sigmoid(self) -> None:
t1 = Tensor(np.zeros(shape=(3, 3, 3)), requires_grad=True)
t2 = sigmoid(t1)
assert t2.shape == (3, 3, 3)
np.testing.assert_allclose(t2.data, 0.5)
def test_sigmoid_grad(self) -> None:
t1 = Tensor(np.zeros(shape=(3, 3, 3)), requires_grad=True)
t2 = sigmoid(t1)
t2.backward(1)
np.testing.assert_allclose(t1.grad, 0.25)
def test_sigmoid_no_grad(self) -> None:
t1 = Tensor(np.zeros(shape=(3, 3, 3)), requires_grad=False)
t2 = sigmoid(t1)
assert t2.shape == (3, 3, 3)
assert not t2.requires_grad
def test_tanh(self) -> None:
np.random.seed(0)
t1 = Tensor(np.ones(shape=(3, 3, 3)) * 1_000, requires_grad=True)
t2 = tanh(t1)
assert t2.shape == (3, 3, 3)
np.testing.assert_allclose(t2.data, 1)
def test_tanh_grad(self) -> None:
np.random.seed(0)
t1 = Tensor(np.ones(shape=(3, 3, 3)) * 1_000, requires_grad=True)
t2 = tanh(t1)
t2.backward(1)
np.testing.assert_allclose(t1.grad, 0)
def test_tanh_no_grad(self) -> None:
t1 = Tensor(np.zeros(shape=(3, 3, 3)), requires_grad=False)
t2 = tanh(t1)
assert t2.shape == (3, 3, 3)
assert not t2.requires_grad
| 28.117647
| 73
| 0.591353
| 218
| 1,434
| 3.770642
| 0.183486
| 0.048662
| 0.085158
| 0.097324
| 0.777372
| 0.777372
| 0.777372
| 0.777372
| 0.777372
| 0.76399
| 0
| 0.07531
| 0.26848
| 1,434
| 50
| 74
| 28.68
| 0.708294
| 0
| 0
| 0.611111
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.277778
| 1
| 0.166667
| false
| 0
| 0.083333
| 0
| 0.277778
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 0
|
0
| 5
|
34a27b401b6e2f9976cd3366a41af38b9cd56d0d
| 149
|
py
|
Python
|
HW7/AndriiBabii/CW_6.py
|
kolyasalubov/Lv-677.PythonCore
|
c9f9107c734a61e398154a90b8a3e249276c2704
|
[
"MIT"
] | null | null | null |
HW7/AndriiBabii/CW_6.py
|
kolyasalubov/Lv-677.PythonCore
|
c9f9107c734a61e398154a90b8a3e249276c2704
|
[
"MIT"
] | null | null | null |
HW7/AndriiBabii/CW_6.py
|
kolyasalubov/Lv-677.PythonCore
|
c9f9107c734a61e398154a90b8a3e249276c2704
|
[
"MIT"
] | 6
|
2022-02-22T22:30:49.000Z
|
2022-03-28T12:51:19.000Z
|
#https://www.codewars.com/kata/convert-boolean-values-to-strings-yes-or-no
def bool_to_word(boolean):
return "Yes" if boolean == True else "No"
| 29.8
| 74
| 0.731544
| 25
| 149
| 4.28
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107383
| 149
| 4
| 75
| 37.25
| 0.804511
| 0.489933
| 0
| 0
| 0
| 0
| 0.066667
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
34a970ccb17863c49f00d5b985d7f3b0fb330de2
| 57
|
py
|
Python
|
lightbus/schema/__init__.py
|
gcollard/lightbus
|
d04deeda8ccef5a582b79255725ca2025a085c02
|
[
"Apache-2.0"
] | 178
|
2017-07-22T12:35:00.000Z
|
2022-03-28T07:53:13.000Z
|
lightbus/schema/__init__.py
|
adamcharnock/warren
|
5e7069da06cd37a8131e8c592ee957ccb73603d5
|
[
"Apache-2.0"
] | 26
|
2017-08-03T12:09:29.000Z
|
2021-10-19T16:47:18.000Z
|
lightbus/schema/__init__.py
|
adamcharnock/warren
|
5e7069da06cd37a8131e8c592ee957ccb73603d5
|
[
"Apache-2.0"
] | 19
|
2017-09-15T17:51:24.000Z
|
2022-02-28T13:00:16.000Z
|
from .schema import Schema, Parameter, WildcardParameter
| 28.5
| 56
| 0.842105
| 6
| 57
| 8
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 57
| 1
| 57
| 57
| 0.941176
| 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
| 1
| 0
|
0
| 5
|
550622b0ea83f487425d2bb547963ab1e28b0464
| 95
|
py
|
Python
|
src/advanced python/executatble_dirs/package1/__init__.py
|
sudeep0901/python
|
7a50af12e72d21ca4cad7f2afa4c6f929552043f
|
[
"MIT"
] | null | null | null |
src/advanced python/executatble_dirs/package1/__init__.py
|
sudeep0901/python
|
7a50af12e72d21ca4cad7f2afa4c6f929552043f
|
[
"MIT"
] | 3
|
2019-12-26T05:13:55.000Z
|
2020-03-07T06:59:56.000Z
|
src/advanced python/executatble_dirs/package1/__init__.py
|
sudeep0901/python
|
7a50af12e72d21ca4cad7f2afa4c6f929552043f
|
[
"MIT"
] | null | null | null |
from pprint import pprint
pprint(locals())
pprint("I am in package 1")
from .. import package2
| 19
| 27
| 0.747368
| 15
| 95
| 4.733333
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.024691
| 0.147368
| 95
| 5
| 28
| 19
| 0.851852
| 0
| 0
| 0
| 0
| 0
| 0.177083
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.75
| 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
| 1
|
0
| 5
|
5517f2f169db6cc097a7a2359857b3bed12bacb3
| 62,712
|
py
|
Python
|
experiments/experiments_toy/convergence/nmtf_icm.py
|
ThomasBrouwer/BNMTF
|
34df0c3cebc5e67a5e39762b9305b75d73a2a0e0
|
[
"Apache-2.0"
] | 16
|
2017-04-19T12:04:47.000Z
|
2021-12-03T00:50:43.000Z
|
experiments/experiments_toy/convergence/nmtf_icm.py
|
ThomasBrouwer/BNMTF
|
34df0c3cebc5e67a5e39762b9305b75d73a2a0e0
|
[
"Apache-2.0"
] | 1
|
2017-04-20T11:26:16.000Z
|
2017-04-20T11:26:16.000Z
|
experiments/experiments_toy/convergence/nmtf_icm.py
|
ThomasBrouwer/BNMTF
|
34df0c3cebc5e67a5e39762b9305b75d73a2a0e0
|
[
"Apache-2.0"
] | 8
|
2015-12-15T05:29:43.000Z
|
2019-06-05T03:14:11.000Z
|
"""
Recover the toy dataset using ICM.
We can plot the MSE, R2 and Rp as it converges, on the entire dataset.
We have I=100, J=80, K=5, L=5, and no test data.
We give flatter priors (1/10) than what was used to generate the data (1).
"""
import sys, os
project_location = os.path.dirname(__file__)+"/../../../../"
sys.path.append(project_location)
from BNMTF.code.models.nmtf_icm import nmtf_icm
import numpy, matplotlib.pyplot as plt
##########
input_folder = project_location+"BNMTF/data_toy/bnmtf/"
iterations = 1000
init_FG = 'kmeans'
init_S = 'random'
I, J, K, L = 100, 80, 5, 5
minimum_TN = 0.
alpha, beta = 1., 1.
lambdaF = numpy.ones((I,K))/10.
lambdaS = numpy.ones((K,L))/10.
lambdaG = numpy.ones((J,L))/10.
priors = { 'alpha':alpha, 'beta':beta, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG }
# Load in data
R = numpy.loadtxt(input_folder+"R.txt")
M = numpy.ones((I,J))
# Give the same random initialisation
numpy.random.seed(3)
# Run the Gibbs sampler
NMTF = nmtf_icm(R,M,K,L,priors)
NMTF.initialise(init_S=init_S,init_FG=init_FG)
NMTF.run(iterations,minimum_TN=minimum_TN)
# Plot the tau expectation values to check convergence
plt.plot(NMTF.all_tau)
# Extract the performances across all iterations
print "icm_all_performances = %s" % NMTF.all_performances
'''
icm_all_performances = {'R^2': [0.9288177442589758, 0.9382287011236542, 0.9418203899864597, 0.9460128280404809, 0.9522883341388004, 0.9595457624926458, 0.9656885834266021, 0.9699748031764066, 0.9725999243410073, 0.974172214388124, 0.9751600751033723, 0.9758339191664401, 0.9763306418389491, 0.9767265135122127, 0.9770435156342856, 0.9773156898933042, 0.9775581979741197, 0.9777792516208544, 0.9779848864143771, 0.9781797334003397, 0.9783684955978944, 0.9785519239167353, 0.9787346370702479, 0.9789196522092244, 0.9791101181706288, 0.979309927497815, 0.9795224632110561, 0.9797498421420886, 0.9799983905326746, 0.9802732667024991, 0.9805791511830504, 0.9809203269838284, 0.9813011607831511, 0.9817290522918887, 0.9822062551135903, 0.9827368747345924, 0.9833244560179435, 0.9839666017876979, 0.984659952168261, 0.9854029585781526, 0.9861900386678588, 0.9870112862799908, 0.9878499309050247, 0.9886805982448128, 0.9894979224362951, 0.9902767181882643, 0.9909985800068991, 0.9916802560992952, 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0.99918683175399758, 0.9991868328829232, 0.99918683400506136, 0.99918683512046846, 0.99918683622920768, 0.99918683733130254, 0.99918683842680533, 0.99918683951577214, 0.99918684059824081, 0.99918684167426464, 0.99918684274389047, 0.99918684380714773, 0.99918684486409182, 0.99918684591477469, 0.99918684695922833, 0.99918684799751545, 0.99918684902966248, 0.99918685005583063, 0.99918685107633143, 0.99918685209073321, 0.99918685309913213, 0.99918685410159536, 0.99918685509816307, 0.99918685608887969, 0.9991868570737954, 0.99918685805295548, 0.99918685902640647, 0.99918685999417889, 0.99918686095632003, 0.99918686191286565, 0.9991868628638636, 0.99918686380935706, 0.99918686474936802, 0.9991868656839511, 0.99918686661313805, 0.99918686753696739, 0.99918686845548454, 0.99918686936871415, 0.99918687027670916, 0.99918687117949456, 0.99918687207710422, 0.99918687296957487, 0.99918687385695892, 0.99918687473926793, 0.99918687561654118, 0.99918687648866678, 0.99918687735570277, 0.99918687821773733, 0.99918687907483494, 0.9991868799270418, 0.99918688077440287, 0.99918688161694225, 0.99918688245650666, 0.99918688329188765, 0.99918688412272116, 0.99918688494903152, 0.99918688577086723, 0.99918688658827559, 0.9991868874012918, 0.99918688820997859, 0.99918688901437858, 0.99918688981455483, 0.99918689061054067, 0.99918689140238293, 0.99918689219015533, 0.99918689297388408, 0.99918689375362346, 0.99918689452942333, 0.99918689530133986, 0.99918689606939148, 0.99918689683365747, 0.9991868975941538, 0.99918689835094265, 0.99918689910405767, 0.99918689985354525, 0.99918690059944637, 0.99918690134179944, 0.99918690208064653, 0.99918690281602196, 0.99918690354797812, 0.99918690427653867, 0.9991869050017449, 0.99918690572363511, 0.99918690644248209, 0.99918690715840774, 0.99918690787135778, 0.99918690858123715, 0.99918690928801468, 0.99918690999172188, 0.9991869106924175, 0.99918691139016524, 0.99918691208503041, 0.99918691277707494, 0.99918691346636723, 0.99918691415296113, 0.99918691483691002, 0.99918691551828642, 0.99918691619712718, 0.99918691687349226, 0.99918691754741651, 0.99918691821893435, 0.99918691888809164, 0.99918691955491312, 0.99918692021945443, 0.99918692088171701, 0.99918692154174515, 0.99918692219956262, 0.99918692285517829, 0.99918692350863658, 0.99918692415994315, 0.99918692480913152, 0.99918692545620014, 0.99918692610119375, 0.99918692674410148, 0.99918692738495229, 0.99918692802376718, 0.99918692866055892, 0.99918692929532449, 0.9991869299280991, 0.99918693055889241, 0.99918693118770396, 0.99918693181455964, 0.99918693243946932, 0.99918693306243267, 0.99918693368286593, 0.99918693430103134, 0.99918693491742339, 0.99918693553202098, 0.99918693614478349, 0.99918693675569448, 0.99918693736475128, 0.99918693797196978, 0.99918693857735552, 0.99918693918090795, 0.99918693978265571, 0.99918694038257339, 0.99918694098070848, 0.99918694157704591, 0.99918694217160664, 0.99918694276439368, 0.99918694335541669, 0.99918694394470442, 0.99918694453222878]}
'''
| 1,140.218182
| 61,407
| 0.850077
| 6,226
| 62,712
| 8.557983
| 0.500964
| 0.000676
| 0.000488
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.880109
| 0.050756
| 62,712
| 55
| 61,408
| 1,140.218182
| 0.014951
| 0.002711
| 0
| 0
| 0
| 0
| 0.12142
| 0.024055
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.125
| null | null | 0.041667
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
9b2ae82c898bc1856cb7dfa44bb822e0d24139aa
| 284
|
py
|
Python
|
opencv_learn/charpter08/demo_08.12.py
|
zhangxinzhou/play_game
|
854448f8416b2d3f98bb2c3ed0f7d834a61593de
|
[
"Apache-2.0"
] | null | null | null |
opencv_learn/charpter08/demo_08.12.py
|
zhangxinzhou/play_game
|
854448f8416b2d3f98bb2c3ed0f7d834a61593de
|
[
"Apache-2.0"
] | null | null | null |
opencv_learn/charpter08/demo_08.12.py
|
zhangxinzhou/play_game
|
854448f8416b2d3f98bb2c3ed0f7d834a61593de
|
[
"Apache-2.0"
] | null | null | null |
import cv2
kernel1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
kernel2 = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))
kernel3 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
print("kernel1=\n", kernel1)
print("kernel2=\n", kernel2)
print("kernel3=\n", kernel3)
| 28.4
| 62
| 0.742958
| 38
| 284
| 5.473684
| 0.342105
| 0.346154
| 0.389423
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.084942
| 0.088028
| 284
| 9
| 63
| 31.555556
| 0.718147
| 0
| 0
| 0
| 0
| 0
| 0.105634
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.142857
| 0
| 0.142857
| 0.428571
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
9b4e5fe5345bdc19728f0a69b2447a1c3513a2ad
| 518
|
py
|
Python
|
extractor/horkos-extractor/stat_technique/statisticaltechnique.py
|
JoeRegnier/horkos
|
aec8fa5dc205ea7a6b751cc17fb470f34e99a1c4
|
[
"Apache-2.0"
] | 2
|
2019-08-29T23:21:43.000Z
|
2020-01-15T23:41:29.000Z
|
extractor/horkos-extractor/stat_technique/statisticaltechnique.py
|
JoeRegnier/horkos
|
aec8fa5dc205ea7a6b751cc17fb470f34e99a1c4
|
[
"Apache-2.0"
] | 12
|
2019-09-26T20:05:09.000Z
|
2022-02-10T10:09:09.000Z
|
extractor/horkos-extractor/stat_technique/statisticaltechnique.py
|
JoeRegnier/horkos
|
aec8fa5dc205ea7a6b751cc17fb470f34e99a1c4
|
[
"Apache-2.0"
] | 2
|
2020-01-15T23:41:33.000Z
|
2020-10-16T00:18:00.000Z
|
__all__ = ('')
"""
Parent class to keep all statistical queries uniform
"""
class StatisticalTechnique():
def __init__(self, freqmap, latest_freqmap):
self.freqmap = freqmap
self.latest_freqmap = latest_freqmap
self.scores = dict()
def get_name(self):
pass
def process(self):
pass
def get_scores(self):
return self.scores
def get_freqmap(self):
return self.freqmap
def get_latest_freqmap(self):
return self.latest_freqmap
| 18.5
| 52
| 0.638996
| 60
| 518
| 5.233333
| 0.35
| 0.207006
| 0.16242
| 0.152866
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.274131
| 518
| 28
| 53
| 18.5
| 0.835106
| 0
| 0
| 0.125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.375
| false
| 0.125
| 0
| 0.1875
| 0.625
| 0
| 0
| 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
| 1
| 0
| 1
| 1
| 0
|
0
| 5
|
9b7de92b2643aca1abd9228dac81dc856799c2e2
| 138
|
py
|
Python
|
cap3/ex6.py
|
felipesch92/livroPython
|
061b1c095c3ec2d25fb1d5fdfbf9e9dbe10b3307
|
[
"MIT"
] | null | null | null |
cap3/ex6.py
|
felipesch92/livroPython
|
061b1c095c3ec2d25fb1d5fdfbf9e9dbe10b3307
|
[
"MIT"
] | null | null | null |
cap3/ex6.py
|
felipesch92/livroPython
|
061b1c095c3ec2d25fb1d5fdfbf9e9dbe10b3307
|
[
"MIT"
] | null | null | null |
s = float(input('Informe o salário: '))
a = float(input('Informe o aumento em %: '))
t = s + (s * a) / 100
print(f'Novo salário: R$ {t}')
| 27.6
| 44
| 0.57971
| 24
| 138
| 3.333333
| 0.625
| 0.25
| 0.425
| 0.45
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027027
| 0.195652
| 138
| 4
| 45
| 34.5
| 0.693694
| 0
| 0
| 0
| 0
| 0
| 0.456522
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.25
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
9b895a088ecc7fb9c6cc4fd4c991043052e5cf09
| 101
|
py
|
Python
|
src/lightuptraining/sources/antplus/device.py
|
marcelblijleven/light-up-training
|
e0310ec024c03064934f5c01d3b336dd81fac93c
|
[
"MIT"
] | 1
|
2021-12-05T13:55:04.000Z
|
2021-12-05T13:55:04.000Z
|
src/lightuptraining/sources/antplus/device.py
|
marcelblijleven/light-up-training
|
e0310ec024c03064934f5c01d3b336dd81fac93c
|
[
"MIT"
] | null | null | null |
src/lightuptraining/sources/antplus/device.py
|
marcelblijleven/light-up-training
|
e0310ec024c03064934f5c01d3b336dd81fac93c
|
[
"MIT"
] | null | null | null |
from typing import Protocol, runtime_checkable
@runtime_checkable
class Device(Protocol):
pass
| 14.428571
| 46
| 0.80198
| 12
| 101
| 6.583333
| 0.75
| 0.405063
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148515
| 101
| 6
| 47
| 16.833333
| 0.918605
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.25
| 0.25
| 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
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
9b99d0b6c3178578349d84800a825f595cba5bbb
| 69
|
py
|
Python
|
gui/component/__init__.py
|
timothyhalim/Render-Manager
|
b919a6a2290c25fe7799d661fa7839f99bf0a5cc
|
[
"MIT"
] | null | null | null |
gui/component/__init__.py
|
timothyhalim/Render-Manager
|
b919a6a2290c25fe7799d661fa7839f99bf0a5cc
|
[
"MIT"
] | null | null | null |
gui/component/__init__.py
|
timothyhalim/Render-Manager
|
b919a6a2290c25fe7799d661fa7839f99bf0a5cc
|
[
"MIT"
] | null | null | null |
from .ImageButton import ImageButton
from .SearchBar import SearchBar
| 34.5
| 36
| 0.869565
| 8
| 69
| 7.5
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.101449
| 69
| 2
| 37
| 34.5
| 0.967742
| 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
| 1
| 0
|
0
| 5
|
9bbf141f88690bad45e6c6a5fb82654bc40afb7d
| 33
|
py
|
Python
|
django-stdimage/__init__.py
|
gitdaniel228/realtor
|
4366d57b064be87b31c8a036b3ed7a99b2036461
|
[
"BSD-3-Clause"
] | null | null | null |
django-stdimage/__init__.py
|
gitdaniel228/realtor
|
4366d57b064be87b31c8a036b3ed7a99b2036461
|
[
"BSD-3-Clause"
] | null | null | null |
django-stdimage/__init__.py
|
gitdaniel228/realtor
|
4366d57b064be87b31c8a036b3ed7a99b2036461
|
[
"BSD-3-Clause"
] | null | null | null |
from fields import StdImageField
| 16.5
| 32
| 0.878788
| 4
| 33
| 7.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 33
| 1
| 33
| 33
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
32d86c367669c8ecfea9f2827b191948fd22429d
| 9,527
|
py
|
Python
|
generate-code/configs.py
|
honzaskypala/osloveni
|
3574f13aa895e48c98d4fd41c5571adf2ad71fae
|
[
"WTFPL",
"Unlicense"
] | 21
|
2017-12-15T23:39:59.000Z
|
2022-02-15T09:38:50.000Z
|
generate-code/configs.py
|
honzaskypala/osloveni
|
3574f13aa895e48c98d4fd41c5571adf2ad71fae
|
[
"WTFPL",
"Unlicense"
] | 3
|
2019-01-16T14:11:31.000Z
|
2020-09-22T19:59:58.000Z
|
generate-code/configs.py
|
honzaskypala/osloveni
|
3574f13aa895e48c98d4fd41c5571adf2ad71fae
|
[
"WTFPL",
"Unlicense"
] | 7
|
2019-06-07T02:39:01.000Z
|
2021-03-17T01:10:04.000Z
|
configs = {
"python" :
{
"filesuffix" : ".py",
"commentstart" : "'''",
"commentend" : "'''",
"indent" : " ",
"blockstart" : "",
"blockend" : "",
"function" : "def {fnname}({var}):",
"var" : "{varname}",
"if" : "if {cond}:",
"elseif" : "elif {cond}:",
"else" : "else:",
"switchsupport" : False,
"assignement" : "{var} = {exp}",
"conditional" : "{exp1} if {cond} else {exp2}",
"equal" : "{exp1} == {exp2}",
"and" : "{exp1} and {exp2}",
"or" : "{exp1} or {exp2}",
"charquote" : "'",
"strquote" : "'",
"return" : "return {exp}",
"charatpos" : "{var}[{pos}]",
"leftstr" : "{var}[:{length}]",
"rightstr" : "{var}[-{length}:]",
"lowercase" : "{var}.lower()",
"uppercase" : "{var}.upper()",
"titlecase" : "{var}.title()",
"islowercase" : "{var}.islower()",
"isuppercase" : "{var}.isupper()",
"istitlecase" : "{var}.istitle()",
"concat" : "{str1} + {str2}",
"tuple" : "({exp1}, {exp2})",
"strlen" : "len({var})",
"strnegativepos" : True,
"fetchcharoptimization": True,
"funcdoc" : "\"\"\"Vrací pátý pád jména k prvnímu pádu\n\nArgumenty:\njmeno -- první pád jména\n\"\"\"",
"docinsidefunction" : True,
},
"php" :
{
"filesuffix" : ".php",
"filestart" : "<?php",
"fileend" : "?>",
"commentstart" : "/*",
"commentend" : "*/",
"indent" : "\t",
"blockstart" : "{",
"blockend" : "}",
"function" : "function {fnname}({var}) {{",
"functionend" : "}",
"var" : "${varname}",
"if" : "if ({cond}) {{",
"elseif" : "}} elseif ({cond}) {{",
"else" : "} else {",
"endif" : "}",
"switchsupport" : True,
"switch" : "switch ({var}) {{",
"endswitch" : "}",
"case" : "case {exp}:",
"endcase" : "\tbreak;",
"default" : "default:",
"enddefault" : False,
"assignement" : "{var} = {exp};",
"conditional" : "{cond} ? {exp1} : {exp2}",
"equal" : "{exp1} == {exp2}",
"and" : "{exp1} && {exp2}",
"or" : "{exp1} || {exp2}",
"charquote" : "'",
"strquote" : "\"",
"return" : "return {exp};",
"charatpos" : "{var}[{pos}]",
"strlen" : "strlen({var})",
"leftstr" : "substr({var}, 0, {length})",
"rightstr" : "substr({var}, -{length})",
"lowercase" : "mb_convert_case({var}, MB_CASE_LOWER, \"UTF-8\")",
"uppercase" : "mb_convert_case({var}, MB_CASE_UPPER, \"UTF-8\")",
"titlecase" : "mb_convert_case({var}, MB_CASE_TITLE, \"UTF-8\")",
"islowercase" : "mb_convert_case({var}, MB_CASE_LOWER) == {var}",
"isuppercase" : "mb_convert_case({var}, MB_CASE_UPPER) == {var}",
"istitlecase" : "preg_match(\"/^[A-ZÁČĎÉÍŇÓŘŠŤÚÝŽ][a-záčďéěíňóřšťúůýž]*$/u\", {var})",
"concat" : "{str1} . {str2}",
"tuple" : "[{exp1}, {exp2}]",
# "tuple" : "array({exp1}, {exp2})", # PHP < 5.4
"strnegativepos" : False,
"fetchcharoptimization": True,
"funcdoc" : "/**\n * Vrací pátý pád jména k prvnímu pádu\n * @param string $jmeno první pád jména\n*/",
"docinsidefunction" : False,
},
"javascript" :
{
"filesuffix" : ".js",
"commentstart" : "/*",
"commentend" : "*/",
"indent" : "\t",
"blockstart" : "{",
"blockend" : "}",
"function" : "function {fnname}({var}) {{",
"functionend" : "}",
"var" : "{varname}",
"vardeclaration" : "var {var};",
"if" : "if ({cond}) {{",
"elseif" : "}} else if ({cond}) {{",
"else" : "} else {",
"endif" : "}",
"switchsupport" : True,
"switch" : "switch ({var}) {{",
"endswitch" : "}",
"case" : "case {exp}:",
"endcase" : "\tbreak;",
"default" : "default:",
"enddefault" : False,
"assignement" : "{var} = {exp};",
"conditional" : "{cond} ? {exp1} : {exp2}",
"equal" : "{exp1} == {exp2}",
"and" : "{exp1} && {exp2}",
"or" : "{exp1} || {exp2}",
"charquote" : "'",
"strquote" : "\"",
"return" : "return {exp};",
"charatpos" : "{var}.charAt({pos})",
"strlen" : "{var}.length",
"leftstr" : "{var}.substr(0, {length})",
"rightstr" : "{var}.substr({var}.length - {length})",
"lowercase" : "{var}.toLowerCase()",
"uppercase" : "{var}.toUpperCase()",
"titlecase" : "{var}.replace(/\w\S*/g, function(txt){{return txt.charAt(0).toUpperCase() + txt.substr(1).toLowerCase();}})",
"islowercase" : "{var}.toLowerCase() == {var}",
"isuppercase" : "{var}.toUpperCase() == {var}",
"istitlecase" : "{var}.match(/^[A-ZÁČĎÉÍŇÓŘŠŤÚÝŽ][a-záčďéěíňóřšťúůýž]*$/u)",
"concat" : "{str1} + {str2}",
"tuple" : "[{exp1}, {exp2}]",
"strnegativepos" : False,
"fetchcharoptimization": True,
"funcdoc" : "/**\n * Vrací pátý pád jména k prvnímu pádu\n * @param {String} jmeno první pád jména\n*/",
"docinsidefunction" : False,
},
"micropython" :
{
"filesuffix" : ".py",
"commentstart" : "'''",
"commentend" : "'''",
"indent" : " ",
"blockstart" : "",
"blockend" : "",
"function" : "def {fnname}({var}):",
"var" : "{varname}",
"if" : "if {cond}:",
"elseif" : "elif {cond}:",
"else" : "else:",
"switchsupport" : False,
"assignement" : "{var} = {exp}",
"conditional" : "{exp1} if {cond} else {exp2}",
"equal" : "{exp1} == {exp2}",
"and" : "{exp1} and {exp2}",
"or" : "{exp1} or {exp2}",
"charquote" : "'",
"strquote" : "'",
"return" : "return {exp}",
"charatpos" : "{var}[{pos}]",
"leftstr" : "{var}[:{length}]",
"rightstr" : "{var}[-{length}:]",
"lowercase" : "{var}.lower()",
"uppercase" : "{var}.upper()",
"titlecase" : "{var}[0].upper() + {var}[1:].lower()",
"islowercase" : "{var}.islower()",
"isuppercase" : "{var}.isupper()",
"istitlecase" : "{var}[0].isupper() and {var}[1:].islower()",
"concat" : "{str1} + {str2}",
"tuple" : "({exp1}, {exp2})",
"strlen" : "len({var})",
"strnegativepos" : True,
"fetchcharoptimization": True,
"funcdoc" : "\"\"\"Vrací pátý pád jména k prvnímu pádu\n\nArgumenty:\njmeno -- první pád jména\n\"\"\"",
"docinsidefunction" : True,
},
}
| 53.223464
| 148
| 0.313005
| 545
| 9,527
| 5.433028
| 0.211009
| 0.040527
| 0.021952
| 0.027018
| 0.788585
| 0.788585
| 0.732523
| 0.696049
| 0.658224
| 0.65721
| 0
| 0.013413
| 0.50698
| 9,527
| 178
| 149
| 53.522472
| 0.61699
| 0.006508
| 0
| 0.740113
| 0
| 0.011299
| 0.381409
| 0.040715
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
fd433f4fac94107a19028d7554a36bd7532e7930
| 125
|
py
|
Python
|
pypy/annotation/__init__.py
|
camillobruni/pygirl
|
ddbd442d53061d6ff4af831c1eab153bcc771b5a
|
[
"MIT"
] | 12
|
2016-01-06T07:10:28.000Z
|
2021-05-13T23:02:02.000Z
|
pypy/annotation/__init__.py
|
woodrow/pyoac
|
b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7
|
[
"MIT"
] | null | null | null |
pypy/annotation/__init__.py
|
woodrow/pyoac
|
b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7
|
[
"MIT"
] | 2
|
2016-07-29T07:09:50.000Z
|
2016-10-16T08:50:26.000Z
|
# workaround for a circular imports problem
# e.g. if you import pypy.annotation.listdef first
import pypy.annotation.model
| 25
| 50
| 0.8
| 19
| 125
| 5.263158
| 0.842105
| 0.2
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136
| 125
| 4
| 51
| 31.25
| 0.925926
| 0.72
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
b5c4d6c5bc6a509c46ea5cc7ea6ab2da5a57faa7
| 362
|
py
|
Python
|
students/k3343/practical_works/Tsybaeva_Arina/practise3/second_app/models.py
|
TonikX/ITMO_ICT_-WebProgramming_2020
|
ba566c1b3ab04585665c69860b713741906935a0
|
[
"MIT"
] | 10
|
2020-03-20T09:06:12.000Z
|
2021-07-27T13:06:02.000Z
|
students/k3343/practical_works/Tsybaeva_Arina/practise3/second_app/models.py
|
TonikX/ITMO_ICT_-WebProgramming_2020
|
ba566c1b3ab04585665c69860b713741906935a0
|
[
"MIT"
] | 134
|
2020-03-23T09:47:48.000Z
|
2022-03-12T01:05:19.000Z
|
students/k3343/practical_works/Tsybaeva_Arina/practise3/second_app/models.py
|
TonikX/ITMO_ICT_-WebProgramming_2020
|
ba566c1b3ab04585665c69860b713741906935a0
|
[
"MIT"
] | 71
|
2020-03-20T12:45:56.000Z
|
2021-10-31T19:22:25.000Z
|
from django.db import models
from django.contrib.auth.models import AbstractUser
# Create your models here.
class User(AbstractUser):
passport_number = models.CharField(max_length=10, blank=True, null=True)
home_address = models.CharField(max_length=40, blank=True, null=True)
nationality = models.CharField(max_length=40, blank=True, null=True)
| 30.166667
| 76
| 0.773481
| 51
| 362
| 5.392157
| 0.509804
| 0.163636
| 0.196364
| 0.261818
| 0.312727
| 0.312727
| 0.312727
| 0.312727
| 0.312727
| 0
| 0
| 0.018987
| 0.127072
| 362
| 11
| 77
| 32.909091
| 0.851266
| 0.066298
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.166667
| 0.333333
| 0
| 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
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
b5f64cdbeb6b4b8cfd49611e65211aab5ef86c35
| 179
|
py
|
Python
|
volunteer/views.py
|
MissionBit/MB_Portal
|
a8bbde9c25b0863a193cb4adb7a419493dd322ff
|
[
"PostgreSQL"
] | 1
|
2019-08-12T01:57:11.000Z
|
2019-08-12T01:57:11.000Z
|
volunteer/views.py
|
MissionBit/MB_Portal
|
a8bbde9c25b0863a193cb4adb7a419493dd322ff
|
[
"PostgreSQL"
] | 35
|
2019-06-25T01:09:43.000Z
|
2022-02-10T08:13:09.000Z
|
volunteer/views.py
|
MissionBit/MB_Portal
|
a8bbde9c25b0863a193cb4adb7a419493dd322ff
|
[
"PostgreSQL"
] | 2
|
2019-07-02T17:25:42.000Z
|
2019-07-18T00:05:58.000Z
|
from django.shortcuts import render
from home.decorators import group_required
@group_required("volunteer")
def volunteer(request):
return render(request, "volunteer.html")
| 22.375
| 44
| 0.798883
| 22
| 179
| 6.409091
| 0.636364
| 0.184397
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111732
| 179
| 7
| 45
| 25.571429
| 0.886792
| 0
| 0
| 0
| 0
| 0
| 0.128492
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0.2
| 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
| 1
| 1
| 0
|
0
| 5
|
bd1f851ba3856fc353fcb266c956097df4455daf
| 238
|
py
|
Python
|
odincal/tests/test_datatypes.py
|
Odin-SMR/odincal
|
4c40f0d762b5ee8cbfd7f305cf6aa7ed9ec50206
|
[
"MIT"
] | null | null | null |
odincal/tests/test_datatypes.py
|
Odin-SMR/odincal
|
4c40f0d762b5ee8cbfd7f305cf6aa7ed9ec50206
|
[
"MIT"
] | null | null | null |
odincal/tests/test_datatypes.py
|
Odin-SMR/odincal
|
4c40f0d762b5ee8cbfd7f305cf6aa7ed9ec50206
|
[
"MIT"
] | null | null | null |
from ctypes import sizeof
from odincal.data_structures import AC, ACData, HK
def test_len_acd():
assert sizeof(AC) == 150
def test_len_shkd():
assert sizeof(HK) == 150
def test_len_acdata():
assert sizeof(ACData) == 150
| 15.866667
| 50
| 0.705882
| 36
| 238
| 4.472222
| 0.472222
| 0.130435
| 0.186335
| 0.161491
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.046875
| 0.193277
| 238
| 14
| 51
| 17
| 0.791667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.375
| 1
| 0.375
| true
| 0
| 0.25
| 0
| 0.625
| 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
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
bd39ee4918673bc40b4a85c9747a05ce49d44ade
| 182
|
py
|
Python
|
Curso de Cisco/Actividades/Algunas funcione simples - recursividad parte 3.py
|
tomasfriz/Curso-de-Cisco
|
a50ee5fa96bd86d468403e29ccdc3565a181af60
|
[
"MIT"
] | null | null | null |
Curso de Cisco/Actividades/Algunas funcione simples - recursividad parte 3.py
|
tomasfriz/Curso-de-Cisco
|
a50ee5fa96bd86d468403e29ccdc3565a181af60
|
[
"MIT"
] | null | null | null |
Curso de Cisco/Actividades/Algunas funcione simples - recursividad parte 3.py
|
tomasfriz/Curso-de-Cisco
|
a50ee5fa96bd86d468403e29ccdc3565a181af60
|
[
"MIT"
] | null | null | null |
def factorialFun(n):
if n < 0:
return None
if n < 2:
return 1
return n * factorialFun(n - 1)
for n in range(1, 10):
print(n, "->", factorialFun(n))
| 18.2
| 35
| 0.521978
| 29
| 182
| 3.310345
| 0.517241
| 0.40625
| 0.291667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057851
| 0.335165
| 182
| 9
| 36
| 20.222222
| 0.727273
| 0
| 0
| 0
| 0
| 0
| 0.01105
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.125
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
bd4a38305c8d81c4ec907a802429244f46741c94
| 338
|
py
|
Python
|
main/dto/hint.py
|
wangjingjing/wx-5268
|
5828208a513ffbe1c32097414ef96fd0fa078656
|
[
"Apache-2.0"
] | null | null | null |
main/dto/hint.py
|
wangjingjing/wx-5268
|
5828208a513ffbe1c32097414ef96fd0fa078656
|
[
"Apache-2.0"
] | null | null | null |
main/dto/hint.py
|
wangjingjing/wx-5268
|
5828208a513ffbe1c32097414ef96fd0fa078656
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
class Hint():
def __init__(self, label, value):
self.label = label
self.value = value
def __repr__(self):
return '<Hint %r>' % self.label
def serialize(self):
return {
'label': self.label,
'value': self.value
}
| 18.777778
| 39
| 0.508876
| 38
| 338
| 4.315789
| 0.473684
| 0.219512
| 0.170732
| 0.219512
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004505
| 0.343195
| 338
| 17
| 40
| 19.882353
| 0.734234
| 0.12426
| 0
| 0
| 0
| 0
| 0.064626
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.272727
| false
| 0
| 0
| 0.181818
| 0.545455
| 0
| 0
| 0
| 0
| null | 1
| 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
| 5
|
1fabf92a7e3450739b243f5f28fd03068979a62e
| 263
|
py
|
Python
|
sickbeard/lib/hachoir_parser/video/__init__.py
|
Branlala/docker-sickbeardfr
|
3ac85092dc4cc8a4171fb3c83e9682162245e13e
|
[
"MIT"
] | null | null | null |
sickbeard/lib/hachoir_parser/video/__init__.py
|
Branlala/docker-sickbeardfr
|
3ac85092dc4cc8a4171fb3c83e9682162245e13e
|
[
"MIT"
] | null | null | null |
sickbeard/lib/hachoir_parser/video/__init__.py
|
Branlala/docker-sickbeardfr
|
3ac85092dc4cc8a4171fb3c83e9682162245e13e
|
[
"MIT"
] | null | null | null |
from lib.hachoir_parser.video.asf import AsfFile
from lib.hachoir_parser.video.flv import FlvFile
from lib.hachoir_parser.video.mov import MovFile
from lib.hachoir_parser.video.mpeg_video import MPEGVideoFile
from lib.hachoir_parser.video.mpeg_ts import MPEG_TS
| 37.571429
| 61
| 0.863118
| 43
| 263
| 5.093023
| 0.348837
| 0.159817
| 0.319635
| 0.456621
| 0.607306
| 0.26484
| 0
| 0
| 0
| 0
| 0
| 0
| 0.079848
| 263
| 6
| 62
| 43.833333
| 0.904959
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
1faf3cfbbfbb7a4a74d56e772b47e899d37f05fc
| 74
|
py
|
Python
|
mundo01/ex001.py
|
lucasadsr/Curso-Em-Video-Python
|
c5593eefcdea3aebda79a892054398062a70a29f
|
[
"MIT"
] | null | null | null |
mundo01/ex001.py
|
lucasadsr/Curso-Em-Video-Python
|
c5593eefcdea3aebda79a892054398062a70a29f
|
[
"MIT"
] | null | null | null |
mundo01/ex001.py
|
lucasadsr/Curso-Em-Video-Python
|
c5593eefcdea3aebda79a892054398062a70a29f
|
[
"MIT"
] | null | null | null |
# Crie um programa que escreva "Olá mundo" na tela.
print('Olá, mundo!')
| 18.5
| 51
| 0.689189
| 12
| 74
| 4.25
| 0.833333
| 0.313725
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175676
| 74
| 3
| 52
| 24.666667
| 0.836066
| 0.662162
| 0
| 0
| 0
| 0
| 0.478261
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
951e967be6e0b57624c35e8d98d0f4a292fa84d9
| 98
|
py
|
Python
|
math/fractions/0.py
|
admariner/playground
|
02a3104472c8fa3589fe87f7265e70c61d5728c7
|
[
"MIT"
] | 3
|
2021-06-12T04:42:32.000Z
|
2021-06-24T13:57:38.000Z
|
math/fractions/0.py
|
admariner/playground
|
02a3104472c8fa3589fe87f7265e70c61d5728c7
|
[
"MIT"
] | null | null | null |
math/fractions/0.py
|
admariner/playground
|
02a3104472c8fa3589fe87f7265e70c61d5728c7
|
[
"MIT"
] | 1
|
2021-08-19T14:57:17.000Z
|
2021-08-19T14:57:17.000Z
|
from fractions import Fraction
f = Fraction(3, 4)
print(repr(f)) # Fraction(3, 4)
print(f) # 3/4
| 16.333333
| 31
| 0.673469
| 18
| 98
| 3.666667
| 0.5
| 0.090909
| 0.30303
| 0.333333
| 0.484848
| 0
| 0
| 0
| 0
| 0
| 0
| 0.073171
| 0.163265
| 98
| 6
| 32
| 16.333333
| 0.731707
| 0.183673
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 0
| 0.25
| 0.5
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
1f029aa6276f059f5e4bd5adfe52b1c18f0e1f0a
| 288
|
py
|
Python
|
kite-go/lang/python/pythonparser/epytext/testdata/literal-block.py
|
kiteco/kiteco-public
|
74aaf5b9b0592153b92f7ed982d65e15eea885e3
|
[
"BSD-3-Clause"
] | 17
|
2022-01-10T11:01:50.000Z
|
2022-03-25T03:21:08.000Z
|
kite-go/lang/python/pythonparser/epytext/testdata/literal-block.py
|
kiteco/kiteco-public
|
74aaf5b9b0592153b92f7ed982d65e15eea885e3
|
[
"BSD-3-Clause"
] | 1
|
2022-01-13T14:28:47.000Z
|
2022-01-13T14:28:47.000Z
|
kite-go/lang/python/pythonparser/epytext/testdata/literal-block.py
|
kiteco/kiteco-public
|
74aaf5b9b0592153b92f7ed982d65e15eea885e3
|
[
"BSD-3-Clause"
] | 7
|
2022-01-07T03:58:10.000Z
|
2022-03-24T07:38:20.000Z
|
# The first two are treated as literal blocks.
def example():
"""
This is a paragraph::
With a literal block.
While this one has trailing space::
So is this a literal block?
And this one has trailing chars:: !
- So this clearly isn't a literal block.
"""
return 1
| 24
| 46
| 0.663194
| 46
| 288
| 4.152174
| 0.630435
| 0.125654
| 0.204188
| 0.188482
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004673
| 0.256944
| 288
| 11
| 47
| 26.181818
| 0.88785
| 0.819444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 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
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
1f23ca06f8abd7aa7ded9b926c3b41157d17f03f
| 81
|
py
|
Python
|
user/MiLAB_Lowlevel_Controller/exlcm/__init__.py
|
Tomato1107/MiLAB-Cheetah-Software
|
6d00421de49970b31bbeb8a6e165ba5608128d33
|
[
"MIT"
] | 8
|
2021-09-23T06:38:14.000Z
|
2022-03-02T17:29:58.000Z
|
user/MiLAB_Lowlevel_Controller/exlcm/__init__.py
|
raess1/MiLAB-Cheetah-Software
|
6d00421de49970b31bbeb8a6e165ba5608128d33
|
[
"MIT"
] | 1
|
2022-01-14T10:14:32.000Z
|
2022-01-14T10:14:32.000Z
|
user/MiLAB_Lowlevel_Controller/exlcm/__init__.py
|
AWang-Cabin/MILAB-Cheetah-Software
|
6d00421de49970b31bbeb8a6e165ba5608128d33
|
[
"MIT"
] | 10
|
2021-08-14T07:52:12.000Z
|
2022-03-02T02:07:00.000Z
|
from .lowlevel_cmd import lowlevel_cmd
from .lowlevel_state import lowlevel_state
| 40.5
| 42
| 0.888889
| 12
| 81
| 5.666667
| 0.416667
| 0.352941
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08642
| 81
| 2
| 42
| 40.5
| 0.918919
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
1f3c999b26cc67f7e762c4fdae51581524d6c706
| 234
|
py
|
Python
|
masks/admin.py
|
AlexPersaud17/MasksByLiz
|
7c1dbdae82b5a0f6e7a54e1355904dffe42dd165
|
[
"MIT"
] | null | null | null |
masks/admin.py
|
AlexPersaud17/MasksByLiz
|
7c1dbdae82b5a0f6e7a54e1355904dffe42dd165
|
[
"MIT"
] | null | null | null |
masks/admin.py
|
AlexPersaud17/MasksByLiz
|
7c1dbdae82b5a0f6e7a54e1355904dffe42dd165
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Product, Order, Cart, AdminMgmt
# Register your models here.
# admin.site.register(Product)
# admin.site.register(Order)
# admin.site.register(Cart)
# admin.site.register(AdminMgmt)
| 33.428571
| 51
| 0.786325
| 32
| 234
| 5.75
| 0.4375
| 0.195652
| 0.369565
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.098291
| 234
| 7
| 52
| 33.428571
| 0.872038
| 0.594017
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
1f50500861c366126e5867b09ca5c08e6e844b3f
| 138
|
py
|
Python
|
will/backends/generation/__init__.py
|
afoster757/pcobot
|
72b42d42ab53613fd45e2267d83c278372bb48ea
|
[
"MIT"
] | 349
|
2015-01-15T05:12:02.000Z
|
2022-01-11T09:21:01.000Z
|
will/backends/generation/__init__.py
|
afoster757/pcobot
|
72b42d42ab53613fd45e2267d83c278372bb48ea
|
[
"MIT"
] | 350
|
2015-01-02T16:33:14.000Z
|
2022-02-06T17:34:34.000Z
|
will/backends/generation/__init__.py
|
afoster757/pcobot
|
72b42d42ab53613fd45e2267d83c278372bb48ea
|
[
"MIT"
] | 184
|
2015-01-08T13:20:50.000Z
|
2021-12-31T05:57:21.000Z
|
from .strict_regex import RegexBackend
from .fuzzy_best_match import FuzzyBestMatch
from .fuzzy_all_matches import FuzzyAllMatchesBackend
| 34.5
| 53
| 0.891304
| 17
| 138
| 6.941176
| 0.705882
| 0.152542
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.086957
| 138
| 3
| 54
| 46
| 0.936508
| 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
| 1
| 0
|
0
| 5
|
1f5290451f073f5b1d0ad85db49224528ace6de8
| 38
|
py
|
Python
|
tests/__init__.py
|
heroku/pghstore
|
31de76a7431ca280b1d9138bd6baf1ac767ea0ea
|
[
"MIT"
] | 2
|
2021-03-29T06:39:04.000Z
|
2021-08-04T06:40:17.000Z
|
tests/__init__.py
|
heroku/pghstore
|
31de76a7431ca280b1d9138bd6baf1ac767ea0ea
|
[
"MIT"
] | 12
|
2017-08-22T15:43:09.000Z
|
2020-05-06T17:12:49.000Z
|
tests/__init__.py
|
heroku/pghstore
|
31de76a7431ca280b1d9138bd6baf1ac767ea0ea
|
[
"MIT"
] | 2
|
2017-08-19T12:24:52.000Z
|
2019-10-06T18:53:49.000Z
|
"""Unit tests module for pghstore."""
| 19
| 37
| 0.684211
| 5
| 38
| 5.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.131579
| 38
| 1
| 38
| 38
| 0.787879
| 0.815789
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 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
| 5
|
1f70c0651dcef958bc2fb10997a56bb8c1bd17e4
| 88
|
py
|
Python
|
pyNastran/gui/errors.py
|
ACea15/pyNastran
|
5ffc37d784b52c882ea207f832bceb6b5eb0e6d4
|
[
"BSD-3-Clause"
] | 293
|
2015-03-22T20:22:01.000Z
|
2022-03-14T20:28:24.000Z
|
pyNastran/gui/errors.py
|
ACea15/pyNastran
|
5ffc37d784b52c882ea207f832bceb6b5eb0e6d4
|
[
"BSD-3-Clause"
] | 512
|
2015-03-14T18:39:27.000Z
|
2022-03-31T16:15:43.000Z
|
pyNastran/gui/errors.py
|
ACea15/pyNastran
|
5ffc37d784b52c882ea207f832bceb6b5eb0e6d4
|
[
"BSD-3-Clause"
] | 136
|
2015-03-19T03:26:06.000Z
|
2022-03-25T22:14:54.000Z
|
class NoGeometry(RuntimeError):
pass
class NoSuperelements(RuntimeError):
pass
| 14.666667
| 36
| 0.761364
| 8
| 88
| 8.375
| 0.625
| 0.477612
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.170455
| 88
| 5
| 37
| 17.6
| 0.917808
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 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
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
2f452b29c4306ef917160f330d8f9eb1e911cdc6
| 74
|
py
|
Python
|
lib/datasets/__init__.py
|
chensong1995/E-CIR
|
cfecce92cb4cb6e93af1c8be9f8b6b62a91bbf56
|
[
"MIT"
] | 16
|
2022-03-03T06:21:45.000Z
|
2022-03-30T08:57:31.000Z
|
lib/datasets/__init__.py
|
chensong1995/E-CIR
|
cfecce92cb4cb6e93af1c8be9f8b6b62a91bbf56
|
[
"MIT"
] | 1
|
2022-03-21T14:14:52.000Z
|
2022-03-21T17:48:26.000Z
|
lib/datasets/__init__.py
|
chensong1995/E-CIR
|
cfecce92cb4cb6e93af1c8be9f8b6b62a91bbf56
|
[
"MIT"
] | 1
|
2022-03-11T03:15:31.000Z
|
2022-03-11T03:15:31.000Z
|
from .edi_dataset import EDIDataset
from .reds_dataset import REDSDataset
| 24.666667
| 37
| 0.864865
| 10
| 74
| 6.2
| 0.7
| 0.419355
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108108
| 74
| 2
| 38
| 37
| 0.939394
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
85e0b5c8fef27b4f76b6312deb34681ea4f626ac
| 105
|
py
|
Python
|
spikewidgets/widgets/mapswidget/__init__.py
|
KnierimLab/spikewidgets
|
5ee37a43df21676db646942141c60e9bde95362c
|
[
"MIT"
] | 6
|
2019-01-23T03:51:31.000Z
|
2021-02-15T07:54:39.000Z
|
spikewidgets/widgets/mapswidget/__init__.py
|
KnierimLab/spikewidgets
|
5ee37a43df21676db646942141c60e9bde95362c
|
[
"MIT"
] | 52
|
2019-01-23T10:10:30.000Z
|
2021-06-27T10:23:10.000Z
|
spikewidgets/widgets/mapswidget/__init__.py
|
KnierimLab/spikewidgets
|
5ee37a43df21676db646942141c60e9bde95362c
|
[
"MIT"
] | 7
|
2019-01-23T10:06:03.000Z
|
2020-10-29T18:38:37.000Z
|
from .activitymapwidget import plot_activity_map
from .templatemapswidget import plot_unit_template_maps
| 35
| 55
| 0.904762
| 13
| 105
| 6.923077
| 0.769231
| 0.222222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.07619
| 105
| 2
| 56
| 52.5
| 0.927835
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
85e5fba094f711567d2004bd55c86ffd5b047d6f
| 82
|
py
|
Python
|
core/api/request/reading_request.py
|
rits-dajare/DaaS
|
ab8483250a1a2b2838c316ba71fdaf748130dff1
|
[
"MIT"
] | 7
|
2020-07-20T12:03:06.000Z
|
2021-05-22T15:57:18.000Z
|
core/api/request/reading_request.py
|
averak/DaaS
|
ab8483250a1a2b2838c316ba71fdaf748130dff1
|
[
"MIT"
] | 19
|
2020-08-28T10:23:53.000Z
|
2021-11-17T23:48:45.000Z
|
core/api/request/reading_request.py
|
averak/DaaS
|
ab8483250a1a2b2838c316ba71fdaf748130dff1
|
[
"MIT"
] | 2
|
2020-08-08T21:20:01.000Z
|
2021-05-20T01:37:46.000Z
|
from pydantic import BaseModel
class ReadingRequest(BaseModel):
dajare: str
| 13.666667
| 32
| 0.780488
| 9
| 82
| 7.111111
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.170732
| 82
| 5
| 33
| 16.4
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 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
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
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