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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dca31a817ae55a971a7621e33258eb5e0e2581bb
| 291
|
py
|
Python
|
apis/covid_api.py
|
MistrBot/Fleeks-Ticket
|
558dd69baf1ef9d3f13dafbda8b069ad74a9d597
|
[
"MIT"
] | 28
|
2021-04-18T02:46:11.000Z
|
2022-03-20T19:13:27.000Z
|
apis/covid_api.py
|
MistrBot/Fleeks-Ticket
|
558dd69baf1ef9d3f13dafbda8b069ad74a9d597
|
[
"MIT"
] | 4
|
2022-02-03T18:24:32.000Z
|
2022-02-03T19:24:51.000Z
|
apis/covid_api.py
|
MistrBot/Fleeks-Ticket
|
558dd69baf1ef9d3f13dafbda8b069ad74a9d597
|
[
"MIT"
] | 47
|
2021-03-14T16:59:16.000Z
|
2022-03-18T16:41:50.000Z
|
import json
import urllib.request
def covid_api_request(endpoint):
covid_request_url = urllib.request.Request('https://api.covid19api.com/' + endpoint)
covid_request_data = json.loads(urllib.request.urlopen(covid_request_url).read().decode('utf-8'))
return covid_request_data
| 29.1
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0
| 4
|
dcb32128c89baa8cfce9985f9960bc2ea98b4b61
| 149
|
py
|
Python
|
python/metagoofil.py
|
reverland/scripts
|
c7ad9005b26adcd5df8726b3c3d86a7f4a19e513
|
[
"MIT"
] | 13
|
2015-02-09T05:40:51.000Z
|
2020-05-18T00:56:27.000Z
|
python/metagoofil.py
|
reverland/scripts
|
c7ad9005b26adcd5df8726b3c3d86a7f4a19e513
|
[
"MIT"
] | null | null | null |
python/metagoofil.py
|
reverland/scripts
|
c7ad9005b26adcd5df8726b3c3d86a7f4a19e513
|
[
"MIT"
] | 5
|
2015-01-23T15:35:10.000Z
|
2021-12-29T10:06:50.000Z
|
#! /bin/env python
# -*- coding: utf-8 -*-
'''
Something like metagoofil
Nothing... I just write it to work just for google...
Practice for OOP
'''
| 16.555556
| 53
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0
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f4d1861c267fc351483bd4958114715d852a1197
| 220,369
|
py
|
Python
|
music_manager/resource_rc.py
|
cedi4155476/musicmanager
|
f430e92ba85ef7fb77f0d688a094e1efd7feeda3
|
[
"MIT"
] | 1
|
2015-08-07T14:08:13.000Z
|
2015-08-07T14:08:13.000Z
|
music_manager/resource_rc.py
|
cedi4155476/musicmanager
|
f430e92ba85ef7fb77f0d688a094e1efd7feeda3
|
[
"MIT"
] | null | null | null |
music_manager/resource_rc.py
|
cedi4155476/musicmanager
|
f430e92ba85ef7fb77f0d688a094e1efd7feeda3
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# Resource object code
#
# Created: Mi. Mai 27 11:21:40 2015
# by: The Resource Compiler for PyQt (Qt v4.8.6)
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore
qt_resource_data = "\
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qt_resource_name = "\
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qt_resource_struct = "\
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def qInitResources():
QtCore.qRegisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
| 65.178645
| 96
| 0.727149
| 53,352
| 220,369
| 3.00313
| 0.005642
| 0.005467
| 0.003764
| 0.001273
| 0.027861
| 0.020315
| 0.018069
| 0.016177
| 0.015073
| 0.01423
| 0
| 0.307738
| 0.015615
| 220,369
| 3,380
| 97
| 65.197929
| 0.430862
| 0.000821
| 0
| 0.012184
| 0
| 0.98841
| 0
| 0
| 0
| 1
| 0.000036
| 0
| 0
| 1
| 0.000594
| false
| 0
| 0.000297
| 0
| 0.000892
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
760372e4e30529c106b939e0952bf1a361ffcd88
| 447
|
py
|
Python
|
manet/utils/__init__.py
|
jonasteuwen/manet-old
|
fb20c98f7e5c89a5ffe89d851ee84e7b65c5e229
|
[
"BSD-2-Clause"
] | 1
|
2021-02-23T04:51:19.000Z
|
2021-02-23T04:51:19.000Z
|
manet/utils/__init__.py
|
jonasteuwen/manet-old
|
fb20c98f7e5c89a5ffe89d851ee84e7b65c5e229
|
[
"BSD-2-Clause"
] | null | null | null |
manet/utils/__init__.py
|
jonasteuwen/manet-old
|
fb20c98f7e5c89a5ffe89d851ee84e7b65c5e229
|
[
"BSD-2-Clause"
] | 1
|
2021-02-23T04:51:20.000Z
|
2021-02-23T04:51:20.000Z
|
# encoding: utf-8
from .file_readers import read_yml, read_json, write_yml, write_json
from .file_readers import read_list, write_list
from .image_readers import read_image, read_dcm, read_dcm_series
from .numpy_utils import prob_round, cast_numpy
from .patch_utils import extract_patch, rebuild_bbox, sym_bbox_from_bbox, sym_bbox_from_point
from .mask_utils import bounding_box, random_mask_idx
from .bbox_utils import _split_bbox, _combine_bbox
| 49.666667
| 93
| 0.854586
| 75
| 447
| 4.64
| 0.44
| 0.126437
| 0.146552
| 0.12069
| 0.143678
| 0
| 0
| 0
| 0
| 0
| 0
| 0.002475
| 0.096197
| 447
| 8
| 94
| 55.875
| 0.858911
| 0.033557
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
763192f1ad2d8081f7146fdc4614a6d7b33f6320
| 38
|
py
|
Python
|
notebooks/_solutions/pandas_06_groupby_operations35.py
|
rprops/Python_DS-WS
|
b2fc449a74be0c82863e5fcf1ddbe7d64976d530
|
[
"BSD-3-Clause"
] | 183
|
2016-08-24T12:32:07.000Z
|
2022-03-26T14:05:04.000Z
|
notebooks/_solutions/pandas_06_groupby_operations35.py
|
rprops/Python_DS-WS
|
b2fc449a74be0c82863e5fcf1ddbe7d64976d530
|
[
"BSD-3-Clause"
] | 100
|
2016-12-15T03:44:06.000Z
|
2022-03-07T08:14:07.000Z
|
notebooks/_solutions/pandas_06_groupby_operations35.py
|
rprops/Python_DS-WS
|
b2fc449a74be0c82863e5fcf1ddbe7d64976d530
|
[
"BSD-3-Clause"
] | 204
|
2016-08-24T14:22:58.000Z
|
2022-03-29T15:09:03.000Z
|
cast.character.value_counts().head(11)
| 38
| 38
| 0.815789
| 6
| 38
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.052632
| 0
| 38
| 1
| 38
| 38
| 0.736842
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
520354de1ae669c8bd5978bbad510ecff100673d
| 132
|
py
|
Python
|
patchcore/samplers/__init__.py
|
bilzard/PatchCore
|
f9061b50bdc0f71661fba9459d2ac379b79d3161
|
[
"MIT"
] | 4
|
2021-07-28T07:15:39.000Z
|
2021-12-27T08:14:18.000Z
|
patchcore/samplers/__init__.py
|
bilzard/PatchCore
|
f9061b50bdc0f71661fba9459d2ac379b79d3161
|
[
"MIT"
] | 2
|
2021-07-08T01:30:58.000Z
|
2021-08-16T15:04:05.000Z
|
patchcore/samplers/__init__.py
|
bilzard/PatchCore
|
f9061b50bdc0f71661fba9459d2ac379b79d3161
|
[
"MIT"
] | 3
|
2021-07-24T18:02:25.000Z
|
2021-09-02T01:37:05.000Z
|
from .k_center_greedy import KCenterGreedy
from .sampling_def import SamplingMethod
__all__ = ["KCenterGreedy", "SamplingMethod"]
| 22
| 45
| 0.818182
| 14
| 132
| 7.214286
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.106061
| 132
| 5
| 46
| 26.4
| 0.855932
| 0
| 0
| 0
| 0
| 0
| 0.204545
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
520a54a7ca42cf43ccf988c942ef36c53d5306cf
| 217
|
py
|
Python
|
03_introduicing_lists/changing_guest_list.py
|
simonhoch/python_basics
|
4ecf12c074e641e3cdeb0a6690846eb9133f96af
|
[
"MIT"
] | null | null | null |
03_introduicing_lists/changing_guest_list.py
|
simonhoch/python_basics
|
4ecf12c074e641e3cdeb0a6690846eb9133f96af
|
[
"MIT"
] | null | null | null |
03_introduicing_lists/changing_guest_list.py
|
simonhoch/python_basics
|
4ecf12c074e641e3cdeb0a6690846eb9133f96af
|
[
"MIT"
] | null | null | null |
guests = ['bobi', 'veli', 'rumen']
for guest in guests :
print ('Hi, ' + guest.title() + ' I invite u.')
guests[0] = 'slavi'
for guest in guests :
print ('Hi, ' + guest.title() + ' I invite u.')
print (len(guests))
| 27.125
| 48
| 0.585253
| 32
| 217
| 3.96875
| 0.5
| 0.125984
| 0.15748
| 0.251969
| 0.645669
| 0.645669
| 0.645669
| 0.645669
| 0.645669
| 0.645669
| 0
| 0.005714
| 0.193548
| 217
| 7
| 49
| 31
| 0.72
| 0
| 0
| 0.571429
| 0
| 0
| 0.230415
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.428571
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
52314d293386bf67b87dafafb29759728b628f27
| 1,042
|
py
|
Python
|
espnet/nets/pytorch_backend/transformer/mask.py
|
creatorscan/espnet-asrtts
|
e516601bd550aeb5d75ee819749c743fc4777eee
|
[
"Apache-2.0"
] | 5
|
2021-04-17T13:12:20.000Z
|
2022-02-22T09:36:45.000Z
|
espnet/nets/pytorch_backend/transformer/mask.py
|
creatorscan/espnet-asrtts
|
e516601bd550aeb5d75ee819749c743fc4777eee
|
[
"Apache-2.0"
] | null | null | null |
espnet/nets/pytorch_backend/transformer/mask.py
|
creatorscan/espnet-asrtts
|
e516601bd550aeb5d75ee819749c743fc4777eee
|
[
"Apache-2.0"
] | 5
|
2020-02-24T08:13:54.000Z
|
2022-02-22T09:03:09.000Z
|
import torch
def subsequent_mask(size, device="cpu", dtype=torch.uint8):
"""Create mask for subsequent steps (1, size, size)
:param int size: size of mask
:param str device: "cpu" or "cuda" or torch.Tensor.device
:param torch.dtype dtype: result dtype
:rtype: torch.Tensor
>>> subsequent_mask(3)
[[1, 0, 0],
[1, 1, 0],
[1, 1, 1]]
"""
ret = torch.ones(size, size, device=device, dtype=dtype)
return torch.tril(ret, out=ret)
def non_subsequent_mask(size, ctxt=-2, device="cpu", dtype=torch.uint8):
"""Create mask for subsequent steps (1, size, size)
:param int size: size of mask
:param str device: "cpu" or "cuda" or torch.Tensor.device
:param torch.dtype dtype: result dtype
:rtype: torch.Tensor
>>> subsequent_mask(3)
[[1, 0, 0],
[1, 1, 0],
[1, 1, 1]]
"""
ones = torch.ones(size, size, device=device, dtype=dtype)
ltri_ = torch.tril(ones, diagonal=0)
ltri_d = torch.tril(ones, diagonal=ctxt)
ret = ltri_ - ltri_d
return ret
| 28.944444
| 72
| 0.621881
| 156
| 1,042
| 4.096154
| 0.230769
| 0.075117
| 0.018779
| 0.059468
| 0.735524
| 0.735524
| 0.735524
| 0.735524
| 0.613459
| 0.613459
| 0
| 0.032419
| 0.230326
| 1,042
| 35
| 73
| 29.771429
| 0.764339
| 0.492322
| 0
| 0
| 0
| 0
| 0.013514
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.1
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
525c9c63b594deda2fb947a13a7a6f915b9e021f
| 120
|
py
|
Python
|
user_app/apps.py
|
CarliJoy/RoWoOekostromDB
|
dc4602a537cc54c0642b0865a8376317311deb3f
|
[
"MIT"
] | null | null | null |
user_app/apps.py
|
CarliJoy/RoWoOekostromDB
|
dc4602a537cc54c0642b0865a8376317311deb3f
|
[
"MIT"
] | 5
|
2020-02-10T15:49:58.000Z
|
2021-11-09T10:15:18.000Z
|
user_app/apps.py
|
CarliJoy/RoWoOekostromDB
|
dc4602a537cc54c0642b0865a8376317311deb3f
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class UserAppConfig(AppConfig):
name = "user_app"
verbose_name = "User App"
| 17.142857
| 33
| 0.725
| 15
| 120
| 5.666667
| 0.733333
| 0.188235
| 0.258824
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.191667
| 120
| 6
| 34
| 20
| 0.876289
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 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
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
526801f435effb095db8df5817610d8652a9f492
| 117
|
py
|
Python
|
src/python/vcoco_eval_img_features.py
|
tengyu-liu/Part-GPNN
|
d2092917edeee835fc1888101dddf42ad76ec5e5
|
[
"MIT"
] | null | null | null |
src/python/vcoco_eval_img_features.py
|
tengyu-liu/Part-GPNN
|
d2092917edeee835fc1888101dddf42ad76ec5e5
|
[
"MIT"
] | null | null | null |
src/python/vcoco_eval_img_features.py
|
tengyu-liu/Part-GPNN
|
d2092917edeee835fc1888101dddf42ad76ec5e5
|
[
"MIT"
] | null | null | null |
import os
import pickle
import numpy as np
for fn in os.listdir('/mnt/hdd-12t/share/v-coco/processed/resnet/'):
| 19.5
| 68
| 0.726496
| 21
| 117
| 4.047619
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.02
| 0.145299
| 117
| 6
| 69
| 19.5
| 0.83
| 0
| 0
| 0
| 0
| 0
| 0.364407
| 0.364407
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.75
| null | null | 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
5276a315db9fc4d457da8f8a3ceec3afef538d38
| 430
|
py
|
Python
|
tethysext/atcore/tests/integrated_tests/controllers/resource_workflows/workflow_view_tests/__init__.py
|
Aquaveo/tethysext-atcore
|
7a83ccea24fdbbe806f12154f938554dd6c8015f
|
[
"BSD-3-Clause"
] | 3
|
2020-11-05T23:50:47.000Z
|
2021-02-26T21:43:29.000Z
|
tethysext/atcore/tests/integrated_tests/controllers/resource_workflows/workflow_view_tests/__init__.py
|
Aquaveo/tethysext-atcore
|
7a83ccea24fdbbe806f12154f938554dd6c8015f
|
[
"BSD-3-Clause"
] | 7
|
2020-10-29T16:53:49.000Z
|
2021-05-07T19:46:47.000Z
|
tethysext/atcore/tests/integrated_tests/controllers/resource_workflows/workflow_view_tests/__init__.py
|
Aquaveo/tethysext-atcore
|
7a83ccea24fdbbe806f12154f938554dd6c8015f
|
[
"BSD-3-Clause"
] | null | null | null |
"""
********************************************************************************
* Name: lock_methods_tests.py
* Author: nswain
* Created On: September 23, 2019
* Copyright: (c) Aquaveo 2019
********************************************************************************
"""
from .base_methods_tests import WorkflowViewBaseMethodsTests # noqa: F401
from .lock_methods_tests import WorkflowViewLockMethodsTests # noqa: F401
| 39.090909
| 80
| 0.486047
| 32
| 430
| 6.34375
| 0.6875
| 0.17734
| 0.157635
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.040712
| 0.086047
| 430
| 10
| 81
| 43
| 0.475827
| 0.683721
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| 1
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| true
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| null | 0
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| 0
| 0
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| 0
| 0
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| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
5280c65d2ad8697416e3ac7fb00bd2a208de2144
| 279
|
py
|
Python
|
ramda/drop_repeats_with_test.py
|
jakobkolb/ramda.py
|
982b2172f4bb95b9a5b09eff8077362d6f2f0920
|
[
"MIT"
] | 56
|
2018-08-06T08:44:58.000Z
|
2022-03-17T09:49:03.000Z
|
ramda/drop_repeats_with_test.py
|
jakobkolb/ramda.py
|
982b2172f4bb95b9a5b09eff8077362d6f2f0920
|
[
"MIT"
] | 28
|
2019-06-17T11:09:52.000Z
|
2022-02-18T16:59:21.000Z
|
ramda/drop_repeats_with_test.py
|
jakobkolb/ramda.py
|
982b2172f4bb95b9a5b09eff8077362d6f2f0920
|
[
"MIT"
] | 5
|
2019-09-18T09:24:38.000Z
|
2021-07-21T08:40:23.000Z
|
from ramda.drop_repeats_with import drop_repeats_with
from ramda.private.asserts import *
from ramda.eq_by import eq_by
list = [1, -1, 1, 3, 4, -4, -4, -5, 5, 3, 3]
def drop_repeats_with_nocurry_test():
assert_equal(drop_repeats_with(eq_by(abs), list), [1, 3, 4, -5, 3])
| 25.363636
| 71
| 0.706093
| 53
| 279
| 3.45283
| 0.396226
| 0.240437
| 0.327869
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.067511
| 0.150538
| 279
| 10
| 72
| 27.9
| 0.704641
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.166667
| false
| 0
| 0.5
| 0
| 0.666667
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
5292414fd10c1cb673ca0f75623a6cf80e88d429
| 8,335
|
py
|
Python
|
mayan/apps/acls/tests/test_api.py
|
Syunkolee9891/Mayan-EDMS
|
3759a9503a264a180b74cc8518388f15ca66ac1a
|
[
"Apache-2.0"
] | 1
|
2021-06-17T18:24:25.000Z
|
2021-06-17T18:24:25.000Z
|
mayan/apps/acls/tests/test_api.py
|
Syunkolee9891/Mayan-EDMS
|
3759a9503a264a180b74cc8518388f15ca66ac1a
|
[
"Apache-2.0"
] | 7
|
2020-06-06T00:01:04.000Z
|
2022-01-13T01:47:17.000Z
|
mayan/apps/acls/tests/test_api.py
|
Syunkolee9891/Mayan-EDMS
|
3759a9503a264a180b74cc8518388f15ca66ac1a
|
[
"Apache-2.0"
] | null | null | null |
from __future__ import absolute_import, unicode_literals
from rest_framework import status
from mayan.apps.permissions.tests.literals import TEST_ROLE_LABEL
from mayan.apps.rest_api.tests import BaseAPITestCase
from ..models import AccessControlList
from ..permissions import permission_acl_edit, permission_acl_view
from .mixins import ACLTestMixin
class ACLAPITestCase(ACLTestMixin, BaseAPITestCase):
auto_create_test_object = True
def _request_acl_create_api_view(self, extra_data=None):
data = {'role_pk': self.test_role.pk}
if extra_data:
data.update(extra_data)
return self.post(
viewname='rest_api:accesscontrollist-list',
kwargs=self.test_content_object_view_kwargs, data=data
)
def test_acl_create_api_api_view_with_access(self):
self.grant_access(obj=self.test_object, permission=permission_acl_edit)
response = self._request_acl_create_api_view()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
pk = response.data['id']
test_object_acl = self.test_object.acls.get(pk=pk)
self.assertEqual(
test_object_acl.role, self.test_role
)
self.assertEqual(
test_object_acl.content_object, self.test_object
)
self.assertEqual(
test_object_acl.permissions.count(), 0
)
def test_acl_create_post_api_extra_data_view_with_access(self):
self.grant_access(obj=self.test_object, permission=permission_acl_edit)
response = self._request_acl_create_api_view(
extra_data={'permissions_pk_list': permission_acl_view.pk}
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
pk = response.data['id']
test_object_acl = self.test_object.acls.get(pk=pk)
self.assertEqual(
test_object_acl.content_object, self.test_object
)
self.assertEqual(
test_object_acl.role, self.test_role
)
self.assertEqual(
test_object_acl.permissions.first(),
permission_acl_view.stored_permission
)
def _request_test_acl_delete_api_view(self):
return self.delete(
viewname='rest_api:accesscontrollist-detail', kwargs={
'app_label': self.test_object_content_type.app_label,
'model': self.test_object_content_type.model,
'object_id': self.test_object.pk,
'pk': self.test_acl.pk
}
)
def test_acl_delete_api_view_with_access(self):
self.expected_content_type = None
self._create_test_acl()
self.grant_access(self.test_object, permission=permission_acl_edit)
acl_count = AccessControlList.objects.count()
response = self._request_test_acl_delete_api_view()
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
self.assertEqual(AccessControlList.objects.count(), acl_count - 1)
def _request_test_acl_permission_delete_api_view(self):
return self.delete(
viewname='rest_api:accesscontrollist-permission-detail', kwargs={
'app_label': self.test_object_content_type.app_label,
'model': self.test_object_content_type.model,
'object_id': self.test_object.pk,
'pk': self.test_acl.pk,
'permission_pk': self.test_permission.stored_permission.pk
}
)
def test_acl_permission_delete_view_with_access(self):
self.expected_content_type = None
self._create_test_acl()
self.test_acl.permissions.add(self.test_permission.stored_permission)
self.grant_access(obj=self.test_object, permission=permission_acl_edit)
response = self._request_test_acl_permission_delete_api_view()
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
self.assertEqual(self.test_acl.permissions.count(), 0)
def test_acl_detail_api_view_with_access(self):
self._create_test_acl()
self.grant_access(obj=self.test_object, permission=permission_acl_view)
response = self._request_test_acl_detail_api_view()
self.assertEqual(
response.data['content_type']['app_label'],
self.test_object_content_type.app_label
)
self.assertEqual(
response.data['role']['label'], TEST_ROLE_LABEL
)
def _request_test_acl_permission_detail_api_view(self):
return self.get(
viewname='rest_api:accesscontrollist-permission-detail', kwargs={
'app_label': self.test_object_content_type.app_label,
'model': self.test_object_content_type.model,
'object_id': self.test_object.pk,
'pk': self.test_acl.pk,
'permission_pk': self.test_acl.permissions.first().pk
}
)
def test_acl_permission_detail_api_view_with_access(self):
self._create_test_acl()
self.test_acl.permissions.add(self.test_permission.stored_permission)
self.grant_access(obj=self.test_object, permission=permission_acl_view)
response = self._request_test_acl_permission_detail_api_view()
self.assertEqual(
response.data['pk'], self.test_permission.pk
)
def test_acl_list_api_view_with_access(self):
self._create_test_acl()
self.grant_access(obj=self.test_object, permission=permission_acl_view)
response = self.get(
viewname='rest_api:accesscontrollist-list', kwargs={
'app_label': self.test_object_content_type.app_label,
'model': self.test_object_content_type.model,
'object_id': self.test_object.pk
}
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertContains(
response=response, text=self.test_object_content_type.app_label,
status_code=200
)
self.assertContains(
response=response, text=self.test_acl.role.label,
status_code=200
)
def _request_test_acl_detail_api_view(self):
return self.get(
viewname='rest_api:accesscontrollist-detail', kwargs={
'app_label': self.test_object_content_type.app_label,
'model': self.test_object_content_type.model,
'object_id': self.test_object.pk,
'pk': self.test_acl.pk
}
)
def _request_test_acl_permission_list_api_get_view(self):
return self.get(
viewname='rest_api:accesscontrollist-permission-list', kwargs={
'app_label': self.test_object_content_type.app_label,
'model': self.test_object_content_type.model,
'object_id': self.test_object.pk,
'pk': self.test_acl.pk
}
)
def test_acl_permission_list_api_get_view_with_access(self):
self._create_test_acl()
self.test_acl.permissions.add(self.test_permission.stored_permission)
self.grant_access(obj=self.test_object, permission=permission_acl_view)
response = self._request_test_acl_permission_list_api_get_view()
self.assertEqual(
response.data['results'][0]['pk'],
self.test_permission.pk
)
def _request_acl_permssion_list_api_post_view(self):
return self.post(
viewname='rest_api:accesscontrollist-permission-list', kwargs={
'app_label': self.test_object_content_type.app_label,
'model': self.test_object_content_type.model,
'object_id': self.test_object.pk,
'pk': self.test_acl.pk
}, data={'permission_pk': self.test_permission.pk}
)
def test_acl_permission_list_api_post_view_with_access(self):
self._create_test_acl()
self.grant_access(obj=self.test_object, permission=permission_acl_edit)
response = self._request_acl_permssion_list_api_post_view()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertTrue(
self.test_permission.stored_permission in self.test_acl.permissions.all()
)
| 36.880531
| 85
| 0.668146
| 1,004
| 8,335
| 5.126494
| 0.083665
| 0.094813
| 0.097921
| 0.065281
| 0.83369
| 0.809015
| 0.777346
| 0.694191
| 0.645813
| 0.627744
| 0
| 0.004449
| 0.244991
| 8,335
| 225
| 86
| 37.044444
| 0.813444
| 0
| 0
| 0.485876
| 0
| 0
| 0.069946
| 0.035993
| 0
| 0
| 0
| 0
| 0.118644
| 1
| 0.090395
| false
| 0
| 0.039548
| 0.033898
| 0.180791
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
874376cc51d821f063a191c4591ae6f9bf4f69a4
| 122
|
py
|
Python
|
src/RGT/gridMng/error/wrongGridType.py
|
danrg/RGT-tool
|
115ba9a93686595699b3e182958921b3d60382b3
|
[
"MIT"
] | 7
|
2015-02-09T12:12:04.000Z
|
2019-03-31T08:23:36.000Z
|
src/RGT/gridMng/error/wrongGridType.py
|
danrg/RGT-tool
|
115ba9a93686595699b3e182958921b3d60382b3
|
[
"MIT"
] | 3
|
2015-07-05T20:49:14.000Z
|
2017-07-03T19:45:18.000Z
|
src/RGT/gridMng/error/wrongGridType.py
|
danrg/RGT-tool
|
115ba9a93686595699b3e182958921b3d60382b3
|
[
"MIT"
] | 1
|
2021-03-23T14:01:22.000Z
|
2021-03-23T14:01:22.000Z
|
class WrongGridType(Exception):
def __init__(self):
Exception.__init__(self, 'Unexpected type grid found')
| 30.5
| 63
| 0.704918
| 13
| 122
| 6
| 0.769231
| 0.205128
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.196721
| 122
| 3
| 64
| 40.666667
| 0.795918
| 0
| 0
| 0
| 0
| 0
| 0.218487
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
875058f015c51f89c93ceba7e63b659b8ec13350
| 85
|
py
|
Python
|
Rapid/apps.py
|
AeroYoung/PyCSE
|
4cc73bf8d0b949c0225acd44fd32f992135a5ca1
|
[
"MIT"
] | null | null | null |
Rapid/apps.py
|
AeroYoung/PyCSE
|
4cc73bf8d0b949c0225acd44fd32f992135a5ca1
|
[
"MIT"
] | null | null | null |
Rapid/apps.py
|
AeroYoung/PyCSE
|
4cc73bf8d0b949c0225acd44fd32f992135a5ca1
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class RapidConfig(AppConfig):
name = 'Rapid'
| 14.166667
| 33
| 0.741176
| 10
| 85
| 6.3
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176471
| 85
| 5
| 34
| 17
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
87571fc32061f81a4348985387a2c78bb483cf86
| 20
|
py
|
Python
|
src/test.py
|
Leoyll/PythonDemo
|
e3e8e9110a31c65aa6c766e6a6528bf1829592f2
|
[
"MIT"
] | null | null | null |
src/test.py
|
Leoyll/PythonDemo
|
e3e8e9110a31c65aa6c766e6a6528bf1829592f2
|
[
"MIT"
] | null | null | null |
src/test.py
|
Leoyll/PythonDemo
|
e3e8e9110a31c65aa6c766e6a6528bf1829592f2
|
[
"MIT"
] | null | null | null |
a = []
print(len(a))
| 10
| 13
| 0.5
| 4
| 20
| 2.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15
| 20
| 2
| 13
| 10
| 0.588235
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
5e5f2714d72c0443d5b96f20edfd69765ffcc829
| 741
|
py
|
Python
|
workflow/torch/__init__.py
|
Aiwizo/ml-workflow
|
88e104fce571dd3b76914626a52f9001342c07cc
|
[
"Apache-2.0"
] | 4
|
2020-09-23T15:39:24.000Z
|
2021-09-12T22:11:00.000Z
|
workflow/torch/__init__.py
|
Aiwizo/ml-workflow
|
88e104fce571dd3b76914626a52f9001342c07cc
|
[
"Apache-2.0"
] | 4
|
2020-09-23T15:07:39.000Z
|
2020-10-30T10:26:24.000Z
|
workflow/torch/__init__.py
|
Aiwizo/ml-workflow
|
88e104fce571dd3b76914626a52f9001342c07cc
|
[
"Apache-2.0"
] | null | null | null |
from workflow.torch.is_float import is_float
from workflow.torch.get_conv_output_size import get_conv_output_size
from workflow.torch.get_model_summary import get_model_summary
from workflow.torch.initialize_weights import initialize_weights
from workflow.torch.module_device import module_device
from workflow.torch.module_compose import ModuleCompose
from workflow.torch.to_device import to_device
from workflow.torch.to_shapes import to_shapes
from workflow.torch.module_train import module_train, module_eval
from workflow.torch.requires_grad import requires_grad, requires_nograd
from workflow.torch.set_learning_rate import set_learning_rate
from workflow.torch.set_seeds import set_seeds
# from datastream import Dataset, Datastream
| 49.4
| 71
| 0.88529
| 112
| 741
| 5.5625
| 0.276786
| 0.23114
| 0.327448
| 0.110754
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 741
| 14
| 72
| 52.928571
| 0.910819
| 0.05668
| 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
| 0
| 0
|
0
| 4
|
5e89a8e6a0ec862c2351cf963599c839a438c337
| 204
|
py
|
Python
|
app/app/api/domain/services/factories/UserQueryRepositoryFactory.py
|
GPortas/Playgroundb
|
60f98a4dd62ce34fbb8abfa0d9ee63697e82c57e
|
[
"Apache-2.0"
] | 1
|
2019-01-30T19:59:20.000Z
|
2019-01-30T19:59:20.000Z
|
app/app/api/domain/services/factories/UserQueryRepositoryFactory.py
|
GPortas/Playgroundb
|
60f98a4dd62ce34fbb8abfa0d9ee63697e82c57e
|
[
"Apache-2.0"
] | null | null | null |
app/app/api/domain/services/factories/UserQueryRepositoryFactory.py
|
GPortas/Playgroundb
|
60f98a4dd62ce34fbb8abfa0d9ee63697e82c57e
|
[
"Apache-2.0"
] | null | null | null |
from app.api.data.query.UserMongoQueryRepository import UserMongoQueryRepository
class UserQueryRepositoryFactory:
def create_user_query_repository(self):
return UserMongoQueryRepository()
| 25.5
| 80
| 0.828431
| 18
| 204
| 9.222222
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122549
| 204
| 7
| 81
| 29.142857
| 0.927374
| 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
| 0
| 0
|
0
| 4
|
5ebd17245068ee6b0f0d2f024ba12a90ad465516
| 60
|
py
|
Python
|
malware.py
|
Dani-Hacker/Malware
|
414aedefe239f8038b2fc3dd29fee080f5a13dd3
|
[
"MIT"
] | 1
|
2021-12-07T14:29:59.000Z
|
2021-12-07T14:29:59.000Z
|
malware.py
|
Dani-Hacker/Malware
|
414aedefe239f8038b2fc3dd29fee080f5a13dd3
|
[
"MIT"
] | 1
|
2021-12-08T10:13:06.000Z
|
2021-12-09T05:14:50.000Z
|
malware.py
|
Dani-Hacker/Malware
|
414aedefe239f8038b2fc3dd29fee080f5a13dd3
|
[
"MIT"
] | null | null | null |
import os
while True:
os.startfile(__file__[:-2]+"exe")
| 15
| 37
| 0.666667
| 9
| 60
| 4
| 0.888889
| 0
| 0
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| 0
| 0.019608
| 0.15
| 60
| 3
| 38
| 20
| 0.686275
| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
0d5cfe7aa495ba4791706f0a12f08ca67bd93385
| 145
|
py
|
Python
|
wagtailpurge/testapp/utils.py
|
ababic/wagtail-purge
|
9f48b1fa11f738fc11b6f3b708190daef3e80926
|
[
"MIT"
] | null | null | null |
wagtailpurge/testapp/utils.py
|
ababic/wagtail-purge
|
9f48b1fa11f738fc11b6f3b708190daef3e80926
|
[
"MIT"
] | 2
|
2021-08-24T18:38:43.000Z
|
2021-08-24T18:43:25.000Z
|
wagtailpurge/testapp/utils.py
|
ababic/wagtailpurge
|
9f48b1fa11f738fc11b6f3b708190daef3e80926
|
[
"MIT"
] | null | null | null |
from wagtail.contrib.frontend_cache.backends import BaseBackend
class DummyFECacheBackend(BaseBackend):
def purge(self, url):
pass
| 20.714286
| 63
| 0.765517
| 16
| 145
| 6.875
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.165517
| 145
| 6
| 64
| 24.166667
| 0.909091
| 0
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| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0.25
| 0.25
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| 0.75
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| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
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| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
0d6d8ea483ade75b47cd28d283d0b2a7f25d772c
| 92
|
py
|
Python
|
src/cities/admin.py
|
Potisin/find_route
|
56408714ab70d8d58cbdfe9eccfea95d5918d355
|
[
"BSD-3-Clause"
] | null | null | null |
src/cities/admin.py
|
Potisin/find_route
|
56408714ab70d8d58cbdfe9eccfea95d5918d355
|
[
"BSD-3-Clause"
] | null | null | null |
src/cities/admin.py
|
Potisin/find_route
|
56408714ab70d8d58cbdfe9eccfea95d5918d355
|
[
"BSD-3-Clause"
] | null | null | null |
from django.contrib import admin
from cities.models import City
admin.site.register(City)
| 15.333333
| 32
| 0.815217
| 14
| 92
| 5.357143
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119565
| 92
| 5
| 33
| 18.4
| 0.925926
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
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| 0.666667
| 0
| 0.666667
| 0
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| null | 0
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| null | 0
| 0
| 0
| 0
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| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
0da32519dab6bb27b67bea9f06ef24b51336373c
| 37
|
py
|
Python
|
custom_components/dhmz/__init__.py
|
kpisacic/DHMZ-home-assistant-custom-component
|
d8b2177c7190a659eedc4142d212b26862895d88
|
[
"MIT"
] | 4
|
2020-09-26T09:21:28.000Z
|
2022-01-08T21:21:40.000Z
|
custom_components/dhmz/__init__.py
|
kpisacic/DHMZ-home-assistant-custom-component
|
d8b2177c7190a659eedc4142d212b26862895d88
|
[
"MIT"
] | 7
|
2020-09-18T09:50:05.000Z
|
2022-03-12T11:25:25.000Z
|
custom_components/dhmz/__init__.py
|
kpisacic/DHMZ-home-assistant-custom-component
|
d8b2177c7190a659eedc4142d212b26862895d88
|
[
"MIT"
] | null | null | null |
"""A component for DHMZ weather."""
| 18.5
| 36
| 0.648649
| 5
| 37
| 4.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.162162
| 37
| 1
| 37
| 37
| 0.774194
| 0.783784
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0dabbf737a9949626eeabdb65d345adcf41990c8
| 169
|
py
|
Python
|
version.py
|
Vinen88/CMPUT404_Lab1
|
7472dcb4413de0aef68ba0739beaebf710649921
|
[
"MIT"
] | null | null | null |
version.py
|
Vinen88/CMPUT404_Lab1
|
7472dcb4413de0aef68ba0739beaebf710649921
|
[
"MIT"
] | null | null | null |
version.py
|
Vinen88/CMPUT404_Lab1
|
7472dcb4413de0aef68ba0739beaebf710649921
|
[
"MIT"
] | null | null | null |
import requests
r = requests.get('https://raw.githubusercontent.com/Vinen88/CMPUT404_Lab1/main/version.py')
print(r.text)
print("Requests version:",requests.__version__)
| 42.25
| 91
| 0.804734
| 23
| 169
| 5.695652
| 0.695652
| 0.229008
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.037037
| 0.04142
| 169
| 4
| 92
| 42.25
| 0.771605
| 0
| 0
| 0
| 0
| 0
| 0.517647
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 0
| 0.25
| 0.5
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
0db08c870d9ac5dc35e8f6feac607e3f0bcb1eef
| 251
|
py
|
Python
|
torchfes/opt/utils/__init__.py
|
AkihideHayashi/torchfes1
|
83f01525e6071ffd7a884c8e108f9c25ba2b009b
|
[
"MIT"
] | null | null | null |
torchfes/opt/utils/__init__.py
|
AkihideHayashi/torchfes1
|
83f01525e6071ffd7a884c8e108f9c25ba2b009b
|
[
"MIT"
] | null | null | null |
torchfes/opt/utils/__init__.py
|
AkihideHayashi/torchfes1
|
83f01525e6071ffd7a884c8e108f9c25ba2b009b
|
[
"MIT"
] | null | null | null |
# flake8: noqa
from .derivative import (hessian, jacobian,
set_directional_gradient, set_directional_hessian,
set_hessian)
from .generalize import generalize, Lagrangian
from .linalg import solve, dot
| 35.857143
| 75
| 0.665339
| 25
| 251
| 6.48
| 0.6
| 0.17284
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005556
| 0.282869
| 251
| 6
| 76
| 41.833333
| 0.894444
| 0.047809
| 0
| 0
| 0
| 0
| 0
| 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
| 0
| 0
|
0
| 4
|
217111c4f240baefe032315e86bf953b9e6392a1
| 1,439
|
py
|
Python
|
test/test_remote_image_service_api.py
|
stanionascu/python-embyapi
|
a3f7aa49aea4052277cc43605c0d89bc6ff21913
|
[
"BSD-3-Clause"
] | null | null | null |
test/test_remote_image_service_api.py
|
stanionascu/python-embyapi
|
a3f7aa49aea4052277cc43605c0d89bc6ff21913
|
[
"BSD-3-Clause"
] | null | null | null |
test/test_remote_image_service_api.py
|
stanionascu/python-embyapi
|
a3f7aa49aea4052277cc43605c0d89bc6ff21913
|
[
"BSD-3-Clause"
] | null | null | null |
# coding: utf-8
"""
Emby Server API
Explore the Emby Server API # noqa: E501
OpenAPI spec version: 4.1.1.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import unittest
import embyapi
from embyapi.api.remote_image_service_api import RemoteImageServiceApi # noqa: E501
from embyapi.rest import ApiException
class TestRemoteImageServiceApi(unittest.TestCase):
"""RemoteImageServiceApi unit test stubs"""
def setUp(self):
self.api = RemoteImageServiceApi() # noqa: E501
def tearDown(self):
pass
def test_get_images_remote(self):
"""Test case for get_images_remote
Gets a remote image # noqa: E501
"""
pass
def test_get_items_by_id_remoteimages(self):
"""Test case for get_items_by_id_remoteimages
Gets available remote images for an item # noqa: E501
"""
pass
def test_get_items_by_id_remoteimages_providers(self):
"""Test case for get_items_by_id_remoteimages_providers
Gets available remote image providers for an item # noqa: E501
"""
pass
def test_post_items_by_id_remoteimages_download(self):
"""Test case for post_items_by_id_remoteimages_download
Downloads a remote image for an item # noqa: E501
"""
pass
if __name__ == '__main__':
unittest.main()
| 23.209677
| 84
| 0.678944
| 182
| 1,439
| 5.082418
| 0.362637
| 0.060541
| 0.058378
| 0.136216
| 0.35027
| 0.330811
| 0.217297
| 0.217297
| 0.177297
| 0.092973
| 0
| 0.024052
| 0.248784
| 1,439
| 61
| 85
| 23.590164
| 0.831637
| 0.43016
| 0
| 0.25
| 1
| 0
| 0.011478
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.3
| false
| 0.25
| 0.25
| 0
| 0.6
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 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
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
217386449ccf5520d27421e33e49129ce391ad98
| 25
|
py
|
Python
|
patchflow/__init__.py
|
rnogueras/patchflow
|
f7409ca60d33a72a418b52f3371ff75795180db4
|
[
"Apache-2.0"
] | null | null | null |
patchflow/__init__.py
|
rnogueras/patchflow
|
f7409ca60d33a72a418b52f3371ff75795180db4
|
[
"Apache-2.0"
] | 1
|
2021-12-31T20:23:25.000Z
|
2022-01-01T16:12:41.000Z
|
patchflow/__init__.py
|
rnogueras/patchflow
|
f7409ca60d33a72a418b52f3371ff75795180db4
|
[
"Apache-2.0"
] | null | null | null |
"""Package patchflow."""
| 12.5
| 24
| 0.64
| 2
| 25
| 8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08
| 25
| 1
| 25
| 25
| 0.695652
| 0.72
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
219a97eaab7f6d8b62daaa6a48705b0170485008
| 93
|
py
|
Python
|
testapp/auditavel/apps.py
|
luzfcb/versionamento_testes
|
b9982663a1e11b320d6565c4ffeb6acdd870f77e
|
[
"MIT"
] | null | null | null |
testapp/auditavel/apps.py
|
luzfcb/versionamento_testes
|
b9982663a1e11b320d6565c4ffeb6acdd870f77e
|
[
"MIT"
] | null | null | null |
testapp/auditavel/apps.py
|
luzfcb/versionamento_testes
|
b9982663a1e11b320d6565c4ffeb6acdd870f77e
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class AuditavelConfig(AppConfig):
name = 'auditavel'
| 15.5
| 33
| 0.763441
| 10
| 93
| 7.1
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16129
| 93
| 5
| 34
| 18.6
| 0.910256
| 0
| 0
| 0
| 0
| 0
| 0.097826
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
21c95b5bab3cc95882d2d370e594c1a6bf653850
| 103
|
py
|
Python
|
panadora_problems/problem_1.py
|
loftwah/Daily-Coding-Problem
|
0327f0b4f69ef419436846c831110795c7a3c1fe
|
[
"MIT"
] | 129
|
2018-10-14T17:52:29.000Z
|
2022-01-29T15:45:57.000Z
|
panadora_problems/problem_1.py
|
loftwah/Daily-Coding-Problem
|
0327f0b4f69ef419436846c831110795c7a3c1fe
|
[
"MIT"
] | 2
|
2019-11-30T23:28:23.000Z
|
2020-01-03T16:30:32.000Z
|
panadora_problems/problem_1.py
|
loftwah/Daily-Coding-Problem
|
0327f0b4f69ef419436846c831110795c7a3c1fe
|
[
"MIT"
] | 60
|
2019-02-21T09:18:31.000Z
|
2022-03-25T21:01:04.000Z
|
"""This problem was asked by Pandora.
Given an undirected graph, determine if it contains a cycle.
"""
| 25.75
| 60
| 0.747573
| 16
| 103
| 4.8125
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.165049
| 103
| 4
| 61
| 25.75
| 0.895349
| 0.932039
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
21d10f3698a9935277f690b2621f426ce6e1cd25
| 1,764
|
py
|
Python
|
scripts/mell/losses/kd_loss.py
|
zhenhua32/EasyTransfer
|
07940087b80b7dc001fb688c81d9420a2055a2bd
|
[
"Apache-2.0"
] | 806
|
2020-09-02T03:05:24.000Z
|
2022-03-26T03:45:23.000Z
|
scripts/mell/losses/kd_loss.py
|
zhenhua32/EasyTransfer
|
07940087b80b7dc001fb688c81d9420a2055a2bd
|
[
"Apache-2.0"
] | 48
|
2020-09-16T12:53:32.000Z
|
2022-03-09T09:34:44.000Z
|
scripts/mell/losses/kd_loss.py
|
zhenhua32/EasyTransfer
|
07940087b80b7dc001fb688c81d9420a2055a2bd
|
[
"Apache-2.0"
] | 151
|
2020-09-16T12:31:06.000Z
|
2022-03-24T08:51:47.000Z
|
import torch
def soft_cross_entropy(input, targets):
""" Soft Cross Entropy loss for hinton's dark knowledge
Args:
input (`Tensor`): shape of [None, N]
targets (`Tensor`): shape of [None, N]
Returns:
loss (`Tensor`): scalar tensor
"""
student_likelihood = torch.nn.functional.log_softmax(input, dim=-1)
targets_prob = torch.nn.functional.softmax(targets, dim=-1)
return (- targets_prob * student_likelihood).sum(dim=-1).mean()
def soft_cross_entropy_tinybert(input, targets):
""" Soft Cross Entropy loss for hinton's dark knowledge
Args:
input (`Tensor`): shape of [None, N]
targets (`Tensor`): shape of [None, N]
Returns:
loss (`Tensor`): scalar tensor
"""
student_likelihood = torch.nn.functional.log_softmax(input, dim=-1)
targets_prob = torch.nn.functional.softmax(targets, dim=-1)
return (- targets_prob * student_likelihood).mean()
def soft_kl_div_loss(input, targets, reduction="batchmean", **kwargs):
student_likelihood = torch.nn.functional.log_softmax(input, dim=-1)
targets_prob = torch.nn.functional.softmax(targets, dim=-1)
return torch.nn.functional.kl_div(student_likelihood, targets_prob, reduction=reduction, **kwargs)
def mse_loss(inputs, targets, **kwargs):
""" MSE loss """
return torch.nn.functional.mse_loss(inputs, targets, **kwargs)
def soft_input_mse_loss(inputs, targets, **kwargs):
targets = torch.softmax(targets, dim=-1)
return torch.nn.functional.mse_loss(inputs, targets, **kwargs)
def cosine_embedding_loss(input1, input2, target, **kwargs):
return torch.nn.functional.cosine_embedding_loss(input1, input2, target, reduction="mean", **kwargs)
| 36
| 104
| 0.681406
| 225
| 1,764
| 5.191111
| 0.208889
| 0.059932
| 0.145548
| 0.058219
| 0.777397
| 0.732877
| 0.669521
| 0.669521
| 0.639555
| 0.639555
| 0
| 0.00838
| 0.188209
| 1,764
| 49
| 104
| 36
| 0.807263
| 0.21712
| 0
| 0.4
| 0
| 0
| 0.010212
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.3
| false
| 0
| 0.05
| 0.05
| 0.65
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
21d287b47d7b558ed27bfd84398db195dc0a259f
| 609
|
py
|
Python
|
src/parmatter/parmatters/base.py
|
Ricyteach/parmatter
|
4eb3bd500c0aaa4331c114aac5a020d7a6327363
|
[
"BSD-2-Clause"
] | null | null | null |
src/parmatter/parmatters/base.py
|
Ricyteach/parmatter
|
4eb3bd500c0aaa4331c114aac5a020d7a6327363
|
[
"BSD-2-Clause"
] | null | null | null |
src/parmatter/parmatters/base.py
|
Ricyteach/parmatter
|
4eb3bd500c0aaa4331c114aac5a020d7a6327363
|
[
"BSD-2-Clause"
] | null | null | null |
'''Base class for custom parmatters. The `args_parse` method is provided
to be overridden in case it is more convenient to pass arguments in a
manner different from the initializer signature (capability used in the
format_group module).'''
from ..parmatter import Parmatter
class ArgsParseMixin():
'''Provides default arg parsing behavior.'''
@staticmethod
def args_parse(*args):
return args, {}
class ParmatterBase(Parmatter, ArgsParseMixin):
'''A modified parsing formatter with an args_parse method.
Use to modify signature of arguments sent to initializer.'''
pass
| 35.823529
| 72
| 0.740558
| 79
| 609
| 5.658228
| 0.658228
| 0.060403
| 0.067114
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.187192
| 609
| 17
| 73
| 35.823529
| 0.90303
| 0.635468
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| true
| 0.142857
| 0.142857
| 0.142857
| 0.714286
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
21ee91f40c5b5b36dd1e86024097dbbafee81fbb
| 91
|
py
|
Python
|
dataAnalytics/utils.py
|
abiraihan/spatialDataScience
|
079e5ccff7d94d6eee807a37ceb094ad22852ca6
|
[
"MIT"
] | null | null | null |
dataAnalytics/utils.py
|
abiraihan/spatialDataScience
|
079e5ccff7d94d6eee807a37ceb094ad22852ca6
|
[
"MIT"
] | null | null | null |
dataAnalytics/utils.py
|
abiraihan/spatialDataScience
|
079e5ccff7d94d6eee807a37ceb094ad22852ca6
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 29 14:19:14 2021
@author: ABIR RAIHAN
"""
| 11.375
| 35
| 0.582418
| 15
| 91
| 3.533333
| 0.933333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.180556
| 0.208791
| 91
| 7
| 36
| 13
| 0.555556
| 0.879121
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
1d0a264b7cbde4dbe3acc2f61bc6d5ae2f39060d
| 100
|
py
|
Python
|
test/unittests/analysis_osr.py
|
aisk/pyston
|
ac69cfef0621dbc8901175e84fa2b5cb5781a646
|
[
"BSD-2-Clause",
"Apache-2.0"
] | 1
|
2020-02-06T14:28:45.000Z
|
2020-02-06T14:28:45.000Z
|
test/unittests/analysis_osr.py
|
aisk/pyston
|
ac69cfef0621dbc8901175e84fa2b5cb5781a646
|
[
"BSD-2-Clause",
"Apache-2.0"
] | null | null | null |
test/unittests/analysis_osr.py
|
aisk/pyston
|
ac69cfef0621dbc8901175e84fa2b5cb5781a646
|
[
"BSD-2-Clause",
"Apache-2.0"
] | 1
|
2020-02-06T14:29:00.000Z
|
2020-02-06T14:29:00.000Z
|
def f():
if True:
for i in xrange(20000):
pass
else:
a = 1
f()
| 11.111111
| 31
| 0.38
| 14
| 100
| 2.714286
| 0.928571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122449
| 0.51
| 100
| 8
| 32
| 12.5
| 0.653061
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| false
| 0.142857
| 0
| 0
| 0.142857
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
df05be897359b38583528bf729cb59040de62ee4
| 24
|
py
|
Python
|
meterparser/src/app/__init__.py
|
junalmeida/ha-meterparser-addon
|
1dc57035b8e26a629450a9af6f11f1ac447df67e
|
[
"MIT"
] | null | null | null |
meterparser/src/app/__init__.py
|
junalmeida/ha-meterparser-addon
|
1dc57035b8e26a629450a9af6f11f1ac447df67e
|
[
"MIT"
] | 3
|
2022-01-30T18:32:17.000Z
|
2022-03-03T06:21:58.000Z
|
meterparser/src/app/__init__.py
|
junalmeida/ha-meterparser-addon
|
1dc57035b8e26a629450a9af6f11f1ac447df67e
|
[
"MIT"
] | null | null | null |
""" Meter Parser """
| 4.8
| 20
| 0.458333
| 2
| 24
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.291667
| 24
| 4
| 21
| 6
| 0.647059
| 0.5
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
df1353a90902e36fff07ed10221d29dac3b23482
| 324
|
py
|
Python
|
examples/__too_long_init__.py
|
nmakeenkov/simple-lint
|
26cfc46b118f4cae6f88fc957bb68cfb7ee4367a
|
[
"MIT"
] | null | null | null |
examples/__too_long_init__.py
|
nmakeenkov/simple-lint
|
26cfc46b118f4cae6f88fc957bb68cfb7ee4367a
|
[
"MIT"
] | null | null | null |
examples/__too_long_init__.py
|
nmakeenkov/simple-lint
|
26cfc46b118f4cae6f88fc957bb68cfb7ee4367a
|
[
"MIT"
] | null | null | null |
class Foo(object):
def a(self, arg):
badVariableName = 1
return badVariableName + arg
def b(self):
pass
def c(self):
pass
def d(self):
pass
def e(self):
pass
def f(self):
pass
def g(self):
pass
def h(self):
pass
| 12
| 36
| 0.459877
| 40
| 324
| 3.725
| 0.45
| 0.375839
| 0.442953
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005587
| 0.447531
| 324
| 26
| 37
| 12.461538
| 0.826816
| 0
| 0
| 0.388889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.444444
| false
| 0.388889
| 0
| 0
| 0.555556
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
df408babcd2937e216fa6d0896bed829b674648d
| 33
|
py
|
Python
|
pythonDesafios/aula09.py
|
mateusdev7/desafios-python
|
6160ddc84548c7af7f5775f9acabe58238f83008
|
[
"MIT"
] | null | null | null |
pythonDesafios/aula09.py
|
mateusdev7/desafios-python
|
6160ddc84548c7af7f5775f9acabe58238f83008
|
[
"MIT"
] | null | null | null |
pythonDesafios/aula09.py
|
mateusdev7/desafios-python
|
6160ddc84548c7af7f5775f9acabe58238f83008
|
[
"MIT"
] | null | null | null |
frase = 'Curso em Vídeo Python'
| 11
| 31
| 0.69697
| 5
| 33
| 4.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.212121
| 33
| 2
| 32
| 16.5
| 0.884615
| 0
| 0
| 0
| 0
| 0
| 0.65625
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
df49e73f296e6099f98274f212c4513d71c4caf6
| 60
|
py
|
Python
|
run/module/exception.py
|
run-hub/run
|
67072c4e837982aca4962f006c7eb180337e8ebc
|
[
"Unlicense"
] | 1
|
2016-01-14T19:37:31.000Z
|
2016-01-14T19:37:31.000Z
|
run/module/exception.py
|
roll/run
|
67072c4e837982aca4962f006c7eb180337e8ebc
|
[
"Unlicense"
] | null | null | null |
run/module/exception.py
|
roll/run
|
67072c4e837982aca4962f006c7eb180337e8ebc
|
[
"Unlicense"
] | null | null | null |
class GetattrError(AttributeError):
# Public
pass
| 10
| 35
| 0.683333
| 5
| 60
| 8.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 60
| 5
| 36
| 12
| 0.911111
| 0.1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
df83ee1cb8b068417a912672f30833992c652ae7
| 48
|
py
|
Python
|
WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/mini-scripts/python_Read_Only_Parts_of_the_File.txt.py
|
webdevhub42/Lambda
|
b04b84fb5b82fe7c8b12680149e25ae0d27a0960
|
[
"MIT"
] | 5
|
2021-06-02T23:44:25.000Z
|
2021-12-27T16:21:57.000Z
|
WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/mini-scripts/python_Read_Only_Parts_of_the_File.txt.py
|
webdevhub42/Lambda
|
b04b84fb5b82fe7c8b12680149e25ae0d27a0960
|
[
"MIT"
] | 22
|
2021-05-31T01:33:25.000Z
|
2021-10-18T18:32:39.000Z
|
WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/mini-scripts/python_Read_Only_Parts_of_the_File.txt.py
|
webdevhub42/Lambda
|
b04b84fb5b82fe7c8b12680149e25ae0d27a0960
|
[
"MIT"
] | 3
|
2021-06-19T03:37:47.000Z
|
2021-08-31T00:49:51.000Z
|
f = open("demofile.txt", "r")
print(f.read(5))
| 12
| 29
| 0.583333
| 9
| 48
| 3.111111
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.02381
| 0.125
| 48
| 3
| 30
| 16
| 0.642857
| 0
| 0
| 0
| 0
| 0
| 0.270833
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
10c072c1e0969540e12ec96bc16927c48e7c0334
| 684
|
py
|
Python
|
array_api_tests/function_stubs/searching_functions.py
|
leofang/array-api-tests
|
6789a05da5595e1e53b30db22fc51206dd825c1d
|
[
"MIT"
] | 1
|
2021-07-07T14:50:28.000Z
|
2021-07-07T14:50:28.000Z
|
array_api_tests/function_stubs/searching_functions.py
|
leofang/array-api-tests
|
6789a05da5595e1e53b30db22fc51206dd825c1d
|
[
"MIT"
] | null | null | null |
array_api_tests/function_stubs/searching_functions.py
|
leofang/array-api-tests
|
6789a05da5595e1e53b30db22fc51206dd825c1d
|
[
"MIT"
] | null | null | null |
"""
Function stubs for searching functions.
NOTE: This file is generated automatically by the generate_stubs.py script. Do
not modify it directly.
See
https://github.com/data-apis/array-api/blob/master/spec/API_specification/searching_functions.md
"""
from __future__ import annotations
from ._types import Tuple, array
def argmax(x: array, /, *, axis: int = None, keepdims: bool = False) -> array:
pass
def argmin(x: array, /, *, axis: int = None, keepdims: bool = False) -> array:
pass
def nonzero(x: array, /) -> Tuple[array, ...]:
pass
def where(condition: array, x1: array, x2: array, /) -> array:
pass
__all__ = ['argmax', 'argmin', 'nonzero', 'where']
| 24.428571
| 96
| 0.687135
| 93
| 684
| 4.924731
| 0.612903
| 0.078603
| 0.078603
| 0.056769
| 0.200873
| 0.200873
| 0.200873
| 0.200873
| 0.200873
| 0.200873
| 0
| 0.003515
| 0.168129
| 684
| 27
| 97
| 25.333333
| 0.801406
| 0.358187
| 0
| 0.363636
| 1
| 0
| 0.055684
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.363636
| false
| 0.363636
| 0.181818
| 0
| 0.545455
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 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
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
10f8cb65d6b5cb34e67d50823bfad08505c92470
| 171
|
py
|
Python
|
{{cookiecutter.project_folder}}/code/{{cookiecutter.python_libname}}/process/__init__.py
|
gaulinmp/cookiecutter-research-project-template
|
7d5feff90d2303d8bfd8a2916c8dac9fa1ba185c
|
[
"MIT"
] | null | null | null |
{{cookiecutter.project_folder}}/code/{{cookiecutter.python_libname}}/process/__init__.py
|
gaulinmp/cookiecutter-research-project-template
|
7d5feff90d2303d8bfd8a2916c8dac9fa1ba185c
|
[
"MIT"
] | null | null | null |
{{cookiecutter.project_folder}}/code/{{cookiecutter.python_libname}}/process/__init__.py
|
gaulinmp/cookiecutter-research-project-template
|
7d5feff90d2303d8bfd8a2916c8dac9fa1ba185c
|
[
"MIT"
] | 2
|
2021-07-01T14:44:06.000Z
|
2022-02-05T22:57:39.000Z
|
# STDlib imports
# import os
# 3rd party imports
# from reslib import config as __config
# current module imports
# from {{ cookiecutter.python_libname }} import config
| 19
| 54
| 0.760234
| 22
| 171
| 5.772727
| 0.681818
| 0.173228
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007092
| 0.175439
| 171
| 8
| 55
| 21.375
| 0.893617
| 0.912281
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
802970a9be3d0a389f0d615974e3169f488281df
| 2,303
|
py
|
Python
|
molpy/base.py
|
Roy-Kid/molpy
|
08e7985b21fabaf78e9064a25ed80759eb373465
|
[
"BSD-3-Clause"
] | 6
|
2021-11-04T12:28:44.000Z
|
2021-12-22T13:49:52.000Z
|
molpy/base.py
|
Roy-Kid/molpy
|
08e7985b21fabaf78e9064a25ed80759eb373465
|
[
"BSD-3-Clause"
] | 2
|
2021-11-26T12:06:24.000Z
|
2021-11-28T11:03:11.000Z
|
molpy/base.py
|
Roy-Kid/molpy
|
08e7985b21fabaf78e9064a25ed80759eb373465
|
[
"BSD-3-Clause"
] | 4
|
2021-11-04T10:36:51.000Z
|
2021-12-17T11:58:47.000Z
|
# author: Roy Kid
# contact: lijichen365@126.com
# date: 2021-10-17
# version: 0.0.2
__all__ = ['Node', 'Graph', 'Edge']
from copy import deepcopy
class Item:
def __init__(self, name, **attr) -> None:
self.update(attr)
# self._uuid = id(self)
self._name = name
self._itemType = self.__class__.__name__
#TODO: need to redesign class attribute:
# type 1: attributes belongs to this python instance, not properties
# type 2: attributes are properties but can not be copy
# type 3: attributes are properties and to copy
def __copy__(self):
cls = self.__class__
result = cls.__new__(cls, self._name)
result.__dict__.update(self.__dict__)
return result
def __deepcopy__(self, memo):
cls = self.__class__
result = cls.__new__(cls, self._name)
memo[id(self)] = result
for k, v in self.__dict__.items():
setattr(result, k, deepcopy(v, memo))
return result
@property
def uuid(self):
return id(self)
@property
def itemType(self):
return self._itemType
def __hash__(self) -> int:
return id(self)
def __repr__(self) -> str:
return f'< {self._itemType} {self._name} >'
def update(self, attr):
for k, v in attr.items():
setattr(self,k, v)
def __eq__(self, o):
return self.uuid == o.uuid
def __lt__(self, o):
return self.uuid < o.uuid
@property
def properties(self):
return self.__dict__
@property
def name(self):
return self._name
@name.setter
def name(self, name):
self._name = name
def __call__(self, **attr):
item = deepcopy(self)
item.update(attr)
return item
class Node(Item):
"""A Atom DataView class for a molpy Group
"""
def __init__(self, name, **attr) -> None:
super().__init__(name, **attr)
class Edge(Item):
def __init__(self, name, **attr) -> None:
super().__init__(name, **attr)
class Graph(Item):
def __init__(self, name, **attr) -> None:
super().__init__(name, **attr)
| 24.5
| 81
| 0.555797
| 276
| 2,303
| 4.23913
| 0.300725
| 0.075214
| 0.037607
| 0.051282
| 0.24188
| 0.24188
| 0.24188
| 0.177778
| 0.177778
| 0.117949
| 0
| 0.01297
| 0.330439
| 2,303
| 94
| 82
| 24.5
| 0.745785
| 0.158923
| 0
| 0.362069
| 0
| 0
| 0.023909
| 0
| 0
| 0
| 0
| 0.010638
| 0
| 1
| 0.293103
| false
| 0
| 0.017241
| 0.137931
| 0.568966
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
8049ea61f2acd6ad90fa7db5a2259bc457b8022e
| 153
|
py
|
Python
|
src/modern_python/__init__.py
|
ChadHattabaugh/modern-python
|
2c7603b99c333c8b2b02f5ec7db0bf9064d4e21b
|
[
"MIT"
] | null | null | null |
src/modern_python/__init__.py
|
ChadHattabaugh/modern-python
|
2c7603b99c333c8b2b02f5ec7db0bf9064d4e21b
|
[
"MIT"
] | null | null | null |
src/modern_python/__init__.py
|
ChadHattabaugh/modern-python
|
2c7603b99c333c8b2b02f5ec7db0bf9064d4e21b
|
[
"MIT"
] | null | null | null |
# ./src/modern_python/__init__.py
"""The modern python development template."""
from importlib.metadata import version
__version__ = version(__name__)
| 21.857143
| 45
| 0.784314
| 18
| 153
| 5.944444
| 0.777778
| 0.224299
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.104575
| 153
| 6
| 46
| 25.5
| 0.781022
| 0.470588
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
337e7c53624dd060e85a383aa93fdb6b4277891d
| 376
|
py
|
Python
|
src/custom_auth/permissions.py
|
julada/bullet-train-api
|
1d9a62ab917ffa4744e9d47eb80454c46556c64e
|
[
"BSD-3-Clause"
] | null | null | null |
src/custom_auth/permissions.py
|
julada/bullet-train-api
|
1d9a62ab917ffa4744e9d47eb80454c46556c64e
|
[
"BSD-3-Clause"
] | null | null | null |
src/custom_auth/permissions.py
|
julada/bullet-train-api
|
1d9a62ab917ffa4744e9d47eb80454c46556c64e
|
[
"BSD-3-Clause"
] | null | null | null |
from rest_framework.permissions import IsAuthenticated
class CurrentUser(IsAuthenticated):
"""
Class to ensure that users of the platform can only retrieve details of themselves.
"""
def has_permission(self, request, view):
return view.action == "me"
def has_object_permission(self, request, view, obj):
return obj.id == request.user.id
| 28.923077
| 87
| 0.707447
| 47
| 376
| 5.574468
| 0.702128
| 0.152672
| 0.160305
| 0.19084
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.207447
| 376
| 12
| 88
| 31.333333
| 0.879195
| 0.220745
| 0
| 0
| 0
| 0
| 0.00722
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.166667
| 0.333333
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
338fcaac901938de8145ef1d94cdafcdf557e4ba
| 191
|
py
|
Python
|
1 Python Basics/6_lower_upper.py
|
narayanants/python-mega-course
|
2ba2980ab21dfbed5f86f00695559f7831b5c566
|
[
"MIT"
] | null | null | null |
1 Python Basics/6_lower_upper.py
|
narayanants/python-mega-course
|
2ba2980ab21dfbed5f86f00695559f7831b5c566
|
[
"MIT"
] | null | null | null |
1 Python Basics/6_lower_upper.py
|
narayanants/python-mega-course
|
2ba2980ab21dfbed5f86f00695559f7831b5c566
|
[
"MIT"
] | null | null | null |
god = 'THoR'
print(god.lower())
print(god.upper())
a = " Hello, World! "
print(a.strip()) # returns "Hello, World!"
b = "Hello, World!"
print(b.split(",")) # returns ['Hello', ' World!']
| 15.916667
| 50
| 0.581152
| 26
| 191
| 4.269231
| 0.461538
| 0.36036
| 0.27027
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157068
| 191
| 11
| 51
| 17.363636
| 0.689441
| 0.272251
| 0
| 0
| 0
| 0
| 0.242647
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.571429
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
33a353cb615f7b8a228c223b10d2051cf78b002d
| 2,600
|
py
|
Python
|
Leak #5 - Lost In Translation/windows/Resources/Ops/PyScripts/lib/ops/data/processinfo.py
|
bidhata/EquationGroupLeaks
|
1ff4bc115cb2bd5bf2ed6bf769af44392926830c
|
[
"Unlicense"
] | 9
|
2019-11-22T04:58:40.000Z
|
2022-02-26T16:47:28.000Z
|
Python.Fuzzbunch/Resources/Ops/PyScripts/lib/ops/data/processinfo.py
|
010001111/Vx-Suites
|
6b4b90a60512cce48aa7b87aec5e5ac1c4bb9a79
|
[
"MIT"
] | null | null | null |
Python.Fuzzbunch/Resources/Ops/PyScripts/lib/ops/data/processinfo.py
|
010001111/Vx-Suites
|
6b4b90a60512cce48aa7b87aec5e5ac1c4bb9a79
|
[
"MIT"
] | 8
|
2017-09-27T10:31:18.000Z
|
2022-01-08T10:30:46.000Z
|
from ops.data import OpsClass, OpsField, DszObject, DszCommandObject, cmd_definitions
import dsz
if ('processinfo' not in cmd_definitions):
dszprocessinfo = OpsClass('processinfo', {'id': OpsField('id', dsz.TYPE_INT), 'groups': OpsClass('groups', {'group': OpsClass('group', {'type': OpsField('type', dsz.TYPE_STRING), 'name': OpsField('name', dsz.TYPE_STRING), 'attributes': OpsClass('attributes', {'groupusedeny': OpsField('groupusedeny', dsz.TYPE_BOOL), 'groupmandatory': OpsField('groupmandatory', dsz.TYPE_BOOL), 'groupenabled': OpsField('groupenabled', dsz.TYPE_BOOL), 'grouplogonid': OpsField('grouplogonid', dsz.TYPE_BOOL), 'groupresource': OpsField('groupresource', dsz.TYPE_BOOL), 'groupenabledbydefault': OpsField('groupenabledbydefault', dsz.TYPE_BOOL), 'groupowner': OpsField('groupowner', dsz.TYPE_BOOL), 'mask': OpsField('mask', dsz.TYPE_INT)}, DszObject)}, DszObject, single=False)}, DszObject), 'privileges': OpsClass('privileges', {'privilege': OpsClass('privilege', {'name': OpsField('name', dsz.TYPE_STRING), 'attributes': OpsClass('attributes', {'priv_enabled': OpsField('priv_enabled', dsz.TYPE_BOOL), 'priv_enabled_by_defaul': OpsField('priv_enabled_by_defaul', dsz.TYPE_BOOL), 'priv_used_access': OpsField('priv_used_access', dsz.TYPE_BOOL), 'mask': OpsField('mask', dsz.TYPE_INT)}, DszObject)}, DszObject, single=False)}, DszObject), 'basicinfo': OpsClass('basicinfo', {'user': OpsClass('user', {'attributes': OpsField('attributes', dsz.TYPE_STRING), 'type': OpsField('type', dsz.TYPE_STRING), 'name': OpsField('name', dsz.TYPE_STRING)}, DszObject), 'owner': OpsClass('owner', {'attributes': OpsField('attributes', dsz.TYPE_STRING), 'type': OpsField('type', dsz.TYPE_STRING), 'name': OpsField('name', dsz.TYPE_STRING)}, DszObject), 'primarygroup': OpsClass('primarygroup', {'attributes': OpsField('attributes', dsz.TYPE_STRING), 'type': OpsField('type', dsz.TYPE_STRING), 'name': OpsField('name', dsz.TYPE_STRING)}, DszObject)}, DszObject), 'modules': OpsClass('modules', {'module': OpsClass('module', {'baseaddress': OpsField('baseaddress', dsz.TYPE_INT), 'imagesize': OpsField('imagesize', dsz.TYPE_INT), 'entrypoint': OpsField('entrypoint', dsz.TYPE_INT), 'modulename': OpsField('modulename', dsz.TYPE_STRING), 'checksum': OpsClass('checksum', {'type': OpsField('type', dsz.TYPE_STRING), 'value': OpsField('value', dsz.TYPE_STRING)}, DszObject, single=False)}, DszObject, single=False)}, DszObject)}, DszObject)
processinfocommand = OpsClass('processinfo', {'processinfo': dszprocessinfo}, DszCommandObject)
cmd_definitions['processinfo'] = processinfocommand
| 371.428571
| 2,303
| 0.741538
| 290
| 2,600
| 6.489655
| 0.193103
| 0.115303
| 0.103613
| 0.050478
| 0.343252
| 0.343252
| 0.327843
| 0.327843
| 0.327843
| 0.282678
| 0
| 0
| 0.067692
| 2,600
| 7
| 2,304
| 371.428571
| 0.776403
| 0
| 0
| 0
| 0
| 0
| 0.305769
| 0.033077
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
33a42a18e58ead5427bf8e1e2fe5dc35c8bad45b
| 206
|
py
|
Python
|
create_python_app/create_root_package.py
|
xuchaoqian/create-python-app
|
70745432b972b96a3faf95a378d54d922b77f2be
|
[
"MIT"
] | null | null | null |
create_python_app/create_root_package.py
|
xuchaoqian/create-python-app
|
70745432b972b96a3faf95a378d54d922b77f2be
|
[
"MIT"
] | null | null | null |
create_python_app/create_root_package.py
|
xuchaoqian/create-python-app
|
70745432b972b96a3faf95a378d54d922b77f2be
|
[
"MIT"
] | null | null | null |
from create_python_app.path_utils import *
def create_root_package(base_dir, **kwargs):
create_dir(base_dir, kwargs["app_name"])
create_file(base_dir, kwargs["app_name"], '__init__.py', content="")
| 41.2
| 72
| 0.752427
| 31
| 206
| 4.483871
| 0.580645
| 0.151079
| 0.280576
| 0.230216
| 0.28777
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.101942
| 206
| 5
| 72
| 41.2
| 0.751351
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
33cd28b4de6385f6d26e339b191f871e2f07b3d1
| 1,394
|
py
|
Python
|
cstar/remote.py
|
smarsching/cstar
|
872bb4c4a541eb01a87ac1b9073aeca881ba6bdc
|
[
"Apache-2.0"
] | 244
|
2018-08-07T14:49:00.000Z
|
2022-02-15T19:16:06.000Z
|
cstar/remote.py
|
smarsching/cstar
|
872bb4c4a541eb01a87ac1b9073aeca881ba6bdc
|
[
"Apache-2.0"
] | 46
|
2018-08-13T15:28:21.000Z
|
2022-02-16T15:09:55.000Z
|
cstar/remote.py
|
smarsching/cstar
|
872bb4c4a541eb01a87ac1b9073aeca881ba6bdc
|
[
"Apache-2.0"
] | 36
|
2018-08-13T08:03:54.000Z
|
2022-03-18T09:01:54.000Z
|
# Copyright 2017 Spotify AB
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from cstar.remote_paramiko import RemoteParamiko
from cstar.output import debug
from cstar.exceptions import BadArgument
class Remote(object):
def __init__(self, hostname, ssh_username, ssh_password, ssh_identity_file, ssh_lib, host_variables):
debug("Using ssh lib : ", ssh_lib)
self.remote = RemoteParamiko(hostname, ssh_username, ssh_password, ssh_identity_file, host_variables)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
self.remote.close()
def run_job(self, file, jobid, timeout=None, env={}):
return self.remote.run_job(file, jobid, timeout, env)
def get_job_status(self, jobid):
pass
def run(self, argv):
return self.remote.run(argv)
def close(self):
self.remote.close()
| 34
| 109
| 0.728121
| 200
| 1,394
| 4.915
| 0.525
| 0.061038
| 0.02645
| 0.032553
| 0.091556
| 0.091556
| 0.091556
| 0.091556
| 0
| 0
| 0
| 0.007073
| 0.188666
| 1,394
| 40
| 110
| 34.85
| 0.862069
| 0.389527
| 0
| 0.105263
| 0
| 0
| 0.019116
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.368421
| false
| 0.157895
| 0.157895
| 0.157895
| 0.736842
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
d5080a9acc430f5624139cf608d1b9c7ef4c1f8b
| 45,630
|
py
|
Python
|
contrib/palettes.py
|
ConnectionMaster/qgis-earthengine-plugin
|
dfb7cbaeca856e03ca3560bb52454a1c17ce347a
|
[
"MIT"
] | 307
|
2018-04-28T02:50:27.000Z
|
2022-03-31T09:39:25.000Z
|
contrib/palettes.py
|
ConnectionMaster/qgis-earthengine-plugin
|
dfb7cbaeca856e03ca3560bb52454a1c17ce347a
|
[
"MIT"
] | 72
|
2019-09-18T00:03:00.000Z
|
2022-01-26T17:33:55.000Z
|
contrib/palettes.py
|
ConnectionMaster/qgis-earthengine-plugin
|
dfb7cbaeca856e03ca3560bb52454a1c17ce347a
|
[
"MIT"
] | 95
|
2018-05-21T04:12:53.000Z
|
2022-03-24T11:52:46.000Z
|
# Copyright (c) 2018 Gennadii Donchyts. All rights reserved.
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
# Contributors:
# * 2018-08-01: Fedor Baart (f.baart@gmail.com) - added cmocean
# * 2019-01-18: Justin Braaten (jstnbraaten@gmail.com) - added niccoli, matplotlib, kovesi, misc
cmocean = {
'Thermal': {7: ['042333', '2c3395', '744992', 'b15f82', 'eb7958', 'fbb43d', 'e8fa5b']},
'Haline': {7: ['2a186c', '14439c', '206e8b', '3c9387', '5ab978', 'aad85c', 'fdef9a']},
'Solar': {7: ['331418', '682325', '973b1c', 'b66413', 'cb921a', 'dac62f', 'e1fd4b']},
'Ice': {7: ['040613', '292851', '3f4b96', '427bb7', '61a8c7', '9cd4da', 'eafdfd']},
'Gray': {7: ['000000', '232323', '4a4a49', '727171', '9b9a9a', 'cacac9', 'fffffd']},
'Oxy': {7: ['400505', '850a0b', '6f6f6e', '9b9a9a', 'cbcac9', 'ebf34b', 'ddaf19']},
'Deep': {7: ['fdfecc', 'a5dfa7', '5dbaa4', '488e9e', '3e6495', '3f396c', '281a2c']},
'Dense': {7: ['e6f1f1', 'a2cee2', '76a4e5', '7871d5', '7642a5', '621d62', '360e24']},
'Algae': {7: ['d7f9d0', 'a2d595', '64b463', '129450', '126e45', '1a482f', '122414']},
'Matter': {7: ['feedb0', 'f7b37c', 'eb7858', 'ce4356', '9f2462', '66185c', '2f0f3e']},
'Turbid': {7: ['e9f6ab', 'd3c671', 'bf9747', 'a1703b', '795338', '4d392d', '221f1b']},
'Speed': {7: ['fffdcd', 'e1cd73', 'aaac20', '5f920c', '187328', '144b2a', '172313']},
'Amp': {7: ['f1edec', 'dfbcb0', 'd08b73', 'c0583b', 'a62225', '730e27', '3c0912']},
'Tempo': {7: ['fff6f4', 'c3d1ba', '7db390', '2a937f', '156d73', '1c455b', '151d44']},
'Phase': {7: ['a8780d', 'd74957', 'd02fd0', '7d73f0', '1e93a8', '359943', 'a8780d']},
'Balance': {7: ['181c43', '0c5ebe', '75aabe', 'f1eceb', 'd08b73','a52125', '3c0912']},
'Delta': {7: ['112040', '1c67a0', '6db6b3', 'fffccc', 'abac21', '177228', '172313']},
'Curl': {7: ['151d44', '156c72', '7eb390', 'fdf5f4', 'db8d77', '9c3060', '340d35']}
}
colorbrewer = {
'YlGn':{3:["f7fcb9","addd8e","31a354"],4:["ffffcc","c2e699","78c679","238443"],5:["ffffcc","c2e699","78c679","31a354","006837"],6:["ffffcc","d9f0a3","addd8e","78c679","31a354","006837"],7:["ffffcc","d9f0a3","addd8e","78c679","41ab5d","238443","005a32"],8:["ffffe5","f7fcb9","d9f0a3","addd8e","78c679","41ab5d","238443","005a32"],9:["ffffe5","f7fcb9","d9f0a3","addd8e","78c679","41ab5d","238443","006837","004529"]},
'YlGnBu':{3:["edf8b1","7fcdbb","2c7fb8"],4:["ffffcc","a1dab4","41b6c4","225ea8"],5:["ffffcc","a1dab4","41b6c4","2c7fb8","253494"],6:["ffffcc","c7e9b4","7fcdbb","41b6c4","2c7fb8","253494"],7:["ffffcc","c7e9b4","7fcdbb","41b6c4","1d91c0","225ea8","0c2c84"],8:["ffffd9","edf8b1","c7e9b4","7fcdbb","41b6c4","1d91c0","225ea8","0c2c84"],9:["ffffd9","edf8b1","c7e9b4","7fcdbb","41b6c4","1d91c0","225ea8","253494","081d58"]},
'GnBu':{3:["e0f3db","a8ddb5","43a2ca"],4:["f0f9e8","bae4bc","7bccc4","2b8cbe"],5:["f0f9e8","bae4bc","7bccc4","43a2ca","0868ac"],6:["f0f9e8","ccebc5","a8ddb5","7bccc4","43a2ca","0868ac"],7:["f0f9e8","ccebc5","a8ddb5","7bccc4","4eb3d3","2b8cbe","08589e"],8:["f7fcf0","e0f3db","ccebc5","a8ddb5","7bccc4","4eb3d3","2b8cbe","08589e"],9:["f7fcf0","e0f3db","ccebc5","a8ddb5","7bccc4","4eb3d3","2b8cbe","0868ac","084081"]},
'BuGn':{3:["e5f5f9","99d8c9","2ca25f"],4:["edf8fb","b2e2e2","66c2a4","238b45"],5:["edf8fb","b2e2e2","66c2a4","2ca25f","006d2c"],6:["edf8fb","ccece6","99d8c9","66c2a4","2ca25f","006d2c"],7:["edf8fb","ccece6","99d8c9","66c2a4","41ae76","238b45","005824"],8:["f7fcfd","e5f5f9","ccece6","99d8c9","66c2a4","41ae76","238b45","005824"],9:["f7fcfd","e5f5f9","ccece6","99d8c9","66c2a4","41ae76","238b45","006d2c","00441b"]},
'PuBuGn':{3:["ece2f0","a6bddb","1c9099"],4:["f6eff7","bdc9e1","67a9cf","02818a"],5:["f6eff7","bdc9e1","67a9cf","1c9099","016c59"],6:["f6eff7","d0d1e6","a6bddb","67a9cf","1c9099","016c59"],7:["f6eff7","d0d1e6","a6bddb","67a9cf","3690c0","02818a","016450"],8:["fff7fb","ece2f0","d0d1e6","a6bddb","67a9cf","3690c0","02818a","016450"],9:["fff7fb","ece2f0","d0d1e6","a6bddb","67a9cf","3690c0","02818a","016c59","014636"]},
'PuBu':{3:["ece7f2","a6bddb","2b8cbe"],4:["f1eef6","bdc9e1","74a9cf","0570b0"],5:["f1eef6","bdc9e1","74a9cf","2b8cbe","045a8d"],6:["f1eef6","d0d1e6","a6bddb","74a9cf","2b8cbe","045a8d"],7:["f1eef6","d0d1e6","a6bddb","74a9cf","3690c0","0570b0","034e7b"],8:["fff7fb","ece7f2","d0d1e6","a6bddb","74a9cf","3690c0","0570b0","034e7b"],9:["fff7fb","ece7f2","d0d1e6","a6bddb","74a9cf","3690c0","0570b0","045a8d","023858"]},
'BuPu':{3:["e0ecf4","9ebcda","8856a7"],4:["edf8fb","b3cde3","8c96c6","88419d"],5:["edf8fb","b3cde3","8c96c6","8856a7","810f7c"],6:["edf8fb","bfd3e6","9ebcda","8c96c6","8856a7","810f7c"],7:["edf8fb","bfd3e6","9ebcda","8c96c6","8c6bb1","88419d","6e016b"],8:["f7fcfd","e0ecf4","bfd3e6","9ebcda","8c96c6","8c6bb1","88419d","6e016b"],9:["f7fcfd","e0ecf4","bfd3e6","9ebcda","8c96c6","8c6bb1","88419d","810f7c","4d004b"]},
'RdPu':{3:["fde0dd","fa9fb5","c51b8a"],4:["feebe2","fbb4b9","f768a1","ae017e"],5:["feebe2","fbb4b9","f768a1","c51b8a","7a0177"],6:["feebe2","fcc5c0","fa9fb5","f768a1","c51b8a","7a0177"],7:["feebe2","fcc5c0","fa9fb5","f768a1","dd3497","ae017e","7a0177"],8:["fff7f3","fde0dd","fcc5c0","fa9fb5","f768a1","dd3497","ae017e","7a0177"],9:["fff7f3","fde0dd","fcc5c0","fa9fb5","f768a1","dd3497","ae017e","7a0177","49006a"]},
'PuRd':{3:["e7e1ef","c994c7","dd1c77"],4:["f1eef6","d7b5d8","df65b0","ce1256"],5:["f1eef6","d7b5d8","df65b0","dd1c77","980043"],6:["f1eef6","d4b9da","c994c7","df65b0","dd1c77","980043"],7:["f1eef6","d4b9da","c994c7","df65b0","e7298a","ce1256","91003f"],8:["f7f4f9","e7e1ef","d4b9da","c994c7","df65b0","e7298a","ce1256","91003f"],9:["f7f4f9","e7e1ef","d4b9da","c994c7","df65b0","e7298a","ce1256","980043","67001f"]},
'OrRd':{3:["fee8c8","fdbb84","e34a33"],4:["fef0d9","fdcc8a","fc8d59","d7301f"],5:["fef0d9","fdcc8a","fc8d59","e34a33","b30000"],6:["fef0d9","fdd49e","fdbb84","fc8d59","e34a33","b30000"],7:["fef0d9","fdd49e","fdbb84","fc8d59","ef6548","d7301f","990000"],8:["fff7ec","fee8c8","fdd49e","fdbb84","fc8d59","ef6548","d7301f","990000"],9:["fff7ec","fee8c8","fdd49e","fdbb84","fc8d59","ef6548","d7301f","b30000","7f0000"]},
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'Purples':{3:["efedf5","bcbddc","756bb1"],4:["f2f0f7","cbc9e2","9e9ac8","6a51a3"],5:["f2f0f7","cbc9e2","9e9ac8","756bb1","54278f"],6:["f2f0f7","dadaeb","bcbddc","9e9ac8","756bb1","54278f"],7:["f2f0f7","dadaeb","bcbddc","9e9ac8","807dba","6a51a3","4a1486"],8:["fcfbfd","efedf5","dadaeb","bcbddc","9e9ac8","807dba","6a51a3","4a1486"],9:["fcfbfd","efedf5","dadaeb","bcbddc","9e9ac8","807dba","6a51a3","54278f","3f007d"]},
'Blues':{3:["deebf7","9ecae1","3182bd"],4:["eff3ff","bdd7e7","6baed6","2171b5"],5:["eff3ff","bdd7e7","6baed6","3182bd","08519c"],6:["eff3ff","c6dbef","9ecae1","6baed6","3182bd","08519c"],7:["eff3ff","c6dbef","9ecae1","6baed6","4292c6","2171b5","084594"],8:["f7fbff","deebf7","c6dbef","9ecae1","6baed6","4292c6","2171b5","084594"],9:["f7fbff","deebf7","c6dbef","9ecae1","6baed6","4292c6","2171b5","08519c","08306b"]},
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'RdYlGn':{3:["fc8d59","ffffbf","91cf60"],4:["d7191c","fdae61","a6d96a","1a9641"],5:["d7191c","fdae61","ffffbf","a6d96a","1a9641"],6:["d73027","fc8d59","fee08b","d9ef8b","91cf60","1a9850"],7:["d73027","fc8d59","fee08b","ffffbf","d9ef8b","91cf60","1a9850"],8:["d73027","f46d43","fdae61","fee08b","d9ef8b","a6d96a","66bd63","1a9850"],9:["d73027","f46d43","fdae61","fee08b","ffffbf","d9ef8b","a6d96a","66bd63","1a9850"],10:["a50026","d73027","f46d43","fdae61","fee08b","d9ef8b","a6d96a","66bd63","1a9850","006837"],11:["a50026","d73027","f46d43","fdae61","fee08b","ffffbf","d9ef8b","a6d96a","66bd63","1a9850","006837"]},
'Accent':{3:["7fc97f","beaed4","fdc086"],4:["7fc97f","beaed4","fdc086","ffff99"],5:["7fc97f","beaed4","fdc086","ffff99","386cb0"],6:["7fc97f","beaed4","fdc086","ffff99","386cb0","f0027f"],7:["7fc97f","beaed4","fdc086","ffff99","386cb0","f0027f","bf5b17"],8:["7fc97f","beaed4","fdc086","ffff99","386cb0","f0027f","bf5b17","666666"]},
'Dark2':{3:["1b9e77","d95f02","7570b3"],4:["1b9e77","d95f02","7570b3","e7298a"],5:["1b9e77","d95f02","7570b3","e7298a","66a61e"],6:["1b9e77","d95f02","7570b3","e7298a","66a61e","e6ab02"],7:["1b9e77","d95f02","7570b3","e7298a","66a61e","e6ab02","a6761d"],8:["1b9e77","d95f02","7570b3","e7298a","66a61e","e6ab02","a6761d","666666"]},
'Pastel1':{3:["fbb4ae","b3cde3","ccebc5"],4:["fbb4ae","b3cde3","ccebc5","decbe4"],5:["fbb4ae","b3cde3","ccebc5","decbe4","fed9a6"],6:["fbb4ae","b3cde3","ccebc5","decbe4","fed9a6","ffffcc"],7:["fbb4ae","b3cde3","ccebc5","decbe4","fed9a6","ffffcc","e5d8bd"],8:["fbb4ae","b3cde3","ccebc5","decbe4","fed9a6","ffffcc","e5d8bd","fddaec"],9:["fbb4ae","b3cde3","ccebc5","decbe4","fed9a6","ffffcc","e5d8bd","fddaec","f2f2f2"]},
'Pastel2':{3:["b3e2cd","fdcdac","cbd5e8"],4:["b3e2cd","fdcdac","cbd5e8","f4cae4"],5:["b3e2cd","fdcdac","cbd5e8","f4cae4","e6f5c9"],6:["b3e2cd","fdcdac","cbd5e8","f4cae4","e6f5c9","fff2ae"],7:["b3e2cd","fdcdac","cbd5e8","f4cae4","e6f5c9","fff2ae","f1e2cc"],8:["b3e2cd","fdcdac","cbd5e8","f4cae4","e6f5c9","fff2ae","f1e2cc","cccccc"]},
'Paired':{3:["a6cee3","1f78b4","b2df8a"],4:["a6cee3","1f78b4","b2df8a","33a02c"],5:["a6cee3","1f78b4","b2df8a","33a02c","fb9a99"],6:["a6cee3","1f78b4","b2df8a","33a02c","fb9a99","e31a1c"],7:["a6cee3","1f78b4","b2df8a","33a02c","fb9a99","e31a1c","fdbf6f"],8:["a6cee3","1f78b4","b2df8a","33a02c","fb9a99","e31a1c","fdbf6f","ff7f00"],9:["a6cee3","1f78b4","b2df8a","33a02c","fb9a99","e31a1c","fdbf6f","ff7f00","cab2d6"],10:["a6cee3","1f78b4","b2df8a","33a02c","fb9a99","e31a1c","fdbf6f","ff7f00","cab2d6","6a3d9a"],11:["a6cee3","1f78b4","b2df8a","33a02c","fb9a99","e31a1c","fdbf6f","ff7f00","cab2d6","6a3d9a","ffff99"],12:["a6cee3","1f78b4","b2df8a","33a02c","fb9a99","e31a1c","fdbf6f","ff7f00","cab2d6","6a3d9a","ffff99","b15928"]},
'Set1':{3:["e41a1c","377eb8","4daf4a"],4:["e41a1c","377eb8","4daf4a","984ea3"],5:["e41a1c","377eb8","4daf4a","984ea3","ff7f00"],6:["e41a1c","377eb8","4daf4a","984ea3","ff7f00","ffff33"],7:["e41a1c","377eb8","4daf4a","984ea3","ff7f00","ffff33","a65628"],8:["e41a1c","377eb8","4daf4a","984ea3","ff7f00","ffff33","a65628","f781bf"],9:["e41a1c","377eb8","4daf4a","984ea3","ff7f00","ffff33","a65628","f781bf","999999"]},
'Set2':{3:["66c2a5","fc8d62","8da0cb"],4:["66c2a5","fc8d62","8da0cb","e78ac3"],5:["66c2a5","fc8d62","8da0cb","e78ac3","a6d854"],6:["66c2a5","fc8d62","8da0cb","e78ac3","a6d854","ffd92f"],7:["66c2a5","fc8d62","8da0cb","e78ac3","a6d854","ffd92f","e5c494"],8:["66c2a5","fc8d62","8da0cb","e78ac3","a6d854","ffd92f","e5c494","b3b3b3"]},
'Set3':{3:["8dd3c7","ffffb3","bebada"],4:["8dd3c7","ffffb3","bebada","fb8072"],5:["8dd3c7","ffffb3","bebada","fb8072","80b1d3"],6:["8dd3c7","ffffb3","bebada","fb8072","80b1d3","fdb462"],7:["8dd3c7","ffffb3","bebada","fb8072","80b1d3","fdb462","b3de69"],8:["8dd3c7","ffffb3","bebada","fb8072","80b1d3","fdb462","b3de69","fccde5"],9:["8dd3c7","ffffb3","bebada","fb8072","80b1d3","fdb462","b3de69","fccde5","d9d9d9"],10:["8dd3c7","ffffb3","bebada","fb8072","80b1d3","fdb462","b3de69","fccde5","d9d9d9","bc80bd"],11:["8dd3c7","ffffb3","bebada","fb8072","80b1d3","fdb462","b3de69","fccde5","d9d9d9","bc80bd","ccebc5"],12:["8dd3c7","ffffb3","bebada","fb8072","80b1d3","fdb462","b3de69","fccde5","d9d9d9","bc80bd","ccebc5","ffed6f"]},
}
cb = colorbrewer
misc = {
'coolwarm': {7:['#3B4CC0', '#6F91F2', '#A9C5FC', '#DDDDDD', '#F6B69B', '#E6745B', '#B40426']},
'warmcool': {7:['#B40426', '#E6745B', '#F6B69B', '#DDDDDD', '#A9C5FC', '#6F91F2', '#3B4CC0']},
'cubehelix': {7:['#000000', '#182E49', '#2B6F39', '#A07949', '#D490C6', '#C2D8F3', '#FFFFFF']},
'gnuplot': {7:['#000033', '#0000CC', '#5000FF', '#C729D6', '#FF758A', '#FFC23D', '#FFFF60']},
'jet': {7:['#00007F', '#002AFF', '#00D4FF', '#7FFF7F', '#FFD400', '#FF2A00', '#7F0000']},
'parula': {7:['#352A87', '#056EDE', '#089BCE', '#33B7A0', '#A3BD6A', '#F9BD3F', '#F9FB0E']},
'tol_rainbow': {7:['#781C81', '#3F60AE', '#539EB6', '#6DB388', '#CAB843', '#E78532', '#D92120']},
'cividis': {7:['#00204C', '#213D6B', '#555B6C', '#7B7A77', '#A59C74', '#D3C064', '#FFE945']}
}
niccoli = {
'cubicyf': {7:['#830CAB', '#7556F3', '#5590E7', '#3BBCAC', '#52D965', '#86EA50', '#CCEC5A']},
'cubicl': {7:['#780085', '#7651EE', '#4C9ED9', '#49CF7F', '#85EB50', '#D4E35B', '#F9965B']},
'isol': {7:['#E839E5', '#7C58FA', '#2984B9', '#0A9A4D', '#349704', '#9E7C09', '#FF3A2A']},
'linearl': {7:['#040404', '#2C1C5D', '#114E81', '#00834B', '#37B200', '#C4CA39', '#F7ECE5']},
'linearlhot': {7:['#060303', '#620100', '#B20022', '#DE2007', '#D78E00', '#C9CE00', '#F2F2B7']}
}
matplotlib = {
'magma': {7:['#000004', '#2C105C', '#711F81', '#B63679', '#EE605E', '#FDAE78', '#FCFDBF']},
'inferno': {7:['#000004', '#320A5A', '#781B6C', '#BB3654', '#EC6824', '#FBB41A', '#FCFFA4']},
'plasma': {7:['#0D0887', '#5B02A3', '#9A179B', '#CB4678', '#EB7852', '#FBB32F', '#F0F921']},
'viridis': {7:['#440154', '#433982', '#30678D', '#218F8B', '#36B677', '#8ED542', '#FDE725']}
}
kovesi = {
'cyclic_grey_15_85_c0': {7:['#787878', '#B0B0B0', '#B0B0B0', '#767676', '#414141', '#424242', '#767676']},
'cyclic_grey_15_85_c0_s25': {7:['#2D2D2D', '#5B5B5B', '#949494', '#CACACA', '#949494', '#5A5A5A', '#2D2D2D']},
'cyclic_mrybm_35_75_c68': {7:['#F985F8', '#D82D5F', '#C14E04', '#D0AA25', '#2C76B1', '#7556F9', '#F785F9']},
'cyclic_mrybm_35_75_c68_s25': {7:['#3E3FF0', '#B976FC', '#F55CB1', '#B71C18', '#D28004', '#8E9871', '#3C40EE']},
'cyclic_mygbm_30_95_c78': {7:['#EF55F2', '#FCC882', '#B8E014', '#32AD26', '#2F5DB9', '#712AF7', '#ED53F3']},
'cyclic_mygbm_30_95_c78_s25': {7:['#2E22EA', '#B341FB', '#FC93C0', '#F1ED37', '#77C80D', '#458873', '#2C24E9']},
'cyclic_wrwbw_40_90_c42': {7:['#DFD5D8', '#D9694D', '#D86449', '#DDD1D6', '#6C81E5', '#6F83E5', '#DDD5DA']},
'cyclic_wrwbw_40_90_c42_s25': {7:['#1A63E5', '#B0B2E4', '#E4A695', '#C93117', '#E3A18F', '#ADB0E4', '#1963E5']},
'diverging_isoluminant_cjm_75_c23': {7:['#00C9FF', '#69C3E8', '#98BED0', '#B8B8BB', '#CBB1C6', '#DCA8D5', '#ED9EE4']},
'diverging_isoluminant_cjm_75_c24': {7:['#00CBFE', '#62C5E7', '#96BFD0', '#B8B8BB', '#CCB1C8', '#DEA7D6', '#F09DE6']},
'diverging_isoluminant_cjo_70_c25': {7:['#00B6FF', '#67B2E4', '#8FAFC7', '#ABABAB', '#C7A396', '#E09A81', '#F6906D']},
'diverging_linear_bjr_30_55_c53': {7:['#002AD7', '#483FB0', '#5E528A', '#646464', '#A15C49', '#D44A2C', '#FF1900']},
'diverging_linear_bjy_30_90_c45': {7:['#1431C1', '#5A50B2', '#796FA2', '#938F8F', '#B8AB74', '#DAC652', '#FDE409']},
'diverging_rainbow_bgymr_45_85_c67': {7:['#085CF8', '#3C9E49', '#98BB18', '#F3CC1D', '#FE8F7B', '#F64497', '#D70500']},
'diverging_bkr_55_10_c35': {7:['#1981FA', '#315CA9', '#2D3B5E', '#221F21', '#5C2F28', '#9E4035', '#E65041']},
'diverging_bky_60_10_c30': {7:['#0E94FA', '#2F68A9', '#2D405E', '#212020', '#4C3E20', '#7D6321', '#B38B1A']},
'diverging_bwr_40_95_c42': {7:['#2151DB', '#8182E3', '#BCB7EB', '#EBE2E6', '#EEAD9D', '#DC6951', '#C00206']},
'diverging_bwr_55_98_c37': {7:['#2480FF', '#88A4FD', '#C4CDFC', '#F8F6F7', '#FDC1B3', '#F58B73', '#E65037']},
'diverging_cwm_80_100_c22': {7:['#00D9FF', '#89E6FF', '#C9F2FF', '#FEFFFF', '#FEE3FA', '#FCC9F5', '#FAAEF0']},
'diverging_gkr_60_10_c40': {7:['#36A616', '#347420', '#2B4621', '#22201D', '#633226', '#AC462F', '#FD5838']},
'diverging_gwr_55_95_c38': {7:['#39970E', '#7DB461', '#B7D2A7', '#EDEAE6', '#F9BAB2', '#F78579', '#ED4744']},
'diverging_gwv_55_95_c39': {7:['#39970E', '#7DB461', '#B7D2A7', '#EBEBEA', '#E0BEED', '#CD8DE9', '#B859E4']},
'isoluminant_cgo_70_c39': {7:['#37B7EC', '#4DBAC6', '#63BB9E', '#86B876', '#B3AE60', '#D8A05F', '#F6906D']},
'isoluminant_cgo_80_c38': {7:['#70D1FF', '#74D4E0', '#80D6BA', '#9BD594', '#C4CC7D', '#EABF77', '#FFB281']},
'isoluminant_cm_70_c39': {7:['#14BAE6', '#5DB2EA', '#8CAAEB', '#B0A1E3', '#CF98D3', '#E98FC1', '#FE85AD']},
'rainbow_bgyr_35_85_c72': {7:['#0034F5', '#1E7D83', '#4DA910', '#B3C120', '#FCC228', '#FF8410', '#FD3000']},
'rainbow_bgyr_35_85_c73': {7:['#0035F9', '#1E7D83', '#4DA910', '#B3C01A', '#FDC120', '#FF8303', '#FF2A00']},
'rainbow_bgyrm_35_85_c69': {7:['#0030F5', '#36886A', '#82B513', '#EDC823', '#F68E19', '#F45A44', '#FD92FA']},
'rainbow_bgyrm_35_85_c71': {7:['#0035F9', '#34886A', '#80B412', '#F1CA24', '#FD8814', '#FE4E41', '#FD92FA']},
'linear_bgy_10_95_c74': {7:['#000C7D', '#002CB9', '#005EA3', '#198E61', '#32BA1A', '#70E21A', '#FFF123']},
'linear_bgyw_15_100_c67': {7:['#1B0084', '#1D26C7', '#2E68AB', '#4C9A41', '#95BE16', '#E1DB41', '#FFFFFF']},
'linear_bgyw_15_100_c68': {7:['#1A0086', '#1B27C8', '#2469AD', '#4B9B41', '#95BE16', '#E1DB41', '#FFFFFF']},
'linear_blue_5_95_c73': {7:['#00014E', '#0E02A8', '#2429F4', '#2D6CFD', '#36A3FD', '#2CD8FA', '#B3FFF6']},
'linear_blue_95_50_c20': {7:['#F1F1F1', '#D0DCEC', '#B1C8E6', '#93B5DC', '#7BA1CA', '#5E8EBC', '#3B7CB2']},
'linear_bmw_5_95_c86': {7:['#00024B', '#0708A6', '#451AF4', '#B621FE', '#F957FE', '#FEA8FD', '#FEEBFE']},
'linear_bmw_5_95_c89': {7:['#000558', '#0014BF', '#251EFA', '#B71EFF', '#F655FF', '#FFA6FF', '#FEEBFE']},
'linear_bmy_10_95_c71': {7:['#000F5D', '#48188F', '#A60B8A', '#E4336F', '#F97E4A', '#FCBE39', '#F5F94E']},
'linear_bmy_10_95_c78': {7:['#000C7D', '#3013A7', '#A7018B', '#EE1774', '#FF7051', '#FFB722', '#FFF123']},
'linear_gow_60_85_c27': {7:['#669B90', '#87A37D', '#B4A671', '#D4AC6A', '#D8B97A', '#D7C6A6', '#D4D4D4']},
'linear_gow_65_90_c35': {7:['#70AD5C', '#A3B061', '#CCB267', '#E6B86D', '#E7C786', '#E5D5B3', '#E2E2E2']},
'linear_green_5_95_c69': {7:['#011506', '#093805', '#146007', '#1F890B', '#2AB610', '#35E415', '#D8FF15']},
'linear_grey_0_100_c0': {7:['#000000', '#272727', '#4E4E4E', '#777777', '#A2A2A2', '#CFCFCF', '#FFFFFF']},
'linear_grey_10_95_c0': {7:['#1B1B1B', '#393939', '#5A5A5A', '#7D7D7D', '#A2A2A2', '#C9C9C9', '#F1F1F1']},
'linear_kry_5_95_c72': {7:['#111111', '#660304', '#A80502', '#E72205', '#FE7310', '#F4BE26', '#F7F909']},
'linear_kry_5_98_c75': {7:['#111111', '#6B0004', '#AF0000', '#F50C00', '#FF7705', '#FFBF13', '#FFFE1C']},
'linear_kryw_5_100_c64': {7:['#111111', '#6A0303', '#B00703', '#F02C06', '#FE8714', '#F3CE4C', '#FFFFFF']},
'linear_kryw_5_100_c67': {7:['#111111', '#6C0004', '#B20000', '#F81300', '#FF7D05', '#FFC43E', '#FFFFFF']},
'linear_ternary_blue_0_44_c57': {7:['#000000', '#051238', '#091F5E', '#0D2B83', '#1139AB', '#1546D3', '#1A54FF']},
'linear_ternary_green_0_46_c42': {7:['#000000', '#001C00', '#002E00', '#004100', '#005500', '#006900', '#008000']},
'linear_ternary_red_0_50_c52': {7:['#000000', '#320900', '#531000', '#761600', '#991C00', '#BE2400', '#E62B00']}
}
crameri = {
'acton': {
10: ['2E214D','4B3B66','6E5480','926390','B26795','D17BA5','D495B8','D4ADC9','DBC9DC','E6E6F0'],
25: ['2E214D','392B57','443460','503E6A','5C4974','69517D','775A86','855F8C','926390','9F6593','AA6694','B76896','C46E9B','CE77A2','D482AA','D58CB1','D495B8','D39EBE','D4A6C4','D5B0CB','D7BBD2','DAC5D9','DED0E1','E1DAE8','E6E6F0'],
50: ['2E214D','332651','382A56','3E305B','443460','493964','4F3D69','54426E','5B4873','614C78','68507C','6E5480','755884','7D5C88','835F8B','8A618E','906390','966491','9D6592','A26693','A86694','AD6795','B36795','BA6997','C06C99','C6709C','CB74A0','D07AA4','D37FA8','D484AC','D589AF','D58DB2','D492B6','D496B9','D49ABC','D39FBF','D3A3C2','D4A8C5','D4ADC9','D5B1CC','D6B6CF','D7BBD2','D9C1D7','DAC6DA','DCCBDD','DED0E1','DFD5E4','E2DBE9','E4E0EC','E6E6F0']
},
'bamako': {
10: ['00404D','134B42','265737','3A652A','52741C','71870B','969206','C5AE32','E7CD68','FFE599'],
25: ['00404D','084449','0F4845','154C41','1C513C','235538','2B5A34','325F2F','3A652A','436A25','4C7020','56771A','617E14','6C840E','7A8B06','878E03','969206','A89A14','B9A525','CBB33A','D9BF4F','E3C961','EDD375','F6DC86','FFE599'],
50: ['00404D','04424B','074449','0B4647','0F4845','124A43','154C41','184E3F','1C513D','1F533B','225539','265737','295A34','2D5C32','315F30','35612D','39642B','3D6629','416926','466C24','4A6F21','4E721F','53751C','597819','5E7C16','637F13','698210','70860C','768908','7D8C05','838E03','8A8F03','929104','999308','A1960F','AA9B16','B2A01E','BCA829','C5AE32','CCB43B','D3BA45','D9BF4F','DFC55A','E4CA63','E8CF6C','EDD375','F1D87D','F6DD88','FBE190','FFE599']
},
'batlow': {
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50: ['DED9FF','D0CFF9','C3C6F3','B3BBEC','A6B1E6','98A8E1','8B9FDB','7E95D4','6E89CB','617FC3','5575B8','4A6BAC','4262A0','395790','334F83','2E4776','293F6A','24385D','1F304F','1B2943','172338','141D2E','111824','0F151B','0D1516','0D1712','0F1B12','112113','142716','162F19','1A361C','1D3E20','224825','26512A','2A5A2E','2E6233','336C38','39773E','3F8144','488B4A','529551','5FA059','70AB63','7FB46B','8EBD73','9DC57B','ACCD83','BDD68C','CCDE94','DBE69B']
},
'tokyo': {
10: ['1A0E34','45204C','6E3E67','855E78','8D7982','929489','97AE91','A7CE9D','D5F2BC','FEFED8'],
25: ['1A0E34','2B143D','3A1A46','4B2350','5C2E5A','693964','76466C','7E5273','855E78','89697D','8C7380','8E7D83','908786','919088','939A8B','95A48E','97AE91','9BB994','A1C599','ACD3A0','BCE2AB','CEEEB7','E1F7C4','F0FCCE','FEFED8'],
50: ['1A0E34','221138','2A143C','331741','3A1A46','421E4A','4A224F','512754','5A2D59','61325E','683863','6E3E67','74446B','7A4B6F','7E5172','815775','845C77','86617A','88677C','8A6C7E','8B717F','8C7681','8D7A82','8E8084','8F8485','908986','918D88','929389','92988A','939C8C','94A18D','95A58E','96AB90','98B091','99B593','9BBB95','9EC097','A2C79A','A7CE9D','ADD4A1','B4DBA6','BCE2AB','C7EAB2','D0EFB8','D9F3BE','E1F7C4','E9FAC9','F1FCCF','F8FDD4','FEFED8']
},
'turku': {
10: ['000000','242420','424235','5F5F44','7E7C52','A99965','CFA67C','EAAD98','FCC7C3','FFE6E6'],
25: ['000000','121211','1D1D1A','282823','34332C','3E3E33','49493A','54533F','5F5F44','6B6A49','76744E','838054','938C5B','A29562','B39E6B','C2A373','CFA67C','DBA885','E4AA8F','EEB09E','F6B9AE','FBC3BD','FECFCC','FFDAD9','FFE6E6'],
50: ['000000','090908','111110','181816','1D1D1A','22221F','272723','2C2C27','33322B','38382F','3D3D32','424235','474738','4E4D3C','53523E','585841','5D5D43','626246','686848','6E6D4B','73724D','797750','7F7D52','878356','8E8859','968E5C','9D9360','A79864','AF9C68','B69F6C','BEA270','C4A474','CCA579','D1A67D','D7A781','DBA886','E0A98B','E6AB92','EAAD98','EFB09F','F2B4A7','F6B9AE','F9BFB7','FBC4BF','FDC9C6','FECFCC','FED4D3','FFDBDA','FFE0E0','FFE6E6']
},
'vik': {
10: ['001261','033E7D','1E6F9D','71A8C4','C9DDE7','EACEBD','D39774','BE6533','8B2706','590008'],
25: ['001261','02236C','023376','034481','06568C','156798','307DA6','4E92B4','71A8C4','94BED2','B3D1DF','D5E3E9','ECE5E0','EDD5C8','E4BFAA','DCAC90','D39774','CB835A','C37243','BA5E2A','A94512','942F06','7E1D06','6C0E07','590008'],
50: ['001261','011A66','02226B','022B71','023376','023A7B','034280','034A85','06548B','0B5D91','136697','1E6F9D','2B79A4','3C85AC','4B90B3','5A9ABA','6AA4C1','7AAEC8','8DBAD0','9DC4D6','ADCDDD','BDD6E3','CCDFE8','DEE6E9','E8E7E5','EEE3DC','EEDBD0','EBD0C0','E7C6B2','E3BCA5','DFB298','DBA88B','D69D7C','D29470','CE8B64','CA8258','C6794C','C26E3F','BE6533','B85C28','B2511D','A94512','9C3709','912D06','872406','7E1D06','741506','6A0D07','620607','590008']
}
}
| 167.142857
| 731
| 0.623669
| 5,160
| 45,630
| 5.474806
| 0.563566
| 0.005097
| 0.006372
| 0.007646
| 0.22315
| 0.202478
| 0.164814
| 0.099398
| 0.051823
| 0.013593
| 0
| 0.359495
| 0.036927
| 45,630
| 273
| 732
| 167.142857
| 0.283354
| 0.007539
| 0
| 0
| 0
| 0
| 0.620208
| 0.020075
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
1d24f515cedc1f9df7a59d14c3e5082192fe97ba
| 14
|
py
|
Python
|
Q-learning-control/tempCodeRunnerFile.py
|
KaguraTart/SUMO-RL-ramp-control
|
45a6015aee120e86fdcc28a519a7aeb4da5e3f5c
|
[
"MIT"
] | 6
|
2021-06-03T14:57:49.000Z
|
2022-03-29T08:05:58.000Z
|
Q-learning-control/tempCodeRunnerFile.py
|
KaguraTart/SUMO-RL-ramp-control
|
45a6015aee120e86fdcc28a519a7aeb4da5e3f5c
|
[
"MIT"
] | null | null | null |
Q-learning-control/tempCodeRunnerFile.py
|
KaguraTart/SUMO-RL-ramp-control
|
45a6015aee120e86fdcc28a519a7aeb4da5e3f5c
|
[
"MIT"
] | 2
|
2022-01-05T20:50:11.000Z
|
2022-03-15T08:31:30.000Z
|
if gui == 1:
| 7
| 13
| 0.428571
| 3
| 14
| 2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 0.357143
| 14
| 2
| 13
| 7
| 0.555556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
1d31fe391b53fc4659701237dbe6e3f947a9fb71
| 531
|
py
|
Python
|
api/models.py
|
UoW-CPC/Asclepios-Server
|
d4563c26ca751e08be9d71c985d21ee54cb0c5a5
|
[
"Apache-1.1"
] | null | null | null |
api/models.py
|
UoW-CPC/Asclepios-Server
|
d4563c26ca751e08be9d71c985d21ee54cb0c5a5
|
[
"Apache-1.1"
] | 5
|
2020-03-25T16:06:48.000Z
|
2020-10-01T12:15:08.000Z
|
api/models.py
|
UoW-CPC/Asclepios-Server
|
d4563c26ca751e08be9d71c985d21ee54cb0c5a5
|
[
"Apache-1.1"
] | 1
|
2020-03-24T12:28:28.000Z
|
2020-03-24T12:28:28.000Z
|
from django.db import models
# Create your models here.
class CipherText(models.Model):
jsonId = models.CharField(max_length=300)
data = models.TextField()
keyId = models.CharField(max_length=255)
def __str__(self):
return '%s %s' % (self.jsonId, self.data, self.keyId)
class Map(models.Model):
address = models.CharField(max_length=300)
value = models.CharField(max_length=300) # file identifiers
keyId = models.CharField(max_length=255)
def __str__(self):
return '%s %s' % (self.address, self.value, self.keyId)
| 29.5
| 60
| 0.736347
| 76
| 531
| 4.973684
| 0.394737
| 0.198413
| 0.238095
| 0.31746
| 0.5
| 0.285714
| 0.285714
| 0.285714
| 0.285714
| 0.285714
| 0
| 0.032468
| 0.129944
| 531
| 17
| 61
| 31.235294
| 0.785714
| 0.077213
| 0
| 0.307692
| 0
| 0
| 0.020534
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.153846
| false
| 0
| 0.076923
| 0.153846
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
1d353abb70424c370825289af97980529b3b3202
| 4,008
|
py
|
Python
|
tests/test_parser_record_symbolic.py
|
robertopreste/vcfpy2
|
03942712a8b398d3339c15bb60c441551c6c4623
|
[
"MIT"
] | null | null | null |
tests/test_parser_record_symbolic.py
|
robertopreste/vcfpy2
|
03942712a8b398d3339c15bb60c441551c6c4623
|
[
"MIT"
] | 1
|
2021-11-15T17:48:45.000Z
|
2021-11-15T17:48:45.000Z
|
tests/test_parser_record_symbolic.py
|
robertopreste/vcfpy2
|
03942712a8b398d3339c15bb60c441551c6c4623
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# Created by Roberto Preste
"""Test parsing of symbolic records
"""
import io
import sys
from vcfpy2 import parser
__author__ = "Manuel Holtgrewe <manuel.holtgrewe@bihealth.de>"
MEDIUM_HEADER = """
##fileformat=VCFv4.3
##fileDate=20090805
##source=myImputationProgramV3.1
##reference=file:///seq/references/1000GenomesPilot-NCBI36.fasta
##contig=<ID=20,length=62435964,assembly=B36,md5=f126cdf8a6e0c7f379d618ff66beb2da,species="Homo sapiens",taxonomy=x>
##phasing=partial
##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data">
##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">
##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">
##INFO=<ID=AA,Number=1,Type=String,Description="Ancestral Allele">
##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP membership, build 129">
##INFO=<ID=H2,Number=0,Type=Flag,Description="HapMap2 membership">
##INFO=<ID=ANNO,Number=.,Type=String,Description="Additional annotation">
##INFO=<ID=SVTYPE,Number=1,Type=String,Description="SV type">
##INFO=<ID=END,Number=1,Type=Integer,Description="SV end position">
##INFO=<ID=SVLEN,Number=1,Type=Integer,Description="SV length">
##FILTER=<ID=q10,Description="Quality below 10">
##FILTER=<ID=s50,Description="Less than 50% of samples have data">
##FILTER=<ID=PASS,Description="All filters passed">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth">
##FORMAT=<ID=HQ,Number=2,Type=Integer,Description="Haplotype Quality">
##FORMAT=<ID=FT,Number=1,Type=String,Description="Call-wise filters">
##ALT=<ID=DUP,Description="Duplication">
##ALT=<ID=R,Description="IUPAC code R = A/G">
#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tNA00001\tNA00002\tNA00003
""".lstrip()
def vcf_parser(lines):
return parser.Parser(io.StringIO(MEDIUM_HEADER + lines), "<builtin>")
def test_parse_dup():
# Setup parser with stock header and lines to parse
LINES = "2\t321681\t.\tN\t<DUP>\t.\tPASS\tSVTYPE=DUP;END=324681;SVLEN=3000\tGT\t0/1\t0/0\t0/0\n"
p = vcf_parser(LINES)
p.parse_header()
# Perform the actual test
if sys.version_info < (3, 6):
EXPECTED = (
"Record('2', 321681, [], 'N', [SymbolicAllele('DUP')], None, ['PASS'], "
"OrderedDict([('SVTYPE', 'DUP'), ('END', 324681), ('SVLEN', 3000)]), ['GT'], ["
"Call('NA00001', OrderedDict([('GT', '0/1')])), Call('NA00002', OrderedDict([('GT', '0/0')])), "
"Call('NA00003', OrderedDict([('GT', '0/0')]))])"
)
else:
EXPECTED = (
"Record('2', 321681, [], 'N', [SymbolicAllele('DUP')], None, ['PASS'], "
"{'SVTYPE': 'DUP', 'END': 324681, 'SVLEN': 3000}, ['GT'], ["
"Call('NA00001', {'GT': '0/1'}), Call('NA00002', {'GT': '0/0'}), Call('NA00003', {'GT': '0/0'})])"
)
rec = p.parse_next_record()
assert str(rec) == EXPECTED
assert rec.ALT[0].serialize() == "<DUP>"
def test_parse_iupac():
# Setup parser with stock header and lines to parse
LINES = "2\t321681\t.\tC\t<R>\t.\tPASS\t.\tGT\t0/1\t0/0\t0/0\n"
p = vcf_parser(LINES)
p.parse_header()
# Perform the actual test
if sys.version_info < (3, 6):
EXPECTED = (
"Record('2', 321681, [], 'C', [SymbolicAllele('R')], None, ['PASS'], OrderedDict(), ['GT'], "
"[Call('NA00001', OrderedDict([('GT', '0/1')])), Call('NA00002', OrderedDict([('GT', '0/0')])), "
"Call('NA00003', OrderedDict([('GT', '0/0')]))])"
)
else:
EXPECTED = (
"Record('2', 321681, [], 'C', [SymbolicAllele('R')], None, ['PASS'], {}, ['GT'], "
"[Call('NA00001', {'GT': '0/1'}), Call('NA00002', {'GT': '0/0'}), Call('NA00003', {'GT': '0/0'})])"
)
rec = p.parse_next_record()
assert str(rec) == EXPECTED
assert rec.ALT[0].serialize() == "<R>"
| 42.638298
| 116
| 0.627246
| 537
| 4,008
| 4.642458
| 0.348231
| 0.01444
| 0.044124
| 0.043321
| 0.505014
| 0.407541
| 0.382671
| 0.356197
| 0.356197
| 0.294424
| 0
| 0.077351
| 0.148453
| 4,008
| 93
| 117
| 43.096774
| 0.653091
| 0.062126
| 0
| 0.273973
| 0
| 0.205479
| 0.736252
| 0.418046
| 0
| 0
| 0
| 0
| 0.054795
| 1
| 0.041096
| false
| 0.09589
| 0.041096
| 0.013699
| 0.09589
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
1d4505bc4e9328311196808197ba7a4a0179f93f
| 335
|
py
|
Python
|
Lib/ldap/async.py
|
reqa/python-ldap
|
e75c24dd70dcf10c8315d6f30ecf98f2c30f08e8
|
[
"MIT"
] | 299
|
2017-11-23T14:24:32.000Z
|
2022-03-25T08:45:24.000Z
|
Lib/ldap/async.py
|
reqa/python-ldap
|
e75c24dd70dcf10c8315d6f30ecf98f2c30f08e8
|
[
"MIT"
] | 412
|
2017-11-23T22:21:36.000Z
|
2022-03-18T11:20:59.000Z
|
Lib/ldap/async.py
|
reqa/python-ldap
|
e75c24dd70dcf10c8315d6f30ecf98f2c30f08e8
|
[
"MIT"
] | 114
|
2017-11-23T14:24:37.000Z
|
2022-03-24T20:55:42.000Z
|
"""
ldap.asyncsearch - handle async LDAP search operations
See https://www.python-ldap.org/ for details.
"""
import warnings
from ldap.asyncsearch import *
from ldap.asyncsearch import __version__
warnings.warn(
"'ldap.async module' is deprecated, import 'ldap.asyncsearch' instead.",
DeprecationWarning,
stacklevel=2
)
| 20.9375
| 76
| 0.749254
| 40
| 335
| 6.175
| 0.625
| 0.242915
| 0.153846
| 0.202429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003497
| 0.146269
| 335
| 15
| 77
| 22.333333
| 0.86014
| 0.301493
| 0
| 0
| 0
| 0
| 0.30531
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 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
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
1d570dbcc420301ebd4a771e678a192f1b51a2b4
| 3,842
|
py
|
Python
|
scripts/configs.py
|
wilkie/covid
|
e2c6447f83647ed269b368df888430a7fee59cea
|
[
"MIT"
] | 1
|
2021-05-18T22:46:20.000Z
|
2021-05-18T22:46:20.000Z
|
scripts/configs.py
|
lzlzlizi/covid
|
5dedfb31e9e6a66e7ac218095f52911183b30995
|
[
"MIT"
] | null | null | null |
scripts/configs.py
|
lzlzlizi/covid
|
5dedfb31e9e6a66e7ac218095f52911183b30995
|
[
"MIT"
] | null | null | null |
import covid.models.SEIRD
import covid.models.SEIRD_variable_detection
import covid.models.SEIRD_incident
import covid.util as util
# 2020-04-25 forecast (?)
SEIRD = {
'model' : covid.models.SEIRD.SEIRD,
'args' : {} # use defaults
}
# 2020-05-03 forecast
strongest_prior = {
'model' : covid.models.SEIRD_variable_detection.SEIRD,
'args' : {
'gamma_shape': 100,
'sigma_shape': 100
}
}
# 2020-05-10 forecast
fit_dispersion = {
'model' : covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 100,
'sigma_shape': 100
}
}
# State forecasts starting 2020-05-17, all US forecasts
resample_80_last_10 = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 100,
'sigma_shape': 100,
'resample_high': 80,
'rw_use_last': 10
}
}
# State and US forecasts starting 2020-09-06
longer_H = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 100,
'sigma_shape': 100,
'resample_high': 80,
'rw_use_last': 10,
'H_duration_est': 18.0
}
}
# State and US forecasts starting 2020-09-20, except 2020-10-20
# changed gamma_shape and sigma_shape from 100 to 1000 on 2021-01-10
llonger_H = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 1000,
'sigma_shape': 1000,
'resample_high': 80,
'rw_use_last': 10,
'H_duration_est': 25.0
}
}
# Less rw
llonger_H_fix = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 1000,
'sigma_shape': 1000,
'resample_high': 80,
'rw_use_last': 10,
'rw_scale': 1e-1,
'H_duration_est': 25.0
}
}
lower_det = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 1000,
'sigma_shape': 1000,
'resample_high': 80,
'rw_use_last': 10,
'rw_scale': 1e-1,
'H_duration_est': 25.0,
'det_prob_est': 0.1
}
}
# For debugging on Jan 3
debug = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 1000,
'sigma_shape': 1000,
'resample_high': 80,
'rw_use_last': 10,
'H_duration_est': 25.0,
'num_warmup': 100,
'num_samples': 100
}
}
debug2 = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 1000,
'sigma_shape': 1000,
'resample_high': 80,
'rw_use_last': 10,
'H_duration_est': 25.0
}
}
# For debugging on Jan 3
fix = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 100,
'sigma_shape': 100,
'resample_high': 80,
'rw_use_last': 10,
'H_duration_est': 25.0,
'beta_shape': 1,
'rw_scale': 1e-1,
}
}
# State and US forecasts 2020-10-20
lllonger_H = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 100,
'sigma_shape': 100,
'resample_high': 80,
'rw_use_last': 10,
'H_duration_est': 35.0
}
}
# changed gamma_shape and sigma_shape from 100 to 1000 on 2021-01-10
counties = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 1000,
'sigma_shape': 1000,
'resample_high': 80,
'rw_use_last': 10,
'rw_scale': 1e-1,
'T_future': 8*7
}
}
counties_fix = {
'model': covid.models.SEIRD_incident.SEIRD,
'args' : {
'gamma_shape': 100,
'sigma_shape': 100,
'resample_high': 80,
'rw_use_last': 10,
'rw_scale': 1e-1,
'T_future': 8*7,
'H_duration_est': 25.0,
'beta_shape': 1
}
}
| 21.58427
| 69
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| 479
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0
| 4
|
1d883741fa02528930f7dcfc8658a29ab0e08d64
| 122
|
py
|
Python
|
Chapter06_code/Ch06_R07/some_model_ch06r07/__openerp__.py
|
PacktPublishing/Odoo-Development-Cookbook
|
5553110c0bc352c4541f11904e236cad3c443b8b
|
[
"MIT"
] | 55
|
2016-05-23T16:05:50.000Z
|
2021-07-19T00:16:46.000Z
|
Chapter06_code/Ch06_R07/some_model_ch06r07/__openerp__.py
|
kogkog098/Odoo-Development-Cookbook
|
166c9b98efbc9108b30d719213689afb1f1c294d
|
[
"MIT"
] | 1
|
2016-12-09T02:14:21.000Z
|
2018-07-02T09:02:20.000Z
|
Chapter06_code/Ch06_R07/some_model_ch06r07/__openerp__.py
|
kogkog098/Odoo-Development-Cookbook
|
166c9b98efbc9108b30d719213689afb1f1c294d
|
[
"MIT"
] | 52
|
2016-06-01T20:03:59.000Z
|
2020-10-31T23:58:25.000Z
|
{
'name': 'Chapter 06, Recipe 07 code',
'summary': 'Port old API code to the new API',
'depends': ['base'],
}
| 20.333333
| 50
| 0.557377
| 17
| 122
| 4
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| 122
| 5
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0
| 4
|
1d917c7fde2aaa645ffce0920d9af4e6218c05c9
| 319
|
py
|
Python
|
app/db/mongodb.py
|
BoroviyOrest/QuizzesAPI
|
4db3de618ef73140a6a1b659ea458d6a90d3d365
|
[
"MIT"
] | null | null | null |
app/db/mongodb.py
|
BoroviyOrest/QuizzesAPI
|
4db3de618ef73140a6a1b659ea458d6a90d3d365
|
[
"MIT"
] | 11
|
2021-02-02T13:12:29.000Z
|
2021-04-05T20:50:52.000Z
|
app/db/mongodb.py
|
BoroviyOrest/SpanishQuizzesAPI
|
4db3de618ef73140a6a1b659ea458d6a90d3d365
|
[
"MIT"
] | null | null | null |
from typing import Callable
from starlette.requests import Request
def get_client(model_class) -> Callable:
"""Get mongoDB client from the request object ind initialize model class with it"""
def wrapper(request: Request) -> object:
return model_class(request.app.state.mongodb)
return wrapper
| 24.538462
| 87
| 0.742947
| 42
| 319
| 5.571429
| 0.547619
| 0.128205
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| 319
| 12
| 88
| 26.583333
| 0.9
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| false
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| 1
| 1
| 1
| 0
|
0
| 4
|
1d985ab1526227fdd2166b4b809112def710914e
| 156
|
py
|
Python
|
admin/backend/main.py
|
thanhhongquang9/BookStore_StrangersTeam
|
e959c9d283d12dc2416ce022be516a18d5ae4984
|
[
"Apache-2.0"
] | null | null | null |
admin/backend/main.py
|
thanhhongquang9/BookStore_StrangersTeam
|
e959c9d283d12dc2416ce022be516a18d5ae4984
|
[
"Apache-2.0"
] | null | null | null |
admin/backend/main.py
|
thanhhongquang9/BookStore_StrangersTeam
|
e959c9d283d12dc2416ce022be516a18d5ae4984
|
[
"Apache-2.0"
] | null | null | null |
from starlette.routing import Host
import uvicorn
if __name__ == '__main__':
uvicorn.run("app.mainapi:app",host = "0.0.0.0", port= 8000,reload= True)
| 26
| 76
| 0.705128
| 24
| 156
| 4.25
| 0.708333
| 0.058824
| 0.058824
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0
| 4
|
d551a4f421c6d2d1b21385a2c77042fe711cf915
| 302
|
py
|
Python
|
fastApi_backend/app/api/api.py
|
forrest57/rise-up
|
30e03b1b64183bb454e8b20e3541611b9128ec69
|
[
"MIT"
] | null | null | null |
fastApi_backend/app/api/api.py
|
forrest57/rise-up
|
30e03b1b64183bb454e8b20e3541611b9128ec69
|
[
"MIT"
] | null | null | null |
fastApi_backend/app/api/api.py
|
forrest57/rise-up
|
30e03b1b64183bb454e8b20e3541611b9128ec69
|
[
"MIT"
] | null | null | null |
from fastapi import APIRouter
from .endpoints import posts, users, login
api_router = APIRouter()
api_router.include_router(login.router, tags=['login'])
api_router.include_router(users.router, prefix='/users', tags=['users'])
api_router.include_router(posts.router, prefix='/posts', tags=['posts'])
| 33.555556
| 72
| 0.771523
| 41
| 302
| 5.512195
| 0.317073
| 0.159292
| 0.212389
| 0.292035
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| 0.076159
| 302
| 8
| 73
| 37.75
| 0.810036
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| 0
| 0
| 0
|
0
| 4
|
d5634ff759f1c09a67bee86ea3bb600844cbc8ed
| 87
|
py
|
Python
|
server.py
|
toschoch/python-storageapi
|
8b48fddb46ac729942f92c85234c8bc9c3ff3ea2
|
[
"MIT"
] | null | null | null |
server.py
|
toschoch/python-storageapi
|
8b48fddb46ac729942f92c85234c8bc9c3ff3ea2
|
[
"MIT"
] | null | null | null |
server.py
|
toschoch/python-storageapi
|
8b48fddb46ac729942f92c85234c8bc9c3ff3ea2
|
[
"MIT"
] | null | null | null |
import uvicorn
if __name__ == '__main__':
uvicorn.run("api.app:app", reload=True)
| 17.4
| 43
| 0.689655
| 12
| 87
| 4.333333
| 0.833333
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| 87
| 4
| 44
| 21.75
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| 0
|
0
| 4
|
63383052cbaf2836c36a1363b69d64a5d6874c8d
| 550
|
py
|
Python
|
commands.py
|
GetmeUK/h51
|
17d4003336857514765a42a0853995fbe3da6525
|
[
"MIT"
] | null | null | null |
commands.py
|
GetmeUK/h51
|
17d4003336857514765a42a0853995fbe3da6525
|
[
"MIT"
] | 4
|
2021-06-08T22:58:13.000Z
|
2022-03-12T00:53:18.000Z
|
commands.py
|
GetmeUK/h51
|
17d4003336857514765a42a0853995fbe3da6525
|
[
"MIT"
] | null | null | null |
from manhattan.manage import commands as manage
from blueprints.accounts.manage import commands as accounts
from blueprints.assets.manage import commands as assets
from blueprints.users.manage import commands as users
from dispatcher import create_dispatcher
def create_app(env):
dispatcher = create_dispatcher(env)
# Register commands with the app
accounts.add_commands(dispatcher.app)
assets.add_commands(dispatcher.app)
manage.add_commands(dispatcher.app)
users.add_commands(dispatcher.app)
return dispatcher.app
| 27.5
| 59
| 0.8
| 72
| 550
| 6.013889
| 0.277778
| 0.150115
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| 0.203233
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| 550
| 19
| 60
| 28.947368
| 0.919321
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| 1
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|
0
| 4
|
63654ecbb13836e7c267a672684484efc31c2fb4
| 162
|
py
|
Python
|
services/leekduck/test_leekduck.py
|
pogotulz/aiogram_bot
|
62bb4edff4a968262812e5179cddd409159f6018
|
[
"MIT"
] | null | null | null |
services/leekduck/test_leekduck.py
|
pogotulz/aiogram_bot
|
62bb4edff4a968262812e5179cddd409159f6018
|
[
"MIT"
] | null | null | null |
services/leekduck/test_leekduck.py
|
pogotulz/aiogram_bot
|
62bb4edff4a968262812e5179cddd409159f6018
|
[
"MIT"
] | null | null | null |
import pytest
from services.leekduck import LeekDuck
@pytest.fisture
def leek(duck):
""" фікстура """
return LeekDuck()
def test_init(leek):
pass
| 12.461538
| 38
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| 20
| 162
| 5.55
| 0.7
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| 0.203704
| 162
| 12
| 39
| 13.5
| 0.860465
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| false
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| 1
| 0
|
0
| 4
|
636e1cd0a3709dc409c3e6febd4bd946dd3f8413
| 186
|
py
|
Python
|
kivy_garden/splittergrid/tests/test_import.py
|
kivy-garden/splittergrid
|
5164cdf31bd77d79b6cdea922d36726296c79b40
|
[
"MIT"
] | 7
|
2020-02-17T17:36:48.000Z
|
2020-09-14T14:15:18.000Z
|
kivy_garden/splittergrid/tests/test_import.py
|
kivy-garden/splittergrid
|
5164cdf31bd77d79b6cdea922d36726296c79b40
|
[
"MIT"
] | null | null | null |
kivy_garden/splittergrid/tests/test_import.py
|
kivy-garden/splittergrid
|
5164cdf31bd77d79b6cdea922d36726296c79b40
|
[
"MIT"
] | null | null | null |
import pytest
def test_flower():
from kivy_garden.splittergrid import SplitterGrid
grid = SplitterGrid(cols=5)
assert grid.cols == 5
assert grid.orientation == 'lr-tb'
| 20.666667
| 53
| 0.709677
| 24
| 186
| 5.416667
| 0.666667
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| 0.169231
| 0.230769
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| 0
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| 0.198925
| 186
| 8
| 54
| 23.25
| 0.85906
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| 0.166667
| false
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
638c1217d29698f2db35a1ee500cc734f0aa9ba3
| 23
|
py
|
Python
|
apache/datadog_checks/apache/__about__.py
|
vbarbaresi/integrations-core
|
ab26ab1cd6c28a97c1ad1177093a93659658c7aa
|
[
"BSD-3-Clause"
] | 31
|
2016-11-14T14:26:24.000Z
|
2021-11-19T15:43:45.000Z
|
apache/datadog_checks/apache/__about__.py
|
vbarbaresi/integrations-core
|
ab26ab1cd6c28a97c1ad1177093a93659658c7aa
|
[
"BSD-3-Clause"
] | 296
|
2016-11-11T20:52:59.000Z
|
2022-02-23T13:34:37.000Z
|
apache/datadog_checks/apache/__about__.py
|
vbarbaresi/integrations-core
|
ab26ab1cd6c28a97c1ad1177093a93659658c7aa
|
[
"BSD-3-Clause"
] | 40
|
2016-11-11T20:48:13.000Z
|
2021-04-22T17:47:09.000Z
|
__version__ = "1.16.0"
| 11.5
| 22
| 0.652174
| 4
| 23
| 2.75
| 1
| 0
| 0
| 0
| 0
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| 0
| 0.2
| 0.130435
| 23
| 1
| 23
| 23
| 0.35
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| 1
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| 0
| 0
| 0
| 0
|
0
| 4
|
63a39dd2b74d99d3ae37afd2f983746c46666c93
| 67
|
py
|
Python
|
lectures/code/list_filter.py
|
naskoch/python_course
|
84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3
|
[
"MIT"
] | 4
|
2015-08-10T17:46:55.000Z
|
2020-04-18T21:09:03.000Z
|
lectures/code/list_filter.py
|
naskoch/python_course
|
84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3
|
[
"MIT"
] | null | null | null |
lectures/code/list_filter.py
|
naskoch/python_course
|
84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3
|
[
"MIT"
] | 2
|
2019-04-24T03:31:02.000Z
|
2019-05-13T07:36:06.000Z
|
l = range(8)
print filter(lambda x: x % 2 == 0, l)
# [0, 2, 4, 6]
| 13.4
| 37
| 0.492537
| 15
| 67
| 2.2
| 0.733333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 0.268657
| 67
| 4
| 38
| 16.75
| 0.530612
| 0.179104
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
63d3dda0bd3f08e89628fcbf417d00d8f5128a8d
| 53
|
py
|
Python
|
breakout game/test.py
|
PLChen1/sc-project
|
30b248000c22bbd0ff309a3cffcf90b0bf422962
|
[
"MIT"
] | null | null | null |
breakout game/test.py
|
PLChen1/sc-project
|
30b248000c22bbd0ff309a3cffcf90b0bf422962
|
[
"MIT"
] | null | null | null |
breakout game/test.py
|
PLChen1/sc-project
|
30b248000c22bbd0ff309a3cffcf90b0bf422962
|
[
"MIT"
] | null | null | null |
from testgraphics import
def main():
print(str)
| 17.666667
| 25
| 0.698113
| 7
| 53
| 5.285714
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.207547
| 53
| 3
| 26
| 17.666667
| 0.880952
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.333333
| null | null | 0.333333
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
8944888194689ecb0619e438ea10d6586253e2ad
| 158
|
py
|
Python
|
coding/learn_itsdangerous/__init__.py
|
yatao91/learning_road
|
e88dc43de98e35922bfc71c222ec71766851e618
|
[
"MIT"
] | 3
|
2021-05-25T16:58:52.000Z
|
2022-02-05T09:37:17.000Z
|
coding/learn_itsdangerous/__init__.py
|
yataosu/learning_road
|
e88dc43de98e35922bfc71c222ec71766851e618
|
[
"MIT"
] | null | null | null |
coding/learn_itsdangerous/__init__.py
|
yataosu/learning_road
|
e88dc43de98e35922bfc71c222ec71766851e618
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
author: 苏亚涛
email: yataosu@gmail.com
create_time: 2019/10/24 13:42
file: __init__.py.py
ide: PyCharm
"""
| 19.75
| 30
| 0.556962
| 22
| 158
| 3.772727
| 0.954545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113043
| 0.272152
| 158
| 8
| 31
| 19.75
| 0.608696
| 0.943038
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
895b19a36a37032cd83d01216cd0cbfe46aee507
| 134
|
py
|
Python
|
financial_data/tasks/celery_worker.py
|
fredericoqueiroz/scraperfy-api
|
124385de0780c70f320f800d1af1de39d8e1d62d
|
[
"MIT"
] | null | null | null |
financial_data/tasks/celery_worker.py
|
fredericoqueiroz/scraperfy-api
|
124385de0780c70f320f800d1af1de39d8e1d62d
|
[
"MIT"
] | null | null | null |
financial_data/tasks/celery_worker.py
|
fredericoqueiroz/scraperfy-api
|
124385de0780c70f320f800d1af1de39d8e1d62d
|
[
"MIT"
] | null | null | null |
from financial_data.app import create_app
from . import celery, init_celery
flask_app = create_app()
init_celery(celery, flask_app)
| 19.142857
| 41
| 0.813433
| 21
| 134
| 4.857143
| 0.428571
| 0.176471
| 0.27451
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119403
| 134
| 6
| 42
| 22.333333
| 0.864407
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 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
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
896169ae2a40c1f97e3fa36ab3d304dd26a6eed5
| 5,764
|
py
|
Python
|
typedb/api/concept/concept.py
|
rpatil524/client-python
|
e8daba79842a81669f4f4c2799bcc8610e610551
|
[
"Apache-2.0"
] | 47
|
2019-01-22T19:17:13.000Z
|
2021-02-06T15:39:59.000Z
|
typedb/api/concept/concept.py
|
rpatil524/client-python
|
e8daba79842a81669f4f4c2799bcc8610e610551
|
[
"Apache-2.0"
] | 85
|
2019-01-22T14:51:34.000Z
|
2021-04-08T15:41:43.000Z
|
typedb/api/concept/concept.py
|
rpatil524/client-python
|
e8daba79842a81669f4f4c2799bcc8610e610551
|
[
"Apache-2.0"
] | 24
|
2019-01-22T13:21:42.000Z
|
2021-03-02T18:06:03.000Z
|
#
# Copyright (C) 2021 Vaticle
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING
from typedb.common.exception import TypeDBClientException, INVALID_CONCEPT_CASTING
if TYPE_CHECKING:
from typedb.api.concept.thing.attribute import Attribute, RemoteAttribute
from typedb.api.concept.thing.entity import Entity, RemoteEntity
from typedb.api.concept.thing.relation import Relation, RemoteRelation
from typedb.api.concept.thing.thing import Thing, RemoteThing
from typedb.api.concept.type.attribute_type import AttributeType, RemoteAttributeType
from typedb.api.concept.type.entity_type import EntityType, RemoteEntityType
from typedb.api.concept.type.relation_type import RelationType, RemoteRelationType
from typedb.api.concept.type.role_type import RoleType, RemoteRoleType
from typedb.api.concept.type.thing_type import ThingType, RemoteThingType
from typedb.api.concept.type.type import Type, RemoteType
from typedb.api.connection.transaction import TypeDBTransaction
class Concept(ABC):
def is_type(self) -> bool:
return False
def is_thing_type(self) -> bool:
return False
def is_entity_type(self) -> bool:
return False
def is_attribute_type(self) -> bool:
return False
def is_relation_type(self) -> bool:
return False
def is_role_type(self) -> bool:
return False
def is_thing(self) -> bool:
return False
def is_entity(self) -> bool:
return False
def is_attribute(self) -> bool:
return False
def is_relation(self) -> bool:
return False
def as_type(self) -> "Type":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "Type"))
def as_thing_type(self) -> "ThingType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "ThingType"))
def as_entity_type(self) -> "EntityType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "EntityType"))
def as_attribute_type(self) -> "AttributeType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "AttributeType"))
def as_relation_type(self) -> "RelationType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "RelationType"))
def as_role_type(self) -> "RoleType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "RoleType"))
def as_thing(self) -> "Thing":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "Thing"))
def as_entity(self) -> "Entity":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "Entity"))
def as_attribute(self) -> "Attribute":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "Attribute"))
def as_relation(self) -> "Relation":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "Relation"))
@abstractmethod
def as_remote(self, transaction: "TypeDBTransaction") -> "RemoteConcept":
pass
@abstractmethod
def is_remote(self) -> bool:
pass
class RemoteConcept(Concept, ABC):
@abstractmethod
def delete(self) -> None:
pass
@abstractmethod
def is_deleted(self) -> bool:
pass
def as_type(self) -> "RemoteType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "Type"))
def as_thing_type(self) -> "RemoteThingType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "ThingType"))
def as_entity_type(self) -> "RemoteEntityType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "EntityType"))
def as_attribute_type(self) -> "RemoteAttributeType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "AttributeType"))
def as_relation_type(self) -> "RemoteRelationType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "RelationType"))
def as_role_type(self) -> "RemoteRoleType":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "RoleType"))
def as_thing(self) -> "RemoteThing":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "Thing"))
def as_entity(self) -> "RemoteEntity":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "Entity"))
def as_attribute(self) -> "RemoteAttribute":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "Attribute"))
def as_relation(self) -> "RemoteRelation":
raise TypeDBClientException.of(INVALID_CONCEPT_CASTING, (self.__class__.__name__, "Relation"))
| 38.426667
| 107
| 0.735253
| 669
| 5,764
| 5.950673
| 0.19133
| 0.073851
| 0.110776
| 0.175835
| 0.575735
| 0.508917
| 0.508917
| 0.451143
| 0.434564
| 0.434564
| 0
| 0.001669
| 0.168459
| 5,764
| 149
| 108
| 38.684564
| 0.828917
| 0.135149
| 0
| 0.426966
| 0
| 0
| 0.085818
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.382022
| false
| 0.044944
| 0.157303
| 0.11236
| 0.674157
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
8978c4887c382891171bd0e70e92b6ff5279f071
| 574
|
py
|
Python
|
0x08-python-more_classes/test/4-main.py
|
malu17/alx-higher_level_programming
|
75a24d98c51116b737f339697c75855e34254d3a
|
[
"MIT"
] | 1
|
2022-02-07T12:13:18.000Z
|
2022-02-07T12:13:18.000Z
|
0x08-python-more_classes/test/4-main.py
|
malu17/alx-higher_level_programming
|
75a24d98c51116b737f339697c75855e34254d3a
|
[
"MIT"
] | null | null | null |
0x08-python-more_classes/test/4-main.py
|
malu17/alx-higher_level_programming
|
75a24d98c51116b737f339697c75855e34254d3a
|
[
"MIT"
] | 1
|
2021-12-06T18:15:54.000Z
|
2021-12-06T18:15:54.000Z
|
#!/usr/bin/python3
Rectangle = __import__('4-rectangle').Rectangle
my_rectangle = Rectangle(2, 4)
print(str(my_rectangle))
print("--")
print(my_rectangle)
print("--")
print(repr(my_rectangle))
print("--")
print(hex(id(my_rectangle)))
print("--")
# create new instance based on representation
new_rectangle = eval(repr(my_rectangle))
print(str(new_rectangle))
print("--")
print(new_rectangle)
print("--")
print(repr(new_rectangle))
print("--")
print(hex(id(new_rectangle)))
print("--")
print(new_rectangle is my_rectangle)
print(type(new_rectangle) is type(my_rectangle))
| 21.259259
| 48
| 0.733449
| 79
| 574
| 5.088608
| 0.278481
| 0.348259
| 0.330846
| 0.218905
| 0.288557
| 0.169154
| 0
| 0
| 0
| 0
| 0
| 0.007533
| 0.074913
| 574
| 26
| 49
| 22.076923
| 0.749529
| 0.106272
| 0
| 0.380952
| 0
| 0
| 0.052838
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.047619
| 0
| 0.047619
| 0.857143
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
897de1bb99afaecfbc6eae43e7060c74204f7617
| 134
|
py
|
Python
|
reporting/urls.py
|
Nivocse/notam
|
2bfd1088caf00ec6cabce3d2b183ef466930a8bb
|
[
"MIT"
] | null | null | null |
reporting/urls.py
|
Nivocse/notam
|
2bfd1088caf00ec6cabce3d2b183ef466930a8bb
|
[
"MIT"
] | null | null | null |
reporting/urls.py
|
Nivocse/notam
|
2bfd1088caf00ec6cabce3d2b183ef466930a8bb
|
[
"MIT"
] | null | null | null |
from django.urls import path
from .views import verbose_report
urlpatterns = [
path('', verbose_report, name='verbose_report'),
]
| 22.333333
| 52
| 0.746269
| 17
| 134
| 5.705882
| 0.588235
| 0.402062
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141791
| 134
| 6
| 53
| 22.333333
| 0.843478
| 0
| 0
| 0
| 0
| 0
| 0.103704
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 0.4
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
89878a8b923cee12a25f177b3da4e029fc097cbb
| 223
|
py
|
Python
|
trio_run_in_process/__init__.py
|
goodboy/trio-run-in-process
|
b1f366ff0c4ef4f6055799ba073bbf69efe7d864
|
[
"MIT"
] | null | null | null |
trio_run_in_process/__init__.py
|
goodboy/trio-run-in-process
|
b1f366ff0c4ef4f6055799ba073bbf69efe7d864
|
[
"MIT"
] | null | null | null |
trio_run_in_process/__init__.py
|
goodboy/trio-run-in-process
|
b1f366ff0c4ef4f6055799ba073bbf69efe7d864
|
[
"MIT"
] | null | null | null |
from .exceptions import ( # noqa: F401
BaseRunInProcessException,
InvalidState,
ProcessKilled,
)
from .run_in_process import open_in_process, run_in_process # noqa: F401
from .state import State # noqa: F401
| 27.875
| 73
| 0.748879
| 27
| 223
| 5.962963
| 0.481481
| 0.149068
| 0.149068
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.049451
| 0.183857
| 223
| 7
| 74
| 31.857143
| 0.835165
| 0.143498
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.428571
| 0
| 0.428571
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
9834e8d6b9bd28a7d7fd380c6c48483c65a0f2f5
| 83,732
|
py
|
Python
|
train.py
|
ycao5602/SAL
|
85a363ed1b0d18451daefe11357c565a991f18b5
|
[
"MIT"
] | 13
|
2020-08-25T00:58:00.000Z
|
2022-02-28T01:33:26.000Z
|
train.py
|
ycao5602/SAL
|
85a363ed1b0d18451daefe11357c565a991f18b5
|
[
"MIT"
] | 3
|
2021-02-05T04:32:39.000Z
|
2021-12-22T11:05:25.000Z
|
train.py
|
ycao5602/SAL
|
85a363ed1b0d18451daefe11357c565a991f18b5
|
[
"MIT"
] | 1
|
2020-12-04T00:26:04.000Z
|
2020-12-04T00:26:04.000Z
|
from __future__ import print_function
from __future__ import division
import os
import sys
import time
import datetime
import os.path as osp
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torch import Tensor
from args import argument_parser, image_dataset_kwargs, optimizer_kwargs, optimizer_kwargs_sc, optimizer_kwargs_pt, \
optimizer_kwargs_al, optimizer_kwargs_gf, optimizer_kwargs_df
from torchreid.data_manager import ImageDataManager
from torchreid import models
from torchreid.losses import CrossEntropyLoss, DeepSupervision, BCELoss
from torchreid.utils.iotools import save_checkpoint, check_isfile
from torchreid.utils.avgmeter import AverageMeter
from torchreid.utils.loggers import Logger, RankLogger
from torchreid.utils.torchtools import count_num_param, open_all_layers, open_specified_layers
from torchreid.utils.reidtools import visualize_ranked_results
from torchreid.eval_metrics import evaluate
from torchreid.optimizers import init_optimizer
from models import model_g, model_d, model_x, model_a, model_i2, model_da, model_ga, model_dx, model_gx
from random import sample, choices
'''
========================================================================================
gan: triangle structure. Three discriminators.
attention: None
UNIT
https://arxiv.org/pdf/1703.00848.pdf
triange with reverse from the shared space. orders changed.
========================================================================================
'''
# global variables
parser = argument_parser()
args = parser.parse_args()
def main():
global args
torch.manual_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
if args.use_cpu:
use_gpu = False
log_name = 'log_test.txt' if args.evaluate else 'log_train.txt'
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
device = torch.device('cuda')
else:
print("Currently using CPU, however, GPU is highly recommended")
device = torch.device('cpu')
print("Initializing image data manager")
dm = ImageDataManager(use_gpu, **image_dataset_kwargs(args))
trainloader, testloader_dict = dm.return_dataloaders()
w = dm.w
average = dm.average
print("Initializing model: {}".format(args.arch))
size_embed = args.size_embed
num_attr = dm.num_train_attrs
# image branch extractor
model_I1 = models.init_model(name=args.arch, num_classes=512, loss={'xent'}, use_gpu=use_gpu)
print("Model size: {:.3f} M".format(count_num_param(model_I1)))
# attribute branch extractor
model_G = model_g(dm.num_train_attrs, 512).to(device)
# attribute classifier
model_I2 = model_i2(size_embed, dm.num_train_attrs).to(device)
# category classifier
model_I2_sem = model_i2(size_embed, dm.num_train_sems).to(device)
######## Synthesis-GAN ##################
model_GA = model_ga(512, 512).to(device)
model_GX = model_gx(512+args.noise, 512).to(device)
model_DC = model_d(2 * 512).to(device)
######## Alignment-GAN ##################
model_DS = model_d(size_embed).to(device)
model_xencoder = model_x(512, size_embed).to(device) # Ev
model_aencoder = model_a(512, size_embed).to(device) # Ea
if use_gpu:
model_I1 = nn.DataParallel(model_I1).cuda()
model_I2 = nn.DataParallel(model_I2).cuda()
model_I2_sem = nn.DataParallel(model_I2_sem).cuda()
model_G = nn.DataParallel(model_G).cuda()
model_DC = nn.DataParallel(model_DC).cuda()
model_DS = nn.DataParallel(model_DS).cuda()
model_xencoder = nn.DataParallel(model_xencoder).cuda()
model_aencoder = nn.DataParallel(model_aencoder).cuda()
model_GA = nn.DataParallel(model_GA).cuda()
model_GX = nn.DataParallel(model_GX).cuda()
criterion = BCELoss(num_classes=dm.num_train_attrs, use_gpu=use_gpu, label_smooth=args.label_smooth)
criterion_sem = CrossEntropyLoss(num_classes=dm.num_train_sems, use_gpu=use_gpu, label_smooth=args.label_smooth)
criterion_gan = BCELoss(num_classes=1, use_gpu=use_gpu, label_smooth=args.label_smooth)
# consistency loss, l2 or l1
if args.loss == 'l2':
criterion_mse = nn.MSELoss()
elif args.loss == 'l1':
criterion_mse = nn.L1Loss()
else:
criterion_mse = nn.MSELoss()
print('mse loss is used since the argument is not correctly given from l1 and l2')
# optimizer for the image branch in single branch pretrain
optimizer = init_optimizer(
list(model_I1.parameters()) + list(model_xencoder.parameters()) + list(model_I2.parameters()) + list(
model_I2_sem.parameters()), **optimizer_kwargs_pt(args))
# optimizer for the attribute branch in joint pretrain
optimizer_G = init_optimizer(
list(model_G.parameters()) + list(model_aencoder.parameters()) + list(model_I2.parameters()) + list(
model_I2_sem.parameters()), **optimizer_kwargs_sc(args))
# optimizer for the image branch in joint pretrain
optimizer_I = init_optimizer(
list(model_I1.parameters()) + list(model_xencoder.parameters()) + list(model_I2.parameters()) + list(
model_I2_sem.parameters()), **optimizer_kwargs(args))
# optimizer for the attribute branch in SAL
optimizer_A = init_optimizer(
list(model_G.parameters()) + list(model_aencoder.parameters()) + list(model_I2.parameters()) + list(
model_I2_sem.parameters()),
**optimizer_kwargs_al(args))
# optimizer for the image branch in SAL
optimizer_X = init_optimizer(
list(model_I1.parameters()) + list(model_xencoder.parameters()) + list(model_I2.parameters()) + list(
model_I2_sem.parameters()),
**optimizer_kwargs_al(args))
# optimizer for the synthesis-GAN discriminator
optimizer_DC = init_optimizer(model_DC.parameters(), **optimizer_kwargs_df(args))
# optimizer for the synthesis-GAN generators
optimizer_GC = init_optimizer(list(model_GX.parameters()) + list(model_GA.parameters()),
**optimizer_kwargs_gf(args))
# optimizer for the alignment-GAN discriminator
optimizer_DS = init_optimizer(model_DS.parameters(), **optimizer_kwargs_al(args))
# optimizer for Ea, Ev
optimizer_ES = init_optimizer(list(model_xencoder.parameters()) + list(model_aencoder.parameters()),
**optimizer_kwargs_al(args))
# simple checkpoint loading
if args.folder and args.resume_epoch:
if args.resume_stage == 'pt':
args.resume_i1 = 'log/' + args.folder + '/checkpoint_ep_pt_I1' + args.resume_epoch + '.pth.tar'
args.resume_i2 = 'log/' + args.folder + '/checkpoint_ep_pt_I2' + args.resume_epoch + '.pth.tar'
args.resume_i2_sem = 'log/' + args.folder + '/checkpoint_ep_pt_I2sem' + args.resume_epoch + '.pth.tar'
args.resume_x = 'log/' + args.folder + '/checkpoint_ep_pt_x' + args.resume_epoch + '.pth.tar'
elif args.resume_stage == 'jt':
args.resume_i1 = 'log/' + args.folder + '/checkpoint_ep_jt_I1' + args.resume_epoch + '.pth.tar'
args.resume_i2 = 'log/' + args.folder + '/checkpoint_ep_jt_I2' + args.resume_epoch + '.pth.tar'
args.resume_i2_sem = 'log/' + args.folder + '/checkpoint_ep_jt_I2sem' + args.resume_epoch + '.pth.tar'
args.resume_a = 'log/' + args.folder + '/checkpoint_ep_jt_a' + args.resume_epoch + '.pth.tar'
args.resume_x = 'log/' + args.folder + '/checkpoint_ep_jt_x' + args.resume_epoch + '.pth.tar'
args.resume_g = 'log/' + args.folder + '/checkpoint_ep_jt_G' + args.resume_epoch + '.pth.tar'
elif args.resume_stage == 'al':
args.resume_i1 = 'log/' + args.folder + '/checkpoint_ep_al_I1' + args.resume_epoch + '.pth.tar'
args.resume_g = 'log/' + args.folder + '/checkpoint_ep_al_G' + args.resume_epoch + '.pth.tar'
args.resume_i2 = 'log/' + args.folder + '/checkpoint_ep_al_I2' + args.resume_epoch + '.pth.tar'
args.resume_i2_sem = 'log/' + args.folder + '/checkpoint_ep_al_I2sem' + args.resume_epoch + '.pth.tar'
args.resume_a = 'log/' + args.folder + '/checkpoint_ep_al_a' + args.resume_epoch + '.pth.tar'
args.resume_x = 'log/' + args.folder + '/checkpoint_ep_al_x' + args.resume_epoch + '.pth.tar'
args.resume_ga = 'log/' + args.folder + '/checkpoint_ep_al_GA' + args.resume_epoch + '.pth.tar'
args.resume_gx = 'log/' + args.folder + '/checkpoint_ep_al_GX' + args.resume_epoch + '.pth.tar'
args.resume_dc = 'log/' + args.folder + '/checkpoint_ep_al_DC' + args.resume_epoch + '.pth.tar'
args.resume_ds = 'log/' + args.folder + '/checkpoint_ep_al_DS' + args.resume_epoch + '.pth.tar'
# load pretrained weights for the image extractor
if args.load_weights and check_isfile(args.load_weights):
# load pretrained weights but ignore layers that don't match in size
checkpoint = torch.load(args.load_weights)
pretrain_dict = checkpoint['state_dict']
model_dict = model_I1.state_dict()
pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
model_dict.update(pretrain_dict)
if use_gpu:
model_I1.module.load_state_dict(model_dict)
else:
model_I1.load_state_dict(model_dict)
print("Loaded pretrained weights from '{}'".format(args.load_weights))
# Functions for resuming from checkpoints individually
if args.resume_i1 and check_isfile(args.resume_i1):
checkpoint = torch.load(args.resume_i1)
if use_gpu:
model_I1.module.load_state_dict(checkpoint['state_dict'])
else:
model_I1.load_state_dict(checkpoint['state_dict'])
if not args.resume_g and not args.resume_gx:
optimizer.load_state_dict(checkpoint['optimizer'])
for group in optimizer.param_groups:
group['initial_lr'] = group['lr']
args.start_epoch_pt = checkpoint['epoch'] + 1
print("Loaded checkpoint from '{}'".format(args.resume_i1))
print("- rank1: {}".format(checkpoint['rank1']))
if args.resume_i2 and check_isfile(args.resume_i2):
checkpoint = torch.load(args.resume_i2)
if use_gpu:
model_I2.module.load_state_dict(checkpoint['state_dict'])
else:
model_I2.load_state_dict(checkpoint['state_dict'])
print("Loaded checkpoint from '{}'".format(args.resume_i2))
print("- rank1: {}".format(checkpoint['rank1']))
if args.resume_i2_sem and check_isfile(args.resume_i2_sem):
checkpoint = torch.load(args.resume_i2_sem)
if use_gpu:
model_I2_sem.module.load_state_dict(checkpoint['state_dict'])
else:
model_I2_sem.load_state_dict(checkpoint['state_dict'])
print("Loaded checkpoint from '{}'".format(args.resume_i2_sem))
print("- rank1: {}".format(checkpoint['rank1']))
if args.resume_x and check_isfile(args.resume_x):
checkpoint = torch.load(args.resume_x)
if use_gpu:
model_xencoder.module.load_state_dict(checkpoint['state_dict'])
else:
model_xencoder.load_state_dict(checkpoint['state_dict'])
if args.resume_gx:
args.start_epoch_jt = args.max_epoch_jt
args.start_epoch_pt = args.max_epoch_pt
optimizer_X.load_state_dict(checkpoint['optimizer_x'])
for group in optimizer_X.param_groups:
group['initial_lr'] = group['lr']
print("Loaded checkpoint from '{}'".format(args.resume_x))
print("- rank1: {}".format(checkpoint['rank1']))
if args.resume_a and check_isfile(args.resume_a):
checkpoint = torch.load(args.resume_a)
if use_gpu:
model_aencoder.module.load_state_dict(checkpoint['state_dict'])
else:
model_aencoder.load_state_dict(checkpoint['state_dict'])
if args.resume_gx:
args.start_epoch_jt = args.max_epoch_jt
args.start_epoch_pt = args.max_epoch_pt
optimizer_A.load_state_dict(checkpoint['optimizer_a'])
for group in optimizer_A.param_groups:
group['initial_lr'] = group['lr']
print("Loaded checkpoint from '{}'".format(args.resume_a))
print("- rank1: {}".format(checkpoint['rank1']))
if args.resume_g and check_isfile(args.resume_g):
checkpoint = torch.load(args.resume_g)
if use_gpu:
model_G.module.load_state_dict(checkpoint['state_dict'])
else:
model_G.load_state_dict(checkpoint['state_dict'])
# loading saved generative model means the pretraining is completed
if not args.resume_gx:
args.start_epoch_pt = args.max_epoch_pt
args.start_epoch_jt = checkpoint['epoch'] + 1
optimizer_I.load_state_dict(checkpoint['optimizer_i'])
for group in optimizer_I.param_groups:
group['initial_lr'] = group['lr']
optimizer_G.load_state_dict(checkpoint['optimizer_g'])
for group in optimizer_G.param_groups:
group['initial_lr'] = group['lr']
print("Loaded checkpoint from '{}'".format(args.resume_g))
print("- rank1: {}".format(checkpoint['rank1']))
if args.resume_ga and check_isfile(args.resume_ga):
checkpoint = torch.load(args.resume_ga)
if use_gpu:
model_GA.module.load_state_dict(checkpoint['state_dict'])
else:
model_GA.load_state_dict(checkpoint['state_dict'])
# loading saved generative model means the pretraining is completed
args.start_epoch_jt = args.max_epoch_jt
args.start_epoch_pt = args.max_epoch_pt
print("Loaded checkpoint from '{}'".format(args.resume_ga))
if args.resume_gx and check_isfile(args.resume_gx):
checkpoint = torch.load(args.resume_gx)
if use_gpu:
model_GX.module.load_state_dict(checkpoint['state_dict'])
else:
model_GX.load_state_dict(checkpoint['state_dict'])
# loading saved generative model means the pretraining is completed
print("Loaded checkpoint from '{}'".format(args.resume_gx))
if args.resume_dc and check_isfile(args.resume_dc):
checkpoint = torch.load(args.resume_dc)
if use_gpu:
model_DC.module.load_state_dict(checkpoint['state_dict'])
else:
model_DC.load_state_dict(checkpoint['state_dict'])
optimizer_DC.load_state_dict(checkpoint['optimizer_dc'])
for group in optimizer_DC.param_groups:
group['initial_lr'] = group['lr']
optimizer_GC.load_state_dict(checkpoint['optimizer_gc'])
for group in optimizer_GC.param_groups:
group['initial_lr'] = group['lr']
print("Loaded checkpoint from '{}'".format(args.resume_dc))
print("- rank1: {}".format(checkpoint['rank1']))
if args.resume_ds and check_isfile(args.resume_ds):
checkpoint = torch.load(args.resume_ds)
if use_gpu:
model_DS.module.load_state_dict(checkpoint['state_dict'])
else:
model_DS.load_state_dict(checkpoint['state_dict'])
optimizer_DS.load_state_dict(checkpoint['optimizer_ds'])
for group in optimizer_DS.param_groups:
group['initial_lr'] = group['lr']
optimizer_ES.load_state_dict(checkpoint['optimizer_es'])
for group in optimizer_ES.param_groups:
group['initial_lr'] = group['lr']
print("Loaded checkpoint from '{}'".format(args.resume_ds))
if args.evaluate:
print("Evaluate only")
for name in args.dataset:
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
distmat = test_WOadv(model_I1, model_G, model_xencoder, model_aencoder, queryloader, galleryloader, use_gpu,
return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(
dm.label,
distmat, dm.return_testdataset_by_name(name),
save_dir=osp.join(args.save_dir, 'ranked_results', name),
topk=20
)
return
if args.attribute_prediction:
print('evaluate the attribute predicting ability of the model')
for name in args.dataset:
print("Evaluating {} ...".format(name))
galleryloader = testloader_dict[name]['gallery']
test_attr(model_I1, model_I2, model_G, galleryloader, use_gpu)
return
start_time = time.time()
ranklogger = RankLogger(args.dataset, args.dataset)
train_time = 0
print("=> Start training")
if args.fixbase_epoch > 0:
print("Train {} for {} epochs while keeping other layers frozen".format(args.open_layers, args.fixbase_epoch))
initial_optim_state = optimizer.state_dict()
for epoch in range(args.fixbase_epoch):
start_train_time = time.time()
train_I(epoch, model_I1, model_I2, model_I2_sem, model_xencoder, criterion, criterion_sem, optimizer,
trainloader, use_gpu, fixbase=False)
print("Done. All layers are open to train for {} epochs".format(args.max_epoch_pt))
optimizer.load_state_dict(initial_optim_state)
# learning rate decay
# singe pretrain
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.stepsize, gamma=args.gamma)
# joint pretrain
scheduler_G = lr_scheduler.MultiStepLR(optimizer_G, milestones=args.stepsize, gamma=args.gamma)
scheduler_I = lr_scheduler.MultiStepLR(optimizer_I, milestones=args.stepsize, gamma=args.gamma)
# sal
scheduler_GC = lr_scheduler.MultiStepLR(optimizer_GC, milestones=args.stepsize_sal, gamma=args.gamma)
scheduler_ES = lr_scheduler.MultiStepLR(optimizer_ES, milestones=args.stepsize_sal, gamma=args.gamma)
scheduler_DC = lr_scheduler.MultiStepLR(optimizer_DC, milestones=args.stepsize_sal, gamma=args.gamma)
scheduler_DS = lr_scheduler.MultiStepLR(optimizer_DS, milestones=args.stepsize_sal, gamma=args.gamma)
scheduler_X = lr_scheduler.MultiStepLR(optimizer_X, milestones=args.stepsize_sal, gamma=args.gamma)
scheduler_A = lr_scheduler.MultiStepLR(optimizer_A, milestones=args.stepsize_sal, gamma=args.gamma)
print("***************stage 1: Pretrain the single image network******************")
for epoch in range(args.start_epoch_pt, args.max_epoch_pt):
start_train_time = time.time()
train_I(epoch, model_I1, model_I2, model_I2_sem, model_xencoder, criterion, criterion_sem, optimizer,
trainloader, use_gpu, fixbase=False)
scheduler.step()
if (epoch + 1) % args.save_pt == 0 or (epoch + 1) == args.max_epoch_pt:
print("=> Save pretrained model")
if use_gpu:
state_dict_I1 = model_I1.module.state_dict()
state_dict_I2 = model_I2.module.state_dict()
state_dict_I2_sem = model_I2_sem.module.state_dict()
state_dict_xencoder = model_xencoder.module.state_dict()
else:
state_dict_I1 = model_I1.state_dict()
state_dict_I2 = model_I2.state_dict()
state_dict_I2_sem = model_I2_sem.state_dict()
state_dict_xencoder = model_xencoder.state_dict()
optim_state_dict = optimizer.state_dict()
if not args.no_save:
save_checkpoint({
'state_dict': state_dict_I1,
'rank1': 0,
'epoch': epoch,
'optimizer': optim_state_dict,
}, False, osp.join(args.save_dir, 'checkpoint_ep_pt_I1' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_I2,
'rank1': 0,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_pt_I2' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_I2_sem,
'rank1': 0,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_pt_I2sem' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_xencoder,
'rank1': 0,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_pt_x' + str(epoch + 1) + '.pth.tar'))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))
for name in args.dataset:
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1 = test_WOadv(model_I1, model_G, model_xencoder, model_aencoder, queryloader, galleryloader, use_gpu,
ranks=[1, 5, 10, 20],
return_distmat=False)
ranklogger.write(name, args.max_epoch_pt, rank1)
ranklogger.show_summary()
start_time = time.time()
ranklogger = RankLogger(args.dataset, args.dataset)
train_time = 0
print("***************stage 2: jointly pretrain both branches******************")
for epoch in range(args.start_epoch_jt, args.max_epoch_jt):
start_train_time = time.time()
train_WOadv(epoch, model_I1, model_I2, model_I2_sem, model_G, model_xencoder, model_aencoder, criterion,
criterion_sem, optimizer_I,
optimizer_G, trainloader, use_gpu, fixbase=False)
train_time += round(time.time() - start_train_time)
scheduler_G.step()
scheduler_I.step()
if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (epoch + 1) % args.eval_freq == 0 or (
epoch + 1) == args.max_epoch_jt:
print("=> Test")
for name in args.dataset:
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1 = test_WOadv(model_I1, model_G, model_xencoder, model_aencoder, queryloader, galleryloader,
use_gpu, ranks=[1, 5, 10, 20], return_distmat=False)
ranklogger.write(name, epoch + 1, rank1)
if use_gpu:
state_dict_I1 = model_I1.module.state_dict()
state_dict_I2 = model_I2.module.state_dict()
state_dict_I2_sem = model_I2_sem.module.state_dict()
state_dict_G = model_G.module.state_dict()
state_dict_xencoder = model_xencoder.module.state_dict()
state_dict_aencoder = model_aencoder.module.state_dict()
else:
state_dict_I1 = model_I1.state_dict()
state_dict_I2 = model_I2.state_dict()
state_dict_I2_sem = model_I2_sem.state_dict()
state_dict_G = model_G.state_dict()
state_dict_xencoder = model_xencoder.state_dict()
state_dict_aencoder = model_aencoder.state_dict()
optimizer_I_state_dict = optimizer_I.state_dict()
optimizer_G_state_dict = optimizer_G.state_dict()
if not args.no_save:
save_checkpoint({
'state_dict': state_dict_I1,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_jt_I1' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_I2,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_jt_I2' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_I2_sem,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_jt_I2sem' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_G,
'rank1': rank1,
'epoch': epoch,
'optimizer_i': optimizer_I_state_dict,
'optimizer_g': optimizer_G_state_dict,
}, False, osp.join(args.save_dir, 'checkpoint_ep_jt_G' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_xencoder,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_jt_x' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_aencoder,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_jt_a' + str(epoch + 1) + '.pth.tar'))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))
ranklogger.show_summary()
train_time = 0
print("***************stage 3: train the full SAL******************")
for epoch in range(args.start_epoch_al, args.max_epoch_al):
start_train_time = time.time()
train_gan(epoch, w, average, model_I1, model_I2, model_I2_sem, model_G, model_xencoder, model_aencoder, model_GA,
model_GX, model_DC,
model_DS, criterion, criterion_sem, criterion_gan, criterion_mse, optimizer_GC,
optimizer_DS,
optimizer_ES, optimizer_X, optimizer_A, optimizer_DC, trainloader, use_gpu,
fixbase=False)
train_time += round(time.time() - start_train_time)
scheduler_X.step()
scheduler_A.step()
scheduler_DS.step()
scheduler_ES.step()
scheduler_DC.step()
scheduler_GC.step()
if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (epoch + 1) % args.eval_freq == 0 or (
epoch + 1) == args.max_epoch_al:
print("=> Test")
for name in args.dataset:
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1 = test_WOadv(model_I1, model_G, model_xencoder, model_aencoder, queryloader, galleryloader,
use_gpu, ranks=[1, 5, 10, 20], return_distmat=False)
ranklogger.write(name, epoch + 1, rank1)
if use_gpu:
state_dict_I1 = model_I1.module.state_dict()
state_dict_G = model_G.module.state_dict()
state_dict_I2 = model_I2.module.state_dict()
state_dict_I2_sem = model_I2_sem.module.state_dict()
state_dict_xencoder = model_xencoder.module.state_dict()
state_dict_aencoder = model_aencoder.module.state_dict()
state_dict_GA = model_GA.module.state_dict()
state_dict_GX = model_GX.module.state_dict()
state_dict_DC = model_DC.module.state_dict()
state_dict_DS = model_DS.module.state_dict()
else:
state_dict_I1 = model_I1.state_dict()
state_dict_G = model_G.state_dict()
state_dict_I2_sem = model_I2_sem.state_dict()
state_dict_xencoder = model_xencoder.state_dict()
state_dict_aencoder = model_aencoder.state_dict()
state_dict_GA = model_GA.state_dict()
state_dict_GX = model_GX.state_dict()
state_dict_DC = model_DC.module.state_dict()
state_dict_DS = model_DS.module.state_dict()
optimizer_A_state_dict = optimizer_A.state_dict()
optimizer_X_state_dict = optimizer_X.state_dict()
optimizer_DC_state_dict = optimizer_DC.state_dict()
optimizer_GC_state_dict = optimizer_GC.state_dict()
optimizer_DS_state_dict = optimizer_DS.state_dict()
optimizer_ES_state_dict = optimizer_ES.state_dict()
save_checkpoint({
'state_dict': state_dict_I1,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_al_I1' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_G,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_al_G' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_I2,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_al_I2' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_I2_sem,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_al_I2sem' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_xencoder,
'rank1': rank1,
'epoch': epoch,
'optimizer_x': optimizer_X_state_dict,
}, False, osp.join(args.save_dir, 'checkpoint_ep_al_x' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_aencoder,
'rank1': rank1,
'epoch': epoch,
'optimizer_a': optimizer_A_state_dict,
}, False, osp.join(args.save_dir, 'checkpoint_ep_al_a' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_GX,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_al_GX' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_GA,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep_al_GA' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_DS,
'rank1': rank1,
'epoch': epoch,
'optimizer_ds': optimizer_DS_state_dict,
'optimizer_es': optimizer_ES_state_dict,
}, False, osp.join(args.save_dir, 'checkpoint_ep_al_DS' + str(epoch + 1) + '.pth.tar'))
save_checkpoint({
'state_dict': state_dict_DC,
'rank1': rank1,
'epoch': epoch,
'optimizer_dc': optimizer_DC_state_dict,
'optimizer_gc': optimizer_GC_state_dict,
}, False, osp.join(args.save_dir, 'checkpoint_ep_al_DC' + str(epoch + 1) + '.pth.tar'))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))
print('ordinary testing')
ranklogger.show_summary()
# stage 1: pretrain with the image branch only
def train_I(epoch, model_I1, model_I2, model_I2_sem, model_xencoder, criterion, criterion_sem, optimizer, trainloader,
use_gpu, fixbase=False):
########################
# Training image network#
########################
losses = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
model_I1.train()
model_I2.train()
model_xencoder.train()
end = time.time()
for batch_idx, (imgs, _, attrs, _, _, _, sems) in enumerate(trainloader):
data_time.update(time.time() - end)
if use_gpu:
imgs, attrs, sems = imgs.cuda(), attrs.cuda(), sems.cuda()
sems = sems.long()
attrs = attrs.float()
img_feature = model_xencoder(model_I1(imgs))
output_attr = model_I2(img_feature)
output_sem = model_I2_sem(img_feature)
loss = (2 - args.lamb_sem) * criterion(output_attr, attrs) + args.lamb_sem * criterion_sem(output_sem, sems)
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
losses.update(loss.item(), sems.size(0))
if (batch_idx + 1) % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.4f} ({data_time.avg:.4f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
epoch + 1, batch_idx + 1, len(trainloader), batch_time=batch_time,
data_time=data_time, loss=losses))
end = time.time()
# stage 2: jointly train the two branches without adversarial learning
def train_WOadv(epoch, model_I1, model_I2, model_I2_sem, model_G, model_xencoder, model_aencoder, criterion,
criterion_sem, optimizer_I,
optimizer_G, trainloader, use_gpu, fixbase=False):
losses = AverageMeter()
losses_G = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
model_I1.train()
model_I2.train()
model_G.train()
model_aencoder.train()
model_xencoder.train()
end = time.time()
for batch_idx, (imgs, _, attrs, _, _, _, sems) in enumerate(trainloader):
data_time.update(time.time() - end)
if use_gpu:
imgs, attrs, sems = imgs.cuda(), attrs.cuda(), sems.cuda()
sems = sems.long()
attrs = attrs.float()
# update image branch
img_feature = model_xencoder(model_I1(imgs))
loss = criterion(model_I2(img_feature), attrs) \
+ criterion_sem(model_I2_sem(img_feature),
sems)
optimizer_I.zero_grad()
loss.backward()
optimizer_I.step()
# update attribute branch
attr_feature = model_aencoder(model_G(attrs))
loss_G = (2 - args.lamb_sem) * criterion(model_I2(attr_feature), attrs) \
+ args.lamb_sem * criterion_sem(model_I2_sem(attr_feature),
sems)
optimizer_G.zero_grad()
loss_G.backward()
optimizer_G.step()
# ###########################end of GAN structure############################ #
batch_time.update(time.time() - end)
losses.update(loss.item(), sems.size(0))
losses_G.update(loss_G.item(), sems.size(0))
if (batch_idx + 1) % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.4f} ({data_time.avg:.4f})\t'
'Loss {loss.val:.4f}+{loss_g.val:.4f} ({loss.avg:.4f}+{loss_g.avg:.4f})\t'.format(
epoch + 1, batch_idx + 1, len(trainloader), batch_time=batch_time,
data_time=data_time, loss=losses, loss_g=losses_G))
end = time.time()
def train_gan(epoch, w, average, model_I1, model_I2, model_I2_sem, model_G, model_xencoder, model_aencoder, model_GA, model_GX,
model_DC,
model_DS, criterion, criterion_sem, criterion_gan, criterion_mse, optimizer_GC,
optimizer_DS,
optimizer_ES, optimizer_X, optimizer_A, optimizer_DC, trainloader, use_gpu,
fixbase=False):
#################################################################################
# 1. extract image concept by removing softmax and adding fc to resnet-50. #
# 2. generate image-analogous concept by adding multiple fully connected layers.#
# 3. concept discriminator #
#################################################################################
losses_I = AverageMeter()
losses_G = AverageMeter()
losses_DC = AverageMeter()
losses_GX = AverageMeter()
losses_GA = AverageMeter()
losses_CYCLE = AverageMeter()
losses_DS = AverageMeter()
losses_ES = AverageMeter()
losses_EMSE = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
model_I1.train()
model_I2.train()
model_G.train()
model_xencoder.eval()
model_aencoder.eval()
model_GX.train()
model_GA.train()
model_DC.train()
model_DS.train()
''' DISABLING FIXBASE
if fixbase or args.always_fixbase:
open_specified_layers(model, args.open_layers)
else:
open_all_layers(model)
'''
end = time.time()
ratio = args.ssl_loss_ratio
if epoch >= args.start_mse:
lamb_mse = args.lamb_mse
else:
lamb_mse = 0
ratio_l = args.train_batch_size / (args.train_batch_size + args.num_fake)
ratio_u = 1 - ratio_l
if epoch >= args.epoch_add_fake:
for batch_idx, (imgs, _, attrs, _, _, _, sems) in enumerate(trainloader):
# print('iter')
data_time.update(time.time() - end)
############################################################################
# sample fake attributes from prior distribution (training distribution) #
############################################################################
fake_attrs = fake_attributes(w, average, args.num_fake)
ua_valid = Variable(Tensor(fake_attrs.size(0), 1).fill_(1.0), requires_grad=False)
ua_fake = Variable(Tensor(fake_attrs.size(0), 1).fill_(0.0), requires_grad=False)
if batch_idx == 0:
print('train with randomly sampled (based on prior distribution) fake attributes:')
valid = Variable(Tensor(imgs.size(0), 1).fill_(1.0), requires_grad=False)
fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False)
if use_gpu:
imgs, attrs, sems, fake_attrs \
= imgs.cuda(), attrs.cuda(), sems.cuda(), fake_attrs.cuda()
valid, fake, ua_valid, ua_fake \
= valid.cuda(), fake.cuda(), ua_valid.cuda(), ua_fake.cuda()
sems = sems.long()
attrs = attrs.float()
fake_attrs = fake_attrs.float()
#############################
# train the image network #
#############################
x_open = model_I1(imgs)
a_open = model_G(attrs)
faa_open = model_G(fake_attrs)
x = x_open.detach()
a = a_open.detach()
faa = faa_open.detach()
# ############################# Synthesis-GAN structure ################################# #
# ########################## D(A,I) ############################ #
# update the discriminator for the synthesis-GAN #
##################################################################
model_GX.train()
model_GA.train()
model_xencoder.eval()
model_aencoder.eval()
noise = torch.randn(attrs.size(0), args.noise)
noise2 = torch.randn(fake_attrs.size(0), args.noise)
if use_gpu:
noise = noise.cuda()
noise2 = noise2.cuda()
# add noise to image feature generator
x_hat = model_GX(torch.cat((a, noise), 1).detach())
fax_hat = model_GX(torch.cat((faa, noise2), 1).detach())
a_hat = model_GA(x)
pair_ax = torch.cat((a, x), 1)
pair_ax1 = torch.cat((a, x_hat), 1)
pair_a1x = torch.cat((a_hat, x), 1)
pair_faafax1 = torch.cat((faa, fax_hat), 1)
loss_DC = criterion_gan(model_DC(pair_ax.detach()), valid)\
+ ratio_l*criterion_gan(model_DC(pair_ax1.detach()),fake) \
+ ratio_u*criterion_gan(model_DC(pair_faafax1.detach()),ua_fake) \
+ criterion_gan(model_DC(pair_a1x.detach()), fake)
optimizer_DC.zero_grad()
loss_DC.backward()
optimizer_DC.step()
# ########################## GX and GA ############################ #
# update the generators for the synthesis-GAN with both #
# adversarial and consistency loss # #
################################################################### #
encoded_x_hat = model_xencoder(x_hat)
encoded_a_hat = model_aencoder(a_hat)
encoded_fax_hat = model_xencoder(fax_hat)
encoded_x = model_xencoder(x)
encoded_a = model_aencoder(a)
encoded_faa = model_aencoder(faa)
loss_GX = ratio_l*criterion_gan(model_DC(pair_ax1), valid) \
+ ratio_l*criterion_mse(encoded_x_hat, encoded_a.detach()) \
+ criterion_mse(encoded_x_hat, encoded_x.detach()) \
+ ratio_u*ratio * criterion_gan(model_DC(pair_faafax1), ua_valid) \
+ ratio_u*ratio * criterion_mse(encoded_fax_hat, encoded_faa.detach())
loss_GA = criterion_gan(model_DC(pair_a1x), valid) \
+ criterion_mse(encoded_a_hat, encoded_x.detach()) \
+ criterion_mse(encoded_a_hat, encoded_a.detach())
loss_cycle = ratio_l*criterion_mse(model_GA(x_hat), a) \
+ ratio_u*ratio * criterion_mse(model_GA(fax_hat), faa)
optimizer_GC.zero_grad()
loss_GC = loss_GX + loss_GA + loss_cycle
loss_GC.backward()
optimizer_GC.step()
# ############################# Alignment-GAN structure ################################# #
# ########################## D(Sa,Sx) ############################ #
# update the discriminator in the alignment-GAN #
# ################################################################ #
model_GX.eval()
model_GA.eval()
model_xencoder.train()
model_aencoder.train()
encoded_x_hat = model_xencoder(x_hat.detach())
encoded_a_hat = model_aencoder(a_hat.detach())
encoded_x = model_xencoder(x)
encoded_a = model_aencoder(a)
encoded_fax_hat = model_xencoder(fax_hat.detach())
encoded_faa = model_aencoder(faa.detach())
loss_DS = ratio_u*criterion_gan(model_DS(encoded_a.detach()), fake) \
+ criterion_gan(model_DS(encoded_x.detach()), valid) \
+ lamb_mse*criterion_gan(model_DS(encoded_a_hat.detach()), fake) \
+ lamb_mse*ratio_u*criterion_gan(model_DS(encoded_x_hat.detach()), valid) \
+ ratio_u*ratio * criterion_gan(model_DS(encoded_faa.detach()), ua_fake) \
+ ratio_u*ratio * criterion_gan(model_DS(encoded_fax_hat.detach()), ua_valid)
optimizer_DS.zero_grad()
loss_DS.backward()
optimizer_DS.step()
# ########################## E(Sa,Sx) ############################ #
# update the encoders in the alignment-GAN #
# ################################################################ #
encoded_a = model_aencoder(a)
encoded_faa = model_aencoder(faa)
loss_ES = ratio_l*criterion_gan(model_DS(encoded_a), valid) \
+ lamb_mse*criterion_gan(model_DS(encoded_a_hat), valid) \
+ ratio_u*ratio * criterion_gan(model_DS(encoded_faa), ua_valid)
optimizer_ES.zero_grad()
(loss_ES).backward()
optimizer_ES.step()
##########################
# train the classifier #
##########################
x_feature = model_xencoder(x_open)
x_hat_feature = model_xencoder(x_hat.detach())
loss_I = (2 - args.lamb_sem) * (criterion(model_I2(x_feature), attrs)
+ lamb_mse * criterion(model_I2(x_hat_feature),attrs)) / 2 \
+ args.lamb_sem * (criterion_sem(model_I2_sem(x_feature), sems)
+ lamb_mse * criterion_sem(model_I2_sem(x_hat_feature), sems)) / 2
optimizer_X.zero_grad()
loss_I.backward()
optimizer_X.step()
#######################################
# regularising the attribute branch #
#######################################
a_feature = model_aencoder(a_open)
faa_feature = model_aencoder(faa_open)
a_hat_feature = model_aencoder(a_hat.detach())
optimizer_A.zero_grad()
loss_G = (2 - args.lamb_sem) * (ratio_l*criterion(model_I2(a_feature), attrs)
+ lamb_mse * criterion(model_I2(a_hat_feature), attrs)
+ ratio_u*criterion(model_I2(faa_feature), fake_attrs)) / 2 \
+ args.lamb_sem * (criterion_sem(model_I2_sem(a_feature), sems)
+ lamb_mse * criterion_sem(model_I2_sem(a_hat_feature), sems)) / 2
loss_G.backward()
optimizer_A.step()
# ##########################end of updating process############################ #
batch_time.update(time.time() - end)
losses_I.update(loss_I.item(), sems.size(0))
losses_G.update(loss_G.item(), sems.size(0))
losses_DC.update(loss_DC.item(), sems.size(0))
losses_GX.update(loss_GX.item(), sems.size(0))
losses_GA.update(loss_GA.item(), sems.size(0))
losses_CYCLE.update(loss_cycle.item(), sems.size(0))
losses_DS.update(loss_DS.item(), sems.size(0))
losses_ES.update(loss_ES.item(), sems.size(0))
del loss_I
del loss_G
del loss_GC
del loss_DC
del loss_DS
del loss_ES
if (batch_idx + 1) % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.4f} ({data_time.avg:.4f})\t'
'Loss i {loss_i.val:.4f}+g {loss_g.val:.4f}'
'+dc {loss_dc.val:.4f}+gx {loss_gx.val:.4f}'
'+ga {loss_ga.val:.4f}+cycle {loss_cycle.val:.4f}'
'+ds {loss_ds.val:.4f}+es {loss_es.val:.4f}'
'(i {loss_i.avg:.4f}+g {loss_g.avg:.4f}'
'+dc {loss_dc.avg:.4f}+gx {loss_gx.avg:.4f}'
'+ga {loss_ga.avg:.4f}+cycle {loss_cycle.avg:.4f}'
'+ds {loss_ds.avg:.4f}+es {loss_es.avg:.4f})\t'.format(
epoch + 1, batch_idx + 1, len(trainloader), batch_time=batch_time,
data_time=data_time, loss_i=losses_I, loss_g=losses_G, loss_dc=losses_DC, loss_gx=losses_GX,
loss_ga=losses_GA, loss_cycle=losses_CYCLE, loss_ds=losses_DS, loss_es=losses_ES))
end = time.time()
torch.cuda.empty_cache()
else:
for batch_idx, (imgs, _, attrs, _, _, _, sems) in enumerate(trainloader):
# print('iter')
data_time.update(time.time() - end)
if batch_idx == 0:
print('train with only labelled data:')
valid = Variable(Tensor(attrs.size(0), 1).fill_(1.0), requires_grad=False)
fake = Variable(Tensor(attrs.size(0), 1).fill_(0.0), requires_grad=False)
if use_gpu:
imgs, attrs, sems = imgs.cuda(), attrs.cuda(), sems.cuda()
valid, fake = valid.cuda(), fake.cuda()
sems = sems.long()
attrs = attrs.float()
#############################
# train the image network #
#############################
x_open = model_I1(imgs)
a_open = model_G(attrs)
x = x_open.detach()
a = a_open.detach()
# ############################# Synthesis-GAN structure ################################# #
# ########################## D(A,I) ############################ #
# update the discriminator for the synthesis-GAN #
##################################################################
model_GX.train()
model_GA.train()
model_xencoder.eval()
model_aencoder.eval()
noise = torch.randn(attrs.size(0), args.noise)
if use_gpu:
noise = noise.cuda()
# add noise to image feature generator
x_hat = model_GX(torch.cat((a, noise), 1).detach())
a_hat = model_GA(x)
pair_ax = torch.cat((a, x), 1)
pair_ax1 = torch.cat((a, x_hat), 1)
pair_a1x = torch.cat((a_hat, x), 1)
loss_DC = criterion_gan(model_DC(pair_ax.detach()), valid) + criterion_gan(model_DC(pair_ax1.detach()),
fake) \
+ criterion_gan(model_DC(pair_a1x.detach()), fake)
optimizer_DC.zero_grad()
loss_DC.backward()
optimizer_DC.step()
# ########################## GX and GA ############################ #
# update the generators for synthesis-GAN with both #
# adversarial and consistency loss # #
################################################################### #
encoded_x_hat = model_xencoder(x_hat)
encoded_a_hat = model_aencoder(a_hat)
encoded_x = model_xencoder(x)
encoded_a = model_aencoder(a)
loss_GX = criterion_gan(model_DC(pair_ax1), valid) \
+ criterion_mse(encoded_x_hat, encoded_a.detach()) \
+ criterion_mse(encoded_x_hat, encoded_x.detach())
loss_GA = criterion_gan(model_DC(pair_a1x), valid) \
+ criterion_mse(encoded_a_hat, encoded_x.detach()) \
+ criterion_mse(encoded_a_hat, encoded_a.detach())
loss_cycle = criterion_mse(model_GA(x_hat), a)
optimizer_GC.zero_grad()
loss_GC = loss_GX + loss_GA + loss_cycle
loss_GC.backward()
optimizer_GC.step()
# ############################# Alignment-GAN structure ################################# #
# ########################## D(Sa,Sx) ############################ #
# update the discriminator in the alignment-GAN #
# ################################################################ #
model_GX.eval()
model_GA.eval()
model_xencoder.train()
model_aencoder.train()
encoded_x_hat = model_xencoder(x_hat.detach())
encoded_a_hat = model_aencoder(a_hat.detach())
loss_DS = criterion_gan(model_DS(encoded_a.detach()), fake) \
+ criterion_gan(model_DS(encoded_x.detach()), valid) \
+ lamb_mse * criterion_gan(model_DS(encoded_a_hat.detach()), fake) \
+ lamb_mse * criterion_gan(model_DS(encoded_x_hat.detach()), valid)
optimizer_DS.zero_grad()
loss_DS.backward()
optimizer_DS.step()
# ########################## E(Sa,Sx) ############################ #
# update the encoders in the alignment-GAN #
# ################################################################ #
encoded_a = model_aencoder(a)
loss_ES = criterion_gan(model_DS(encoded_a), valid) \
+ lamb_mse * criterion_gan(model_DS(encoded_a_hat), valid)
optimizer_ES.zero_grad()
(loss_ES).backward()
optimizer_ES.step()
##########################
# train the classifier #
##########################
x_feature = model_xencoder(x_open)
x_hat_feature = model_xencoder(x_hat.detach())
loss_I = (2 - args.lamb_sem) * (
criterion(model_I2(x_feature), attrs) + lamb_mse * criterion(model_I2(x_hat_feature),
attrs)) / 2 \
+ args.lamb_sem * (criterion_sem(model_I2_sem(x_feature), sems) + lamb_mse * criterion_sem(
model_I2_sem(x_hat_feature), sems)) / 2
optimizer_X.zero_grad()
loss_I.backward()
optimizer_X.step()
#######################################
# regularising the attribute branch #
#######################################
a_feature = model_aencoder(a_open)
a_hat_feature = model_aencoder(a_hat.detach())
optimizer_A.zero_grad()
loss_G = (2 - args.lamb_sem) * (
criterion(model_I2(a_feature), attrs) + lamb_mse * criterion(model_I2(a_hat_feature),
attrs)) / 2 \
+ args.lamb_sem * (criterion_sem(model_I2_sem(a_feature), sems) + lamb_mse * criterion_sem(
model_I2_sem(a_hat_feature), sems)) / 2
loss_G.backward()
optimizer_A.step()
# ##########################end of updating process############################ #
batch_time.update(time.time() - end)
losses_I.update(loss_I.item(), sems.size(0))
losses_G.update(loss_G.item(), sems.size(0))
losses_DC.update(loss_DC.item(), sems.size(0))
losses_GX.update(loss_GX.item(), sems.size(0))
losses_GA.update(loss_GA.item(), sems.size(0))
losses_CYCLE.update(loss_cycle.item(), sems.size(0))
losses_DS.update(loss_DS.item(), sems.size(0))
losses_ES.update(loss_ES.item(), sems.size(0))
del loss_I
del loss_G
del loss_GC
del loss_DC
del loss_DS
del loss_ES
if (batch_idx + 1) % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.4f} ({data_time.avg:.4f})\t'
'Loss i {loss_i.val:.4f}+g {loss_g.val:.4f}'
'+dc {loss_dc.val:.4f}+gx {loss_gx.val:.4f}'
'+ga {loss_ga.val:.4f}+cycle {loss_cycle.val:.4f}'
'+ds {loss_ds.val:.4f}+es {loss_es.val:.4f}'
'(i {loss_i.avg:.4f}+g {loss_g.avg:.4f}'
'+dc {loss_dc.avg:.4f}+gx {loss_gx.avg:.4f}'
'+ga {loss_ga.avg:.4f}+cycle {loss_cycle.avg:.4f}'
'+ds {loss_ds.avg:.4f}+es {loss_es.avg:.4f})\t'.format(
epoch + 1, batch_idx + 1, len(trainloader), batch_time=batch_time,
data_time=data_time, loss_i=losses_I, loss_g=losses_G, loss_dc=losses_DC, loss_gx=losses_GX,
loss_ga=losses_GA, loss_cycle=losses_CYCLE, loss_ds=losses_DS, loss_es=losses_ES))
end = time.time()
torch.cuda.empty_cache()
# ################################## generate sampled attributes ############################# #
def fake_attributes(w, average, batch_size):
dataset = args.dataset[0]
fake_attrs = None
if dataset == 'Market-1501':
# first 4 as a group, next 9 binary, next 8 as a group, next 9 as a group
w = np.array(w)
if args.prob == 'inverse':
w[w == 0] = 0.99999
w[w == 1] = 0.00001
w_bin = w[4:13]
prob = 1 / w_bin / (1 / w_bin + 1 / (1 - w_bin)) # generate infrequent attributes
prob_binary = torch.FloatTensor(prob).unsqueeze(0).repeat(batch_size, 1)
binary_attrs = torch.bernoulli(prob_binary)
age_prob = 1 / torch.tensor(w[:4])
age_dist = torch.distributions.categorical.Categorical(age_prob / age_prob.sum())
age = age_dist.sample(sample_shape=(batch_size,)).long()
up_prob = 1 / torch.tensor(w[13:21])
up_dist = torch.distributions.categorical.Categorical(up_prob / up_prob.sum())
up = up_dist.sample(sample_shape=(batch_size,)).long()
down_prob = 1 / torch.tensor(w[21:])
down_dist = torch.distributions.categorical.Categorical(down_prob / down_prob.sum())
down = down_dist.sample(sample_shape=(batch_size,)).long()
elif args.prob == 'prior':
w_bin = w[4:13]
bin_prob = torch.tensor(w_bin) # generate infrequent attributes
# prob_binary = torch.FloatTensor(prob).unsqueeze(0).repeat(batch_size, 1)
# binary_attrs = torch.bernoulli(prob_binary)
age_prob = torch.tensor(w[:4])
age_dist = torch.distributions.categorical.Categorical(age_prob / age_prob.sum())
age = age_dist.sample(sample_shape=(batch_size,)).long()
up_prob = torch.tensor(w[13:21])
up_dist = torch.distributions.categorical.Categorical(up_prob / up_prob.sum())
up = up_dist.sample(sample_shape=(batch_size,)).long()
down_prob = torch.tensor(w[21:])
down_dist = torch.distributions.categorical.Categorical(down_prob / down_prob.sum())
down = down_dist.sample(sample_shape=(batch_size,)).long()
bin_list = []
for i in range(batch_size):
bin_list.append(torch.multinomial(bin_prob / bin_prob.sum(), int(average - 2)).unsqueeze(0))
bin = torch.cat(bin_list, 0).long()
bin_vector = torch.zeros(len(bin), len(w_bin)).scatter_(1, bin, 1.)
age_one_hot = torch.zeros(len(age), 4).scatter_(1, age.unsqueeze(1), 1.)
up_one_hot = torch.zeros(len(up), 8).scatter_(1, up.unsqueeze(1), 1.)
down_one_hot = torch.zeros(len(down), 9).scatter_(1, down.unsqueeze(1), 1.)
fake_attrs = torch.cat((age_one_hot, bin_vector, up_one_hot, down_one_hot), 1)
elif dataset == 'PETA':
# first 8 are binary, next 7 as a group, next 8 as a group
w = np.array(w)
if args.prob=='inverse':
w[w == 0] = 0.99999
w[w == 1] = 0.00001
w_bin = np.concatenate((w[4:35], w[79:]))
bin_prob = 1 / torch.tensor(w_bin)
age_prob = 1 / torch.tensor(w[0:4])
up_prob = 1 / torch.tensor(w[35:46])
down_prob = 1 / torch.tensor(w[46:57])
hair_prob = 1 / torch.tensor(w[57:68])
foot_prob = 1 / torch.tensor(w[68:79])
elif args.prob=='equal':
w[w == 0] = 0.99999
w[w == 1] = 0.00001
w_bin = np.concatenate((w[4:35], w[79:]))
bin_prob = torch.ones_like(torch.tensor(w_bin))
age_prob = torch.ones_like(torch.tensor(w[0:4]))
up_prob = torch.ones_like(torch.tensor(w[35:46]))
down_prob = torch.ones_like(torch.tensor(w[46:57]))
hair_prob = torch.ones_like(torch.tensor(w[57:68]))
foot_prob = torch.ones_like(torch.tensor(w[68:79]))
elif args.prob=='prior':
w_bin = np.concatenate((w[4:35], w[79:]))
bin_prob = torch.tensor(w_bin)
age_prob = torch.tensor(w[0:4])
up_prob = torch.tensor(w[35:46])
down_prob = torch.tensor(w[46:57])
hair_prob = torch.tensor(w[57:68])
foot_prob = torch.tensor(w[68:79])
bin_list = []
for i in range(batch_size):
bin_list.append(torch.multinomial(bin_prob / bin_prob.sum(), int(average-5)).unsqueeze(0))
bin = torch.cat(bin_list, 0).long()
# actually no need to divide by the sum of probabilities.
age_dist = torch.distributions.categorical.Categorical(age_prob / age_prob.sum())
age = age_dist.sample(sample_shape=(batch_size,)).long()
up_dist = torch.distributions.categorical.Categorical(up_prob / up_prob.sum())
up = up_dist.sample(sample_shape=(batch_size,)).long()
# actually no need to divide by the sum of probabilities.
down_dist = torch.distributions.categorical.Categorical(down_prob / down_prob.sum())
down = down_dist.sample(sample_shape=(batch_size,)).long()
# actually no need to divide by the sum of probabilities.
hair_dist = torch.distributions.categorical.Categorical(hair_prob / hair_prob.sum())
hair = hair_dist.sample(sample_shape=(batch_size,)).long()
# actually no need to divide by the sum of probabilities.
foot_dist = torch.distributions.categorical.Categorical(foot_prob / foot_prob.sum())
foot = foot_dist.sample(sample_shape=(batch_size,)).long()
bin_vector = torch.zeros(len(bin), len(w_bin)).scatter_(1, bin, 1.)
age_one_hot = torch.zeros(len(age), 4).scatter_(1, age.unsqueeze(1), 1.)
down_one_hot = torch.zeros(len(down), 11).scatter_(1, down.unsqueeze(1), 1.)
up_one_hot = torch.zeros(len(up), 11).scatter_(1, up.unsqueeze(1), 1.)
hair_one_hot = torch.zeros(len(hair), 11).scatter_(1, hair.unsqueeze(1), 1.)
foot_one_hot = torch.zeros(len(foot), 11).scatter_(1, foot.unsqueeze(1), 1.)
fake_attrs = torch.cat((age_one_hot,bin_vector[:,:31], up_one_hot, down_one_hot, hair_one_hot, foot_one_hot, bin_vector[:,31:]), 1)
return fake_attrs
def test_WOadv(model_I1, model_G, model_xencoder, model_aencoder, queryloader, galleryloader, use_gpu,
ranks=[1, 5, 10, 20], return_distmat=False):
batch_time = AverageMeter()
model_G.eval()
model_I1.eval()
model_xencoder.eval()
model_aencoder.eval()
with torch.no_grad():
qf, q_pids, q_camids = [], [], []
for batch_idx, (_, attrs, ids, camids, names, sems) in enumerate(queryloader):
if use_gpu: attrs = attrs.cuda()
attrs = attrs.float()
end = time.time()
features = model_aencoder(model_G(attrs))
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
q_pids.extend(sems)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids = [], [], []
end = time.time()
# data, i, label, id, cam, name, sem
for batch_idx, (imgs, attrs, id, camids, name, sems) in enumerate(galleryloader):
if use_gpu:
sems = sems.cuda()
imgs = imgs.cuda()
end = time.time()
features = model_xencoder(model_I1(imgs))
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
g_pids.extend(sems)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
print("=> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size))
m, n = qf.size(0), gf.size(0)
########################################
# change euclidean dist to cosine dist #
########################################
qf_norm = qf / (qf.norm(dim=1).expand_as(qf.t()).t())
gf_norm = gf / (gf.norm(dim=1).expand_as(gf.t()).t())
distmat = qf_norm.mm(gf_norm.t())
distmat = -distmat.numpy()
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=False)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0]
#################### The following test functions are only used for reference ########################
def test_Image_space(model_I1, model_G, model_GX, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20],
return_distmat=False):
batch_time = AverageMeter()
model_G.eval()
model_I1.eval()
model_GX.eval()
with torch.no_grad():
qf, q_pids, q_camids = [], [], []
for batch_idx, (_, attrs, ids, camids, names, sems) in enumerate(queryloader):
if use_gpu: attrs = attrs.cuda()
attrs = attrs.float()
end = time.time()
noise = torch.randn(attrs.size(0), args.noise)
a = model_G(attrs)
if use_gpu:
noise = noise.cuda()
# add noise to image feature generator
features = model_GX(torch.cat((a, noise), 1).detach())
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
q_pids.extend(sems)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids = [], [], []
end = time.time()
# data, i, label, id, cam, name, sem
for batch_idx, (imgs, attrs, id, camids, name, sems) in enumerate(galleryloader):
if use_gpu:
sems = sems.cuda()
imgs = imgs.cuda()
end = time.time()
features = model_I1(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
g_pids.extend(sems)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
print("=> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size))
m, n = qf.size(0), gf.size(0)
########################################
# change euclidean dist to cosine dist #
########################################
qf_norm = qf / (qf.norm(dim=1).expand_as(qf.t()).t())
gf_norm = gf / (gf.norm(dim=1).expand_as(gf.t()).t())
distmat = qf_norm.mm(gf_norm.t())
distmat = -distmat.numpy()
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=False)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0]
def test_attribute_space(model_I1, model_G, model_GA, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20],
return_distmat=False):
batch_time = AverageMeter()
model_G.eval()
model_I1.eval()
model_GA.eval()
with torch.no_grad():
qf, q_pids, q_camids = [], [], []
for batch_idx, (_, attrs, ids, camids, names, sems) in enumerate(queryloader):
if use_gpu: attrs = attrs.cuda()
attrs = attrs.float()
end = time.time()
features = model_G(attrs)
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
q_pids.extend(sems)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids = [], [], []
end = time.time()
# data, i, label, id, cam, name, sem
for batch_idx, (imgs, attrs, id, camids, name, sems) in enumerate(galleryloader):
if use_gpu:
sems = sems.cuda()
imgs = imgs.cuda()
end = time.time()
features = model_GA(model_I1(imgs))
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
g_pids.extend(sems)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
print("=> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size))
m, n = qf.size(0), gf.size(0)
########################################
# change euclidean dist to cosine dist #
########################################
qf_norm = qf / (qf.norm(dim=1).expand_as(qf.t()).t())
gf_norm = gf / (gf.norm(dim=1).expand_as(gf.t()).t())
distmat = qf_norm.mm(gf_norm.t())
distmat = -distmat.numpy()
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=False)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0]
def test_2way(model_I1, model_G, model_xencoder, model_aencoder, model_GX, model_GA, queryloader, galleryloader,
use_gpu, ranks=[1, 5, 10, 20], return_distmat=False):
batch_time = AverageMeter()
model_G.eval()
model_I1.eval()
model_xencoder.eval()
model_aencoder.eval()
model_GA.eval()
model_GX.eval()
with torch.no_grad():
qf, q_pids, q_camids = [], [], []
for batch_idx, (_, attrs, ids, camids, names, sems) in enumerate(queryloader):
if use_gpu: attrs = attrs.cuda()
attrs = attrs.float()
end = time.time()
features1 = model_aencoder(model_G(attrs))
noise = torch.randn(attrs.size(0), args.noise)
a = model_G(attrs)
if use_gpu:
noise = noise.cuda()
# add noise to image feature generator
x_hat = model_GX(torch.cat((a, noise), 1).detach())
features2 = model_xencoder(x_hat)
features = features1 + features2
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
q_pids.extend(sems)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids = [], [], []
end = time.time()
# data, i, label, id, cam, name, sem
for batch_idx, (imgs, attrs, id, camids, name, sems) in enumerate(galleryloader):
if use_gpu:
sems = sems.cuda()
imgs = imgs.cuda()
end = time.time()
features1 = model_xencoder(model_I1(imgs))
features2 = model_aencoder(model_GA(model_I1(imgs)))
features = features1 + features2
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
g_pids.extend(sems)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
print("=> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size))
m, n = qf.size(0), gf.size(0)
########################################
# change euclidean dist to cosine dist #
########################################
qf_norm = qf / (qf.norm(dim=1).expand_as(qf.t()).t())
gf_norm = gf / (gf.norm(dim=1).expand_as(gf.t()).t())
distmat = qf_norm.mm(gf_norm.t())
distmat = -distmat.numpy()
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=False)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0]
def test_1way(model_I1, model_G, model_xencoder, model_aencoder, model_GA, queryloader, galleryloader, use_gpu,
ranks=[1, 5, 10, 20], return_distmat=False):
batch_time = AverageMeter()
model_G.eval()
model_I1.eval()
model_xencoder.eval()
model_aencoder.eval()
model_GA.eval()
with torch.no_grad():
qf, q_pids, q_camids = [], [], []
for batch_idx, (_, attrs, ids, camids, names, sems) in enumerate(queryloader):
if use_gpu: attrs = attrs.cuda()
attrs = attrs.float()
end = time.time()
features = model_aencoder(model_G(attrs))
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
q_pids.extend(sems)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids = [], [], []
end = time.time()
# data, i, label, id, cam, name, sem
for batch_idx, (imgs, attrs, id, camids, name, sems) in enumerate(galleryloader):
if use_gpu:
sems = sems.cuda()
imgs = imgs.cuda()
end = time.time()
features = model_aencoder(model_GA(model_I1(imgs)))
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
g_pids.extend(sems)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
print("=> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size))
m, n = qf.size(0), gf.size(0)
########################################
# change euclidean dist to cosine dist #
########################################
qf_norm = qf / (qf.norm(dim=1).expand_as(qf.t()).t())
gf_norm = gf / (gf.norm(dim=1).expand_as(gf.t()).t())
distmat = qf_norm.mm(gf_norm.t())
distmat = -distmat.numpy()
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=False)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0]
def test_1way_alt(model_I1, model_G, model_xencoder, model_aencoder, model_GA, model_GX, queryloader, galleryloader,
use_gpu, ranks=[1, 5, 10, 20], return_distmat=False):
batch_time = AverageMeter()
model_G.eval()
model_I1.eval()
model_xencoder.eval()
model_aencoder.eval()
model_GA.eval()
model_GX.eval()
with torch.no_grad():
qf, q_pids, q_camids = [], [], []
for batch_idx, (_, attrs, ids, camids, names, sems) in enumerate(queryloader):
if use_gpu: attrs = attrs.cuda()
attrs = attrs.float()
end = time.time()
noise = torch.randn(attrs.size(0), args.noise)
a = model_G(attrs)
if use_gpu:
noise = noise.cuda()
# add noise to image feature generator
x_hat = model_GX(torch.cat((a, noise), 1).detach())
features = model_xencoder(x_hat)
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
q_pids.extend(sems)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids = [], [], []
end = time.time()
# data, i, label, id, cam, name, sem
for batch_idx, (imgs, attrs, id, camids, name, sems) in enumerate(galleryloader):
if use_gpu:
sems = sems.cuda()
imgs = imgs.cuda()
end = time.time()
features = model_xencoder(model_I1(imgs))
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
g_pids.extend(sems)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
print("=> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size))
m, n = qf.size(0), gf.size(0)
########################################
# change euclidean dist to cosine dist #
########################################
qf_norm = qf / (qf.norm(dim=1).expand_as(qf.t()).t())
gf_norm = gf / (gf.norm(dim=1).expand_as(gf.t()).t())
distmat = qf_norm.mm(gf_norm.t())
distmat = -distmat.numpy()
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=False)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0]
def test_attr(model_I1, model_I2, model_G, galleryloader, use_gpu):
batch_time = AverageMeter()
model_G.eval()
model_I1.eval()
with torch.no_grad():
gf, g_pids, g_camids = [], [], []
end = time.time()
# data, label, id, camid, img_path, sem
tp_i = None # true positive for image network
pp_i = None # positive prediction
tp_g = None # true positive for attribute network
pp_g = None # positive prediction
for batch_idx, (imgs, attrs, id, camids, name, sems) in enumerate(galleryloader):
ones = torch.ones(attrs.size())
zeros = torch.zeros(attrs.size())
if use_gpu:
sems = sems.cuda()
imgs = imgs.cuda()
attrs = attrs.cuda()
ones = ones.cuda()
zeros = zeros.cuda()
batch_tp_i = zeros.clone()
batch_pp_i = zeros.clone()
batch_tp_g = zeros.clone()
batch_pp_g = zeros.clone()
end = time.time()
attrs = attrs.float()
###################################
# calculate map for image network #
###################################
image_predictions = model_I2(model_I1(imgs))
# print('image_outputs size: ',image_outputs.size())
# image_predictions = torch.argmax(image_outputs, dim=2)
image_predictions = torch.where(image_predictions > 0, ones, zeros)
image_results = image_predictions - attrs
image_results = torch.where(image_results == 0, ones, zeros)
image_predictions = torch.where(image_predictions == 1, ones, zeros)
index = torch.where(image_results + image_predictions == 2, ones, zeros)
batch_tp_i += index
batch_pp_i += image_predictions
if tp_i is not None:
tp_i += batch_tp_i.sum(dim=0)
pp_i += batch_pp_i.sum(dim=0)
else:
tp_i = batch_tp_i.sum(dim=0)
pp_i = batch_pp_i.sum(dim=0)
#######################################
# calculate map for attribute network #
#######################################
attr_predictions = model_I2(model_G(attrs))
attr_predictions = torch.where(attr_predictions > 0, ones, zeros)
attr_results = attr_predictions - attrs
attr_results = torch.where(attr_results == 0, ones, zeros)
attr_predictions = torch.where(attr_predictions == 1, ones, zeros)
index = torch.where(attr_results + attr_predictions == 2, ones, zeros)
batch_tp_g += index
batch_pp_g += attr_predictions
if tp_g is not None:
tp_g += batch_tp_g.sum(dim=0)
pp_g += batch_pp_g.sum(dim=0)
else:
tp_g = batch_tp_g.sum(dim=0)
pp_g = batch_pp_g.sum(dim=0)
pp_i = torch.add(pp_i, 1e-10)
pp_g = torch.add(pp_g, 1e-10)
map_i = torch.div(tp_i, pp_i).mean()
map_g = torch.div(tp_g, pp_g).mean()
print('mean average precision of image network predictions: ', map_i)
print('mean average precision of attribute network predictions: ', map_g)
return
if __name__ == '__main__':
main()
| 41.574975
| 139
| 0.562127
| 10,234
| 83,732
| 4.33506
| 0.050322
| 0.040573
| 0.016725
| 0.021503
| 0.804035
| 0.761276
| 0.733033
| 0.694376
| 0.662504
| 0.639175
| 0
| 0.015789
| 0.28141
| 83,732
| 2,013
| 140
| 41.595628
| 0.721551
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| 0
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| 0.088725
| 0.009905
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| 0.00827
| false
| 0
| 0.018608
| 0
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| 0.077188
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| null | 0
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| 0
| 0
| 0
|
0
| 4
|
983867a7e8583bceb6a3407de42f5ba5e333aab9
| 92
|
py
|
Python
|
runserver.py
|
kellya/hostthedocs
|
ec1c0127668a7ba40fbd596a1537588031483fc4
|
[
"MIT"
] | 92
|
2015-01-06T18:04:46.000Z
|
2022-03-18T12:43:40.000Z
|
runserver.py
|
kellya/hostthedocs
|
ec1c0127668a7ba40fbd596a1537588031483fc4
|
[
"MIT"
] | 32
|
2015-01-14T15:37:59.000Z
|
2022-03-24T18:42:42.000Z
|
runserver.py
|
kellya/hostthedocs
|
ec1c0127668a7ba40fbd596a1537588031483fc4
|
[
"MIT"
] | 48
|
2015-01-08T16:41:47.000Z
|
2022-03-24T00:05:14.000Z
|
from hostthedocs import app, getconfig
if __name__ == '__main__':
getconfig.serve(app)
| 18.4
| 38
| 0.73913
| 11
| 92
| 5.454545
| 0.818182
| 0
| 0
| 0
| 0
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| 0.163043
| 92
| 4
| 39
| 23
| 0.779221
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| 1
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| 0
| 0
|
0
| 4
|
986b703102becff58f9fc311bec586dc52b707b1
| 262
|
py
|
Python
|
dicttoobject/type.py
|
oneofthezombies/dict-to-object
|
35a743af6805bbccfb6e3e96ff946a30d5265380
|
[
"MIT"
] | null | null | null |
dicttoobject/type.py
|
oneofthezombies/dict-to-object
|
35a743af6805bbccfb6e3e96ff946a30d5265380
|
[
"MIT"
] | null | null | null |
dicttoobject/type.py
|
oneofthezombies/dict-to-object
|
35a743af6805bbccfb6e3e96ff946a30d5265380
|
[
"MIT"
] | null | null | null |
from types import SimpleNamespace
from . import error
class WritableObject(SimpleNamespace):
pass
class ReadOnlyObject(SimpleNamespace):
def __setattr__(self, attr_name, attr_value):
raise error.DoNotWriteError(attr_name, attr_value)
| 21.833333
| 58
| 0.755725
| 28
| 262
| 6.785714
| 0.607143
| 0.084211
| 0.126316
| 0.178947
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.183206
| 262
| 11
| 59
| 23.818182
| 0.88785
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| false
| 0.142857
| 0.285714
| 0
| 0.714286
| 0
| 1
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
98a59148643385310737b53175d32916a6d91c6c
| 54
|
py
|
Python
|
challenge_1/python/nebi/src/reverse.py
|
rchicoli/2017-challenges
|
44f0b672e5dea34de1dde131b6df837d462f8e29
|
[
"Apache-2.0"
] | 271
|
2017-01-01T22:58:36.000Z
|
2021-11-28T23:05:29.000Z
|
challenge_1/python/nebi/src/reverse.py
|
AakashOfficial/2017Challenges
|
a8f556f1d5b43c099a0394384c8bc2d826f9d287
|
[
"Apache-2.0"
] | 283
|
2017-01-01T23:26:05.000Z
|
2018-03-23T00:48:55.000Z
|
challenge_1/python/nebi/src/reverse.py
|
AakashOfficial/2017Challenges
|
a8f556f1d5b43c099a0394384c8bc2d826f9d287
|
[
"Apache-2.0"
] | 311
|
2017-01-01T22:59:23.000Z
|
2021-09-23T00:29:12.000Z
|
"""Reverse the string hello """
print('hello'[::-1])
| 13.5
| 31
| 0.592593
| 7
| 54
| 4.571429
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021277
| 0.12963
| 54
| 3
| 32
| 18
| 0.659574
| 0.444444
| 0
| 0
| 0
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| 0.227273
| 0
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| true
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| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
7f6b704acd2246ad0f7620ce5b124ea48875734e
| 81
|
py
|
Python
|
holomorphefrontend/cad/apps.py
|
Jay4C/Holomorphe_Company
|
81d0034b381a5a45216fd3933727bf00e542fb2c
|
[
"MIT"
] | null | null | null |
holomorphefrontend/cad/apps.py
|
Jay4C/Holomorphe_Company
|
81d0034b381a5a45216fd3933727bf00e542fb2c
|
[
"MIT"
] | null | null | null |
holomorphefrontend/cad/apps.py
|
Jay4C/Holomorphe_Company
|
81d0034b381a5a45216fd3933727bf00e542fb2c
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class CadConfig(AppConfig):
name = 'cad'
| 13.5
| 33
| 0.728395
| 10
| 81
| 5.9
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0.185185
| 81
| 5
| 34
| 16.2
| 0.893939
| 0
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| 0.037037
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
7f724532812281a3d83d1aa2d7d79f593bed1b59
| 166
|
py
|
Python
|
ramda/pluck.py
|
zydmayday/pamda
|
6740d0294f3bedbeeef3bbc3042a43dceb3239b2
|
[
"MIT"
] | 1
|
2022-03-14T07:35:13.000Z
|
2022-03-14T07:35:13.000Z
|
ramda/pluck.py
|
zydmayday/pamda
|
6740d0294f3bedbeeef3bbc3042a43dceb3239b2
|
[
"MIT"
] | 3
|
2022-03-24T02:30:18.000Z
|
2022-03-31T07:46:04.000Z
|
ramda/pluck.py
|
zydmayday/pamda
|
6740d0294f3bedbeeef3bbc3042a43dceb3239b2
|
[
"MIT"
] | null | null | null |
from .map import map
from .private._curry2 import _curry2
from .prop import prop
def inner_pluck(p, arr):
return map(prop(p), arr)
pluck = _curry2(inner_pluck)
| 15.090909
| 36
| 0.740964
| 27
| 166
| 4.37037
| 0.444444
| 0.169492
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021583
| 0.162651
| 166
| 10
| 37
| 16.6
| 0.827338
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.5
| 0.166667
| 0.833333
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 4
|
7f73e4927aa34eaf9a6ad89e7174cad011ffb333
| 87
|
py
|
Python
|
Password Validation/createuserfile.py
|
iitsunil/self-projects
|
010e3698fc5f6536c3ab038b3f8278b23ddeab21
|
[
"Apache-2.0"
] | null | null | null |
Password Validation/createuserfile.py
|
iitsunil/self-projects
|
010e3698fc5f6536c3ab038b3f8278b23ddeab21
|
[
"Apache-2.0"
] | null | null | null |
Password Validation/createuserfile.py
|
iitsunil/self-projects
|
010e3698fc5f6536c3ab038b3f8278b23ddeab21
|
[
"Apache-2.0"
] | null | null | null |
import pickle
with open('users.pck', 'wb') as file:
pickle.dump([],file)
| 14.5
| 38
| 0.574713
| 12
| 87
| 4.166667
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.252874
| 87
| 5
| 39
| 17.4
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0.135802
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
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| 0
| null | 0
| 0
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| 0
| 0
| 0
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| 0
| 0
| 0
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| 0
| 1
| 0
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| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
7f8791631414037c8f1d8fbd95931f63a94fb32c
| 4,378
|
py
|
Python
|
tests/_internal/clients/test_archive.py
|
unparalleled-js/py42
|
8c6b054ddd8c2bfea92bf77b0d648af76f1efcf1
|
[
"MIT"
] | 1
|
2020-08-18T22:00:22.000Z
|
2020-08-18T22:00:22.000Z
|
tests/_internal/clients/test_archive.py
|
unparalleled-js/py42
|
8c6b054ddd8c2bfea92bf77b0d648af76f1efcf1
|
[
"MIT"
] | null | null | null |
tests/_internal/clients/test_archive.py
|
unparalleled-js/py42
|
8c6b054ddd8c2bfea92bf77b0d648af76f1efcf1
|
[
"MIT"
] | 1
|
2021-05-10T23:33:34.000Z
|
2021-05-10T23:33:34.000Z
|
import json
import pytest
from requests import Response
import py42.settings
from py42._internal.clients.archive import ArchiveClient
from py42.response import Py42Response
MOCK_GET_ORG_RESTORE_HISTORY_RESPONSE = """{"totalCount": 3000, "restoreEvents": [{"eventName": "foo", "eventUid": "123"}]}"""
MOCK_EMPTY_GET_ORG_RESTORE_HISTORY_RESPONSE = (
"""{"totalCount": 3000, "restoreEvents": []}"""
)
MOCK_GET_ORG_COLD_STORAGE_RESPONSE = (
"""{"coldStorageRows": [{"archiveGuid": "fakeguid"}]}"""
)
MOCK_EMPTY_GET_ORG_COLD_STORAGE_RESPONSE = """{"coldStorageRows": []}"""
class TestArchiveClient(object):
@pytest.fixture
def mock_get_all_restore_history_response(self, mocker):
response = mocker.MagicMock(spec=Response)
response.status_code = 200
response.encoding = "utf-8"
response.text = MOCK_GET_ORG_RESTORE_HISTORY_RESPONSE
return Py42Response(response)
@pytest.fixture
def mock_get_all_restore_history_empty_response(self, mocker):
response = mocker.MagicMock(spec=Response)
response.status_code = 200
response.encoding = "utf-8"
response.text = MOCK_EMPTY_GET_ORG_RESTORE_HISTORY_RESPONSE
return Py42Response(response)
@pytest.fixture
def mock_get_all_org_cold_storage_response(self, mocker):
response = mocker.MagicMock(spec=Response)
response.status_code = 200
response.encoding = "utf-8"
response.text = MOCK_GET_ORG_COLD_STORAGE_RESPONSE
return Py42Response(response)
@pytest.fixture
def mock_get_all_org_cold_storage_empty_response(self, mocker):
response = mocker.MagicMock(spec=Response)
response.status_code = 200
response.encoding = "utf-8"
response.text = MOCK_EMPTY_GET_ORG_COLD_STORAGE_RESPONSE
return Py42Response(response)
def test_get_all_restore_history_calls_get_expected_number_of_times(
self,
mock_session,
mock_get_all_restore_history_response,
mock_get_all_restore_history_empty_response,
):
py42.settings.items_per_page = 1
client = ArchiveClient(mock_session)
mock_session.get.side_effect = [
mock_get_all_restore_history_response,
mock_get_all_restore_history_response,
mock_get_all_restore_history_empty_response,
]
for _ in client.get_all_restore_history(10, "orgId", "123"):
pass
py42.settings.items_per_page = 500
assert mock_session.get.call_count == 3
def test_update_cold_storage_purge_date_calls_coldstorage_with_expected_data(
self, mock_session
):
client = ArchiveClient(mock_session)
client.update_cold_storage_purge_date(u"123", u"2020-04-24")
mock_session.put.assert_called_once_with(
u"/api/coldStorage/123",
params={u"idType": u"guid"},
data=json.dumps({u"archiveHoldExpireDate": u"2020-04-24"}),
)
def test_get_all_org_cold_storage_archives_calls_get_expected_number_of_times(
self,
mock_session,
mock_get_all_org_cold_storage_response,
mock_get_all_org_cold_storage_empty_response,
):
py42.settings.items_per_page = 1
client = ArchiveClient(mock_session)
mock_session.get.side_effect = [
mock_get_all_org_cold_storage_response,
mock_get_all_org_cold_storage_response,
mock_get_all_org_cold_storage_empty_response,
]
for _ in client.get_all_org_cold_storage_archives("orgId"):
pass
py42.settings.items_per_page = 500
assert mock_session.get.call_count == 3
def test_get_all_org_cold_storage_archives_calls_get_with_expected_uri_and_params(
self, mock_session, mock_get_all_org_cold_storage_empty_response
):
client = ArchiveClient(mock_session)
mock_session.get.side_effect = [mock_get_all_org_cold_storage_empty_response]
for _ in client.get_all_org_cold_storage_archives("orgId"):
break
params = {
"orgId": "orgId",
"incChildOrgs": True,
"pgNum": 1,
"pgSize": 500,
"srtDir": "asc",
"srtKey": "archiveHoldExpireDate",
}
mock_session.get.assert_called_once_with("/api/ColdStorage", params=params)
| 36.483333
| 126
| 0.693467
| 525
| 4,378
| 5.31619
| 0.19619
| 0.047295
| 0.085274
| 0.060552
| 0.766392
| 0.747761
| 0.725188
| 0.70369
| 0.620208
| 0.606234
| 0
| 0.027027
| 0.222476
| 4,378
| 119
| 127
| 36.789916
| 0.792891
| 0
| 0
| 0.534653
| 0
| 0
| 0.070175
| 0.009825
| 0
| 0
| 0
| 0
| 0.039604
| 1
| 0.079208
| false
| 0.019802
| 0.059406
| 0
| 0.188119
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
7fa1a0b49b0257b5e0671d37d69eed12f1540bfd
| 455
|
py
|
Python
|
entities/tech_user.py
|
vaultboy324/ExecBot
|
f65d505433e0266f89f16a7af255ae834a09071a
|
[
"Apache-2.0"
] | 2
|
2020-05-16T03:27:14.000Z
|
2020-05-18T07:52:34.000Z
|
entities/tech_user.py
|
vaultboy324/ExecBot
|
f65d505433e0266f89f16a7af255ae834a09071a
|
[
"Apache-2.0"
] | null | null | null |
entities/tech_user.py
|
vaultboy324/ExecBot
|
f65d505433e0266f89f16a7af255ae834a09071a
|
[
"Apache-2.0"
] | null | null | null |
from helper.crypt_helper import Crypt_helper
import json
class TechUser:
def __init__(self):
self.__user_info = Crypt_helper.get_tech_user_info()
def get_uname(self):
return self.__user_info['login']
def get_password(self):
return self.__user_info['password'].encode()
def get_decrypt_password(self):
return Crypt_helper.decrypt_string(self.get_password(), Crypt_helper.get_hash_key()).decode("utf-8")
| 26.764706
| 108
| 0.720879
| 63
| 455
| 4.746032
| 0.412698
| 0.183946
| 0.120401
| 0.120401
| 0.147157
| 0
| 0
| 0
| 0
| 0
| 0
| 0.00266
| 0.173626
| 455
| 17
| 108
| 26.764706
| 0.792553
| 0
| 0
| 0
| 0
| 0
| 0.039474
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.363636
| false
| 0.363636
| 0.181818
| 0.272727
| 0.909091
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
7fa94bc8159ecda73c89111d6156bc786cfc4802
| 189
|
py
|
Python
|
FabricUI/device/__init__.py
|
shuaih7/FabricUI
|
6501e8e6370d1f90174002f5768b5ef63e8412bc
|
[
"Apache-2.0"
] | null | null | null |
FabricUI/device/__init__.py
|
shuaih7/FabricUI
|
6501e8e6370d1f90174002f5768b5ef63e8412bc
|
[
"Apache-2.0"
] | 2
|
2020-11-27T05:21:12.000Z
|
2020-11-27T05:24:04.000Z
|
FabricUI/device/__init__.py
|
shuaih7/QtUI
|
6501e8e6370d1f90174002f5768b5ef63e8412bc
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/python
# -*- coding: utf-8 -*-
'''
Created on 02.04.2021
Updated on 02.05.2021
Author: haoshuai@handaotech.com
'''
from .camera import GXCamera
from .machine import Machine
| 12.6
| 31
| 0.693122
| 28
| 189
| 4.678571
| 0.785714
| 0.061069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.10625
| 0.153439
| 189
| 14
| 32
| 13.5
| 0.7125
| 0.608466
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
7fb925984f57f3c7ed7c325d0669375ad31f9653
| 520
|
py
|
Python
|
dist/py/hidadapter.py
|
microsoft/jacdac
|
2c6548b7e55ac34141e5152c664ca268e873cf09
|
[
"CC-BY-4.0",
"MIT"
] | 31
|
2020-07-24T14:49:32.000Z
|
2022-03-20T12:20:56.000Z
|
dist/py/hidadapter.py
|
microsoft/jacdac
|
2c6548b7e55ac34141e5152c664ca268e873cf09
|
[
"CC-BY-4.0",
"MIT"
] | 747
|
2020-07-31T22:05:45.000Z
|
2022-03-31T23:27:35.000Z
|
dist/py/hidadapter.py
|
microsoft/jacdac
|
2c6548b7e55ac34141e5152c664ca268e873cf09
|
[
"CC-BY-4.0",
"MIT"
] | 17
|
2020-07-31T10:49:01.000Z
|
2022-03-15T03:21:43.000Z
|
# Autogenerated file for HID Adapter
# Add missing from ... import const
_JD_SERVICE_CLASS_HID_ADAPTER = const(0x1e5758b5)
_JD_HID_ADAPTER_REG_NUM_CONFIGURATIONS = const(0x80)
_JD_HID_ADAPTER_REG_CURRENT_CONFIGURATION = const(0x81)
_JD_HID_ADAPTER_CMD_GET_CONFIGURATION = const(0x80)
_JD_HID_ADAPTER_CMD_SET_BINDING = const(0x82)
_JD_HID_ADAPTER_CMD_CLEAR_BINDING = const(0x83)
_JD_HID_ADAPTER_CMD_CLEAR_CONFIGURATION = const(0x84)
_JD_HID_ADAPTER_CMD_CLEAR = const(0x85)
_JD_HID_ADAPTER_EV_CHANGED = const(JD_EV_CHANGE)
| 47.272727
| 55
| 0.863462
| 81
| 520
| 4.888889
| 0.407407
| 0.252525
| 0.242424
| 0.189394
| 0.257576
| 0
| 0
| 0
| 0
| 0
| 0
| 0.058212
| 0.075
| 520
| 11
| 56
| 47.272727
| 0.765073
| 0.130769
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0.084444
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f6a8048ecd365802eb777f4b8c900ce298dd6b28
| 20,420
|
py
|
Python
|
tests/mappers/test_fs_mapper.py
|
adolnik/oozie-to-airflow
|
e4eed9098fb91c234a2c9c6505ca84a54e6a9e32
|
[
"Apache-2.0"
] | 61
|
2019-05-23T14:41:53.000Z
|
2022-03-03T11:33:38.000Z
|
tests/mappers/test_fs_mapper.py
|
adolnik/oozie-to-airflow
|
e4eed9098fb91c234a2c9c6505ca84a54e6a9e32
|
[
"Apache-2.0"
] | 505
|
2019-05-20T15:21:09.000Z
|
2022-03-01T23:10:31.000Z
|
tests/mappers/test_fs_mapper.py
|
adolnik/oozie-to-airflow
|
e4eed9098fb91c234a2c9c6505ca84a54e6a9e32
|
[
"Apache-2.0"
] | 33
|
2019-05-23T01:30:47.000Z
|
2022-03-28T10:25:09.000Z
|
# -*- coding: utf-8 -*-
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests fs mapper"""
import ast
import unittest
from typing import Dict
from xml.etree import ElementTree as ET
from parameterized import parameterized
from o2a.converter.task import Task
from o2a.converter.relation import Relation
from o2a.mappers import fs_mapper
from o2a.o2a_libs.property_utils import PropertySet
TEST_JOB_PROPS: Dict[str, str] = {"user.name": "pig", "nameNode": "hdfs://localhost:8020"}
TEST_CONFIG: Dict[str, str] = {}
class PrepareCommandsTest(unittest.TestCase):
@parameterized.expand(
[
(
"<mkdir path='hdfs://localhost:8020/home/pig/test-fs/test-mkdir-1'/>",
"fs -mkdir -p /home/pig/test-fs/test-mkdir-1",
),
(
"<mkdir path='${nameNode}/home/pig/test-fs/DDD-mkdir-1'/>",
"fs -mkdir -p /home/pig/test-fs/DDD-mkdir-1",
),
]
)
def test_prepare_mkdir_command(self, xml, command):
node = ET.fromstring(xml)
self.assertEqual(
command,
fs_mapper.prepare_mkdir_command(
node, props=PropertySet(job_properties=TEST_JOB_PROPS, config=TEST_CONFIG)
),
)
@parameterized.expand(
[
(
"<delete path='hdfs://localhost:8020/home/pig/test-fsXXX/test-delete-3'/>",
"fs -rm -f -r /home/pig/test-fsXXX/test-delete-3",
),
(
"<delete path='hdfs://localhost:8020/home/pig/test-fs/test-delete-3'/>",
"fs -rm -f -r /home/pig/test-fs/test-delete-3",
),
]
)
def test_prepare_delete_command(self, xml, command):
node = ET.fromstring(xml)
self.assertEqual(
command,
fs_mapper.prepare_delete_command(
node, props=PropertySet(job_properties=TEST_JOB_PROPS, config=TEST_CONFIG)
),
)
@parameterized.expand(
[
(
"<move source='hdfs://localhost:8020/home/pig/test-fs/test-move-1' "
"target='/home/pig/test-fs/test-move-2' />",
"fs -mv /home/pig/test-fs/test-move-1 /home/pig/test-fs/test-move-2",
),
(
"<move source='${nameNode}/home/pig/test-fs/test-move-1' "
"target='/home/pig/test-DDD/test-move-2' />",
"fs -mv /home/pig/test-fs/test-move-1 /home/pig/test-DDD/test-move-2",
),
(
"<move source='${nameNode}/home/pig/test-fs/test-move-1' "
"target='/home/pig/test-DDD/test-move-2' />",
"fs -mv /home/pig/test-fs/test-move-1 /home/pig/test-DDD/test-move-2",
),
]
)
def test_prepare_move_command(self, xml, command):
node = ET.fromstring(xml)
self.assertEqual(
command,
fs_mapper.prepare_move_command(
node, props=PropertySet(job_properties=TEST_JOB_PROPS, config=TEST_CONFIG)
),
)
@parameterized.expand(
[
(
"<chmod path='hdfs://localhost:8020/home/pig/test-fs/test-chmod-1' "
"permissions='777' dir-files='false' />",
"fs -chmod 777 /home/pig/test-fs/test-chmod-1",
),
(
"<chmod path='hdfs://localhost:8020/home/pig/test-fs/test-chmod-2' "
"permissions='777' dir-files='true' />",
"fs -chmod 777 /home/pig/test-fs/test-chmod-2",
),
(
"<chmod path='${nameNode}/home/pig/test-fs/test-chmod-3' permissions='777' />",
"fs -chmod 777 /home/pig/test-fs/test-chmod-3",
),
(
"""<chmod path='hdfs://localhost:8020/home/pig/test-fs/test-chmod-4'
permissions='777' dir-files='false' >
<recursive/>
</chmod>""",
"fs -chmod -R 777 /home/pig/test-fs/test-chmod-4",
),
]
)
def test_prepare_chmod_command(self, xml, command):
node = ET.fromstring(xml)
self.assertEqual(
command,
fs_mapper.prepare_chmod_command(
node, props=PropertySet(job_properties=TEST_JOB_PROPS, config=TEST_CONFIG)
),
)
@parameterized.expand(
[
(
"<touchz path='hdfs://localhost:8020/home/pig/test-fs/test-touchz-1' />",
"fs -touchz /home/pig/test-fs/test-touchz-1",
),
(
"<touchz path='${nameNode}/home/pig/test-fs/DDDD-touchz-1' />",
"fs -touchz /home/pig/test-fs/DDDD-touchz-1",
),
]
)
def test_prepare_touchz_command(self, xml, command):
node = ET.fromstring(xml)
self.assertEqual(
command,
fs_mapper.prepare_touchz_command(
node, props=PropertySet(job_properties=TEST_JOB_PROPS, config=TEST_CONFIG)
),
)
@parameterized.expand(
[
(
"<chgrp path='hdfs://localhost:8020/home/pig/test-fs/test-chgrp-1' group='hadoop' />",
"fs -chgrp hadoop /home/pig/test-fs/test-chgrp-1",
),
(
"<chgrp path='${nameNode}/home/pig/test-fs/DDD-chgrp-1' group='hadoop' />",
"fs -chgrp hadoop /home/pig/test-fs/DDD-chgrp-1",
),
]
)
def test_prepare_chgrp_command(self, xml, command):
node = ET.fromstring(xml)
self.assertEqual(
command,
fs_mapper.prepare_chgrp_command(
node, props=PropertySet(job_properties=TEST_JOB_PROPS, config=TEST_CONFIG)
),
)
class FsMapperSingleTestCase(unittest.TestCase):
def setUp(self):
# language=XML
node_str = """
<fs>
<mkdir path='hdfs://localhost:9200/home/pig/test-delete-1'/>
</fs>"""
self.node = ET.fromstring(node_str)
self.mapper = _get_fs_mapper(oozie_node=self.node)
self.mapper.on_parse_node()
def test_to_tasks_and_relations(self):
tasks, relations = self.mapper.to_tasks_and_relations()
self.assertEqual(
[
Task(
task_id="test_id",
template_name="fs_op.tpl",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-delete-1",
"action_node_properties": {},
},
)
],
tasks,
)
self.assertEqual([], relations)
def test_required_imports(self):
imps = self.mapper.required_imports()
imp_str = "\n".join(imps)
self.assertIsNotNone(ast.parse(imp_str))
class FsMapperEmptyTestCase(unittest.TestCase):
def setUp(self):
self.node = ET.Element("fs")
self.mapper = _get_fs_mapper(oozie_node=self.node)
self.mapper.on_parse_node()
def test_to_tasks_and_relations(self):
tasks, relations = self.mapper.to_tasks_and_relations()
self.assertEqual([Task(task_id="test_id", template_name="dummy.tpl")], tasks)
self.assertEqual([], relations)
def test_required_imports(self):
imps = self.mapper.required_imports()
imp_str = "\n".join(imps)
self.assertIsNotNone(ast.parse(imp_str))
class FsMapperComplexTestCase(unittest.TestCase):
def setUp(self):
# language=XML
node_str = """
<fs>
<configuration>
<property>
<name>test.property.node</name>
<value>${nameNode}</value>
</property>
</configuration>
<!-- mkdir -->
<mkdir path='hdfs://localhost:9200/home/pig/test-delete-1'/>
<mkdir path='hdfs:///home/pig/test-delete-2'/>
<!-- delete -->
<mkdir path='hdfs://localhost:9200/home/pig/test-delete-1'/>
<mkdir path='hdfs://localhost:9200/home/pig/test-delete-2'/>
<mkdir path='hdfs://localhost:9200/home/pig/test-delete-3'/>
<delete path='hdfs://localhost:9200/home/pig/test-delete-1'/>
<!-- move -->
<mkdir path='hdfs://localhost:9200/home/pig/test-delete-1'/>
<move source='hdfs://localhost:9200/home/pig/test-chmod-1' target='/home/pig/test-chmod-2' />
<!-- chmod -->
<mkdir path='hdfs://localhost:9200/home/pig/test-chmod-1'/>
<mkdir path='hdfs://localhost:9200/home/pig/test-chmod-2'/>
<mkdir path='hdfs://localhost:9200/home/pig/test-chmod-3'/>
<mkdir path='hdfs://localhost:9200/home/pig/test-chmod-4'/>
<chmod path='hdfs://localhost:9200/home/pig/test-chmod-1'
permissions='-rwxrw-rw-' dir-files='false' />
<chmod path='hdfs://localhost:9200/home/pig/test-chmod-2'
permissions='-rwxrw-rw-' dir-files='true' />
<chmod path='hdfs://localhost:9200/home/pig/test-chmod-3'
permissions='-rwxrw-rw-' />
<chmod path='hdfs://localhost:9200/home/pig/test-chmod-4'
permissions='-rwxrw-rw-' dir-files='false' >
<recursive/>
</chmod>
<!-- touchz -->
<touchz path='hdfs://localhost:9200/home/pig/test-touchz-1' />
<!-- chgrp -->
<chgrp path='hdfs://localhost:9200/home/pig/test-touchz-1' group='pig' />
</fs>"""
self.node = ET.fromstring(node_str)
self.mapper = _get_fs_mapper(oozie_node=self.node)
self.mapper.on_parse_node()
def test_to_tasks_and_relations(self):
tasks, relations = self.mapper.to_tasks_and_relations()
self.assertEqual(
[
Task(
task_id="test_id_fs_0_mkdir",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-delete-1",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_1_mkdir",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-delete-2",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_2_mkdir",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-delete-1",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_3_mkdir",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-delete-2",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_4_mkdir",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-delete-3",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_5_delete",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -rm -f -r /home/pig/test-delete-1",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_6_mkdir",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-delete-1",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_7_move",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mv /home/pig/test-chmod-1 /home/pig/test-chmod-2",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_8_mkdir",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-chmod-1",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_9_mkdir",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-chmod-2",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_10_mkdir",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-chmod-3",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_11_mkdir",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -mkdir -p /home/pig/test-chmod-4",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_12_chmod",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -chmod -rwxrw-rw- /home/pig/test-chmod-1",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_13_chmod",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -chmod -rwxrw-rw- /home/pig/test-chmod-2",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_14_chmod",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -chmod -rwxrw-rw- /home/pig/test-chmod-3",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_15_chmod",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -chmod -R -rwxrw-rw- /home/pig/test-chmod-4",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_16_touchz",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -touchz /home/pig/test-touchz-1",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
Task(
task_id="test_id_fs_17_chgrp",
template_name="fs_op.tpl",
trigger_rule="one_success",
template_params={
"pig_command": "fs -chgrp pig /home/pig/test-touchz-1",
"action_node_properties": {"test.property.node": "{{nameNode}}"},
},
),
],
tasks,
)
self.assertEqual(
relations,
[
Relation(from_task_id="test_id_fs_0_mkdir", to_task_id="test_id_fs_1_mkdir"),
Relation(from_task_id="test_id_fs_1_mkdir", to_task_id="test_id_fs_2_mkdir"),
Relation(from_task_id="test_id_fs_2_mkdir", to_task_id="test_id_fs_3_mkdir"),
Relation(from_task_id="test_id_fs_3_mkdir", to_task_id="test_id_fs_4_mkdir"),
Relation(from_task_id="test_id_fs_4_mkdir", to_task_id="test_id_fs_5_delete"),
Relation(from_task_id="test_id_fs_5_delete", to_task_id="test_id_fs_6_mkdir"),
Relation(from_task_id="test_id_fs_6_mkdir", to_task_id="test_id_fs_7_move"),
Relation(from_task_id="test_id_fs_7_move", to_task_id="test_id_fs_8_mkdir"),
Relation(from_task_id="test_id_fs_8_mkdir", to_task_id="test_id_fs_9_mkdir"),
Relation(from_task_id="test_id_fs_9_mkdir", to_task_id="test_id_fs_10_mkdir"),
Relation(from_task_id="test_id_fs_10_mkdir", to_task_id="test_id_fs_11_mkdir"),
Relation(from_task_id="test_id_fs_11_mkdir", to_task_id="test_id_fs_12_chmod"),
Relation(from_task_id="test_id_fs_12_chmod", to_task_id="test_id_fs_13_chmod"),
Relation(from_task_id="test_id_fs_13_chmod", to_task_id="test_id_fs_14_chmod"),
Relation(from_task_id="test_id_fs_14_chmod", to_task_id="test_id_fs_15_chmod"),
Relation(from_task_id="test_id_fs_15_chmod", to_task_id="test_id_fs_16_touchz"),
Relation(from_task_id="test_id_fs_16_touchz", to_task_id="test_id_fs_17_chgrp"),
],
)
def test_required_imports(self):
imps = self.mapper.required_imports()
imp_str = "\n".join(imps)
self.assertIsNotNone(ast.parse(imp_str))
def _get_fs_mapper(oozie_node):
return fs_mapper.FsMapper(
oozie_node=oozie_node,
name="test_id",
dag_name="DAG_NAME_B",
props=PropertySet(job_properties={"nameNode": "hdfs://"}, config={}),
input_directory_path="/tmp/input-directory-path/",
)
| 41.336032
| 109
| 0.505877
| 2,216
| 20,420
| 4.396661
| 0.087094
| 0.054603
| 0.085805
| 0.066509
| 0.84204
| 0.818536
| 0.808375
| 0.725033
| 0.668172
| 0.586678
| 0
| 0.023131
| 0.362733
| 20,420
| 493
| 110
| 41.419878
| 0.725582
| 0.029775
| 0
| 0.509132
| 0
| 0.045662
| 0.383175
| 0.178349
| 0
| 0
| 0
| 0
| 0.034247
| 1
| 0.03653
| false
| 0
| 0.034247
| 0.002283
| 0.082192
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f6be56c91850284f33a994024ba711543b3ccd6f
| 92
|
py
|
Python
|
python/hello.py
|
ddeeff/sandbox
|
fed673ebdb630e0f5e8484ef36f43c95333dcebf
|
[
"MIT"
] | null | null | null |
python/hello.py
|
ddeeff/sandbox
|
fed673ebdb630e0f5e8484ef36f43c95333dcebf
|
[
"MIT"
] | null | null | null |
python/hello.py
|
ddeeff/sandbox
|
fed673ebdb630e0f5e8484ef36f43c95333dcebf
|
[
"MIT"
] | null | null | null |
import datetime
if __name__ == "__main__":
print "Hello python", datetime.datetime.now()
| 15.333333
| 46
| 0.728261
| 11
| 92
| 5.363636
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141304
| 92
| 5
| 47
| 18.4
| 0.746835
| 0
| 0
| 0
| 0
| 0
| 0.21978
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.333333
| null | null | 0.333333
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
f6bedc1dbf8bd89c5e7d9003e5e8c8bca658acd7
| 247
|
py
|
Python
|
COM220/IntroducaoPython/exerc09.py
|
MarcosPaul0/Materias-de-Programacao
|
2032f4d4ca983631b637846c02dc9feee94489ea
|
[
"MIT"
] | null | null | null |
COM220/IntroducaoPython/exerc09.py
|
MarcosPaul0/Materias-de-Programacao
|
2032f4d4ca983631b637846c02dc9feee94489ea
|
[
"MIT"
] | null | null | null |
COM220/IntroducaoPython/exerc09.py
|
MarcosPaul0/Materias-de-Programacao
|
2032f4d4ca983631b637846c02dc9feee94489ea
|
[
"MIT"
] | null | null | null |
def fahrenheitConverter(celsius):
return celsius * 1.8 + 32
def celsiusConverter(fahrenheit):
return (fahrenheit - 32) / 1.8
print('{} °C = {} °F'.format(5, fahrenheitConverter(5)))
print('{} °F = {} °C'.format(50, celsiusConverter(50)))
| 30.875
| 56
| 0.65587
| 34
| 247
| 4.882353
| 0.470588
| 0.024096
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.066667
| 0.149798
| 247
| 8
| 57
| 30.875
| 0.704762
| 0
| 0
| 0
| 0
| 0
| 0.104839
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.333333
| 0.666667
| 0.333333
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
f6dfd74b15011e52db8771a43b8b6838b5f746ad
| 239
|
py
|
Python
|
mw/xml_dump/errors.py
|
frankier/python-mediawiki-utilities
|
aa066d3d955daa3d20cf09bf5b0d46778dd67a7c
|
[
"MIT"
] | 23
|
2015-09-13T04:42:24.000Z
|
2021-05-28T23:28:57.000Z
|
mw/xml_dump/errors.py
|
frankier/python-mediawiki-utilities
|
aa066d3d955daa3d20cf09bf5b0d46778dd67a7c
|
[
"MIT"
] | 23
|
2015-01-14T04:48:59.000Z
|
2015-08-25T19:25:43.000Z
|
mw/xml_dump/errors.py
|
frankier/python-mediawiki-utilities
|
aa066d3d955daa3d20cf09bf5b0d46778dd67a7c
|
[
"MIT"
] | 14
|
2015-09-15T16:04:50.000Z
|
2022-01-09T19:18:39.000Z
|
class FileTypeError(Exception):
"""
Thrown when an XML dump file is not of an expected type.
"""
pass
class MalformedXML(Exception):
"""
Thrown when an XML dump file is not formatted as expected.
"""
pass
| 18.384615
| 62
| 0.635983
| 31
| 239
| 4.903226
| 0.580645
| 0.197368
| 0.25
| 0.276316
| 0.486842
| 0.486842
| 0.486842
| 0.486842
| 0.486842
| 0
| 0
| 0
| 0.280335
| 239
| 12
| 63
| 19.916667
| 0.883721
| 0.481172
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 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
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
f6ebee3151d7ebc21ad0f6173e8ad2b71bfe2910
| 79
|
py
|
Python
|
tests/perfs/test_ozone_perf_measure_L2.py
|
shaido987/pyaf
|
b9afd089557bed6b90b246d3712c481ae26a1957
|
[
"BSD-3-Clause"
] | 377
|
2016-10-13T20:52:44.000Z
|
2022-03-29T18:04:14.000Z
|
tests/perfs/test_ozone_perf_measure_L2.py
|
ysdede/pyaf
|
b5541b8249d5a1cfdc01f27fdfd99b6580ed680b
|
[
"BSD-3-Clause"
] | 160
|
2016-10-13T16:11:53.000Z
|
2022-03-28T04:21:34.000Z
|
tests/perfs/test_ozone_perf_measure_L2.py
|
ysdede/pyaf
|
b5541b8249d5a1cfdc01f27fdfd99b6580ed680b
|
[
"BSD-3-Clause"
] | 63
|
2017-03-09T14:51:18.000Z
|
2022-03-27T20:52:57.000Z
|
import tests.perfs.test_ozone_perf_measure as tperf
tperf.build_model("L2");
| 15.8
| 51
| 0.810127
| 13
| 79
| 4.615385
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013889
| 0.088608
| 79
| 4
| 52
| 19.75
| 0.819444
| 0
| 0
| 0
| 0
| 0
| 0.025641
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
f6f3f38299d1efd9f176a4a5af8dc943678447a2
| 463
|
py
|
Python
|
chapter05/mypackage/mymathlib.py
|
codingEzio/code_python_book_001
|
d9dd7a3d40c4c34c2ae8d08222aba989631abd88
|
[
"Unlicense"
] | null | null | null |
chapter05/mypackage/mymathlib.py
|
codingEzio/code_python_book_001
|
d9dd7a3d40c4c34c2ae8d08222aba989631abd88
|
[
"Unlicense"
] | null | null | null |
chapter05/mypackage/mymathlib.py
|
codingEzio/code_python_book_001
|
d9dd7a3d40c4c34c2ae8d08222aba989631abd88
|
[
"Unlicense"
] | null | null | null |
class mymathlib:
def __init__(self):
"""Constructor for this class..."""
print("Creating object : "+self.__class__.__name__)
def add(self, x, y):
return (x+y)
def mul(self, x, y):
return (x*y)
def sub(self, x, y):
return (x-y)
def div(self, x, y):
return (x/y)
def __del__(self):
"""Destructor for this class..."""
print("Destroying object : "+self.__class__.__name__)
| 21.045455
| 61
| 0.539957
| 60
| 463
| 3.766667
| 0.366667
| 0.070796
| 0.106195
| 0.212389
| 0.300885
| 0.300885
| 0.300885
| 0
| 0
| 0
| 0
| 0
| 0.302376
| 463
| 21
| 62
| 22.047619
| 0.69969
| 0.12527
| 0
| 0
| 0
| 0
| 0.096692
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.461538
| false
| 0
| 0
| 0.307692
| 0.846154
| 0.153846
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
f6f6eb5c0891c385c016b58128cfaf24f3d4e248
| 222
|
py
|
Python
|
chap18/exercises.py
|
theChad/ThinkPython
|
825e1215766d2abfab58600ea4862c7f2e89b347
|
[
"MIT"
] | null | null | null |
chap18/exercises.py
|
theChad/ThinkPython
|
825e1215766d2abfab58600ea4862c7f2e89b347
|
[
"MIT"
] | null | null | null |
chap18/exercises.py
|
theChad/ThinkPython
|
825e1215766d2abfab58600ea4862c7f2e89b347
|
[
"MIT"
] | null | null | null |
# Exercise from Section 18.3
class Time:
def time_to_int(self):
return self.second + 60 * (self.minute + 60* self.hour)
def __lt__(self, other):
return self.time_to_int() < other.time_to_int()
| 18.5
| 63
| 0.63964
| 34
| 222
| 3.882353
| 0.529412
| 0.136364
| 0.204545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.041667
| 0.243243
| 222
| 11
| 64
| 20.181818
| 0.744048
| 0.117117
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0
| 0.4
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
f6f97cb2cbbb3cd1b5973d186e87522df848c7e1
| 130
|
py
|
Python
|
src/Errors.py
|
aaryanrr/DownDetector-CLI
|
944b29b3c22ceb870144696f56a3512a0009dc53
|
[
"MIT"
] | 2
|
2021-04-03T10:02:45.000Z
|
2021-10-31T03:19:28.000Z
|
src/Errors.py
|
aaryanrr/DownDetector-CLI
|
944b29b3c22ceb870144696f56a3512a0009dc53
|
[
"MIT"
] | 1
|
2021-07-19T02:55:31.000Z
|
2021-07-24T18:53:15.000Z
|
src/Errors.py
|
aaryanrr/DownDetector-CLI
|
944b29b3c22ceb870144696f56a3512a0009dc53
|
[
"MIT"
] | null | null | null |
# Defining the Error Classes
class InvalidServiceName(Exception):
# Raised when the Service Name entered is Invalid
pass
| 21.666667
| 53
| 0.761538
| 16
| 130
| 6.1875
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 130
| 5
| 54
| 26
| 0.951923
| 0.569231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
101324058191865d1e3e0f522f1b8e816f866dd6
| 412
|
py
|
Python
|
script_runs.py
|
DanielJAM/pytorch-mask-rcnn
|
4848bc11fb7be112ee1c3ce62dbf618b2d5d65af
|
[
"MIT"
] | null | null | null |
script_runs.py
|
DanielJAM/pytorch-mask-rcnn
|
4848bc11fb7be112ee1c3ce62dbf618b2d5d65af
|
[
"MIT"
] | null | null | null |
script_runs.py
|
DanielJAM/pytorch-mask-rcnn
|
4848bc11fb7be112ee1c3ce62dbf618b2d5d65af
|
[
"MIT"
] | null | null | null |
"""
@Daniel Maaskant
"""
import train_test
# img_dir, annos_dir, ONLY_TEST=1, STEPS_IS_LEN_TRAIN_SET=0, n_epochs=5, layer_string="5+", name="Faster_RCNN-"
train_test.run("../Master_Thesis_GvA_project/data/4_external/PanorAMS_panoramas_GT/",
"../Master_Thesis_GvA_project/data/4_external/PanorAMS_GT_pascal-VOC_selection-50/",
1, 1, 100, "all", "100e_selection50-lr00001") # 5+
| 31.692308
| 111
| 0.711165
| 61
| 412
| 4.377049
| 0.704918
| 0.067416
| 0.11236
| 0.164794
| 0.322097
| 0.322097
| 0.322097
| 0.322097
| 0
| 0
| 0
| 0.067797
| 0.140777
| 412
| 12
| 112
| 34.333333
| 0.686441
| 0.315534
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| 0.630037
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| 1
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| 0
| 0
| 0
| 0
|
0
| 4
|
63e4d5c908fa74dca259e75e7e6f6b34b3878089
| 97
|
py
|
Python
|
src/algorithms/main/maths/perfect_square_number.py
|
JoelGRod/Algorithms-py
|
085dbf5d2d62adf41cfd59313827f83e2da6bc81
|
[
"MIT"
] | null | null | null |
src/algorithms/main/maths/perfect_square_number.py
|
JoelGRod/Algorithms-py
|
085dbf5d2d62adf41cfd59313827f83e2da6bc81
|
[
"MIT"
] | null | null | null |
src/algorithms/main/maths/perfect_square_number.py
|
JoelGRod/Algorithms-py
|
085dbf5d2d62adf41cfd59313827f83e2da6bc81
|
[
"MIT"
] | null | null | null |
def is_square(n):
if n == -1: return False
return True if (n ** 0.5) % 1 == 0 else False
| 24.25
| 49
| 0.556701
| 19
| 97
| 2.789474
| 0.631579
| 0.113208
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.072464
| 0.28866
| 97
| 3
| 50
| 32.333333
| 0.695652
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
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| 0.666667
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| null | 0
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| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
63e7a8adc441c08db794018f3590fecc4d18f5aa
| 92
|
py
|
Python
|
WASD.py
|
ponyatov/wasd
|
27955799f2ea5f6a0c69e0f132fb3bb84e7e9098
|
[
"MIT"
] | null | null | null |
WASD.py
|
ponyatov/wasd
|
27955799f2ea5f6a0c69e0f132fb3bb84e7e9098
|
[
"MIT"
] | null | null | null |
WASD.py
|
ponyatov/wasd
|
27955799f2ea5f6a0c69e0f132fb3bb84e7e9098
|
[
"MIT"
] | null | null | null |
import config
import os, sys
def test_any(): assert True
import queue
A = queue.Queue()
| 9.2
| 27
| 0.717391
| 15
| 92
| 4.333333
| 0.733333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.195652
| 92
| 9
| 28
| 10.222222
| 0.878378
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 1
| 0.2
| false
| 0
| 0.6
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
120370dd622b74739ba7e59ae542751e47db1457
| 182
|
py
|
Python
|
tests/conftest.py
|
avito-tech/trainspotting
|
7fd5dba1db6f95c70a24462b4975fdffc021b677
|
[
"MIT"
] | 4
|
2022-01-23T11:25:35.000Z
|
2022-01-26T13:59:23.000Z
|
tests/conftest.py
|
avito-tech/trainspotting
|
7fd5dba1db6f95c70a24462b4975fdffc021b677
|
[
"MIT"
] | 2
|
2022-01-23T11:21:42.000Z
|
2022-01-29T06:43:59.000Z
|
tests/conftest.py
|
avito-tech/trainspotting
|
7fd5dba1db6f95c70a24462b4975fdffc021b677
|
[
"MIT"
] | null | null | null |
import pytest
from trainspotting import DependencyInjector
@pytest.fixture
def injector_fabric():
def _fabric(cfg):
return DependencyInjector(cfg)
return _fabric
| 15.166667
| 44
| 0.752747
| 19
| 182
| 7.052632
| 0.578947
| 0.134328
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.192308
| 182
| 11
| 45
| 16.545455
| 0.911565
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0
| 0.285714
| 0.142857
| 0.857143
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
120ac981a05415fc089180334f8f2234466d2d73
| 199
|
py
|
Python
|
hotair/html.py
|
serviper/hota
|
b132d94af7217ce90636bf1af4f207dc01d00116
|
[
"MIT"
] | null | null | null |
hotair/html.py
|
serviper/hota
|
b132d94af7217ce90636bf1af4f207dc01d00116
|
[
"MIT"
] | null | null | null |
hotair/html.py
|
serviper/hota
|
b132d94af7217ce90636bf1af4f207dc01d00116
|
[
"MIT"
] | null | null | null |
from .template import Element, element
@element()
class h1(Element):
...
@element()
class b(Element):
...
@element()
class i(Element):
...
@element()
class span(Element):
...
| 9.045455
| 38
| 0.582915
| 21
| 199
| 5.52381
| 0.428571
| 0.603448
| 0.655172
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006494
| 0.226131
| 199
| 21
| 39
| 9.47619
| 0.746753
| 0
| 0
| 0.615385
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.076923
| 0
| 0.384615
| 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
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
122013cd4ab1da9678ccad1a62c9ac62b197b7c1
| 203
|
py
|
Python
|
src/admin/widgets/integer.py
|
aimanow/sft
|
dce87ffe395ae4bd08b47f28e07594e1889da819
|
[
"Apache-2.0"
] | 280
|
2016-07-19T09:59:02.000Z
|
2022-03-05T19:02:48.000Z
|
widgets/integer.py
|
YAR-SEN/GodMode2
|
d8a79b45c6d8b94f3d2af3113428a87d148d20d0
|
[
"WTFPL"
] | 3
|
2016-07-20T05:36:49.000Z
|
2018-12-10T16:16:19.000Z
|
widgets/integer.py
|
YAR-SEN/GodMode2
|
d8a79b45c6d8b94f3d2af3113428a87d148d20d0
|
[
"WTFPL"
] | 20
|
2016-07-20T10:51:34.000Z
|
2022-01-12T23:15:22.000Z
|
from wtforms.fields.html5 import IntegerField
from godmode.widgets.base import BaseWidget
class IntegerWidget(BaseWidget):
field = IntegerField()
field_kwargs = {"style": "max-width: 100px;"}
| 22.555556
| 49
| 0.753695
| 23
| 203
| 6.608696
| 0.782609
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022989
| 0.142857
| 203
| 8
| 50
| 25.375
| 0.850575
| 0
| 0
| 0
| 0
| 0
| 0.108374
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
1227accf8365e3d3f1f1db7e0b5043b2c55640e1
| 34
|
py
|
Python
|
dmsky/file_io/__init__.py
|
kadrlica/dmsky
|
a8f0c47c43164851b738b4a59a723addf89054bc
|
[
"MIT"
] | 1
|
2021-01-19T05:19:31.000Z
|
2021-01-19T05:19:31.000Z
|
dmsky/file_io/__init__.py
|
kadrlica/dmsky
|
a8f0c47c43164851b738b4a59a723addf89054bc
|
[
"MIT"
] | 10
|
2017-10-24T23:32:40.000Z
|
2021-04-16T23:49:59.000Z
|
dmsky/file_io/__init__.py
|
kadrlica/dmsky
|
a8f0c47c43164851b738b4a59a723addf89054bc
|
[
"MIT"
] | 4
|
2017-05-18T19:01:11.000Z
|
2021-01-08T18:37:27.000Z
|
"""
File IO for dmsky package
"""
| 8.5
| 25
| 0.617647
| 5
| 34
| 4.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205882
| 34
| 3
| 26
| 11.333333
| 0.777778
| 0.735294
| 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
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
1231ac2f75dbe1b2025760dfd2990bec17a40884
| 112
|
py
|
Python
|
pyzu/__init__.py
|
chason/pyOGP
|
cecf89b68dffabd5d4dfe1cd7d39195a38dcc73a
|
[
"MIT"
] | 1
|
2018-08-14T17:25:37.000Z
|
2018-08-14T17:25:37.000Z
|
pyzu/__init__.py
|
chason/pyOGP
|
cecf89b68dffabd5d4dfe1cd7d39195a38dcc73a
|
[
"MIT"
] | 2
|
2018-08-02T09:25:06.000Z
|
2018-08-03T08:08:37.000Z
|
pyzu/__init__.py
|
chason/pyOGP
|
cecf89b68dffabd5d4dfe1cd7d39195a38dcc73a
|
[
"MIT"
] | 1
|
2018-08-02T08:28:47.000Z
|
2018-08-02T08:28:47.000Z
|
from typing import Sequence
from .pyzu import OGP
__all__: Sequence[str] = ["OGP"]
__version__: str = "0.1.4"
| 16
| 32
| 0.705357
| 17
| 112
| 4.176471
| 0.705882
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.031915
| 0.160714
| 112
| 6
| 33
| 18.666667
| 0.723404
| 0
| 0
| 0
| 0
| 0
| 0.071429
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
12370c303fbf7b7616357316ebb77a9b1965c697
| 1,288
|
py
|
Python
|
tests/test_graphql.py
|
seermedical/seer-py
|
1190f9c93e46bc42ae0344b1630290808991efcb
|
[
"MIT"
] | 22
|
2018-09-14T23:17:53.000Z
|
2022-03-17T04:45:07.000Z
|
tests/test_graphql.py
|
seermedical/seer-py
|
1190f9c93e46bc42ae0344b1630290808991efcb
|
[
"MIT"
] | 53
|
2018-06-15T01:09:22.000Z
|
2021-10-12T02:38:22.000Z
|
tests/test_graphql.py
|
seermedical/seer-py
|
1190f9c93e46bc42ae0344b1630290808991efcb
|
[
"MIT"
] | 16
|
2018-05-09T02:31:27.000Z
|
2022-03-31T13:15:32.000Z
|
from gql import gql
import seerpy.graphql as graphql
def test_graphql_query_string():
"""Ensure query strings parse correctly."""
# TODO: it would be good if all queries were iterable so we could do this in a loop, either by
# looping through all module variables if possible (or all CAPS), or having them in a class
# the main advantage being we wouldn't miss any then.
gql(graphql.GET_STUDY_WITH_DATA)
gql(graphql.GET_LABELS_PAGED)
gql(graphql.GET_LABELS_STRING)
gql(graphql.GET_ALL_LABEL_GROUPS_FOR_STUDY_ID_PAGED)
gql(graphql.GET_STUDIES_BY_SEARCH_TERM_PAGED)
gql(graphql.GET_STUDIES_BY_STUDY_ID_PAGED)
gql(graphql.ADD_LABELS)
gql(graphql.GET_TAG_IDS)
gql(graphql.EDIT_STUDY_LABEL_GROUP)
gql(graphql.GET_ORGANISATIONS)
gql(graphql.GET_PATIENTS)
gql(graphql.GET_DIARY_INSIGHTS_PAGED)
gql(graphql.GET_DIARY_LABELS)
gql(graphql.GET_DIARY_MEDICATION_ALERTS)
gql(graphql.GET_DIARY_MEDICATION_ALERT_WINDOWS)
gql(graphql.GET_DIARY_MEDICATION_COMPLIANCE)
gql(graphql.GET_DOCUMENTS_FOR_STUDY_IDS_PAGED)
gql(graphql.GET_LABELS_FOR_DIARY_STUDY_PAGED)
gql(graphql.GET_STUDY_IDS_IN_STUDY_COHORT_PAGED)
gql(graphql.GET_MOOD_SURVEY_RESULTS_PAGED)
gql(graphql.GET_USER_IDS_IN_USER_COHORT_PAGED)
| 37.882353
| 98
| 0.788043
| 201
| 1,288
| 4.671642
| 0.427861
| 0.223642
| 0.263046
| 0.153355
| 0.228967
| 0.057508
| 0
| 0
| 0
| 0
| 0
| 0
| 0.146739
| 1,288
| 33
| 99
| 39.030303
| 0.854413
| 0.211957
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.030303
| 0
| 1
| 0.041667
| true
| 0
| 0.083333
| 0
| 0.125
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
1245f6977e159e24247ea554374a146e2dd915ce
| 126
|
py
|
Python
|
python/testData/refactoring/makeFunctionTopLevel/methodCalledViaClass.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/refactoring/makeFunctionTopLevel/methodCalledViaClass.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/refactoring/makeFunctionTopLevel/methodCalledViaClass.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
class C():
def me<caret>thod(self, x):
print(self.foo, self.bar, x)
C.method(C(), 42)
C.method()
| 18
| 36
| 0.492063
| 20
| 126
| 3.1
| 0.65
| 0.225806
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.023256
| 0.31746
| 126
| 7
| 37
| 18
| 0.697674
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.2
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
1263976c7bb2af310e8c79e53e545b21764f2d36
| 616
|
py
|
Python
|
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/cms/djangoapps/contentstore/views/__init__.py
|
osoco/better-ways-of-thinking-about-software
|
83e70d23c873509e22362a09a10d3510e10f6992
|
[
"MIT"
] | 3
|
2021-12-15T04:58:18.000Z
|
2022-02-06T12:15:37.000Z
|
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/cms/djangoapps/contentstore/views/__init__.py
|
osoco/better-ways-of-thinking-about-software
|
83e70d23c873509e22362a09a10d3510e10f6992
|
[
"MIT"
] | null | null | null |
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/cms/djangoapps/contentstore/views/__init__.py
|
osoco/better-ways-of-thinking-about-software
|
83e70d23c873509e22362a09a10d3510e10f6992
|
[
"MIT"
] | 1
|
2019-01-02T14:38:50.000Z
|
2019-01-02T14:38:50.000Z
|
"All view functions for contentstore, broken out into submodules"
from .assets import *
from .checklists import *
from .component import *
from .course import * # lint-amnesty, pylint: disable=redefined-builtin
from .entrance_exam import *
from .error import *
from .export_git import *
from .helpers import *
from .import_export import *
from .item import *
from .library import *
from .preview import *
from .public import *
from .tabs import *
from .transcript_settings import *
from .transcripts_ajax import *
from .user import *
from .videos import *
try:
from .dev import *
except ImportError:
pass
| 23.692308
| 72
| 0.751623
| 81
| 616
| 5.654321
| 0.530864
| 0.349345
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.168831
| 616
| 25
| 73
| 24.64
| 0.894531
| 0.181818
| 0
| 0
| 0
| 0
| 0.111111
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.043478
| 0.869565
| 0
| 0.869565
| 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
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
89dee0e638cb0d1829a0276cdcf8d54573aa8a96
| 223
|
py
|
Python
|
backend/db_hero.py
|
joeriddles/FAPP
|
53b3788e3d7069ccf75c0337aca783847f5c40dd
|
[
"MIT"
] | 2
|
2021-08-19T21:05:12.000Z
|
2022-01-29T22:06:43.000Z
|
backend/db_hero.py
|
joeriddles/FAPP
|
53b3788e3d7069ccf75c0337aca783847f5c40dd
|
[
"MIT"
] | null | null | null |
backend/db_hero.py
|
joeriddles/FAPP
|
53b3788e3d7069ccf75c0337aca783847f5c40dd
|
[
"MIT"
] | 1
|
2022-01-29T22:06:44.000Z
|
2022-01-29T22:06:44.000Z
|
from sqlalchemy import Column, Integer, String
from .db_base import Base
class DbHero(Base):
__tablename__ = 'heroes'
id = Column(Integer, primary_key=True, nullable=False)
name = Column(String, unique=True)
| 22.3
| 58
| 0.730942
| 29
| 223
| 5.413793
| 0.689655
| 0.165605
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.174888
| 223
| 9
| 59
| 24.777778
| 0.853261
| 0
| 0
| 0
| 0
| 0
| 0.026906
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
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