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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
767b028eb1faebc818fb5278f2250f1caa9c9134
| 779
|
py
|
Python
|
xbaydnsweb/urls.py
|
bopopescu/xbaydns-2
|
606e8d9848d42fe5c0c5847a5a0e62044f58e486
|
[
"BSD-2-Clause"
] | 1
|
2019-01-16T05:20:51.000Z
|
2019-01-16T05:20:51.000Z
|
xbaydnsweb/urls.py
|
bopopescu/xbaydns-2
|
606e8d9848d42fe5c0c5847a5a0e62044f58e486
|
[
"BSD-2-Clause"
] | null | null | null |
xbaydnsweb/urls.py
|
bopopescu/xbaydns-2
|
606e8d9848d42fe5c0c5847a5a0e62044f58e486
|
[
"BSD-2-Clause"
] | 3
|
2015-12-29T11:22:28.000Z
|
2020-07-26T04:11:28.000Z
|
from django.conf.urls.defaults import *
urlpatterns = patterns('',
# Example:
# (r'^xbaydnsweb/', include('xbaydnsweb.foo.urls')),
# Uncomment this for admin:
#(r'^admin/', include('django.contrib.admin.urls')),
(r'admin/loadgen', 'xbaydnsweb.web.views.loadgenview'),
(r'admin/preview', 'xbaydnsweb.web.views.preview'),
(r'admin/smartload', 'xbaydnsweb.web.views.smartload'),
(r'agent/create/(?P<authzcode>.*)/(?P<pubkey>.*)/$', 'xbaydnsweb.web.views.create_agent'),
(r'slave/create/(?P<authzcode>.*)/(?P<pubkey>.*)/$', 'xbaydnsweb.web.views.create_slave'),
# (r'agent/create/', 'xbaydnsweb.web.views.create_agent'),
# (r'agent/create', 'xbaydnsweb.web.views.create_agent'),
(r'', include('django.contrib.admin.urls')),
)
| 43.277778
| 95
| 0.6457
| 93
| 779
| 5.365591
| 0.322581
| 0.182365
| 0.252505
| 0.192385
| 0.482966
| 0.366733
| 0.354709
| 0.354709
| 0.354709
| 0
| 0
| 0
| 0.121951
| 779
| 17
| 96
| 45.823529
| 0.729532
| 0.32991
| 0
| 0
| 0
| 0
| 0.613592
| 0.533981
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.111111
| 0
| 0.111111
| 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
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
768071cf9be66d04722a18052866d662605308da
| 90
|
py
|
Python
|
vmail/apps.py
|
Abhinaygunjal/V-mail
|
43179dfd98302c5cc33019527f306ed7cce06e4d
|
[
"MIT"
] | null | null | null |
vmail/apps.py
|
Abhinaygunjal/V-mail
|
43179dfd98302c5cc33019527f306ed7cce06e4d
|
[
"MIT"
] | null | null | null |
vmail/apps.py
|
Abhinaygunjal/V-mail
|
43179dfd98302c5cc33019527f306ed7cce06e4d
|
[
"MIT"
] | 2
|
2020-02-02T15:45:52.000Z
|
2020-03-25T09:50:50.000Z
|
from django.apps import AppConfig
class VmailConfig(AppConfig):
name = 'vmail'
| 15
| 34
| 0.7
| 10
| 90
| 6.3
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.222222
| 90
| 5
| 35
| 18
| 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
|
76837f0cb6632eecff4458b577e54734acaa3106
| 59
|
py
|
Python
|
code-everyday-challenge/n230_n_stairs.py
|
ved93/Deliberate-Practice-code-everyday-challenge
|
f32ced6452fa6b503b332df195e2b14791dd3094
|
[
"MIT"
] | null | null | null |
code-everyday-challenge/n230_n_stairs.py
|
ved93/Deliberate-Practice-code-everyday-challenge
|
f32ced6452fa6b503b332df195e2b14791dd3094
|
[
"MIT"
] | null | null | null |
code-everyday-challenge/n230_n_stairs.py
|
ved93/Deliberate-Practice-code-everyday-challenge
|
f32ced6452fa6b503b332df195e2b14791dd3094
|
[
"MIT"
] | null | null | null |
# https://www.geeksforgeeks.org/count-ways-reach-nth-stair/
| 59
| 59
| 0.779661
| 9
| 59
| 5.111111
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.016949
| 59
| 1
| 59
| 59
| 0.793103
| 0.966102
| 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
|
76b811abcd1bef37d2249f3a0d6cb412d78dd56c
| 356
|
py
|
Python
|
Python/Mundo 1/ex018.py
|
henrique-tavares/Coisas
|
f740518b1bedec5b0ea8c12ae07a2cac21eb51ae
|
[
"MIT"
] | 1
|
2020-02-07T20:39:26.000Z
|
2020-02-07T20:39:26.000Z
|
Python/Mundo 1/ex018.py
|
neptune076/Coisas
|
85c064cc0e134465aaf6ef41acf747d47f108fc9
|
[
"MIT"
] | null | null | null |
Python/Mundo 1/ex018.py
|
neptune076/Coisas
|
85c064cc0e134465aaf6ef41acf747d47f108fc9
|
[
"MIT"
] | null | null | null |
from math import sin, cos, tan, radians
angulo = float(input("Digite o ângulo que você deseja: "))
print("O ângulo de {}° tem o SENO de {:.2f}".format(angulo, sin(radians(angulo))))
print("O ângulo de {}° tem o COSSENO de {:.2f}".format(angulo, cos(radians(angulo))))
print("O ângulo de {}° tem a TANGENTE de {:.2f}".format(angulo, tan(radians(angulo))))
| 50.857143
| 86
| 0.676966
| 61
| 356
| 4
| 0.42623
| 0.213115
| 0.147541
| 0.172131
| 0.336066
| 0.336066
| 0.336066
| 0.254098
| 0
| 0
| 0
| 0.009709
| 0.132022
| 356
| 7
| 86
| 50.857143
| 0.770227
| 0
| 0
| 0
| 0
| 0
| 0.414566
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 0.2
| 0.6
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
76bd28036ce94e9a35dd39c07242dbe8caef3101
| 35,264
|
py
|
Python
|
musicbrainz/genrelist.py
|
yoshiask/PyZuneCatalogServer
|
b176155f89e3a990456248175b458ba309b808e0
|
[
"MIT"
] | 7
|
2021-02-21T10:45:25.000Z
|
2021-03-28T10:29:36.000Z
|
musicbrainz/genrelist.py
|
yoshiask/PyZuneCatalogServer
|
b176155f89e3a990456248175b458ba309b808e0
|
[
"MIT"
] | 1
|
2022-02-16T08:18:36.000Z
|
2022-02-20T04:17:02.000Z
|
musicbrainz/genrelist.py
|
yoshiask/PyZuneCatalogServer
|
b176155f89e3a990456248175b458ba309b808e0
|
[
"MIT"
] | null | null | null |
import urllib.parse
def get_zune_genre_id(genre_name: str) -> str:
"""
Returns the [original] Zune ID for the supplied genre.
"""
transformed: str = genre_name.replace("/", "_").replace("&", "!")
return "xm-" + urllib.parse.quote(transformed, safe="!")
def get_zune_genre_name(genre_id: str) -> str:
"""
Returns the [original] Zune name for the supplied genre.
"""
unescaped: str = urllib.parse.unquote(genre_id[3:])
return unescaped.replace("_", "/").replace("!", "&")
GENRES = {
"Rock": {
"ef063b60-3206-466c-a7d8-7d317fcdf9fc": "acid rock",
"00055e8b-b951-46e2-af1e-58b5624e7952": "acoustic rock",
"aca1caab-09d1-44b5-ae84-590ff1d218fc": "aor",
"7983ff25-ddf9-411e-a7f9-6cca238bff79": "alternative metal",
"e096efc6-6c9c-4240-b627-d7bbb5e8fa2a": "alternative punk",
"ceeaa283-5d7b-4202-8d1d-e25d116b2a18": "alternative rock",
"e12fe025-4c13-485e-a2fe-ad5bf2cd75eb": "anarcho-punk",
"26af3760-750a-4b5e-a699-78c87697585a": "arena rock",
"6e105daf-c06d-431e-8e33-1b8da3e48f7e": "art punk",
"b7ef058e-6d83-4ca4-8123-9724bff4648b": "art rock",
"d14a4c4a-bea5-4b08-b945-59b048acd2d5": "atmospheric black metal",
"77c0160b-8090-4493-b229-890addfee1df": "avant-garde metal",
"e5abef91-49eb-491c-b0ce-c6dd4e1f2d19": "avant-prog",
"12568698-97cc-49a9-80ab-9c5e3fe4dd33": "black metal",
"f976d48b-965e-4fe9-aaa6-105aab988c7c": "blackened death metal",
"a687ea57-487a-440d-b5fd-2f1b2f8b1f57": "blackgaze",
"58e325d5-54fd-4e98-b39a-3aa6bc319273": "blues rock",
"f07bf7f4-1b55-42ea-8c4d-9906fce84cdf": "boogie rock",
"12d11d01-cecc-4f9b-8d3a-99e4590952fe": "brutal death metal",
"9c2f8b08-9bac-4bfd-b9ed-6381c65791e9": "canterbury scene",
"36c0f210-b6c1-4481-bc7c-33de031a4046": "celtic punk",
"f0843e91-c025-4dd7-8aef-6fe3d35470d9": "chamber pop",
"5fef47af-772b-48e2-8e49-855f7d999e81": "city pop",
"93244085-20e5-4f16-9067-1d19143b3810": "classic rock",
"8b945b77-ccf8-4e55-a9a5-103cca5c1c96": "comedy rock",
"fb07b1e3-411d-4146-a0d6-3479c0e7557b": "country rock",
"ac4806f4-b2a8-43e8-8014-4f2bd9d64dad": "cowpunk",
"eee90717-3eee-41bd-b463-003c180544bd": "crossover prog",
"f66d7266-eb3d-4ef3-b4d8-b7cd992f918b": "crust punk",
"ad41288f-0803-4e2e-859f-148e7f7b2eae": "d-beat",
"509c006f-67c2-4c54-a568-d2b415cb0642": "dance-punk",
"c4a72fcd-e291-43c8-a546-9ce1764ff31b": "dark wave",
"eacfa027-2fad-413f-a2f1-80fa43674f0b": "death metal",
"2bd1ff1a-777d-4c5c-a1cb-92792adcf10c": "death-doom metal",
"d39cbc30-7dc6-4194-8f99-6d35dd602451": "deathcore",
"619c5945-6484-4a68-96e9-a48d4a4e1f73": "deathgrind",
"d5e21e4f-f6fe-464d-b269-14cf670a6949": "deathrock",
"8b6f2489-4f8a-4bff-80d6-9f39ef672493": "depressive black metal",
"bc1b8c46-5a69-4262-b909-c376f5de491a": "desert rock",
"2c63c5f4-280d-466c-bcd1-7905a4795d5b": "djent",
"db815815-60fe-44a3-9859-2232df479dc9": "doom metal",
"6242615b-5620-465b-b6fb-2e593e90cbad": "dream pop",
"c4a69842-f891-4569-9506-1882aa5db433": "electronic rock",
"c187da0e-48a0-4de5-b759-4ca2a1503008": "electropunk",
"ef0c2769-9c8f-49fc-b66e-27b8242b004a": "emo",
"17466ce1-b750-4f75-9341-c68b16531ba3": "emo pop",
"1d6489a9-5837-43c9-8155-94a126e8a951": "emocore",
"65c97e89-b42b-45c2-a70e-0eca1b8f0ff7": "experimental rock",
"f5b0242a-ede0-4929-9361-4eddeb0516b5": "folk metal",
"6cec5b4d-3c64-44a6-914d-3e833432f631": "folk punk",
"445dc48f-5233-4b4f-987f-867ff9ade836": "folk rock",
"0caaa092-27c4-413e-841c-7c4deeec57a7": "funk metal",
"eca476ac-d63b-431f-9347-c45f530dc15d": "funk rock",
"fd5bea65-15ef-44c4-83fb-ba15013b3596": "garage punk",
"b7bb11c3-bb83-4047-af65-1e8aba89039f": "garage rock",
"bc08993e-b188-43e8-b358-b84f2db6bfe6": "glam",
"818134e3-9392-4624-a13c-c4b19c54bccd": "glam metal",
"54d89e62-5bfb-42bc-a321-9230e6fdcd75": "glam rock",
"ca315be3-733b-4630-92d9-cd8ecafe0419": "goregrind",
"d9f42dc1-3a92-4217-8ae5-e9672f595b8e": "gothic",
"c73392a9-adaf-4ee4-b911-97a5892ac917": "gothic metal",
"d9858aef-25bf-478b-b135-b900aa1f0fd1": "gothic rock",
"b9c9a69d-5fc2-4317-b25d-5a96c8c9478a": "grebo",
"093149ea-d1bc-4f93-bd5a-8fdcd055ea2f": "grindcore",
"1a85800e-945e-4a9d-824e-dbd21ca15757": "groove metal",
"77f8d0ee-059a-4420-9ac7-84890730ae2a": "group sounds",
"3f08faa9-6c3e-4b6d-b4b8-08eea45954e2": "grunge",
"4ab0dfbd-1c0b-45e6-8cb8-3acf78a3bd77": "gypsy punk",
"51cb9f91-e6a2-41bf-891f-e78e3f1e52ab": "hard rock",
"055a6e4d-d929-42ac-ba7a-9d063b254ea5": "hardcore punk",
"a6719055-99c4-47eb-beaa-71081f2376f9": "heavy metal",
"3bc3d7dd-2bc5-4848-a9ef-4b67c6f7c6f9": "heavy psych",
"80778e31-3eca-4391-b01d-68c0e1cf40f5": "heavy rock",
"5fdbe79f-e728-4caa-81d3-669f1c6e4de2": "hopepunk",
"36471baf-3fd6-459d-918c-40dc64adea36": "horror punk",
"ccd19ebf-052c-4afe-8ad9-fbb0a73f94a7": "indie rock",
"fde4b353-2d97-4b57-af38-e7c53efa84f3": "indorock",
"d4df54b5-67b4-4fb7-8f73-79e71717a501": "industrial metal",
"ffbc9907-c9be-4ace-876b-b7fd5b9d51f9": "industrial rock",
"6b3903aa-8abe-4645-b363-558483b1586f": "instrumental rock",
"d181e978-c908-4f33-b2a2-f7454d0cb2c1": "j-rock",
"5eea976c-1ab2-4d94-9b63-7d1297d5dba5": "jazz rock",
"5319fada-431d-4b16-ac6f-37e24b2201b5": "kawaii metal",
"ba318056-9ddf-46cd-8b95-61fc993b962d": "krautrock",
"b07b5eec-c7f0-4529-a876-e0dd6b350084": "latin rock",
"caec8f8d-d862-4b62-9942-5622c2c18446": "mainstream rock",
"a6b55d73-bba3-49fc-a361-ca4bde004717": "math rock",
"8df1241b-2ba1-40b8-bb8f-2dc15ce6dc1e": "mathcore",
"74ed98e0-d2f4-4d34-9a51-0c18c1303726": "melodic black metal",
"5eeb0431-8160-40a7-ae44-c2ad01fed156": "melodic death metal",
"4f2cd717-7259-48df-8d0a-c17ccd9203b9": "melodic metalcore",
"e6d5e340-d969-4bb1-8342-18abc1d8c611": "melodic rock",
"cc28f5aa-5e19-487c-88e1-e56b022f1fdd": "metal",
"62f88295-0605-4ce8-958b-2e0063ece47e": "metalcore",
"d024e2e6-842c-43f7-b46e-8d0b87a4cd8e": "mincecore",
"d2327987-362f-4d70-8bfd-a8b47c3b0a54": "neo-progressive rock",
"cd99451c-f6d5-47c7-8d49-6c08b51e61aa": "neo-psychedelia",
"66366e89-590e-424f-b13d-0d0564eb4f27": "neo-rockabilly",
"28f8cf28-d1d6-4df9-8bee-ab0c6a9f0721": "neue deutsche härte",
"3ea5e230-c0e1-4c04-af91-2a4fb07b8afc": "neue deutsche welle",
"391a797d-50d7-46df-b380-39ccacd52a09": "new romantic",
"56407f9d-3398-4bf3-bbbd-ea372fa5adeb": "new wave",
"3188a961-88e0-4b12-9b9e-47f79e53d780": "no wave",
"8e5474b7-f445-49a7-866d-0f8e34b0a519": "noise rock",
"0cd76818-fdde-4f56-93a1-040f09ca7f23": "noisecore",
"bbad9cda-bde2-448e-821c-7ebf85a54020": "nu metal",
"290afc44-ad41-4d51-9ca6-4ee88df0f4f8": "occult rock",
"221ff39c-4521-49e3-ab52-e4dde85efd1e": "oi",
"2692f2d7-d680-4a5c-83f0-48e6e7c3ec6b": "old school death metal",
"77365c92-f25b-4ed3-8d02-03aae8b6df0e": "pop metal",
"60e21d01-ce21-4127-86b7-aada79558dab": "pop punk",
"797e2e85-5ffd-495c-a757-8b4079052f0e": "pop rock",
"bbb1310c-24cb-4fc9-97c4-f93e4549f1e2": "post-grunge",
"aec382b7-b635-470c-9051-2f84e0b5ea1e": "post-hardcore",
"e214700e-dae1-4b69-81e9-276e4e88fa32": "post-metal",
"f6d8cadd-6a5e-4ec9-9c2b-996b5332a639": "post-punk",
"0bfffa19-4e4a-4bf7-bf53-6ec44f0a2f8e": "post-rock",
"f0b0aa5f-ed81-456b-85ab-492002a29eeb": "power metal",
"4e6e388c-a9d2-45cb-80ad-bf64db4a0759": "powerviolence",
"5c7785e6-a0b4-4441-a1e6-4fd4c0066a5f": "progressive metal",
"ae9b8279-3959-48d8-8a88-741a7f6d4a48": "progressive rock",
"146ef761-5ad9-48b4-b0b3-483104f7da48": "psychedelic rock",
"8a14f570-7297-438d-996d-8797f9b8cfcc": "psychobilly",
"8cc9b280-230b-4a3a-b1e2-8acab0744dd3": "punk",
"bd7e1d40-43b1-4ba5-97b3-05b891466962": "punk rock",
"2481fbdc-05ed-458d-9928-46f3336c343e": "queercore",
"22623d74-4676-4853-aed9-921ae6fad495": "rap metal",
"b4b184b7-6224-478e-a273-f9da9ee30136": "rap rock",
"2cd7891d-c012-45f1-b1a1-86e34793f7fa": "rapcore",
"0e3fc579-2d24-4f20-9dae-736e1ec78798": "rock",
"e4581b76-a842-440b-9b0f-1aa7a00a5c57": "rock and roll",
"f1a053eb-61bb-448a-964d-35784c4ae8c1": "rockabilly",
"8fea0cb0-4205-49cb-95e8-bc1122857c7d": "screamo",
"a4012b2e-5c27-4f81-8781-9ae5998d882c": "shoegaze",
"455f264b-db00-4716-991d-fbd32dc24523": "singer-songwriter",
"8d3d9402-79df-4d91-946c-ea041acfb3cc": "slowcore",
"66544dbb-08a8-44c2-a21a-4d251481d2b6": "sludge metal",
"7d0c6a6b-5d56-4b1e-a8c7-e32194f246b9": "soft rock",
"905c1899-3027-4d29-8d9c-bbeef2c8628a": "southern rock",
"532dcf83-5641-446d-b007-51292b401650": "space rock",
"13b9a3d0-cadd-483d-9bf5-9c2b32a946a7": "speed metal",
"5c76eb54-7342-4d1f-9d98-a93f2f6aa7f6": "stoner metal",
"aa5f0254-403e-496a-a388-c01abf62f39a": "stoner rock",
"c2ca3275-7e83-40cc-8f13-7ccae588144d": "street punk",
"b76ee78d-ec97-49e9-8ea6-cfc46ecc3b23": "surf rock",
"82e38e98-8a93-4ea9-a07f-ff0aeba07444": "swamp rock",
"86a383d3-a4cc-4690-9a0f-f8ee83f7492d": "symphonic black metal",
"2a4cae89-5a5a-44b7-9cf8-bf92c38a31dc": "symphonic metal",
"166be36f-febb-4523-a005-1fb3603bd3f6": "symphonic prog",
"f729e6f8-30dc-4b81-9ff4-f4e7de82225d": "symphonic rock",
"db37c56d-e6a7-4809-b549-6f1cb559b559": "technical death metal",
"cc4cf136-5690-4ee3-a62b-b172febfe322": "thrash metal",
"e8a3ec42-4427-4365-8180-f1e05b46459e": "thrashcore",
"fc736acd-9413-4b86-b8d4-47e9ce61d561": "viking metal",
"823e4a7f-ade7-4d34-927a-721c60469b81": "visual kei",
"6d39ad19-fbd6-407b-baf3-ad6ad46a5518": "yacht rock",
"281e2c3f-80ad-4bc4-8b30-54222dee3b9c": "zamrock",
"6992b6b1-c275-4728-89b0-8aebab5b7d94": "zeuhl",
},
"Hip hop": {
"924943cd-73c8-45c0-96eb-74f2a15e5d6e": "alternative hip hop",
"79fb8e4d-f508-40c3-9043-524811ee9897": "bongo flava",
"d8048b43-9edb-4e1a-aa88-bb91fa847d4a": "boom bap",
"ccd103a5-bf71-43a2-b715-244a81ac9950": "bounce",
"8a2e8d9c-3402-4e83-b35d-a23e8016461a": "chopped and screwed",
"c0e73bb6-37a4-485e-aaea-11c63b40c9f3": "conscious hip hop",
"1e5e18e5-4e70-4a73-942a-c6c79c1aefe2": "drill",
"6732427a-0d04-4a15-b9e5-b4b5b9532f8f": "drill and bass",
"1bafab01-c092-47bc-84ee-a6b503b3cb86": "east coast hip hop",
"f52f5fc5-3b75-4757-9d1f-4cabe6808856": "emo rap",
"dfdf3e46-3b2b-417e-9346-86f9a5bac360": "funk carioca",
"12b013ff-cf03-4fa0-8e1b-c9b7f5d27f90": "hardcore hip hop",
"52faa157-6bad-4d86-a0ab-d4dec7d2513c": "hip hop",
"66d01f06-21dc-4216-ac63-c5efb7b6338e": "horrorcore",
"5f216b90-4ed2-4297-a243-d623db4c8235": "industrial hip hop",
"6061fb1b-d733-4643-a3d4-940c9a5e2ab8": "jazz rap",
"d92aaf30-5941-4a65-b741-d9d48c80772a": "lo-fi hip hop",
"432290bd-f667-4871-9fdc-127996f3ad93": "miami bass",
"9c8ba153-740e-4b88-b7ff-31d004944c95": "nerdcore",
"38dd5ce2-96d8-4202-b477-75a857d78adb": "phonk",
"da2fc3b4-b97d-491a-ac0f-8a731bee7237": "pop rap",
"4080e55b-ac2e-4907-a724-491b3bfdba63": "ragga hip-hop",
"9a339aa8-a0e5-450c-95a7-b11517bb508f": "trap",
"cfe47781-460e-498a-92f5-399d68076727": "turntablism",
"ca5d2892-93bf-4aae-85d5-a4589a1680b9": "uk drill",
"03a8749c-93e6-4b71-8204-b33de116d8fd": "underground hip hop",
"149a3524-0fd3-4bdc-9876-0361dfee8908": "west coast hip hop",
},
"R&B / Soul": {
"650bda08-20ca-4012-b873-3b68c5768bdf": "blue-eyed soul",
"fe12b346-a10e-450f-bf81-fa20894b5ea2": "blues",
"310c2c5c-38a7-49ce-9d24-6f27e22bd203": "boogie-woogie",
"15f9b448-6f2d-45a4-b11b-1fcdf45be203": "classic blues",
"4bb4043a-0ee5-4b84-93fa-6ba4567a6ba0": "contemporary r&b",
"a04d680b-b5c5-4f6a-87a3-f9f1ff98e3ea": "disco",
"1696f12e-9f01-4309-be9d-116e31ac65b7": "doo-wop",
"fe4ba6a1-9873-4fd0-a12b-a70c81818514": "funk",
"1fbd6e82-0d33-4d78-a9b1-7d145a8eefa6": "funk soul",
"b37aef4d-0029-4fc7-b634-6338197335a8": "g-funk",
"bc8c2f79-dcea-43f3-962b-f0663868d42c": "gangsta rap",
"0c44a04f-f7ba-46a0-9dbe-1a21359cfa1e": "motown",
"286f9168-9c46-4e41-94a1-1aa4aeb93b8c": "neo soul",
"dd497eac-e915-49a9-b1e4-482792acf397": "new jack swing",
"a18a254b-67ca-4c8b-a13c-87154055e47e": "northern soul",
"fadab481-3ced-466e-b828-74aed72d5c2f": "p-funk",
"64e0bd76-822f-4549-a6fe-9858561282d1": "pop soul",
"31be54b2-4d0c-42df-aa44-c496c7b4c3c3": "r&b",
"d4eafdc7-1a86-422b-b598-79e6bb81745c": "smooth soul",
"1b5e7b16-336b-473e-a364-5120413a9827": "soul",
"bd61d502-8906-4073-a4ea-18c04e946dbc": "southern soul",
},
"Pop": {
"d3ebe947-7c03-43f5-b333-09379881b0f6": "alternative pop",
"930ef127-3653-4677-9b95-ecc90c7c1a14": "art pop",
"0e7a3c6a-4fbc-4b66-8201-9196032768ac": "avant-garde pop",
"f1c9b874-7ef2-4e79-9419-c0bcb5f33a30": "britpop",
"6fd83b83-9b63-47fa-a208-5f9e762f40ab": "bubblegum pop",
"78caea3b-84fd-4664-878a-fe70a6e1d9d1": "cantopop",
"7232e5df-8466-421f-ad5a-ce2048e5522d": "chillout",
"50e3ff0e-1abb-4577-9bf3-95d5c539ce2d": "europop",
"abb2374f-30e5-4d3e-b417-2caf78ef1f6c": "folk pop",
"05039e37-4520-4d1e-8173-92a7915a452b": "hauntology",
"f390be72-360b-41bb-a310-6a2e638779d2": "indie pop",
"eba7715e-ee26-4989-8d49-9db382955419": "j-pop",
"b74b3b6c-0700-46b1-aa55-1f2869a3bd1a": "k-pop",
"7054e8e2-7c7b-4432-83b9-93423b361792": "kayōkyoku",
"2b9b7ea8-cb90-428c-adb2-d55b2faa4f1d": "latin pop",
"924bddc4-5e86-46b8-adf7-ba23ed764c40": "luk krung",
"489cf2f9-0edd-4fbc-aa05-ae248706cb3e": "mandopop",
"6c76d9ec-0924-4fc9-bb22-91a695ca09d6": "new age",
"1208a8e7-e5bd-4891-9f81-9a9fdfceeb39": "noise pop",
"911c7bbb-172d-4df8-9478-dbff4296e791": "pop",
"618704fc-daac-44a2-92c9-89e3b1a2fc8c": "power pop",
"d698ece0-6fed-4262-b446-42c98b73b65a": "psychedelic",
"2aeb5340-c474-4677-b9a6-35ddac9b6a58": "psychedelic pop",
"7edde935-8094-409e-ba33-34779b4668f8": "schlager",
"9864d280-a987-466f-bac2-ce103721c3e2": "shibuya-kei",
"18e297f0-8fd3-46ac-9953-1a9a91b69596": "space age pop",
"42fabe6b-0d6a-469d-b83c-21515bb2b490": "swamp pop",
"4b7c8e31-462b-4e06-acfd-fa508ad26e0c": "teen pop",
"e6d6a58f-6c00-484e-9bc7-12e2c4d024e2": "yé-yé",
},
"Electronic / Dance": {
"54c01942-22fd-4184-9877-1db0089b18f1": "acid house",
"ba64013e-27bb-4f14-a530-8d25b296e0da": "acid techno",
"8301f73c-9166-4108-bfeb-4fd22dc19083": "alternative dance",
"608b0471-7531-4854-a348-e698c69cb699": "ambient",
"9c143ab1-1506-4134-ba40-1660fbad7f51": "ambient dub",
"cc2327cf-88a3-476b-950c-296c382eabd9": "ambient house",
"1c6e4eae-915d-4662-a7ff-038920b67d9e": "ambient techno",
"2d5cd6af-6195-4d12-9788-9a9b03ac0a12": "bass house",
"decc967b-5b70-453b-9107-0b4e7a00505f": "beat music",
"aac07ae0-8acf-4249-b5c0-2762b53947a2": "big beat",
"43ef7823-5f16-4c9b-840d-f6b0de83a9c4": "bleep techno",
"3b8f3f65-6caa-47c8-b076-fa59dfb69c43": "breakbeat",
"1c5db6d3-9ce6-48c0-9bff-e29f7d7cb1af": "breakbeat hardcore",
"8aad8a03-be38-48e4-aa43-5ca0f826c569": "breakcore",
"63eddc75-dbad-4a05-a365-e30d1e87bca8": "breaks",
"cf9daa9d-74e2-4eff-b0eb-7269bbca6501": "broken beat",
"1dfc0cd3-bf39-4240-9265-96ced154425a": "brostep",
"103a65bb-35d9-4d27-9c9b-f35a458621ba": "chiptune",
"bab69b07-8bb9-4415-b666-c37609cef80b": "club",
"d20c40f8-935f-43f3-80cb-b65857abb7fc": "complextro",
"9d966bd4-89d5-4d6a-a408-eeb8c300c426": "coupé-décalé",
"75858cef-eb30-4787-b524-3e04a41698cc": "cyberpunk",
"e5bba957-8c91-496a-a675-c6d0c6b51c33": "dance",
"b739a895-85ed-4ad3-8717-4e9ef5387dd8": "dance-pop",
"01ecf8e4-7f7a-4145-ae97-504d3109633d": "dark ambient",
"24db3574-7c54-4d9b-b238-8d17eb7e8d63": "dark electro",
"7fa479e7-3d35-40d1-96f7-b619a5ca9d9f": "deep house",
"88b01b1f-9151-4a1b-a9f7-608accdeaf20": "detroit techno",
"6d12e828-40a5-4ebc-a5b0-aa5506e2efd0": "digital hardcore",
"cc38aba3-48ed-439a-83b9-f81a34a66598": "downtempo",
"462f9321-6103-49c9-b6db-96219bce6f62": "drum and bass",
"c72a5d45-75a8-4b35-9f48-67e49eb4b5e5": "dub",
"16aab171-3dce-454e-bb96-2636f15813fc": "dub techno",
"1b50083b-1afa-4778-82c8-548b309af783": "dubstep",
"c89a76aa-2149-499e-946b-8299aa2f4ab3": "dungeon synth",
"f3818817-6235-48d5-82f7-b223eb0261d9": "ebm",
"3976e9ba-16d0-461d-b732-99408a0374ba": "edm",
"60f00d05-df4d-496e-8f5a-c45c03a56ad4": "electro",
"05247982-e44f-4c58-96f8-a5453be69f5f": "electro house",
"9b1af4d0-2196-43d4-bbca-845a2e02d962": "electro swing",
"4f476710-cd29-400f-98fe-7782e1f00e06": "electro-funk",
"6e2e809f-8c54-4e0f-aca0-0642771ab3cf": "electro-industrial",
"cae57e98-c939-4b0e-ae49-8e0cb1bdc91d": "electroclash",
"89255676-1f14-4dd8-bbad-fca839d6aff4": "electronic",
"53a3cea3-17af-4421-a07a-5824b540aeb5": "electronica",
"f9720c77-3574-4fe8-823b-c290b117222e": "electronicore",
"3f3388b4-e36a-4bf8-a0be-66b10d92aaa4": "electropop",
"845d2209-0b82-4a87-9405-453fc2e3086c": "ethereal",
"8f8cbcde-3948-4be5-8bf0-8073f13e4b60": "euro house",
"8de96944-cabb-4864-893a-ed072d802046": "eurobeat",
"e9a67960-9b49-4967-a1c9-c26360e9c612": "eurodance",
"6d588e79-30b0-4f8c-9924-d4a951d60aef": "extratone",
"60cbcb55-852b-4329-9392-30a697802b71": "fidget house",
"ec5a14c7-7793-46dc-b858-470183eb63f7": "folktronica",
"405b97cf-e1d9-48aa-97cd-53212730589c": "footwork",
"5acda04e-995d-4f79-9a66-5fe6a977ce15": "french house",
"2fe74485-a939-4aa5-958f-ce09ba16906c": "funktronica",
"62e4c7cb-a2b3-462b-8d96-1b0d66ee6cf3": "funky house",
"e254f865-58ea-4483-9b2f-1bc65febcd71": "future bass",
"ad6d7f8d-2224-4708-8a00-0fdd838d4676": "future garage",
"cdc54f47-79fc-42e2-80d8-8ef58bd21122": "future jazz",
"6df6e38b-2a6f-4990-94dc-ea48eba4f8ad": "futurepop",
"2f2118d7-315b-4241-bf40-d5d6ca11348d": "gabber",
"60c9c6a6-1de7-4010-9f5b-7692d3231d22": "garage",
"092f40b5-9157-41f2-8243-68b704cd0ed1": "garage house",
"75ddea23-a5f4-466c-951c-900d45fc352c": "ghetto house",
"511a3aa8-abdb-4e63-b11c-2d50ab19b373": "ghettotech",
"18b010d7-7d85-4445-a4a8-1889a4688308": "glitch",
"aa3215ae-28e6-4993-908e-ed3807e1ce54": "glitch hop",
"997baf8b-203b-4567-a475-1c1fe348d206": "glitch pop",
"ade8f293-c049-438a-a5f0-b51e6776d9c5": "goa trance",
"51cfaac4-6696-480b-8f1b-27cfc789109c": "grime",
"bd477a3e-9235-4597-9ce5-3caa994baef7": "happy hardcore",
"9c5052e0-36ff-4b92-9447-58f44ffdf682": "hard house",
"c8acb78a-8337-4eae-9d19-48220dba03ac": "hard trance",
"d3a28fe9-40f8-47d7-8fe9-b741307f5b49": "hardbass",
"3308d0b0-3c7f-45e2-b835-5c2a0d433f02": "hardcore techno",
"4a74a604-f702-424c-984f-93c5b04360ea": "hardstyle",
"61d50e81-17bb-4b8b-ab36-7ae1759bc589": "hi-nrg",
"b99d935b-cc28-404f-a306-50b7d468304f": "hip house",
"a2782cb6-1cd0-477c-a61d-b3f8b42dd1b3": "house",
"8eb583f1-4fd7-460c-8246-dcdccc0e3ef9": "idm",
"da52c9d1-73f2-456e-a0d0-94bd854cb338": "illbient",
"9e7ae1de-d5f2-46a5-b105-11c7367dc70d": "indietronica",
"39b5ca66-873c-4a3d-94a3-3d31176b545e": "industrial techno",
"97a4a75a-6e49-4519-9684-ae2ccb2fc0f9": "italo-disco",
"36cdb9a1-a19e-4c8f-a887-c451e71cca46": "juke",
"ea9b6ec3-fc8f-4c55-b2f9-19bd4511a986": "jungle",
"32bea60c-e201-4241-8ed4-228636d34a97": "leftfield",
"013161b9-3f21-468f-aa1f-28adf1eae085": "madchester",
"13e409d0-fe4a-4dd4-a957-6e06462b085f": "melodic trance",
"bbce54eb-a477-4b23-ac81-d3b9aac43756": "microhouse",
"c1313278-b276-4a79-9fc1-770dd62a8b83": "minimal techno",
"556be6ff-df60-4bb1-bd88-3ec7c69dfa28": "minimal wave",
"edc96a6f-e8d0-4006-b065-d0fe188191c9": "musique concrète",
"9094f251-8379-4ced-9541-a1c91045c327": "neurofunk",
"4f2977aa-6943-404a-b8ec-295639ff0adf": "nightcore",
"351424dd-91a3-4cf2-9edb-5df647743ec0": "nu disco",
"848ecccf-e7fa-4439-a8ab-9dd3c31b4cf6": "nu jazz",
"1e661112-612a-487a-a3de-180816200eab": "onkyo",
"18527512-634e-48bb-81b2-6bfcf1818b95": "power electronics",
"ceadd212-a669-4981-b05c-7e8889df55ae": "power noise",
"70adb285-e1f7-458a-823f-5cbda5e291c4": "progressive house",
"6bd228be-3374-4c4e-bc0a-46779fafe8f1": "progressive trance",
"d1091878-2332-4f82-9a5c-f6d67cf4b7ef": "psy-trance",
"b3cccf71-e843-4546-87fa-728055ba3147": "psytrance",
"7aae791f-dfff-4910-b133-ba1b9e9c1560": "ragga jungle",
"660e755a-1b66-440b-9b7c-fbcf245f4415": "rave",
"9d4f50e8-5056-435e-8786-2d09e3407622": "rhythmic noise",
"1deaf1c4-d01b-4a3c-adc4-ff45cf09264c": "ritual ambient",
"23b9af26-1442-4dd9-8b40-a0db8a22e149": "schranz",
"59c7776f-fd87-4cfb-a7a5-9fe66ec02b1b": "speed garage",
"03544ca7-48ef-449b-abed-67aebdcd8120": "speedcore",
"988e91a3-3341-416d-b7f8-7dbef6848dac": "synth-pop",
"19565d07-8460-4773-8d54-149208a61bb4": "synthwave",
"1f9c4b4a-c43d-40bd-b806-357153641fd0": "tech house",
"ec039eb4-e175-44af-82ed-f152349eca35": "tech trance",
"41fe3260-fcc1-450b-bd5a-803886c56912": "techno",
"8a757534-0928-434c-accd-6be5517c1af3": "terrorcore",
"2afc2320-384f-43a4-a71e-1b763c485045": "trance",
"a21ea484-efba-4119-8120-429b26f98728": "trap edm",
"94bd08a1-367d-49ad-a9f5-6db1c08f196a": "tribal ambient",
"57159d7c-faa8-4266-abe1-7504e312fc88": "tribal house",
"45eb1d9c-588c-4dc8-9394-a14b7c8f02bc": "trip hop",
"31ec78f2-15c9-464b-a9e4-19e531028544": "uk funky",
"95c57cd7-7fb8-4405-ade1-775c2d27e54e": "uk garage",
"e837fa08-f314-428f-af96-ceed9858e3b2": "vaporwave",
"9405d427-8b33-4670-a41a-040e23cef8ce": "vocal house",
"f4dec9c1-abe2-4f3f-adee-5aa8f395d5ce": "vocal trance",
"34ec5538-a91a-454f-b500-18253bb45c20": "witch house",
"449e7cdf-0d7e-46aa-852d-96d81078abf3": "wonky",
},
"Latin": {
"49fa872d-561c-4f8a-aea4-fbe7f67e1bd6": "bolero",
"ae4e05d3-6ae6-41b2-9b63-7c9e27784e27": "bolero son",
"e8a66eb6-882a-49fe-81d3-73f05a0a027f": "chachachá",
"55d05e59-4829-4e98-8cb7-5d769cafb576": "champeta",
"42419c8a-48ab-4b1e-a3d2-48b2a4d9ebdb": "changüí",
"cbb6cb49-6671-46ba-8d35-c5f703d881a3": "compas",
"7cd4173c-fef8-4e99-a527-98a25b6487c0": "cumbia",
"09754a03-bf20-40e9-bfbe-ba1172d4a966": "cumbia villera",
"396d532a-19e2-47da-9f05-f98ff961407d": "descarga",
"2fb0a213-28f9-4e67-bb02-3df6d396d2de": "fado",
"961c6dad-8cf0-47ab-bc09-ccd011f9ef28": "flamenco",
"2cd172f7-d191-4cfd-8c4f-0a648b155fdf": "guaguancó",
"e17eaeab-2d3f-4dae-8344-1dd5d9cb3c00": "guajira",
"3c9097f9-10d9-4086-98e3-e8c54ef470b3": "guaracha",
"7369bb8b-3399-4180-8cb2-4e37e0e7b0e7": "latin",
"f6fb8e67-cd02-4fb4-b256-2970000dcb46": "mambo",
"3f5f955d-f505-484a-b397-d0bf0760d8f3": "merengue",
"7588308f-e08b-444e-8d6b-55549c58cf3c": "milonga",
"7b447f98-1e94-4e57-9bb8-373743fbcdd8": "mpb",
"3e226375-1286-40e3-92b3-d257e3a1babb": "norteño",
"ebf28819-ebd3-4c00-9c42-3b8dc00f897c": "nueva canción",
"4bcd6118-46ff-4903-a466-e1485005a3ba": "pagode",
"ca20f188-68d2-4af5-966e-8e2e0f848758": "ranchera",
"81638671-c67c-47cf-b21d-e92261068cf6": "rocksteady",
"190bba5b-909f-4501-9a00-f96969983459": "rumba",
"717f442d-59d1-4349-a14e-c4874acd3509": "salsa",
"2447a0ec-e65f-48c7-b402-05cfe5c62cb7": "samba",
"65877068-2116-4af6-89a2-838179a7e103": "ska",
"89e34d8e-6562-412f-8da3-d040e159e6cd": "ska punk",
"7aabc697-7c79-4f12-bb6f-ee5d70298a3f": "skacore",
"b251cd9a-990c-49f6-8da3-db3457aa2114": "soca",
"db48f46a-6467-49b0-a6b2-beb059584d9d": "son cubano",
"7f0d2b34-d0ea-4ab5-a169-934f131bc40c": "son montuno",
"267b14aa-3e3c-4615-803c-0255a81c8203": "tango",
"6b8e3c88-add3-4b27-b2ec-a2135edf6e95": "timba",
"bff33e5d-d591-498f-927f-b26c5527d817": "vallenato",
"34d6235e-91cd-452f-95fb-2f8ce6508471": "zouk",
},
"Reggae / Dancehall": {
"2346fb3e-8546-4119-a929-601db8e0a733": "cloud rap",
"6c3503e3-bae3-42de-9e4c-7861f2053fbe": "dancehall",
"97b15117-c3e2-47ec-9d64-79f90b528a9c": "lovers rock",
"ce41a887-cdb3-4c52-bbce-604287d74573": "mento",
"0d15e596-3374-4590-bdb0-0a95d38ab4dc": "ragga",
"02b2f720-d06e-42ce-85c2-1ecd4191ffcb": "reggae",
"bb14fb18-af6b-4547-9130-47fc4b208f48": "reggaeton",
"f2d4991d-97fa-4c1c-8e09-15ec7668aa43": "roots reggae",
},
"World / Traditional": {
"fcc58a18-9326-4c92-8b29-c294d44379c3": "afrobeat",
"b413acf5-112a-4d44-a61f-9abe28f0dfac": "bachata",
"9e44c2c9-8c44-4aa6-9de3-0f3e75b49f1b": "baroque",
"b44a8ffd-e717-4d73-99ea-9fe5d245339d": "calypso",
"b72d3108-3360-447e-bb08-0895deff7ae5": "candombe",
"5b8be5dd-44b1-4461-9266-cfed3e16f55c": "carnatic classical",
"dc726e7d-7f0d-47e8-9172-c9311d7bd4a8": "chutney",
"b73d4a65-091e-4f08-8422-93f1b90f95d7": "enka",
"1b757a82-3b28-4522-81d9-82d3d10b00c0": "exotica",
"07b217fc-7798-4e74-aad6-2c08b32c2810": "gagaku",
"92ccf7a7-c886-4e06-9aff-e432593653df": "hindustani classical",
"53c3e4ea-ce29-4dc1-9133-09b6be18a607": "joik",
"5c8b654c-0f2b-40d5-990b-e070a527b1df": "kizomba",
"06eeec6e-f389-4783-b601-909bb10bd04e": "klezmer",
"2b756f35-79bc-4424-8061-026b431d036a": "luk thung",
"4c1cfd87-002d-49de-8e88-0f3cbcc090fe": "maloya",
"16929e76-e49b-4ca3-9d26-11f7dc7e665a": "maskanda", # [sic]
"b0ac2e92-b41d-46e9-8c88-cd254f3a86e0": "min'yō",
"bbe70cad-e184-42e5-9b90-e14438ba4363": "pachanga",
"df6d324a-ff39-4055-9289-b14da57ab4e4": "polka",
"ab01fbff-c7da-462a-93a9-71ad62b64d80": "qawwali",
"deec5f2f-ccfc-4e0c-b1ac-8e5a17cd5cb7": "raï",
"15d8d1e8-302d-4919-93a7-740099aef3c0": "ryūkōka",
},
"Country": {
"5f9cba3d-1a9f-46cd-8c49-7ed78d1f3354": "alternative country",
"2c765e49-a12e-45eb-b628-f8b9cc0a0250": "americana",
"a1a7b40d-f6e6-48c1-a5fc-b15af73d7381": "bluegrass",
"9cc9ea3b-e189-4def-9e47-86e9dc643eee": "classic country",
"2f7f569a-9348-4384-8508-2d0ac7d6a778": "contra",
"5f665615-7fb3-49d8-b541-62a7b239edbe": "country",
"2de61e9c-de1d-47c8-999d-5b44c0cfeca1": "country pop",
"ef7f6b36-cf15-408d-9eb4-fd7a46d0602b": "gothic country",
"0582ba9e-320b-431d-a925-e1b22cad22d8": "modern country",
"b5220905-bd0a-41d5-b534-ed5401be80c2": "neo-traditional country",
"ed67ff3f-8f00-4321-badd-e9f183f7584b": "old-time",
"f30d5ce4-559c-45c2-897c-089599ac9a83": "outlaw country",
"037150a6-d83b-4307-908a-84201867186d": "traditional country",
},
"Classical": {
"19d31930-9e5f-450d-8fa1-120e622773a5": "bardcore",
"b7864789-29e6-4965-84e4-463baaa869df": "chanson",
"bf9955d6-c3eb-466d-95ca-30f3848fc9ea": "classical",
"5e6d5394-6508-4ce2-87ee-90388b1a6b9c": "classical crossover",
"a190bb32-e0a7-4c56-b142-5cf9cfcfa713": "contemporary classical",
"59b8a806-8bdd-45b5-978a-b5e404c910aa": "honky tonk",
"4fc769b7-acac-4061-9129-ee8c6875b4c3": "medieval",
"e328c306-8e35-46d2-93c3-623412ec7c13": "modern classical",
"f3fe6570-1e79-4ed5-8eb9-9ed64d631871": "orchestral",
"842008b8-1c67-4c8c-b450-7f5adf20fb7f": "post-classical",
"bdebe8fd-c5b6-4b89-bb2a-ece612f1caa7": "romantic classical",
"a4704a31-7c69-44d9-a590-8fcc2b258f77": "slow waltz",
"50294a4d-61f0-4dc8-a20f-4708d52fe78a": "symphony",
"4bf6058e-0736-4836-8eae-6d63bf78b9fb": "waltz",
},
"Jazz": {
"7dc2b20f-3953-4874-b9bf-41b8ba06d20c": "acid jazz",
"8d901306-d4a3-436c-a1c3-75550e5b6eaf": "avant-garde jazz",
"ac30756b-55a7-4dc3-8908-8f94c93318d0": "bebop",
"e7bb9a21-9556-4b85-bc78-5f69a3d0577d": "big band",
"573a284f-2ae3-4636-af36-10fc1078d295": "bossa nova",
"f0e90b41-4499-493f-b6e3-05d3c3ea1b20": "classic jazz",
"e2b06d21-13df-4a48-aa81-6ddd99344dc7": "contemporary jazz",
"5fc86669-d9ba-4716-8090-eff39081090e": "cool jazz",
"2126f16b-13b6-45c9-b02e-c63be8bbdaea": "dansband",
"fd7b5582-e69c-40ad-90b1-d31db33be612": "dark jazz",
"3c31635b-a5a7-44eb-ab9d-4bbde11da2d4": "free jazz",
"8c083468-2dce-4a31-ae77-433af3deafd3": "hard bop",
"c1a4860e-720f-4f4c-8a54-a8032aac08fa": "instrumental jazz",
"a715278f-1580-409f-8078-4ffbc800e08b": "jazz",
"2377ff19-83e7-4873-b425-dce6d1ad4ef4": "jazz fusion",
"f3c43ae8-839a-4523-b295-cfa4eba6c911": "jazz-funk",
"48095aaf-c31d-4bfc-9578-8755c71d44af": "latin jazz",
"94ce0ba0-9043-4b5b-87d6-3c1a8458f75d": "post-bop",
"a1f1dcc8-6078-4c12-90b7-47e3080e330c": "smooth jazz",
"e70f0aa3-9531-448c-8182-7ff34e3d74f8": "soul jazz",
"063d0461-1e12-4246-87bf-69c5ca611cc2": "swing",
"782dc157-d1a5-42b9-9174-d329c24a7ef3": "vocal jazz",
"bb071d13-8c80-411b-9c0b-16b37bf71ae6": "west coast swing",
"60e19df0-99e8-4863-be31-7ec7e5c98c7a": "western swing",
},
"Blues / Folk": {
"37f85b9c-c3fc-4b5a-8545-51aeb78c8786": "acoustic blues",
"d2d6de50-4dd8-4838-92ac-aa6d1e600bba": "afoxê",
"0b48a36c-630f-4ee7-8cf3-480e3dd8be65": "alternative folk",
"59a849e1-73fe-428d-8a61-3788875f9644": "bhangra",
"0af301ff-3e33-4f5f-934f-5916cfc04157": "cajun",
"34edc6ac-d431-4b36-be15-ecd17533e5e2": "celtic",
"f1e26a78-3e85-4365-a34d-dc4b5389b29d": "chicago blues",
"c11e4253-a888-4a2a-adde-c274b748d0b3": "contemporary folk",
"1a46845c-ed4d-48c5-a93f-45bb85cf56d1": "country blues",
"984e6cb2-823e-4709-a23b-233f7f2ee3aa": "country folk",
"16103855-c0f1-4221-81d8-eafc53b1b41c": "dark folk",
"b0702eb4-2951-4e1f-bbcd-dd9b0ec63c7c": "delta blues",
"8164d906-055d-4ca8-9ab2-14204b301cc3": "electric blues",
"18a0d235-c8e0-4575-b0dc-e3bb0cc2ad2c": "filk",
"a91eed3a-fdd0-494e-ade0-95ea4117f6eb": "folk",
"8103927c-5f4b-4bbc-9d8c-7a1241d2bab8": "freak folk",
"36917620-240a-4fed-abcb-7be611ac3834": "indie folk",
"3d88ed41-624d-4be9-bf9d-ca11991eb250": "irish folk",
"9702d99a-823f-47ba-b8c2-af0538c9640b": "jazz blues",
"6fb5fe60-b6c2-414f-8011-4f8dc023039b": "modern blues",
"e94c28db-21a2-43de-9e58-8113fba2c732": "neofolk",
"34d08b64-7426-42e1-959e-48af44352fcd": "progressive folk",
"507757f0-a63c-422b-93f1-f1d77462033f": "psychedelic folk",
"d017dd87-704b-4ad4-8b67-39fb401b8339": "ragtime",
"498229df-4956-4ed7-91f7-a9d4b1877af1": "zydeco",
},
"Comedy / Spoken word": {
"966d3d2d-15bd-4f6f-a830-15ab2c5e662f": "audiobook",
"17b58d38-1110-4f5a-9412-b0314329622b": "ballad",
"119ecf69-7d87-4f63-bc83-2959a0111a55": "comedy",
"c774791e-1b78-4794-a109-befb712ef44d": "spoken word",
},
"Christian / Gospel": {
"c02cd2c5-9316-4046-a241-f74b773fee06": "christian rock",
"2b4f0036-79a0-4ce0-a38c-4c0c8e61c579": "contemporary christian",
"6aeacd9a-3964-4ca4-9e3c-23ec8bd8848d": "contemporary gospel",
"08289b42-7e41-4987-9263-c28bb4c59da0": "gospel",
},
"Soundtracks": {
"ff6d73e8-bf1a-431e-9911-88ae7ffcfdfb": "musical",
"56e0b906-1018-4a55-8ec7-86746272116e": "opera",
"5a56d32f-81e4-4301-be70-4a060ab3b4a0": "production music",
"db8a5e56-60da-4438-8e2a-f2f51f0d4092": "vaudeville",
},
"Kids'": {
},
"More": {
"b1c0c2ad-e5ae-4bb6-a1be-d66e195fd2d7": "avant-garde",
"f8f6157d-5ace-4844-90c2-2e2a829b8932": "barbershop",
"31aee5bc-c1e8-43d7-a7bc-902df802ccbb": "christmas music",
"75c5866f-0212-4c02-9650-de475dda157e": "drone",
"9e5374b1-1f09-4c27-827a-d4e6aff4cc29": "drone metal",
"68c81274-5770-4e7b-a4bf-ab0d7d425d99": "experimental",
"6a1ba2f9-1e5b-446f-9ffa-c0b0963aa08f": "free improvisation",
"7af339eb-e666-4aa6-a6ca-8444ca573fe0": "fusion",
"5c61cf56-7dcd-4bd8-87e4-b5bb0c25255a": "harsh noise",
"344dc18a-b982-49e7-918c-23c7d69fb6ba": "harsh noise wall",
"538c905b-642d-43c9-b071-439af11cc471": "indie",
"060beed7-e597-4c42-8e25-5bf8bd5dd3cb": "industrial",
"b97dda04-d712-4527-80fe-7cfa0e71ecf1": "industrial musical",
"4ae63770-68af-4fae-b0f1-5e53f372c743": "instrumental",
"e1e37f00-9bd9-4510-b6ca-9bb128b665f4": "line dance",
"65e2f3ad-e0bd-4627-bd83-cba7c8b6d011": "lo-fi",
"de2785b2-f282-4718-a422-37dc93964bdd": "lounge",
"449e6224-5a56-4146-9463-3feda95a5f15": "lowercase",
"482ff4ed-fc37-49fb-9858-92f955a19e6c": "martial industrial",
"299c1b38-5c7b-4dfc-ab3a-f541c936987a": "microsound",
"8d9a00db-0b8b-41ff-9af7-d0f96793bdc5": "minimal",
"89c7c9b3-215e-482f-bce3-3219ffd0a222": "noise",
"018b7630-8941-4966-90e4-3774daea4e04": "non-music",
"8302fd70-150f-4f74-b11c-10a7ff7586b9": "plunderphonics",
"aef2077e-6cbc-47ba-9698-9eb5f842a412": "pornogrind",
"8d275a8d-154a-4bcb-8b5a-f56306ad4bcb": "progressive",
"518102e9-8d09-4f6b-b1e2-141bdcce6a82": "red song",
"3571d5f1-b4f1-446c-87f3-5033c3ea0bcd": "spectralism",
"8a729b73-1ef1-44da-a0d8-ce90cfc181c3": "steampunk",
}
}
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| 74
| 0.661836
| 3,769
| 35,264
| 6.189175
| 0.835235
| 0.002829
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| 0.001286
| 0.002401
| 0.002401
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| 35,264
| 623
| 75
| 56.603531
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| 0.564887
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| 0.003367
| false
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| 0
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| 0
| 0
| 0
|
0
| 4
|
4f1f6dd6020ecd021ea0ef7e60ffc5c499974c8f
| 1,120
|
py
|
Python
|
pypco/models/giving.py
|
andrew-rfc/pypco
|
d6d3cf66d1f2c794660badd7d8a4142d3440cd26
|
[
"MIT"
] | null | null | null |
pypco/models/giving.py
|
andrew-rfc/pypco
|
d6d3cf66d1f2c794660badd7d8a4142d3440cd26
|
[
"MIT"
] | null | null | null |
pypco/models/giving.py
|
andrew-rfc/pypco
|
d6d3cf66d1f2c794660badd7d8a4142d3440cd26
|
[
"MIT"
] | null | null | null |
"""Models for PCO Giving.
To add additional models, simply add additional classes
subclassing the GivingModel class.
"""
#pylint: disable=C0321,R0903,C0111
from .base_model import BaseModel
# The base Giving model
class GivingModel(BaseModel): pass
# Giving models
class Batch(GivingModel): ENDPOINT_END='batches'
class BatchGroup(GivingModel): ENDPOINT_END='batch_groups'
class Designation(GivingModel): ENDPOINT_END='designations'
class DesignationRefund(GivingModel): ENDPOINT_END='designation_refunds'
class Donation(GivingModel): ENDPOINT_END='donations'
class Fund(GivingModel): ENDPOINT_END='funds'
class Label(GivingModel): ENDPOINT_END='labels'
class Organization(GivingModel): ENDPOINT_END='organizations'
class PaymentMethod(GivingModel): ENDPOINT_END='payment_methods'
class PaymentSource(GivingModel): ENDPOINT_END='payment_sources'
class Peoples(GivingModel): ENDPOINT_END='people'
class RecurringDonation(GivingModel): ENDPOINT_END='recurring_donations'
class RecurringDonationDesignation(GivingModel): ENDPOINT_END='recurring_donation_designations'
class Refunds(GivingModel): ENDPOINT_END='refund'
| 38.62069
| 95
| 0.834821
| 127
| 1,120
| 7.188976
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| 0.337349
| 0.063527
| 0
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| 0
| 0
| 0
| 0
| 0
| 0.01155
| 0.072321
| 1,120
| 28
| 96
| 40
| 0.86718
| 0.164286
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| 0
| 0.188781
| 0.033441
| 0
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| 1
| 0
| false
| 0.0625
| 0.0625
| 0
| 1.875
| 0
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| 0
| null | 1
| 1
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| 0
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| 0
| 0
| 0
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| 0
| 0
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| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
4f242179c0183079bae5df5d5ea615df66b82ff4
| 72
|
py
|
Python
|
tests/__init__.py
|
felixonmars/rfc3339-validator
|
e6f70e2fc10f5383d2485340271be4f940113927
|
[
"MIT"
] | 6
|
2019-10-29T13:10:54.000Z
|
2022-01-19T00:22:57.000Z
|
tests/__init__.py
|
rmcgibbo/rfc3339-validator
|
193ede1932729570de6df9514fc134b38b037512
|
[
"MIT"
] | 3
|
2019-11-14T22:40:56.000Z
|
2022-01-25T16:01:17.000Z
|
tests/__init__.py
|
rmcgibbo/rfc3339-validator
|
193ede1932729570de6df9514fc134b38b037512
|
[
"MIT"
] | 4
|
2019-11-14T11:48:23.000Z
|
2021-09-03T08:58:13.000Z
|
# -*- coding: utf-8 -*-
"""Unit test package for rfc3339_validator."""
| 18
| 46
| 0.625
| 9
| 72
| 4.888889
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.081967
| 0.152778
| 72
| 3
| 47
| 24
| 0.639344
| 0.875
| 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
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| 0
| 0
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| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
4f459ca99d2ddc57e42687fe095e08da293937eb
| 104
|
py
|
Python
|
test/__init__.py
|
ZJCV/X3D
|
1635fe4ade5ac5e0bd8f272262cec73c7a12f0fb
|
[
"Apache-2.0"
] | 10
|
2021-02-08T06:47:09.000Z
|
2022-03-31T03:02:55.000Z
|
test/__init__.py
|
ZJCV/SlowFast
|
eb049e2d23d60c02b25f20c6ec438e3fd884db46
|
[
"Apache-2.0"
] | 3
|
2021-05-17T10:19:23.000Z
|
2021-07-22T05:57:17.000Z
|
test/__init__.py
|
ZJCV/SlowFast
|
eb049e2d23d60c02b25f20c6ec438e3fd884db46
|
[
"Apache-2.0"
] | 2
|
2021-06-15T02:39:50.000Z
|
2021-09-30T05:03:29.000Z
|
# -*- coding: utf-8 -*-
"""
@date: 2020/9/10 上午11:21
@file: __init__.py
@author: zj
@description:
"""
| 11.555556
| 24
| 0.576923
| 15
| 104
| 3.733333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 0.163462
| 104
| 8
| 25
| 13
| 0.505747
| 0.894231
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
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| null | 0
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| 0
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| 0
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| 0
| 0
| 0
| 1
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| null | 0
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| 0
| 0
| 0
| 0
|
0
| 4
|
4f4715859fe9dc007b00ec3be5213aa5af27d3d6
| 230
|
py
|
Python
|
apps/crawler/admin.py
|
AniPython/ani
|
2536ac9ddae2b8396b634f982fb1083339b4a389
|
[
"MIT"
] | null | null | null |
apps/crawler/admin.py
|
AniPython/ani
|
2536ac9ddae2b8396b634f982fb1083339b4a389
|
[
"MIT"
] | null | null | null |
apps/crawler/admin.py
|
AniPython/ani
|
2536ac9ddae2b8396b634f982fb1083339b4a389
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import LearningResources
@admin.register(LearningResources)
class ArticleAdmin(admin.ModelAdmin):
list_display = ['title', 'order', 'url', 'desc']
list_editable = ['order']
| 23
| 52
| 0.73913
| 25
| 230
| 6.72
| 0.72
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.134783
| 230
| 9
| 53
| 25.555556
| 0.844221
| 0
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| 0
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| 0.095652
| 0
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| 0
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| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.833333
| 0
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| 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
|
4f540c35b8fef6ecde9829b6620d5c25f3bc335a
| 179
|
py
|
Python
|
cargo/db/db.py
|
shipperizer/cargo
|
f56529b37a70ac5cdbc7bc7398b340796c49d404
|
[
"MIT"
] | null | null | null |
cargo/db/db.py
|
shipperizer/cargo
|
f56529b37a70ac5cdbc7bc7398b340796c49d404
|
[
"MIT"
] | null | null | null |
cargo/db/db.py
|
shipperizer/cargo
|
f56529b37a70ac5cdbc7bc7398b340796c49d404
|
[
"MIT"
] | null | null | null |
from flask.ext.sqlalchemy import SQLAlchemy
from flask.ext.migrate import Migrate
db = SQLAlchemy()
def init_database(app):
db.init_app(app)
migrate = Migrate(app, db)
| 17.9
| 43
| 0.743017
| 26
| 179
| 5.038462
| 0.423077
| 0.137405
| 0.183206
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.162011
| 179
| 9
| 44
| 19.888889
| 0.873333
| 0
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.333333
| 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
|
4f62f0f97a4abc270aee7785c3f03ce62bd2ba2f
| 1,041
|
py
|
Python
|
7/cvicenie/exercises.py
|
sevo/FLP-2020
|
6b5ecebc30f4a771aebdd8c147245557aa8901c8
|
[
"MIT"
] | null | null | null |
7/cvicenie/exercises.py
|
sevo/FLP-2020
|
6b5ecebc30f4a771aebdd8c147245557aa8901c8
|
[
"MIT"
] | 1
|
2020-03-25T13:04:56.000Z
|
2020-03-25T15:24:06.000Z
|
7/cvicenie/exercises.py
|
sevo/FLP-2020
|
6b5ecebc30f4a771aebdd8c147245557aa8901c8
|
[
"MIT"
] | null | null | null |
from functools import reduce
from operator import add
# Uloha 1:
def square_generator(numbers):
pass
# Uloha 2:
def square_map(numbers):
pass
# Uloha 3:
def square_comprehension(numbers):
pass
# Uloha 4:
def cycle(list):
pass
# Uloha 5:
def factorial():
pass
# Uloha 6:
def digits(n, base):
pass
# Uloha 7:
def factorial_digit_sum(N):
pass
# Uloha 8:
def my_range(start, stop, step=1):
pass
# Uloha 9:
def my_range_negative(start, stop, step=1):
pass
# Uloha 10:
def items(dictionary):
pass
# Uloha 11:
def pseudorandom(modulus, multiplier, increment, seed):
pass
# Uloha 12:
def sample(items):
pass
# Uloha 13:
def sample_no_rep(items):
pass
# Uloha 14:
def primes():
pass
# Uloha 15:
def primes_memory():
pass
# Uloha 16:
def nth_prime(n):
pass
# Uloha 17:
def pairs(list1, list2):
pass
# Uloha 18:
def groups(*lists):
pass
# Uloha 19:
def trange(start, stop, step):
pass
# Uloha 20:
def k_permutations(items, n):
pass
| 10.106796
| 55
| 0.639769
| 152
| 1,041
| 4.296053
| 0.460526
| 0.261868
| 0.073507
| 0.042879
| 0.070444
| 0.070444
| 0
| 0
| 0
| 0
| 0
| 0.044814
| 0.24976
| 1,041
| 102
| 56
| 10.205882
| 0.791293
| 0.182517
| 0
| 0.47619
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.47619
| false
| 0.47619
| 0.047619
| 0
| 0.52381
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
4f916aec2ed3c358041e84d6130d938f09eb63d2
| 141
|
py
|
Python
|
reddit2telegram/channels/r_proseporn/app.py
|
mainyordle/reddit2telegram
|
1163e15aed3b6ff0fba65b222d3d9798f644c386
|
[
"MIT"
] | 187
|
2016-09-20T09:15:54.000Z
|
2022-03-29T12:22:33.000Z
|
reddit2telegram/channels/r_proseporn/app.py
|
mainyordle/reddit2telegram
|
1163e15aed3b6ff0fba65b222d3d9798f644c386
|
[
"MIT"
] | 84
|
2016-09-22T14:25:07.000Z
|
2022-03-19T01:26:17.000Z
|
reddit2telegram/channels/r_proseporn/app.py
|
mainyordle/reddit2telegram
|
1163e15aed3b6ff0fba65b222d3d9798f644c386
|
[
"MIT"
] | 172
|
2016-09-21T15:39:39.000Z
|
2022-03-16T15:15:58.000Z
|
#encoding:utf-8
subreddit = 'ProsePorn'
t_channel = '@r_proseporn'
def send_post(submission, r2t):
return r2t.send_simple(submission)
| 15.666667
| 38
| 0.744681
| 19
| 141
| 5.315789
| 0.789474
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.02459
| 0.134752
| 141
| 8
| 39
| 17.625
| 0.803279
| 0.099291
| 0
| 0
| 0
| 0
| 0.166667
| 0
| 0
| 0
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| 1
| 0.25
| false
| 0
| 0
| 0.25
| 0.5
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| 0
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| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 4
|
4fa281a1bda3bba28724fb39b1344dc08077398f
| 79,943
|
py
|
Python
|
backend/pyrogram/raw/all.py
|
appheap/social-media-analyzer
|
0f9da098bfb0b4f9eb38e0244aa3a168cf97d51c
|
[
"Apache-2.0"
] | 5
|
2021-09-11T22:01:15.000Z
|
2022-03-16T21:33:42.000Z
|
backend/pyrogram/raw/all.py
|
iamatlasss/social-media-analyzer
|
429d1d2bbd8bfce80c50c5f8edda58f87ace668d
|
[
"Apache-2.0"
] | null | null | null |
backend/pyrogram/raw/all.py
|
iamatlasss/social-media-analyzer
|
429d1d2bbd8bfce80c50c5f8edda58f87ace668d
|
[
"Apache-2.0"
] | 3
|
2022-01-18T11:06:22.000Z
|
2022-02-26T13:39:28.000Z
|
# Pyrogram - Telegram MTProto API Client Library for Python
# Copyright (C) 2017-2021 Dan <https://github.com/delivrance>
#
# This file is part of Pyrogram.
#
# Pyrogram is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Pyrogram is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with Pyrogram. If not, see <http://www.gnu.org/licenses/>.
# # # # # # # # # # # # # # # # # # # # # # # #
# !!! WARNING !!! #
# This is a generated file! #
# All changes made in this file will be lost! #
# # # # # # # # # # # # # # # # # # # # # # # #
layer = 123
objects = {
0x05162463: "pyrogram.raw.types.ResPQ",
0x83c95aec: "pyrogram.raw.types.PQInnerData",
0xa9f55f95: "pyrogram.raw.types.PQInnerDataDc",
0x3c6a84d4: "pyrogram.raw.types.PQInnerDataTemp",
0x56fddf88: "pyrogram.raw.types.PQInnerDataTempDc",
0x75a3f765: "pyrogram.raw.types.BindAuthKeyInner",
0x79cb045d: "pyrogram.raw.types.ServerDHParamsFail",
0xd0e8075c: "pyrogram.raw.types.ServerDHParamsOk",
0xb5890dba: "pyrogram.raw.types.ServerDHInnerData",
0x6643b654: "pyrogram.raw.types.ClientDHInnerData",
0x3bcbf734: "pyrogram.raw.types.DhGenOk",
0x46dc1fb9: "pyrogram.raw.types.DhGenRetry",
0xa69dae02: "pyrogram.raw.types.DhGenFail",
0xf660e1d4: "pyrogram.raw.types.DestroyAuthKeyOk",
0x0a9f2259: "pyrogram.raw.types.DestroyAuthKeyNone",
0xea109b13: "pyrogram.raw.types.DestroyAuthKeyFail",
0x60469778: "pyrogram.raw.functions.ReqPq",
0xbe7e8ef1: "pyrogram.raw.functions.ReqPqMulti",
0xd712e4be: "pyrogram.raw.functions.ReqDHParams",
0xf5045f1f: "pyrogram.raw.functions.SetClientDHParams",
0xd1435160: "pyrogram.raw.functions.DestroyAuthKey",
0x62d6b459: "pyrogram.raw.types.MsgsAck",
0xa7eff811: "pyrogram.raw.types.BadMsgNotification",
0xedab447b: "pyrogram.raw.types.BadServerSalt",
0xda69fb52: "pyrogram.raw.types.MsgsStateReq",
0x04deb57d: "pyrogram.raw.types.MsgsStateInfo",
0x8cc0d131: "pyrogram.raw.types.MsgsAllInfo",
0x276d3ec6: "pyrogram.raw.types.MsgDetailedInfo",
0x809db6df: "pyrogram.raw.types.MsgNewDetailedInfo",
0x7d861a08: "pyrogram.raw.types.MsgResendReq",
0x8610baeb: "pyrogram.raw.types.MsgResendAnsReq",
0xf35c6d01: "pyrogram.raw.types.RpcResult",
0x2144ca19: "pyrogram.raw.types.RpcError",
0x5e2ad36e: "pyrogram.raw.types.RpcAnswerUnknown",
0xcd78e586: "pyrogram.raw.types.RpcAnswerDroppedRunning",
0xa43ad8b7: "pyrogram.raw.types.RpcAnswerDropped",
0x347773c5: "pyrogram.raw.types.Pong",
0xe22045fc: "pyrogram.raw.types.DestroySessionOk",
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0x3fedc75f: "pyrogram.raw.functions.help.GetDeepLinkInfo",
0x98914110: "pyrogram.raw.functions.help.GetAppConfig",
0x6f02f748: "pyrogram.raw.functions.help.SaveAppLog",
0xc661ad08: "pyrogram.raw.functions.help.GetPassportConfig",
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0x38a08d3: "pyrogram.raw.functions.help.GetUserInfo",
0x66b91b70: "pyrogram.raw.functions.help.EditUserInfo",
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0x1e251c95: "pyrogram.raw.functions.help.HidePromoData",
0x77fa99f: "pyrogram.raw.functions.help.DismissSuggestion",
0x735787a8: "pyrogram.raw.functions.help.GetCountriesList",
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0x84c1fd4e: "pyrogram.raw.functions.channels.DeleteMessages",
0xd10dd71b: "pyrogram.raw.functions.channels.DeleteUserHistory",
0xfe087810: "pyrogram.raw.functions.channels.ReportSpam",
0xad8c9a23: "pyrogram.raw.functions.channels.GetMessages",
0x123e05e9: "pyrogram.raw.functions.channels.GetParticipants",
0x546dd7a6: "pyrogram.raw.functions.channels.GetParticipant",
0xa7f6bbb: "pyrogram.raw.functions.channels.GetChannels",
0x8736a09: "pyrogram.raw.functions.channels.GetFullChannel",
0x3d5fb10f: "pyrogram.raw.functions.channels.CreateChannel",
0xd33c8902: "pyrogram.raw.functions.channels.EditAdmin",
0x566decd0: "pyrogram.raw.functions.channels.EditTitle",
0xf12e57c9: "pyrogram.raw.functions.channels.EditPhoto",
0x10e6bd2c: "pyrogram.raw.functions.channels.CheckUsername",
0x3514b3de: "pyrogram.raw.functions.channels.UpdateUsername",
0x24b524c5: "pyrogram.raw.functions.channels.JoinChannel",
0xf836aa95: "pyrogram.raw.functions.channels.LeaveChannel",
0x199f3a6c: "pyrogram.raw.functions.channels.InviteToChannel",
0xc0111fe3: "pyrogram.raw.functions.channels.DeleteChannel",
0xe63fadeb: "pyrogram.raw.functions.channels.ExportMessageLink",
0x1f69b606: "pyrogram.raw.functions.channels.ToggleSignatures",
0xf8b036af: "pyrogram.raw.functions.channels.GetAdminedPublicChannels",
0x72796912: "pyrogram.raw.functions.channels.EditBanned",
0x33ddf480: "pyrogram.raw.functions.channels.GetAdminLog",
0xea8ca4f9: "pyrogram.raw.functions.channels.SetStickers",
0xeab5dc38: "pyrogram.raw.functions.channels.ReadMessageContents",
0xaf369d42: "pyrogram.raw.functions.channels.DeleteHistory",
0xeabbb94c: "pyrogram.raw.functions.channels.TogglePreHistoryHidden",
0x8341ecc0: "pyrogram.raw.functions.channels.GetLeftChannels",
0xf5dad378: "pyrogram.raw.functions.channels.GetGroupsForDiscussion",
0x40582bb2: "pyrogram.raw.functions.channels.SetDiscussionGroup",
0x8f38cd1f: "pyrogram.raw.functions.channels.EditCreator",
0x58e63f6d: "pyrogram.raw.functions.channels.EditLocation",
0xedd49ef0: "pyrogram.raw.functions.channels.ToggleSlowMode",
0x11e831ee: "pyrogram.raw.functions.channels.GetInactiveChannels",
0xaa2769ed: "pyrogram.raw.functions.bots.SendCustomRequest",
0xe6213f4d: "pyrogram.raw.functions.bots.AnswerWebhookJSONQuery",
0x805d46f6: "pyrogram.raw.functions.bots.SetBotCommands",
0x99f09745: "pyrogram.raw.functions.payments.GetPaymentForm",
0xa092a980: "pyrogram.raw.functions.payments.GetPaymentReceipt",
0x770a8e74: "pyrogram.raw.functions.payments.ValidateRequestedInfo",
0x2b8879b3: "pyrogram.raw.functions.payments.SendPaymentForm",
0x227d824b: "pyrogram.raw.functions.payments.GetSavedInfo",
0xd83d70c1: "pyrogram.raw.functions.payments.ClearSavedInfo",
0x2e79d779: "pyrogram.raw.functions.payments.GetBankCardData",
0xf1036780: "pyrogram.raw.functions.stickers.CreateStickerSet",
0xf7760f51: "pyrogram.raw.functions.stickers.RemoveStickerFromSet",
0xffb6d4ca: "pyrogram.raw.functions.stickers.ChangeStickerPosition",
0x8653febe: "pyrogram.raw.functions.stickers.AddStickerToSet",
0x9a364e30: "pyrogram.raw.functions.stickers.SetStickerSetThumb",
0x55451fa9: "pyrogram.raw.functions.phone.GetCallConfig",
0x42ff96ed: "pyrogram.raw.functions.phone.RequestCall",
0x3bd2b4a0: "pyrogram.raw.functions.phone.AcceptCall",
0x2efe1722: "pyrogram.raw.functions.phone.ConfirmCall",
0x17d54f61: "pyrogram.raw.functions.phone.ReceivedCall",
0xb2cbc1c0: "pyrogram.raw.functions.phone.DiscardCall",
0x59ead627: "pyrogram.raw.functions.phone.SetCallRating",
0x277add7e: "pyrogram.raw.functions.phone.SaveCallDebug",
0xff7a9383: "pyrogram.raw.functions.phone.SendSignalingData",
0xbd3dabe0: "pyrogram.raw.functions.phone.CreateGroupCall",
0x5f9c8e62: "pyrogram.raw.functions.phone.JoinGroupCall",
0x500377f9: "pyrogram.raw.functions.phone.LeaveGroupCall",
0xa5e76cd8: "pyrogram.raw.functions.phone.EditGroupCallMember",
0x7b393160: "pyrogram.raw.functions.phone.InviteToGroupCall",
0x7a777135: "pyrogram.raw.functions.phone.DiscardGroupCall",
0x74bbb43d: "pyrogram.raw.functions.phone.ToggleGroupCallSettings",
0xc7cb017: "pyrogram.raw.functions.phone.GetGroupCall",
0xc9f1d285: "pyrogram.raw.functions.phone.GetGroupParticipants",
0xb74a7bea: "pyrogram.raw.functions.phone.CheckGroupCall",
0xf2f2330a: "pyrogram.raw.functions.langpack.GetLangPack",
0xefea3803: "pyrogram.raw.functions.langpack.GetStrings",
0xcd984aa5: "pyrogram.raw.functions.langpack.GetDifference",
0x42c6978f: "pyrogram.raw.functions.langpack.GetLanguages",
0x6a596502: "pyrogram.raw.functions.langpack.GetLanguage",
0x6847d0ab: "pyrogram.raw.functions.folders.EditPeerFolders",
0x1c295881: "pyrogram.raw.functions.folders.DeleteFolder",
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0x621d5fa0: "pyrogram.raw.functions.stats.LoadAsyncGraph",
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0x5630281b: "pyrogram.raw.functions.stats.GetMessagePublicForwards",
0xb6e0a3f5: "pyrogram.raw.functions.stats.GetMessageStats",
0xbc799737: "pyrogram.raw.core.BoolFalse",
0x997275b5: "pyrogram.raw.core.BoolTrue",
0x1cb5c415: "pyrogram.raw.core.Vector",
0x73f1f8dc: "pyrogram.raw.core.MsgContainer",
0xae500895: "pyrogram.raw.core.FutureSalts",
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0x3072cfa1: "pyrogram.raw.core.GzipPacked",
0x5bb8e511: "pyrogram.raw.core.Message",
}
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| 103
| 0.777879
| 7,138
| 79,943
| 8.711964
| 0.375035
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| 0.234394
| 0.059885
| 0.001608
| 0.001608
| 0.001093
| 0
| 0
| 0
| 0
| 0.107527
| 0.100747
| 79,943
| 1,324
| 104
| 60.379909
| 0.757501
| 0.011708
| 0
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| 0
| 0
| 0.671778
| 0.671778
| 0
| 0
| 0.163338
| 0
| 0
| 1
| 0
| false
| 0.021555
| 0.010778
| 0
| 0.010778
| 0
| 0
| 0
| 0
| null | 1
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| null | 0
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| 0
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| 0
| 0
| 0
|
0
| 4
|
8c0f77e03cdb93e3a5ec6fa08649a6327fe83778
| 1,144
|
py
|
Python
|
stubs/micropython-v1_13-95-pyboard/logging.py
|
mattytrentini/micropython-stubs
|
4d596273823b69e9e5bcf5fa67f249c374ee0bbc
|
[
"MIT"
] | null | null | null |
stubs/micropython-v1_13-95-pyboard/logging.py
|
mattytrentini/micropython-stubs
|
4d596273823b69e9e5bcf5fa67f249c374ee0bbc
|
[
"MIT"
] | null | null | null |
stubs/micropython-v1_13-95-pyboard/logging.py
|
mattytrentini/micropython-stubs
|
4d596273823b69e9e5bcf5fa67f249c374ee0bbc
|
[
"MIT"
] | null | null | null |
"""
Module: 'logging' on pyboard 1.13.0-95
"""
# MCU: (sysname='pyboard', nodename='pyboard', release='1.13.0', version='v1.13-95-g0fff2e03f on 2020-10-03', machine='PYBv1.1 with STM32F405RG')
# Stubber: 1.3.4 - updated
from typing import Any
CRITICAL = 50
DEBUG = 10
ERROR = 40
INFO = 20
class Logger:
""""""
def _level_str(self, *args) -> Any:
pass
def critical(self, *args) -> Any:
pass
def debug(self, *args) -> Any:
pass
def error(self, *args) -> Any:
pass
def exc(self, *args) -> Any:
pass
def exception(self, *args) -> Any:
pass
def info(self, *args) -> Any:
pass
def isEnabledFor(self, *args) -> Any:
pass
level = 0
def log(self, *args) -> Any:
pass
def setLevel(self, *args) -> Any:
pass
def warning(self, *args) -> Any:
pass
NOTSET = 0
WARNING = 30
_level = 20
_level_dict = None
_loggers = None
_stream = None
def basicConfig(*args) -> Any:
pass
def debug(*args) -> Any:
pass
def getLogger(*args) -> Any:
pass
def info(*args) -> Any:
pass
sys = None
| 14.666667
| 145
| 0.560315
| 154
| 1,144
| 4.116883
| 0.38961
| 0.165615
| 0.260252
| 0.264984
| 0.328076
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| 0
| 0
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| 0
| 0
| 0.063041
| 0.292832
| 1,144
| 77
| 146
| 14.857143
| 0.720643
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| 0
| 0.340909
| 0
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| 0
| 0
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| 0
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| 1
| 0.340909
| false
| 0.340909
| 0.022727
| 0
| 0.409091
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| 0
| null | 0
| 1
| 1
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| 1
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| 0
| 0
| 0
|
0
| 4
|
8c19382c2e35ee7b646bd9ee3e0c8280b2772bd3
| 1,573
|
py
|
Python
|
maxsubarraysum.py
|
mutakubwa/python_practice
|
5fef46659ee162c3accfee44e2675eeb0e9c6e95
|
[
"Apache-2.0"
] | null | null | null |
maxsubarraysum.py
|
mutakubwa/python_practice
|
5fef46659ee162c3accfee44e2675eeb0e9c6e95
|
[
"Apache-2.0"
] | null | null | null |
maxsubarraysum.py
|
mutakubwa/python_practice
|
5fef46659ee162c3accfee44e2675eeb0e9c6e95
|
[
"Apache-2.0"
] | null | null | null |
def get_subarray_sum_1(s_array): #O(n**2)
'''
Return max subarray and max subarray sum
'''
#initialise variables
max_sum = 0
max_sub_array = []
#get all items in array and go through them
for item in range(len(s_array)):
#get all possible sub arrays while maintaining their max sum and max subarray
#for every tiem go to items after it and record max sum and max sub array if greater than current max sum
index1 = item
index2 = item
while index2 < len(s_array):
index2 += 1
if ( sum(s_array[index1:index2+1]) > (max_sum) ):
max_sum = sum(s_array[index1:index2+1])
max_sub_array = s_array[index1:index2+1]
return (max_sub_array,max_sum)
def get_subarray_sum_2(s_array): #O(n**2)
'''
Return max subarray and max subarray sum
'''
#initialise variables
max_sum = 0
max_sub_array = []
#get all items in array and go through them
for item in range(len(s_array)):
#get all possible sub arrays while maintaining their max sum and max subarray
#for every tiem go to items after it and record max sum and max sub array if greater than current max sum
if item == 0:
continue
if( sum( s_array[:item+1] ) > max_sum):
max_sum = sum( s_array[:item+1] )
max_sub_array = s_array[:item+1]
return (max_sub_array,max_sum)
#get subarray
s_array = [2, 4, -3, 5, -9 ,6, 2, 1 ,-5]
#get subarray and subarray sum
print(get_subarray_sum_1(s_array))
| 31.46
| 115
| 0.624921
| 251
| 1,573
| 3.749004
| 0.203187
| 0.089267
| 0.093518
| 0.05101
| 0.843783
| 0.831031
| 0.740701
| 0.650372
| 0.603613
| 0.603613
| 0
| 0.030357
| 0.287985
| 1,573
| 50
| 116
| 31.46
| 0.809821
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| 0
|
0
| 4
|
8c309c39c554950ac644da729739e693ee88ad73
| 209
|
py
|
Python
|
ffta/pixel_utils/peakdetect.py
|
lindat18/ffta
|
f510a2068b7626e2984e54afc1a577450e560e97
|
[
"MIT"
] | null | null | null |
ffta/pixel_utils/peakdetect.py
|
lindat18/ffta
|
f510a2068b7626e2984e54afc1a577450e560e97
|
[
"MIT"
] | null | null | null |
ffta/pixel_utils/peakdetect.py
|
lindat18/ffta
|
f510a2068b7626e2984e54afc1a577450e560e97
|
[
"MIT"
] | null | null | null |
import numpy as np
from scipy.signal import argrelextrema
def get_peaks(x):
maxpeaks = argrelextrema(x, np.greater, order=2)
minpeaks = argrelextrema(x, np.less, order=2)
return maxpeaks[0], minpeaks[0]
| 23.222222
| 49
| 0.751196
| 32
| 209
| 4.875
| 0.625
| 0.179487
| 0.205128
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022222
| 0.138756
| 209
| 9
| 50
| 23.222222
| 0.844444
| 0
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| 0
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| 1
| 0.166667
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
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| null | 0
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| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
8c5b9e224ddf3d596e3306885d3b9ac8abb869b8
| 78
|
py
|
Python
|
src/masonite/facades/Queue.py
|
cercos/masonite
|
f7f220efa7fae833683e9f07ce13c3795a87d3b8
|
[
"MIT"
] | 35
|
2018-01-08T01:20:16.000Z
|
2018-02-06T02:37:14.000Z
|
src/masonite/facades/Queue.py
|
cercos/masonite
|
f7f220efa7fae833683e9f07ce13c3795a87d3b8
|
[
"MIT"
] | 55
|
2018-01-03T02:42:03.000Z
|
2018-02-06T13:35:54.000Z
|
src/masonite/facades/Queue.py
|
cercos/masonite
|
f7f220efa7fae833683e9f07ce13c3795a87d3b8
|
[
"MIT"
] | 4
|
2018-01-08T13:13:14.000Z
|
2018-01-12T19:35:32.000Z
|
from .Facade import Facade
class Queue(metaclass=Facade):
key = "queue"
| 13
| 30
| 0.705128
| 10
| 78
| 5.5
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0.192308
| 78
| 5
| 31
| 15.6
| 0.873016
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| 0.064103
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| 1
| 0
|
0
| 4
|
4fcb1b782c0b007e743c018ef5e00b03a66db7a1
| 129
|
py
|
Python
|
dptpy/__init__.py
|
decave27/dptpy
|
d462c471c98208a0dc0aea8b0cf302fb06e09347
|
[
"MIT"
] | 13
|
2021-01-07T04:41:33.000Z
|
2021-02-21T10:50:37.000Z
|
dptpy/__init__.py
|
decave27/dptpy
|
d462c471c98208a0dc0aea8b0cf302fb06e09347
|
[
"MIT"
] | null | null | null |
dptpy/__init__.py
|
decave27/dptpy
|
d462c471c98208a0dc0aea8b0cf302fb06e09347
|
[
"MIT"
] | null | null | null |
from .template import Node_Ignore, Node_Docker, Python_Ignore, Python_Docker
from .__main__ import console
__version__ = "0.1.1"
| 32.25
| 76
| 0.813953
| 19
| 129
| 4.894737
| 0.631579
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.026087
| 0.108527
| 129
| 3
| 77
| 43
| 0.782609
| 0
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| 0
| 0.03876
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| false
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| 0
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| 0
| 0
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| 0
| 0
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| 0
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
8b059bb7fef9f689a0bb4a72b17b7d00f5b1beec
| 268
|
py
|
Python
|
nablapps/interactive/models/__init__.py
|
pettaroni/nablaweb
|
5e610698a276884b9cd779a718dfdee641713636
|
[
"MIT"
] | null | null | null |
nablapps/interactive/models/__init__.py
|
pettaroni/nablaweb
|
5e610698a276884b9cd779a718dfdee641713636
|
[
"MIT"
] | null | null | null |
nablapps/interactive/models/__init__.py
|
pettaroni/nablaweb
|
5e610698a276884b9cd779a718dfdee641713636
|
[
"MIT"
] | null | null | null |
from .advent import AdventCalendar, AdventDoor
from .quiz import Quiz, QuizQuestion, QuizReply
from .user_test import Test, TestResult, TestQuestion, TestQuestionAlternative
from .code_golf import CodeTask, Result
from .color_picker import ColorChoice, validate_color
| 44.666667
| 78
| 0.847015
| 32
| 268
| 6.96875
| 0.65625
| 0
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| 0
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| 0
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| 0
| 0
| 0
| 0.104478
| 268
| 5
| 79
| 53.6
| 0.929167
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
8b0f4aff215460cd5cd48c33c1a58312aa309c7e
| 160
|
py
|
Python
|
cowsay/lib/cows/owl.py
|
Ovlic/cowsay_py
|
1ee8d11d6d895d7695d57e26003d71ce18379d3b
|
[
"MIT"
] | null | null | null |
cowsay/lib/cows/owl.py
|
Ovlic/cowsay_py
|
1ee8d11d6d895d7695d57e26003d71ce18379d3b
|
[
"MIT"
] | null | null | null |
cowsay/lib/cows/owl.py
|
Ovlic/cowsay_py
|
1ee8d11d6d895d7695d57e26003d71ce18379d3b
|
[
"MIT"
] | null | null | null |
def Owl(thoughts, eyes, eye, tongue):
return f"""
{thoughts}
{thoughts}
___
(o o)
( V )
/--m-m-
"""
| 17.777778
| 37
| 0.3625
| 15
| 160
| 3.666667
| 0.733333
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| 0.48125
| 160
| 9
| 38
| 17.777778
| 0.662651
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| 0.111111
| false
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| 0.222222
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| null | 0
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| 1
| 0
| 0
|
0
| 4
|
8b31be76a266d67c6aca0fe606b5f78f6f1fbde4
| 825
|
py
|
Python
|
mava/utils/debugging/scenarios/__init__.py
|
sash-a/Mava
|
976d0863e058fd92f066d8a8fabe2f5e2f3f60ce
|
[
"Apache-2.0"
] | 337
|
2021-07-06T09:09:56.000Z
|
2022-03-30T21:42:37.000Z
|
mava/utils/debugging/scenarios/__init__.py
|
sash-a/Mava
|
976d0863e058fd92f066d8a8fabe2f5e2f3f60ce
|
[
"Apache-2.0"
] | 125
|
2021-07-12T11:38:37.000Z
|
2022-03-30T19:22:53.000Z
|
mava/utils/debugging/scenarios/__init__.py
|
sash-a/Mava
|
976d0863e058fd92f066d8a8fabe2f5e2f3f60ce
|
[
"Apache-2.0"
] | 43
|
2021-07-06T19:44:37.000Z
|
2022-03-07T09:29:07.000Z
|
# python3
# Copyright 2021 InstaDeep Ltd. All rights reserved.
#
# 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.
# Adapted from https://github.com/openai/multiagent-particle-envs.
import importlib
from typing import Any
def load(name: str) -> Any:
return importlib.import_module(f"mava.utils.debugging.scenarios.{name}")
| 34.375
| 76
| 0.762424
| 124
| 825
| 5.064516
| 0.725806
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| 0.041401
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| 0.156364
| 825
| 23
| 77
| 35.869565
| 0.889368
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| 1
| 1
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|
0
| 4
|
8b40a56dfbe2cb9bcddd077bc9b29d99e096eebe
| 35
|
py
|
Python
|
src/handler/__init__.py
|
HalbardHobby/git-LFS-for-Lambda
|
d19ba6fc4605d5dc2dba52acb4236c68787f8bde
|
[
"MIT"
] | null | null | null |
src/handler/__init__.py
|
HalbardHobby/git-LFS-for-Lambda
|
d19ba6fc4605d5dc2dba52acb4236c68787f8bde
|
[
"MIT"
] | null | null | null |
src/handler/__init__.py
|
HalbardHobby/git-LFS-for-Lambda
|
d19ba6fc4605d5dc2dba52acb4236c68787f8bde
|
[
"MIT"
] | null | null | null |
"""Package for Lambda handling."""
| 17.5
| 34
| 0.685714
| 4
| 35
| 6
| 1
| 0
| 0
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| 0
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| 0
| 0.114286
| 35
| 1
| 35
| 35
| 0.774194
| 0.8
| 0
| null | 0
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| 0
| 0
|
0
| 4
|
8c68c05c01069325a1c83ea95cda7035254e9477
| 200
|
py
|
Python
|
Algorithms/Merge Strings Alternately/solution.py
|
rawat9/leetcode
|
936ced4a447c3eab7485d00b9eea480100da1ddb
|
[
"MIT"
] | null | null | null |
Algorithms/Merge Strings Alternately/solution.py
|
rawat9/leetcode
|
936ced4a447c3eab7485d00b9eea480100da1ddb
|
[
"MIT"
] | null | null | null |
Algorithms/Merge Strings Alternately/solution.py
|
rawat9/leetcode
|
936ced4a447c3eab7485d00b9eea480100da1ddb
|
[
"MIT"
] | null | null | null |
from itertools import zip_longest
class Solution:
def mergeAlternately(self, word1: str, word2: str) -> str:
return "".join(w1 + w2 for w1, w2 in zip_longest(word1, word2, fillvalue=""))
| 33.333333
| 85
| 0.69
| 28
| 200
| 4.857143
| 0.714286
| 0.147059
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| 0.19
| 200
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| 0
| 0
|
0
| 4
|
8c8b6155bb31d5c802474b5a258bdcbd50045b48
| 313
|
py
|
Python
|
parking_permits_app/pricing/value_added_tax.py
|
mingfeng/parking-permits
|
d0f5534bbf5a00dda07066d7b3a5dd68befedd59
|
[
"MIT"
] | null | null | null |
parking_permits_app/pricing/value_added_tax.py
|
mingfeng/parking-permits
|
d0f5534bbf5a00dda07066d7b3a5dd68befedd59
|
[
"MIT"
] | null | null | null |
parking_permits_app/pricing/value_added_tax.py
|
mingfeng/parking-permits
|
d0f5534bbf5a00dda07066d7b3a5dd68befedd59
|
[
"MIT"
] | null | null | null |
from decimal import Decimal
from parking_permits_app.constants import VAT_PERCENTAGE
def calculate_price_without_vat(price_with_vat):
vat_multiplier = Decimal("1") + (Decimal(VAT_PERCENTAGE) / Decimal("100"))
price_without_vat = price_with_vat / vat_multiplier
return round(price_without_vat, 2)
| 26.083333
| 78
| 0.789137
| 43
| 313
| 5.348837
| 0.465116
| 0.156522
| 0.195652
| 0.173913
| 0.347826
| 0.347826
| 0.347826
| 0.347826
| 0
| 0
| 0
| 0.01845
| 0.134185
| 313
| 11
| 79
| 28.454545
| 0.830258
| 0
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| 0.01278
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| 0.166667
| false
| 0
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| 0
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| 0
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| null | 0
| 1
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| null | 0
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| 0
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| 1
| 0
| 1
| 0
|
0
| 4
|
8c955dc7641c6d020a64e87356af07f309ee9db5
| 221
|
py
|
Python
|
src/random_survey/urls.py
|
shreyagupta995/random-survey
|
e5aa8dd9233a81b54716e534b7e2189be0edfd91
|
[
"MIT"
] | null | null | null |
src/random_survey/urls.py
|
shreyagupta995/random-survey
|
e5aa8dd9233a81b54716e534b7e2189be0edfd91
|
[
"MIT"
] | null | null | null |
src/random_survey/urls.py
|
shreyagupta995/random-survey
|
e5aa8dd9233a81b54716e534b7e2189be0edfd91
|
[
"MIT"
] | null | null | null |
from django.urls import path
from . import views
from .admin import custom_admin
urlpatterns = [
path('', views.survey, name='survey'),
path('submit_response/', views.submit_response, name='submit_response'),
]
| 22.1
| 76
| 0.723982
| 28
| 221
| 5.571429
| 0.464286
| 0.269231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.144796
| 221
| 9
| 77
| 24.555556
| 0.825397
| 0
| 0
| 0
| 0
| 0
| 0.167421
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.428571
| 0
| 0.428571
| 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
|
8c9ed7cd53b9722c510b8562cbed9db1f5efd02a
| 87
|
py
|
Python
|
PTGRIS/apps.py
|
Angel2113/Lenguaje_y_Razonamiento
|
77ec8b759768d09d5b1ac5fe95847efe43ade1aa
|
[
"MIT"
] | null | null | null |
PTGRIS/apps.py
|
Angel2113/Lenguaje_y_Razonamiento
|
77ec8b759768d09d5b1ac5fe95847efe43ade1aa
|
[
"MIT"
] | null | null | null |
PTGRIS/apps.py
|
Angel2113/Lenguaje_y_Razonamiento
|
77ec8b759768d09d5b1ac5fe95847efe43ade1aa
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class PtgrisConfig(AppConfig):
name = 'PTGRIS'
| 14.5
| 33
| 0.747126
| 10
| 87
| 6.5
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.172414
| 87
| 5
| 34
| 17.4
| 0.902778
| 0
| 0
| 0
| 0
| 0
| 0.068966
| 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
|
8cd6f5fc7fa327254728da9d1d3dc308a52fd81d
| 166
|
py
|
Python
|
ababe/cmdline/apps/model.py
|
shaobinqiu/pyabc
|
eefd322bdd0bb04ae6d42554d24140c6ffbd5c34
|
[
"MIT"
] | null | null | null |
ababe/cmdline/apps/model.py
|
shaobinqiu/pyabc
|
eefd322bdd0bb04ae6d42554d24140c6ffbd5c34
|
[
"MIT"
] | null | null | null |
ababe/cmdline/apps/model.py
|
shaobinqiu/pyabc
|
eefd322bdd0bb04ae6d42554d24140c6ffbd5c34
|
[
"MIT"
] | null | null | null |
# coding: utf-8
# Distributed under the terms of the MIT License.
class AppModel(object):
def __init__(self):
pass
def __run__(self):
pass
| 15.090909
| 49
| 0.63253
| 22
| 166
| 4.409091
| 0.818182
| 0.164948
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008403
| 0.283133
| 166
| 10
| 50
| 16.6
| 0.806723
| 0.36747
| 0
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0.4
| 0
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
8ce17d327f83c578bb276dd274bb48726daea7ae
| 5,274
|
py
|
Python
|
Msgrpc/Handle/socks.py
|
evi1hack/viperpython
|
04bf8e31e21385edb58ea9d25296df062197df39
|
[
"BSD-3-Clause"
] | null | null | null |
Msgrpc/Handle/socks.py
|
evi1hack/viperpython
|
04bf8e31e21385edb58ea9d25296df062197df39
|
[
"BSD-3-Clause"
] | null | null | null |
Msgrpc/Handle/socks.py
|
evi1hack/viperpython
|
04bf8e31e21385edb58ea9d25296df062197df39
|
[
"BSD-3-Clause"
] | null | null | null |
# -*- coding: utf-8 -*-
# @File : socks.py
# @Date : 2021/2/25
# @Desc :
from Lib.api import data_return
from Lib.configs import Socks_MSG_ZH, RPC_JOB_API_REQ, Socks_MSG_EN
from Lib.msfmodule import MSFModule
from Lib.notice import Notice
from Msgrpc.Handle.job import Job
class Socks(object):
"""socks代理"""
@staticmethod
def list_msf_socks():
socks_list = []
infos = Job.list_msfrpc_jobs()
if infos is None:
return socks_list
for key in infos.keys():
info = infos.get(key)
jobid = int(key)
if info.get('name') == 'Auxiliary: server/socks4a_api':
datastore = info.get('datastore')
if datastore is not None:
onesocks4a = {'ID': jobid,
"type": "msf_socks4a",
"port": datastore.get("SRVPORT"),
'datastore': datastore}
socks_list.append(onesocks4a)
elif info.get('name') == 'Auxiliary: server/socks5_api':
datastore = info.get('datastore')
if datastore is not None:
onesocks4a = {'ID': jobid,
"type": "msf_socks5",
"port": datastore.get("SRVPORT"),
'datastore': datastore}
socks_list.append(onesocks4a)
return socks_list
@staticmethod
def create(socks_type=None, port=None):
if socks_type == "msf_socks4a":
opts = {'SRVHOST': '0.0.0.0', 'SRVPORT': port}
result = MSFModule.run_msf_module_realtime(module_type="auxiliary", mname="server/socks4a_api", opts=opts,
runasjob=True,
timeout=RPC_JOB_API_REQ)
if isinstance(result, dict) is not True or result.get('job_id') is None:
opts['job_id'] = None
context = data_return(303, opts, Socks_MSG_ZH.get(303), Socks_MSG_EN.get(303))
else:
job_id = int(result.get('job_id'))
if Job.is_msf_job_alive(job_id):
opts['job_id'] = int(result.get('job_id'))
Notice.send_success(f"新建msf_socks4a代理成功,Port: {opts.get('SRVPORT')}",
f"Create msf_socks4a successfully,Port: {opts.get('SRVPORT')}")
context = data_return(201, opts, Socks_MSG_ZH.get(201), Socks_MSG_EN.get(201))
else:
context = data_return(306, opts, Socks_MSG_ZH.get(306), Socks_MSG_EN.get(306))
return context
elif socks_type == "msf_socks5":
opts = {'SRVHOST': '0.0.0.0', 'SRVPORT': port}
result = MSFModule.run_msf_module_realtime(module_type="auxiliary", mname="server/socks5_api", opts=opts,
runasjob=True,
timeout=RPC_JOB_API_REQ)
if isinstance(result, dict) is not True or result.get('job_id') is None:
opts['job_id'] = None
context = data_return(303, opts, Socks_MSG_ZH.get(303), Socks_MSG_EN.get(303))
else:
job_id = int(result.get('job_id'))
if Job.is_msf_job_alive(job_id):
opts['job_id'] = int(result.get('job_id'))
Notice.send_success(f"新建msf_socks5代理成功,Port: {opts.get('SRVPORT')}",
f"Create msf_socks5 successfully,Port: {opts.get('SRVPORT')}")
context = data_return(201, opts, Socks_MSG_ZH.get(201), Socks_MSG_EN.get(201))
else:
context = data_return(306, opts, Socks_MSG_ZH.get(306), Socks_MSG_EN.get(306))
return context
@staticmethod
def destory(socks_type=None, jobid=None):
if socks_type == "msf_socks4a":
flag = Job.destroy(jobid)
if flag:
if Job.is_msf_job_alive(jobid) is not True:
Notice.send_success(f"删除msf_socks4a代理 JobID:{jobid}", f"Delete msf_socks4a JobID:{jobid}")
context = data_return(204, {}, Socks_MSG_ZH.get(204), Socks_MSG_EN.get(204))
else:
context = data_return(304, {}, Socks_MSG_ZH.get(304), Socks_MSG_EN.get(304))
else:
context = data_return(304, {}, Socks_MSG_ZH.get(304), Socks_MSG_EN.get(304))
return context
elif socks_type == "msf_socks5":
flag = Job.destroy(jobid)
if flag:
if Job.is_msf_job_alive(jobid) is not True:
Notice.send_success(f"删除msf_socks5代理 JobID:{jobid}", f"Delete msf_socks5 JobID:{jobid}")
context = data_return(204, {}, Socks_MSG_ZH.get(204), Socks_MSG_EN.get(204))
else:
context = data_return(304, {}, Socks_MSG_ZH.get(304), Socks_MSG_EN.get(304))
else:
context = data_return(304, {}, Socks_MSG_ZH.get(404), Socks_MSG_EN.get(404))
return context
| 49.28972
| 118
| 0.525787
| 617
| 5,274
| 4.259319
| 0.163695
| 0.079148
| 0.049467
| 0.059361
| 0.777778
| 0.74277
| 0.723744
| 0.680746
| 0.680746
| 0.680746
| 0
| 0.043375
| 0.361775
| 5,274
| 106
| 119
| 49.754717
| 0.737374
| 0.01441
| 0
| 0.702128
| 0
| 0
| 0.130854
| 0.02486
| 0
| 0
| 0
| 0
| 0
| 1
| 0.031915
| false
| 0
| 0.053191
| 0
| 0.159574
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
50756e01d6448605300aba9c368ffa90b0f3001a
| 238
|
py
|
Python
|
print-month.py
|
sorcerer-armani/hackerrank-solutions-advanced
|
89f57f1aee52bf062008a78144e1dfac1e980911
|
[
"MIT"
] | null | null | null |
print-month.py
|
sorcerer-armani/hackerrank-solutions-advanced
|
89f57f1aee52bf062008a78144e1dfac1e980911
|
[
"MIT"
] | null | null | null |
print-month.py
|
sorcerer-armani/hackerrank-solutions-advanced
|
89f57f1aee52bf062008a78144e1dfac1e980911
|
[
"MIT"
] | 4
|
2021-10-01T14:47:47.000Z
|
2021-10-01T16:28:54.000Z
|
import calendar
year =int( input("Enter the year of the required calendar "))
month_no = int( input("Enter the month of the required calendar "))
print(calendar.month(year,month_no))
| 47.6
| 91
| 0.584034
| 29
| 238
| 4.724138
| 0.413793
| 0.116788
| 0.189781
| 0.233577
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.331933
| 238
| 5
| 91
| 47.6
| 0.861635
| 0
| 0
| 0
| 0
| 0
| 0.338912
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 0
| 0.25
| 0.25
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
50896d534e16c7fd6fc62bc3c13d9ca9ee2108fc
| 370
|
py
|
Python
|
python/matrix/matrix.py
|
rob93c/Exercism
|
51e38b6559a5650a56d81e733be22b7ee349cedd
|
[
"MIT"
] | null | null | null |
python/matrix/matrix.py
|
rob93c/Exercism
|
51e38b6559a5650a56d81e733be22b7ee349cedd
|
[
"MIT"
] | null | null | null |
python/matrix/matrix.py
|
rob93c/Exercism
|
51e38b6559a5650a56d81e733be22b7ee349cedd
|
[
"MIT"
] | 1
|
2019-03-19T12:18:39.000Z
|
2019-03-19T12:18:39.000Z
|
class Matrix(object):
def __init__(self, matrix_string):
self.matrix = []
rows = matrix_string.splitlines()
for row in rows:
self.matrix.append([int(num) for num in row.split()])
def row(self, index):
return self.matrix[index - 1]
def column(self, index):
return [row[index - 1] for row in self.matrix]
| 26.428571
| 65
| 0.594595
| 50
| 370
| 4.28
| 0.4
| 0.233645
| 0.074766
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007547
| 0.283784
| 370
| 13
| 66
| 28.461538
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.3
| false
| 0
| 0
| 0.2
| 0.6
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
5092b6c4f412cc8aaade4199b595ea80a363f9f2
| 915
|
py
|
Python
|
src/locations/models.py
|
hijal/event-management-system
|
92fa74035bec846e3448bf210bb5d5088bbf639e
|
[
"bzip2-1.0.6"
] | 3
|
2021-09-27T00:45:42.000Z
|
2021-09-28T16:42:05.000Z
|
src/locations/models.py
|
hijal/event-management-system
|
92fa74035bec846e3448bf210bb5d5088bbf639e
|
[
"bzip2-1.0.6"
] | null | null | null |
src/locations/models.py
|
hijal/event-management-system
|
92fa74035bec846e3448bf210bb5d5088bbf639e
|
[
"bzip2-1.0.6"
] | null | null | null |
from django.db import models
class City(models.Model):
name = models.CharField(max_length=30)
def __str__(self):
return self.name
class Place(models.Model):
city = models.ForeignKey(City, on_delete=models.CASCADE)
name = models.CharField(max_length=30)
def __str__(self):
return self.name
class Center(models.Model):
place = models.ForeignKey(Place, on_delete=models.CASCADE)
name = models.CharField(max_length=30)
def __str__(self):
return self.name
class Location(models.Model):
city = models.ForeignKey(City, on_delete=models.SET_NULL, null=True)
place = models.ForeignKey(Place, on_delete=models.SET_NULL, null=True)
center = models.ForeignKey(Center, on_delete=models.SET_NULL, null=True)
def __str__(self):
return '{}, {}, {}'.format(self.city, self.place, self.center)
| 32.678571
| 81
| 0.662295
| 117
| 915
| 4.948718
| 0.239316
| 0.138169
| 0.120898
| 0.110535
| 0.739206
| 0.739206
| 0.739206
| 0.523316
| 0.523316
| 0.378238
| 0
| 0.008427
| 0.221858
| 915
| 28
| 82
| 32.678571
| 0.804775
| 0
| 0
| 0.47619
| 0
| 0
| 0.010917
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.190476
| false
| 0
| 0.047619
| 0.190476
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
50d4e3a9485b2d1b309ebb576429596fc7cf62bb
| 8,560
|
py
|
Python
|
chess/game/piece.py
|
ryomakawakami/chess-ai
|
61cd605c2bdba2cc568d028954e16379fa2dae73
|
[
"MIT"
] | null | null | null |
chess/game/piece.py
|
ryomakawakami/chess-ai
|
61cd605c2bdba2cc568d028954e16379fa2dae73
|
[
"MIT"
] | 2
|
2020-05-16T19:38:49.000Z
|
2020-05-26T00:27:11.000Z
|
chess/game/piece.py
|
ryomakawakami/chess-ai
|
61cd605c2bdba2cc568d028954e16379fa2dae73
|
[
"MIT"
] | null | null | null |
from chess.game.piece_type import PieceType
class Piece:
def __init__(self, pieceType, color):
self.pieceType = pieceType
self.color = color
self.moves = []
def updateMoves(self, squares, attacks, position):
self.moves = []
if self.pieceType == PieceType.PAWN:
self.addPawnMoves(squares, attacks, position)
elif self.pieceType == PieceType.ROOK:
self.addRookMoves(squares, attacks, position)
# see knight_moves.png for option numbering
elif self.pieceType == PieceType.KNIGHT:
self.addKnightMoves(squares, attacks, position)
elif self.pieceType == PieceType.BISHOP:
self.addBishopMoves(squares, attacks, position)
elif self.pieceType == PieceType.QUEEN:
self.addRookMoves(squares, attacks, position)
self.addBishopMoves(squares, attacks, position)
else:
self.addKingMoves(squares, attacks, position)
def addPawnMoves(self, squares, attacks, position):
x, y = position
if self.color == 0 and y == 7:
return
elif self.color == 1 and y == 0:
return
fwd = 1
if self.color == 1:
fwd = -1
# 1 square forward
if squares[x][y + fwd] == None:
self.moves.append((x, y + fwd))
# 2 squares forward (pawn first move or idk)
if (y == 1 and self.color == 0) or (y == 6 and self.color == 1):
if squares[x][y + fwd] == None and squares[x][y + 2 * fwd] == None:
self.moves.append((x, y + 2 * fwd))
# Capture left
if x > 0 and squares[x - 1][y + fwd] != None and squares[x - 1][y + fwd].color != self.color:
self.moves.append((x - 1, y + fwd))
# Capture right
if x < 7 and squares[x + 1][y + fwd] != None and squares[x + 1][y + fwd].color != self.color:
self.moves.append((x + 1, y + fwd))
def addRookMoves(self, squares, attacks, position):
x, y = position
tempX = x
while tempX < 7:
nextPiece = squares[tempX + 1][y]
if nextPiece == None:
self.moves.append((tempX + 1, y))
tempX += 1
continue
if nextPiece.color == self.color:
break
self.moves.append((tempX + 1, y))
break
tempX = x
while tempX > 0:
nextPiece = squares[tempX - 1][y]
if nextPiece == None:
self.moves.append((tempX - 1, y))
tempX -= 1
continue
if nextPiece.color == self.color:
break
self.moves.append((tempX - 1, y))
break
tempY = y
while tempY < 7:
nextPiece = squares[x][tempY + 1]
if nextPiece == None:
self.moves.append((x, tempY + 1))
tempY += 1
continue
if nextPiece.color == self.color:
break
self.moves.append((x, tempY + 1))
break
tempY = y
while tempY > 0:
nextPiece = squares[x][tempY - 1]
if nextPiece == None:
self.moves.append((x, tempY - 1))
tempY -= 1
continue
if nextPiece.color == self.color:
break
self.moves.append((x, tempY - 1))
break
def addKnightMoves(self, squares, attacks, position):
x, y = position
#Attack val is 1 for whites, multiply by 100 if black.
#Attacks are same as moves, regardless.
# option 1
if x < 7 and y < 6:
if squares[x + 1][y + 2] == None or squares[x + 1][y + 2].color != self.color:
self.moves.append((x + 1, y + 2))
# option 2
if x < 6 and y < 7:
if squares[x + 2][y + 1] == None or squares[x + 2][y + 1].color != self.color:
self.moves.append((x + 2, y + 1))
# option 3
if x < 6 and y > 0:
if squares[x + 2][y - 1] == None or squares[x + 2][y - 1].color != self.color:
self.moves.append((x + 2, y - 1))
# option 4
if x < 7 and y > 1:
if squares[x + 1][y - 2] == None or squares[x + 1][y - 2].color != self.color:
self.moves.append((x + 1, y - 2))
# option 5
if x > 0 and y > 1:
if squares[x - 1][y - 2] == None or squares[x - 1][y - 2].color != self.color:
self.moves.append((x - 1, y - 2))
# option 6
if x > 1 and y > 0:
if squares[x - 2][y - 1] == None or squares[x - 2][y - 1].color != self.color:
self.moves.append((x - 2, y - 1))
# option 7
if x > 1 and y < 7:
if squares[x - 2][y + 1] == None or squares[x - 2][y + 1].color != self.color:
self.moves.append((x - 2, y + 1))
# option 8
if x > 0 and y < 6:
if squares[x - 1][y + 2] == None or squares[x - 1][y + 2].color != self.color:
self.moves.append((x - 1, y + 2))
def addBishopMoves(self, squares, attacks, position):
x, y = position
offset = 0
while x + offset < 7 and y + offset < 7:
nextPiece = squares[x + offset + 1][y + offset + 1]
if nextPiece == None:
offset += 1
self.moves.append((x + offset, y + offset))
continue
if nextPiece.color == self.color:
break
offset += 1
self.moves.append((x + offset, y + offset))
break
offset = 0
while x + offset < 7 and y - offset > 0:
nextPiece = squares[x + offset + 1][y - offset - 1]
if nextPiece == None:
offset += 1
self.moves.append((x + offset, y - offset))
continue
if nextPiece.color == self.color:
break
offset += 1
self.moves.append((x + offset, y - offset))
break
offset = 0
while x - offset > 0 and y + offset < 7:
nextPiece = squares[x - offset - 1][y + offset + 1]
if nextPiece == None:
offset += 1
self.moves.append((x - offset, y + offset))
continue
if nextPiece.color == self.color:
break
offset += 1
self.moves.append((x - offset, y + offset))
break
offset = 0
while x - offset > 0 and y - offset > 0:
nextPiece = squares[x - offset - 1][y - offset - 1]
if nextPiece == None:
offset += 1
self.moves.append((x - offset, y - offset))
continue
if nextPiece.color == self.color:
break
offset += 1
self.moves.append((x - offset, y - offset))
break
def addKingMoves(self, squares, attacks, position):
x, y = position
if x > 0:
if squares[x - 1][y] == None or squares[x - 1][y].color != self.color:
self.moves.append((x - 1, y))
if y > 0:
if squares[x - 1][y - 1] == None or squares[x - 1][y - 1].color != self.color:
self.moves.append((x - 1, y - 1))
if y < 7:
if squares[x - 1][y + 1] == None or squares[x - 1][y + 1].color != self.color:
self.moves.append((x - 1, y + 1))
if x < 7:
if squares[x + 1][y] == None or squares[x + 1][y].color != self.color:
self.moves.append((x + 1, y))
if y > 0:
if squares[x + 1][y - 1] == None or squares[x + 1][y - 1].color != self.color:
self.moves.append((x + 1, y - 1))
if y < 7:
if squares[x + 1][y + 1] == None or squares[x + 1][y + 1].color != self.color:
self.moves.append((x + 1, y + 1))
if y > 0:
if squares[x][y - 1] == None or squares[x][y - 1].color != self.color:
self.moves.append((x, y - 1))
if y < 7:
if squares[x][y + 1] == None or squares[x][y + 1].color != self.color:
self.moves.append((x, y + 1))
| 32.424242
| 101
| 0.456308
| 1,069
| 8,560
| 3.648269
| 0.073901
| 0.106154
| 0.138462
| 0.131282
| 0.809744
| 0.758974
| 0.747949
| 0.671026
| 0.650513
| 0.645128
| 0
| 0.038323
| 0.417757
| 8,560
| 264
| 102
| 32.424242
| 0.744181
| 0.033995
| 0
| 0.486631
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.037433
| false
| 0
| 0.005348
| 0
| 0.058824
| 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
|
0fd2e9fb02b75f3f1e2694df6918435b26c2f4cf
| 167
|
py
|
Python
|
models/Agent.py
|
KevinJeon/The-Tragedy-of-the-commons
|
7151faf25fd91732de19a843b39cd1f2614f34ca
|
[
"Apache-2.0"
] | 5
|
2021-03-21T15:04:36.000Z
|
2021-06-22T14:09:00.000Z
|
models/Agent.py
|
KevinJeon/The-Tragedy-of-the-commons
|
7151faf25fd91732de19a843b39cd1f2614f34ca
|
[
"Apache-2.0"
] | 5
|
2021-04-10T08:16:16.000Z
|
2021-09-12T09:28:42.000Z
|
models/Agent.py
|
KevinJeon/The-Tragedy-of-the-commons
|
7151faf25fd91732de19a843b39cd1f2614f34ca
|
[
"Apache-2.0"
] | 2
|
2021-04-26T22:33:19.000Z
|
2021-06-08T18:13:49.000Z
|
class Agent(object):
def __init__(self):
pass
def act(self, obs):
pass
def train(self, replay_buffer, logger, step):
pass
| 15.181818
| 49
| 0.54491
| 20
| 167
| 4.3
| 0.7
| 0.162791
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.359281
| 167
| 11
| 50
| 15.181818
| 0.803738
| 0
| 0
| 0.428571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.428571
| false
| 0.428571
| 0
| 0
| 0.571429
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
0fdfac8b9b55a0f0082e186d98cadec47e3d6900
| 213
|
py
|
Python
|
chuck-pyth/code/largest.py
|
KaiserPhemi/python-101
|
703facf41f1b47c486e5044696ce87b6d28a9ab3
|
[
"MIT"
] | 41
|
2015-02-27T22:13:41.000Z
|
2021-11-14T15:37:29.000Z
|
chuck-pyth/code/largest.py
|
KaiserPhemi/python-101
|
703facf41f1b47c486e5044696ce87b6d28a9ab3
|
[
"MIT"
] | 2
|
2015-12-15T04:03:15.000Z
|
2017-01-13T15:29:47.000Z
|
chuck-pyth/code/largest.py
|
KaiserPhemi/python-101
|
703facf41f1b47c486e5044696ce87b6d28a9ab3
|
[
"MIT"
] | 45
|
2015-01-03T17:26:02.000Z
|
2022-01-09T16:06:04.000Z
|
largest = None
print 'Before:', largest
for iterval in [3, 41, 12, 9, 74, 15]:
if largest == None or largest < iterval:
largest = iterval
print 'Loop:', iterval, largest
print 'Largest:', largest
| 23.666667
| 44
| 0.638498
| 29
| 213
| 4.689655
| 0.551724
| 0.161765
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.06135
| 0.234742
| 213
| 8
| 45
| 26.625
| 0.773006
| 0
| 0
| 0
| 0
| 0
| 0.09434
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.428571
| 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
|
0ff05f1a4dd08d62846eff1b5c75bab43e56ee8d
| 99
|
py
|
Python
|
scripts/item/consume_2435199.py
|
G00dBye/YYMS
|
1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb
|
[
"MIT"
] | 54
|
2019-04-16T23:24:48.000Z
|
2021-12-18T11:41:50.000Z
|
scripts/item/consume_2435199.py
|
G00dBye/YYMS
|
1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb
|
[
"MIT"
] | 3
|
2019-05-19T15:19:41.000Z
|
2020-04-27T16:29:16.000Z
|
scripts/item/consume_2435199.py
|
G00dBye/YYMS
|
1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb
|
[
"MIT"
] | 49
|
2020-11-25T23:29:16.000Z
|
2022-03-26T16:20:24.000Z
|
# Black Mage Chat Ring Coupon
if sm.canHold(1115021):
sm.giveItem(1115021)
sm.consumeItem()
| 24.75
| 29
| 0.717172
| 14
| 99
| 5.071429
| 0.785714
| 0.253521
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.170732
| 0.171717
| 99
| 4
| 30
| 24.75
| 0.695122
| 0.272727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0ff933e3270e8f1162064129fbec6b9f3261e451
| 51
|
py
|
Python
|
09 Lambda Functions/sumoftwonumbers.py
|
Himanshu44626748/Learn-Python
|
f3a4d997f2d29b146e5f7434f4801ae94bc3483f
|
[
"MIT"
] | 2
|
2020-05-30T20:31:17.000Z
|
2020-07-07T15:15:43.000Z
|
09 Lambda Functions/sumoftwonumbers.py
|
Himanshu44626748/Learn-Python
|
f3a4d997f2d29b146e5f7434f4801ae94bc3483f
|
[
"MIT"
] | null | null | null |
09 Lambda Functions/sumoftwonumbers.py
|
Himanshu44626748/Learn-Python
|
f3a4d997f2d29b146e5f7434f4801ae94bc3483f
|
[
"MIT"
] | 1
|
2020-08-13T07:59:02.000Z
|
2020-08-13T07:59:02.000Z
|
l = lambda a,b : a+b
print(l(10,20))
print(l(10,2))
| 17
| 20
| 0.588235
| 14
| 51
| 2.142857
| 0.571429
| 0.133333
| 0.533333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.159091
| 0.137255
| 51
| 3
| 21
| 17
| 0.522727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.666667
| 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
| 0
| 1
|
0
| 4
|
ba097ec36e96b38243b00af5b7f4af9967ce066b
| 289
|
py
|
Python
|
Scripts/selected-glyphs/layers/mask.py
|
miguelsousa/hTools2
|
eab400677c1b21bb2519a7354a142e167c2b39ba
|
[
"BSD-3-Clause"
] | null | null | null |
Scripts/selected-glyphs/layers/mask.py
|
miguelsousa/hTools2
|
eab400677c1b21bb2519a7354a142e167c2b39ba
|
[
"BSD-3-Clause"
] | null | null | null |
Scripts/selected-glyphs/layers/mask.py
|
miguelsousa/hTools2
|
eab400677c1b21bb2519a7354a142e167c2b39ba
|
[
"BSD-3-Clause"
] | null | null | null |
# [h] copy glyphs to mask
'''Copy selected glyphs from and to the mask layer.'''
# debug
import hTools2
reload(hTools2)
if hTools2.DEBUG:
import hTools2.dialogs.glyphs
reload(hTools2.dialogs.glyphs)
# import
from hTools2.dialogs.glyphs import maskDialog
# run
maskDialog()
| 13.761905
| 54
| 0.733564
| 39
| 289
| 5.435897
| 0.461538
| 0.198113
| 0.283019
| 0.245283
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025105
| 0.17301
| 289
| 20
| 55
| 14.45
| 0.861925
| 0.311419
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.428571
| 0
| 0.428571
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
ba194f0a613a8ae30a3ccdc58fda4e015908ced2
| 106
|
py
|
Python
|
api/website_analysis/apps.py
|
gpiechnik2/senter
|
6f64f5410fe02a5215ba148553dec45feaadcc09
|
[
"CC0-1.0"
] | 2
|
2021-12-08T19:38:33.000Z
|
2022-01-26T15:02:57.000Z
|
api/website_analysis/apps.py
|
gpiechnik2/senter
|
6f64f5410fe02a5215ba148553dec45feaadcc09
|
[
"CC0-1.0"
] | null | null | null |
api/website_analysis/apps.py
|
gpiechnik2/senter
|
6f64f5410fe02a5215ba148553dec45feaadcc09
|
[
"CC0-1.0"
] | 1
|
2021-12-08T19:38:39.000Z
|
2021-12-08T19:38:39.000Z
|
from django.apps import AppConfig
class WebsiteAnalysisConfig(AppConfig):
name = 'website_analysis'
| 17.666667
| 39
| 0.792453
| 11
| 106
| 7.545455
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141509
| 106
| 5
| 40
| 21.2
| 0.912088
| 0
| 0
| 0
| 0
| 0
| 0.150943
| 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
|
e85a59a0e8c6505391131daf573bba0e80baf87f
| 221
|
py
|
Python
|
02 - Bibliotecas/MeuArquivo.py
|
Ivoneideduarte/minicurso-de-python
|
496dbe19ba696aecc91f3275c1c1c1092c8f9cf5
|
[
"MIT"
] | null | null | null |
02 - Bibliotecas/MeuArquivo.py
|
Ivoneideduarte/minicurso-de-python
|
496dbe19ba696aecc91f3275c1c1c1092c8f9cf5
|
[
"MIT"
] | null | null | null |
02 - Bibliotecas/MeuArquivo.py
|
Ivoneideduarte/minicurso-de-python
|
496dbe19ba696aecc91f3275c1c1c1092c8f9cf5
|
[
"MIT"
] | null | null | null |
import pandas as pd
# Importar a base de dados
# Visualizar a base de dados
# Faturamento por loja
# Quantidade de produtos vendidos por loja
# Ticket médio por produto em cada loja
# Enviar um email com o relatório
| 17
| 42
| 0.760181
| 36
| 221
| 4.666667
| 0.75
| 0.059524
| 0.083333
| 0.142857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.21267
| 221
| 13
| 43
| 17
| 0.965517
| 0.828054
| 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
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
e885ee274f0f15c38956f7c7d8514dcf9b08b47b
| 90
|
py
|
Python
|
src/flickr/secrets.py
|
dballesteros7/master-thesis-2015
|
8c0bf9a6eef172fc8167a30780ae0666f8ea2d88
|
[
"MIT"
] | null | null | null |
src/flickr/secrets.py
|
dballesteros7/master-thesis-2015
|
8c0bf9a6eef172fc8167a30780ae0666f8ea2d88
|
[
"MIT"
] | null | null | null |
src/flickr/secrets.py
|
dballesteros7/master-thesis-2015
|
8c0bf9a6eef172fc8167a30780ae0666f8ea2d88
|
[
"MIT"
] | null | null | null |
FLICKR_API_KEY = '1844fefcac361892270130124dd9862f'
FLICKR_API_SECRET = '2ed025843bc2daf3'
| 45
| 51
| 0.877778
| 8
| 90
| 9.375
| 0.75
| 0.24
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.376471
| 0.055556
| 90
| 2
| 52
| 45
| 0.505882
| 0
| 0
| 0
| 0
| 0
| 0.527473
| 0.351648
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
2cd4c17ecd500e3edbe3fd44b8927b2ef3990f89
| 193
|
py
|
Python
|
moodle/base/general/type_value.py
|
Hardikris/moodlepy
|
8f5cb0cb4c2297e10f48396de681f6bb250f7751
|
[
"MIT"
] | null | null | null |
moodle/base/general/type_value.py
|
Hardikris/moodlepy
|
8f5cb0cb4c2297e10f48396de681f6bb250f7751
|
[
"MIT"
] | null | null | null |
moodle/base/general/type_value.py
|
Hardikris/moodlepy
|
8f5cb0cb4c2297e10f48396de681f6bb250f7751
|
[
"MIT"
] | null | null | null |
from moodle.attr import dataclass
@dataclass
class GeneralTypeValue:
"""GeneralTypeValue
Args:
type (str): ...
value (str): ...
"""
type: str
value: str
| 12.866667
| 33
| 0.564767
| 18
| 193
| 6.055556
| 0.611111
| 0.12844
| 0.220183
| 0.275229
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.310881
| 193
| 14
| 34
| 13.785714
| 0.819549
| 0.331606
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.2
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
fa0e7a67f0569d2c26a7dd955f3d3d886c6931aa
| 117
|
py
|
Python
|
dlflow/models/__init__.py
|
12860/dlflow
|
6fb974fd800649af82b20c5f4e40aea123559d10
|
[
"Apache-2.0"
] | 156
|
2020-04-22T10:59:26.000Z
|
2022-02-28T09:09:01.000Z
|
dlflow/models/__init__.py
|
12860/dlflow
|
6fb974fd800649af82b20c5f4e40aea123559d10
|
[
"Apache-2.0"
] | 5
|
2020-07-10T05:39:48.000Z
|
2022-03-15T14:38:23.000Z
|
dlflow/models/__init__.py
|
12860/dlflow
|
6fb974fd800649af82b20c5f4e40aea123559d10
|
[
"Apache-2.0"
] | 31
|
2020-04-22T12:51:32.000Z
|
2022-03-15T07:02:05.000Z
|
from .model_base import ModelBase
from .input_base import InputBase
__all__ = [
"ModelBase",
"InputBase"
]
| 13
| 33
| 0.709402
| 13
| 117
| 5.923077
| 0.615385
| 0.25974
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205128
| 117
| 8
| 34
| 14.625
| 0.827957
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
fa1c5cb1ba8b7a175512972df8f0bb479f236cc2
| 82
|
py
|
Python
|
backend/src/helpers/status_code_model.py
|
nokia-wroclaw/innovativeproject-noticrawl
|
2569dfe39492695cc33d0bbcbc675465b14756c0
|
[
"MIT"
] | 5
|
2020-03-08T14:38:47.000Z
|
2021-02-24T12:17:57.000Z
|
backend/src/helpers/status_code_model.py
|
nokia-wroclaw/innovativeproject-noticrawl
|
2569dfe39492695cc33d0bbcbc675465b14756c0
|
[
"MIT"
] | 35
|
2020-03-13T14:18:00.000Z
|
2020-06-16T22:17:39.000Z
|
backend/src/helpers/status_code_model.py
|
nokia-wroclaw/innovativeproject-noticrawl
|
2569dfe39492695cc33d0bbcbc675465b14756c0
|
[
"MIT"
] | 2
|
2020-03-03T20:56:19.000Z
|
2020-03-08T14:38:53.000Z
|
from pydantic import BaseModel
class StatusCodeBase(BaseModel):
detail: str
| 13.666667
| 32
| 0.780488
| 9
| 82
| 7.111111
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.170732
| 82
| 5
| 33
| 16.4
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
fa32533b181c00601a990a4ea4a267807c1f599d
| 59
|
py
|
Python
|
sbot/vision/calibrations/__init__.py
|
sourcebots/sbot
|
6d40c8ad989c52e30b26119fb0b56df88892ce3d
|
[
"MIT"
] | 2
|
2019-08-22T14:31:58.000Z
|
2020-03-02T18:39:06.000Z
|
sbot/vision/calibrations/__init__.py
|
sourcebots/sbot
|
6d40c8ad989c52e30b26119fb0b56df88892ce3d
|
[
"MIT"
] | 150
|
2019-05-19T11:15:33.000Z
|
2022-03-25T12:24:02.000Z
|
sbot/vision/calibrations/__init__.py
|
sourcebots/sbot
|
6d40c8ad989c52e30b26119fb0b56df88892ce3d
|
[
"MIT"
] | 1
|
2020-03-02T18:40:33.000Z
|
2020-03-02T18:40:33.000Z
|
"""This module houses OpenCV camera calibration files."""
| 29.5
| 58
| 0.745763
| 7
| 59
| 6.285714
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135593
| 59
| 1
| 59
| 59
| 0.862745
| 0.864407
| 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
|
fa390d17e08db98d4406a99dbba9fd498297765f
| 350
|
py
|
Python
|
ACME/utility/isfinite.py
|
mauriziokovacic/ACME
|
2615b66dd4addfd5c03d9d91a24c7da414294308
|
[
"MIT"
] | 3
|
2019-10-23T23:10:55.000Z
|
2021-09-01T07:30:14.000Z
|
ACME/utility/isfinite.py
|
mauriziokovacic/ACME-Python
|
2615b66dd4addfd5c03d9d91a24c7da414294308
|
[
"MIT"
] | null | null | null |
ACME/utility/isfinite.py
|
mauriziokovacic/ACME-Python
|
2615b66dd4addfd5c03d9d91a24c7da414294308
|
[
"MIT"
] | 1
|
2020-07-11T11:35:43.000Z
|
2020-07-11T11:35:43.000Z
|
from .isnan import *
from .isinf import *
def isfinite(*obj):
"""
Returns whether or not the input is finite
Parameters
----------
*obj : object...
a sequence of objects
Returns
-------
bool
True if the inputs are finite, False otherwise
"""
return (not isnan(*obj)) and (not isinf(*obj))
| 16.666667
| 54
| 0.56
| 42
| 350
| 4.666667
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.305714
| 350
| 20
| 55
| 17.5
| 0.806584
| 0.517143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0
| 0.5
| 0
| 1
| 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
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
fa40f079d15fcf5ef97360a7e78ac79fde57161f
| 85
|
py
|
Python
|
APICanalLuciernaga/lugar/apps.py
|
ErickMurillo/canal-luciernaga
|
3839b2bf03aea4f5336e0b2899845dc6ec38ee04
|
[
"MIT"
] | null | null | null |
APICanalLuciernaga/lugar/apps.py
|
ErickMurillo/canal-luciernaga
|
3839b2bf03aea4f5336e0b2899845dc6ec38ee04
|
[
"MIT"
] | 12
|
2020-02-12T01:04:09.000Z
|
2022-02-10T12:22:14.000Z
|
APICanalLuciernaga/lugar/apps.py
|
ErickMurillo/canal-luciernaga
|
3839b2bf03aea4f5336e0b2899845dc6ec38ee04
|
[
"MIT"
] | 3
|
2019-07-04T19:51:17.000Z
|
2019-07-30T17:23:13.000Z
|
from django.apps import AppConfig
class LugarConfig(AppConfig):
name = 'lugar'
| 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
|
fa6e9126fda1e0d8bf8be9e4ac02aa36240fa256
| 65
|
py
|
Python
|
fundamentals/introduction/hello_world.py
|
ZhouSusan/CodingDojoPython
|
8d89c9a94a3be18e79fbf24e25348eae8c96a338
|
[
"MIT"
] | null | null | null |
fundamentals/introduction/hello_world.py
|
ZhouSusan/CodingDojoPython
|
8d89c9a94a3be18e79fbf24e25348eae8c96a338
|
[
"MIT"
] | null | null | null |
fundamentals/introduction/hello_world.py
|
ZhouSusan/CodingDojoPython
|
8d89c9a94a3be18e79fbf24e25348eae8c96a338
|
[
"MIT"
] | null | null | null |
print("Hello World!")
x = "Hello World"
print(x)
y = 42
print(y)
| 10.833333
| 21
| 0.630769
| 12
| 65
| 3.416667
| 0.5
| 0.487805
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.037037
| 0.169231
| 65
| 6
| 22
| 10.833333
| 0.722222
| 0
| 0
| 0
| 0
| 0
| 0.348485
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.6
| 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
|
3afb9b6ef61c00eceba4efefe4772d211252bdf5
| 2,481
|
py
|
Python
|
features/steps/ade/steps.py
|
unity-sds/unity-system-test
|
abb5d6b50e87f751fe65e534e0867820f9e20c4b
|
[
"Apache-2.0"
] | null | null | null |
features/steps/ade/steps.py
|
unity-sds/unity-system-test
|
abb5d6b50e87f751fe65e534e0867820f9e20c4b
|
[
"Apache-2.0"
] | null | null | null |
features/steps/ade/steps.py
|
unity-sds/unity-system-test
|
abb5d6b50e87f751fe65e534e0867820f9e20c4b
|
[
"Apache-2.0"
] | null | null | null |
from behave import *
from behave.runner import Context
import json
@given(u'an algorithm binary in an accessible repository')
def step_impl(context):
raise NotImplementedError(u'STEP: Given an algorithm binary in an accessible repository')
@given(u'an algorithm descriptor')
def step_impl(context):
raise NotImplementedError(u'STEP: Given an algorithm descriptor')
@when(u'the algorithm descriptor is registered in the Algorithm Catalog')
def step_impl(context):
raise NotImplementedError(u'STEP: When the algorithm descriptor is registered in the Algorithm Catalog')
@then(u'the algorithm is searchable in the Algorithm catalog')
def step_impl(context):
raise NotImplementedError(u'STEP: Then the algorithm is searchable in the Algorithm catalog')
@then(u'the search result maps back to the algorithm binary')
def step_impl(context):
raise NotImplementedError(u'STEP: Then the search result maps back to the algorithm binary')
@given(u'a set of catalog algorithms')
def step_impl(context):
for row in context.table:
print(row['name'])
print(row['binary'])
raise NotImplementedError(u'STEP: Given a set of catalog algorithms')
@given(u'the catalog algorithms are registered in the Algorithm Catalog')
def step_impl(context):
raise NotImplementedError(u'STEP: Given the catalog algorithms are registered in the Algorithm Catalog')
@when(u'a user searches the algorithm catalog')
def step_impl(context):
raise NotImplementedError(u'STEP: When a user searches the algorithm catalog')
@then(u'the search retrieves 3 results')
def step_impl(context):
raise NotImplementedError(u'STEP: Then the search retrieves 3 results')
@given(u'a set of catalog algorthms')
def step_impl(context):
raise NotImplementedError(u'STEP: Given a set of catalog algorthms')
@when(u'a user searches the algorithm catalog by name')
def step_impl(context):
raise NotImplementedError(u'STEP: When a user searches the algorithm catalog by name')
@then(u'the user gets the algorithm package back')
def step_impl(context):
raise NotImplementedError(u'STEP: Then the user gets the algorithm package back')
@then(u'the algorithm package references the correct binary')
def step_impl(context):
raise NotImplementedError(u'STEP: Then the algorithm package references the correct binary')
@then(u'the binary is accessible')
def step_impl(context):
raise NotImplementedError(u'STEP: Then the binary is accessible')
| 32.220779
| 108
| 0.769448
| 359
| 2,481
| 5.278552
| 0.147632
| 0.126649
| 0.081266
| 0.132982
| 0.880211
| 0.842216
| 0.822164
| 0.699736
| 0.634301
| 0.45277
| 0
| 0.000949
| 0.150746
| 2,481
| 76
| 109
| 32.644737
| 0.898434
| 0
| 0
| 0.291667
| 0
| 0
| 0.534059
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.291667
| false
| 0
| 0.0625
| 0
| 0.354167
| 0.041667
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
3afd58c67adde8bc45e4dbc0eba3b2d77e157002
| 24
|
py
|
Python
|
data/studio21_generated/introductory/2779/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
data/studio21_generated/introductory/2779/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
data/studio21_generated/introductory/2779/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
def fib_rabbits(n, b):
| 12
| 22
| 0.666667
| 5
| 24
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 24
| 2
| 23
| 12
| 0.75
| 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
| 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
|
d70ded6ce1f8ac7816555813f237588e0c379d9a
| 168
|
py
|
Python
|
tests/testing.py
|
akkana/python-mammoth
|
abc07c40f2a80468292b13ddb6714972bb73b7af
|
[
"BSD-2-Clause"
] | 1
|
2015-08-15T21:53:03.000Z
|
2015-08-15T21:53:03.000Z
|
tests/testing.py
|
JoshBarr/python-mammoth
|
e24c3d90fee2bcc4c2c3ad47e38c0cbba42945d8
|
[
"BSD-2-Clause"
] | null | null | null |
tests/testing.py
|
JoshBarr/python-mammoth
|
e24c3d90fee2bcc4c2c3ad47e38c0cbba42945d8
|
[
"BSD-2-Clause"
] | 1
|
2020-11-01T17:04:56.000Z
|
2020-11-01T17:04:56.000Z
|
import os
from nose.tools import nottest
@nottest
def test_path(path):
this_dir = os.path.dirname(__file__)
return os.path.join(this_dir, "test-data", path)
| 16.8
| 52
| 0.72619
| 27
| 168
| 4.259259
| 0.592593
| 0.121739
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.160714
| 168
| 9
| 53
| 18.666667
| 0.815603
| 0
| 0
| 0
| 0
| 0
| 0.053571
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| 0
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| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
d73192ba99f971cad7d28aa4a87973e5e66f9eb3
| 56
|
py
|
Python
|
dpaw_utils/requests/__init__.py
|
xzzy/dpaw-utils
|
043817b95271e951ca86c4b20b0d9ce587d4cc59
|
[
"Apache-2.0"
] | null | null | null |
dpaw_utils/requests/__init__.py
|
xzzy/dpaw-utils
|
043817b95271e951ca86c4b20b0d9ce587d4cc59
|
[
"Apache-2.0"
] | null | null | null |
dpaw_utils/requests/__init__.py
|
xzzy/dpaw-utils
|
043817b95271e951ca86c4b20b0d9ce587d4cc59
|
[
"Apache-2.0"
] | 5
|
2016-01-15T03:01:23.000Z
|
2017-03-10T06:01:46.000Z
|
from .api import get,options,head,post,put,patch,delete
| 28
| 55
| 0.803571
| 10
| 56
| 4.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.071429
| 56
| 1
| 56
| 56
| 0.865385
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
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| 1
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| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
d74959756733254db36bba1a1ce74fe4b76b1ffd
| 255
|
py
|
Python
|
src/capturing_device.py
|
vkhaydarov/vkhaydarov-planteye-vision
|
9afe405d7985ec6aa660a972c159b2e1804a7073
|
[
"MIT"
] | null | null | null |
src/capturing_device.py
|
vkhaydarov/vkhaydarov-planteye-vision
|
9afe405d7985ec6aa660a972c159b2e1804a7073
|
[
"MIT"
] | null | null | null |
src/capturing_device.py
|
vkhaydarov/vkhaydarov-planteye-vision
|
9afe405d7985ec6aa660a972c159b2e1804a7073
|
[
"MIT"
] | null | null | null |
from abc import ABC, abstractmethod
class CapturingDevice(ABC):
"""
This class describes generic capturing device
"""
@abstractmethod
def capture_frame(self):
pass
@abstractmethod
def get_status(self):
pass
| 15.9375
| 49
| 0.65098
| 26
| 255
| 6.307692
| 0.692308
| 0.207317
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.278431
| 255
| 15
| 50
| 17
| 0.891304
| 0.176471
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0.25
| 0.125
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
d754613213235d448cb34f733e6c8134f93081e7
| 291
|
py
|
Python
|
nmdc_runtime/api/boot/capabilities.py
|
elais/nmdc-runtime
|
0c03684bc01fd1c0ce443aa271c234067d6d7c09
|
[
"Apache-2.0"
] | 2
|
2021-09-14T16:24:58.000Z
|
2022-02-22T21:45:08.000Z
|
nmdc_runtime/api/boot/capabilities.py
|
elais/nmdc-runtime
|
0c03684bc01fd1c0ce443aa271c234067d6d7c09
|
[
"Apache-2.0"
] | 79
|
2021-05-04T22:00:22.000Z
|
2022-03-31T16:35:46.000Z
|
nmdc_runtime/api/boot/capabilities.py
|
elais/nmdc-runtime
|
0c03684bc01fd1c0ce443aa271c234067d6d7c09
|
[
"Apache-2.0"
] | 1
|
2021-04-21T16:07:21.000Z
|
2021-04-21T16:07:21.000Z
|
from nmdc_runtime.api.models.capability import Capability
import nmdc_runtime.api.boot.workflows as workflows_boot
# Include 1-to-1 "I can run this workflow" capabilities.
_raw = [item for item in workflows_boot._raw]
def construct():
return [Capability(**kwargs) for kwargs in _raw]
| 29.1
| 57
| 0.780069
| 44
| 291
| 5
| 0.613636
| 0.1
| 0.127273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007937
| 0.134021
| 291
| 9
| 58
| 32.333333
| 0.865079
| 0.185567
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0.2
| 0.8
| 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
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 4
|
d7560aadcd7afa57a20b073deeb471544fabc836
| 111
|
py
|
Python
|
key_bot/imageFunctions/__init__.py
|
Jinaz/Py_datascraping
|
e3f1f8819fe98b257cba356b7378cb6cf90b5d62
|
[
"Apache-2.0"
] | null | null | null |
key_bot/imageFunctions/__init__.py
|
Jinaz/Py_datascraping
|
e3f1f8819fe98b257cba356b7378cb6cf90b5d62
|
[
"Apache-2.0"
] | null | null | null |
key_bot/imageFunctions/__init__.py
|
Jinaz/Py_datascraping
|
e3f1f8819fe98b257cba356b7378cb6cf90b5d62
|
[
"Apache-2.0"
] | null | null | null |
from win32_connectors.screenshotter import screenshot
if __name__ == '__main__':
#demo2()
screenshot()
| 22.2
| 53
| 0.738739
| 11
| 111
| 6.636364
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.032258
| 0.162162
| 111
| 5
| 54
| 22.2
| 0.752688
| 0.063063
| 0
| 0
| 0
| 0
| 0.076923
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
d770b87d9f36ea7bab8fc03e00ebdd5cc96f8e0c
| 106
|
py
|
Python
|
faker/providers/ssn/ru_RU/__init__.py
|
StabbarN/faker
|
57882ff73255cb248d8f995b2abfce5cfee45ab3
|
[
"MIT"
] | 12,077
|
2015-01-01T18:30:07.000Z
|
2022-03-31T23:22:01.000Z
|
faker/providers/ssn/ru_RU/__init__.py
|
StabbarN/faker
|
57882ff73255cb248d8f995b2abfce5cfee45ab3
|
[
"MIT"
] | 1,306
|
2015-01-03T05:18:55.000Z
|
2022-03-31T02:43:04.000Z
|
faker/providers/ssn/ru_RU/__init__.py
|
StabbarN/faker
|
57882ff73255cb248d8f995b2abfce5cfee45ab3
|
[
"MIT"
] | 1,855
|
2015-01-08T14:20:10.000Z
|
2022-03-25T17:23:32.000Z
|
from .. import Provider as SsnProvider
class Provider(SsnProvider):
ssn_formats = ("############",)
| 17.666667
| 38
| 0.622642
| 10
| 106
| 6.5
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.160377
| 106
| 5
| 39
| 21.2
| 0.730337
| 0
| 0
| 0
| 0
| 0
| 0.113208
| 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
|
d7762cddacfef5efda3259ac07ade7a46737278b
| 288
|
py
|
Python
|
server/vcr-server/api/v4/search/filters/credential.py
|
esune/indy-catalyst
|
009e1113fc9761aad8742a70b0ab16832f7ee04d
|
[
"Apache-2.0"
] | 38
|
2019-01-07T02:49:55.000Z
|
2020-01-27T17:26:09.000Z
|
server/vcr-server/api/v4/search/filters/credential.py
|
esune/indy-catalyst
|
009e1113fc9761aad8742a70b0ab16832f7ee04d
|
[
"Apache-2.0"
] | 364
|
2019-01-07T20:22:15.000Z
|
2020-03-10T21:59:23.000Z
|
server/vcr-server/api/v4/search/filters/credential.py
|
esune/indy-catalyst
|
009e1113fc9761aad8742a70b0ab16832f7ee04d
|
[
"Apache-2.0"
] | 34
|
2019-01-04T19:16:04.000Z
|
2020-02-20T19:24:25.000Z
|
from haystack.inputs import Clean, Exact, Raw
from api.v2.search.filters import CredNameFilter, CredNameFilterBuilder
class CredQueryFilterBuilder(CredNameFilterBuilder):
query_param = "q"
class CredQueryFilter(CredNameFilter):
query_builder_class = CredQueryFilterBuilder
| 20.571429
| 71
| 0.815972
| 28
| 288
| 8.285714
| 0.714286
| 0.232759
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003968
| 0.125
| 288
| 13
| 72
| 22.153846
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0.003472
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
d77a454a59f2f8aa28421724793ffa4620414f39
| 177
|
py
|
Python
|
tests/confused_math_bc/confused_math.py
|
aroberge/importhooks
|
57483ce24d265d391587f6321954f2ed60f04afd
|
[
"MIT"
] | null | null | null |
tests/confused_math_bc/confused_math.py
|
aroberge/importhooks
|
57483ce24d265d391587f6321954f2ed60f04afd
|
[
"MIT"
] | null | null | null |
tests/confused_math_bc/confused_math.py
|
aroberge/importhooks
|
57483ce24d265d391587f6321954f2ed60f04afd
|
[
"MIT"
] | null | null | null |
def add(a, b):
return a + b
def multiply(a, b):
return a * b
assert add(10, 10) == 100, "confused addition"
assert multiply(10, 10) == 20, "confused multiplication"
| 16.090909
| 56
| 0.627119
| 28
| 177
| 3.964286
| 0.464286
| 0.072072
| 0.144144
| 0.162162
| 0.18018
| 0
| 0
| 0
| 0
| 0
| 0
| 0.094891
| 0.225989
| 177
| 10
| 57
| 17.7
| 0.715328
| 0
| 0
| 0
| 0
| 0
| 0.225989
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| false
| 0
| 0
| 0.333333
| 0.666667
| 0
| 1
| 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
| 0
| 0
|
0
| 4
|
ad01cb404937e567ccc42f55f2771fc3066543ff
| 199
|
py
|
Python
|
Lib/site-packages/rest_framework/authtoken/apps.py
|
inging44/python3
|
fcd8d9d2ee54b46b757ecf34f284b4e60a43097a
|
[
"bzip2-1.0.6"
] | 5,079
|
2015-01-01T03:39:46.000Z
|
2022-03-31T07:38:22.000Z
|
Lib/site-packages/rest_framework/authtoken/apps.py
|
inging44/python3
|
fcd8d9d2ee54b46b757ecf34f284b4e60a43097a
|
[
"bzip2-1.0.6"
] | 1,623
|
2015-01-01T08:06:24.000Z
|
2022-03-30T19:48:52.000Z
|
Lib/site-packages/rest_framework/authtoken/apps.py
|
inging44/python3
|
fcd8d9d2ee54b46b757ecf34f284b4e60a43097a
|
[
"bzip2-1.0.6"
] | 2,033
|
2015-01-04T07:18:02.000Z
|
2022-03-28T19:55:47.000Z
|
from django.apps import AppConfig
from django.utils.translation import ugettext_lazy as _
class AuthTokenConfig(AppConfig):
name = 'rest_framework.authtoken'
verbose_name = _("Auth Token")
| 24.875
| 55
| 0.778894
| 24
| 199
| 6.25
| 0.791667
| 0.133333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.145729
| 199
| 7
| 56
| 28.428571
| 0.882353
| 0
| 0
| 0
| 0
| 0
| 0.170854
| 0.120603
| 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
|
ad30ca5d0210aaff48296f606f13d97a8916c0f6
| 30
|
py
|
Python
|
data/studio21_generated/introductory/4036/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
data/studio21_generated/introductory/4036/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
data/studio21_generated/introductory/4036/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
def days_represented(trips):
| 15
| 28
| 0.8
| 4
| 30
| 5.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 30
| 2
| 29
| 15
| 0.851852
| 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
| 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
|
a8e760bf105831d0526c34bc816bb0ce0adb1f9c
| 42
|
py
|
Python
|
numpy_sugar/ma/__init__.py
|
glimix/numpy-sugar
|
d28ae23d0972cead9e9c83dd4c577678ed587f79
|
[
"MIT"
] | 1
|
2017-07-25T17:28:54.000Z
|
2017-07-25T17:28:54.000Z
|
numpy_sugar/ma/__init__.py
|
limix/numpy-sugar
|
d28ae23d0972cead9e9c83dd4c577678ed587f79
|
[
"MIT"
] | null | null | null |
numpy_sugar/ma/__init__.py
|
limix/numpy-sugar
|
d28ae23d0972cead9e9c83dd4c577678ed587f79
|
[
"MIT"
] | null | null | null |
from .dot import dotd
__all__ = ["dotd"]
| 10.5
| 21
| 0.666667
| 6
| 42
| 4
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.190476
| 42
| 3
| 22
| 14
| 0.705882
| 0
| 0
| 0
| 0
| 0
| 0.095238
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
a8edbaef8a262aecababd26f5bd0c61753683566
| 864
|
py
|
Python
|
rapidplugin/analysis/graph_query.py
|
Rheddes/quality-analyzer
|
1bb8ec037c42c2c1ab202f961cebf5956f2a449b
|
[
"Apache-2.0"
] | null | null | null |
rapidplugin/analysis/graph_query.py
|
Rheddes/quality-analyzer
|
1bb8ec037c42c2c1ab202f961cebf5956f2a449b
|
[
"Apache-2.0"
] | 19
|
2020-08-20T13:36:28.000Z
|
2021-12-06T21:01:32.000Z
|
rapidplugin/analysis/graph_query.py
|
Rheddes/quality-analyzer
|
1bb8ec037c42c2c1ab202f961cebf5956f2a449b
|
[
"Apache-2.0"
] | 3
|
2020-08-20T08:29:27.000Z
|
2021-10-19T04:33:34.000Z
|
# Copyright 2020 Software Improvement Group
#
# 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.
#
"""
Module provides graph query and json query of a call graph in FASTEN json format.
"""
class GraphQuery:
def __init__(self,cg):
pass
def get_trans_callees(self, method):
return []
def get_trans_callers(self, method):
return []
| 27.870968
| 81
| 0.725694
| 127
| 864
| 4.874016
| 0.661417
| 0.096931
| 0.042003
| 0.051696
| 0
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| 0
| 0
| 0
| 0
| 0
| 0.011611
| 0.202546
| 864
| 30
| 82
| 28.8
| 0.886792
| 0.747685
| 0
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| 0.428571
| false
| 0.142857
| 0
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| 0.857143
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| null | 0
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| 0
| 0
| 1
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| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
d1194b237dd3d38c89107aa438cbd69025836d47
| 1,391
|
py
|
Python
|
azext_cdf/_help.py
|
ahelal/cdf
|
0c0e50123a42d701ca5133383dd20d5eabf43e7c
|
[
"MIT"
] | 1
|
2021-11-25T11:45:47.000Z
|
2021-11-25T11:45:47.000Z
|
azext_cdf/_help.py
|
ahelal/cdf
|
0c0e50123a42d701ca5133383dd20d5eabf43e7c
|
[
"MIT"
] | null | null | null |
azext_cdf/_help.py
|
ahelal/cdf
|
0c0e50123a42d701ca5133383dd20d5eabf43e7c
|
[
"MIT"
] | null | null | null |
""" Help commands """
from knack.help_files import helps # pylint: disable=unused-import
helps[
"cdf"
] = """
type: group
short-summary: Commands to manage CDF https://github.com/ahelal/cdf
"""
helps[
"cdf debug"
] = """
type: group
short-summary: Commands to help you debug your CDF deployment.
"""
helps[
"cdf up"
] = """
type: command
short-summary: Bring up an environment.
"""
helps[
"cdf down"
] = """
type: command
short-summary: Destroy an environment.
"""
helps[
"cdf hook"
] = """
type: command
short-summary: Manage CDF hooks.
"""
helps[
"cdf status"
] = """
type: command
short-summary: Show status.
"""
helps[
"cdf test"
] = """
type: command
short-summary: test and environment
"""
helps[
"cdf init"
] = """
type: command
short-summary: Create the cdf directory structure.
"""
helps[
"cdf debug interpolate"
] = """
type: command
short-summary: Run an interactive session to test/debug your jinja2 expressions.
"""
helps[
"cdf debug version"
] = """
type: command
short-summary: Version of CDF.
"""
helps[
"cdf debug config"
] = """
type: command
short-summary: Dump the configuration file.
"""
helps[
"cdf debug deployment-errors"
] = """
type: command
short-summary: Check deployment error and all nested deployments.
"""
helps[
"cdf debug result"
] = """
type: command
short-summary: Print out results.
"""
| 14.642105
| 80
| 0.652768
| 172
| 1,391
| 5.273256
| 0.377907
| 0.114664
| 0.194046
| 0.278942
| 0.068357
| 0.068357
| 0
| 0
| 0
| 0
| 0
| 0.000893
| 0.194824
| 1,391
| 94
| 81
| 14.797872
| 0.808929
| 0.032351
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| 0.658228
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| 0.718447
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| true
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| 0.012658
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| null | 0
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| 1
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
d11b0296d3c11b2bcc95dd9eabc4ae84ae825ba7
| 39
|
py
|
Python
|
interview/main.py
|
jiz148/rift-projects
|
59949f7dc9b217f1fc6c1a9558505d50287b008d
|
[
"MIT"
] | null | null | null |
interview/main.py
|
jiz148/rift-projects
|
59949f7dc9b217f1fc6c1a9558505d50287b008d
|
[
"MIT"
] | null | null | null |
interview/main.py
|
jiz148/rift-projects
|
59949f7dc9b217f1fc6c1a9558505d50287b008d
|
[
"MIT"
] | 2
|
2019-09-01T02:21:42.000Z
|
2019-09-01T02:21:51.000Z
|
"""
Main program to run interview.
"""
| 9.75
| 30
| 0.641026
| 5
| 39
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.179487
| 39
| 3
| 31
| 13
| 0.78125
| 0.769231
| 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
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0f7971c394ac437648743e41ffa9ce9f4347a4c8
| 201
|
py
|
Python
|
fontin/__init__.py
|
laxyapahuja/font-in
|
4adf4ce2b2c6ca127ff96fe35c8085f59707e9c3
|
[
"MIT"
] | 6
|
2020-09-28T18:34:31.000Z
|
2021-07-17T10:55:51.000Z
|
fontin/__init__.py
|
laxyapahuja/font-in
|
4adf4ce2b2c6ca127ff96fe35c8085f59707e9c3
|
[
"MIT"
] | null | null | null |
fontin/__init__.py
|
laxyapahuja/font-in
|
4adf4ce2b2c6ca127ff96fe35c8085f59707e9c3
|
[
"MIT"
] | null | null | null |
import sys, os, ctypes
def clear():
os.system('cls' if os.name == 'nt' else 'clear')
def is_admin():
try:
return ctypes.windll.shell32.IsUserAnAdmin()
except:
return False
| 20.1
| 52
| 0.616915
| 27
| 201
| 4.555556
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013245
| 0.248756
| 201
| 10
| 53
| 20.1
| 0.801325
| 0
| 0
| 0
| 0
| 0
| 0.049505
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0
| 0.125
| 0
| 0.625
| 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
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
0f8ca1f4359e39cdef20555de62e0af7766fe526
| 176
|
py
|
Python
|
apps/www/od/wsgi-bin/web_cfg.py
|
Roy-Tuhin/maskrcnn_sophisticate-
|
a5a2300abbe2633d66847cdbfa7ed2bc2f901ec3
|
[
"Apache-2.0"
] | null | null | null |
apps/www/od/wsgi-bin/web_cfg.py
|
Roy-Tuhin/maskrcnn_sophisticate-
|
a5a2300abbe2633d66847cdbfa7ed2bc2f901ec3
|
[
"Apache-2.0"
] | 14
|
2021-02-02T22:32:47.000Z
|
2022-03-12T00:20:40.000Z
|
apps/www/od/wsgi-bin/web_cfg.py
|
Boyetuhin/maskrcnn_sophisticate-
|
a5a2300abbe2633d66847cdbfa7ed2bc2f901ec3
|
[
"Apache-2.0"
] | 1
|
2021-05-03T22:48:36.000Z
|
2021-05-03T22:48:36.000Z
|
import os
AI_CODE_BASE_PATH = '/codehub'
BASE_PATH_CONFIG = os.path.join(AI_CODE_BASE_PATH,'config')
APP_ROOT_DIR = os.path.join(AI_CODE_BASE_PATH,'apps')
WEB_APP_NAME = 'od'
| 25.142857
| 59
| 0.784091
| 33
| 176
| 3.727273
| 0.484848
| 0.260163
| 0.243902
| 0.341463
| 0.390244
| 0.390244
| 0.390244
| 0
| 0
| 0
| 0
| 0
| 0.085227
| 176
| 6
| 60
| 29.333333
| 0.763975
| 0
| 0
| 0
| 0
| 0
| 0.113636
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
7e195db285484a4136432c767df9581adddf9fca
| 1,037
|
py
|
Python
|
src/compas/com/__init__.py
|
gonzalocasas/compas
|
2fabc7e5c966a02d823fa453564151e1a1e7e3c6
|
[
"MIT"
] | null | null | null |
src/compas/com/__init__.py
|
gonzalocasas/compas
|
2fabc7e5c966a02d823fa453564151e1a1e7e3c6
|
[
"MIT"
] | null | null | null |
src/compas/com/__init__.py
|
gonzalocasas/compas
|
2fabc7e5c966a02d823fa453564151e1a1e7e3c6
|
[
"MIT"
] | null | null | null |
"""
********************************************************************************
compas.com
********************************************************************************
.. module:: compas.com
:mod:`compas.com` provides functionality for communicating with external software.
Matlab
======
.. autosummary::
:toctree: generated/
:nosignatures:
MatlabClient
MatlabEngine
MatlabProcess
MatlabSession
Rhino
=====
.. autosummary::
:toctree: generated/
:nosignatures:
RhinoClient
ssh
===
.. autosummary::
:toctree: generated/
:nosignatures:
connect_to_server
local_command
receive_file
send_file
send_folder
sync_folder
server_command
"""
class Process(object):
pass
class Client(object):
pass
from .matlab_ import *
from .ssh import *
from .rhino import *
from .matlab_ import __all__ as a
from .ssh import __all__ as b
from .rhino import __all__ as b
__all__ = a + b
| 15.477612
| 83
| 0.52459
| 88
| 1,037
| 5.886364
| 0.5
| 0.052124
| 0.156371
| 0.225869
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.24783
| 1,037
| 66
| 84
| 15.712121
| 0.664103
| 0.689489
| 0
| 0.181818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.181818
| 0.545455
| 0
| 0.727273
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
|
0
| 4
|
7e2cd48a084a5083964a25cad9c9d2af17ab1a24
| 96
|
py
|
Python
|
CodeWars/7 Kyu/Basic Math (Add or Subtract).py
|
anubhab-code/Competitive-Programming
|
de28cb7d44044b9e7d8bdb475da61e37c018ac35
|
[
"MIT"
] | null | null | null |
CodeWars/7 Kyu/Basic Math (Add or Subtract).py
|
anubhab-code/Competitive-Programming
|
de28cb7d44044b9e7d8bdb475da61e37c018ac35
|
[
"MIT"
] | null | null | null |
CodeWars/7 Kyu/Basic Math (Add or Subtract).py
|
anubhab-code/Competitive-Programming
|
de28cb7d44044b9e7d8bdb475da61e37c018ac35
|
[
"MIT"
] | null | null | null |
def calculate(s):
ret = eval(s.replace("plus","+").replace("minus","-"))
return str(ret)
| 32
| 58
| 0.583333
| 13
| 96
| 4.307692
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.145833
| 96
| 3
| 59
| 32
| 0.682927
| 0
| 0
| 0
| 0
| 0
| 0.113402
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
7e33bc42054788a5995a22bef471ead7f6b37c31
| 78
|
py
|
Python
|
lib/subl/__init__.py
|
mrmansano/sublime-ycmd
|
fece62f0ce4e9cbf96ed8ba07f5cecb24b21427e
|
[
"MIT"
] | 12
|
2018-01-24T20:58:10.000Z
|
2021-12-21T15:02:10.000Z
|
lib/subl/__init__.py
|
mrmansano/sublime-ycmd
|
fece62f0ce4e9cbf96ed8ba07f5cecb24b21427e
|
[
"MIT"
] | 4
|
2018-01-13T14:39:45.000Z
|
2020-11-25T00:05:27.000Z
|
lib/subl/__init__.py
|
mrmansano/sublime-ycmd
|
fece62f0ce4e9cbf96ed8ba07f5cecb24b21427e
|
[
"MIT"
] | 2
|
2018-10-23T17:13:44.000Z
|
2019-05-12T04:10:17.000Z
|
#!/usr/bin/env python3
'''
lib/subl
sublime-ycmd sublime utility module.
'''
| 11.142857
| 36
| 0.692308
| 11
| 78
| 4.909091
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.014706
| 0.128205
| 78
| 6
| 37
| 13
| 0.779412
| 0.858974
| 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
|
7e7c0ef2961fcf14822b06867a4995f061feba82
| 187
|
py
|
Python
|
core/serializers.py
|
victor-s-santos/simple-django-ajax
|
7a2e9cc832bdf54e39b0aac43fa08d6d587a916c
|
[
"MIT"
] | null | null | null |
core/serializers.py
|
victor-s-santos/simple-django-ajax
|
7a2e9cc832bdf54e39b0aac43fa08d6d587a916c
|
[
"MIT"
] | null | null | null |
core/serializers.py
|
victor-s-santos/simple-django-ajax
|
7a2e9cc832bdf54e39b0aac43fa08d6d587a916c
|
[
"MIT"
] | null | null | null |
from rest_framework.serializers import ModelSerializer
from core.models import Perguntas
class PerguntasSerializer(ModelSerializer):
class Meta:
model = Perguntas
fields = '__all__'
| 26.714286
| 54
| 0.823529
| 20
| 187
| 7.45
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122995
| 187
| 7
| 55
| 26.714286
| 0.908537
| 0
| 0
| 0
| 0
| 0
| 0.037234
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
7e8d7763d67b6636377c320a0cce716ab56a5abd
| 224
|
py
|
Python
|
core/__init__.py
|
ItsFabby/connect4
|
4bad1ce4f25e1533cebb890105fedbbbfcf51eb8
|
[
"MIT"
] | null | null | null |
core/__init__.py
|
ItsFabby/connect4
|
4bad1ce4f25e1533cebb890105fedbbbfcf51eb8
|
[
"MIT"
] | null | null | null |
core/__init__.py
|
ItsFabby/connect4
|
4bad1ce4f25e1533cebb890105fedbbbfcf51eb8
|
[
"MIT"
] | null | null | null |
import os
import sys
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
# from . import constants
# from . import db_connector
# from . import nnet
# from . import game
# from . import gui
# from . import trainer
| 20.363636
| 60
| 0.732143
| 33
| 224
| 4.818182
| 0.484848
| 0.377358
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.160714
| 224
| 10
| 61
| 22.4
| 0.845745
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 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
|
7e8f8f8e74978abfd3cc0b920d053396add5a92e
| 93
|
pyw
|
Python
|
play.pyw
|
Tauseef-Hilal/Pong-Multiplayer
|
aee412fc9a25e75994a28302cad293fa80340cf6
|
[
"MIT"
] | null | null | null |
play.pyw
|
Tauseef-Hilal/Pong-Multiplayer
|
aee412fc9a25e75994a28302cad293fa80340cf6
|
[
"MIT"
] | null | null | null |
play.pyw
|
Tauseef-Hilal/Pong-Multiplayer
|
aee412fc9a25e75994a28302cad293fa80340cf6
|
[
"MIT"
] | null | null | null |
import os
if os.name == "nt":
os.system("python src")
else:
os.system("python3 src")
| 15.5
| 28
| 0.612903
| 15
| 93
| 3.8
| 0.666667
| 0.280702
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013514
| 0.204301
| 93
| 6
| 28
| 15.5
| 0.756757
| 0
| 0
| 0
| 0
| 0
| 0.244681
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.2
| 0
| 0.2
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
7eaba175801c9347573b0828cc5b51b220808bed
| 82
|
py
|
Python
|
beagle/header/mpu_gyro_fsr_t.py
|
ts03408751/beagle
|
c9c2d37c081768e3fb6dbd88e563deb168c9e33b
|
[
"MIT"
] | null | null | null |
beagle/header/mpu_gyro_fsr_t.py
|
ts03408751/beagle
|
c9c2d37c081768e3fb6dbd88e563deb168c9e33b
|
[
"MIT"
] | null | null | null |
beagle/header/mpu_gyro_fsr_t.py
|
ts03408751/beagle
|
c9c2d37c081768e3fb6dbd88e563deb168c9e33b
|
[
"MIT"
] | 1
|
2018-10-06T09:01:45.000Z
|
2018-10-06T09:01:45.000Z
|
GYRO_FSR_250DPS = 0
GYRO_FSR_500DPS = 1
GYRO_FSR_1000DPS = 2
GYRO_FSR_2000DPS = 3
| 16.4
| 20
| 0.804878
| 16
| 82
| 3.625
| 0.625
| 0.482759
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.257143
| 0.146341
| 82
| 4
| 21
| 20.5
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0e2000dac55c0cddc1c8b24dc2ef305e55876e9b
| 77
|
py
|
Python
|
test/test_import_class/ClassC.py
|
songofhawk/simplerpa
|
69491f3e9a84c106921be972242d05c5e3db5849
|
[
"MIT"
] | 1
|
2022-02-16T08:37:25.000Z
|
2022-02-16T08:37:25.000Z
|
test/test_import_class/ClassC.py
|
songofhawk/simplerpa
|
69491f3e9a84c106921be972242d05c5e3db5849
|
[
"MIT"
] | null | null | null |
test/test_import_class/ClassC.py
|
songofhawk/simplerpa
|
69491f3e9a84c106921be972242d05c5e3db5849
|
[
"MIT"
] | null | null | null |
from ClassA import A
class C(A):
def walk(self):
print(A.test)
| 11
| 21
| 0.584416
| 13
| 77
| 3.461538
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.298701
| 77
| 6
| 22
| 12.833333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.75
| 0.25
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
0e5ab5fdaaa9f4983eea073e2695ac4ba67c08ce
| 1,096
|
py
|
Python
|
attic/testsuite/lrucache.py
|
AaronWebster/attic
|
be7e636b6de677776d57b788c9a435070063575d
|
[
"BSD-3-Clause"
] | 1
|
2022-02-11T21:46:40.000Z
|
2022-02-11T21:46:40.000Z
|
attic/testsuite/lrucache.py
|
AaronWebster/attic
|
be7e636b6de677776d57b788c9a435070063575d
|
[
"BSD-3-Clause"
] | 20
|
2022-02-14T07:54:49.000Z
|
2022-02-14T07:57:38.000Z
|
attic/testsuite/lrucache.py
|
AaronWebster/attic
|
be7e636b6de677776d57b788c9a435070063575d
|
[
"BSD-3-Clause"
] | null | null | null |
from attic.lrucache import LRUCache
from attic.testsuite import AtticTestCase
class LRUCacheTestCase(AtticTestCase):
def test(self):
c = LRUCache(2)
self.assert_equal(len(c), 0)
for i, x in enumerate('abc'):
c[x] = i
self.assert_equal(len(c), 2)
self.assert_equal(set(c), set(['b', 'c']))
self.assert_equal(set(c.items()), set([('b', 1), ('c', 2)]))
self.assert_equal(False, 'a' in c)
self.assert_equal(True, 'b' in c)
self.assert_raises(KeyError, lambda: c['a'])
self.assert_equal(c['b'], 1)
self.assert_equal(c['c'], 2)
c['d'] = 3
self.assert_equal(len(c), 2)
self.assert_equal(c['c'], 2)
self.assert_equal(c['d'], 3)
c['c'] = 22
c['e'] = 4
self.assert_equal(len(c), 2)
self.assert_raises(KeyError, lambda: c['d'])
self.assert_equal(c['c'], 22)
self.assert_equal(c['e'], 4)
del c['c']
self.assert_equal(len(c), 1)
self.assert_raises(KeyError, lambda: c['c'])
self.assert_equal(c['e'], 4)
def test_pop(self):
c = LRUCache(2)
c[1] = 1
c[2] = 2
c.pop(1)
c[3] = 3
| 26.731707
| 64
| 0.586679
| 185
| 1,096
| 3.367568
| 0.216216
| 0.304976
| 0.385233
| 0.179775
| 0.568218
| 0.410915
| 0.160514
| 0.160514
| 0.11236
| 0
| 0
| 0.032407
| 0.211679
| 1,096
| 40
| 65
| 27.4
| 0.688657
| 0
| 0
| 0.25
| 0
| 0
| 0.020985
| 0
| 0
| 0
| 0
| 0
| 0.527778
| 1
| 0.055556
| false
| 0
| 0.055556
| 0
| 0.138889
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
0e6b16aeef7a4ae3587ca3994e407cf135a70ce2
| 1,949
|
py
|
Python
|
3rdparty/huawei-lte-api/huawei_lte_api/api/OnlineUpdate.py
|
tux1c0/plugin-huawei4g
|
af83ce6321db23fcfd51eb204e00c287d3ea2325
|
[
"MIT"
] | null | null | null |
3rdparty/huawei-lte-api/huawei_lte_api/api/OnlineUpdate.py
|
tux1c0/plugin-huawei4g
|
af83ce6321db23fcfd51eb204e00c287d3ea2325
|
[
"MIT"
] | 1
|
2020-06-21T20:35:27.000Z
|
2020-07-14T20:08:55.000Z
|
3rdparty/huawei-lte-api/huawei_lte_api/api/OnlineUpdate.py
|
tux1c0/plugin-huawei4g
|
af83ce6321db23fcfd51eb204e00c287d3ea2325
|
[
"MIT"
] | 3
|
2020-02-27T11:35:55.000Z
|
2021-03-25T08:51:51.000Z
|
from huawei_lte_api.ApiGroup import ApiGroup
from huawei_lte_api.Connection import GetResponseType, SetResponseType
class OnlineUpdate(ApiGroup):
def check_new_version(self) -> GetResponseType:
return self._connection.get('online-update/check-new-version')
def set_check_new_version(self) -> SetResponseType:
return self._connection.post_set('online-update/check-new-version')
def status(self) -> GetResponseType:
return self._connection.get('online-update/status')
def url_list(self) -> GetResponseType:
return self._connection.get('online-update/url-list')
def ack_newversion(self) -> GetResponseType:
return self._connection.get('online-update/ack-newversion')
def set_ack_newversion(self) -> SetResponseType:
return self._connection.post_set('online-update/ack-newversion', {
'userAckNewVersion': 0
})
def cancel_downloading(self) -> GetResponseType:
"""
Invoking this method is known to cause some devices to reboot.
"""
return self._connection.get('online-update/cancel-downloading')
def set_cancel_downloading(self) -> SetResponseType:
return self._connection.post_set('online-update/cancel-downloading')
def upgrade_messagebox(self) -> GetResponseType:
return self._connection.get('online-update/upgrade-messagebox')
def set_upgrade_messagebox(self, messagebox: str) -> SetResponseType:
return self._connection.post_set('online-update/upgrade-messagebox', {
'messagebox': messagebox
})
def configuration(self) -> GetResponseType:
return self._connection.get('online-update/configuration')
def autoupdate_config(self) -> GetResponseType:
return self._connection.get('online-update/autoupdate-config')
def redirect_cancel(self) -> GetResponseType:
return self._connection.get('online-update/redirect_cancel')
| 38.215686
| 78
| 0.715752
| 212
| 1,949
| 6.396226
| 0.226415
| 0.09587
| 0.19174
| 0.152655
| 0.568584
| 0.539086
| 0.486726
| 0.486726
| 0.128319
| 0
| 0
| 0.000624
| 0.177527
| 1,949
| 50
| 79
| 38.98
| 0.84529
| 0.031811
| 0
| 0.060606
| 0
| 0
| 0.215781
| 0.190553
| 0
| 0
| 0
| 0
| 0
| 1
| 0.393939
| false
| 0
| 0.060606
| 0.363636
| 0.878788
| 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
|
0ea12adb9722703e9c104a984270ca74a2bb8d59
| 85
|
py
|
Python
|
backend/app_esp/models/__init__.py
|
waltercassiano/micropython-spa-react
|
110fcd88c170251adad2b7e2e14dbdad94657370
|
[
"MIT"
] | 6
|
2020-05-29T01:39:21.000Z
|
2022-03-08T02:29:29.000Z
|
backend/app_esp/models/__init__.py
|
waltercassiano/micropython-spa-react
|
110fcd88c170251adad2b7e2e14dbdad94657370
|
[
"MIT"
] | 2
|
2022-02-13T11:59:34.000Z
|
2022-02-27T03:49:49.000Z
|
backend/app_esp/models/__init__.py
|
waltercassiano/micropython-spa-react
|
110fcd88c170251adad2b7e2e14dbdad94657370
|
[
"MIT"
] | null | null | null |
from .config import ConfigModel
from .model import Model
from .user import UserModel
| 21.25
| 31
| 0.823529
| 12
| 85
| 5.833333
| 0.583333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141176
| 85
| 3
| 32
| 28.333333
| 0.958904
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
7d02af496397b9cb9f0d7faabf0a2d1ce67c288d
| 401
|
py
|
Python
|
library/source1/game_model/parts/mesh.py
|
anderlli0053/SourceIO
|
3c0c4839939ce698439987ac52154f89ee2f5341
|
[
"MIT"
] | 199
|
2019-04-02T02:30:58.000Z
|
2022-03-30T21:29:49.000Z
|
source1/game_model/parts/mesh.py
|
syborg64/SourceIO
|
e4ba86d801f518e192260af08ef533759c2e1cc3
|
[
"MIT"
] | 113
|
2019-03-03T19:36:25.000Z
|
2022-03-31T19:44:05.000Z
|
source1/game_model/parts/mesh.py
|
syborg64/SourceIO
|
e4ba86d801f518e192260af08ef533759c2e1cc3
|
[
"MIT"
] | 38
|
2019-05-15T16:49:30.000Z
|
2022-03-22T03:40:43.000Z
|
from abc import ABC
class SourceMesh(ABC):
@property
def vertices(self):
raise NotImplementedError
@property
def indices(self):
raise NotImplementedError
@property
def normals(self):
raise NotImplementedError
@property
def weights(self):
raise NotImplementedError
@property
def uv(self):
raise NotImplementedError
| 16.04
| 33
| 0.648379
| 37
| 401
| 7.027027
| 0.405405
| 0.211538
| 0.538462
| 0.553846
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.294264
| 401
| 24
| 34
| 16.708333
| 0.918728
| 0
| 0
| 0.588235
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.294118
| false
| 0
| 0.058824
| 0
| 0.411765
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
7d03080a049d4ccf582ad4dbb85f32f92ea44aeb
| 1,495
|
py
|
Python
|
pennylane/interfaces/__init__.py
|
MoritzWillmann/pennylane
|
2b07d22cfcc6406ba28e5c647062340b240a4ee5
|
[
"Apache-2.0"
] | 539
|
2018-11-13T08:45:42.000Z
|
2020-07-27T18:17:16.000Z
|
pennylane/interfaces/__init__.py
|
MoritzWillmann/pennylane
|
2b07d22cfcc6406ba28e5c647062340b240a4ee5
|
[
"Apache-2.0"
] | 588
|
2018-11-14T10:21:47.000Z
|
2020-07-28T06:27:14.000Z
|
pennylane/interfaces/__init__.py
|
MoritzWillmann/pennylane
|
2b07d22cfcc6406ba28e5c647062340b240a4ee5
|
[
"Apache-2.0"
] | 165
|
2018-11-13T18:58:56.000Z
|
2020-07-27T17:18:17.000Z
|
# Copyright 2018-2021 Xanadu Quantum Technologies Inc.
# 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.
"""
This subpackage defines functions for interfacing devices' execution
capabilities with different machine learning libraries.
.. currentmodule:: pennylane
Execution functions and utilities
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: api
~execute
~interfaces.cache_execute
~interfaces.set_shots
Supported interfaces
~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: api
~interfaces.autograd
~interfaces.jax
~interfaces.jax_jit
~interfaces.tensorflow
~interfaces.tensorflow_autograph
~interfaces.torch
"""
from .execution import cache_execute, execute, INTERFACE_MAP, SUPPORTED_INTERFACES
from .set_shots import set_shots
class InterfaceUnsupportedError(NotImplementedError):
"""Exception raised when features not supported by an interface are
attempted to be used."""
| 29.313725
| 83
| 0.713043
| 175
| 1,495
| 6.04
| 0.628571
| 0.056764
| 0.024598
| 0.030274
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009909
| 0.189967
| 1,495
| 50
| 84
| 29.9
| 0.862923
| 0.820736
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
7d4e177821f684dfde5e42d0b9326f33cd29e78b
| 96
|
py
|
Python
|
openmaps_auth/settings/dev.py
|
radiant-maxar/openmaps-auth
|
3c4205deae85815ee8a7bfec0a21b1511fbe8640
|
[
"Apache-2.0"
] | null | null | null |
openmaps_auth/settings/dev.py
|
radiant-maxar/openmaps-auth
|
3c4205deae85815ee8a7bfec0a21b1511fbe8640
|
[
"Apache-2.0"
] | null | null | null |
openmaps_auth/settings/dev.py
|
radiant-maxar/openmaps-auth
|
3c4205deae85815ee8a7bfec0a21b1511fbe8640
|
[
"Apache-2.0"
] | null | null | null |
from .common import *
DEBUG = True
INSTALLED_APPS = ("django.contrib.admin",) + INSTALLED_APPS
| 19.2
| 59
| 0.739583
| 12
| 96
| 5.75
| 0.833333
| 0.376812
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135417
| 96
| 4
| 60
| 24
| 0.831325
| 0
| 0
| 0
| 0
| 0
| 0.208333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
adb5fa873bc763b8905967ae23bab7153d4ed887
| 158
|
py
|
Python
|
vizdoomaze/envs/vizdoomazeone7.py
|
fanyuzeng/Vizdoomaze
|
5b444f2d861c908c4d96ae374bcce660d364f22e
|
[
"MIT"
] | 3
|
2020-09-25T16:00:49.000Z
|
2020-10-29T10:32:30.000Z
|
vizdoomaze/envs/vizdoomazeone7.py
|
fanyuzeng/Vizdoomaze
|
5b444f2d861c908c4d96ae374bcce660d364f22e
|
[
"MIT"
] | null | null | null |
vizdoomaze/envs/vizdoomazeone7.py
|
fanyuzeng/Vizdoomaze
|
5b444f2d861c908c4d96ae374bcce660d364f22e
|
[
"MIT"
] | 1
|
2021-12-17T07:50:47.000Z
|
2021-12-17T07:50:47.000Z
|
from vizdoomaze.envs.vizdoomenv import VizdoomEnv
class vizdoomazeOne7(VizdoomEnv):
def __init__(self):
super(vizdoomazeOne7, self).__init__(20)
| 26.333333
| 49
| 0.765823
| 17
| 158
| 6.647059
| 0.705882
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.02963
| 0.14557
| 158
| 6
| 50
| 26.333333
| 0.807407
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
adec483755cd5577f9d85117bdd804f89bd9e557
| 1,278
|
py
|
Python
|
openiPrototype/appUI/jsonModel.py
|
OPENi-ict/ntua_demo
|
104118fbe1f54db35386ca96286317ceb64cb658
|
[
"Apache-2.0"
] | null | null | null |
openiPrototype/appUI/jsonModel.py
|
OPENi-ict/ntua_demo
|
104118fbe1f54db35386ca96286317ceb64cb658
|
[
"Apache-2.0"
] | null | null | null |
openiPrototype/appUI/jsonModel.py
|
OPENi-ict/ntua_demo
|
104118fbe1f54db35386ca96286317ceb64cb658
|
[
"Apache-2.0"
] | null | null | null |
from django.db import models
class Responses(models.Model):
requests = models.ForeignKey("Requests", blank=True)
responses = models.CharField(max_length=255, blank=True)
class Requests(models.Model):
data_mode = models.CharField(max_length=255, blank=True)
name = models.CharField(max_length=255, blank=True)
method = models.CharField(max_length=255, blank=True)
openi-object = models.ForeignKey("openi-object", blank=True)
collection_id = models.CharField(max_length=255, blank=True)
headers = models.CharField(max_length=255, blank=True)
version = models.FloatField(blank=True)
id_property = models.CharField(max_length=255, blank=True)
timestamp = models.FloatField(blank=True)
description = models.CharField(max_length=255, blank=True)
url = models.CharField(max_length=255, blank=True)
class openiObject(models.Model):
timestamp = models.FloatField(blank=True)
name = models.CharField(max_length=255, blank=True)
id_property = models.CharField(max_length=255, blank=True)
class Order(models.Model):
openiObject = models.ForeignKey("openi-object", blank=True)
order = models.CharField(max_length=255, blank=True)
class Data(models.Model):
requests = models.ForeignKey("Requests", blank=True)
data = models.CharField(max_length=255, blank=True)
| 34.540541
| 61
| 0.77856
| 175
| 1,278
| 5.588571
| 0.188571
| 0.184049
| 0.239264
| 0.319018
| 0.786299
| 0.725971
| 0.652352
| 0.440695
| 0.203476
| 0.203476
| 0
| 0.033708
| 0.094679
| 1,278
| 36
| 62
| 35.5
| 0.811582
| 0
| 0
| 0.307692
| 0
| 0
| 0.031323
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.038462
| null | null | 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
bc1f69f17c523e90a9ca90168599771e39f2f7ca
| 100
|
py
|
Python
|
Python/Canvas/Commands/__init__.py
|
yoann01/FabricUI
|
d4d24f25245b8ccd2d206aded2b6c5f2aca09155
|
[
"BSD-3-Clause"
] | null | null | null |
Python/Canvas/Commands/__init__.py
|
yoann01/FabricUI
|
d4d24f25245b8ccd2d206aded2b6c5f2aca09155
|
[
"BSD-3-Clause"
] | null | null | null |
Python/Canvas/Commands/__init__.py
|
yoann01/FabricUI
|
d4d24f25245b8ccd2d206aded2b6c5f2aca09155
|
[
"BSD-3-Clause"
] | null | null | null |
__all__ = [
'CommandHelpers',
'CommandManager',
'CommandRegistry',
'CommandManagerCallback'
]
| 14.285714
| 26
| 0.72
| 5
| 100
| 13.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.14
| 100
| 6
| 27
| 16.666667
| 0.790698
| 0
| 0
| 0
| 0
| 0
| 0.65
| 0.22
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 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
|
bc2d68df4383c7dc796252b31cac6c875f1353b3
| 29,162
|
py
|
Python
|
dlra/algorithms.py
|
cohenjer/dlra
|
043daa9a626fa52ef95a6d54d4bf790830cecf92
|
[
"MIT"
] | null | null | null |
dlra/algorithms.py
|
cohenjer/dlra
|
043daa9a626fa52ef95a6d54d4bf790830cecf92
|
[
"MIT"
] | null | null | null |
dlra/algorithms.py
|
cohenjer/dlra
|
043daa9a626fa52ef95a6d54d4bf790830cecf92
|
[
"MIT"
] | null | null | null |
import numpy as np
from numpy.linalg import lstsq, solve
import warnings
from mscode.methods.algorithms import ista, homp, iht_mix, ista_nn
from mscode.methods.proxs import SoftT, HardT
from matplotlib import pyplot as plt
import tensorly as tl
from tensorly.random import random_cp
from tensorly.base import unfold
from tensorly.tenalg import khatri_rao, mode_dot
from tensorly.cp_tensor import (cp_to_tensor, CPTensor,
unfolding_dot_khatri_rao, cp_norm,
cp_normalize, validate_cp_rank)
from tensorly.decomposition._cp import initialize_cp, error_calc
from tensorly.tenalg.proximal import hals_nnls
import copy
# Adapting tensorly's ALS for DCPD or DMF
def palm_Dparafac(tensor, rank, D, k, step_rel=0.5, n_iter_max=100, init='svd', X0=None, tol=1e-8, random_state=None,
verbose=0, return_errors=False,
cvg_criterion='abs_rec_error', nonnegative=False, accelerate=True):
"""Dictionary-based Parafac Decomposition via PALM
Computes a rank-`rank` decomposition of `tensor` such that::
tensor = [|weights; factors[0], ..., factors[-1] |].
with factors[0] = DX, for input dictionary D and columnwise k-sparse X.
This solves approximately the following problem
:math:`minimize\; \\frac{1}{2} \\|tensor - DX(B\\odot C)^T\\|_F^2\; wrt\; X, B, C \; s.t. \; \\|X_i\\|_0\\leq k`
Constrained mode is necessarily the first one in this implementation.
Parameters
----------
tensor : ndarray
Input data
rank : int
Number of components.
D : ndarray
Dictionary for the first mode
k : int
Columnwise sparsity level for the first mode
lamb_rel: float in [0,1]. Default: 0.1
Amount of regularization for MSC Fista solver.
step_rel : float in [0,1]. Default: 0.1
Percentage of theoretical largest stepsize actually used. Set smaller than 1 if divergence occurs.
n_iter_max : int
Maximum number of iteration
init : {'svd', 'random', CPTensor}, optional
Type of factor matrix initialization.
If a CPTensor is passed, this is directly used for initalization.
See `initialize_factors`.
svd : str, default is 'numpy_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
of shape (rank, ), which will contain the norms of the factors
tol : float, optional
(Default: 1e-6) Relative reconstruction error tolerance. The
algorithm is considered to have found a local minimum when the
reconstruction error is less than `tol`.
random_state : {None, int, np.random.RandomState}
verbose : int, optional
Level of verbosity
return_errors : bool, optional
Activate return of iteration errors
cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
Stopping criterion for ALS, works if `tol` is not None. If 'rec_error', ALS stops at current iteration if ``(previous rec_error - current rec_error) < tol``. If 'abs_rec_error', ALS terminates when `|previous rec_error - current rec_error| < tol`.
accelerate : boolean, default True
If true, uses iPALM instead of PALM.
Returns
-------
CPTensor : (weight, factors)
Estimated DCPD tensor in cp_tensor tensorly format.
X : ndarray
The sparse coefficients of the first factor.
S : numpy array
Support of X.
errors : list
A list of reconstruction errors at each iteration of the algorithms.
"""
# initial error
weights, factors = initialize_cp(tensor, rank, init=init, svd='numpy_svd',
random_state=random_state)
if X0 is None:
if nonnegative:
X = np.random.rand(D.shape[1],rank)
else:
X = np.random.randn(D.shape[1],rank)
else:
X = X0
# Precomputations
#DtY = mode_dot(tensor, D.T, 0)
DtD = D.T@D
Dttensor = mode_dot(tensor,D.T,0)
#Y = unfold(tensor,0)
rec_errors = []
norm_tensor = tl.norm(tensor, 2)
beta=0
X_old = np.copy(X)
factors_old = copy.deepcopy(factors)
for iteration in range(n_iter_max):
if verbose > 1:
print("Starting iteration", iteration + 1)
if accelerate:
beta = (iteration-1)/(iteration+2)
# Updating X
mttkrp = unfolding_dot_khatri_rao(Dttensor, (None,factors), 0)
pseudo_inverse = tl.tensor(np.ones((rank, rank)), **tl.context(tensor))
for i, factor in enumerate(factors):
if i != 0:
pseudo_inverse = pseudo_inverse * tl.dot(tl.transpose(factor), factor)
# Stepsize computation, following PALM
eta = step_rel/(tl.norm(DtD)*tl.norm(pseudo_inverse))
Z = X + beta*(X - X_old)
X_old = np.copy(X)
X = HardT(Z - eta * (DtD@Z@pseudo_inverse - mttkrp), k)
factors[0] = D@X
# Updating other factors
for mode in range(1,tensor.ndim):
if verbose > 1:
print("Mode", mode, "of", tl.ndim(tensor))
pseudo_inverse = tl.tensor(np.ones((rank, rank)), **tl.context(tensor))
for i, factor in enumerate(factors):
#if i==0:
# pseudo_inverse = pseudo_inverse * tl.dot(tl.dot(X.T, DtD), X)
if i != mode:
pseudo_inverse = pseudo_inverse * tl.dot(tl.transpose(factor), factor)
# Concatenating X in front instead of the first factor, to use precomputed DtY
#factors_mod = [X] + factors[1:]
mttkrp = unfolding_dot_khatri_rao(tensor, (None, factors), mode)
# Stepsize computation following PALM and acceleration
eta = step_rel/tl.norm(pseudo_inverse)
Z = factors[mode] + beta*(factors[mode] - factors_old[mode])
factors_old[mode] = np.copy(factors[mode])
if nonnegative:
factors[mode] = tl.clip(Z - eta * (Z@pseudo_inverse - mttkrp), 0)
else:
factors[mode] = Z - eta * (Z@pseudo_inverse - mttkrp)
# Calculate the current unnormalized error if we need it
if (tol or return_errors):
unnorml_rec_error, tensor, norm_tensor = error_calc(tensor, norm_tensor, weights, factors, 0, None, mttkrp)
rec_error = unnorml_rec_error / norm_tensor
rec_errors.append(rec_error)
if tol:
if iteration >= 1:
rec_error_decrease = rec_errors[-2] - rec_errors[-1]
if verbose:
print("iteration {}, reconstruction error: {}, decrease = {}, unnormalized = {}".format(iteration, rec_error, rec_error_decrease, unnorml_rec_error))
if cvg_criterion == 'abs_rec_error':
stop_flag = abs(rec_error_decrease) < tol
elif cvg_criterion == 'rec_error':
stop_flag = rec_error_decrease < tol
else:
raise TypeError("Unknown convergence criterion")
if stop_flag:
if verbose:
print("PARAFAC converged after {} iterations".format(iteration))
break
else:
if verbose:
print('reconstruction error={}'.format(rec_errors[-1]))
cp_tensor = CPTensor((weights, factors))
# Estimating support (np arrays with supports)
Sx = np.argsort(np.abs(X), 0)
# truncating the support
Sx = Sx[-k:, :]
if return_errors:
return cp_tensor, X, Sx, rec_errors
else:
return cp_tensor, X, Sx
def dlra_parafac(tensor, rank, D, k, lamb_rel=0.01, n_iter_max=100, init='svd', X0=None, tol=1e-8, random_state=None,
verbose=0, return_errors=False,
cvg_criterion='abs_rec_error',
fixed_modes=None, inner_iter_max=100, nonnegative=False, tau=20, itermax_calib=10):
"""Dictionary-based CANDECOMP/PARAFAC decomposition via alternating least squares (ALS) and Mixed Sparse Coding (MSC)
Computes a rank-`rank` decomposition of `tensor` such that::
tensor = [|weights; factors[0], ..., factors[-1] |].
with factors[0] = DX, for input dictionary D and columnwise k-sparse X.
This solves approximately the following problem
:math:`minimize\; \\frac{1}{2} \\|tensor - DX(B\\odot C)^T\\|_F^2\; wrt\; X, B, C \; s.t. \; \\|X_i\\|_0\\leq k`
Constrained mode is necessarily the first one in this implementation.
Parameters
----------
tensor : ndarray
rank : int
Number of components.
D : list of ndarray
Dictionary for the first mode, or dictionary modes. E.g. [D1, D2] constraints modes 0 and 1.
k : list of int
Columnwise sparsity level for the first mode or for dictionary modes
lamb_rel: list of float in [0,1]. Default: 0.1
Amount of regularization for MSC Fista solver.
n_iter_max : int
Maximum number of
X0 : list of arrays
Initial values for the sparse coefficients.
init : {'svd', 'random', CPTensor}, optional
Type of factor matrix initialization.
If a CPTensor is passed, this is directly used for initalization.
See `initialize_factors`.
tol : float, optional
(Default: 1e-8) Relative reconstruction error tolerance. The
algorithm is considered to have found a local minimum when the
reconstruction error is less than `tol`.
random_state : {None, int, np.random.RandomState}
verbose : int, optional
Level of verbosity
return_errors : bool, optional
Activate return of iteration errors
cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
Stopping criterion for ALS, works if `tol` is not None.
If 'rec_error', ALS stops at current iteration if ``(previous rec_error - current rec_error) < tol``.
If 'abs_rec_error', ALS terminates when `|previous rec_error - current rec_error| < tol`.
fixed_modes : list, default is None
A list of modes for which the initial value is not modified.
The last mode cannot be fixed due to error computation.
nonnegative: bool
Set to True to have nn constraints everywhere, with nnls solver HALS as implemented in Tensorly.
Returns
-------
CPTensor : (weight, factors)
Estimated DCPD tensor in cp_tensor tensorly format.
X : numpy array
The sparse coefficients of the first factor.
S : numpy array
Support of X
errors : list
A list of reconstruction errors at each iteration of the algorithms.
"""
weights, factors = initialize_cp(tensor, rank, init=init, svd='numpy_svd',
random_state=random_state)
nbr_dic = len(D)
if X0 is None:
if nonnegative:
X = [np.random.rand(D[l].shape[1],rank) for l in range(nbr_dic)]
else:
X = [np.random.randn(D[l].shape[1],rank) for l in range(nbr_dic)]
else:
X = X0
Sx = [None for l in range(nbr_dic)]
rec_errors = []
norm_tensor = tl.norm(tensor, 2)
if fixed_modes is None:
fixed_modes = []
if tl.ndim(tensor)-1 in fixed_modes:
warnings.warn('You asked for fixing the last mode, which is not supported.\n The last mode will not be fixed. Consider using tl.moveaxis()')
fixed_modes.remove(tl.ndim(tensor)-1)
modes_list = [mode for mode in range(tl.ndim(tensor)) if mode not in fixed_modes]
# Precomputations
DtY = [mode_dot(tensor, D[l].T, l) for l in range(nbr_dic)]
DtD = [D[l].T@D[l] for l in range(nbr_dic)]
Y = [unfold(tensor,l) for l in range(nbr_dic)]
# Turning lamb_rel into a list
lamb_rel = [lamb_rel[l]*np.ones(rank) for l in range(nbr_dic)]
# to compute stepsizes
singvalD = [np.linalg.svd(DtD[l])[1][0] for l in range(nbr_dic)]
# store best
err_best = np.Inf
for iteration in range(n_iter_max):
if verbose > 1:
print("Starting iteration", iteration + 1)
for mode in modes_list:
if verbose > 1:
print("Mode", mode, "of", tl.ndim(tensor))
pseudo_inverse = tl.tensor(np.ones((rank, rank)), **tl.context(tensor))
for i, factor in enumerate(factors):
if i != mode:
pseudo_inverse = pseudo_inverse * tl.dot(tl.transpose(factor), factor)
if mode>nbr_dic-1:
mttkrp = unfolding_dot_khatri_rao(tensor, (None, factors), mode)
if nonnegative:
factor = hals_nnls(mttkrp.T, pseudo_inverse, factors[mode].T, n_iter_max=100)[0]
factor = factor.T
else:
factor = tl.transpose(tl.solve(tl.transpose(pseudo_inverse), tl.transpose(mttkrp)))
else:
# BtB is pseudo-inverse
singvalB = np.linalg.svd(pseudo_inverse)[1][0]
eta = 1/singvalD[mode]/singvalB
# first mode update using a Mixed Sparse Coding solver
# suboptimal :( rewrite ista for innerproducts
mttkrp = unfolding_dot_khatri_rao(DtY[mode], (None,factors),mode)
B = khatri_rao(factors, skip_matrix=mode)
# Tuning lambda
sparsity_balance=np.zeros(rank)
iter_calib=0
# First ISTA without final projection
while np.sum(sparsity_balance)<rank and iter_calib<itermax_calib:
nz=[]
iter_calib+=1
if nonnegative:
X[mode],_,rec,_,X_old = ista_nn(Y[mode], D[mode], B, lamb_rel[mode], k=None, itermax=1000, verbose=False, X0=X[mode], DtD=DtD[mode], DtYB=mttkrp, BtB=pseudo_inverse, eta=eta, tol=1e-8, return_old=True)
else:
X[mode],_,rec,_,X_old = ista(Y[mode], D[mode], B, lamb_rel[mode], k=None, itermax=1000, verbose=False, X0=X[mode], DtD=DtD[mode], DtYB=mttkrp, BtB=pseudo_inverse, eta=eta, tol=1e-8, return_old=True)
# Automatic lambda tuning
for i in range(rank):
# first we get the nnz numbers
nz.append(np.sum(np.abs(X_old[:,i])>0))
for i in range(rank):
# then we increase lambda if needed, or decrease if needed
# tolerance on k is [k, k+10]
# We prefear that lambda is slightly too small to avoid picking zeroes arbitrarily in the unbiaising LS step
if nz[i]<k[mode]:
sparsity_balance[i]=0
lamb_rel[mode][i] = lamb_rel[mode][i]/1.3
elif nz[i]>(k[mode]+tau):
#elif nz[i]>(2*k):
sparsity_balance[i]=0
#lamb_rel[mode][i] = lamb_rel[mode][i]*1.01
# TODO Test
lamb_rel[mode][i] = np.minimum(lamb_rel[mode][i]*1.01,1)
else:
sparsity_balance[i]=1
#print(nz)
#print(lamb_rel)
# Now the real ista
if nonnegative:
X[mode],_,rec,Sx[mode],X_old = ista_nn(Y[mode], D[mode], B, lamb_rel[mode], k=k[mode], itermax=inner_iter_max, verbose=False, X0=X[mode], DtD=DtD[mode], DtYB=mttkrp, BtB=pseudo_inverse, eta=eta, tol=1e-8, return_old=True)
else:
X[mode],_,rec,Sx[mode],X_old = ista(Y[mode], D[mode], B, lamb_rel[mode], k=k[mode], itermax=inner_iter_max, verbose=False, X0=X[mode], DtD=DtD[mode], DtYB=mttkrp, BtB=pseudo_inverse, eta=eta, tol=1e-8, return_old=True)
if verbose:
nz = []
# printing sparsity levels for hand tuning
for i in range(rank):
nz.append(np.sum(np.abs(X_old[:,i])>0))
print('number of nonzeroes in X_old (before thres) is currently', nz)
factor = D[mode]@X[mode]
factors[mode] = factor
# Calculate the current unnormalized error if we need it
unnorml_rec_error, tensor, norm_tensor = error_calc(tensor, norm_tensor, weights, factors, 0, None, mttkrp)
rec_error = unnorml_rec_error / norm_tensor
rec_errors.append(rec_error)
# TODO test
if rec_error < err_best:
X_best = np.copy(X)
S_best = np.copy(Sx)
err_best = rec_error
cp_tensor = CPTensor(copy.deepcopy((weights,factors)))
if tol:
if iteration >= 1:
rec_error_decrease = rec_errors[-2] - rec_errors[-1]
if verbose:
print("iteration {}, reconstruction error: {}, decrease = {}, unnormalized = {}".format(iteration, rec_error, rec_error_decrease, unnorml_rec_error))
if cvg_criterion == 'abs_rec_error':
stop_flag = abs(rec_error_decrease) < tol
elif cvg_criterion == 'rec_error':
stop_flag = rec_error_decrease < tol
else:
raise TypeError("Unknown convergence criterion")
if stop_flag:
if verbose:
print("PARAFAC converged after {} iterations".format(iteration))
break
else:
if verbose:
print('reconstruction error={}'.format(rec_errors[-1]))
if return_errors:
return cp_tensor, X_best, S_best, rec_errors
else:
return cp_tensor, X_best, S_best
def dlra_mf(Y, rank, D, k, lamb_rel=0.01, n_iter_max=100, init='random', tol=1e-8,
verbose=0, return_errors=False,
cvg_criterion='abs_rec_error', inner_iter_max=20, method='ista', tau=20, itermax_calib=20):
"""Dictionary-based Matrix Factorization via alternating least squares (ALS) and Mixed Sparse Coding (MSC)
Computes a rank-`rank` decomposition of Y such that
:math:`Y \\approx DXB^T`
with columnwise k-sparse X.
Parameters
----------
Y : numpy array
input data matrix
rank : int
Number of components.
D : ndarray
Dictionary for the first mode
k : int
Columnwise sparsity level for the first mode
lamb_rel: float in [0,1], default 0.01
Amount of regularization for MSC Fista solver.
n_iter_max : int, default 100
Maximum number of iteration
init : {'random', CPTensor}, optional
Type of factor matrix initialization. If a CPTensor is passed, this is directly used for initalization.
tol : float, optional, default: 1e-8
Relative reconstruction error tolerance. The
algorithm is considered to have found a local minimum when the
reconstruction error is less than `tol`.
verbose : int, optional
Level of verbosity
return_errors : bool, optional
Activate return of iteration errors
cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
Stopping criterion for the outer iterations, works if `tol` is not None. If 'rec_error', iterations stop at current iteration if :math:`(previous rec_error - current rec_error) < tol`. If 'abs_rec_error', iterations terminate when :math:`|previous rec_error - current rec_error| < tol`.
method : string, default 'ista'
update strategy for the MSC problem. Can be 'ista', 'iht' or 'homp'.
tau : int, default 20
tolerance for the sparsity level of estimated X in the inner ista loops.
itermax_calib : int, default 20
maximum number of regularization calibration steps.
Returns
-------
Decomposed Matrix : list of numpy arrays
Estimated DMF factors [DX, B]
X : numpy array
The sparse coefficients of the first factor
S : numpy array
Support of X
errors : list
A list of reconstruction errors at each iteration of the algorithms.
"""
# only need to initialize B
if init=='random':
B = np.random.randn(Y.shape[1],rank)
X = np.random.randn(D.shape[1],rank)
DX = D@X
else:
X = np.copy(init[0])
DX = D@X
B = np.copy(init[1])
Ynorm = np.linalg.norm(Y,'fro')
rec_errors = [np.linalg.norm(Y - DX@B.T, 'fro')/Ynorm]
DtD = D.T@D
DtY = D.T@Y
r = X.shape[1]
# Turning lamb_rel into a list
lamb_rel = lamb_rel*np.ones(r)
# to compute stepsizes
singvalD = np.linalg.svd(DtD)[1][0]
# Storing best results
err_best = np.inf
for iteration in range(n_iter_max):
if verbose > 1:
print("Starting iteration", iteration + 1)
singvalB = np.linalg.svd(B.T@B)[1][0]
eta = 1/singvalD/singvalB
if method=='ista':
sparsity_balance=np.zeros(r)
iter_calib=0
# First ISTA without final projection
while np.sum(sparsity_balance)<r and iter_calib<itermax_calib:
nz=[]
iter_calib+=1
X,_,rec,_,X_old = ista(Y, D, B, lamb_rel, k=None, itermax=100, verbose=False, X0=X, tol=1e-8, warning=False, DtD=DtD, DtY=DtY, return_old=True, eta=eta) # lambda hard to tune
# Automatic lambda tuning
for i in range(r):
# first we get the nnz numbers
nz.append(np.sum(np.abs(X_old[:,i])>0))
for i in range(r):
# then we increase lambda if needed, or decrease if needed
# tolerance on k is [k, k+10]
# We prefear that lambda is slightly too small to avoid picking zeroes arbitrarily
if nz[i]<k:
sparsity_balance[i]=0
lamb_rel[i] = lamb_rel[i]/1.3
elif nz[i]>(k+tau):
#elif nz[i]>(3*k):
sparsity_balance[i]=0
lamb_rel[i] = np.minimum(lamb_rel[i]*1.01, 1)
else:
sparsity_balance[i]=1
#print(nz)
#print(lamb_rel)
# Now the real ista
X,_,rec,Sx,X_old = ista(Y, D, B, lamb_rel, k=k, itermax=inner_iter_max, verbose=False, X0=X, tol=1e-8, warning=False, DtD=DtD, DtY=DtY, return_old=True, eta=eta) # lambda hard to tune
if verbose:
nz = []
# printing sparsity levels for hand tuning
for i in range(X.shape[1]):
nz.append(np.sum(np.abs(X_old[:,i])>0))
print('number of nonzeroes in X_old (before thres) is currently', nz)
elif method=='homp':
X, rec, Sx = homp(Y,D,B,k,Xin=X, itermax=50) # too slow for large k
elif method=='iht':
X,rec,Sx = iht_mix(Y,D,B,k,X_in=X,itermax=inner_iter_max, DtD=DtD, DtY=DtY, eta=eta) # not great
else:
print('method not supported')
break
DX = D@X
DXtDX = X.T@DtD@X
DXY = X.T@DtY
Bt = np.linalg.solve(DXtDX, DXY)
B = Bt.T
# Calculate the current unnormalized error
err = np.linalg.norm(Y - DX@B.T, 'fro')/Ynorm
# check if better than before and storing best results
if err < err_best:
X_best = np.copy(X)
B_best = np.copy(B)
S_best = np.copy(Sx)
err_best = err
rec_errors.append(err)
if tol:
if iteration >= 1:
rec_error_decrease = rec_errors[-2] - rec_errors[-1]
if verbose:
print("iteration {}, reconstruction error: {}, decrease = {}".format(iteration, err, rec_error_decrease))
if cvg_criterion == 'abs_rec_error':
stop_flag = abs(rec_error_decrease) < tol
elif cvg_criterion == 'rec_error':
stop_flag = rec_error_decrease < tol
else:
raise TypeError("Unknown convergence criterion")
if stop_flag:
if verbose:
print("DMF converged after {} iterations".format(iteration))
break
else:
if verbose:
print('reconstruction error={}'.format(rec_errors[-1]))
if return_errors:
return [D@X_best, B_best], X_best, S_best, rec_errors
else:
return [D@X_best, B_best], X_best, S_best
def dlra_mf_iht(Y, rank, D, k, step_rel=0.5, n_iter_max=100, init='random', tol=1e-8,
verbose=0, return_errors=False,
cvg_criterion='abs_rec_error', accelerate=True):#, unbiais=False):
"""Dictionary-based Matrix Factorization via PALM
solves approximately the following problem
minimize 1/2 \|Y - D@X@B.T\|_F^2 wrt X, B s.t. \|X_i\|_0\leq k
Parameters
----------
Y : numpy array
input data matrix
rank : int
Number of components.
D : ndarray
Dictionary for the first mode
k : int
Columnwise sparsity level for the first mode
step_rel : float in [0,1]. Default: 0.5
Percentage of theoretical largest stepsize actually used. Set smaller than 1 if divergence occurs.
n_iter_max : int, default 100
Maximum number of iteration
init : {'random', [DX, B]]}, optional
Type of factor matrix initialization. If a list of factors is passed, it is directly used for initalization. See `initialize_factors`.
tol : float, default: 1e-8
Relative reconstruction error tolerance. The algorithm is considered to have found a local minimum when the
reconstruction error is less than `tol`.
verbose : int, optional
Level of verbosity
return_errors : bool, optional
Activate return of iteration errors
cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
Stopping criterion for the outer iterations, works if `tol` is not None. If 'rec_error', iterations stop at current iteration if :math:`(previous rec_error - current rec_error) < tol`. If 'abs_rec_error', iterations terminate when :math:`|previous rec_error - current rec_error| < tol`.
Returns
-------
Decomposed Matrix : list of numpy arrays
Estimated DMF factors [DX, B]
X : numpy array
The sparse coefficients of the first factor
S : numpy array
Support of X
errors : list
A list of reconstruction errors at each iteration of the algorithms.
"""
# only need to initialize B
if init=='random':
B = np.random.randn(Y.shape[1],rank)
X = np.random.randn(D.shape[1],rank)
DX = D@X
else:
X = np.copy(init[0])
DX = D@X
B = np.copy(init[1])
Ynorm = np.linalg.norm(Y)
rec_errors = [np.linalg.norm(Y - DX@B.T)/Ynorm]
# Precomputations
DtY = D.T@Y
DtD = D.T@D
X_old = np.copy(X)
B_old = np.copy(B)
# Default: no acceleration
beta=0
for iteration in range(n_iter_max):
if verbose > 1:
print("Starting iteration", iteration + 1)
# Acceleration heuristic from iPALM
if accelerate:
beta = (iteration - 1)/(iteration + 2)
# X proximal gradient update
BtB = B.T@B
eta = step_rel/np.linalg.norm(BtB)
Zx = X + beta*(X - X_old)
X_old = np.copy(X)
X = HardT(Zx - eta * (DtD@Zx@(B.T@B) - DtY@B), k)
DX = D@X
# B gradient step
DXtDX = DX.T@DX
eta = step_rel/np.linalg.norm(DXtDX)
Zb = B.T + beta*(B.T - B_old.T)
B_old = np.copy(B)
Bt = Zb - eta*(-X.T@DtY + DXtDX@Zb)
B = Bt.T
# Calculate the current unnormalized error if we need it
if (tol or return_errors):
err = np.linalg.norm(Y - DX@B.T)/Ynorm
rec_errors.append(err)
if tol:
if iteration >= 1:
rec_error_decrease = rec_errors[-2] - rec_errors[-1]
if verbose:
print("iteration {}, reconstruction error: {}, decrease = {}".format(iteration, err, rec_error_decrease))
if cvg_criterion == 'abs_rec_error':
stop_flag = abs(rec_error_decrease) < tol
elif cvg_criterion == 'rec_error':
stop_flag = rec_error_decrease < tol
else:
raise TypeError("Unknown convergence criterion")
if stop_flag:
if verbose:
print("DMF converged after {} iterations".format(iteration))
break
else:
if verbose:
print('reconstruction error={}'.format(rec_errors[-1]))
# Estimating support (np arrays with supports)
Sx = np.argsort(np.abs(X), 0)
# truncating the support
Sx = Sx[-k:, :]
return [DX, B], X, Sx, rec_errors
| 38.830892
| 294
| 0.584185
| 3,892
| 29,162
| 4.256937
| 0.1074
| 0.035732
| 0.013279
| 0.013037
| 0.773177
| 0.750302
| 0.728151
| 0.70099
| 0.670087
| 0.655541
| 0
| 0.013139
| 0.316199
| 29,162
| 750
| 295
| 38.882667
| 0.817712
| 0.36311
| 0
| 0.635593
| 0
| 0.002825
| 0.064919
| 0
| 0
| 0
| 0
| 0.001333
| 0
| 1
| 0.011299
| false
| 0
| 0.039548
| 0
| 0.070621
| 0.059322
| 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
|
70cad4dae4d1a30bba201be5ed21c439ae1a279e
| 153
|
py
|
Python
|
sympy/3sympy_square_root.py
|
chenliangold4j/MyPyDictionnary
|
3428333f42249f33732da71e420bdc41a412f594
|
[
"Apache-2.0"
] | null | null | null |
sympy/3sympy_square_root.py
|
chenliangold4j/MyPyDictionnary
|
3428333f42249f33732da71e420bdc41a412f594
|
[
"Apache-2.0"
] | null | null | null |
sympy/3sympy_square_root.py
|
chenliangold4j/MyPyDictionnary
|
3428333f42249f33732da71e420bdc41a412f594
|
[
"Apache-2.0"
] | null | null | null |
# 平方根是一个数字，乘以它会产生指定的数量。
from sympy import sqrt, pprint, Mul
x = sqrt(2)
y = sqrt(2)
pprint(Mul(x, y, evaluate=False))
print('equals to ')
print(x * y)
| 15.3
| 35
| 0.673203
| 26
| 153
| 3.961538
| 0.615385
| 0.174757
| 0.194175
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.015625
| 0.163399
| 153
| 9
| 36
| 17
| 0.789063
| 0.137255
| 0
| 0
| 0
| 0
| 0.076923
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.166667
| 0
| 0.166667
| 0.666667
| 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
| 0
| 1
|
0
| 4
|
70d14a26d237ffc7eaeffb5aca944d2a69e5e54c
| 11,228
|
py
|
Python
|
crystalpy/tests/polarization/MuellerMatrixTest.py
|
oasys-kit/crystalpy
|
4852851c622ef914844178577dea4075e92cd8dd
|
[
"MIT"
] | null | null | null |
crystalpy/tests/polarization/MuellerMatrixTest.py
|
oasys-kit/crystalpy
|
4852851c622ef914844178577dea4075e92cd8dd
|
[
"MIT"
] | null | null | null |
crystalpy/tests/polarization/MuellerMatrixTest.py
|
oasys-kit/crystalpy
|
4852851c622ef914844178577dea4075e92cd8dd
|
[
"MIT"
] | null | null | null |
"""
Unittest for MuellerMatrix class.
"""
import unittest
import numpy as np
from numpy.testing import assert_array_almost_equal
from crystalpy.polarization.MuellerMatrix import MuellerMatrix
def _generate_matrix():
matrix = np.asarray([[0.3259811941363535, 0.47517601596586845, 0.5518825026595213, 0.309697589767706],
[0.9302512754240655, 0.31523109368760793, 0.14892732688907617, 0.6794292697143903],
[0.4445952221208559, 0.3645127814592628, 0.1683036723562683, 0.6726938039088263],
[0.5613952867979658, 0.1067151292280123, 0.4887844163526709, 0.27447059819338904]])
return matrix
class MuellerMatrixTest(unittest.TestCase):
def setUp(self):
self.matrix = _generate_matrix()
self.mueller_matrix = MuellerMatrix(self.matrix)
def test_constructor(self):
matrix = self.mueller_matrix.matrix
self.assertEqual(type(matrix), np.ndarray)
self.assertListEqual(matrix.flatten().tolist(), self.matrix.flatten().tolist())
def test_from_matrix_to_elements(self):
element_list = [0.3259811941363535, 0.47517601596586845, 0.5518825026595213, 0.309697589767706,
0.9302512754240655, 0.31523109368760793, 0.14892732688907617, 0.6794292697143903,
0.4445952221208559, 0.3645127814592628, 0.1683036723562683, 0.6726938039088263,
0.5613952867979658, 0.1067151292280123, 0.4887844163526709, 0.27447059819338904]
matrix1 = self.mueller_matrix.from_matrix_to_elements(return_numpy=False)
matrix2 = self.mueller_matrix.from_matrix_to_elements(return_numpy=True)
self.assertEqual(type(matrix1), list)
self.assertEqual(type(matrix2), np.ndarray)
np.testing.assert_array_almost_equal(matrix2, np.asarray(element_list))
self.assertListEqual(matrix1, element_list)
def test_matrix_by_scalar(self):
matrix = self.mueller_matrix.matrix
scalar1 = 2.2562276967724735
scalar2 = scalar1 * 1e-8
scalar3 = -5.010632784090829
self.assertIsInstance(self.mueller_matrix.matrix_by_scalar(scalar1), MuellerMatrix)
# once I have checked the products are MuellerMatrix objects, I use the __eq__ method.
self.assertTrue(self.mueller_matrix.matrix_by_scalar(scalar1) == MuellerMatrix(matrix * scalar1))
self.assertIsInstance(self.mueller_matrix.matrix_by_scalar(scalar2), MuellerMatrix)
self.assertTrue(self.mueller_matrix.matrix_by_scalar(scalar2) == MuellerMatrix(matrix * scalar2))
self.assertIsInstance(self.mueller_matrix.matrix_by_scalar(scalar3), MuellerMatrix)
self.assertTrue(self.mueller_matrix.matrix_by_scalar(scalar3) == MuellerMatrix(matrix * scalar3))
def test_matrix_by_vector(self):
vector = np.array([2.3305949456965642, 8.395751778702131, 1.8896988291928611, -9.515370772747518])
res1 = self.mueller_matrix.matrix_by_vector(vector, return_numpy=False)
res2 = self.mueller_matrix.matrix_by_vector(vector, return_numpy=True)
self.assertEqual(type(res1), list)
self.assertEqual(type(res2), np.ndarray)
self.assertListEqual(res1, list(np.dot(self.mueller_matrix.matrix, vector)))
np.testing.assert_array_almost_equal(res2, np.dot(self.mueller_matrix.matrix, vector))
def test_vector_by_matrix(self):
vector = np.array([2.3305949456965642, 8.395751778702131, 1.8896988291928611, -9.515370772747518])
res1 = self.mueller_matrix.vector_by_matrix(vector, return_numpy=False)
res2 = self.mueller_matrix.vector_by_matrix(vector, return_numpy=True)
self.assertEqual(type(res1), list)
self.assertEqual(type(res2), np.ndarray)
self.assertListEqual(res1, list(np.dot(vector, self.mueller_matrix.matrix)))
np.testing.assert_array_almost_equal(res2, np.dot(vector, self.mueller_matrix.matrix))
def test_mueller_times_mueller(self):
mueller_matrix1 = self.mueller_matrix
matrix2 = np.asarray([[-6.17688112, 5.02032824, 6.23225893, -0.32032499],
[7.27366317, 1.43011512, 1.81816476, 9.62888406],
[5.27430283, -1.37894077, -0.99747857, 8.80405558],
[1.89547814, -1.17966381, -7.85411672, -7.73368115]])
product1 = mueller_matrix1.mueller_times_mueller(matrix2, mod=False)
product2 = mueller_matrix1.mueller_times_mueller(matrix2, mod=True)
self.assertIsInstance(product1, MuellerMatrix)
self.assertIsInstance(product2, MuellerMatrix)
# once I have checked the products are MuellerMatrix objects, I use the __eq__ method.
self.assertTrue(product1 == MuellerMatrix(np.dot(matrix2, mueller_matrix1.matrix)))
self.assertTrue(product2 == MuellerMatrix(np.dot(mueller_matrix1.matrix, matrix2)))
def test_operator_equal(self):
candidate1 = np.asarray([[0.3259811941363535, 0.47517601596586845, 0.55188250265952130, 0.30969758976770600],
[0.9302512754240655, 0.31523109368760793, 0.14892732688907617, 0.67942926971439030],
[0.4445952221208559, 0.36451278145926280, 0.16830367235626830, 0.67269380390882630],
[0.5613952867979658, 0.10671512922801230, 0.48878441635267090, 0.27447059819338904]])
candidate1 = MuellerMatrix(candidate1)
candidate2 = np.asarray([[-6.17688112, 5.02032824, 6.23225893, -0.32032499],
[7.27366317, 1.43011512, 1.81816476, 9.62888406],
[5.27430283, -1.37894077, -0.99747857, 8.80405558],
[1.89547814, -1.17966381, -7.85411672, -7.73368115]])
candidate2 = MuellerMatrix(candidate2)
self.assertTrue(self.mueller_matrix == self.mueller_matrix) # identity
self.assertTrue(candidate1 == self.mueller_matrix)
self.assertFalse(candidate2 == self.mueller_matrix)
def test_operator_not_equal(self):
candidate1 = np.asarray([[0.3259811941363535, 0.47517601596586845, 0.55188250265952130, 0.30969758976770600],
[0.9302512754240655, 0.31523109368760793, 0.14892732688907617, 0.67942926971439030],
[0.4445952221208559, 0.36451278145926280, 0.16830367235626830, 0.67269380390882630],
[0.5613952867979658, 0.10671512922801230, 0.48878441635267090, 0.27447059819338904]])
candidate1 = MuellerMatrix(candidate1)
candidate2 = np.asarray([[-6.17688112, 5.02032824, 6.23225893, -0.32032499],
[7.27366317, 1.43011512, 1.81816476, 9.62888406],
[5.27430283, -1.37894077, -0.99747857, 8.80405558],
[1.89547814, -1.17966381, -7.85411672, -7.73368115]])
candidate2 = MuellerMatrix(candidate2)
self.assertFalse(self.mueller_matrix != self.mueller_matrix) # identity
self.assertFalse(candidate1 != self.mueller_matrix)
self.assertTrue(candidate2 != self.mueller_matrix)
def test_linear_polarizers(self):
#
assert_array_almost_equal( MuellerMatrix.initialize_as_linear_polarizer_horizontal().get_matrix(),
0.5 * np.array([
[1.0,1.0,0.0,0.0],
[1.0,1.0,0.0,0.0],
[0.0,0.0,0.0,0.0],
[0.0,0.0,0.0,0.0] ]))
#
assert_array_almost_equal( MuellerMatrix.initialize_as_linear_polarizer_horizontal().get_matrix(),
0.5 * np.array([
[1.0,1.0,0.0,0.0],
[1.0,1.0,0.0,0.0],
[0.0,0.0,0.0,0.0],
[0.0,0.0,0.0,0.0] ]))
#
assert_array_almost_equal( MuellerMatrix.initialize_as_linear_polarizer_vertical().get_matrix(),
0.5 * np.array([
[1.0,-1.0,0.0,0.0],
[-1.0,1.0,0.0,0.0],
[0.0,0.0,0.0,0.0],
[0.0,0.0,0.0,0.0] ]))
#
assert_array_almost_equal( MuellerMatrix.initialize_as_linear_polarizer_plus45().get_matrix(),
0.5 * np.array([
[1.0,0.0,1.0,0.0],
[0.0,0.0,0.0,0.0],
[1.0,0.0,1.0,0.0],
[0.0,0.0,0.0,0.0] ]))
#
assert_array_almost_equal( MuellerMatrix.initialize_as_linear_polarizer_minus45().get_matrix(),
0.5 * np.array([
[1.0,0.0,-1.0,0.0],
[0.0,0.0,0.0,0.0],
[-1.0,0.0,1.0,0.0],
[0.0,0.0,0.0,0.0] ]))
def test_linear_retarders(self):
#
assert_array_almost_equal( MuellerMatrix.initialize_as_quarter_wave_plate_fast_vertical().get_matrix(),
np.array([
[1.0,0.0,0.0,0.0],
[0.0,1.0,0.0,0.0],
[0.0,0.0,0.0,-1.0],
[0.0,0.0,1.0,0.0] ]))
#
assert_array_almost_equal( MuellerMatrix.initialize_as_quarter_wave_plate_fast_horizontal().get_matrix(),
np.array([
[1.0,0.0,0.0,0.0],
[0.0,1.0,0.0,0.0],
[0.0,0.0,0.0,1.0],
[0.0,0.0,-1.0,0.0] ]))
#
assert_array_almost_equal( MuellerMatrix.initialize_as_half_wave_plate().get_matrix(),
np.array([
[1.0,0.0,0.0,0.0],
[0.0,1.0,0.0,0.0],
[0.0,0.0,-1.0,0.0],
[0.0,0.0,0.0,-1.0] ]))
def test_filter(self):
#
assert_array_almost_equal( MuellerMatrix.initialize_as_filter(55.5).get_matrix(),
55.5*np.array([
[1.0,0.0,0.0,0.0],
[0.0,1.0,0.0,0.0],
[0.0,0.0,1.0,0.0],
[0.0,0.0,0.0,1.0] ]))
| 58.176166
| 118
| 0.544532
| 1,201
| 11,228
| 4.926728
| 0.121565
| 0.07301
| 0.095319
| 0.108163
| 0.790096
| 0.777928
| 0.746155
| 0.710326
| 0.636978
| 0.566672
| 0
| 0.283119
| 0.34316
| 11,228
| 192
| 119
| 58.479167
| 0.519186
| 0.019772
| 0
| 0.424837
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.261438
| 1
| 0.084967
| false
| 0
| 0.026144
| 0
| 0.124183
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 1
| 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
|
70f5fc0ad1fdf41a1b0d9567d0437481b924a528
| 314,743
|
py
|
Python
|
resources.py
|
halils70/CustomerManagement
|
5c65d5ccf9d200506fe6f23214948da508460f54
|
[
"MIT"
] | null | null | null |
resources.py
|
halils70/CustomerManagement
|
5c65d5ccf9d200506fe6f23214948da508460f54
|
[
"MIT"
] | null | null | null |
resources.py
|
halils70/CustomerManagement
|
5c65d5ccf9d200506fe6f23214948da508460f54
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.11.1)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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qt_resource_struct_v1 = b"\
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qt_resource_struct_v2 = b"\
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qt_version = QtCore.qVersion().split('.')
if qt_version < ['5', '8', '0']:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
| 64.207058
| 129
| 0.726799
| 76,167
| 314,743
| 3.002836
| 0.004004
| 0.030824
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| 314,743
| 4,901
| 130
| 64.220159
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| 0
|
0
| 4
|
cb2716e581d42ca1bb32ab961d38018fc0cec55a
| 30
|
py
|
Python
|
homeassistant/components/fail2ban/__init__.py
|
domwillcode/home-assistant
|
f170c80bea70c939c098b5c88320a1c789858958
|
[
"Apache-2.0"
] | 30,023
|
2016-04-13T10:17:53.000Z
|
2020-03-02T12:56:31.000Z
|
homeassistant/components/fail2ban/__init__.py
|
jagadeeshvenkatesh/core
|
1bd982668449815fee2105478569f8e4b5670add
|
[
"Apache-2.0"
] | 31,101
|
2020-03-02T13:00:16.000Z
|
2022-03-31T23:57:36.000Z
|
homeassistant/components/fail2ban/__init__.py
|
jagadeeshvenkatesh/core
|
1bd982668449815fee2105478569f8e4b5670add
|
[
"Apache-2.0"
] | 11,956
|
2016-04-13T18:42:31.000Z
|
2020-03-02T09:32:12.000Z
|
"""The fail2ban component."""
| 15
| 29
| 0.666667
| 3
| 30
| 6.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.037037
| 0.1
| 30
| 1
| 30
| 30
| 0.703704
| 0.766667
| 0
| null | 0
| null | 0
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| null | 0
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| null | 1
| null | true
| 0
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| null | null | null | 1
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| 0
| 0
| 0
|
0
| 4
|
cb47ba8247b93991f65b4147270a52759015ee13
| 168
|
py
|
Python
|
localshop/templatetags/permission_tags.py
|
agoragames/localshop
|
c677da85b063d8d794db498ca6b5222c50290a44
|
[
"BSD-3-Clause"
] | 162
|
2015-01-03T07:59:18.000Z
|
2022-02-04T12:37:14.000Z
|
localshop/templatetags/permission_tags.py
|
agoragames/localshop
|
c677da85b063d8d794db498ca6b5222c50290a44
|
[
"BSD-3-Clause"
] | 110
|
2015-01-06T00:58:21.000Z
|
2022-02-10T07:16:54.000Z
|
localshop/templatetags/permission_tags.py
|
agoragames/localshop
|
c677da85b063d8d794db498ca6b5222c50290a44
|
[
"BSD-3-Clause"
] | 67
|
2015-01-15T18:12:18.000Z
|
2021-09-27T09:45:55.000Z
|
from django import template
register = template.Library()
@register.filter
def is_owner_of(user, repository):
return repository.check_user_role(user, ['owner'])
| 18.666667
| 54
| 0.767857
| 22
| 168
| 5.681818
| 0.727273
| 0
| 0
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| 0
| 0
| 0.125
| 168
| 8
| 55
| 21
| 0.85034
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| 0
| 0.029762
| 0
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| 0.2
| false
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| 0.6
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| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
cb5af0651756d0821507db30cb1957baf0384d82
| 74
|
py
|
Python
|
abc/predict/fixed.py
|
hexin010101/NAM-genomes
|
778ebb94c3451614e1037eaddbadf9fe6c0baf54
|
[
"MIT"
] | null | null | null |
abc/predict/fixed.py
|
hexin010101/NAM-genomes
|
778ebb94c3451614e1037eaddbadf9fe6c0baf54
|
[
"MIT"
] | null | null | null |
abc/predict/fixed.py
|
hexin010101/NAM-genomes
|
778ebb94c3451614e1037eaddbadf9fe6c0baf54
|
[
"MIT"
] | 1
|
2022-02-11T13:33:10.000Z
|
2022-02-11T13:33:10.000Z
|
loci = 20000000 #!!!
sample = 26
mu = 2e-6
rr = 1.05e-6
Na = 1000
Bt = 67
| 10.571429
| 20
| 0.567568
| 15
| 74
| 2.8
| 0.933333
| 0
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| 0.256757
| 74
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| 0.363636
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0
| 4
|
cb5e675c4b3a09573944dca2e879b37c7b882ae9
| 115
|
py
|
Python
|
jserrorlogging/signals.py
|
moqada/django-js-error-logging
|
c9ac21051a4e598b5f9b37585c671fd834fbe46e
|
[
"BSD-3-Clause"
] | 1
|
2015-12-03T06:53:29.000Z
|
2015-12-03T06:53:29.000Z
|
jserrorlogging/signals.py
|
moqada/django-js-error-logging
|
c9ac21051a4e598b5f9b37585c671fd834fbe46e
|
[
"BSD-3-Clause"
] | null | null | null |
jserrorlogging/signals.py
|
moqada/django-js-error-logging
|
c9ac21051a4e598b5f9b37585c671fd834fbe46e
|
[
"BSD-3-Clause"
] | 4
|
2015-04-16T07:19:17.000Z
|
2020-03-19T14:24:42.000Z
|
# -*- coding: utf-8 -*-
import django.dispatch
add_log = django.dispatch.Signal(providing_args=['data', 'meta'])
| 19.166667
| 65
| 0.686957
| 15
| 115
| 5.133333
| 0.866667
| 0.363636
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| 0.113043
| 115
| 5
| 66
| 23
| 0.745098
| 0.182609
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|
0
| 4
|
cbbcedaad45f89e280f6e506af6994fbcbadbb69
| 5,407
|
py
|
Python
|
paralleldomain/model/unordered_scene.py
|
parallel-domain/pd-sdk
|
20e3d052a5cb612a2dd84bda7b1b5487a6a60edc
|
[
"Apache-2.0"
] | 10
|
2021-11-17T17:23:49.000Z
|
2022-03-18T09:51:23.000Z
|
paralleldomain/model/unordered_scene.py
|
parallel-domain/pd-sdk
|
20e3d052a5cb612a2dd84bda7b1b5487a6a60edc
|
[
"Apache-2.0"
] | 3
|
2021-12-02T17:16:20.000Z
|
2022-01-07T12:47:13.000Z
|
paralleldomain/model/unordered_scene.py
|
parallel-domain/pd-sdk
|
20e3d052a5cb612a2dd84bda7b1b5487a6a60edc
|
[
"Apache-2.0"
] | 2
|
2022-03-09T07:03:54.000Z
|
2022-03-23T15:53:48.000Z
|
from datetime import datetime
from itertools import chain
from typing import Any, Dict, Generator, Generic, List, Optional, Set, Type, TypeVar, Union
from paralleldomain.model.annotation import AnnotationType
from paralleldomain.model.class_mapping import ClassMap
from paralleldomain.model.map.map import Map
try:
from typing import Protocol
except ImportError:
from typing_extensions import Protocol # type: ignore
from paralleldomain.model.frame import Frame
from paralleldomain.model.sensor import CameraSensor, LidarSensor
from paralleldomain.model.type_aliases import FrameId, SceneName, SensorName
T = TypeVar("T")
TDateTime = TypeVar("TDateTime", bound=Union[None, datetime])
class UnorderedSceneDecoderProtocol(Protocol[TDateTime]):
def get_set_description(self, scene_name: SceneName) -> str:
pass
def get_set_metadata(self, scene_name: SceneName) -> Dict[str, Any]:
pass
def get_frame(
self,
scene_name: SceneName,
frame_id: FrameId,
) -> Frame[TDateTime]:
pass
def get_sensor_names(self, scene_name: SceneName) -> List[str]:
pass
def get_camera_names(self, scene_name: SceneName) -> List[str]:
pass
def get_lidar_names(self, scene_name: SceneName) -> List[str]:
pass
def get_frame_ids(self, scene_name: SceneName) -> Set[FrameId]:
pass
def get_class_maps(self, scene_name: SceneName) -> Dict[AnnotationType, ClassMap]:
pass
def get_camera_sensor(self, scene_name: SceneName, camera_name: SensorName) -> CameraSensor[TDateTime]:
pass
def get_lidar_sensor(self, scene_name: SceneName, lidar_name: SensorName) -> LidarSensor[TDateTime]:
pass
def get_map(self, scene_name: SceneName) -> Optional[Map]:
pass
class UnorderedScene(Generic[TDateTime]):
"""The sensor frames of a UnorderedScene are not temporally ordered.
Args:
name: Name of scene
available_annotation_types: List of available annotation types for this scene.
decoder: Decoder instance to be used for loading all relevant objects (frames, annotations etc.)
"""
def __init__(
self,
name: SceneName,
available_annotation_types: List[AnnotationType],
decoder: UnorderedSceneDecoderProtocol[TDateTime],
):
self._name = name
self._available_annotation_types = available_annotation_types
self._decoder = decoder
@property
def name(self) -> str:
return self._name
@property
def map(self) -> Optional[Map]:
return self._decoder.get_map(scene_name=self._name)
@property
def description(self) -> str:
return self._decoder.get_set_description(scene_name=self._name)
@property
def metadata(self) -> Dict[str, Any]:
return self._decoder.get_set_metadata(scene_name=self._name)
@property
def frames(self) -> Set[Frame[TDateTime]]:
return {self.get_frame(frame_id=frame_id) for frame_id in self.frame_ids}
@property
def frame_ids(self) -> Set[FrameId]:
return self._decoder.get_frame_ids(scene_name=self.name)
@property
def available_annotation_types(self):
return self._available_annotation_types
@property
def sensor_names(self) -> List[str]:
return self._decoder.get_sensor_names(scene_name=self.name)
@property
def camera_names(self) -> List[str]:
return self._decoder.get_camera_names(scene_name=self.name)
@property
def lidar_names(self) -> List[str]:
return self._decoder.get_lidar_names(scene_name=self.name)
def get_frame(self, frame_id: FrameId) -> Frame[TDateTime]:
return self._decoder.get_frame(scene_name=self.name, frame_id=frame_id)
@property
def sensors(self) -> Generator[Union[CameraSensor[TDateTime], LidarSensor[TDateTime]], None, None]:
return (a for a in chain(self.cameras, self.lidars))
@property
def cameras(self) -> Generator[CameraSensor[TDateTime], None, None]:
return (self.get_camera_sensor(camera_name=camera_name) for camera_name in self.camera_names)
@property
def lidars(self) -> Generator[LidarSensor[TDateTime], None, None]:
return (self.get_lidar_sensor(lidar_name=lidar_name) for lidar_name in self.lidar_names)
def get_sensor(self, sensor_name: SensorName) -> Union[CameraSensor[TDateTime], LidarSensor[TDateTime]]:
if sensor_name in self.camera_names:
return self.get_camera_sensor(camera_name=sensor_name)
else:
return self.get_lidar_sensor(lidar_name=sensor_name)
def get_camera_sensor(self, camera_name: SensorName) -> CameraSensor[TDateTime]:
return self._decoder.get_camera_sensor(scene_name=self.name, camera_name=camera_name)
def get_lidar_sensor(self, lidar_name: SensorName) -> LidarSensor[TDateTime]:
return self._decoder.get_lidar_sensor(scene_name=self.name, lidar_name=lidar_name)
@property
def class_maps(self) -> Dict[AnnotationType, ClassMap]:
return self._decoder.get_class_maps(scene_name=self.name)
def get_class_map(self, annotation_type: Type[T]) -> ClassMap:
if annotation_type not in self._available_annotation_types:
raise ValueError(f"No annotation type {annotation_type} available in this dataset!")
return self.class_maps[annotation_type]
| 34.883871
| 108
| 0.714074
| 681
| 5,407
| 5.433186
| 0.142438
| 0.053514
| 0.038649
| 0.065405
| 0.377027
| 0.171351
| 0.119459
| 0.064865
| 0.035676
| 0.035676
| 0
| 0
| 0.194193
| 5,407
| 154
| 109
| 35.11039
| 0.849208
| 0.054374
| 0
| 0.247706
| 0
| 0
| 0.014353
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.284404
| false
| 0.100917
| 0.110092
| 0.155963
| 0.59633
| 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
|
cbd2382b6bbff8a4fc652563b62a360d12df8067
| 197
|
py
|
Python
|
handler.py
|
JinhuiLee/miniPyserver
|
fef99f40c6a6de04a2d3f3c7b12cace68d82fa41
|
[
"MIT"
] | 2
|
2017-03-05T01:24:11.000Z
|
2017-07-04T23:51:04.000Z
|
handler.py
|
JinhuiLee/miniPyserver
|
fef99f40c6a6de04a2d3f3c7b12cace68d82fa41
|
[
"MIT"
] | null | null | null |
handler.py
|
JinhuiLee/miniPyserver
|
fef99f40c6a6de04a2d3f3c7b12cace68d82fa41
|
[
"MIT"
] | null | null | null |
def hello(verb, path, query):
return "Server received your " + verb + " request" + str(query) + "\r\n"
def register(urlMapper):
urlMapper["/hello"] = hello
| 10.944444
| 76
| 0.532995
| 21
| 197
| 5
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.324873
| 197
| 17
| 77
| 11.588235
| 0.789474
| 0
| 0
| 0
| 0
| 0
| 0.201031
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.25
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
1db4a02ef92f84dde4db38dd1b2c355586945b56
| 50
|
py
|
Python
|
apps/process/models/__init__.py
|
kane-zh/MES_server
|
d8d28768a054eee6433e3900908afd331fd92281
|
[
"Apache-2.0"
] | null | null | null |
apps/process/models/__init__.py
|
kane-zh/MES_server
|
d8d28768a054eee6433e3900908afd331fd92281
|
[
"Apache-2.0"
] | null | null | null |
apps/process/models/__init__.py
|
kane-zh/MES_server
|
d8d28768a054eee6433e3900908afd331fd92281
|
[
"Apache-2.0"
] | null | null | null |
from apps.process.models.basicinfor_model import *
| 50
| 50
| 0.86
| 7
| 50
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.06
| 50
| 1
| 50
| 50
| 0.893617
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
1db7b177fc793700fdc4b2e8e3e02966e66834ae
| 98
|
py
|
Python
|
ward_mapping/apps.py
|
Suraj1127/ward-mapping-application
|
53fa39bab875ca47fdab814fd28ea0b7d2086c15
|
[
"MIT"
] | 1
|
2019-05-16T04:08:40.000Z
|
2019-05-16T04:08:40.000Z
|
ward_mapping/apps.py
|
Suraj1127/ward-mapping-application
|
53fa39bab875ca47fdab814fd28ea0b7d2086c15
|
[
"MIT"
] | null | null | null |
ward_mapping/apps.py
|
Suraj1127/ward-mapping-application
|
53fa39bab875ca47fdab814fd28ea0b7d2086c15
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class WardMappingConfig(AppConfig):
name = 'ward_mapping'
| 16.333333
| 35
| 0.77551
| 11
| 98
| 6.818182
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153061
| 98
| 5
| 36
| 19.6
| 0.903614
| 0
| 0
| 0
| 0
| 0
| 0.122449
| 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
|
1db974d907dbe9b027ecb01e852faab3e95b023d
| 690
|
py
|
Python
|
src/experimentator/__init__.py
|
hsharrison/experimentator
|
91fabd4e394ebd80d7e91e9b29569ebabbc0ede3
|
[
"MIT"
] | 17
|
2015-03-26T20:48:30.000Z
|
2021-11-10T19:43:56.000Z
|
src/experimentator/__init__.py
|
hsharrison/experimentator
|
91fabd4e394ebd80d7e91e9b29569ebabbc0ede3
|
[
"MIT"
] | 5
|
2015-02-25T14:53:11.000Z
|
2019-04-10T20:32:09.000Z
|
src/experimentator/__init__.py
|
hsharrison/experimentator
|
91fabd4e394ebd80d7e91e9b29569ebabbc0ede3
|
[
"MIT"
] | 3
|
2016-10-17T21:23:46.000Z
|
2021-07-13T08:04:16.000Z
|
from experimentator._patched_yaml import yaml
from experimentator.__version__ import __version__
from experimentator.experiment import Experiment, run_experiment_section, export_experiment_data
from experimentator.design import Design, DesignTree
class QuitSession(BaseException):
"""
An Exception indicating a 'soft' exit from a running experimental session.
Raise this exception to indicate a desired exit;
e.g. when the user presses a quit key.
Parameters
----------
message : str
Message to display in the terminal.
"""
def __init__(self, message):
self.message = message
def __str__(self):
return str(self.message)
| 27.6
| 96
| 0.727536
| 82
| 690
| 5.853659
| 0.585366
| 0.15
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.204348
| 690
| 24
| 97
| 28.75
| 0.874317
| 0.347826
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.222222
| false
| 0
| 0.444444
| 0.111111
| 0.888889
| 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
| 0
| 1
| 1
| 1
| 0
|
0
| 4
|
1db9c4de327cf184c90605c81b74ee731f7fa101
| 472
|
py
|
Python
|
days/day010/bite_107_Filter_numbers_with_a_list_comprehension/test_bite_107.py
|
alex-vegan/100daysofcode-with-python-course
|
b6c12316abe18274b7963371b8f0ed2fd549ef07
|
[
"MIT"
] | 2
|
2018-10-28T17:12:37.000Z
|
2018-10-28T17:12:39.000Z
|
days/day010/bite_107_Filter_numbers_with_a_list_comprehension/test_bite_107.py
|
alex-vegan/100daysofcode-with-python-course
|
b6c12316abe18274b7963371b8f0ed2fd549ef07
|
[
"MIT"
] | 3
|
2018-10-28T17:11:04.000Z
|
2018-10-29T22:36:36.000Z
|
days/day010/bite_107_Filter_numbers_with_a_list_comprehension/test_bite_107.py
|
alex-vegan/100daysofcode-with-python-course
|
b6c12316abe18274b7963371b8f0ed2fd549ef07
|
[
"MIT"
] | null | null | null |
from bite_107 import filter_positive_even_numbers
def test_filter_positive_and_negatives():
numbers = list(range(-10, 11))
assert filter_positive_even_numbers(numbers) == [2, 4, 6, 8, 10]
def test_only_positives():
numbers = [2, 4, 51, 44, 47, 10]
assert filter_positive_even_numbers(numbers) == [2, 4, 44, 10]
def test_filter_zero_and_negatives():
numbers = [0, -1, -3, -5]
assert filter_positive_even_numbers(numbers) == []
| 27.764706
| 69
| 0.680085
| 68
| 472
| 4.382353
| 0.455882
| 0.234899
| 0.241611
| 0.33557
| 0.395973
| 0.395973
| 0.268456
| 0.268456
| 0
| 0
| 0
| 0.087071
| 0.197034
| 472
| 16
| 70
| 29.5
| 0.699208
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.3
| 1
| 0.3
| false
| 0
| 0.1
| 0
| 0.4
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
1dd36b84643a7459fd8cba46fd5706dcbfbebf86
| 115
|
py
|
Python
|
prl/environments/__init__.py
|
sliwy/prl
|
0e4bfa5578d11890d21932f535b095f2657ed4ff
|
[
"MIT"
] | 51
|
2020-02-12T08:57:50.000Z
|
2022-03-14T13:27:40.000Z
|
prl/environments/__init__.py
|
sliwy/prl
|
0e4bfa5578d11890d21932f535b095f2657ed4ff
|
[
"MIT"
] | 4
|
2021-03-19T10:47:07.000Z
|
2022-03-12T00:14:39.000Z
|
prl/environments/__init__.py
|
sliwy/prl
|
0e4bfa5578d11890d21932f535b095f2657ed4ff
|
[
"MIT"
] | 4
|
2020-03-04T07:03:24.000Z
|
2022-03-14T13:27:43.000Z
|
from prl.environments.environments import (
Environment,
FrameSkipEnvironment,
TimeShiftEnvironment,
)
| 19.166667
| 43
| 0.765217
| 8
| 115
| 11
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 115
| 5
| 44
| 23
| 0.926316
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.2
| 0
| 0.2
| 0
| 1
| 0
| 1
| 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
|
1df320c6832b39c8b35bebd6ca8c771e49ce0d2e
| 38
|
py
|
Python
|
tea/helpers/constants/default_values.py
|
corinneherzog/tea-lang
|
48ef37111c14310ca648eb96f625ea9800f428a2
|
[
"Apache-2.0"
] | 218
|
2019-04-10T18:30:19.000Z
|
2020-07-02T02:13:41.000Z
|
tea/helpers/constants/default_values.py
|
corinneherzog/tea-lang
|
48ef37111c14310ca648eb96f625ea9800f428a2
|
[
"Apache-2.0"
] | 45
|
2019-04-12T04:01:12.000Z
|
2020-07-11T16:15:26.000Z
|
tea/helpers/constants/default_values.py
|
corinneherzog/tea-lang
|
48ef37111c14310ca648eb96f625ea9800f428a2
|
[
"Apache-2.0"
] | 24
|
2019-04-15T03:35:37.000Z
|
2020-05-27T04:35:10.000Z
|
DEFAULT_ALPHA_PARAMETER: float = 0.05
| 19
| 37
| 0.815789
| 6
| 38
| 4.833333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088235
| 0.105263
| 38
| 1
| 38
| 38
| 0.764706
| 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
|
380e6096aed9587ffa1e9d1b0ff0a4302b5acc4f
| 243
|
py
|
Python
|
schemaless/guid.py
|
eklitzke/schemaless
|
eb2ca453a69e8af36980c53fcc66725116ae7971
|
[
"0BSD"
] | 58
|
2015-04-30T07:36:45.000Z
|
2022-01-20T03:37:09.000Z
|
schemaless/guid.py
|
eklitzke/schemaless
|
eb2ca453a69e8af36980c53fcc66725116ae7971
|
[
"0BSD"
] | 1
|
2016-01-19T05:28:15.000Z
|
2016-01-19T05:28:15.000Z
|
schemaless/guid.py
|
eklitzke/schemaless
|
eb2ca453a69e8af36980c53fcc66725116ae7971
|
[
"0BSD"
] | 14
|
2015-04-14T09:10:53.000Z
|
2020-05-09T01:53:17.000Z
|
import os
GUID_SIZE = 16
def raw_guid(size=GUID_SIZE):
return os.urandom(size)
def guid(size=GUID_SIZE):
return raw_guid(size=size).encode('hex')
def to_raw(s):
return s.decode('hex')
def to_str(r):
return r.encode('hex')
| 15.1875
| 44
| 0.679012
| 43
| 243
| 3.674419
| 0.372093
| 0.303797
| 0.139241
| 0.202532
| 0.278481
| 0
| 0
| 0
| 0
| 0
| 0
| 0.00995
| 0.17284
| 243
| 15
| 45
| 16.2
| 0.776119
| 0
| 0
| 0
| 0
| 0
| 0.037037
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0.1
| 0.4
| 0.9
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 4
|
383ca5f3eabe57976e154663ed03a9d6f411956c
| 213
|
py
|
Python
|
run-rest-api.py
|
Trapov/osu-recognition
|
703822f6700896f6d3e36b9fa005609f113fef73
|
[
"MIT"
] | null | null | null |
run-rest-api.py
|
Trapov/osu-recognition
|
703822f6700896f6d3e36b9fa005609f113fef73
|
[
"MIT"
] | 2
|
2020-09-24T19:01:18.000Z
|
2020-09-24T19:31:55.000Z
|
run-rest-api.py
|
Trapov/osu-recognition
|
703822f6700896f6d3e36b9fa005609f113fef73
|
[
"MIT"
] | 1
|
2020-03-28T16:04:00.000Z
|
2020-03-28T16:04:00.000Z
|
import uvicorn
RUNNABLE_PACKAGES_DIRECTORY = 'runnable_packages'
REST_MODULE = 'rest.api'
what_package_to_run = f'{RUNNABLE_PACKAGES_DIRECTORY}.{REST_MODULE}:app'
uvicorn.run(what_package_to_run, host="0.0.0.0")
| 30.428571
| 72
| 0.812207
| 33
| 213
| 4.848485
| 0.484848
| 0.3
| 0.3125
| 0.2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.020101
| 0.065728
| 213
| 7
| 73
| 30.428571
| 0.78392
| 0
| 0
| 0
| 0
| 0
| 0.369159
| 0.219626
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
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