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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0acabac25e7f182a0cc9d197e74fb9a54f708fdd | 629 | py | Python | day10/samematrix.py | nikhilsamninan/python-files | 15198459081097058a939b40b5e8ef754e578fe0 | [
"Apache-2.0"
] | null | null | null | day10/samematrix.py | nikhilsamninan/python-files | 15198459081097058a939b40b5e8ef754e578fe0 | [
"Apache-2.0"
] | null | null | null | day10/samematrix.py | nikhilsamninan/python-files | 15198459081097058a939b40b5e8ef754e578fe0 | [
"Apache-2.0"
] | null | null | null | def matrix_form():
r = int(input("Enter the no of rows"))
c = int(input("Enter the no of columns"))
matrix=[]
print("Enter the enteries")
for i in range(r):
a = []
for j in range(c):
a.append(int(input()))
matrix.append(a)
return(matrix)
def check_matrix(fi... | 22.464286 | 45 | 0.63434 | 92 | 629 | 4.173913 | 0.347826 | 0.104167 | 0.15625 | 0.15625 | 0.265625 | 0.265625 | 0 | 0 | 0 | 0 | 0 | 0.004141 | 0.232114 | 629 | 28 | 46 | 22.464286 | 0.79089 | 0 | 0 | 0 | 0 | 0 | 0.179365 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.086957 | false | 0 | 0 | 0 | 0.086957 | 0.304348 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0accac5244ae00b90c3dcaa313e0ad6674cf5f7f | 5,284 | py | Python | kepler.py | mdbernard/astrodynamics | cf98df6cd17086e3675c1f7c2fce342d5322ee51 | [
"MIT"
] | null | null | null | kepler.py | mdbernard/astrodynamics | cf98df6cd17086e3675c1f7c2fce342d5322ee51 | [
"MIT"
] | 14 | 2020-11-10T02:37:15.000Z | 2022-02-07T01:11:29.000Z | kepler.py | mdbernard/astrodynamics | cf98df6cd17086e3675c1f7c2fce342d5322ee51 | [
"MIT"
] | null | null | null | import numpy as np
from stumpff import C, S
from CelestialBody import BODIES
from numerical import newton, laguerre
from lagrange import calc_f, calc_fd, calc_g, calc_gd
def kepler_chi(chi, alpha, r0, vr0, mu, dt):
''' Kepler's Equation of the universal anomaly, modified
for use in numerical solvers. '''
... | 39.140741 | 115 | 0.645912 | 867 | 5,284 | 3.817762 | 0.224913 | 0.019033 | 0.018127 | 0.021752 | 0.306344 | 0.287613 | 0.256798 | 0.250453 | 0.244411 | 0.169789 | 0 | 0.046993 | 0.222748 | 5,284 | 134 | 116 | 39.432836 | 0.758948 | 0.402914 | 0 | 0.126761 | 0 | 0 | 0.048296 | 0.009262 | 0 | 0 | 0 | 0.007463 | 0 | 1 | 0.112676 | false | 0 | 0.070423 | 0.028169 | 0.295775 | 0.028169 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0acd26a6aeb9fbb21484a68cd667f26b74d856f7 | 952 | py | Python | nicos_demo/vpgaa/setups/pgai.py | jkrueger1/nicos | 5f4ce66c312dedd78995f9d91e8a6e3c891b262b | [
"CC-BY-3.0",
"Apache-2.0",
"CC-BY-4.0"
] | 12 | 2019-11-06T15:40:36.000Z | 2022-01-01T16:23:00.000Z | nicos_demo/vpgaa/setups/pgai.py | jkrueger1/nicos | 5f4ce66c312dedd78995f9d91e8a6e3c891b262b | [
"CC-BY-3.0",
"Apache-2.0",
"CC-BY-4.0"
] | 91 | 2020-08-18T09:20:26.000Z | 2022-02-01T11:07:14.000Z | nicos_demo/vpgaa/setups/pgai.py | jkrueger1/nicos | 5f4ce66c312dedd78995f9d91e8a6e3c891b262b | [
"CC-BY-3.0",
"Apache-2.0",
"CC-BY-4.0"
] | 6 | 2020-01-11T10:52:30.000Z | 2022-02-25T12:35:23.000Z | description = 'PGAA setup with XYZOmega sample table'
group = 'basic'
sysconfig = dict(
datasinks = ['mcasink', 'chnsink', 'csvsink', 'livesink']
)
includes = [
'system',
'reactor',
'nl4b',
'pressure',
'sampletable',
'pilz',
'detector',
'collimation',
]
devices = dict(
mcasin... | 23.219512 | 73 | 0.522059 | 77 | 952 | 6.363636 | 0.584416 | 0.067347 | 0.085714 | 0.110204 | 0.271429 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00915 | 0.196429 | 952 | 40 | 74 | 23.8 | 0.631373 | 0 | 0 | 0.285714 | 0 | 0 | 0.543067 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.085714 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ace54f568ea92472966bb73d6fa4f6d624bebbf | 6,859 | py | Python | official/nlp/transformer/utils/tokenizer_test.py | hjkim-haga/TF-OD-API | 22ac477ff4dfb93fe7a32c94b5f0b1e74330902b | [
"Apache-2.0"
] | 1 | 2021-05-22T12:50:50.000Z | 2021-05-22T12:50:50.000Z | official/nlp/transformer/utils/tokenizer_test.py | DemonDamon/mask-detection-based-on-tf2odapi | 192ae544169c1230c21141c033800aa1bd94e9b6 | [
"MIT"
] | null | null | null | official/nlp/transformer/utils/tokenizer_test.py | DemonDamon/mask-detection-based-on-tf2odapi | 192ae544169c1230c21141c033800aa1bd94e9b6 | [
"MIT"
] | null | null | null | # Copyright 2021 The TensorFlow Authors. 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... | 33.458537 | 81 | 0.626185 | 830 | 6,859 | 4.926506 | 0.238554 | 0.055026 | 0.048912 | 0.011739 | 0.167523 | 0.134018 | 0.096845 | 0.096845 | 0.096845 | 0.058694 | 0 | 0.023392 | 0.252078 | 6,859 | 204 | 82 | 33.622549 | 0.773684 | 0.129757 | 0 | 0.13986 | 0 | 0 | 0.079254 | 0 | 0 | 0 | 0 | 0 | 0.223776 | 1 | 0.104895 | false | 0 | 0.027972 | 0 | 0.153846 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0acf3366802d8714bb15485c54ab7f3de9aac778 | 2,776 | py | Python | Z - Tool Box/LaZagne/Windows/lazagne/softwares/windows/ppypykatz.py | dfirpaul/Active-Directory-Exploitation-Cheat-Sheet-1 | 1dcf54522e9d20711ff1114550dc2893ed3e9ed0 | [
"MIT"
] | 1,290 | 2020-05-28T21:24:43.000Z | 2022-03-31T16:38:43.000Z | Z - Tool Box/LaZagne/Windows/lazagne/softwares/windows/ppypykatz.py | dfirpaul/Active-Directory-Exploitation-Cheat-Sheet-1 | 1dcf54522e9d20711ff1114550dc2893ed3e9ed0 | [
"MIT"
] | 1 | 2020-07-03T21:14:52.000Z | 2020-07-03T21:14:52.000Z | Z - Tool Box/LaZagne/Windows/lazagne/softwares/windows/ppypykatz.py | dfirpaul/Active-Directory-Exploitation-Cheat-Sheet-1 | 1dcf54522e9d20711ff1114550dc2893ed3e9ed0 | [
"MIT"
] | 280 | 2020-05-29T17:28:38.000Z | 2022-03-31T13:54:15.000Z | # -*- coding: utf-8 -*-
# Thanks to @skelsec for his awesome tool Pypykatz
# Checks his project here: https://github.com/skelsec/pypykatz
import codecs
import traceback
from lazagne.config.module_info import ModuleInfo
from lazagne.config.constant import constant
from pypykatz.pypykatz import pypykatz
... | 36.526316 | 106 | 0.501801 | 273 | 2,776 | 4.985348 | 0.410256 | 0.070536 | 0.035268 | 0.019104 | 0.080823 | 0.057311 | 0.057311 | 0.057311 | 0 | 0 | 0 | 0.001189 | 0.394092 | 2,776 | 75 | 107 | 37.013333 | 0.807967 | 0.144813 | 0 | 0.12766 | 0 | 0 | 0.112533 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.042553 | false | 0.042553 | 0.106383 | 0 | 0.191489 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0acf54e8a20fd816eda3589c3b616626bb4f33fb | 14,981 | py | Python | test/test_discogs.py | mglukhovsky/beets | 889e30c056a609cf71c8c8200259520230545222 | [
"MIT"
] | null | null | null | test/test_discogs.py | mglukhovsky/beets | 889e30c056a609cf71c8c8200259520230545222 | [
"MIT"
] | null | null | null | test/test_discogs.py | mglukhovsky/beets | 889e30c056a609cf71c8c8200259520230545222 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# This file is part of beets.
# Copyright 2016, Adrian Sampson.
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation t... | 41.269972 | 79 | 0.59235 | 1,866 | 14,981 | 4.594855 | 0.146302 | 0.145206 | 0.069046 | 0.05645 | 0.635059 | 0.607534 | 0.572662 | 0.513646 | 0.456146 | 0.405412 | 0 | 0.032152 | 0.271277 | 14,981 | 362 | 80 | 41.383978 | 0.753229 | 0.177959 | 0 | 0.432 | 0 | 0 | 0.08492 | 0 | 0 | 0 | 0 | 0 | 0.336 | 1 | 0.1 | false | 0 | 0.024 | 0.004 | 0.144 | 0.004 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ad02fbe661ef723ec6b1d7108a2d41a85831a5b | 17,018 | py | Python | darknet2ncnn.py | nihui/gen-ncnn-models | 18523f1920d9afc44ce3058087c07e09f28aa151 | [
"BSD-2-Clause"
] | 4 | 2019-12-24T15:16:18.000Z | 2021-05-14T08:12:17.000Z | darknet2ncnn.py | nihui/gen-ncnn-models | 18523f1920d9afc44ce3058087c07e09f28aa151 | [
"BSD-2-Clause"
] | null | null | null | darknet2ncnn.py | nihui/gen-ncnn-models | 18523f1920d9afc44ce3058087c07e09f28aa151 | [
"BSD-2-Clause"
] | null | null | null | #! /usr/bin/env python
# coding: utf-8
import configparser
import numpy as np
import re,sys,os
from graph import MyGraph
from collections import OrderedDict
def unique_config_sections(config_file):
"""Convert all config sections to have unique names.
Adds unique suffixes to config sections for compability wi... | 36.915401 | 117 | 0.534317 | 1,767 | 17,018 | 5.011885 | 0.166384 | 0.046748 | 0.039521 | 0.033198 | 0.448058 | 0.41294 | 0.359869 | 0.314024 | 0.25734 | 0.24458 | 0 | 0.009554 | 0.360324 | 17,018 | 460 | 118 | 36.995652 | 0.803968 | 0.093078 | 0 | 0.433908 | 0 | 0 | 0.070578 | 0.007416 | 0 | 0 | 0 | 0.002174 | 0.005747 | 1 | 0.011494 | false | 0.008621 | 0.022989 | 0 | 0.048851 | 0.014368 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ad20a796d3e2e784e9676daf81a22cf86a1d3cb | 8,474 | py | Python | liuetal2019/utils.py | wasiahmad/GATE | 1e48504a3641f00265a271a19eb6b6449fdc33bd | [
"MIT"
] | 24 | 2020-12-07T10:22:40.000Z | 2022-03-31T09:24:13.000Z | liuetal2019/utils.py | wasiahmad/GATE | 1e48504a3641f00265a271a19eb6b6449fdc33bd | [
"MIT"
] | 15 | 2021-03-22T04:52:57.000Z | 2022-01-01T18:32:31.000Z | liuetal2019/utils.py | wasiahmad/GATE | 1e48504a3641f00265a271a19eb6b6449fdc33bd | [
"MIT"
] | 8 | 2021-03-04T05:09:42.000Z | 2022-01-25T12:59:19.000Z | import io
import logging
import json
import numpy
import torch
import numpy as np
from tqdm import tqdm
from clie.inputters import constant
from clie.objects import Sentence
from torch.utils.data import Dataset
from torch.utils.data.sampler import Sampler
logger = logging.getLogger(__name__)
def load_word_embeddings... | 34.587755 | 85 | 0.576941 | 1,119 | 8,474 | 4.15639 | 0.151028 | 0.064502 | 0.047302 | 0.056762 | 0.211137 | 0.176736 | 0.125994 | 0.099979 | 0.099979 | 0.099979 | 0 | 0.005215 | 0.275903 | 8,474 | 244 | 86 | 34.729508 | 0.752771 | 0.056998 | 0 | 0.046154 | 0 | 0 | 0.075775 | 0 | 0 | 0 | 0 | 0 | 0.010256 | 1 | 0.05641 | false | 0 | 0.05641 | 0.020513 | 0.169231 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ad2503d07ac5b15fee30f7480f83b4ea51f1515 | 914 | py | Python | build.py | dnanexus/IndexTools | 0392b3be92ff50b401290b59e9ca6c7767fa5a96 | [
"MIT"
] | 15 | 2019-07-17T11:41:36.000Z | 2021-03-02T09:36:34.000Z | build.py | dnanexus/IndexTools | 0392b3be92ff50b401290b59e9ca6c7767fa5a96 | [
"MIT"
] | 22 | 2019-05-15T20:08:12.000Z | 2019-10-11T13:33:42.000Z | build.py | dnanexus/IndexTools | 0392b3be92ff50b401290b59e9ca6c7767fa5a96 | [
"MIT"
] | 3 | 2019-06-01T15:58:06.000Z | 2022-01-21T21:10:01.000Z | from distutils.extension import Extension
cmdclass = {}
try:
# with Cython
from Cython.Build import build_ext
cmdclass["build_ext"] = build_ext
module_src = "cgranges/python/cgranges.pyx"
except ImportError: # without Cython
module_src = "cgranges/python/cgranges.c"
def build(setup_kwargs):
... | 25.388889 | 64 | 0.504376 | 79 | 914 | 5.708861 | 0.493671 | 0.053215 | 0.113082 | 0.101996 | 0.137472 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.399344 | 914 | 35 | 65 | 26.114286 | 0.821494 | 0.09628 | 0 | 0.076923 | 0 | 0 | 0.222497 | 0.10136 | 0 | 0 | 0 | 0 | 0 | 1 | 0.038462 | false | 0 | 0.115385 | 0 | 0.153846 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ad2916f049d06f5df6ddbf5e08b57510f7c1b78 | 17,212 | py | Python | gluoncv/data/kinetics400/classification.py | YvetteGuo/gluon-cv | 123af8cf9f15a879c16a5c7d12f01ce1471d85b6 | [
"Apache-2.0"
] | 1 | 2019-04-02T02:08:04.000Z | 2019-04-02T02:08:04.000Z | gluoncv/data/kinetics400/classification.py | YvetteGuo/gluon-cv | 123af8cf9f15a879c16a5c7d12f01ce1471d85b6 | [
"Apache-2.0"
] | 1 | 2019-06-06T08:39:12.000Z | 2019-06-06T08:39:12.000Z | gluoncv/data/kinetics400/classification.py | YvetteGuo/gluon-cv | 123af8cf9f15a879c16a5c7d12f01ce1471d85b6 | [
"Apache-2.0"
] | 1 | 2019-08-26T09:26:42.000Z | 2019-08-26T09:26:42.000Z | # pylint: disable=line-too-long,too-many-lines,missing-docstring
"""Kinetics400 action classification dataset."""
import os
import random
import numpy as np
from mxnet import nd
from mxnet.gluon.data import dataset
__all__ = ['Kinetics400']
class Kinetics400(dataset.Dataset):
"""Load the Kinetics400 action recogn... | 65.444867 | 152 | 0.625552 | 1,887 | 17,212 | 5.384208 | 0.409115 | 0.014173 | 0.011516 | 0.012992 | 0.081496 | 0.056102 | 0.037992 | 0.026181 | 0.022441 | 0.022441 | 0 | 0.009244 | 0.264641 | 17,212 | 262 | 153 | 65.694656 | 0.793474 | 0.138799 | 0 | 0.078947 | 0 | 0.005263 | 0.407726 | 0.058695 | 0 | 0 | 0 | 0 | 0 | 1 | 0.036842 | false | 0.010526 | 0.036842 | 0.005263 | 0.110526 | 0.005263 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ad331ec8ece0975704ec9214918b2580008a6a0 | 23,842 | py | Python | watcher/api/controllers/v1/action_plan.py | ajaytikoo/watcher | 6dbac1f6ae7f3e10dfdcef5721fa4af7af54e159 | [
"Apache-2.0"
] | 64 | 2015-10-18T02:57:24.000Z | 2022-01-13T11:27:51.000Z | watcher/api/controllers/v1/action_plan.py | ajaytikoo/watcher | 6dbac1f6ae7f3e10dfdcef5721fa4af7af54e159 | [
"Apache-2.0"
] | null | null | null | watcher/api/controllers/v1/action_plan.py | ajaytikoo/watcher | 6dbac1f6ae7f3e10dfdcef5721fa4af7af54e159 | [
"Apache-2.0"
] | 35 | 2015-12-25T13:53:21.000Z | 2021-07-19T15:50:16.000Z | # -*- encoding: utf-8 -*-
# Copyright 2013 Red Hat, Inc.
# 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 re... | 39.149425 | 79 | 0.637279 | 2,766 | 23,842 | 5.267534 | 0.161605 | 0.082361 | 0.020178 | 0.012354 | 0.328552 | 0.273644 | 0.218257 | 0.177763 | 0.174674 | 0.15628 | 0 | 0.00402 | 0.28001 | 23,842 | 608 | 80 | 39.213816 | 0.844751 | 0.195453 | 0 | 0.190722 | 0 | 0 | 0.055565 | 0.003942 | 0 | 0 | 0 | 0.003289 | 0 | 1 | 0.074742 | false | 0.005155 | 0.054124 | 0.012887 | 0.231959 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ad57f93e09c3cfa475ee8a3a4f941a9c684524d | 1,613 | py | Python | run.py | shark803/Torch_serve_example_NLP | 7f7984a1668f21aced3a7a1e8ddac3c8e0ff0105 | [
"MIT"
] | 1 | 2021-11-19T07:59:58.000Z | 2021-11-19T07:59:58.000Z | run.py | shark803/Torch_serve_example_NLP | 7f7984a1668f21aced3a7a1e8ddac3c8e0ff0105 | [
"MIT"
] | null | null | null | run.py | shark803/Torch_serve_example_NLP | 7f7984a1668f21aced3a7a1e8ddac3c8e0ff0105 | [
"MIT"
] | null | null | null | # coding: UTF-8
import time
import torch
import numpy as np
from train_eval import train, init_network
from importlib import import_module
import argparse
parser = argparse.ArgumentParser(description='Chinese Text Classification')
parser.add_argument('--model', type=str, required=True, help='choose a model: TextCNN')
... | 32.918367 | 97 | 0.726596 | 223 | 1,613 | 5.017937 | 0.403587 | 0.04647 | 0.045576 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.002959 | 0.16181 | 1,613 | 48 | 98 | 33.604167 | 0.824704 | 0.081215 | 0 | 0 | 0 | 0 | 0.12483 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.257143 | 0 | 0.257143 | 0.085714 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ad630d29820371f228b1287947197de5ede3fb0 | 5,954 | py | Python | tests/mb_util.py | vasilydenisenko/modbus_rtu_slave | 8a531b776ab82c60b5d335f0565468f19a7801f5 | [
"MIT"
] | null | null | null | tests/mb_util.py | vasilydenisenko/modbus_rtu_slave | 8a531b776ab82c60b5d335f0565468f19a7801f5 | [
"MIT"
] | null | null | null | tests/mb_util.py | vasilydenisenko/modbus_rtu_slave | 8a531b776ab82c60b5d335f0565468f19a7801f5 | [
"MIT"
] | null | null | null | # MIT License
# Copyright (c) 2021 Vasily Denisenko, Sergey Kuznetsov
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# t... | 25.553648 | 81 | 0.673161 | 915 | 5,954 | 4.113661 | 0.204372 | 0.069075 | 0.083688 | 0.042508 | 0.450319 | 0.349362 | 0.276567 | 0.221838 | 0.185707 | 0.107598 | 0 | 0.032054 | 0.203561 | 5,954 | 233 | 82 | 25.553648 | 0.761704 | 0.205576 | 0 | 0.423841 | 0 | 0 | 0.114662 | 0.056325 | 0 | 0 | 0.020787 | 0 | 0 | 1 | 0.066225 | false | 0 | 0.006623 | 0 | 0.10596 | 0.139073 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ad85408ba998c356a370a0f1582159d01f77a69 | 8,390 | py | Python | carto/maps.py | danicarrion/carto-python | 631b018f065960baa35473e2087ce598560b9e17 | [
"BSD-3-Clause"
] | 85 | 2016-08-07T16:46:58.000Z | 2022-03-23T01:44:02.000Z | carto/maps.py | danicarrion/carto-python | 631b018f065960baa35473e2087ce598560b9e17 | [
"BSD-3-Clause"
] | 109 | 2016-08-02T18:40:04.000Z | 2021-08-23T08:08:02.000Z | carto/maps.py | danicarrion/carto-python | 631b018f065960baa35473e2087ce598560b9e17 | [
"BSD-3-Clause"
] | 29 | 2016-11-29T03:42:47.000Z | 2022-01-23T17:37:11.000Z | """
Module for working with named and anonymous maps
.. module:: carto.maps
:platform: Unix, Windows
:synopsis: Module for working with named and anonymous maps
.. moduleauthor:: Daniel Carrion <daniel@carto.com>
.. moduleauthor:: Alberto Romeu <alrocar@carto.com>
"""
try:
from urllib.parse import urljoi... | 33.293651 | 86 | 0.555662 | 938 | 8,390 | 4.815565 | 0.203625 | 0.044277 | 0.013947 | 0.009741 | 0.423733 | 0.410228 | 0.341377 | 0.263006 | 0.193048 | 0.148771 | 0 | 0.001666 | 0.356138 | 8,390 | 251 | 87 | 33.426295 | 0.834506 | 0.288439 | 0 | 0.352941 | 0 | 0 | 0.096576 | 0.050428 | 0 | 0 | 0 | 0 | 0 | 1 | 0.084034 | false | 0 | 0.042017 | 0 | 0.235294 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ad8ce46348b78515a8db8b2c9bc54898f1ab6f9 | 1,208 | py | Python | pytorch-frontend/benchmarks/operator_benchmark/pt/embeddingbag_test.py | AndreasKaratzas/stonne | 2915fcc46cc94196303d81abbd1d79a56d6dd4a9 | [
"MIT"
] | 206 | 2020-11-28T22:56:38.000Z | 2022-03-27T02:33:04.000Z | pytorch-frontend/benchmarks/operator_benchmark/pt/embeddingbag_test.py | AndreasKaratzas/stonne | 2915fcc46cc94196303d81abbd1d79a56d6dd4a9 | [
"MIT"
] | 19 | 2020-12-09T23:13:14.000Z | 2022-01-24T23:24:08.000Z | pytorch-frontend/benchmarks/operator_benchmark/pt/embeddingbag_test.py | AndreasKaratzas/stonne | 2915fcc46cc94196303d81abbd1d79a56d6dd4a9 | [
"MIT"
] | 28 | 2020-11-29T15:25:12.000Z | 2022-01-20T02:16:27.000Z | import operator_benchmark as op_bench
import torch
import numpy
from . import configs
"""EmbeddingBag Operator Benchmark"""
class EmbeddingBagBenchmark(op_bench.TorchBenchmarkBase):
def init(self, embeddingbags, dim, mode, input_size, offset, sparse, include_last_offset, device):
self.embedding = torch.nn... | 38.967742 | 107 | 0.724338 | 144 | 1,208 | 5.826389 | 0.395833 | 0.041716 | 0.060787 | 0.040524 | 0.133492 | 0.133492 | 0 | 0 | 0 | 0 | 0 | 0.006979 | 0.169702 | 1,208 | 30 | 108 | 40.266667 | 0.829511 | 0 | 0 | 0 | 0 | 0 | 0.017079 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.086957 | false | 0 | 0.173913 | 0.043478 | 0.347826 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ad9fee81c50ef01672c1f7b553d66bc07bc9155 | 3,972 | py | Python | python/dgl/geometry/capi.py | lfchener/dgl | 77f4287a4118db64c46f4f413a426e1419a09d53 | [
"Apache-2.0"
] | 9,516 | 2018-12-08T22:11:31.000Z | 2022-03-31T13:04:33.000Z | python/dgl/geometry/capi.py | lfchener/dgl | 77f4287a4118db64c46f4f413a426e1419a09d53 | [
"Apache-2.0"
] | 2,494 | 2018-12-08T22:43:00.000Z | 2022-03-31T21:16:27.000Z | python/dgl/geometry/capi.py | lfchener/dgl | 77f4287a4118db64c46f4f413a426e1419a09d53 | [
"Apache-2.0"
] | 2,529 | 2018-12-08T22:56:14.000Z | 2022-03-31T13:07:41.000Z | """Python interfaces to DGL farthest point sampler."""
from dgl._ffi.base import DGLError
import numpy as np
from .._ffi.function import _init_api
from .. import backend as F
from .. import ndarray as nd
def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result):
r"""Farthest Point Samp... | 38.563107 | 95 | 0.680514 | 558 | 3,972 | 4.646953 | 0.370968 | 0.03818 | 0.029695 | 0.032395 | 0.121481 | 0.04705 | 0.027767 | 0.027767 | 0 | 0 | 0 | 0.00659 | 0.235901 | 3,972 | 102 | 96 | 38.941176 | 0.847776 | 0.558912 | 0 | 0 | 0 | 0 | 0.023529 | 0 | 0 | 0 | 0 | 0.009804 | 0.064516 | 1 | 0.064516 | false | 0 | 0.16129 | 0 | 0.290323 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0adab04d82e555974b5ee3aecff08feca7c75415 | 6,478 | py | Python | scidb/core/data.py | oxdc/sci.db | 0a751a0e05e7ad4c83c350e32e32ea9ce5831cbb | [
"MIT"
] | null | null | null | scidb/core/data.py | oxdc/sci.db | 0a751a0e05e7ad4c83c350e32e32ea9ce5831cbb | [
"MIT"
] | null | null | null | scidb/core/data.py | oxdc/sci.db | 0a751a0e05e7ad4c83c350e32e32ea9ce5831cbb | [
"MIT"
] | null | null | null | import shutil
import hashlib
from pathlib import Path
from typing import TextIO, BinaryIO, IO, Union
from datetime import datetime
from os.path import getmtime
from .low import ObservableDict
class Data:
def __init__(self, data_name: str, parent, bucket,
protected_parent_methods: Union[None, dict... | 35.988889 | 102 | 0.599105 | 749 | 6,478 | 4.878505 | 0.142857 | 0.065681 | 0.045977 | 0.027915 | 0.552271 | 0.493706 | 0.437055 | 0.32266 | 0.289819 | 0.206076 | 0 | 0.014075 | 0.298086 | 6,478 | 179 | 103 | 36.189944 | 0.789532 | 0 | 0 | 0.350649 | 0 | 0 | 0.039673 | 0.004322 | 0 | 0 | 0 | 0 | 0 | 1 | 0.155844 | false | 0 | 0.051948 | 0.058442 | 0.409091 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0adacd25859bed18399a4d523ba68cd8adb2bc90 | 39,932 | py | Python | tensorflow/python/keras/optimizer_v2/optimizer_v2.py | PaulWang1905/tensorflow | ebf12d22b4801fb8dab5034cc94562bf7cc33fa0 | [
"Apache-2.0"
] | 9 | 2019-12-29T01:47:37.000Z | 2021-12-21T13:47:41.000Z | tensorflow/python/keras/optimizer_v2/optimizer_v2.py | PaulWang1905/tensorflow | ebf12d22b4801fb8dab5034cc94562bf7cc33fa0 | [
"Apache-2.0"
] | null | null | null | tensorflow/python/keras/optimizer_v2/optimizer_v2.py | PaulWang1905/tensorflow | ebf12d22b4801fb8dab5034cc94562bf7cc33fa0 | [
"Apache-2.0"
] | 1 | 2020-05-28T11:22:49.000Z | 2020-05-28T11:22:49.000Z | # Copyright 2018 The TensorFlow Authors. 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 applica... | 38.806608 | 101 | 0.698162 | 5,320 | 39,932 | 5.088722 | 0.148496 | 0.015957 | 0.014184 | 0.004137 | 0.242797 | 0.187906 | 0.167886 | 0.140957 | 0.131575 | 0.120789 | 0 | 0.002698 | 0.220375 | 39,932 | 1,028 | 102 | 38.844358 | 0.866889 | 0.470525 | 0 | 0.209644 | 0 | 0 | 0.075533 | 0.007458 | 0 | 0 | 0 | 0.002918 | 0.006289 | 1 | 0.092243 | false | 0.004193 | 0.060797 | 0 | 0.247379 | 0.002096 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0adb9e87674ba38043bf368fb738d4c5e8ba7c5c | 362 | py | Python | escola/teste_get.py | danielrosendos/djangoRestFramework | 946bb95b8dd9976d1920302ce724572ffd9f98cf | [
"MIT"
] | 2 | 2020-07-26T15:17:23.000Z | 2020-07-26T16:50:18.000Z | escola/teste_get.py | sport129/djangoRestFramework | 946bb95b8dd9976d1920302ce724572ffd9f98cf | [
"MIT"
] | 3 | 2021-03-30T14:12:18.000Z | 2021-06-04T23:44:47.000Z | escola/teste_get.py | sport129/djangoRestFramework | 946bb95b8dd9976d1920302ce724572ffd9f98cf | [
"MIT"
] | null | null | null | import requests
headers = {
'content-type': 'application/json',
'Authorization': 'Token 80ca9f249b80e7226cdc7fcaada8d7297352f0f9'
}
url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos'
url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes'
resultado = requests.get(url=url_base_cursos, headers=hea... | 27.846154 | 69 | 0.756906 | 48 | 362 | 5.5625 | 0.5625 | 0.078652 | 0.097378 | 0.067416 | 0.142322 | 0.142322 | 0.142322 | 0.142322 | 0 | 0 | 0 | 0.147239 | 0.099448 | 362 | 13 | 70 | 27.846154 | 0.671779 | 0 | 0 | 0 | 0 | 0 | 0.443526 | 0.110193 | 0 | 0 | 0 | 0 | 0.111111 | 1 | 0 | false | 0 | 0.111111 | 0 | 0.111111 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0adc55ed2f06787ab63a1224266a2dd707ce1b10 | 6,455 | py | Python | python/avi/sdk/utils/waf_policy/vdi_waf_policy.py | aaronjwood/alb-sdk | ae4c47b2228651d3f5095e7c14f081aa4adbb732 | [
"Apache-2.0"
] | null | null | null | python/avi/sdk/utils/waf_policy/vdi_waf_policy.py | aaronjwood/alb-sdk | ae4c47b2228651d3f5095e7c14f081aa4adbb732 | [
"Apache-2.0"
] | null | null | null | python/avi/sdk/utils/waf_policy/vdi_waf_policy.py | aaronjwood/alb-sdk | ae4c47b2228651d3f5095e7c14f081aa4adbb732 | [
"Apache-2.0"
] | null | null | null | # Copyright 2021 VMware, Inc.
import argparse
import json
import re
import logging
import os
import sys
from avi.sdk.avi_api import ApiSession
API_VERSION = "18.2.13"
SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI'
logger = logging.getLogger(__name__)
def add_allowlist_rule(waf_policy_obj):
#add a allowlist rule... | 38.652695 | 219 | 0.632223 | 866 | 6,455 | 4.463049 | 0.243649 | 0.093144 | 0.090039 | 0.019405 | 0.311255 | 0.22044 | 0.170505 | 0.147477 | 0.129884 | 0.105563 | 0 | 0.0165 | 0.239504 | 6,455 | 166 | 220 | 38.885542 | 0.770829 | 0.032533 | 0 | 0.194444 | 0 | 0.013889 | 0.259295 | 0.030449 | 0 | 0 | 0 | 0 | 0.013889 | 1 | 0.048611 | false | 0.034722 | 0.048611 | 0 | 0.131944 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0adcde8b96a5cb82b17bdf29ba072f1b54339883 | 4,101 | py | Python | src/api/bkuser_core/tests/bkiam/test_constants.py | Chace-wang/bk-user | 057f270d66a1834312306c9fba1f4e95521f10b1 | [
"MIT"
] | null | null | null | src/api/bkuser_core/tests/bkiam/test_constants.py | Chace-wang/bk-user | 057f270d66a1834312306c9fba1f4e95521f10b1 | [
"MIT"
] | null | null | null | src/api/bkuser_core/tests/bkiam/test_constants.py | Chace-wang/bk-user | 057f270d66a1834312306c9fba1f4e95521f10b1 | [
"MIT"
] | 1 | 2021-12-31T06:48:41.000Z | 2021-12-31T06:48:41.000Z | # -*- coding: utf-8 -*-
"""
TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available.
Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the Lic... | 39.432692 | 115 | 0.613997 | 477 | 4,101 | 5.075472 | 0.348008 | 0.05948 | 0.020653 | 0.046262 | 0.484924 | 0.403139 | 0.234614 | 0.172656 | 0.135481 | 0.113176 | 0 | 0.033077 | 0.270178 | 4,101 | 103 | 116 | 39.815534 | 0.77581 | 0.176298 | 0 | 0.2625 | 0 | 0 | 0.163847 | 0.078955 | 0 | 0 | 0 | 0 | 0.075 | 1 | 0.0625 | false | 0 | 0.05 | 0 | 0.125 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0adf4b5bea842a306db59cff9711a1e6a19b7ae0 | 3,753 | py | Python | improver_tests/precipitation_type/test_utilities.py | cpelley/improver | ebf77fe2adc85ed7aec74c26671872a2e4388ded | [
"BSD-3-Clause"
] | 77 | 2017-04-26T07:47:40.000Z | 2022-03-31T09:40:49.000Z | improver_tests/precipitation_type/test_utilities.py | cpelley/improver | ebf77fe2adc85ed7aec74c26671872a2e4388ded | [
"BSD-3-Clause"
] | 1,440 | 2017-03-29T10:04:15.000Z | 2022-03-28T10:11:29.000Z | improver_tests/precipitation_type/test_utilities.py | MoseleyS/improver | ca028e3a1c842e3ff00b188c8ea6eaedd0a07149 | [
"BSD-3-Clause"
] | 72 | 2017-03-17T16:53:45.000Z | 2022-02-16T09:41:37.000Z | # -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# (C) British Crown Copyright 2017-2021 Met Office.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions a... | 39.925532 | 79 | 0.742073 | 507 | 3,753 | 5.34714 | 0.402367 | 0.044264 | 0.035042 | 0.033936 | 0.217263 | 0.164146 | 0.151236 | 0.095168 | 0.095168 | 0.095168 | 0 | 0.005146 | 0.171596 | 3,753 | 93 | 80 | 40.354839 | 0.866838 | 0.523848 | 0 | 0.102564 | 0 | 0 | 0.139616 | 0.062245 | 0 | 0 | 0 | 0 | 0.128205 | 1 | 0.102564 | false | 0 | 0.153846 | 0 | 0.282051 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ae04a483b4283bc6fdc84bd651d77ab70b6120c | 5,149 | py | Python | app/api/v1/models/items.py | bryan-munene/Store-Manager-DB | 40b24039189aea6854d7fcf33ccb648bb6642231 | [
"MIT"
] | null | null | null | app/api/v1/models/items.py | bryan-munene/Store-Manager-DB | 40b24039189aea6854d7fcf33ccb648bb6642231 | [
"MIT"
] | 4 | 2018-10-25T00:57:18.000Z | 2018-10-25T21:29:09.000Z | app/api/v1/models/items.py | bryan-munene/Store-Manager-DB | 40b24039189aea6854d7fcf33ccb648bb6642231 | [
"MIT"
] | null | null | null | from .db_conn import ModelSetup
class ItemsModel(ModelSetup):
'''Handles the data logic of the items section'''
def __init__(
self,
name=None,
price=None,
quantity=None,
category_id=None,
reorder_point=None,
auth=None):
... | 31.206061 | 112 | 0.543212 | 595 | 5,149 | 4.594958 | 0.147899 | 0.07169 | 0.061448 | 0.083394 | 0.664228 | 0.618508 | 0.604974 | 0.58376 | 0.501829 | 0.393563 | 0 | 0 | 0.350554 | 5,149 | 164 | 113 | 31.396341 | 0.817584 | 0.126238 | 0 | 0.633588 | 0 | 0.007634 | 0.168615 | 0.00652 | 0 | 0 | 0 | 0 | 0 | 1 | 0.068702 | false | 0 | 0.007634 | 0 | 0.145038 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ae122f08d00736fbd1d09356f366ff9dcd6baf8 | 4,215 | py | Python | site/src/sphinx/_extensions/api.py | linxGnu/armeria | 7f4b10e66acc377dd16929157aeb417b729ce55a | [
"Apache-2.0"
] | null | null | null | site/src/sphinx/_extensions/api.py | linxGnu/armeria | 7f4b10e66acc377dd16929157aeb417b729ce55a | [
"Apache-2.0"
] | null | null | null | site/src/sphinx/_extensions/api.py | linxGnu/armeria | 7f4b10e66acc377dd16929157aeb417b729ce55a | [
"Apache-2.0"
] | null | null | null | from docutils.parsers.rst.roles import register_canonical_role, set_classes
from docutils.parsers.rst import directives
from docutils import nodes
from sphinx.writers.html import HTMLTranslator
from sphinx.errors import ExtensionError
import os
import re
def api_role(role, rawtext, text, lineno, inliner, options={},... | 37.633929 | 103 | 0.629656 | 517 | 4,215 | 5 | 0.29207 | 0.024758 | 0.043327 | 0.040619 | 0.155513 | 0.137718 | 0.102901 | 0.086654 | 0.051838 | 0.051838 | 0 | 0.000931 | 0.235113 | 4,215 | 111 | 104 | 37.972973 | 0.800868 | 0.112218 | 0 | 0.067568 | 0 | 0 | 0.093079 | 0.005633 | 0 | 0 | 0 | 0 | 0 | 1 | 0.067568 | false | 0.013514 | 0.094595 | 0.027027 | 0.216216 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ae22c03054218a911ddc84125341497677c75ac | 2,045 | py | Python | ros_buildfarm/debian_repo.py | j-rivero/ros_buildfarm | 840d2dc1dd5db00d5407da4644cd2bcbc5e0ac88 | [
"Apache-2.0"
] | null | null | null | ros_buildfarm/debian_repo.py | j-rivero/ros_buildfarm | 840d2dc1dd5db00d5407da4644cd2bcbc5e0ac88 | [
"Apache-2.0"
] | 1 | 2019-12-12T21:08:01.000Z | 2019-12-12T21:08:01.000Z | ros_buildfarm/debian_repo.py | j-rivero/ros_buildfarm | 840d2dc1dd5db00d5407da4644cd2bcbc5e0ac88 | [
"Apache-2.0"
] | null | null | null | # Copyright 2014 Open Source Robotics Foundation, 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... | 36.517857 | 91 | 0.695355 | 288 | 2,045 | 4.822917 | 0.444444 | 0.043197 | 0.019438 | 0.028078 | 0.080634 | 0.057595 | 0.057595 | 0.057595 | 0.057595 | 0.057595 | 0 | 0.009208 | 0.203423 | 2,045 | 55 | 92 | 37.181818 | 0.843462 | 0.310513 | 0 | 0 | 0 | 0 | 0.073888 | 0 | 0 | 0 | 0 | 0 | 0.034483 | 1 | 0.034483 | false | 0 | 0.137931 | 0 | 0.206897 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ae2b8b9a2e89b056cf58f74862944546c4ef4a9 | 48,440 | py | Python | Framwork-Backpropagation/utils/utils_v2.py | ConvolutedDog/Implicit-Im2col-for-Backpropagation | 529a62f52903326b9289091b7d0abb45e6c7bb31 | [
"Apache-2.0"
] | null | null | null | Framwork-Backpropagation/utils/utils_v2.py | ConvolutedDog/Implicit-Im2col-for-Backpropagation | 529a62f52903326b9289091b7d0abb45e6c7bb31 | [
"Apache-2.0"
] | null | null | null | Framwork-Backpropagation/utils/utils_v2.py | ConvolutedDog/Implicit-Im2col-for-Backpropagation | 529a62f52903326b9289091b7d0abb45e6c7bb31 | [
"Apache-2.0"
] | null | null | null | # Copyright 2022 ConvolutedDog (https://github.com/ConvolutedDog/)
#
# 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 ... | 34.624732 | 172 | 0.63429 | 6,781 | 48,440 | 4.297744 | 0.060905 | 0.041382 | 0.021 | 0.024706 | 0.689051 | 0.639879 | 0.592458 | 0.556703 | 0.53162 | 0.509556 | 0 | 0.016974 | 0.197275 | 48,440 | 1,399 | 173 | 34.624732 | 0.732512 | 0.040235 | 0 | 0.552056 | 0 | 0 | 0.123108 | 0.019954 | 0 | 0 | 0 | 0.000715 | 0.000875 | 1 | 0.028871 | false | 0.0035 | 0.011374 | 0.000875 | 0.08399 | 0.070866 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ae2d03accd91cc3db5f01917f5d31fdecbb74e5 | 4,372 | py | Python | ark_nlp/factory/utils/attack.py | yubuyuabc/ark-nlp | 165d35cfacd7476791c0aeba19bf43f4f8079553 | [
"Apache-2.0"
] | 1 | 2022-03-23T05:10:55.000Z | 2022-03-23T05:10:55.000Z | ark_nlp/factory/utils/attack.py | yubuyuabc/ark-nlp | 165d35cfacd7476791c0aeba19bf43f4f8079553 | [
"Apache-2.0"
] | null | null | null | ark_nlp/factory/utils/attack.py | yubuyuabc/ark-nlp | 165d35cfacd7476791c0aeba19bf43f4f8079553 | [
"Apache-2.0"
] | null | null | null | import torch
class FGM(object):
"""
基于FGM算法的攻击机制
Args:
module (:obj:`torch.nn.Module`): 模型
Examples::
>>> # 初始化
>>> fgm = FGM(module)
>>> for batch_input, batch_label in data:
>>> # 正常训练
>>> loss = module(batch_input, batch_label)
>>> ... | 31.681159 | 101 | 0.52699 | 478 | 4,372 | 4.648536 | 0.177824 | 0.044554 | 0.040954 | 0.054005 | 0.738074 | 0.670567 | 0.646265 | 0.646265 | 0.588659 | 0.588659 | 0 | 0.009571 | 0.354758 | 4,372 | 137 | 102 | 31.912409 | 0.778093 | 0.368253 | 0 | 0.575758 | 0 | 0 | 0.014879 | 0 | 0 | 0 | 0 | 0 | 0.030303 | 1 | 0.136364 | false | 0 | 0.015152 | 0 | 0.19697 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ae341f931ab8799a80b73c9036820e58b4d7de6 | 5,790 | py | Python | core.py | sreejithr/deepfake | c7115ce90ea281e2eb95d75f436efa102c8f2e3c | [
"MIT"
] | null | null | null | core.py | sreejithr/deepfake | c7115ce90ea281e2eb95d75f436efa102c8f2e3c | [
"MIT"
] | 3 | 2021-09-08T02:24:48.000Z | 2022-03-12T00:44:53.000Z | core.py | sreejithr/deepfake | c7115ce90ea281e2eb95d75f436efa102c8f2e3c | [
"MIT"
] | null | null | null | import cv2
import torch
import yaml
import imageio
import throttle
import numpy as np
import matplotlib.pyplot as plt
from argparse import ArgumentParser
from skimage.transform import resize
from scipy.spatial import ConvexHull
from modules.generator import OcclusionAwareGenerator
from modules.keypoint_detector import... | 34.260355 | 142 | 0.68342 | 755 | 5,790 | 5.007947 | 0.250331 | 0.045226 | 0.046549 | 0.022481 | 0.180376 | 0.123512 | 0.085163 | 0.070881 | 0.070881 | 0.021158 | 0 | 0.017722 | 0.181347 | 5,790 | 168 | 143 | 34.464286 | 0.779958 | 0.126598 | 0 | 0.099099 | 0 | 0 | 0.100418 | 0 | 0 | 0 | 0.000795 | 0 | 0 | 1 | 0.027027 | false | 0 | 0.117117 | 0 | 0.171171 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ae6683abfd956b5c3952439b03a59e007c9300a | 2,402 | py | Python | models/1-Tom/train/kaggle-hubmap-main/src/02_train/transforms.py | navekshasood/HuBMAP---Hacking-the-Kidney | 018100fe4bfa5e8764b9df5a9d188e2c670ac061 | [
"MIT"
] | null | null | null | models/1-Tom/train/kaggle-hubmap-main/src/02_train/transforms.py | navekshasood/HuBMAP---Hacking-the-Kidney | 018100fe4bfa5e8764b9df5a9d188e2c670ac061 | [
"MIT"
] | null | null | null | models/1-Tom/train/kaggle-hubmap-main/src/02_train/transforms.py | navekshasood/HuBMAP---Hacking-the-Kidney | 018100fe4bfa5e8764b9df5a9d188e2c670ac061 | [
"MIT"
] | null | null | null | import numpy as np
from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90,
ShiftScaleRotate, ElasticTransform,
GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop,
RandomBrightnessContrast, HueSatu... | 40.711864 | 113 | 0.572856 | 272 | 2,402 | 4.911765 | 0.352941 | 0.011976 | 0.015719 | 0.011976 | 0.181138 | 0.098802 | 0.098802 | 0.098802 | 0.098802 | 0.098802 | 0 | 0.06411 | 0.305162 | 2,402 | 58 | 114 | 41.413793 | 0.736369 | 0.008326 | 0 | 0.177778 | 0 | 0 | 0.026902 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.066667 | false | 0 | 0.088889 | 0.022222 | 0.222222 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ae709052ebf9505470ee0404f1013ba86cb8e0e | 13,017 | py | Python | cubspack/geometry.py | Majikat/cubspack | 16aa6df0603d48d757d74837d3457a1934601d89 | [
"Apache-2.0"
] | 11 | 2018-06-18T12:05:34.000Z | 2021-02-24T19:00:24.000Z | cubspack/geometry.py | Majikat/cubspack | 16aa6df0603d48d757d74837d3457a1934601d89 | [
"Apache-2.0"
] | null | null | null | cubspack/geometry.py | Majikat/cubspack | 16aa6df0603d48d757d74837d3457a1934601d89 | [
"Apache-2.0"
] | 2 | 2018-04-08T17:30:00.000Z | 2018-09-27T08:38:42.000Z | # -*- coding: utf-8 -*-
from math import sqrt
class Point(object):
__slots__ = ('x', 'y', 'z')
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def __eq__(self, other):
return (self.x == other.x and self.y == other.y and self.z == other.z)
def __repr__... | 29.517007 | 85 | 0.556657 | 1,666 | 13,017 | 4.259304 | 0.103241 | 0.024662 | 0.019166 | 0.021421 | 0.495068 | 0.448985 | 0.402198 | 0.360203 | 0.281285 | 0.257046 | 0 | 0.001385 | 0.334563 | 13,017 | 440 | 86 | 29.584091 | 0.817825 | 0.217101 | 0 | 0.384 | 0 | 0 | 0.008383 | 0 | 0 | 0 | 0 | 0 | 0.02 | 1 | 0.192 | false | 0 | 0.004 | 0.096 | 0.436 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ae84e0cfa142229ba7d5efbff2238d28b93f418 | 16,661 | py | Python | app/recipe/tests/test_recipe_api.py | tahmadvand/recipe_app_api | 40b4cc6960d7dc4858373b5f6ccca980ed0eeac8 | [
"MIT"
] | null | null | null | app/recipe/tests/test_recipe_api.py | tahmadvand/recipe_app_api | 40b4cc6960d7dc4858373b5f6ccca980ed0eeac8 | [
"MIT"
] | null | null | null | app/recipe/tests/test_recipe_api.py | tahmadvand/recipe_app_api | 40b4cc6960d7dc4858373b5f6ccca980ed0eeac8 | [
"MIT"
] | null | null | null | from django.contrib.auth import get_user_model
from django.test import TestCase
from django.urls import reverse
from rest_framework import status
from rest_framework.test import APIClient
# use that for making our API requests
from core.models import Recipe, Tag, Ingredient
from ..serializers import RecipeSerializer... | 40.43932 | 78 | 0.667547 | 2,339 | 16,661 | 4.689183 | 0.174861 | 0.022611 | 0.02954 | 0.015044 | 0.343636 | 0.292943 | 0.244621 | 0.165664 | 0.131109 | 0.093363 | 0 | 0.00967 | 0.248965 | 16,661 | 411 | 79 | 40.537713 | 0.866858 | 0.398596 | 0 | 0.353234 | 0 | 0 | 0.073004 | 0.007045 | 0 | 0 | 0 | 0 | 0.169154 | 1 | 0.109453 | false | 0.014925 | 0.049751 | 0 | 0.199005 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0ae8c65cafc822a3267fba35c6ed220e7f320711 | 11,646 | py | Python | gwcs/coordinate_frames.py | migueldvb/gwcs | 4eb2abdb1d9d49ee10c1edbcae0d1cec4c758c39 | [
"BSD-3-Clause"
] | null | null | null | gwcs/coordinate_frames.py | migueldvb/gwcs | 4eb2abdb1d9d49ee10c1edbcae0d1cec4c758c39 | [
"BSD-3-Clause"
] | null | null | null | gwcs/coordinate_frames.py | migueldvb/gwcs | 4eb2abdb1d9d49ee10c1edbcae0d1cec4c758c39 | [
"BSD-3-Clause"
] | null | null | null | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Defines coordinate frames and ties them to data axes.
"""
from __future__ import absolute_import, division, unicode_literals, print_function
import numpy as np
from astropy import units as u
from astropy import utils as astutil
from astropy import coo... | 32.713483 | 99 | 0.574618 | 1,319 | 11,646 | 4.855193 | 0.156937 | 0.05762 | 0.01827 | 0.022486 | 0.313086 | 0.232355 | 0.199563 | 0.190818 | 0.173329 | 0.150531 | 0 | 0.002846 | 0.336339 | 11,646 | 355 | 100 | 32.805634 | 0.825721 | 0.236133 | 0 | 0.206186 | 0 | 0 | 0.060014 | 0.002858 | 0 | 0 | 0 | 0 | 0 | 1 | 0.123711 | false | 0 | 0.036082 | 0.015464 | 0.28866 | 0.010309 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0aeade2b44478bdc750fc6e4297d377345ef5136 | 500 | py | Python | brownie_fund_me/scripts/fund_and_withdraw.py | WangCHEN9/solidity_demos | cf28111a1e972ab9dde70f6d3fac22c897d8b660 | [
"MIT"
] | null | null | null | brownie_fund_me/scripts/fund_and_withdraw.py | WangCHEN9/solidity_demos | cf28111a1e972ab9dde70f6d3fac22c897d8b660 | [
"MIT"
] | null | null | null | brownie_fund_me/scripts/fund_and_withdraw.py | WangCHEN9/solidity_demos | cf28111a1e972ab9dde70f6d3fac22c897d8b660 | [
"MIT"
] | null | null | null | from brownie import FundMe
from scripts.helpful_scripts import get_account
def fund():
fund_me = FundMe[-1]
account = get_account()
entrance_fee = fund_me.getEntranceFee()
print(f"entrance is {entrance_fee}")
print("funding..")
fund_me.fund({"from": account, "value": entrance_fee})
def withd... | 18.518519 | 58 | 0.654 | 63 | 500 | 4.873016 | 0.380952 | 0.09772 | 0.078176 | 0.084691 | 0.19544 | 0.19544 | 0.19544 | 0 | 0 | 0 | 0 | 0.005025 | 0.204 | 500 | 26 | 59 | 19.230769 | 0.766332 | 0 | 0 | 0.222222 | 0 | 0 | 0.112 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0 | 0.111111 | 0 | 0.277778 | 0.111111 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0aeb5c0e9a64382d41d3447557ec9fb64a32a973 | 409 | py | Python | ex019.py | jefernathan/Python | 2f840a625e8d46d41ab36df07ef50ae15a03c5ab | [
"MIT"
] | null | null | null | ex019.py | jefernathan/Python | 2f840a625e8d46d41ab36df07ef50ae15a03c5ab | [
"MIT"
] | null | null | null | ex019.py | jefernathan/Python | 2f840a625e8d46d41ab36df07ef50ae15a03c5ab | [
"MIT"
] | null | null | null | # Um professor quer sortear um dos seus quatro alunos para apagar o quadro. Faça um programa que ajude ele, lendo o nome dos alunos e escrevendo na tela o nome do escolhido.
from random import choice
nome1 = input('Digite um nome: ')
nome2 = input('Digite outro nome: ')
nome3 = input('Digite mais um nome: ')
nome4 = ... | 34.083333 | 173 | 0.728606 | 66 | 409 | 4.515152 | 0.590909 | 0.147651 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.02381 | 0.178484 | 409 | 11 | 174 | 37.181818 | 0.863095 | 0.418093 | 0 | 0 | 0 | 0 | 0.330508 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.142857 | 0 | 0.142857 | 0.142857 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0aeb7979679122962a3fff866f48391b6b9c9278 | 489 | py | Python | contacts/admin.py | liviamendes/agenda-django-project | d602bb5e762ea477c3c97b5a475ad79036c0c93d | [
"MIT"
] | null | null | null | contacts/admin.py | liviamendes/agenda-django-project | d602bb5e762ea477c3c97b5a475ad79036c0c93d | [
"MIT"
] | null | null | null | contacts/admin.py | liviamendes/agenda-django-project | d602bb5e762ea477c3c97b5a475ad79036c0c93d | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import Categoria, Contact
class ContactAdmin(admin.ModelAdmin):
list_display = ('id', 'name', 'last_name', 'phone', 'email', 'creation_date', 'categoria', 'show')
list_display_links = ('id', 'name', 'last_name')
list_filter = ('categoria',)
list_per_page =... | 30.5625 | 102 | 0.691207 | 59 | 489 | 5.525424 | 0.525424 | 0.07362 | 0.110429 | 0.08589 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.004796 | 0.147239 | 489 | 15 | 103 | 32.6 | 0.776978 | 0 | 0 | 0 | 0 | 0 | 0.208589 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.181818 | 0 | 0.818182 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0aec7fad0f474867079a857e5fa0aa0966e20a00 | 2,472 | py | Python | upload_from_folder.py | robinrobinzon/fastpic | 966f1aa8c6d7e98651727e7ed7f6b25970d5da11 | [
"MIT"
] | null | null | null | upload_from_folder.py | robinrobinzon/fastpic | 966f1aa8c6d7e98651727e7ed7f6b25970d5da11 | [
"MIT"
] | null | null | null | upload_from_folder.py | robinrobinzon/fastpic | 966f1aa8c6d7e98651727e7ed7f6b25970d5da11 | [
"MIT"
] | null | null | null | import datetime
import os
import shutil
import tempfile
from joblib import Parallel, delayed
from fastpic_upload import upload_file_to_fastpic
_n_jobs_for_upload = 20
_root_folders_set = (
'/path/to/folder',
)
_spoiler_for_each_file = True
def process_one_pic(result_key, pic_path, tmp_dir):
pic_url, pic_l... | 29.783133 | 106 | 0.651294 | 335 | 2,472 | 4.465672 | 0.256716 | 0.072193 | 0.102941 | 0.064171 | 0.229947 | 0.141043 | 0.141043 | 0.141043 | 0.104947 | 0.061497 | 0 | 0.00316 | 0.231796 | 2,472 | 82 | 107 | 30.146341 | 0.784623 | 0 | 0 | 0.16129 | 0 | 0 | 0.075709 | 0.019433 | 0 | 0 | 0 | 0 | 0 | 1 | 0.064516 | false | 0 | 0.096774 | 0 | 0.193548 | 0.112903 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0aecc3617c0fed4d5c58d568836e4b90d9b9886f | 1,994 | py | Python | tools/accuracy_checker/openvino/tools/accuracy_checker/postprocessor/clip_segmentation_mask.py | TolyaTalamanov/open_model_zoo | 1697e60712df4ca72635a2080a197b9d3bc24129 | [
"Apache-2.0"
] | 2,201 | 2018-10-15T14:37:19.000Z | 2020-07-16T02:05:51.000Z | tools/accuracy_checker/openvino/tools/accuracy_checker/postprocessor/clip_segmentation_mask.py | Pandinosaurus/open_model_zoo | 2543996541346418919c5cddfb71e33e2cdef080 | [
"Apache-2.0"
] | 759 | 2018-10-18T07:43:55.000Z | 2020-07-16T01:23:12.000Z | tools/accuracy_checker/openvino/tools/accuracy_checker/postprocessor/clip_segmentation_mask.py | Pandinosaurus/open_model_zoo | 2543996541346418919c5cddfb71e33e2cdef080 | [
"Apache-2.0"
] | 808 | 2018-10-16T14:03:49.000Z | 2020-07-15T11:41:45.000Z | """
Copyright (c) 2018-2022 Intel Corporation
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 wri... | 38.346154 | 117 | 0.739218 | 248 | 1,994 | 5.798387 | 0.459677 | 0.05007 | 0.03338 | 0.022253 | 0.157163 | 0.11822 | 0.080668 | 0.080668 | 0.080668 | 0.080668 | 0 | 0.008605 | 0.184052 | 1,994 | 51 | 118 | 39.098039 | 0.87523 | 0.284855 | 0 | 0.074074 | 0 | 0 | 0.083216 | 0.015515 | 0 | 0 | 0 | 0 | 0 | 1 | 0.111111 | false | 0 | 0.148148 | 0 | 0.481481 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0aee1a078e80effb05eed8b8321db099a4b35623 | 1,925 | py | Python | tests/test_utils.py | isabella232/pynacl | b3f6c320569d858ba61d4bdf2ac788564528c1c9 | [
"Apache-2.0"
] | 756 | 2015-01-03T17:49:44.000Z | 2022-03-31T13:54:33.000Z | tests/test_utils.py | isabella232/pynacl | b3f6c320569d858ba61d4bdf2ac788564528c1c9 | [
"Apache-2.0"
] | 540 | 2015-01-02T10:54:33.000Z | 2022-03-05T18:47:01.000Z | tests/test_utils.py | isabella232/pynacl | b3f6c320569d858ba61d4bdf2ac788564528c1c9 | [
"Apache-2.0"
] | 217 | 2015-01-09T00:48:01.000Z | 2022-03-26T08:53:32.000Z | # Copyright 2013 Donald Stufft and individual contributors
#
# 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... | 32.083333 | 75 | 0.725195 | 257 | 1,925 | 5.350195 | 0.540856 | 0.045818 | 0.032727 | 0.037091 | 0.196364 | 0.113455 | 0.055273 | 0.055273 | 0.055273 | 0.055273 | 0 | 0.14864 | 0.178701 | 1,925 | 59 | 76 | 32.627119 | 0.721063 | 0.298182 | 0 | 0 | 0 | 0.090909 | 0.327599 | 0.266268 | 0 | 0 | 0 | 0 | 0.151515 | 1 | 0.151515 | false | 0 | 0.090909 | 0 | 0.242424 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0aefad001e36b9eae9b3eb392972175239563b8d | 2,893 | py | Python | guesstheword.py | Cha0sNation/RandomPython | 7ba41d78f27bd90e9c09efcd4d5c26eac93e74ec | [
"MIT"
] | null | null | null | guesstheword.py | Cha0sNation/RandomPython | 7ba41d78f27bd90e9c09efcd4d5c26eac93e74ec | [
"MIT"
] | null | null | null | guesstheword.py | Cha0sNation/RandomPython | 7ba41d78f27bd90e9c09efcd4d5c26eac93e74ec | [
"MIT"
] | null | null | null | #! /home/cha0snation/anaconda3/bin/python
import random
def setup():
words = ["banana", "apple", "orange", "peach", "grape", "watermelon"]
output = []
word = words[random.randint(0, len(words) - 1)]
playing = True
tries = 5
return [words, output, word, tries, playing]
def check_finished(out... | 24.726496 | 73 | 0.483927 | 306 | 2,893 | 4.506536 | 0.27451 | 0.047861 | 0.049311 | 0.04351 | 0.187092 | 0.108774 | 0.062364 | 0.062364 | 0.062364 | 0.062364 | 0 | 0.009855 | 0.403733 | 2,893 | 116 | 74 | 24.939655 | 0.789565 | 0.027999 | 0 | 0.4 | 0 | 0 | 0.089324 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.052632 | false | 0 | 0.010526 | 0 | 0.147368 | 0.263158 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af0f43e75ad092a7a05698be61aa6dca9c4178e | 2,131 | py | Python | web_app/index.py | svakulenk0/ArtDATIS | 29e646f7bcb931e733ee248cc973411ffb18be64 | [
"MIT"
] | null | null | null | web_app/index.py | svakulenk0/ArtDATIS | 29e646f7bcb931e733ee248cc973411ffb18be64 | [
"MIT"
] | 9 | 2020-03-24T17:57:03.000Z | 2022-03-12T00:08:07.000Z | web_app/index.py | svakulenk0/ArtDATIS | 29e646f7bcb931e733ee248cc973411ffb18be64 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Created on Dec 8, 2019
.. codeauthor: svitlana vakulenko
<svitlana.vakulenko@gmail.com>
Index docs into ES
https://qbox.io/blog/building-an-elasticsearch-index-with-python
'''
from settings import *
import glob
import re
# n first characters for the doc preview
L... | 30.014085 | 99 | 0.603003 | 269 | 2,131 | 4.613383 | 0.442379 | 0.050766 | 0.067687 | 0.025786 | 0.043513 | 0.043513 | 0.043513 | 0 | 0 | 0 | 0 | 0.008755 | 0.249648 | 2,131 | 70 | 100 | 30.442857 | 0.767355 | 0.175504 | 0 | 0.045455 | 0 | 0 | 0.174512 | 0.01837 | 0 | 0 | 0 | 0 | 0 | 1 | 0.022727 | false | 0 | 0.113636 | 0 | 0.136364 | 0.181818 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af106828dec53475f13db7b60f12e654896ac46 | 277 | py | Python | src/tokens.py | PythonIsMagic/ponyup | 3b2630d573cd46d0569f713c6d4c3790688dc62d | [
"MIT"
] | 1 | 2022-03-22T12:41:35.000Z | 2022-03-22T12:41:35.000Z | src/tokens.py | PythonIsMagic/ponyup | 3b2630d573cd46d0569f713c6d4c3790688dc62d | [
"MIT"
] | null | null | null | src/tokens.py | PythonIsMagic/ponyup | 3b2630d573cd46d0569f713c6d4c3790688dc62d | [
"MIT"
] | 1 | 2022-03-22T12:41:37.000Z | 2022-03-22T12:41:37.000Z | """
A Token is a button or other object on the table that represents a position, a game state, layer state, or some other piece of info
"""
class Token(object):
def __init__(self, name, table):
self.table = table
self.name = name
self.seat = None
| 25.181818 | 131 | 0.65343 | 43 | 277 | 4.116279 | 0.627907 | 0.090395 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.263538 | 277 | 10 | 132 | 27.7 | 0.867647 | 0.472924 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0 | 0 | 0.4 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af1366c588c694d1d5fccc2c589b64a4b89883f | 1,089 | py | Python | Chapter09/interpolation_search.py | Xiangs18/Algorithms-with-Python-Second-Edition | 96844e1ae7054e099772dc691c1f41f15c2bfba5 | [
"MIT"
] | null | null | null | Chapter09/interpolation_search.py | Xiangs18/Algorithms-with-Python-Second-Edition | 96844e1ae7054e099772dc691c1f41f15c2bfba5 | [
"MIT"
] | null | null | null | Chapter09/interpolation_search.py | Xiangs18/Algorithms-with-Python-Second-Edition | 96844e1ae7054e099772dc691c1f41f15c2bfba5 | [
"MIT"
] | null | null | null | def nearest_mid(input_list, lower_bound_index, upper_bound_index, search_value):
return lower_bound_index + (
(upper_bound_index - lower_bound_index)
// (input_list[upper_bound_index] - input_list[lower_bound_index])
) * (search_value - input_list[lower_bound_index])
def interpolation_search(o... | 37.551724 | 83 | 0.693297 | 157 | 1,089 | 4.343949 | 0.267516 | 0.102639 | 0.109971 | 0.139296 | 0.409091 | 0.325513 | 0.108504 | 0.108504 | 0 | 0 | 0 | 0.0227 | 0.231405 | 1,089 | 28 | 84 | 38.892857 | 0.792115 | 0 | 0 | 0 | 0 | 0 | 0.02663 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.083333 | false | 0 | 0 | 0.041667 | 0.208333 | 0.041667 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af230c3ec87bec2b40fe4cc74ba6765304b22f0 | 13,752 | py | Python | src/macro_pack.py | lulinsheng/macro_pack | 4e9d0178354bad2aa557298f44ba5d4385a72a2b | [
"Apache-2.0"
] | null | null | null | src/macro_pack.py | lulinsheng/macro_pack | 4e9d0178354bad2aa557298f44ba5d4385a72a2b | [
"Apache-2.0"
] | null | null | null | src/macro_pack.py | lulinsheng/macro_pack | 4e9d0178354bad2aa557298f44ba5d4385a72a2b | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/python3
# encoding: utf-8
import os
import sys
import getopt
import logging
import shutil
import psutil
from modules.com_run import ComGenerator
from modules.web_server import ListenServer
from modules.Wlisten_server import WListenServer
from modules.payload_builder_factory import PayloadBuilderFactory
from... | 40.210526 | 171 | 0.592568 | 1,449 | 13,752 | 5.572119 | 0.257419 | 0.023408 | 0.013624 | 0.015606 | 0.183924 | 0.125093 | 0.076666 | 0.076666 | 0.072331 | 0.06428 | 0 | 0.007341 | 0.296684 | 13,752 | 341 | 172 | 40.328446 | 0.82744 | 0.104639 | 0 | 0.296154 | 0 | 0 | 0.144068 | 0.011653 | 0 | 0 | 0 | 0 | 0 | 1 | 0.003846 | false | 0.011538 | 0.080769 | 0 | 0.084615 | 0.053846 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af340336c716992b681bade66c39e840439919b | 6,148 | py | Python | etl/load/elasticsearch.py | bilalelhoudaigui/plant-brapi-etl-data-lookup-gnpis | 973dc444eac6d1cc80c020dd8b9a4656f70eeafb | [
"BSD-3-Clause"
] | 3 | 2018-06-04T09:14:55.000Z | 2018-10-25T14:32:03.000Z | etl/load/elasticsearch.py | bilalelhoudaigui/plant-brapi-etl-data-lookup-gnpis | 973dc444eac6d1cc80c020dd8b9a4656f70eeafb | [
"BSD-3-Clause"
] | 18 | 2020-06-04T07:08:17.000Z | 2022-02-02T17:02:17.000Z | etl/load/elasticsearch.py | bilalelhoudaigui/plant-brapi-etl-data-lookup-gnpis | 973dc444eac6d1cc80c020dd8b9a4656f70eeafb | [
"BSD-3-Clause"
] | 4 | 2019-04-18T12:53:19.000Z | 2019-11-22T08:53:19.000Z | # Load json bulk files into elasticsearch
import json
import os
import time
import traceback
import elasticsearch
from etl.common.store import list_entity_files
from etl.common.utils import get_folder_path, get_file_path, create_logger, first, replace_template
class ElasticSearchException(Exception):
pass
# I... | 40.183007 | 121 | 0.689655 | 788 | 6,148 | 5.101523 | 0.214467 | 0.071642 | 0.051741 | 0.025373 | 0.218159 | 0.169403 | 0.151244 | 0.08209 | 0.062687 | 0 | 0 | 0.001011 | 0.195348 | 6,148 | 152 | 122 | 40.447368 | 0.811603 | 0.019031 | 0 | 0.018349 | 0 | 0 | 0.177752 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.091743 | false | 0.009174 | 0.06422 | 0 | 0.192661 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af3b89835e63f3225a17831847f039cebf091f8 | 6,798 | py | Python | geoplot/crs.py | redfrexx/geoplot | 8231baab0e286f1dec870dd5e8c6c8218e5b5da7 | [
"MIT"
] | null | null | null | geoplot/crs.py | redfrexx/geoplot | 8231baab0e286f1dec870dd5e8c6c8218e5b5da7 | [
"MIT"
] | null | null | null | geoplot/crs.py | redfrexx/geoplot | 8231baab0e286f1dec870dd5e8c6c8218e5b5da7 | [
"MIT"
] | null | null | null | """
This module defines the ``geoplot`` coordinate reference system classes, wrappers on
``cartopy.crs`` objects meant to be used as parameters to the ``projection`` parameter of all
front-end ``geoplot`` outputs. For the list of Cartopy CRS objects this module derives from,
refer to http://scitools.org.uk/cartopy/docs... | 39.523256 | 98 | 0.624595 | 759 | 6,798 | 5.536232 | 0.380764 | 0.026178 | 0.014993 | 0.00476 | 0.159448 | 0.131842 | 0.131842 | 0.131842 | 0.131842 | 0.131842 | 0 | 0 | 0.288614 | 6,798 | 171 | 99 | 39.754386 | 0.8689 | 0.601942 | 0 | 0.028169 | 0 | 0 | 0.143761 | 0.023056 | 0 | 0 | 0 | 0.005848 | 0 | 1 | 0.056338 | false | 0 | 0.028169 | 0 | 0.211268 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af473baeece942d5629ff430bbc40a3d23df7c3 | 559 | py | Python | tmoga/utils/SDE.py | zjg540066169/tmoga | a3c3ecd0d72fc7c57fd5e5a624780e7ebf199c61 | [
"Apache-2.0"
] | 2 | 2021-10-06T04:45:52.000Z | 2022-03-20T01:18:05.000Z | tmoga/utils/SDE.py | zjg540066169/tmoga | a3c3ecd0d72fc7c57fd5e5a624780e7ebf199c61 | [
"Apache-2.0"
] | 1 | 2022-03-20T01:45:09.000Z | 2022-03-21T15:17:21.000Z | tmoga/utils/SDE.py | zjg540066169/tmoga | a3c3ecd0d72fc7c57fd5e5a624780e7ebf199c61 | [
"Apache-2.0"
] | 3 | 2021-10-09T08:08:44.000Z | 2022-03-20T01:18:07.000Z | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Provide function to calculate SDE distance
@auth: Jungang Zou
@date: 2021/05/05
"""
def SDE(front, values1, values2):
shifted_dict = {}
for i in front:
shifted_dict[i] = [(values1[i], values2[i])]
shifted_list = []
for j in front:
... | 25.409091 | 95 | 0.554562 | 71 | 559 | 4.267606 | 0.507042 | 0.145215 | 0.079208 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.046154 | 0.302326 | 559 | 22 | 96 | 25.409091 | 0.730769 | 0.221825 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.083333 | false | 0 | 0 | 0 | 0.166667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af54c84e47849c156e92dd294fed072b3ed4861 | 1,183 | py | Python | tests/v3_validation/cattlevalidationtest/core/test_logs_api.py | bmdepesa/validation-tests | 23e7ab95ce76744483a0657f790b42a88a93436d | [
"Apache-2.0"
] | 7 | 2015-11-18T17:43:08.000Z | 2021-07-14T09:48:18.000Z | tests/v3_validation/cattlevalidationtest/core/test_logs_api.py | bmdepesa/validation-tests | 23e7ab95ce76744483a0657f790b42a88a93436d | [
"Apache-2.0"
] | 175 | 2015-07-09T18:41:24.000Z | 2021-06-10T21:23:27.000Z | tests/v3_validation/cattlevalidationtest/core/test_logs_api.py | bmdepesa/validation-tests | 23e7ab95ce76744483a0657f790b42a88a93436d | [
"Apache-2.0"
] | 25 | 2015-08-08T04:54:24.000Z | 2021-05-25T21:10:37.000Z | from common_fixtures import * # NOQA
import websocket as ws
import pytest
def get_logs(client):
hosts = client.list_host(kind='docker', removed_null=True)
assert len(hosts) > 0
in_log = random_str()
cmd = '/bin/bash -c "echo {}; sleep 2"'.format(in_log)
c = client.create_container(image=TEST_IMAG... | 28.853659 | 68 | 0.687236 | 176 | 1,183 | 4.426136 | 0.380682 | 0.032092 | 0.066752 | 0.053915 | 0.455712 | 0.455712 | 0.372272 | 0.372272 | 0.372272 | 0.372272 | 0 | 0.008403 | 0.195266 | 1,183 | 40 | 69 | 29.575 | 0.809874 | 0.003381 | 0 | 0.290323 | 0 | 0 | 0.094308 | 0.02294 | 0 | 0 | 0 | 0 | 0.16129 | 1 | 0.129032 | false | 0 | 0.096774 | 0 | 0.258065 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af634a53b2ebcc4683b0c1863c9043af5a4905d | 1,090 | py | Python | drybell/drybell_lfs_spark.py | jsnlp/snorkel-tutorials | b4cda9f918daf77f4011ec1598c08d9bd7e51c39 | [
"Apache-2.0"
] | 315 | 2019-07-27T22:49:20.000Z | 2022-03-30T10:02:02.000Z | drybell/drybell_lfs_spark.py | jsnlp/snorkel-tutorials | b4cda9f918daf77f4011ec1598c08d9bd7e51c39 | [
"Apache-2.0"
] | 133 | 2019-07-25T02:07:37.000Z | 2022-03-29T12:08:32.000Z | drybell/drybell_lfs_spark.py | jsnlp/snorkel-tutorials | b4cda9f918daf77f4011ec1598c08d9bd7e51c39 | [
"Apache-2.0"
] | 173 | 2019-08-13T02:27:11.000Z | 2022-03-30T05:26:40.000Z | from pyspark.sql import Row
from snorkel.labeling.lf import labeling_function
from snorkel.labeling.lf.nlp_spark import spark_nlp_labeling_function
from snorkel.preprocess import preprocessor
from drybell_lfs import load_celebrity_knowledge_base
ABSTAIN = -1
NEGATIVE = 0
POSITIVE = 1
@preprocessor()
def combine_tex... | 26.585366 | 83 | 0.748624 | 155 | 1,090 | 5.019355 | 0.335484 | 0.102828 | 0.141388 | 0.092545 | 0.22108 | 0.22108 | 0.22108 | 0.22108 | 0.22108 | 0.22108 | 0 | 0.003275 | 0.159633 | 1,090 | 40 | 84 | 27.25 | 0.84607 | 0 | 0 | 0.133333 | 0 | 0 | 0.046789 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.133333 | false | 0 | 0.166667 | 0.066667 | 0.5 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af886d3e8e59b20a8f0a8f86ad88dbe765599d2 | 14,441 | py | Python | python/influx/database_tables.py | SA-22C-smoothswing/spectrum-protect-sppmon | 8a9c70f65d9faf6ffc35f3400383dcaa6e0fcbc6 | [
"Apache-2.0"
] | null | null | null | python/influx/database_tables.py | SA-22C-smoothswing/spectrum-protect-sppmon | 8a9c70f65d9faf6ffc35f3400383dcaa6e0fcbc6 | [
"Apache-2.0"
] | null | null | null | python/influx/database_tables.py | SA-22C-smoothswing/spectrum-protect-sppmon | 8a9c70f65d9faf6ffc35f3400383dcaa6e0fcbc6 | [
"Apache-2.0"
] | null | null | null | """Provides all database and table structures used for the influx database.
Classes:
Datatype
Database
Table
RetentionPolicy
"""
from __future__ import annotations
from enum import Enum, unique
import re
import json
from typing import Any, Dict, List, Set, Tuple, Union
import influx.influx_queries as ... | 36.012469 | 119 | 0.628696 | 1,799 | 14,441 | 4.912729 | 0.1801 | 0.037339 | 0.019348 | 0.019009 | 0.259335 | 0.206721 | 0.101607 | 0.086445 | 0.063363 | 0.0568 | 0 | 0.000489 | 0.291531 | 14,441 | 400 | 120 | 36.1025 | 0.863356 | 0.326224 | 0 | 0.277778 | 0 | 0 | 0.09887 | 0.01083 | 0 | 0 | 0 | 0.0025 | 0 | 1 | 0.156566 | false | 0 | 0.045455 | 0.035354 | 0.414141 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0af95702c3886ad24fef9b7d2bef0b353d7f0d8a | 5,779 | py | Python | eval_encoder.py | lithium0003/Image2UTF8-Transformer | 2620af2a8bdaf332e25b39ce05d610e21e6492fc | [
"MIT"
] | null | null | null | eval_encoder.py | lithium0003/Image2UTF8-Transformer | 2620af2a8bdaf332e25b39ce05d610e21e6492fc | [
"MIT"
] | null | null | null | eval_encoder.py | lithium0003/Image2UTF8-Transformer | 2620af2a8bdaf332e25b39ce05d610e21e6492fc | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
try:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
except:
# Invalid device or cannot modify virtual devices once initialized.
pass
import numpy as np
import os, time, csv
import ... | 37.525974 | 167 | 0.554767 | 693 | 5,779 | 4.484848 | 0.310245 | 0.028314 | 0.020914 | 0.015444 | 0.086229 | 0.080438 | 0.080438 | 0.080438 | 0.080438 | 0.058559 | 0 | 0.022977 | 0.307147 | 5,779 | 153 | 168 | 37.771242 | 0.753247 | 0.015055 | 0 | 0.053435 | 0 | 0 | 0.113708 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.038168 | false | 0.007634 | 0.091603 | 0 | 0.152672 | 0.038168 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0afa87a4b421519306afb64f3b1e1263669a468c | 22,351 | py | Python | clipper_admin/clipper_admin/clipper_admin.py | SimonZsx/clipper | 457088be2ebe68c68b94d90389d1308e35b4c844 | [
"Apache-2.0"
] | 2 | 2019-04-24T13:46:28.000Z | 2019-05-28T06:59:26.000Z | clipper_admin/clipper_admin/clipper_admin.py | SimonZsx/clipper | 457088be2ebe68c68b94d90389d1308e35b4c844 | [
"Apache-2.0"
] | null | null | null | clipper_admin/clipper_admin/clipper_admin.py | SimonZsx/clipper | 457088be2ebe68c68b94d90389d1308e35b4c844 | [
"Apache-2.0"
] | 4 | 2019-04-03T11:03:57.000Z | 2019-06-26T08:22:38.000Z | from __future__ import absolute_import, division, print_function
import logging
import docker
import tempfile
import requests
from requests.exceptions import RequestException
import json
import pprint
import time
import re
import os
import tarfile
import sys
from cloudpickle import CloudPickler
import pickle
import num... | 39.629433 | 176 | 0.579437 | 2,542 | 22,351 | 4.894571 | 0.166798 | 0.018325 | 0.027005 | 0.003858 | 0.457402 | 0.39865 | 0.304131 | 0.257836 | 0.257836 | 0.228098 | 0 | 0.011674 | 0.321641 | 22,351 | 563 | 177 | 39.699822 | 0.80893 | 0.291441 | 0 | 0.284698 | 0 | 0 | 0.135436 | 0.013385 | 0 | 0 | 0 | 0.001776 | 0 | 1 | 0.064057 | false | 0 | 0.113879 | 0 | 0.19573 | 0.007117 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0afc21eecdc60b266d8862b6f28eebf607699a5d | 48,451 | py | Python | chevah/compat/testing/testcase.py | chevah/compat | d22e5f551a628f8a1652c9f2eea306e17930cb8f | [
"BSD-3-Clause"
] | 5 | 2016-12-03T22:54:50.000Z | 2021-11-17T11:17:39.000Z | chevah/compat/testing/testcase.py | chevah/compat | d22e5f551a628f8a1652c9f2eea306e17930cb8f | [
"BSD-3-Clause"
] | 76 | 2015-01-22T16:00:31.000Z | 2022-02-09T22:13:34.000Z | chevah/compat/testing/testcase.py | chevah/compat | d22e5f551a628f8a1652c9f2eea306e17930cb8f | [
"BSD-3-Clause"
] | 1 | 2016-12-10T15:57:31.000Z | 2016-12-10T15:57:31.000Z | # -*- coding: utf-8 -*-
# Copyright (c) 2011 Adi Roiban.
# See LICENSE for details.
"""
TestCase used for Chevah project.
"""
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
from six import text_type
from six.moves import range
import contextlib
import inspec... | 33.049795 | 78 | 0.593053 | 5,209 | 48,451 | 5.408716 | 0.156652 | 0.008873 | 0.012778 | 0.016895 | 0.269539 | 0.209945 | 0.184319 | 0.168418 | 0.152623 | 0.134237 | 0 | 0.004362 | 0.32342 | 48,451 | 1,465 | 79 | 33.072355 | 0.855099 | 0.26823 | 0 | 0.30397 | 0 | 0 | 0.059196 | 0.00317 | 0 | 0 | 0 | 0.005461 | 0.065757 | 1 | 0.095534 | false | 0.003722 | 0.042184 | 0 | 0.244417 | 0.002481 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0afe13064838542a197bda7a6f3924d3d020b310 | 1,912 | py | Python | generative_deep_learning/build_network.py | slaily/deep-learning-bits | cb9ce7ec539efbdfcaa023d141466f919bd31b71 | [
"MIT"
] | null | null | null | generative_deep_learning/build_network.py | slaily/deep-learning-bits | cb9ce7ec539efbdfcaa023d141466f919bd31b71 | [
"MIT"
] | null | null | null | generative_deep_learning/build_network.py | slaily/deep-learning-bits | cb9ce7ec539efbdfcaa023d141466f919bd31b71 | [
"MIT"
] | null | null | null | from keras import layers
# Single-layer LSTM model for next-character prediction
model = keras.models.Sequential()
model.add(layers.LSTM(128, input_shape=(maxlen, len(chars))))
model.add(layers.Dense(len(chars), activation='softmax'))
# Model compilation configuration
optimizer = keras.optimizers.RMSprop(lr=0.01)
mod... | 33.54386 | 69 | 0.668933 | 260 | 1,912 | 4.834615 | 0.419231 | 0.072395 | 0.022275 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.028686 | 0.216004 | 1,912 | 56 | 70 | 34.142857 | 0.809873 | 0.1909 | 0 | 0 | 0 | 0 | 0.085231 | 0.015615 | 0 | 0 | 0 | 0 | 0 | 1 | 0.029412 | false | 0 | 0.088235 | 0 | 0.147059 | 0.088235 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
0afe544e807773d996329c44f23a45f84862abbe | 2,610 | py | Python | examples/MDF/states.py | 29riyasaxena/MDF | 476e6950d0f14f29463eb4f6e3be518dfb2160a5 | [
"Apache-2.0"
] | 12 | 2021-01-18T20:38:21.000Z | 2022-03-29T15:01:10.000Z | examples/MDF/states.py | 29riyasaxena/MDF | 476e6950d0f14f29463eb4f6e3be518dfb2160a5 | [
"Apache-2.0"
] | 101 | 2020-12-14T15:23:07.000Z | 2022-03-31T17:06:19.000Z | examples/MDF/states.py | 29riyasaxena/MDF | 476e6950d0f14f29463eb4f6e3be518dfb2160a5 | [
"Apache-2.0"
] | 15 | 2020-12-04T22:37:14.000Z | 2022-03-31T09:48:03.000Z | """
Example of ModECI MDF - Testing state variables
"""
from modeci_mdf.mdf import *
import sys
def main():
mod = Model(id="States")
mod_graph = Graph(id="state_example")
mod.graphs.append(mod_graph)
## Counter node
counter_node = Node(id="counter_node")
p1 = Parameter(id="increment", v... | 25.841584 | 104 | 0.591188 | 333 | 2,610 | 4.438438 | 0.366366 | 0.054127 | 0.081191 | 0.064953 | 0.182679 | 0.142084 | 0.058187 | 0.058187 | 0.058187 | 0 | 0 | 0.021937 | 0.283908 | 2,610 | 100 | 105 | 26.1 | 0.76886 | 0.069349 | 0 | 0 | 0 | 0 | 0.113646 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.014706 | false | 0 | 0.073529 | 0 | 0.102941 | 0.029412 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
e40074d263a071da246090065d0ad8ae39b4da28 | 20,118 | py | Python | gaia_tools/xmatch/__init__.py | henrysky/gaia_tools | c151a1d8f6896d8ef5a379291baa8a1f027bd53b | [
"MIT"
] | 44 | 2016-09-13T06:37:46.000Z | 2022-02-03T20:59:56.000Z | gaia_tools/xmatch/__init__.py | henrysky/gaia_tools | c151a1d8f6896d8ef5a379291baa8a1f027bd53b | [
"MIT"
] | 24 | 2016-10-18T23:26:15.000Z | 2020-12-08T18:24:27.000Z | gaia_tools/xmatch/__init__.py | henrysky/gaia_tools | c151a1d8f6896d8ef5a379291baa8a1f027bd53b | [
"MIT"
] | 18 | 2016-10-18T22:26:45.000Z | 2021-08-20T09:07:31.000Z | # Tools for cross-matching catalogs
import csv
import sys
import os
import os.path
import platform
import shutil
import subprocess
import tempfile
import warnings
WIN32= platform.system() == 'Windows'
import numpy
import astropy.coordinates as acoords
from astropy.table import Table
from astropy import units as u
fro... | 46.786047 | 261 | 0.607814 | 2,669 | 20,118 | 4.52042 | 0.186961 | 0.009532 | 0.011189 | 0.013925 | 0.424782 | 0.390966 | 0.341152 | 0.324824 | 0.287775 | 0.257605 | 0 | 0.037606 | 0.284919 | 20,118 | 429 | 262 | 46.895105 | 0.801057 | 0.329407 | 0 | 0.375 | 0 | 0.006579 | 0.083506 | 0.001615 | 0 | 0 | 0 | 0 | 0 | 1 | 0.023026 | false | 0.003289 | 0.049342 | 0 | 0.105263 | 0.003289 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
e400f6b243c2f7da007de4b3632bc30927997f62 | 14,873 | py | Python | rllib/agents/dqn/dqn_torch_policy.py | ThomasLecat/ray | eb025ea8cb27583e8ef6287f5654f23d1ab270ef | [
"Apache-2.0"
] | null | null | null | rllib/agents/dqn/dqn_torch_policy.py | ThomasLecat/ray | eb025ea8cb27583e8ef6287f5654f23d1ab270ef | [
"Apache-2.0"
] | null | null | null | rllib/agents/dqn/dqn_torch_policy.py | ThomasLecat/ray | eb025ea8cb27583e8ef6287f5654f23d1ab270ef | [
"Apache-2.0"
] | null | null | null | from typing import Dict, List, Tuple
import gym
import ray
from ray.rllib.agents.a3c.a3c_torch_policy import apply_grad_clipping
from ray.rllib.agents.dqn.dqn_tf_policy import (
PRIO_WEIGHTS, Q_SCOPE, Q_TARGET_SCOPE, postprocess_nstep_and_prio)
from ray.rllib.agents.dqn.dqn_torch_model import DQNTorchModel
from ra... | 39.76738 | 79 | 0.638741 | 1,897 | 14,873 | 4.666842 | 0.166579 | 0.010392 | 0.021688 | 0.02485 | 0.347227 | 0.286344 | 0.232125 | 0.228623 | 0.207387 | 0.20061 | 0 | 0.011001 | 0.272709 | 14,873 | 373 | 80 | 39.873995 | 0.807433 | 0.08902 | 0 | 0.255172 | 0 | 0 | 0.034024 | 0.003107 | 0 | 0 | 0 | 0.002681 | 0 | 1 | 0.044828 | false | 0 | 0.086207 | 0.013793 | 0.168966 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
e401cec76e2495c504bab2f84a98dc13530872c1 | 6,865 | py | Python | tests/integration/states/test_cmd.py | l2ol33rt/salt | ff68bbd9f4bda992a3e039822fb32f141e94347c | [
"Apache-2.0"
] | null | null | null | tests/integration/states/test_cmd.py | l2ol33rt/salt | ff68bbd9f4bda992a3e039822fb32f141e94347c | [
"Apache-2.0"
] | null | null | null | tests/integration/states/test_cmd.py | l2ol33rt/salt | ff68bbd9f4bda992a3e039822fb32f141e94347c | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
'''
Tests for the file state
'''
# Import python libs
from __future__ import absolute_import
import errno
import os
import textwrap
import tempfile
# Import Salt Testing libs
from tests.support.case import ModuleCase
from tests.support.paths import TMP_STATE_TREE
from tests.support.mixins impor... | 34.154229 | 94 | 0.57276 | 823 | 6,865 | 4.613609 | 0.207776 | 0.042139 | 0.031604 | 0.022123 | 0.486437 | 0.436134 | 0.410587 | 0.371609 | 0.342112 | 0.342112 | 0 | 0.006984 | 0.311726 | 6,865 | 200 | 95 | 34.325 | 0.796614 | 0.174654 | 0 | 0.441667 | 0 | 0 | 0.231852 | 0.018333 | 0 | 0 | 0 | 0 | 0.133333 | 1 | 0.1 | false | 0.008333 | 0.075 | 0 | 0.2 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
e402affb74681aeffbd7073f07e5537c7f847fc0 | 2,591 | py | Python | mars/tensor/execution/datastore.py | ChenQuan/mars | 46fc9747e99210cebfabfc2d85bcc8272440d1a3 | [
"Apache-2.0"
] | null | null | null | mars/tensor/execution/datastore.py | ChenQuan/mars | 46fc9747e99210cebfabfc2d85bcc8272440d1a3 | [
"Apache-2.0"
] | null | null | null | mars/tensor/execution/datastore.py | ChenQuan/mars | 46fc9747e99210cebfabfc2d85bcc8272440d1a3 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2018 Alibaba Group Holding Ltd.
#
# 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-... | 37.550725 | 89 | 0.63296 | 349 | 2,591 | 4.581662 | 0.406877 | 0.048155 | 0.03252 | 0.020013 | 0.08005 | 0.057536 | 0.057536 | 0.057536 | 0.057536 | 0.057536 | 0 | 0.012144 | 0.269008 | 2,591 | 68 | 90 | 38.102941 | 0.832101 | 0.247781 | 0 | 0.093023 | 0 | 0 | 0.001036 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.046512 | false | 0 | 0.186047 | 0 | 0.232558 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
e4041f8f3f0e170375ff7b152259c16fb293ef71 | 1,689 | py | Python | fastgc/model/mlp.py | ppmlguy/fastgradclip | 0d8bff42ab13fa3471c520a2823050ccf0ff4a21 | [
"MIT"
] | 2 | 2020-10-16T10:14:25.000Z | 2021-03-25T17:19:34.000Z | fastgc/model/mlp.py | ppmlguy/fastgradclip | 0d8bff42ab13fa3471c520a2823050ccf0ff4a21 | [
"MIT"
] | null | null | null | fastgc/model/mlp.py | ppmlguy/fastgradclip | 0d8bff42ab13fa3471c520a2823050ccf0ff4a21 | [
"MIT"
] | null | null | null | import torch
import torch.nn as nn
import torch.nn.functional as F
from fastgc.model.penet import PeGradNet
from fastgc.layers.linear import Linear
from fastgc.activation import activation
class MLP(PeGradNet):
def __init__(self, input_size, hidden_sizes, output_size, act_func='sigmoid',
train_al... | 35.1875 | 102 | 0.605684 | 216 | 1,689 | 4.560185 | 0.342593 | 0.054822 | 0.039594 | 0.034518 | 0.04264 | 0 | 0 | 0 | 0 | 0 | 0 | 0.003378 | 0.298993 | 1,689 | 47 | 103 | 35.93617 | 0.828547 | 0.248668 | 0 | 0 | 0 | 0 | 0.010033 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.074074 | false | 0 | 0.222222 | 0 | 0.37037 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
e407a1b65cd96d68a622c0a025047b036e6148f4 | 21,659 | py | Python | test_vector_handlers/src/awses_test_vectors/manifests/full_message/decrypt_generation.py | farleyb-amazon/aws-encryption-sdk-python | 7950abd73ee333407d2dadd02ef2d57c3df464cf | [
"Apache-2.0"
] | 95 | 2018-08-20T23:10:00.000Z | 2022-02-17T02:54:32.000Z | test_vector_handlers/src/awses_test_vectors/manifests/full_message/decrypt_generation.py | farleyb-amazon/aws-encryption-sdk-python | 7950abd73ee333407d2dadd02ef2d57c3df464cf | [
"Apache-2.0"
] | 220 | 2018-08-01T20:56:29.000Z | 2022-03-28T18:12:35.000Z | test_vector_handlers/src/awses_test_vectors/manifests/full_message/decrypt_generation.py | farleyb-amazon/aws-encryption-sdk-python | 7950abd73ee333407d2dadd02ef2d57c3df464cf | [
"Apache-2.0"
] | 63 | 2018-08-01T19:37:33.000Z | 2022-03-20T17:14:15.000Z | # Copyright 2018 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompa... | 43.755556 | 120 | 0.722009 | 2,431 | 21,659 | 6.191279 | 0.160839 | 0.020929 | 0.02259 | 0.015149 | 0.349678 | 0.303701 | 0.262773 | 0.229021 | 0.20889 | 0.194007 | 0 | 0.004625 | 0.211367 | 21,659 | 494 | 121 | 43.84413 | 0.876529 | 0.273466 | 0 | 0.156934 | 0 | 0 | 0.035686 | 0.008256 | 0 | 0 | 0 | 0 | 0 | 1 | 0.083942 | false | 0.00365 | 0.080292 | 0.00365 | 0.335766 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
e40ca767179088e9b2626907b90dc14b9802c60c | 10,237 | py | Python | atmpro1_vsm2.py | joselynzhao/One-shot-Person-Re-ID-ATM | d039b1a66410f87cfe931774eba54a5f1a1a0260 | [
"MIT"
] | 3 | 2020-07-28T03:16:51.000Z | 2020-11-23T05:39:54.000Z | atmpro1_vsm2.py | joselynzhao/One-shot-Person-Re-ID-ATM | d039b1a66410f87cfe931774eba54a5f1a1a0260 | [
"MIT"
] | null | null | null | atmpro1_vsm2.py | joselynzhao/One-shot-Person-Re-ID-ATM | d039b1a66410f87cfe931774eba54a5f1a1a0260 | [
"MIT"
] | null | null | null | #!/usr/bin/python3.6
# -*- coding: utf-8 -*-
# @Time : 2020/9/3 上午11:03
# @Author : Joselynzhao
# @Email : zhaojing17@forxmail.com
# @File : atmpro1_vsm2.py
# @Software: PyCharm
# @Desc :
#!/usr/bin/python3.6
# -*- coding: utf-8 -*-
# @Time : 2020/9/1 下午7:07
# @Author : Joselynzhao
# @Email : zhaojin... | 41.783673 | 225 | 0.65019 | 1,460 | 10,237 | 4.296575 | 0.197945 | 0.037303 | 0.054201 | 0.01913 | 0.38578 | 0.344492 | 0.295234 | 0.227164 | 0.184441 | 0.184441 | 0 | 0.020705 | 0.207385 | 10,237 | 244 | 226 | 41.954918 | 0.752403 | 0.123376 | 0 | 0.053571 | 0 | 0 | 0.099921 | 0.015061 | 0 | 0 | 0 | 0 | 0 | 1 | 0.011905 | false | 0 | 0.125 | 0 | 0.142857 | 0.065476 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c0e42d68dd892a292e20be61de2cca89811eb9b | 6,252 | py | Python | consumer/tests/test__index_handler.py | eHealthAfrica/aether-elasticsearch-consumer | fc29a1da8cfd7482257b1023b50a1a43372886c5 | [
"Apache-2.0"
] | null | null | null | consumer/tests/test__index_handler.py | eHealthAfrica/aether-elasticsearch-consumer | fc29a1da8cfd7482257b1023b50a1a43372886c5 | [
"Apache-2.0"
] | 8 | 2018-08-02T09:11:22.000Z | 2021-09-13T14:12:22.000Z | consumer/tests/test__index_handler.py | eHealthAfrica/aether-elasticsearch-consumer | fc29a1da8cfd7482257b1023b50a1a43372886c5 | [
"Apache-2.0"
] | 1 | 2019-10-29T11:29:32.000Z | 2019-10-29T11:29:32.000Z | # Copyright (C) 2019 by eHealth Africa : http://www.eHealthAfrica.org
#
# See the NOTICE file distributed with this work for additional information
# regarding copyright ownership.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with
# the License. Y... | 31.736041 | 85 | 0.679303 | 812 | 6,252 | 5.008621 | 0.261084 | 0.044259 | 0.030981 | 0.03762 | 0.3612 | 0.313253 | 0.22744 | 0.198918 | 0.198918 | 0.198918 | 0 | 0.009053 | 0.1873 | 6,252 | 196 | 86 | 31.897959 | 0.79138 | 0.143154 | 0 | 0.288732 | 0 | 0 | 0.15503 | 0.031907 | 0 | 0 | 0 | 0 | 0.176056 | 1 | 0.070423 | false | 0 | 0.06338 | 0 | 0.133803 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c0f8b607ed4a4992f5429c04c93d80a3e6a70fc | 9,656 | py | Python | tests/test_api_transaction.py | preston-wagner/authorizesauce | 130ee30f500c8b5bf9a6384296ca4f5d5bb565e7 | [
"MIT"
] | null | null | null | tests/test_api_transaction.py | preston-wagner/authorizesauce | 130ee30f500c8b5bf9a6384296ca4f5d5bb565e7 | [
"MIT"
] | null | null | null | tests/test_api_transaction.py | preston-wagner/authorizesauce | 130ee30f500c8b5bf9a6384296ca4f5d5bb565e7 | [
"MIT"
] | 1 | 2020-06-17T15:48:46.000Z | 2020-06-17T15:48:46.000Z | from datetime import date
from six import BytesIO, binary_type, u
from six.moves.urllib.parse import parse_qsl, urlencode
from unittest2 import TestCase
import mock
from authorizesauce.apis.transaction import PROD_URL, TEST_URL, TransactionAPI
from authorizesauce.data import Address, CreditCard
from authorizesauce.e... | 40.06639 | 79 | 0.629453 | 1,225 | 9,656 | 4.730612 | 0.149388 | 0.059534 | 0.041415 | 0.041933 | 0.69698 | 0.662985 | 0.630026 | 0.586885 | 0.556514 | 0.541329 | 0 | 0.065495 | 0.222038 | 9,656 | 240 | 80 | 40.233333 | 0.705937 | 0.012842 | 0 | 0.487923 | 0 | 0.009662 | 0.310117 | 0.185629 | 0 | 0 | 0 | 0 | 0.15942 | 1 | 0.082126 | false | 0 | 0.038647 | 0 | 0.15942 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c11512944aa360a8ca2b2179d573b01222bea5e | 2,621 | py | Python | build_json.py | sungpyocho/covid19-aichi-tools | 5170bf405f67b14179fe10838701ec5baa9d6cc1 | [
"MIT"
] | null | null | null | build_json.py | sungpyocho/covid19-aichi-tools | 5170bf405f67b14179fe10838701ec5baa9d6cc1 | [
"MIT"
] | null | null | null | build_json.py | sungpyocho/covid19-aichi-tools | 5170bf405f67b14179fe10838701ec5baa9d6cc1 | [
"MIT"
] | null | null | null | import csv
import io
import json
import pandas as pd
import sys
from dateutil import tz
from datetime import datetime, date, time, timedelta
# Japan Standard Time (UTC + 09:00)
JST = tz.gettz('Asia/Tokyo')
JST_current_time = datetime.now(tz=JST).strftime('%Y/%m/%d %H:%M')
patients_list = []
patients_summary_dic = {}... | 28.48913 | 96 | 0.655857 | 343 | 2,621 | 4.819242 | 0.341108 | 0.108893 | 0.016334 | 0.039927 | 0.195402 | 0.136721 | 0.078645 | 0.078645 | 0.078645 | 0.078645 | 0 | 0.011342 | 0.192675 | 2,621 | 91 | 97 | 28.802198 | 0.769849 | 0.076307 | 0 | 0.147059 | 0 | 0 | 0.137344 | 0.023651 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.102941 | 0 | 0.102941 | 0.014706 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c138f84c229bf0a17e877706fc36f489907d8bf | 23,732 | py | Python | scipy/optimize/_numdiff.py | jeremiedbb/scipy | 2bea64c334b18fd445a7945b350d7ace2dc22913 | [
"BSD-3-Clause"
] | 1 | 2019-12-19T16:51:27.000Z | 2019-12-19T16:51:27.000Z | scipy/optimize/_numdiff.py | jeremiedbb/scipy | 2bea64c334b18fd445a7945b350d7ace2dc22913 | [
"BSD-3-Clause"
] | null | null | null | scipy/optimize/_numdiff.py | jeremiedbb/scipy | 2bea64c334b18fd445a7945b350d7ace2dc22913 | [
"BSD-3-Clause"
] | null | null | null | """Routines for numerical differentiation."""
from __future__ import division
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
EPS = np.finfo(n... | 37.08125 | 79 | 0.583727 | 3,358 | 23,732 | 4.030673 | 0.149792 | 0.013594 | 0.016254 | 0.007093 | 0.334318 | 0.25652 | 0.226745 | 0.185962 | 0.170743 | 0.151385 | 0 | 0.025248 | 0.312405 | 23,732 | 639 | 80 | 37.13928 | 0.804204 | 0.488454 | 0 | 0.296167 | 0 | 0 | 0.050031 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.045296 | false | 0.003484 | 0.020906 | 0 | 0.135889 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c147e3dd10a5e110c033ad9ba1df174aabe3c39 | 20,303 | py | Python | tests/models/test_hparams.py | abhinavg97/pytorch-lightning | 0d54cf25a2dba33e4640ac52768a83406e7a0a94 | [
"Apache-2.0"
] | 1 | 2020-10-26T09:02:08.000Z | 2020-10-26T09:02:08.000Z | tests/models/test_hparams.py | vivektalwar13071999/pytorch-lightning | 7c4f80a1afe3d7b0f1e9ee834aacaf8439195cdf | [
"Apache-2.0"
] | null | null | null | tests/models/test_hparams.py | vivektalwar13071999/pytorch-lightning | 7c4f80a1afe3d7b0f1e9ee834aacaf8439195cdf | [
"Apache-2.0"
] | null | null | null | # Copyright The PyTorch Lightning team.
#
# 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 i... | 33.174837 | 116 | 0.713441 | 2,501 | 20,303 | 5.513395 | 0.153139 | 0.061281 | 0.050548 | 0.025455 | 0.548046 | 0.477192 | 0.430271 | 0.391471 | 0.345348 | 0.308217 | 0 | 0.015879 | 0.184209 | 20,303 | 611 | 117 | 33.229133 | 0.81664 | 0.19736 | 0 | 0.429379 | 0 | 0 | 0.030618 | 0 | 0 | 0 | 0 | 0 | 0.135593 | 1 | 0.144068 | false | 0.002825 | 0.039548 | 0.00565 | 0.276836 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c149f4f2e879ee66f71bed92f16a685a097e92b | 20,142 | py | Python | tests/space_test.py | hadrianmontes/jax-md | cea1cc6b22db6044a502eeeab4bddde35ac15d94 | [
"ECL-2.0",
"Apache-2.0"
] | 713 | 2019-05-14T19:02:00.000Z | 2022-03-31T17:42:23.000Z | tests/space_test.py | hadrianmontes/jax-md | cea1cc6b22db6044a502eeeab4bddde35ac15d94 | [
"ECL-2.0",
"Apache-2.0"
] | 109 | 2019-05-15T13:27:09.000Z | 2022-03-17T16:15:59.000Z | tests/space_test.py | hadrianmontes/jax-md | cea1cc6b22db6044a502eeeab4bddde35ac15d94 | [
"ECL-2.0",
"Apache-2.0"
] | 117 | 2019-05-17T13:23:37.000Z | 2022-03-18T10:32:29.000Z | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 35.52381 | 88 | 0.666369 | 2,840 | 20,142 | 4.416901 | 0.085915 | 0.118622 | 0.073661 | 0.043527 | 0.714126 | 0.670839 | 0.645089 | 0.593192 | 0.563217 | 0.560985 | 0 | 0.015215 | 0.220137 | 20,142 | 566 | 89 | 35.586572 | 0.783359 | 0.031874 | 0 | 0.541667 | 0 | 0 | 0.067248 | 0.020483 | 0 | 0 | 0 | 0 | 0.076389 | 1 | 0.046296 | false | 0 | 0.025463 | 0.00463 | 0.081019 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c14cbf83bd9f7d5d27ebfe3490cc6f31c415451 | 246 | py | Python | functions/batch-custom-action/status-api/lambda.py | TrollPursePublishing/trollpurse-trollops | 27e54cfd1ba1eed27097e2e3038dfab56691cf49 | [
"Xnet",
"Linux-OpenIB",
"X11"
] | 2 | 2020-11-18T06:04:27.000Z | 2021-04-22T12:38:15.000Z | functions/batch-custom-action/status-api/lambda.py | TrollPursePublishing/trollpurse-ops | 27e54cfd1ba1eed27097e2e3038dfab56691cf49 | [
"Xnet",
"Linux-OpenIB",
"X11"
] | null | null | null | functions/batch-custom-action/status-api/lambda.py | TrollPursePublishing/trollpurse-ops | 27e54cfd1ba1eed27097e2e3038dfab56691cf49 | [
"Xnet",
"Linux-OpenIB",
"X11"
] | null | null | null | import boto3
batch_client = boto3.client('batch')
def lambda_handler(event, context):
describe_response = batch_client.describe_jobs(
jobs=[ event.get('jobId', '')]
)
return describe_response.get('jobs', [{}])[0].get('status', '')
| 22.363636 | 65 | 0.678862 | 30 | 246 | 5.366667 | 0.566667 | 0.136646 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014151 | 0.138211 | 246 | 10 | 66 | 24.6 | 0.745283 | 0 | 0 | 0 | 0 | 0 | 0.081301 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.142857 | false | 0 | 0.142857 | 0 | 0.428571 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c170adc77db7c06c4c5968ae2d5e3df343748b4 | 776 | py | Python | python97/chapter05/list_gen.py | youaresherlock/PythonPractice | 2e22d3fdcb26353cb0d8215c150e84d11bc9a022 | [
"Apache-2.0"
] | null | null | null | python97/chapter05/list_gen.py | youaresherlock/PythonPractice | 2e22d3fdcb26353cb0d8215c150e84d11bc9a022 | [
"Apache-2.0"
] | null | null | null | python97/chapter05/list_gen.py | youaresherlock/PythonPractice | 2e22d3fdcb26353cb0d8215c150e84d11bc9a022 | [
"Apache-2.0"
] | 1 | 2019-11-05T01:10:15.000Z | 2019-11-05T01:10:15.000Z | #!usr/bin/python
# -*- coding:utf8 -*-
# 列表生成式(列表推导式)
# 1. 提取出1-20之间的奇数
# odd_list = []
# for i in range(21):
# if i % 2 == 1:
# odd_list.append(i)
# odd_list = [i for i in range(21) if i % 2 == 1]
# print(odd_list)
# 2. 逻辑复杂的情况 如果是奇数将结果平方
# 列表生成式性能高于列表操作
def handle_item(item):
return item * item
odd... | 15.836735 | 61 | 0.627577 | 137 | 776 | 3.408759 | 0.350365 | 0.089936 | 0.051392 | 0.094218 | 0.359743 | 0.359743 | 0.359743 | 0.359743 | 0.359743 | 0.184154 | 0 | 0.047308 | 0.210052 | 776 | 48 | 62 | 16.166667 | 0.714519 | 0.338918 | 0 | 0 | 0 | 0 | 0.043299 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.071429 | false | 0 | 0 | 0.071429 | 0.142857 | 0.357143 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c17743faf77b54c0516f30699a3b1dc9b050a25 | 11,409 | py | Python | src/streamlink/plugin/plugin.py | isqad/streamlink | f6708f1d38d056177ac3d614ebbb740d956d46f0 | [
"BSD-2-Clause"
] | 1 | 2017-11-26T18:48:29.000Z | 2017-11-26T18:48:29.000Z | src/streamlink/plugin/plugin.py | isqad/streamlink | f6708f1d38d056177ac3d614ebbb740d956d46f0 | [
"BSD-2-Clause"
] | null | null | null | src/streamlink/plugin/plugin.py | isqad/streamlink | f6708f1d38d056177ac3d614ebbb740d956d46f0 | [
"BSD-2-Clause"
] | 1 | 2021-06-03T23:08:48.000Z | 2021-06-03T23:08:48.000Z | import ast
import operator
import re
from collections import OrderedDict
from functools import partial
from ..cache import Cache
from ..exceptions import PluginError, NoStreamsError
from ..options import Options
# FIXME: This is a crude attempt at making a bitrate's
# weight end up similar to the weight of a resolut... | 29.104592 | 101 | 0.573582 | 1,333 | 11,409 | 4.781695 | 0.250563 | 0.034515 | 0.009413 | 0.008472 | 0.085817 | 0.037967 | 0.009099 | 0 | 0 | 0 | 0 | 0.012887 | 0.326672 | 11,409 | 391 | 102 | 29.179028 | 0.816845 | 0.22193 | 0 | 0.131579 | 0 | 0.004386 | 0.068714 | 0.015884 | 0 | 0 | 0 | 0.002558 | 0 | 1 | 0.096491 | false | 0.004386 | 0.035088 | 0.017544 | 0.25 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c1898e479d14fbe657ed1376514f87c04d2b942 | 2,971 | py | Python | swav/vissl/vissl/data/ssl_transforms/img_patches_tensor.py | lhoestq/DeDLOC | 36f5a6d043c3d727f9d098a35fba94aa351a5cd4 | [
"Apache-2.0"
] | null | null | null | swav/vissl/vissl/data/ssl_transforms/img_patches_tensor.py | lhoestq/DeDLOC | 36f5a6d043c3d727f9d098a35fba94aa351a5cd4 | [
"Apache-2.0"
] | null | null | null | swav/vissl/vissl/data/ssl_transforms/img_patches_tensor.py | lhoestq/DeDLOC | 36f5a6d043c3d727f9d098a35fba94aa351a5cd4 | [
"Apache-2.0"
] | null | null | null | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import math
from typing import Any, Dict
import numpy as np
from classy_vision.dataset.transforms import register_transform
from classy_vision.dataset.transforms.classy_transform import ClassyTransform
@register_transform("ImgPatc... | 37.607595 | 88 | 0.623023 | 363 | 2,971 | 4.898072 | 0.338843 | 0.092801 | 0.040495 | 0.050619 | 0.17604 | 0.060742 | 0 | 0 | 0 | 0 | 0 | 0.012814 | 0.290811 | 2,971 | 78 | 89 | 38.089744 | 0.831039 | 0.208347 | 0 | 0 | 0 | 0 | 0.148661 | 0.009375 | 0 | 0 | 0 | 0 | 0.06383 | 1 | 0.06383 | false | 0 | 0.12766 | 0 | 0.255319 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c1a65d75547f91601127884078028e187b93021 | 588 | py | Python | prodapt_solutions/config/cliargs.py | DineshDevaraj/interview_answers | 8d3d631dc96dc97ebef80604d6455c2c57c8823d | [
"MIT"
] | null | null | null | prodapt_solutions/config/cliargs.py | DineshDevaraj/interview_answers | 8d3d631dc96dc97ebef80604d6455c2c57c8823d | [
"MIT"
] | null | null | null | prodapt_solutions/config/cliargs.py | DineshDevaraj/interview_answers | 8d3d631dc96dc97ebef80604d6455c2c57c8823d | [
"MIT"
] | null | null | null |
import argparse
from helper.metaclasses_definition import Singleton
class CliArgs(metaclass=Singleton):
LogLevel = None
BankName = None
InputFilepath = None
@staticmethod
def init():
my_parser = argparse.ArgumentParser()
my_parser.add_argument('--bank-name', required=True)
... | 24.5 | 60 | 0.685374 | 65 | 588 | 6 | 0.492308 | 0.102564 | 0.084615 | 0.146154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.221088 | 588 | 23 | 61 | 25.565217 | 0.851528 | 0 | 0 | 0 | 0 | 0 | 0.064736 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.0625 | false | 0 | 0.125 | 0 | 0.4375 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c1c295aedd09d62a7ca4222595cff9f7fd4e5fc | 1,237 | py | Python | plugins/flytekit-papermill/setup.py | TeoZosa/flytekit | c4f33c6deaf36a3feaf397cfc6de3bd62e986733 | [
"Apache-2.0"
] | null | null | null | plugins/flytekit-papermill/setup.py | TeoZosa/flytekit | c4f33c6deaf36a3feaf397cfc6de3bd62e986733 | [
"Apache-2.0"
] | null | null | null | plugins/flytekit-papermill/setup.py | TeoZosa/flytekit | c4f33c6deaf36a3feaf397cfc6de3bd62e986733 | [
"Apache-2.0"
] | null | null | null | from setuptools import setup
PLUGIN_NAME = "papermill"
microlib_name = f"flytekitplugins-{PLUGIN_NAME}"
plugin_requires = [
"flytekit>=0.16.0b0,<1.0.0",
"flytekitplugins-spark>=0.16.0b0,<1.0.0,!=0.24.0b0",
"papermill>=1.2.0",
"nbconvert>=6.0.7",
"ipykernel>=5.0.0",
]
__version__ = "0.0.0+develop... | 30.170732 | 71 | 0.645918 | 133 | 1,237 | 5.864662 | 0.481203 | 0.015385 | 0.092308 | 0.066667 | 0.023077 | 0.023077 | 0 | 0 | 0 | 0 | 0 | 0.040527 | 0.202102 | 1,237 | 40 | 72 | 30.925 | 0.749747 | 0 | 0 | 0 | 0 | 0.028571 | 0.566694 | 0.14228 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.028571 | 0 | 0.028571 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c1e9749d62da31f126224b5dcf3c15abd4025bd | 10,568 | py | Python | base/frontends/views.py | danielecook/upvote.pub | fdda3c0895427ddc76f4680d0d63f2d4bac59da0 | [
"MIT"
] | 1 | 2020-09-13T09:16:44.000Z | 2020-09-13T09:16:44.000Z | base/frontends/views.py | danielecook/upvote.pub | fdda3c0895427ddc76f4680d0d63f2d4bac59da0 | [
"MIT"
] | null | null | null | base/frontends/views.py | danielecook/upvote.pub | fdda3c0895427ddc76f4680d0d63f2d4bac59da0 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
"""
import os
import markdown2
from flask import (Blueprint,
request,
render_template,
flash, g,
session,
redirect,
url_for,
abort,
Markup)
... | 35.582492 | 157 | 0.599924 | 1,281 | 10,568 | 4.79313 | 0.226386 | 0.029642 | 0.018241 | 0.014821 | 0.305212 | 0.267101 | 0.24772 | 0.20456 | 0.176222 | 0.145277 | 0 | 0.001201 | 0.290973 | 10,568 | 296 | 158 | 35.702703 | 0.81823 | 0.099167 | 0 | 0.236715 | 0 | 0 | 0.105976 | 0.008061 | 0 | 0 | 0 | 0 | 0 | 1 | 0.062802 | false | 0.019324 | 0.077295 | 0.004831 | 0.231884 | 0.009662 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c1ed9a736672c0c84e29905bebe37cc7b644280 | 2,949 | py | Python | Jarvis.py | vijayeshmt/Securitylock | 5877663a170a22ab8b5931dcef07c74f149cf9b8 | [
"CC0-1.0"
] | 1 | 2021-05-27T09:05:00.000Z | 2021-05-27T09:05:00.000Z | Jarvis.py | vijayeshmt/Securitylock | 5877663a170a22ab8b5931dcef07c74f149cf9b8 | [
"CC0-1.0"
] | null | null | null | Jarvis.py | vijayeshmt/Securitylock | 5877663a170a22ab8b5931dcef07c74f149cf9b8 | [
"CC0-1.0"
] | null | null | null | import pyttsx3
import datetime
import speech_recognition as sr
import wikipedia
import webbrowser
import os
import smtplib
engine = pyttsx3.init('sapi5')
voices = engine.getProperty('voices')
engine.setProperty('voice', voices[0].id)
# To change the voice to female change 0 to 1.
def speak(au... | 26.097345 | 121 | 0.664632 | 401 | 2,949 | 4.837905 | 0.456359 | 0.028866 | 0.034021 | 0.018557 | 0.024742 | 0.024742 | 0 | 0 | 0 | 0 | 0 | 0.011961 | 0.206172 | 2,949 | 112 | 122 | 26.330357 | 0.816745 | 0.166158 | 0 | 0.075949 | 0 | 0 | 0.261618 | 0.041738 | 0 | 0 | 0 | 0 | 0 | 1 | 0.050633 | false | 0.012658 | 0.088608 | 0 | 0.164557 | 0.075949 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c1ff3b3368700c34adbc70fc88801c1bc52b535 | 2,838 | py | Python | utils/data_loader.py | dilum1995/DAugmentor | 6cc86dccf826415a88b8226265e16ae96b5cc05b | [
"MIT"
] | 1 | 2020-08-02T13:06:03.000Z | 2020-08-02T13:06:03.000Z | utils/data_loader.py | dilum1995/DAugmentor | 6cc86dccf826415a88b8226265e16ae96b5cc05b | [
"MIT"
] | null | null | null | utils/data_loader.py | dilum1995/DAugmentor | 6cc86dccf826415a88b8226265e16ae96b5cc05b | [
"MIT"
] | null | null | null | import pandas as pd
import os
import numpy as np
import cv2
from utils import constants as const
import matplotlib.pyplot as plt
class DataLoader:
def load_data():
'''
This function is handling the data loading and pre-processing
:return: (xtrain, ytrain), (xtest, ytest)
'''
... | 31.88764 | 75 | 0.565891 | 361 | 2,838 | 4.232687 | 0.235457 | 0.031414 | 0.045812 | 0.064136 | 0.541885 | 0.484293 | 0.443717 | 0.443717 | 0.353403 | 0.221204 | 0 | 0.016411 | 0.33439 | 2,838 | 89 | 76 | 31.88764 | 0.792483 | 0.180056 | 0 | 0.16 | 0 | 0 | 0.037281 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.02 | false | 0 | 0.12 | 0 | 0.18 | 0.18 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c2027c5e127752f77dcae4527133dc870a9894e | 288 | py | Python | CompilerPython/LexerPython/main.py | valternunez/Compiler | 879cecbbeb1c21d9d19021664ace62442273d3ba | [
"MIT"
] | null | null | null | CompilerPython/LexerPython/main.py | valternunez/Compiler | 879cecbbeb1c21d9d19021664ace62442273d3ba | [
"MIT"
] | null | null | null | CompilerPython/LexerPython/main.py | valternunez/Compiler | 879cecbbeb1c21d9d19021664ace62442273d3ba | [
"MIT"
] | null | null | null | from lexer import *
import sys
if len(sys.argv) != 2:
print("usage: main.py file")
else:
lex = Lexer(sys.argv[1])
with open(sys.argv[1]) as f:
while True:
c = f.read(1)
if not c:
break
print(lex.scan().toString())
| 19.2 | 40 | 0.496528 | 42 | 288 | 3.404762 | 0.666667 | 0.146853 | 0.111888 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.021858 | 0.364583 | 288 | 14 | 41 | 20.571429 | 0.759563 | 0 | 0 | 0 | 0 | 0 | 0.065972 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.166667 | 0 | 0.166667 | 0.166667 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c20c3110a71ede08c1358d9822f7b43bb07338f | 4,903 | py | Python | 3D/Train_Module_3D.py | geometatqueens/RCNN | 2e1e67264969f05a2f554595577dfb1025938245 | [
"Unlicense"
] | 1 | 2020-04-30T21:31:59.000Z | 2020-04-30T21:31:59.000Z | 3D/Train_Module_3D.py | geometatqueens/RCNN | 2e1e67264969f05a2f554595577dfb1025938245 | [
"Unlicense"
] | null | null | null | 3D/Train_Module_3D.py | geometatqueens/RCNN | 2e1e67264969f05a2f554595577dfb1025938245 | [
"Unlicense"
] | null | null | null | """The present code is the Version 1.0 of the RCNN approach to perform MPS
in 3D for categorical variables. It has been developed by S. Avalos and J. Ortiz in the
Geometallurygical Group at Queen's University as part of a PhD program.
The code is not free of bugs but running end-to-end.
Any comments and further i... | 53.879121 | 343 | 0.713237 | 743 | 4,903 | 4.585464 | 0.312248 | 0.16789 | 0.017611 | 0.056355 | 0.377458 | 0.356325 | 0.335192 | 0.335192 | 0.335192 | 0.267684 | 0 | 0.048894 | 0.124006 | 4,903 | 91 | 344 | 53.879121 | 0.744354 | 0.373241 | 0 | 0.172414 | 0 | 0.034483 | 0.151505 | 0.088577 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.103448 | 0 | 0.103448 | 0.155172 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c21319778186a2abea07c3db5dcc502d14e209f | 1,306 | py | Python | feature_flags_project/feature_flags/providers.py | steuke/django_feature_flags_example | 00e33378999d6d567c37593c17289405fc7b5847 | [
"MIT"
] | null | null | null | feature_flags_project/feature_flags/providers.py | steuke/django_feature_flags_example | 00e33378999d6d567c37593c17289405fc7b5847 | [
"MIT"
] | 3 | 2021-09-22T18:56:38.000Z | 2021-11-29T16:11:59.000Z | feature_flags_project/feature_flags/providers.py | steuke/django_feature_flags_example | 00e33378999d6d567c37593c17289405fc7b5847 | [
"MIT"
] | null | null | null | import logging
from typing import Dict
from django.http import HttpRequest
logger = logging.getLogger(__name__)
class FeatureFlagProvider:
def is_feature_enabled(self, feature_name: str, user_id: str = None, attributes: Dict = None):
raise NotImplementedError("You must override FeatureFlagProvider.is_fe... | 32.65 | 100 | 0.712098 | 153 | 1,306 | 5.856209 | 0.346405 | 0.073661 | 0.071429 | 0.042411 | 0.060268 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.20827 | 1,306 | 39 | 101 | 33.487179 | 0.866538 | 0 | 0 | 0.193548 | 0 | 0 | 0.20827 | 0.030628 | 0 | 0 | 0 | 0 | 0 | 1 | 0.096774 | false | 0 | 0.129032 | 0 | 0.387097 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c23d8601d0a15002cc4ed3c1cea741aa47089e1 | 34,227 | py | Python | src/plottoolbox/functions/kde.py | timcera/plottoolbox | b5f4b634d366eb5ba244e2f1fd33a7ef0eba7298 | [
"BSD-3-Clause"
] | null | null | null | src/plottoolbox/functions/kde.py | timcera/plottoolbox | b5f4b634d366eb5ba244e2f1fd33a7ef0eba7298 | [
"BSD-3-Clause"
] | 6 | 2021-09-06T21:26:12.000Z | 2022-03-30T11:55:56.000Z | src/plottoolbox/functions/kde.py | timcera/plottoolbox | b5f4b634d366eb5ba244e2f1fd33a7ef0eba7298 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
"""Collection of functions for the manipulation of time series."""
from __future__ import absolute_import, division, print_function
import itertools
import os
import warnings
import mando
import numpy as np
import pandas as pd
from mando.rst_text_formatter import RSTHelpFormatter
from tstoolb... | 29.918706 | 100 | 0.530984 | 3,699 | 34,227 | 4.785347 | 0.148418 | 0.01949 | 0.019321 | 0.013672 | 0.402011 | 0.351393 | 0.322581 | 0.298853 | 0.274109 | 0.257726 | 0 | 0.005824 | 0.322815 | 34,227 | 1,143 | 101 | 29.944882 | 0.757874 | 0.563327 | 0 | 0.440909 | 0 | 0 | 0.040892 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.004545 | false | 0 | 0.029545 | 0 | 0.036364 | 0.002273 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c241e9ea6651f1832b530bacf0b946a3f610e8c | 2,255 | py | Python | src/models/GNN.py | 3verlyn/DL-abstract-argumentation | 885e442077f5f8e576092c6648077e00ceb79dff | [
"MIT"
] | 6 | 2020-05-01T10:04:16.000Z | 2021-12-12T06:35:00.000Z | src/models/GNN.py | 3verlyn/DL-abstract-argumentation | 885e442077f5f8e576092c6648077e00ceb79dff | [
"MIT"
] | 3 | 2020-05-01T09:58:16.000Z | 2021-12-05T09:24:42.000Z | src/models/GNN.py | 3verlyn/DL-abstract-argumentation | 885e442077f5f8e576092c6648077e00ceb79dff | [
"MIT"
] | 3 | 2021-12-01T12:09:40.000Z | 2022-03-08T07:35:10.000Z | from collections import OrderedDict
import torch
import torch.nn as nn
from torch_geometric.data.batch import Batch
class GNN(nn.Module):
def __init__(self, mp_steps, **config):
super().__init__()
self.mp_steps = mp_steps
self.update_fns = self.assign_update_fns()
self.readout_fns... | 32.214286 | 91 | 0.613747 | 254 | 2,255 | 5.259843 | 0.314961 | 0.047156 | 0.032934 | 0.022455 | 0.050898 | 0.050898 | 0 | 0 | 0 | 0 | 0 | 0.000627 | 0.292683 | 2,255 | 69 | 92 | 32.681159 | 0.836991 | 0.092683 | 0 | 0.085106 | 0 | 0 | 0.049975 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.12766 | false | 0 | 0.085106 | 0 | 0.297872 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c247e4df77036ee1f8b8a7c4396fc03bed606ad | 977 | py | Python | configs/baselines/DACN/GNN/GCN_res_layer.py | vivek-r-2000/BoundaryNet | fce8d51a516646c1001116d03872dbba9e4c5082 | [
"MIT"
] | 17 | 2021-06-07T12:30:23.000Z | 2022-03-07T06:32:25.000Z | configs/baselines/DACN/GNN/GCN_res_layer.py | vivek-r-2000/BoundaryNet | fce8d51a516646c1001116d03872dbba9e4c5082 | [
"MIT"
] | 2 | 2021-07-13T13:24:14.000Z | 2022-03-08T07:21:09.000Z | configs/baselines/DACN/GNN/GCN_res_layer.py | vivek-r-2000/BoundaryNet | fce8d51a516646c1001116d03872dbba9e4c5082 | [
"MIT"
] | 4 | 2021-06-26T15:12:44.000Z | 2021-11-08T16:36:52.000Z | import math
import torch
import torch.nn as nn
from torch.nn.modules.module import Module
from GNN.GCN_layer import GraphConvolution
class GraphResConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, state_dim, name=''):
super(GraphRes... | 23.829268 | 65 | 0.63869 | 131 | 977 | 4.427481 | 0.328244 | 0.068966 | 0.044828 | 0.055172 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.036885 | 0.250768 | 977 | 41 | 66 | 23.829268 | 0.755464 | 0.062436 | 0 | 0 | 0 | 0 | 0.012209 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.130435 | false | 0 | 0.217391 | 0.043478 | 0.478261 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c24dd7d64e797088cd127f5acf19696ee37ca0f | 28,569 | py | Python | mtools/util/logfile.py | lukasvosyka/mtools | b94620cef48a9eb71b6a7fa93ad88f70cd36982f | [
"Apache-2.0"
] | null | null | null | mtools/util/logfile.py | lukasvosyka/mtools | b94620cef48a9eb71b6a7fa93ad88f70cd36982f | [
"Apache-2.0"
] | null | null | null | mtools/util/logfile.py | lukasvosyka/mtools | b94620cef48a9eb71b6a7fa93ad88f70cd36982f | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
from __future__ import print_function
import os
import re
import sys
from datetime import datetime
from math import ceil
from mtools.util.input_source import InputSource
from mtools.util.logevent import LogEvent
class LogFile(InputSource):
"""Log file wrapper class. Handles open file str... | 35.755945 | 116 | 0.509573 | 2,991 | 28,569 | 4.704112 | 0.14109 | 0.028429 | 0.015352 | 0.018479 | 0.399716 | 0.32914 | 0.291898 | 0.253518 | 0.231059 | 0.201919 | 0 | 0.006625 | 0.397704 | 28,569 | 798 | 117 | 35.800752 | 0.811065 | 0.128846 | 0 | 0.428058 | 0 | 0.001799 | 0.098892 | 0.014378 | 0 | 0 | 0 | 0.001253 | 0 | 1 | 0.061151 | false | 0 | 0.014388 | 0 | 0.154676 | 0.003597 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c26833e5360e6495c23a5b485ec7547b6bafa06 | 2,136 | py | Python | tests/svg.py | Tillsten/pyqtgraph | 0045863165fe526988c58cf4f8232ae2d261a5ee | [
"MIT"
] | null | null | null | tests/svg.py | Tillsten/pyqtgraph | 0045863165fe526988c58cf4f8232ae2d261a5ee | [
"MIT"
] | null | null | null | tests/svg.py | Tillsten/pyqtgraph | 0045863165fe526988c58cf4f8232ae2d261a5ee | [
"MIT"
] | null | null | null | """
SVG export test
"""
import test
import pyqtgraph as pg
app = pg.mkQApp()
class SVGTest(test.TestCase):
#def test_plotscene(self):
#pg.setConfigOption('foreground', (0,0,0))
#w = pg.GraphicsWindow()
#w.show()
#p1 = w.addPlot()
#p2 = w.addPlot()
#p1.plot([1... | 30.514286 | 96 | 0.557116 | 269 | 2,136 | 4.386617 | 0.304833 | 0.013559 | 0.021186 | 0.084746 | 0.285593 | 0.204237 | 0.077966 | 0.023729 | 0 | 0 | 0 | 0.095455 | 0.279026 | 2,136 | 70 | 97 | 30.514286 | 0.670779 | 0.535112 | 0 | 0 | 0 | 0 | 0.016913 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.0625 | false | 0 | 0.125 | 0 | 0.25 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c26b3633189c7cbd7b00d1addad30f94587f9ec | 993 | py | Python | src/api/models/enums/apschedulerevents.py | jedicontributors/pythondataintegrator | 3e877b367ab9b20185476128ec053db41087879f | [
"MIT"
] | 14 | 2020-12-19T15:06:13.000Z | 2022-01-12T19:52:17.000Z | src/api/models/enums/apschedulerevents.py | jedicontributors/pythondataintegrator | 3e877b367ab9b20185476128ec053db41087879f | [
"MIT"
] | 43 | 2021-01-06T22:05:22.000Z | 2022-03-10T10:30:30.000Z | src/api/models/enums/apschedulerevents.py | jedicontributors/pythondataintegrator | 3e877b367ab9b20185476128ec053db41087879f | [
"MIT"
] | 4 | 2020-12-18T23:10:09.000Z | 2021-04-02T13:03:12.000Z | EVENT_SCHEDULER_STARTED = EVENT_SCHEDULER_START = 2 ** 0
EVENT_SCHEDULER_SHUTDOWN = 2 ** 1
EVENT_SCHEDULER_PAUSED = 2 ** 2
EVENT_SCHEDULER_RESUMED = 2 ** 3
EVENT_EXECUTOR_ADDED = 2 ** 4
EVENT_EXECUTOR_REMOVED = 2 ** 5
EVENT_JOBSTORE_ADDED = 2 ** 6
EVENT_JOBSTORE_REMOVED = 2 ** 7
EVENT_ALL_JOBS_REMOVED = 2 ** 8
EVENT_JO... | 45.136364 | 96 | 0.75428 | 145 | 993 | 4.648276 | 0.262069 | 0.189911 | 0.062315 | 0.077151 | 0.103858 | 0 | 0 | 0 | 0 | 0 | 0 | 0.05 | 0.17422 | 993 | 22 | 96 | 45.136364 | 0.771951 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 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 | 1 | 0 |
7c272bc2beff83ce709b4ecff735eaf333a85378 | 25,166 | py | Python | scripts/build/build/targets.py | mrninhvn/matter | c577b233db9d2f3a6f87108a062b1699a40c5169 | [
"Apache-2.0"
] | 2 | 2022-03-29T12:17:41.000Z | 2022-03-30T13:25:20.000Z | scripts/build/build/targets.py | mrninhvn/matter | c577b233db9d2f3a6f87108a062b1699a40c5169 | [
"Apache-2.0"
] | null | null | null | scripts/build/build/targets.py | mrninhvn/matter | c577b233db9d2f3a6f87108a062b1699a40c5169 | [
"Apache-2.0"
] | 2 | 2022-02-24T15:42:39.000Z | 2022-03-04T20:38:07.000Z | # Copyright (c) 2021 Project CHIP Authors
#
# 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 ... | 42.510135 | 140 | 0.695581 | 2,934 | 25,166 | 5.871166 | 0.160873 | 0.072449 | 0.055265 | 0.025543 | 0.409613 | 0.303088 | 0.218913 | 0.133055 | 0.080518 | 0.054453 | 0 | 0.036247 | 0.197528 | 25,166 | 591 | 141 | 42.582064 | 0.816737 | 0.095645 | 0 | 0.162907 | 0 | 0.005013 | 0.129666 | 0.035727 | 0 | 0 | 0 | 0 | 0 | 1 | 0.080201 | false | 0 | 0.047619 | 0.007519 | 0.172932 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c279f6e16ec9934410f291dea61230ff38bf396 | 4,608 | py | Python | src/musegan/data.py | TRINITRONIC/musegan | 0a62e0303a8ff357d7f385dcc6edba76afb132b2 | [
"MIT"
] | null | null | null | src/musegan/data.py | TRINITRONIC/musegan | 0a62e0303a8ff357d7f385dcc6edba76afb132b2 | [
"MIT"
] | null | null | null | src/musegan/data.py | TRINITRONIC/musegan | 0a62e0303a8ff357d7f385dcc6edba76afb132b2 | [
"MIT"
] | null | null | null | """This file contains functions for loading and preprocessing pianoroll data.
"""
import logging
import numpy as np
import tensorflow.compat.v1 as tf
from musegan.config import SHUFFLE_BUFFER_SIZE, PREFETCH_SIZE
LOGGER = logging.getLogger(__name__)
# --- Data loader ----------------------------------------------------... | 39.724138 | 80 | 0.59809 | 556 | 4,608 | 4.771583 | 0.223022 | 0.050886 | 0.040709 | 0.040709 | 0.401055 | 0.352808 | 0.335846 | 0.282699 | 0.232944 | 0.162835 | 0 | 0.009494 | 0.24566 | 4,608 | 115 | 81 | 40.069565 | 0.75374 | 0.175781 | 0 | 0.266667 | 0 | 0 | 0.033663 | 0 | 0 | 0 | 0 | 0 | 0.011111 | 1 | 0.1 | false | 0 | 0.055556 | 0 | 0.277778 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c2b65379c3bd0e388f419a0d07e73a9770aad35 | 48,787 | py | Python | visnav/algo/orig/tools.py | oknuutti/hw_visnav | 5254b8bdd146548413554c00e6e76264a2540e8b | [
"MIT"
] | null | null | null | visnav/algo/orig/tools.py | oknuutti/hw_visnav | 5254b8bdd146548413554c00e6e76264a2540e8b | [
"MIT"
] | null | null | null | visnav/algo/orig/tools.py | oknuutti/hw_visnav | 5254b8bdd146548413554c00e6e76264a2540e8b | [
"MIT"
] | null | null | null | import math
import time
import numpy as np
import numba as nb
import quaternion # adds to numpy # noqa # pylint: disable=unused-import
import sys
import scipy
from astropy.coordinates import SkyCoord
from scipy.interpolate import RectBivariateSpline
from scipy.interpolate import NearestNDInterpolator
# from scipy.s... | 33.576738 | 130 | 0.579335 | 7,551 | 48,787 | 3.631572 | 0.12899 | 0.006491 | 0.029684 | 0.020969 | 0.302494 | 0.248851 | 0.223033 | 0.205492 | 0.191817 | 0.177595 | 0 | 0.039014 | 0.266567 | 48,787 | 1,452 | 131 | 33.599862 | 0.727349 | 0.152377 | 0 | 0.182663 | 0 | 0 | 0.039557 | 0.000562 | 0 | 0 | 0 | 0.000689 | 0.017544 | 1 | 0.093911 | false | 0.004128 | 0.038184 | 0.011352 | 0.231166 | 0.014448 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c2bf254c4e2082b3c9d6ed73d3f8891d0fa09df | 4,245 | py | Python | cirtorch/filters/sobel.py | Tarekbouamer/Image-Retrieval-for-Image-Based-Localization | fcad9af4f558bebb3cbec1d08e49603a452f439d | [
"BSD-3-Clause"
] | 3 | 2021-01-15T13:58:22.000Z | 2021-01-22T00:03:34.000Z | cirtorch/filters/sobel.py | Tarekbouamer/Image-Retrieval-for-Image-Based-Localization | fcad9af4f558bebb3cbec1d08e49603a452f439d | [
"BSD-3-Clause"
] | null | null | null | cirtorch/filters/sobel.py | Tarekbouamer/Image-Retrieval-for-Image-Based-Localization | fcad9af4f558bebb3cbec1d08e49603a452f439d | [
"BSD-3-Clause"
] | null | null | null | import torch
import torch.nn as nn
import torch.nn.functional as F
from .kernels import (
get_spatial_gradient_kernel2d,
get_spatial_gradient_kernel3d,
normalize_kernel2d
)
def spatial_gradient(input, mode='sobel', order=1, normalized=True):
"""
Computes the first order image derivative in bo... | 28.877551 | 103 | 0.627562 | 568 | 4,245 | 4.570423 | 0.209507 | 0.042373 | 0.033898 | 0.01849 | 0.627889 | 0.54584 | 0.440678 | 0.389445 | 0.374807 | 0.374807 | 0 | 0.020051 | 0.259835 | 4,245 | 146 | 104 | 29.075342 | 0.806174 | 0.193404 | 0 | 0.3625 | 0 | 0 | 0.054259 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.1125 | false | 0 | 0.05 | 0.0375 | 0.275 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c2c03c407ba0a2ba9a613836bc2fb4601d6b4a8 | 896 | py | Python | PythonCookbook/concurrent_test/findrobots.py | xu6148152/Binea_Python_Project | d943eb5f4685d08f080b372dcf1a7cbd5d63efed | [
"MIT"
] | null | null | null | PythonCookbook/concurrent_test/findrobots.py | xu6148152/Binea_Python_Project | d943eb5f4685d08f080b372dcf1a7cbd5d63efed | [
"MIT"
] | null | null | null | PythonCookbook/concurrent_test/findrobots.py | xu6148152/Binea_Python_Project | d943eb5f4685d08f080b372dcf1a7cbd5d63efed | [
"MIT"
] | null | null | null | # -*- encoding: utf-8 -*-
import gzip
import io
import glob
from concurrent import futures
def find_robots(filename):
'''
Find all of the hosts that access robots.txt in a single log file
'''
robots = set()
with gzip.open(filename) as f:
for line in io.TextIOWrapper(f, encoding='ascii'):
... | 23.578947 | 69 | 0.618304 | 118 | 896 | 4.550847 | 0.491525 | 0.083799 | 0.048417 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.004587 | 0.270089 | 896 | 37 | 70 | 24.216216 | 0.816514 | 0.157366 | 0 | 0 | 0 | 0 | 0.051176 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.086957 | false | 0 | 0.173913 | 0 | 0.347826 | 0.043478 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c2c664c7e1b0b10556e368192b5c6b6dfeac1d6 | 13,634 | py | Python | cnnblstm_with_adabn/cnnblstm_with_adabn.py | Fassial/Air-Writing-with-TL | 9b9047c5bd5aef3a869e2d5166be1c0cf0c5ccf0 | [
"MIT"
] | 1 | 2021-06-16T16:45:01.000Z | 2021-06-16T16:45:01.000Z | cnnblstm_with_adabn/cnnblstm_with_adabn.py | Fassial/Air-Writing-with-TL | 9b9047c5bd5aef3a869e2d5166be1c0cf0c5ccf0 | [
"MIT"
] | null | null | null | cnnblstm_with_adabn/cnnblstm_with_adabn.py | Fassial/Air-Writing-with-TL | 9b9047c5bd5aef3a869e2d5166be1c0cf0c5ccf0 | [
"MIT"
] | 1 | 2020-04-21T01:31:26.000Z | 2020-04-21T01:31:26.000Z | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
# local model
import sys
sys.path.append("../network")
import Coral
from lstm import LSTMHardSigmoid
from AdaBN import AdaBN
sys.path.append("../network/Aut... | 36.068783 | 211 | 0.710943 | 2,080 | 13,634 | 4.404808 | 0.129327 | 0.022157 | 0.015717 | 0.01277 | 0.465837 | 0.407226 | 0.390963 | 0.359201 | 0.327003 | 0.313032 | 0 | 0.017791 | 0.163122 | 13,634 | 377 | 212 | 36.164456 | 0.785188 | 0.230747 | 0 | 0.1875 | 0 | 0 | 0.03476 | 0.005793 | 0 | 0 | 0 | 0 | 0 | 1 | 0.057692 | false | 0 | 0.057692 | 0 | 0.163462 | 0.024038 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c2d2c77ae28e087d253ce05110db6593a6b0fcc | 26,658 | py | Python | src/emmental/model.py | woffett/emmental | 87884fcd89662cca45f0ea0f78cff73380cc47c8 | [
"MIT"
] | null | null | null | src/emmental/model.py | woffett/emmental | 87884fcd89662cca45f0ea0f78cff73380cc47c8 | [
"MIT"
] | null | null | null | src/emmental/model.py | woffett/emmental | 87884fcd89662cca45f0ea0f78cff73380cc47c8 | [
"MIT"
] | null | null | null | """Emmental model."""
import itertools
import logging
import os
from collections import defaultdict
from collections.abc import Iterable
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
import numpy as np
import torch
from numpy import ndarray
from torch import Tensor, nn as nn
from torch.nn i... | 38.028531 | 88 | 0.534249 | 2,842 | 26,658 | 4.79064 | 0.100985 | 0.050533 | 0.017628 | 0.012119 | 0.465443 | 0.375835 | 0.321924 | 0.292031 | 0.213882 | 0.185163 | 0 | 0.000419 | 0.373734 | 26,658 | 700 | 89 | 38.082857 | 0.815094 | 0.120939 | 0 | 0.297959 | 0 | 0 | 0.071641 | 0.010072 | 0 | 0 | 0 | 0.004286 | 0 | 1 | 0.034694 | false | 0 | 0.034694 | 0 | 0.087755 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c2daa2465bd8777ef8940cbc518e195f59d4ad9 | 4,578 | py | Python | server/ws_server.py | jangxx/OVRT_Soundpad | 2f9b2cd19421bc7b5586a3dcded2998d381ba688 | [
"MIT"
] | 1 | 2021-09-29T01:45:35.000Z | 2021-09-29T01:45:35.000Z | server/ws_server.py | jangxx/OVRT_Soundpad | 2f9b2cd19421bc7b5586a3dcded2998d381ba688 | [
"MIT"
] | 2 | 2021-09-28T08:53:09.000Z | 2021-10-20T01:06:15.000Z | server/ws_server.py | jangxx/OVRT_Soundpad | 2f9b2cd19421bc7b5586a3dcded2998d381ba688 | [
"MIT"
] | null | null | null | import asyncio, json
from config import Config
from soundpad_manager import SoundpadManager
from version import BRIDGE_VERSION
import websockets
from sanic.log import logger
# yes I know that it's very lazy to run a separate WS and HTTP server, when both could be run on the same port
# I don't like sanics ... | 33.661765 | 140 | 0.668633 | 600 | 4,578 | 4.975 | 0.29 | 0.033501 | 0.036181 | 0.023116 | 0.240536 | 0.166499 | 0.137018 | 0.124288 | 0 | 0 | 0 | 0.002714 | 0.195063 | 4,578 | 136 | 141 | 33.661765 | 0.807327 | 0.074487 | 0 | 0.147059 | 0 | 0 | 0.174561 | 0.035156 | 0 | 0 | 0 | 0 | 0 | 1 | 0.019608 | false | 0.019608 | 0.058824 | 0 | 0.107843 | 0.029412 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c2f595fee4e21dc84c6666b03b2174e6d5731e0 | 8,108 | py | Python | tensorforce/tests/test_model_save_restore.py | gian1312/suchen | df863140fd8df1ac2e195cbdfa4756f09f962270 | [
"Apache-2.0"
] | null | null | null | tensorforce/tests/test_model_save_restore.py | gian1312/suchen | df863140fd8df1ac2e195cbdfa4756f09f962270 | [
"Apache-2.0"
] | null | null | null | tensorforce/tests/test_model_save_restore.py | gian1312/suchen | df863140fd8df1ac2e195cbdfa4756f09f962270 | [
"Apache-2.0"
] | 1 | 2019-11-29T12:28:33.000Z | 2019-11-29T12:28:33.000Z | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import unittest
import pytest
from tensorforce import TensorForceError
from tensorforce.core.networks import LayeredNetwork
from tensorforce.models import DistributionModel
from tensorforce.tests.minimal_test ... | 39.940887 | 119 | 0.662309 | 928 | 8,108 | 5.570043 | 0.235991 | 0.042561 | 0.015477 | 0.021668 | 0.479783 | 0.451538 | 0.424453 | 0.394274 | 0.356742 | 0.322113 | 0 | 0.009753 | 0.241243 | 8,108 | 202 | 120 | 40.138614 | 0.830462 | 0.044524 | 0 | 0.291139 | 0 | 0 | 0.049759 | 0.007555 | 0 | 0 | 0 | 0 | 0.075949 | 1 | 0.056962 | false | 0 | 0.094937 | 0.025316 | 0.189873 | 0.025316 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c32d21e81a25b4bfc714d53125ce26089327176 | 263 | py | Python | what_can_i_cook/urls.py | s-maibuecher/what_can_i_cook | 07d0eb1e1862fad299477b800654e895d7f8829a | [
"MIT"
] | null | null | null | what_can_i_cook/urls.py | s-maibuecher/what_can_i_cook | 07d0eb1e1862fad299477b800654e895d7f8829a | [
"MIT"
] | null | null | null | what_can_i_cook/urls.py | s-maibuecher/what_can_i_cook | 07d0eb1e1862fad299477b800654e895d7f8829a | [
"MIT"
] | null | null | null | from django.urls import path
from what_can_i_cook.views import WCICFilterView, WCICResultView
app_name = "wcic"
urlpatterns = [
path("", WCICFilterView.as_view(), name="wcic-start"),
path("results/", WCICResultView.as_view(), name="wcic-results"),
]
| 20.230769 | 68 | 0.722433 | 33 | 263 | 5.575758 | 0.606061 | 0.130435 | 0.108696 | 0.152174 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.13308 | 263 | 12 | 69 | 21.916667 | 0.807018 | 0 | 0 | 0 | 0 | 0 | 0.129771 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.285714 | 0 | 0.285714 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c32daa41ae2a8f92a0d91d061b5264ea9984602 | 436 | py | Python | shared/templates/grub2_bootloader_argument/template.py | justchris1/scap-security-guide | 030097afa80041fcdffc537a49c09896efedadca | [
"BSD-3-Clause"
] | 1,138 | 2018-09-05T06:31:44.000Z | 2022-03-31T03:38:24.000Z | shared/templates/grub2_bootloader_argument/template.py | justchris1/scap-security-guide | 030097afa80041fcdffc537a49c09896efedadca | [
"BSD-3-Clause"
] | 4,743 | 2018-09-04T15:14:04.000Z | 2022-03-31T23:17:57.000Z | shared/templates/grub2_bootloader_argument/template.py | justchris1/scap-security-guide | 030097afa80041fcdffc537a49c09896efedadca | [
"BSD-3-Clause"
] | 400 | 2018-09-08T20:08:49.000Z | 2022-03-30T20:54:32.000Z | import ssg.utils
def preprocess(data, lang):
data["arg_name_value"] = data["arg_name"] + "=" + data["arg_value"]
if lang == "oval":
# escape dot, this is used in oval regex
data["escaped_arg_name_value"] = data["arg_name_value"].replace(".", "\\.")
# replace . with _, this is used in t... | 36.333333 | 83 | 0.623853 | 61 | 436 | 4.229508 | 0.47541 | 0.162791 | 0.170543 | 0.124031 | 0.178295 | 0.178295 | 0 | 0 | 0 | 0 | 0 | 0 | 0.21789 | 436 | 11 | 84 | 39.636364 | 0.756598 | 0.224771 | 0 | 0 | 0 | 0 | 0.304478 | 0.065672 | 0 | 0 | 0 | 0 | 0 | 1 | 0.142857 | false | 0 | 0.142857 | 0 | 0.428571 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c34376a6bdd5ec8372f4490b569f441abff9288 | 3,598 | py | Python | preprocess.py | NNDEV1/NMTWithLuongAttention | e6f11d9e8c5f999d413fa0dc51219e979a8f975c | [
"MIT"
] | 4 | 2021-07-09T19:17:47.000Z | 2022-01-04T14:54:11.000Z | preprocess.py | NNDEV1/NMTWithLuongAttention | e6f11d9e8c5f999d413fa0dc51219e979a8f975c | [
"MIT"
] | null | null | null | preprocess.py | NNDEV1/NMTWithLuongAttention | e6f11d9e8c5f999d413fa0dc51219e979a8f975c | [
"MIT"
] | null | null | null | import tensorflow as tf
import os
import contractions
import tensorflow as tf
import pandas as pd
import numpy as np
import time
import rich
from rich.progress import track
import spacy
from config import params
#Preprocessing Text
class preprocess_text():
def __init__(self):
pass
def remove_pa... | 32.414414 | 106 | 0.644525 | 533 | 3,598 | 4.129456 | 0.242026 | 0.040891 | 0.022263 | 0.031804 | 0.294866 | 0.223989 | 0.210813 | 0.141299 | 0.060881 | 0 | 0 | 0.00577 | 0.229294 | 3,598 | 110 | 107 | 32.709091 | 0.787955 | 0.005003 | 0 | 0.063291 | 0 | 0 | 0.044146 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.151899 | false | 0.012658 | 0.139241 | 0.037975 | 0.481013 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c3462f9e646dbe27aad64fea0cc1723870ee413 | 1,665 | py | Python | setup.py | johannesulf/dsigma | 729337c94669f4a0fdacb51b175df1e13e26304c | [
"MIT"
] | 4 | 2020-06-09T01:09:58.000Z | 2021-09-26T16:39:16.000Z | setup.py | johannesulf/dsigma | 729337c94669f4a0fdacb51b175df1e13e26304c | [
"MIT"
] | null | null | null | setup.py | johannesulf/dsigma | 729337c94669f4a0fdacb51b175df1e13e26304c | [
"MIT"
] | null | null | null | from setuptools import setup, find_packages
from distutils.extension import Extension
from distutils.command.sdist import sdist
try:
from Cython.Build import cythonize
USE_CYTHON = True
except ImportError:
USE_CYTHON = False
ext = 'pyx' if USE_CYTHON else 'c'
extensions = [Extension(
'dsigma.precomput... | 30.272727 | 76 | 0.667868 | 185 | 1,665 | 5.875676 | 0.583784 | 0.033119 | 0.091996 | 0.095676 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.008221 | 0.196396 | 1,665 | 54 | 77 | 30.833333 | 0.804185 | 0 | 0 | 0 | 0 | 0 | 0.34955 | 0.060661 | 0 | 0 | 0 | 0 | 0 | 1 | 0.021739 | false | 0 | 0.108696 | 0 | 0.152174 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c34972839ffa0fc13d463ba6725ab4c70743477 | 1,967 | py | Python | face_detector/modules/mod_faceDetection.py | jtfan3/face_detection | 82e3bc839bf12c956f3166c07012912a0638048f | [
"MIT"
] | null | null | null | face_detector/modules/mod_faceDetection.py | jtfan3/face_detection | 82e3bc839bf12c956f3166c07012912a0638048f | [
"MIT"
] | null | null | null | face_detector/modules/mod_faceDetection.py | jtfan3/face_detection | 82e3bc839bf12c956f3166c07012912a0638048f | [
"MIT"
] | null | null | null | import cv2
import mediapipe as mp
class FaceDetection():
# initialize the face detection class with arguments from https://google.github.io/mediapipe/solutions/face_detection.html
def __init__(self, model_selection = 0, threshold = 0.5):
self.model_selection = model_selection
self.threshold = t... | 40.979167 | 154 | 0.620234 | 254 | 1,967 | 4.574803 | 0.38189 | 0.067126 | 0.046472 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.017731 | 0.283172 | 1,967 | 47 | 155 | 41.851064 | 0.806383 | 0.160142 | 0 | 0 | 0 | 0 | 0.008511 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.090909 | false | 0 | 0.060606 | 0 | 0.212121 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
7c378f7b0a34c442460ca831372ef84873f73309 | 768 | py | Python | pymc/mc_enum.py | cherish-web/pymc | 9c322abfdcceca0a78b633d85da23e1290c036c8 | [
"Apache-2.0"
] | 4 | 2021-05-01T12:43:24.000Z | 2022-01-25T03:44:32.000Z | pymc/mc_enum.py | cherish-web/pymc | 9c322abfdcceca0a78b633d85da23e1290c036c8 | [
"Apache-2.0"
] | null | null | null | pymc/mc_enum.py | cherish-web/pymc | 9c322abfdcceca0a78b633d85da23e1290c036c8 | [
"Apache-2.0"
] | 2 | 2021-07-10T03:56:08.000Z | 2021-09-30T14:59:35.000Z | # _*_ coding: utf-8 _*_
# @Time : 2021/3/29 上午 08:57
# @Author : cherish_peng
# @Email : 1058386071@qq.com
# @File : cmd.py
# @Software : PyCharm
from enum import Enum
class EnumSubTitle(Enum):
Request4e = 0x5400
# 请求
Request = 0x5000
# 应答
Respond = 0xD000
Respond4e = 0xD400
class EnumEndC... | 13.714286 | 36 | 0.584635 | 93 | 768 | 4.774194 | 0.752688 | 0.013514 | 0.031532 | 0.067568 | 0.085586 | 0 | 0 | 0 | 0 | 0 | 0 | 0.172348 | 0.3125 | 768 | 55 | 37 | 13.963636 | 0.668561 | 0.324219 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.146045 | 0 | 0 | 1 | 0 | false | 0 | 0.05 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |