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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
d99a20277c32bb1e28312f42ab6d732f38323169	241	py	Python	quick_search/admin.py	naman1901/django-quick-search	7b93554ed9fa4721e52372f9fd1a395d94cc04a7	[ "MIT" ]	null	null	null	quick_search/admin.py	naman1901/django-quick-search	7b93554ed9fa4721e52372f9fd1a395d94cc04a7	[ "MIT" ]	2	2020-02-11T23:28:22.000Z	2020-06-05T19:27:40.000Z	quick_search/admin.py	HereWithoutPermission/django-quick-search	7b93554ed9fa4721e52372f9fd1a395d94cc04a7	[ "MIT" ]	null	null	null	from django.contrib import admin from .models import SearchResult # Register your models here. class SearchResultAdmin(admin.ModelAdmin): fields = ["query", "heading", "url", "text"] admin.site.register(SearchResult, SearchResultAdmin)	30.125	52	0.771784	27	241	6.888889	0.703704	0	0	0	0	0	0	0	0	0	0	0	0.116183	241	8	52	30.125	0.873239	0.107884	0	0	0	0	0.088785	0	0	0	0	0	0	1	0	false	0	0.4	0	0.8	0	1	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	1	0	0	0	0	3
d9a88e74a4ac032ae6e8218d9ec1ed42e6092d32	375	py	Python	app/views/web/homestack.py	geudrik/hautomation	0baae29e85cd68658a0f8578de2e36e42945053f	[ "MIT" ]	null	null	null	app/views/web/homestack.py	geudrik/hautomation	0baae29e85cd68658a0f8578de2e36e42945053f	[ "MIT" ]	null	null	null	app/views/web/homestack.py	geudrik/hautomation	0baae29e85cd68658a0f8578de2e36e42945053f	[ "MIT" ]	null	null	null	#! /usr/bin/env python2.7 # -- coding: latin-1 -- from flask import Blueprint from flask import current_app from flask import render_template from flask_login import login_required homestack = Blueprint("homestack", __name__, url_prefix="/homestack") @homestack.route("/", methods=["GET"]) @login_required def hom...	22.058824	69	0.749333	49	375	5.510204	0.591837	0.133333	0.166667	0	0	0	0	0	0	0	0	0.009091	0.12	375	16	70	23.4375	0.809091	0.128	0	0	0	0	0.129231	0	0	0	0	0	0	1	0.111111	false	0	0.444444	0.111111	0.666667	0.222222	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	1	1	1	0	0	3
d9b55a7ee025f94a0ef3f125fa9c30f974dd7d6e	211	py	Python	abc/abc165/abc165e.py	c-yan/atcoder	940e49d576e6a2d734288fadaf368e486480a948	[ "MIT" ]	1	2019-08-21T00:49:34.000Z	2019-08-21T00:49:34.000Z	abc/abc165/abc165e.py	c-yan/atcoder	940e49d576e6a2d734288fadaf368e486480a948	[ "MIT" ]	null	null	null	abc/abc165/abc165e.py	c-yan/atcoder	940e49d576e6a2d734288fadaf368e486480a948	[ "MIT" ]	null	null	null	N, M = map(int, input().split()) for i in range(1, M + 1): if i % 2 == 1: j = (i - 1) // 2 print(1 + j, M + 1 - j) else: j = (i - 2) // 2 print(M + 2 + j, 2 * M + 1 - j)	21.1	39	0.336493	40	211	1.775	0.4	0.112676	0.084507	0	0	0	0	0	0	0	0	0.111111	0.445498	211	9	40	23.444444	0.495727	0	0	0	0	0	0	0	0	0	0	0	0	1	0	false	0	0	0	0	0.25	0	0	1	null	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	3
d9b8d42e905cba910e6a30f7d6f38e82d05ab46c	2,110	py	Python	graphdb/transformer.py	muggat0n/graphdb	56dfd5ef8a3321abc6a919faee47494bbe059080	[ "MIT" ]	2	2020-08-28T13:42:38.000Z	2020-09-05T03:13:45.000Z	graphdb/transformer.py	muggat0n/graphdb	56dfd5ef8a3321abc6a919faee47494bbe059080	[ "MIT" ]	null	null	null	graphdb/transformer.py	muggat0n/graphdb	56dfd5ef8a3321abc6a919faee47494bbe059080	[ "MIT" ]	null	null	null	""" A query transformer is a function that accepts a program and returns a program, plus a priority level. Higher priority transformers are placed closer to the front of the list. We’re ensuring is a function, because we’re going to evaluate it later 31 . We’ll assume there won’t be an enormous number of transformer ad...	30.57971	120	0.627962	273	2,110	4.842491	0.47619	0.026475	0.016641	0	0	0	0	0	0	0	0	0.005799	0.264455	2,110	68	121	31.029412	0.845361	0.267299	0	0	0	0	0	0	0	0	0	0	0	1	0.4	false	0	0	0.2	0.8	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	1	0	0	0	1	1	0	0	3
d9b9563b7aae9c46b0fbd98073d96eeedfaec4aa	91	py	Python	Courses/1 month/2 week/day 6/Formula.py	emir-naiz/first_git_lesson	1fecf712290f6da3ef03deff518870d91638eb69	[ "MIT" ]	null	null	null	Courses/1 month/2 week/day 6/Formula.py	emir-naiz/first_git_lesson	1fecf712290f6da3ef03deff518870d91638eb69	[ "MIT" ]	null	null	null	Courses/1 month/2 week/day 6/Formula.py	emir-naiz/first_git_lesson	1fecf712290f6da3ef03deff518870d91638eb69	[ "MIT" ]	null	null	null	summary = 0 i = 0 while i < 5: summary = summary + i print(summary) i = i + 1	11.375	25	0.516484	15	91	3.133333	0.466667	0.340426	0	0	0	0	0	0	0	0	0	0.070175	0.373626	91	7	26	13	0.754386	0	0	0	0	0	0	0	0	0	0	0	0	1	0	false	0	0	0	0	0.166667	1	0	0	null	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	3
d9b9af3bd25b0d2f9357446b0ff43e3ab614b141	243	py	Python	tests/image_saver/image_saver_7.py	Vicken-Ghoubiguian/Imtreat	1f8e8406dc48af3b1e8e0c138a09aa1faee0b8a0	[ "MIT" ]	null	null	null	tests/image_saver/image_saver_7.py	Vicken-Ghoubiguian/Imtreat	1f8e8406dc48af3b1e8e0c138a09aa1faee0b8a0	[ "MIT" ]	null	null	null	tests/image_saver/image_saver_7.py	Vicken-Ghoubiguian/Imtreat	1f8e8406dc48af3b1e8e0c138a09aa1faee0b8a0	[ "MIT" ]	null	null	null	import imtreat img = imtreat.imageManagerClass.openImageFunction("../images/soleil.png", 0) img = imtreat.definedModesClass.detailEnhanceFunction(img) imtreat.imageManagerClass.saveImageFunction("/Téléchargements/", "image_1", ".png", img)	30.375	88	0.794239	23	243	8.347826	0.652174	0.15625	0.28125	0	0	0	0	0	0	0	0	0.008811	0.065844	243	7	89	34.714286	0.837004	0	0	0	0	0	0.197531	0	0	0	0	0	0	1	0	false	0	0.25	0	0.25	0	1	0	0	null	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	3
d9c389b63a2c9720abef56190237f31a2306da19	1,972	py	Python	src/biotite/copyable.py	danijoo/biotite	22072e64676e4e917236eac8493eed4c6a22cc33	[ "BSD-3-Clause" ]	208	2018-04-20T15:59:42.000Z	2022-03-22T07:47:12.000Z	src/biotite/copyable.py	danielmuthama/biotite	cb238a8d8d7dc82b3bcea274d7d91d5c876badcd	[ "BSD-3-Clause" ]	121	2017-11-15T14:52:07.000Z	2022-03-30T16:31:41.000Z	src/biotite/copyable.py	danielmuthama/biotite	cb238a8d8d7dc82b3bcea274d7d91d5c876badcd	[ "BSD-3-Clause" ]	49	2018-07-19T09:06:24.000Z	2022-03-23T17:21:34.000Z	# This source code is part of the Biotite package and is distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst' for further # information. __name__ = "biotite" __author__ = "Patrick Kunzmann" __all__ = ["Copyable"] import abc class Copyable(metaclass=abc.ABCMeta): """ Base class for all obje...	27.774648	70	0.59432	235	1,972	4.787234	0.421277	0.017778	0.017778	0.023111	0.088889	0	0	0	0	0	0	0.00076	0.333164	1,972	71	71	27.774648	0.854753	0.626775	0	0	0	0	0.077114	0	0	0	0	0	0	1	0.230769	false	0.076923	0.076923	0	0.538462	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	1	0	1	0	0	1	0	0	3
d9f1f15178cb9e26d9b4f91695b333a07eaa59d6	74,778	py	Python	sqlova/model/nl2sql/wikisql_models.py	guotong1988/Rule-SQL	e826c0d659c8b35a72b64aa2b50d4d943fdd70f1	[ "Apache-2.0" ]	15	2019-07-25T12:13:31.000Z	2020-10-17T13:42:58.000Z	sqlova/model/nl2sql/wikisql_models.py	guotong1988/Rule-SQL	e826c0d659c8b35a72b64aa2b50d4d943fdd70f1	[ "Apache-2.0" ]	1	2020-01-07T05:49:15.000Z	2020-04-22T01:22:00.000Z	sqlova/model/nl2sql/wikisql_models.py	guotong1988/Rule-SQL	e826c0d659c8b35a72b64aa2b50d4d943fdd70f1	[ "Apache-2.0" ]	3	2019-10-01T09:14:35.000Z	2020-07-18T08:39:48.000Z	# Copyright 2019-present NAVER Corp. # Apache License v2.0 # Wonseok Hwang import os, json from copy import deepcopy from matplotlib.pylab import * import torch import torch.nn as nn import torch.nn.functional as F device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from sqlova.utils.utils impor...	39.419083	161	0.555163	10,973	74,778	3.441903	0.05313	0.039187	0.02187	0.018005	0.766522	0.732075	0.704671	0.683595	0.661115	0.641284	0	0.030277	0.333948	74,778	1,896	162	39.439873	0.728025	0.168378	0	0.597066	0	0	0.006951	0	0	0	0	0	0	1	0.035375	false	0.002588	0.006903	0	0.077653	0	0	0	0	null	0	0	0	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	3
d9f32d2b9e677d6893c7269bf23bcedaa4e7f68a	363	py	Python	chia/components/sample_transformers/__init__.py	cabrust/chia	3eaf815b261dc8a85d64fd698e0079515ec0dde9	[ "BSD-3-Clause" ]	null	null	null	chia/components/sample_transformers/__init__.py	cabrust/chia	3eaf815b261dc8a85d64fd698e0079515ec0dde9	[ "BSD-3-Clause" ]	2	2021-10-06T13:19:09.000Z	2021-10-20T17:32:36.000Z	chia/components/sample_transformers/__init__.py	cabrust/chia	3eaf815b261dc8a85d64fd698e0079515ec0dde9	[ "BSD-3-Clause" ]	null	null	null	from chia import components from chia.components.sample_transformers import identity from chia.components.sample_transformers.sample_transformer import SampleTransformer class SampleTransformerFactory(components.Factory): name_to_class_mapping = {"identity": identity.IdentitySampleTransformer} __all__ = ["Sampl...	33	84	0.85124	34	363	8.794118	0.5	0.080268	0.120401	0.160535	0.240803	0	0	0	0	0	0	0	0.082645	363	10	85	36.3	0.897898	0	0	0	0	0	0.134986	0.066116	0	0	0	0	0	1	0	false	0	0.5	0	0.833333	0	0	0	0	null	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	1	0	1	0	0	3
d9f3cb72d610ec30e4ecf05d60ba2025dc849112	416	py	Python	3/3.6/add_guest.py	singi2016cn/python-scaffold	274e508d1919da67e599aa73be139800c043bce4	[ "MIT" ]	null	null	null	3/3.6/add_guest.py	singi2016cn/python-scaffold	274e508d1919da67e599aa73be139800c043bce4	[ "MIT" ]	null	null	null	3/3.6/add_guest.py	singi2016cn/python-scaffold	274e508d1919da67e599aa73be139800c043bce4	[ "MIT" ]	null	null	null	# 添加嘉宾 names = [] names.append('singi') names.append('lily') names.append('sam') print('I find a big dining-table,I can invite more friends.') names.insert(0, 'xiaoling') names.insert(2, 'fangsi') names.append('zhangqing') greets = ',would you like to have dinner with me ?' print(names[0]+greets) print(names[1]+gre...	20.8	61	0.711538	67	416	4.41791	0.537313	0.202703	0.27027	0	0	0	0	0	0	0	0	0.02139	0.100962	416	20	62	20.8	0.770053	0.009615	0	0	0	0	0.309002	0	0	0	0	0	0	1	0	false	0	0	0	0	0.466667	0	0	0	null	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	1	0	3
8a03248b6fead646cb68e7a6a935435de664969c	14,492	py	Python	anaconda-mode/0.1.13/jedi-0.15.1-py3.7.egg/jedi/evaluate/base_context.py	space-scl/emacs.d	6285c38714023b72a023fe24cbcb5e4fcdcdb949	[ "Apache-2.0" ]	4	2019-07-26T11:32:22.000Z	2019-09-11T05:34:59.000Z	anaconda-mode/0.1.13/jedi-0.15.1-py3.7.egg/jedi/evaluate/base_context.py	space-scl/emacs.d	6285c38714023b72a023fe24cbcb5e4fcdcdb949	[ "Apache-2.0" ]	10	2020-05-11T20:29:28.000Z	2022-01-13T01:41:27.000Z	anaconda-mode/0.1.13/jedi-0.15.1-py3.7.egg/jedi/evaluate/base_context.py	space-scl/emacs.d	6285c38714023b72a023fe24cbcb5e4fcdcdb949	[ "Apache-2.0" ]	2	2019-08-28T14:57:54.000Z	2019-11-26T16:18:30.000Z	""" Contexts are the "values" that Python would return. However Contexts are at the same time also the "contexts" that a user is currently sitting in. A ContextSet is typically used to specify the return of a function or any other static analysis operation. In jedi there are always multiple returns and not just one. "...	33.162471	94	0.637524	1,718	14,492	5.089639	0.185681	0.030878	0.021958	0.011436	0.237077	0.180009	0.141468	0.087374	0.064959	0.042543	0	0.001151	0.280638	14,492	436	95	33.238532	0.837602	0.110199	0	0.248322	0	0	0.045142	0.004025	0	0	0	0.006881	0.003356	1	0.204698	false	0.010067	0.067114	0.110738	0.526846	0	0	0	0	null	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	null	0	0	0	0	0	1	0	0	0	1	1	0	0	3

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