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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5b14c0f520aa2dfc088e43cb4960682061f61a03 | 409 | py | Python | netrd/__init__.py | sdmccabe/netrd | f703c19b02f42c9f54bcab57014381da11dd58da | [
"MIT"
] | 116 | 2019-01-17T18:31:43.000Z | 2022-03-31T13:37:21.000Z | netrd/__init__.py | sdmccabe/netrd | f703c19b02f42c9f54bcab57014381da11dd58da | [
"MIT"
] | 175 | 2019-01-15T01:19:13.000Z | 2021-05-25T16:51:26.000Z | netrd/__init__.py | sdmccabe/netrd | f703c19b02f42c9f54bcab57014381da11dd58da | [
"MIT"
] | 36 | 2019-01-14T20:38:32.000Z | 2022-01-21T20:58:38.000Z | """
netrd
-----
netrd stands for Network Reconstruction and Distances. It is a repository
of different algorithms for constructing a network from time series data,
as well as for comparing two networks. It is the product of the Network
Science Insitute 2019 Collabathon.
"""
from . import distance # noqa
from . impo... | 25.5625 | 73 | 0.760391 |
5b14c2ff1b60260805608d9bdfcac0cbbde63652 | 5,613 | py | Python | pytorch/GPT.py | lyq628/NLP-Tutorials | 7c9d117a3542695e79419c835ba9e98ef80800b8 | [
"MIT"
] | 643 | 2018-11-30T09:14:29.000Z | 2022-03-28T14:04:15.000Z | pytorch/GPT.py | lyq628/NLP-Tutorials | 7c9d117a3542695e79419c835ba9e98ef80800b8 | [
"MIT"
] | 22 | 2019-01-03T17:58:12.000Z | 2022-02-10T01:56:00.000Z | pytorch/GPT.py | lyq628/NLP-Tutorials | 7c9d117a3542695e79419c835ba9e98ef80800b8 | [
"MIT"
] | 258 | 2018-12-03T17:15:04.000Z | 2022-03-30T07:45:49.000Z | from transformer import Encoder
from torch import nn,optim
from torch.nn.functional import cross_entropy,softmax, relu
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
import torch
import utils
import os
import pickle
if __name__ == "__main__":
train()
... | 43.176923 | 136 | 0.637983 |
5b14e976757ac56925070b1b4efc08dd156d8a00 | 22,691 | py | Python | skyportal/plot.py | dannygoldstein/skyportal | 3f3518136530fcf5bd1787a4c890782164627fce | [
"BSD-3-Clause"
] | null | null | null | skyportal/plot.py | dannygoldstein/skyportal | 3f3518136530fcf5bd1787a4c890782164627fce | [
"BSD-3-Clause"
] | null | null | null | skyportal/plot.py | dannygoldstein/skyportal | 3f3518136530fcf5bd1787a4c890782164627fce | [
"BSD-3-Clause"
] | null | null | null | import numpy as np
import pandas as pd
from bokeh.core.json_encoder import serialize_json
from bokeh.core.properties import List, String
from bokeh.document import Document
from bokeh.layouts import row, column
from bokeh.models import CustomJS, HoverTool, Range1d, Slider, Button
from bokeh.models.widgets import Check... | 30.335561 | 88 | 0.534441 |
5b15f03a9e21ad9e630b8c38b2ac80ff1cf06549 | 4,625 | py | Python | lib/session.py | Hiteshsuhas/err-stackstorm | 7579350ac50d9324b64a73b86d57e094270cb275 | [
"Apache-2.0"
] | 15 | 2016-09-19T12:06:12.000Z | 2021-11-30T12:04:44.000Z | lib/session.py | Hiteshsuhas/err-stackstorm | 7579350ac50d9324b64a73b86d57e094270cb275 | [
"Apache-2.0"
] | 22 | 2017-06-19T18:13:54.000Z | 2021-05-28T09:25:01.000Z | lib/session.py | Hiteshsuhas/err-stackstorm | 7579350ac50d9324b64a73b86d57e094270cb275 | [
"Apache-2.0"
] | 7 | 2017-06-19T17:03:59.000Z | 2021-09-27T11:06:31.000Z | # coding:utf-8
import uuid
import string
import hashlib
import logging
from lib.errors import SessionExpiredError, SessionConsumedError
from datetime import datetime as dt
from random import SystemRandom
LOG = logging.getLogger("errbot.plugin.st2.session")
| 34.774436 | 99 | 0.611676 |
5b16bf8ef2577dbc0fa8123ec5c7829b61cd4d77 | 700 | py | Python | junopy/entities/bill.py | robertons/junopy | 1acc64ab99d8ea49bb0dac979cd34da43541f243 | [
"MIT"
] | 3 | 2021-07-12T15:05:13.000Z | 2022-01-31T03:35:43.000Z | junopy/entities/bill.py | robertons/junopy | 1acc64ab99d8ea49bb0dac979cd34da43541f243 | [
"MIT"
] | 2 | 2022-01-29T20:14:51.000Z | 2022-02-07T16:16:24.000Z | junopy/entities/bill.py | robertons/junopy | 1acc64ab99d8ea49bb0dac979cd34da43541f243 | [
"MIT"
] | 1 | 2022-02-01T18:36:10.000Z | 2022-02-01T18:36:10.000Z | # -*- coding: utf-8 -*-
from .lib import *
| 26.923077 | 63 | 0.674286 |
5b18bfb17e1557ac4b871c78c2b1715de071b1e0 | 881 | py | Python | accounts/signals.py | julesc00/challenge | 0f991d07c3fa959e254d1b97d4d393fde13844a9 | [
"MIT"
] | null | null | null | accounts/signals.py | julesc00/challenge | 0f991d07c3fa959e254d1b97d4d393fde13844a9 | [
"MIT"
] | null | null | null | accounts/signals.py | julesc00/challenge | 0f991d07c3fa959e254d1b97d4d393fde13844a9 | [
"MIT"
] | null | null | null | from django.db.models.signals import post_save
from django.contrib.auth.signals import user_logged_in, user_logged_out, user_login_failed
from django.contrib.auth.models import User
from django.contrib.auth.models import Group
from django.dispatch import receiver
from .models import Usuario, LoginLog
post_save.conn... | 24.472222 | 90 | 0.715096 |
5b190f68d89adb80d4fc9ec36ff5f159161ba327 | 2,166 | py | Python | Python Scripting/Python - POC-3/DvdApp.py | vaibhavkrishna-bhosle/Trendnxt-Projects | 6c8a31be2f05ec79cfc5086ee09adff161b836ad | [
"MIT"
] | null | null | null | Python Scripting/Python - POC-3/DvdApp.py | vaibhavkrishna-bhosle/Trendnxt-Projects | 6c8a31be2f05ec79cfc5086ee09adff161b836ad | [
"MIT"
] | null | null | null | Python Scripting/Python - POC-3/DvdApp.py | vaibhavkrishna-bhosle/Trendnxt-Projects | 6c8a31be2f05ec79cfc5086ee09adff161b836ad | [
"MIT"
] | null | null | null | import mysql.connector
from mysql.connector.errors import ProgrammingError
from mysql.connector import Error
from DvdOperations import DvdStore
database = "db4"
Function2() | 24.066667 | 171 | 0.60711 |
5b1919573f3036459523134660e1cde252b7f5d5 | 8,689 | py | Python | cloudshell/rest/api.py | QualiSystems/cloudshell-rest-api | 70d09262c81b8dae55053aae162a7265cf67865f | [
"Apache-2.0"
] | 1 | 2021-11-26T22:52:42.000Z | 2021-11-26T22:52:42.000Z | cloudshell/rest/api.py | katzy687/cloudshell-rest-api | 70d09262c81b8dae55053aae162a7265cf67865f | [
"Apache-2.0"
] | 11 | 2019-01-08T06:37:34.000Z | 2021-06-09T17:39:50.000Z | cloudshell/rest/api.py | katzy687/cloudshell-rest-api | 70d09262c81b8dae55053aae162a7265cf67865f | [
"Apache-2.0"
] | 7 | 2016-09-27T13:14:00.000Z | 2021-11-23T14:02:06.000Z | #!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import json
try:
import urllib2
except:
import urllib.request as urllib2
from requests import delete, get, post, put
from cloudshell.rest.exceptions import ShellNotFoundException, FeatureUnavailable
| 36.662447 | 140 | 0.579008 |
5b19d3c83fe2ac0f121d05692ca3db02ba4ea908 | 1,848 | py | Python | data/scripts/classes/team_row.py | matt-waite/lol-reference | 1042fc0a63f7911ed9434b5bb6ba8f866fc0a9c2 | [
"MIT"
] | 1 | 2020-08-26T17:29:58.000Z | 2020-08-26T17:29:58.000Z | data/scripts/classes/team_row.py | matt-waite/lol-reference | 1042fc0a63f7911ed9434b5bb6ba8f866fc0a9c2 | [
"MIT"
] | null | null | null | data/scripts/classes/team_row.py | matt-waite/lol-reference | 1042fc0a63f7911ed9434b5bb6ba8f866fc0a9c2 | [
"MIT"
] | null | null | null | from classes import oracles_headers
| 24 | 63 | 0.540584 |
5b1a34dd97d2ac3c30c9847cc931832f35fa692e | 7,854 | py | Python | startup/97-standard-plans.py | MikeHart85/SIX_profile_collection | f4b34add0c464006a1310375b084c63597b6baf0 | [
"BSD-3-Clause"
] | null | null | null | startup/97-standard-plans.py | MikeHart85/SIX_profile_collection | f4b34add0c464006a1310375b084c63597b6baf0 | [
"BSD-3-Clause"
] | null | null | null | startup/97-standard-plans.py | MikeHart85/SIX_profile_collection | f4b34add0c464006a1310375b084c63597b6baf0 | [
"BSD-3-Clause"
] | null | null | null |
#TODO put this inside of rixscam
def rixscam_get_threshold(Ei = None):
'''Calculate the minimum and maximum threshold for RIXSCAM single photon counting (LS mode)
Ei\t:\t float - incident energy (default is beamline current energy)
'''
if Ei is None:
Ei = pgm.en.user_readback.value
t_... | 37.222749 | 134 | 0.697734 |
5b1a7c8341406690f20aa12accdb9fc9001deadc | 238 | py | Python | speechpro/cloud/speech/synthesis/rest/cloud_client/api/__init__.py | speechpro/cloud-python | dfcfc19a1f008b55c5290599c594fe8de777018b | [
"MIT"
] | 15 | 2020-05-27T09:35:32.000Z | 2022-03-29T18:35:36.000Z | speechpro/cloud/speech/synthesis/rest/cloud_client/api/__init__.py | speechpro/cloud-python | dfcfc19a1f008b55c5290599c594fe8de777018b | [
"MIT"
] | null | null | null | speechpro/cloud/speech/synthesis/rest/cloud_client/api/__init__.py | speechpro/cloud-python | dfcfc19a1f008b55c5290599c594fe8de777018b | [
"MIT"
] | 1 | 2021-04-06T21:39:29.000Z | 2021-04-06T21:39:29.000Z | from __future__ import absolute_import
# flake8: noqa
# import apis into api package
import speechpro.cloud.speech.synthesis.rest.cloud_client.api.session_api
import speechpro.cloud.speech.synthesis.rest.cloud_client.api.synthesize_api
| 29.75 | 76 | 0.848739 |
5b1aad312b8c27483bc4147a2754724cb8c715fb | 1,039 | py | Python | learn_pyqt5/checkable_bar.py | liusong-cn/python | f67933f0879021a595258e09c4cde5ca1f9f6aed | [
"Apache-2.0"
] | 1 | 2019-11-12T13:38:54.000Z | 2019-11-12T13:38:54.000Z | learn_pyqt5/checkable_bar.py | liusong-cn/python | f67933f0879021a595258e09c4cde5ca1f9f6aed | [
"Apache-2.0"
] | null | null | null | learn_pyqt5/checkable_bar.py | liusong-cn/python | f67933f0879021a595258e09c4cde5ca1f9f6aed | [
"Apache-2.0"
] | null | null | null | # _*_ coding:utf-8 _*_
# author:ls
# time:2020/3/19 0019
import sys
from PyQt5.QtWidgets import QApplication,QAction,QMainWindow
from PyQt5.QtGui import QIcon
if __name__ == '__main__':
app = QApplication(sys.argv)
ex = Example()
sys.exit(app.exec_()) | 25.341463 | 60 | 0.627526 |
5b1aca9be8fbadae0d16bcaf4d8c545808d7368a | 3,451 | py | Python | service/test.py | ksiomelo/cubix | cd9e6dda6696b302a7c0d383259a9d60b15b0d55 | [
"Apache-2.0"
] | 3 | 2015-09-07T00:16:16.000Z | 2019-01-11T20:27:56.000Z | service/test.py | ksiomelo/cubix | cd9e6dda6696b302a7c0d383259a9d60b15b0d55 | [
"Apache-2.0"
] | null | null | null | service/test.py | ksiomelo/cubix | cd9e6dda6696b302a7c0d383259a9d60b15b0d55 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
import pika
import time
import json
import StringIO
#from fca.concept import Concept
from casa import Casa
#from fca.readwrite import cxt
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='localhost'))
channel = connection.channel()
channel.queue_declare(queue='... | 30.8125 | 86 | 0.597508 |
5b1cda3e00260587ee1daafde0d87ed8f1313a59 | 310 | py | Python | src/nia/selections/rank.py | salar-shdk/nia | bb0f1b941240b627291dd8212b8840cbe77b0398 | [
"MIT"
] | 8 | 2021-09-06T07:20:23.000Z | 2022-02-23T23:18:22.000Z | src/nia/selections/rank.py | salar-shdk/nia | bb0f1b941240b627291dd8212b8840cbe77b0398 | [
"MIT"
] | null | null | null | src/nia/selections/rank.py | salar-shdk/nia | bb0f1b941240b627291dd8212b8840cbe77b0398 | [
"MIT"
] | null | null | null | from .selection import Selection
import numpy as np
| 25.833333 | 80 | 0.677419 |
5b1ed26356ab2b3641b50b827cab69738be819bd | 15,878 | py | Python | datasets/imppres/imppres.py | ddhruvkr/datasets-1 | 66f2a7eece98d2778bd22bb5034cb7c2376032d4 | [
"Apache-2.0"
] | 7 | 2021-01-04T22:18:26.000Z | 2021-07-10T09:13:29.000Z | datasets/imppres/imppres.py | ddhruvkr/datasets-1 | 66f2a7eece98d2778bd22bb5034cb7c2376032d4 | [
"Apache-2.0"
] | null | null | null | datasets/imppres/imppres.py | ddhruvkr/datasets-1 | 66f2a7eece98d2778bd22bb5034cb7c2376032d4 | [
"Apache-2.0"
] | 3 | 2021-01-03T22:08:20.000Z | 2021-08-12T20:09:39.000Z | # coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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/lice... | 56.910394 | 1,197 | 0.634463 |
5b201dedf7625f49673a17f90219f4d165f06f5d | 1,322 | py | Python | app.py | juergenpointinger/status-dashboard | 439c7e9b6966ff10ada4062c6b97d5088083f442 | [
"MIT"
] | null | null | null | app.py | juergenpointinger/status-dashboard | 439c7e9b6966ff10ada4062c6b97d5088083f442 | [
"MIT"
] | null | null | null | app.py | juergenpointinger/status-dashboard | 439c7e9b6966ff10ada4062c6b97d5088083f442 | [
"MIT"
] | null | null | null | # Standard library imports
import logging
import os
# Third party imports
import dash
import dash_bootstrap_components as dbc
from flask_caching import Cache
import plotly.io as pio
# Local application imports
from modules.gitlab import GitLab
import settings
# Initialize logging mechanism
logging.bas... | 28.12766 | 77 | 0.729955 |
5b20baf76a7bc453b189c49cad4f4c0139f19706 | 5,154 | py | Python | tests/scanner/test_data/fake_retention_scanner_data.py | ogreface/forseti-security | a7a3573183fa1416c605dad683587717795fe13b | [
"Apache-2.0"
] | null | null | null | tests/scanner/test_data/fake_retention_scanner_data.py | ogreface/forseti-security | a7a3573183fa1416c605dad683587717795fe13b | [
"Apache-2.0"
] | null | null | null | tests/scanner/test_data/fake_retention_scanner_data.py | ogreface/forseti-security | a7a3573183fa1416c605dad683587717795fe13b | [
"Apache-2.0"
] | null | null | null | # Copyright 2018 The Forseti Security 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 ap... | 34.13245 | 85 | 0.684517 |
5b22463c2df2d021f347bc17bcb98562b99edb54 | 4,298 | py | Python | libsonyapi/camera.py | BugsForDays/libsonyapi | c6482b4ad90f199b7fb4e344f8e61d4ed0f9466f | [
"MIT"
] | 13 | 2019-04-19T16:44:58.000Z | 2021-09-20T05:33:10.000Z | libsonyapi/camera.py | BugsForDays/libsonyapi | c6482b4ad90f199b7fb4e344f8e61d4ed0f9466f | [
"MIT"
] | 3 | 2021-04-23T17:21:50.000Z | 2022-01-06T17:21:28.000Z | libsonyapi/camera.py | BugsForDays/libsonyapi | c6482b4ad90f199b7fb4e344f8e61d4ed0f9466f | [
"MIT"
] | 5 | 2019-04-11T20:24:47.000Z | 2021-10-17T22:02:56.000Z | import socket
import requests
import json
import xml.etree.ElementTree as ET
| 37.051724 | 156 | 0.578176 |
5b231e5f06d51cf2896d5d0d0db4095473d26007 | 11,961 | py | Python | utility_functions_flu.py | neherlab/treetime_validation | c9760194712396ea5f5c33a9215eddbd3d13bfc1 | [
"MIT"
] | 4 | 2019-01-28T06:47:48.000Z | 2021-04-22T16:31:37.000Z | utility_functions_flu.py | neherlab/treetime_validation | c9760194712396ea5f5c33a9215eddbd3d13bfc1 | [
"MIT"
] | 1 | 2020-04-03T14:42:11.000Z | 2020-04-03T14:42:11.000Z | utility_functions_flu.py | neherlab/treetime_validation | c9760194712396ea5f5c33a9215eddbd3d13bfc1 | [
"MIT"
] | 1 | 2020-03-25T06:58:45.000Z | 2020-03-25T06:58:45.000Z | #!/usr/bin/env python
"""
This module defines functions to facilitate operations with data specific
to Flu trees and alignments.
"""
import numpy as np
from Bio import AlignIO, Phylo
from Bio.Align import MultipleSeqAlignment
import random
import subprocess
import datetime
import os, copy
import matplotlib.pyplot as ... | 32.239892 | 157 | 0.664995 |
5b24576277ff90503d0b77ea45447ed2cd207807 | 3,443 | py | Python | add_label.py | Mause/pull_requests | 6c3aa3feb8ec775c184eaa70d09b944ba753125b | [
"MIT"
] | null | null | null | add_label.py | Mause/pull_requests | 6c3aa3feb8ec775c184eaa70d09b944ba753125b | [
"MIT"
] | 39 | 2021-02-10T05:59:09.000Z | 2022-03-18T07:21:29.000Z | add_label.py | Mause/pull_requests | 6c3aa3feb8ec775c184eaa70d09b944ba753125b | [
"MIT"
] | null | null | null | from asyncio import get_event_loop
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Union
from aiohttp import ClientSession
from pydantic import BaseModel
from sgqlc.endpoint.base import BaseEndpoint
from sgqlc.operation import Operation
from sgqlc_schemas.github.schema import (
Ad... | 27.544 | 88 | 0.652338 |
5b24e7eb961669dcd20e501b760778d98a071d8b | 851 | py | Python | DataEngineering/Chapter7/7.6/financialdata/financialdata/scheduler.py | yz830620/FinMindBook | 1ffda3541eb73e6d4cb47798bf9d28b66a49939b | [
"MIT"
] | 5 | 2021-12-13T12:03:22.000Z | 2022-03-30T08:51:19.000Z | DataEngineering/Chapter7/7.6/financialdata/financialdata/scheduler.py | yz830620/FinMindBook | 1ffda3541eb73e6d4cb47798bf9d28b66a49939b | [
"MIT"
] | 1 | 2022-01-26T05:42:56.000Z | 2022-03-12T08:24:57.000Z | DataEngineering/Chapter7/7.6/financialdata/financialdata/scheduler.py | yz830620/FinMindBook | 1ffda3541eb73e6d4cb47798bf9d28b66a49939b | [
"MIT"
] | 6 | 2021-12-14T04:32:01.000Z | 2022-03-31T17:15:11.000Z | import time
import datetime
from apscheduler.schedulers.background import BackgroundScheduler
from financialdata.producer import Update
from loguru import logger
if __name__ == "__main__":
main()
while True:
time.sleep(600)
| 24.314286 | 74 | 0.679201 |
d289828efb378099de1d3d6011a5a3e50df04330 | 2,692 | py | Python | openmc_plasma_source/plotting/plot_tokamak_source.py | mdfaisal98/openmc-plasma-source | e55d61ce6d641f4d382ce298b6f6335cd46bc507 | [
"MIT"
] | null | null | null | openmc_plasma_source/plotting/plot_tokamak_source.py | mdfaisal98/openmc-plasma-source | e55d61ce6d641f4d382ce298b6f6335cd46bc507 | [
"MIT"
] | null | null | null | openmc_plasma_source/plotting/plot_tokamak_source.py | mdfaisal98/openmc-plasma-source | e55d61ce6d641f4d382ce298b6f6335cd46bc507 | [
"MIT"
] | null | null | null | import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
def scatter_tokamak_source(source, quantity=None, **kwargs):
"""Create a 2D scatter plot of the tokamak source.
See https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html
for more arguments.
Args:
s... | 33.65 | 103 | 0.658247 |
d28a47045a9d4366365cea9cca22f372e578a38f | 620 | py | Python | Exercício feitos pela primeira vez/ex046.py | Claayton/pythonExerciciosLinux | 696cdb16983638418bd0d0d4fe44dc72662b9c97 | [
"MIT"
] | 1 | 2021-01-23T15:43:34.000Z | 2021-01-23T15:43:34.000Z | Exercício feitos pela primeira vez/ex046.py | Claayton/pythonExerciciosLinux | 696cdb16983638418bd0d0d4fe44dc72662b9c97 | [
"MIT"
] | null | null | null | Exercício feitos pela primeira vez/ex046.py | Claayton/pythonExerciciosLinux | 696cdb16983638418bd0d0d4fe44dc72662b9c97 | [
"MIT"
] | null | null | null | #Exerccio046
from time import sleep
import emoji
print('\033[32mCONTAGEM REGRESSIVA PARA O ANO NOVO:\033[m')
sleep(1)
for c in range(10, 0 - 1, -1):#repete os nmeros de 10 at o 0
print(c)
sleep(1)
print(emoji.emojize("\033[31m:boom::boom::boom:KABUM:boom::boom::boom:", use_aliases=True))
print(emoji.emojize("\0... | 47.692308 | 104 | 0.720968 |
d28ad97667405531526925b2fe6abf6f466b39ff | 10,989 | py | Python | bmds/bmds2/logic/rules.py | shapiromatron/bmds | 57562858f3c45e9b9ec23e1c229a8a1de0ea4a70 | [
"MIT"
] | 2 | 2017-05-01T20:00:26.000Z | 2019-07-09T16:42:25.000Z | bmds/bmds2/logic/rules.py | shapiromatron/bmds | 57562858f3c45e9b9ec23e1c229a8a1de0ea4a70 | [
"MIT"
] | 20 | 2016-11-23T21:30:22.000Z | 2022-02-28T15:42:36.000Z | bmds/bmds2/logic/rules.py | shapiromatron/bmds | 57562858f3c45e9b9ec23e1c229a8a1de0ea4a70 | [
"MIT"
] | 2 | 2016-06-28T20:32:00.000Z | 2017-02-23T20:30:24.000Z | import abc
import math
from ... import constants
class NumericValueExists(Rule):
# Test succeeds if value is numeric and not -999
field_name = None
field_name_verbose = None
class BmdExists(NumericValueExists):
default_rule_name = "BMD exists"
field_name = "BMD"
class BmdlExists(NumericValue... | 31.760116 | 101 | 0.628629 |
d28b5d6c386f989e7b581b7ea7ba92a93a7470b3 | 1,959 | py | Python | nets/static/conv_rnn_convT.py | MaximilienLC/nevo | c701a1202bc18d89a622472918733bf78ba5e304 | [
"Apache-2.0"
] | null | null | null | nets/static/conv_rnn_convT.py | MaximilienLC/nevo | c701a1202bc18d89a622472918733bf78ba5e304 | [
"Apache-2.0"
] | null | null | null | nets/static/conv_rnn_convT.py | MaximilienLC/nevo | c701a1202bc18d89a622472918733bf78ba5e304 | [
"Apache-2.0"
] | 1 | 2022-03-31T20:44:09.000Z | 2022-03-31T20:44:09.000Z | # Copyright 2022 Maximilien Le Clei.
#
# 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 w... | 26.472973 | 74 | 0.598264 |
d28b646d833333371908e74411b14fa7d1f681ca | 3,306 | py | Python | ors2bryton.py | andbue/ors2bryton | 7a843cbf2e4d1fc4ca85497cb23919431d8d3843 | [
"Unlicense"
] | null | null | null | ors2bryton.py | andbue/ors2bryton | 7a843cbf2e4d1fc4ca85497cb23919431d8d3843 | [
"Unlicense"
] | 1 | 2021-02-02T13:11:23.000Z | 2021-09-10T16:38:16.000Z | ors2bryton.py | andbue/ors2bryton | 7a843cbf2e4d1fc4ca85497cb23919431d8d3843 | [
"Unlicense"
] | null | null | null | from sys import argv
from os.path import splitext
from lxml import etree
from struct import pack
if __name__ == "__main__":
main()
| 31.485714 | 151 | 0.468845 |
d28b98aeee69dc1cdd515a34f7751e391f42ef74 | 5,022 | py | Python | src/main/python/smart/smartplots3_run.py | cday97/beam | 7e1ab50eecaefafd04daab360f8b12bc7cab559b | [
"BSD-3-Clause-LBNL"
] | 123 | 2017-04-06T20:17:19.000Z | 2022-03-02T13:42:15.000Z | src/main/python/smart/smartplots3_run.py | cday97/beam | 7e1ab50eecaefafd04daab360f8b12bc7cab559b | [
"BSD-3-Clause-LBNL"
] | 2,676 | 2017-04-26T20:27:27.000Z | 2022-03-31T16:39:53.000Z | src/main/python/smart/smartplots3_run.py | cday97/beam | 7e1ab50eecaefafd04daab360f8b12bc7cab559b | [
"BSD-3-Clause-LBNL"
] | 60 | 2017-04-06T20:14:32.000Z | 2022-03-30T20:10:53.000Z | import pandas as pd
import smartplots3_setup
scenarios_lables = {
"Base_CL_CT": "Base0",
"Base_STL_STT_BAU": "Base2",
"Base_STL_STT_VTO": "Base3",
"Base_LTL_LTT_BAU": "Base5",
"Base_LTL_LTT_VTO": "Base6",
"A_STL_STT_BAU": "A2",
"A_STL_STT_VTO": "A3",
"B_LTL_LTT_BAU": "B5",
"B_LTL_LT... | 50.727273 | 110 | 0.788331 |
d28c4ad642d7e25e12003d4150c60dd4429d8299 | 50 | py | Python | genrl/deep/agents/sac/__init__.py | ajaysub110/JigglypuffRL | 083fd26d05b7eac018e6db7d32c4be4587461766 | [
"MIT"
] | null | null | null | genrl/deep/agents/sac/__init__.py | ajaysub110/JigglypuffRL | 083fd26d05b7eac018e6db7d32c4be4587461766 | [
"MIT"
] | null | null | null | genrl/deep/agents/sac/__init__.py | ajaysub110/JigglypuffRL | 083fd26d05b7eac018e6db7d32c4be4587461766 | [
"MIT"
] | null | null | null | from genrl.deep.agents.sac.sac import SAC # noqa
| 25 | 49 | 0.76 |
d28c64bd9262b8b74070c47f2ceb3b8061a39ebe | 238 | py | Python | contrib/libs/cxxsupp/libsan/generate_symbolizer.py | HeyLey/catboost | f472aed90604ebe727537d9d4a37147985e10ec2 | [
"Apache-2.0"
] | 6,989 | 2017-07-18T06:23:18.000Z | 2022-03-31T15:58:36.000Z | contrib/libs/cxxsupp/libsan/generate_symbolizer.py | HeyLey/catboost | f472aed90604ebe727537d9d4a37147985e10ec2 | [
"Apache-2.0"
] | 1,978 | 2017-07-18T09:17:58.000Z | 2022-03-31T14:28:43.000Z | contrib/libs/cxxsupp/libsan/generate_symbolizer.py | HeyLey/catboost | f472aed90604ebe727537d9d4a37147985e10ec2 | [
"Apache-2.0"
] | 1,228 | 2017-07-18T09:03:13.000Z | 2022-03-29T05:57:40.000Z | import os
import sys
if __name__ == '__main__':
main()
| 18.307692 | 98 | 0.621849 |
d28c678a957ea394e636e4d4799124a81070a2a0 | 775 | py | Python | scripts/scheduler/scheduler.py | OCHA-DAP/hdx-scraper-unosat-flood-portal | 80b0bcd404993e4bd1dae442f794c9f86b6d5328 | [
"MIT"
] | 1 | 2016-07-22T13:32:54.000Z | 2016-07-22T13:32:54.000Z | scripts/scheduler/scheduler.py | OCHA-DAP/hdx-scraper-unosat-flood-portal | 80b0bcd404993e4bd1dae442f794c9f86b6d5328 | [
"MIT"
] | 21 | 2015-07-08T21:30:32.000Z | 2015-08-27T17:52:24.000Z | scripts/scheduler/scheduler.py | OCHA-DAP/hdxscraper-unosat-flood-portal | 80b0bcd404993e4bd1dae442f794c9f86b6d5328 | [
"MIT"
] | null | null | null | #!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import sys
import time
import schedule
dir = os.path.split(os.path.split(os.path.realpath(__file__))[0])[0]
sys.path.append(dir)
from utilities.prompt_format import item
from unosat_flood_portal_collect import collect as Collect
def Wrapper(patch=False):
'''Wrap... | 16.145833 | 68 | 0.68129 |
d28c6e3b8a94c187af7ae1ba6acb241b56167d9b | 1,916 | py | Python | grAdapt/sampling/initializer/Vertices.py | mkduong-ai/grAdapt | 94c2659b0f6ff9a2984a9dc58e3c83213313bf90 | [
"Apache-2.0"
] | 25 | 2020-11-13T05:57:01.000Z | 2021-06-18T11:16:03.000Z | grAdapt/sampling/initializer/Vertices.py | mkduong-ai/grAdapt | 94c2659b0f6ff9a2984a9dc58e3c83213313bf90 | [
"Apache-2.0"
] | null | null | null | grAdapt/sampling/initializer/Vertices.py | mkduong-ai/grAdapt | 94c2659b0f6ff9a2984a9dc58e3c83213313bf90 | [
"Apache-2.0"
] | null | null | null | # python
# import warnings
# Third party imports
import numpy as np
# grAdapt
from .base import Initial
from grAdapt.utils.sampling import sample_corner_bounds
| 34.214286 | 97 | 0.581942 |
d28e9a15ec55f39d2fbe7a6ba1ac7924e04991a1 | 6,456 | py | Python | thirdweb/modules/base.py | princetonwong/python-sdk | f35181d97620e29d055498fca75f3702f3bb2449 | [
"Apache-2.0"
] | 1 | 2022-02-18T16:59:12.000Z | 2022-02-18T16:59:12.000Z | thirdweb/modules/base.py | princetonwong/python-sdk | f35181d97620e29d055498fca75f3702f3bb2449 | [
"Apache-2.0"
] | null | null | null | thirdweb/modules/base.py | princetonwong/python-sdk | f35181d97620e29d055498fca75f3702f3bb2449 | [
"Apache-2.0"
] | null | null | null | """Base Module."""
from abc import ABC, abstractmethod
from typing import Callable, Dict, List, Optional, Union, cast
from eth_account.account import LocalAccount
from thirdweb_web3 import Web3
from thirdweb_web3.types import TxReceipt
from zero_ex.contract_wrappers import TxParams
import json
from ..abi.coin import ... | 33.278351 | 95 | 0.623296 |
d291c41a3b15e20796ea46ca106a1298d83274c2 | 17,356 | py | Python | data_util.py | shiyu-wangbyte/leadopt | ef289ab349a19ba1f8aa581638ef7e8e3810cb41 | [
"Apache-2.0"
] | null | null | null | data_util.py | shiyu-wangbyte/leadopt | ef289ab349a19ba1f8aa581638ef7e8e3810cb41 | [
"Apache-2.0"
] | null | null | null | data_util.py | shiyu-wangbyte/leadopt | ef289ab349a19ba1f8aa581638ef7e8e3810cb41 | [
"Apache-2.0"
] | null | null | null | # Copyright 2021 Jacob Durrant
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy
# of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... | 33.441233 | 93 | 0.589191 |
d291cc8632d543ebd26c04ae26559da840755d11 | 4,181 | py | Python | add_socket_response_event.py | Kur0den/kur0bot | d36722617bb4094bdf636779b20a799f9bd3b419 | [
"MIT"
] | 1 | 2021-09-09T11:17:17.000Z | 2021-09-09T11:17:17.000Z | add_socket_response_event.py | Kur0den/kur0bot | d36722617bb4094bdf636779b20a799f9bd3b419 | [
"MIT"
] | 1 | 2021-09-18T15:46:59.000Z | 2021-09-18T15:46:59.000Z | add_socket_response_event.py | Kur0den/kur0bot | d36722617bb4094bdf636779b20a799f9bd3b419 | [
"MIT"
] | 1 | 2021-09-09T02:34:17.000Z | 2021-09-09T02:34:17.000Z | from discord.gateway import DiscordWebSocket, utils, _log, KeepAliveHandler, ReconnectWebSocket
DiscordWebSocket.received_message = received_message
| 32.664063 | 100 | 0.566611 |
d2936347651280722332cf187a2ad771feb61ab8 | 2,207 | py | Python | Image_detection_codes/Keras_training/test2.py | pasadyash/CitizenServiceApp | 01a0389d70624f04f6df25c1eb842b3bbce652da | [
"MIT"
] | null | null | null | Image_detection_codes/Keras_training/test2.py | pasadyash/CitizenServiceApp | 01a0389d70624f04f6df25c1eb842b3bbce652da | [
"MIT"
] | null | null | null | Image_detection_codes/Keras_training/test2.py | pasadyash/CitizenServiceApp | 01a0389d70624f04f6df25c1eb842b3bbce652da | [
"MIT"
] | null | null | null | import numpy as np
np.random.seed(123) # for reproducibility
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from dataset_pothole import pothole
from keras.models import model_from_j... | 27.5875 | 68 | 0.7372 |
d2945eb56ca24287c1bd0834d603839aee1fedac | 2,094 | py | Python | platform/web/api/device/models.py | JMSHDev/regent.dev | e4cedf04dd241ad00012735b543ee3447a8da8a2 | [
"Apache-2.0"
] | 1 | 2021-12-23T14:06:08.000Z | 2021-12-23T14:06:08.000Z | platform/web/api/device/models.py | JMSHDev/regent.dev | e4cedf04dd241ad00012735b543ee3447a8da8a2 | [
"Apache-2.0"
] | null | null | null | platform/web/api/device/models.py | JMSHDev/regent.dev | e4cedf04dd241ad00012735b543ee3447a8da8a2 | [
"Apache-2.0"
] | 1 | 2021-06-28T22:17:28.000Z | 2021-06-28T22:17:28.000Z | import hashlib
import random
import string
import logging
from django.db import models
LOG = logging.getLogger(__name__)
| 34.327869 | 108 | 0.722063 |
d294cefa293f8d84c96bacb7467d9cfe88246372 | 147 | py | Python | armageddon/__init__.py | acse-ns1321/asteroid-impact-simulator | 986c12ff1276e5d0547a4f760e1d2cb90fe4ba11 | [
"MIT"
] | null | null | null | armageddon/__init__.py | acse-ns1321/asteroid-impact-simulator | 986c12ff1276e5d0547a4f760e1d2cb90fe4ba11 | [
"MIT"
] | null | null | null | armageddon/__init__.py | acse-ns1321/asteroid-impact-simulator | 986c12ff1276e5d0547a4f760e1d2cb90fe4ba11 | [
"MIT"
] | null | null | null | # flake8:NOQA
"""Python asteroid airburst calculator"""
from .solver import *
from .damage import *
from .locator import *
from .mapping import *
| 18.375 | 41 | 0.734694 |
d294d257d8cdf140b519b1d91dd4b68639347768 | 8,235 | py | Python | proxy_server/backend_services.py | lmanzurv/django_proxy_server | 20304829ef1ddcbb281e1373d308e5fa826fcd39 | [
"Apache-2.0"
] | 11 | 2015-07-18T02:23:43.000Z | 2021-11-15T11:43:21.000Z | proxy_server/backend_services.py | lmanzurv/django_proxy_server | 20304829ef1ddcbb281e1373d308e5fa826fcd39 | [
"Apache-2.0"
] | null | null | null | proxy_server/backend_services.py | lmanzurv/django_proxy_server | 20304829ef1ddcbb281e1373d308e5fa826fcd39 | [
"Apache-2.0"
] | 5 | 2015-02-24T15:37:36.000Z | 2021-10-10T16:42:22.000Z | from django.contrib.auth import SESSION_KEY
from django.core.cache import cache
from django.conf import settings
from django.http import HttpResponse, HttpResponseServerError
from proxy_server.response import AJAX_REQUEST
import httplib, json, proxy_server
| 37.094595 | 131 | 0.600364 |
d294ed611a40faaaff54b7db50b237d6a8c768e7 | 1,645 | py | Python | py/trawl_analyzer/TrawlSensorsDB_model.py | nwfsc-fram/pyFieldSoftware | 477ba162b66ede2263693cda8c5a51d27eaa3b89 | [
"MIT"
] | null | null | null | py/trawl_analyzer/TrawlSensorsDB_model.py | nwfsc-fram/pyFieldSoftware | 477ba162b66ede2263693cda8c5a51d27eaa3b89 | [
"MIT"
] | 176 | 2019-11-22T17:44:55.000Z | 2021-10-20T23:40:03.000Z | py/trawl_analyzer/TrawlSensorsDB_model.py | nwfsc-fram/pyFieldSoftware | 477ba162b66ede2263693cda8c5a51d27eaa3b89 | [
"MIT"
] | 1 | 2021-05-07T01:06:32.000Z | 2021-05-07T01:06:32.000Z | # from peewee import *
from playhouse.apsw_ext import TextField, IntegerField, PrimaryKeyField
from py.trawl_analyzer.Settings import SensorsModel as BaseModel
# database = SqliteDatabase('data\clean_sensors.db', **{})
| 38.255814 | 83 | 0.764134 |
d29572229651c45d1ad6870cb96992f7e8dc3c59 | 9,754 | py | Python | src/statemachine.py | CEOAI-ABM/SIR-Modelling | 02ab89d64040b09ddce820a1ecbbc0cfc9b13f29 | [
"MIT"
] | 1 | 2021-06-13T11:50:08.000Z | 2021-06-13T11:50:08.000Z | src/statemachine.py | CEOAI-ABM/SIR-Modelling | 02ab89d64040b09ddce820a1ecbbc0cfc9b13f29 | [
"MIT"
] | null | null | null | src/statemachine.py | CEOAI-ABM/SIR-Modelling | 02ab89d64040b09ddce820a1ecbbc0cfc9b13f29 | [
"MIT"
] | null | null | null | import transitions
from functools import partial
# from transitions import transitions.Machine
# TODO: whenever there is a state chage store the following
# (DAY,function_called) -> Stored for every person for agent status, state and Testing state
| 28.190751 | 159 | 0.705454 |
d295e921737512140cabce35cb8da35469a21633 | 304 | py | Python | hard-gists/5898352/snippet.py | jjhenkel/dockerizeme | eaa4fe5366f6b9adf74399eab01c712cacaeb279 | [
"Apache-2.0"
] | 21 | 2019-07-08T08:26:45.000Z | 2022-01-24T23:53:25.000Z | hard-gists/5898352/snippet.py | jjhenkel/dockerizeme | eaa4fe5366f6b9adf74399eab01c712cacaeb279 | [
"Apache-2.0"
] | 5 | 2019-06-15T14:47:47.000Z | 2022-02-26T05:02:56.000Z | hard-gists/5898352/snippet.py | jjhenkel/dockerizeme | eaa4fe5366f6b9adf74399eab01c712cacaeb279 | [
"Apache-2.0"
] | 17 | 2019-05-16T03:50:34.000Z | 2021-01-14T14:35:12.000Z | import os
import scipy.io.wavfile as wav
# install lame
# install bleeding edge scipy (needs new cython)
fname = 'XC135672-Red-winged\ Blackbird1301.mp3'
oname = 'temp.wav'
cmd = 'lame --decode {0} {1}'.format( fname,oname )
os.system(cmd)
data = wav.read(oname)
# your code goes here
print len(data[1])
| 25.333333 | 51 | 0.720395 |
d29646348f53744d285a4ab6a2096da4edb810a8 | 2,612 | py | Python | examples/home-assistant/custom_components/evacalor/config_flow.py | fredericvl/pyevacalor | 37a3d96f867efffdec4457f11119977e6e887b8a | [
"Apache-2.0"
] | 2 | 2020-10-25T15:42:03.000Z | 2021-01-06T10:25:58.000Z | examples/home-assistant/custom_components/evacalor/config_flow.py | fredericvl/pyevacalor | 37a3d96f867efffdec4457f11119977e6e887b8a | [
"Apache-2.0"
] | 2 | 2021-01-06T09:24:58.000Z | 2021-02-13T21:12:02.000Z | examples/home-assistant/custom_components/evacalor/config_flow.py | fredericvl/pyevacalor | 37a3d96f867efffdec4457f11119977e6e887b8a | [
"Apache-2.0"
] | null | null | null | """Config flow for Eva Calor."""
from collections import OrderedDict
import logging
import uuid
from pyevacalor import ( # pylint: disable=redefined-builtin
ConnectionError,
Error as EvaCalorError,
UnauthorizedError,
evacalor,
)
import voluptuous as vol
from homeassistant import config_entries
from h... | 31.095238 | 87 | 0.616003 |
d2965c42b4aa6f52d9c6e78125bcdb00950f4d9f | 6,608 | py | Python | library_samples/Python3/ocs_sample_library_preview/Dataview/Dataview.py | osi-awoodall/OSI-Samples-OCS | 1995ccda20e4fe2ae66f3b67afbc1127d638a6fc | [
"Apache-2.0"
] | null | null | null | library_samples/Python3/ocs_sample_library_preview/Dataview/Dataview.py | osi-awoodall/OSI-Samples-OCS | 1995ccda20e4fe2ae66f3b67afbc1127d638a6fc | [
"Apache-2.0"
] | null | null | null | library_samples/Python3/ocs_sample_library_preview/Dataview/Dataview.py | osi-awoodall/OSI-Samples-OCS | 1995ccda20e4fe2ae66f3b67afbc1127d638a6fc | [
"Apache-2.0"
] | null | null | null | # Dataview.py
#
import json
from .DataviewQuery import DataviewQuery
from .DataviewMapping import DataviewMapping
from .DataviewIndexConfig import DataviewIndexConfig
from .DataviewGroupRule import DataviewGroupRule
| 25.513514 | 87 | 0.575969 |
d296cec19b3a1e77f406394741a977e6895ca59f | 392 | py | Python | PYTHON_Code/TestGUI.py | ROBO-BEV/BARISTO | 0e87d79966efc111cc38c1a1cf22e2d8ee18c350 | [
"CC-BY-3.0",
"MIT"
] | 8 | 2018-03-12T04:52:28.000Z | 2021-05-19T19:37:01.000Z | PYTHON_Code/TestGUI.py | ROBO-BEV/BARISTO | 0e87d79966efc111cc38c1a1cf22e2d8ee18c350 | [
"CC-BY-3.0",
"MIT"
] | null | null | null | PYTHON_Code/TestGUI.py | ROBO-BEV/BARISTO | 0e87d79966efc111cc38c1a1cf22e2d8ee18c350 | [
"CC-BY-3.0",
"MIT"
] | 1 | 2018-01-30T09:43:36.000Z | 2018-01-30T09:43:36.000Z | from tkinter import *
window0 = Tk()
window0.geometry('960x540')
#tk.iconbitmap(default='ROBO_BEV_LOGO.ico')
window0.title("BARISTO")
photo = PhotoImage(file="Page1.png")
widget = Label(window0, image=photo)
widget.photo = photo
widget = Label(window0, text="10", fg="white", font=("Source Sans Pro",50))
#widget = L... | 19.6 | 75 | 0.709184 |
d297adc463629ff967a82e11d0f42bb013364af4 | 2,354 | py | Python | handlers/play.py | AftahBagas/AlphaMusik | c8c3804a26ad393b6f666fecd4d3464727ce2544 | [
"MIT"
] | null | null | null | handlers/play.py | AftahBagas/AlphaMusik | c8c3804a26ad393b6f666fecd4d3464727ce2544 | [
"MIT"
] | null | null | null | handlers/play.py | AftahBagas/AlphaMusik | c8c3804a26ad393b6f666fecd4d3464727ce2544 | [
"MIT"
] | 1 | 2021-06-22T08:08:43.000Z | 2021-06-22T08:08:43.000Z | from os import path
from telethon import Client
from telethon.types import Message, Voice
from callsmusic import callsmusic, queues
import converter
from downloaders import youtube
from config import BOT_NAME as bn, DURATION_LIMIT
from helpers.filters import command, other_filters
from helpers.decorators import err... | 34.115942 | 116 | 0.658454 |
d2992c7176a1b65595e782d6603b030801317e72 | 2,662 | py | Python | Sindri/Properties.py | mrcsbrn/TCC_software | 17a5335aed17d4740c3bbd0ef828b0fc5dcea1da | [
"MIT"
] | 11 | 2019-10-17T02:01:51.000Z | 2022-03-17T17:39:34.000Z | Sindri/Properties.py | mrcsbrn/TCC_software | 17a5335aed17d4740c3bbd0ef828b0fc5dcea1da | [
"MIT"
] | 2 | 2019-07-25T22:16:16.000Z | 2020-03-28T01:59:59.000Z | Sindri/Properties.py | mrcsbrn/TCC_software | 17a5335aed17d4740c3bbd0ef828b0fc5dcea1da | [
"MIT"
] | 5 | 2019-07-15T18:19:36.000Z | 2021-12-24T08:06:24.000Z | from __future__ import annotations
from constants import DBL_EPSILON
| 24.422018 | 87 | 0.531555 |
d29a434df89a3b05d94919b3e887c98d5f6aef26 | 8,240 | py | Python | algorithms/randcommuns.py | eXascaleInfolab/clubmark | 5c329a5308a39d53f77db790a31d621245a7c693 | [
"Apache-2.0"
] | 14 | 2018-11-20T08:32:30.000Z | 2022-03-14T02:46:35.000Z | algorithms/randcommuns.py | eXascaleInfolab/clubmark | 5c329a5308a39d53f77db790a31d621245a7c693 | [
"Apache-2.0"
] | null | null | null | algorithms/randcommuns.py | eXascaleInfolab/clubmark | 5c329a5308a39d53f77db790a31d621245a7c693 | [
"Apache-2.0"
] | 1 | 2019-05-22T08:39:00.000Z | 2019-05-22T08:39:00.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
:Brief: Produces rand disjoint communities (clusters) for the given network with sizes similar in the ground truth.
:Description:
Takes number of the resulting communities and their sizes from the specified groundtruth (actually any sample
of the community structure, ... | 40.392157 | 116 | 0.701942 |
d29d169f662bf82cfbfb0172089e264d38e0b3c3 | 17,578 | py | Python | utils/save_atten.py | xiaomengyc/SPG | 0006659c5be4c3451f8c9a188f1e91e9ff682fa9 | [
"MIT"
] | 152 | 2018-07-25T01:55:33.000Z | 2022-02-02T15:16:09.000Z | utils/save_atten.py | xiaomengyc/SPG | 0006659c5be4c3451f8c9a188f1e91e9ff682fa9 | [
"MIT"
] | 15 | 2018-09-13T06:35:16.000Z | 2021-08-05T06:23:16.000Z | utils/save_atten.py | xiaomengyc/SPG | 0006659c5be4c3451f8c9a188f1e91e9ff682fa9 | [
"MIT"
] | 27 | 2018-07-26T03:47:55.000Z | 2021-04-05T08:06:41.000Z | import numpy as np
import cv2
import os
import torch
import os
import time
from torchvision import models, transforms
from torch.utils.data import DataLoader
from torch.optim import SGD
from torch.autograd import Variable
idx2catename = {'voc20': ['aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair',... | 42.458937 | 131 | 0.529867 |
d29d26d475e134ec64d93b0a0c67aac73b58249e | 453 | py | Python | integration/config/service_names.py | hawflau/serverless-application-model | d2cf4b7e23d26cdf677c564d53bb58e6a5b6cac2 | [
"Apache-2.0"
] | null | null | null | integration/config/service_names.py | hawflau/serverless-application-model | d2cf4b7e23d26cdf677c564d53bb58e6a5b6cac2 | [
"Apache-2.0"
] | 1 | 2020-03-03T01:46:46.000Z | 2020-03-03T01:46:46.000Z | integration/config/service_names.py | hawflau/serverless-application-model | d2cf4b7e23d26cdf677c564d53bb58e6a5b6cac2 | [
"Apache-2.0"
] | null | null | null | COGNITO = "Cognito"
SERVERLESS_REPO = "ServerlessRepo"
MODE = "Mode"
XRAY = "XRay"
LAYERS = "Layers"
HTTP_API = "HttpApi"
IOT = "IoT"
CODE_DEPLOY = "CodeDeploy"
ARM = "ARM"
GATEWAY_RESPONSES = "GatewayResponses"
MSK = "MSK"
KMS = "KMS"
CWE_CWS_DLQ = "CweCwsDlq"
CODE_SIGN = "CodeSign"
MQ = "MQ"
USAGE_PLANS = "UsagePlans... | 19.695652 | 38 | 0.708609 |
d29e1b642a0cdbe5b86c0d36bda20ce0cce1d92a | 2,373 | py | Python | tools/onnx_utilis/export_vfe_weight.py | neolixcn/OpenPCDet | 32bae37db13711a4fb35ad2980068470bb6cee1c | [
"Apache-2.0"
] | null | null | null | tools/onnx_utilis/export_vfe_weight.py | neolixcn/OpenPCDet | 32bae37db13711a4fb35ad2980068470bb6cee1c | [
"Apache-2.0"
] | null | null | null | tools/onnx_utilis/export_vfe_weight.py | neolixcn/OpenPCDet | 32bae37db13711a4fb35ad2980068470bb6cee1c | [
"Apache-2.0"
] | null | null | null | import onnx
import onnxruntime
import torch
import onnx.numpy_helper
# added by huxi, load rpn config
from pcdet.pointpillar_quantize_config import load_rpn_config_json
# ========================================
config_dict = load_rpn_config_json.get_config()
onnx_model_file = config_dict["vfe_onnx_file"]
onnx_mode... | 28.25 | 93 | 0.702908 |
d29e58f5104bd6d4a19025c66f8dbd6cd3fc3f1a | 1,825 | py | Python | color_extractor/cluster.py | hcoura/color-extractor | a69fc4a9a8b7c90d292f954d289c84a38323eda6 | [
"MIT"
] | 276 | 2016-07-25T10:00:06.000Z | 2022-03-10T16:56:26.000Z | color_extractor/cluster.py | hcoura/color-extractor | a69fc4a9a8b7c90d292f954d289c84a38323eda6 | [
"MIT"
] | 13 | 2017-05-25T12:45:30.000Z | 2022-03-11T23:16:30.000Z | color_extractor/cluster.py | hcoura/color-extractor | a69fc4a9a8b7c90d292f954d289c84a38323eda6 | [
"MIT"
] | 74 | 2016-12-14T07:31:18.000Z | 2022-03-12T18:36:57.000Z | from sklearn.cluster import KMeans
from .exceptions import KMeansException
from .task import Task
| 26.838235 | 78 | 0.572603 |
d29e853085f1e22d6f5c45806ff223b5999daf1d | 315 | py | Python | notebooks/datasets.py | jweill-aws/jupyterlab-data-explorer | 3db8eed9562f35d2b0e44370cf22f32ac9ffbc4d | [
"BSD-3-Clause"
] | 173 | 2019-01-04T05:18:08.000Z | 2022-03-28T11:15:30.000Z | notebooks/datasets.py | jweill-aws/jupyterlab-data-explorer | 3db8eed9562f35d2b0e44370cf22f32ac9ffbc4d | [
"BSD-3-Clause"
] | 115 | 2019-01-04T01:09:41.000Z | 2022-03-24T01:07:00.000Z | notebooks/datasets.py | jweill-aws/jupyterlab-data-explorer | 3db8eed9562f35d2b0e44370cf22f32ac9ffbc4d | [
"BSD-3-Clause"
] | 34 | 2019-06-12T16:46:53.000Z | 2022-02-01T08:41:40.000Z | #
# @license BSD-3-Clause
#
# Copyright (c) 2019 Project Jupyter Contributors.
# Distributed under the terms of the 3-Clause BSD License.
import IPython.display
import pandas
| 21 | 67 | 0.730159 |
d29f3df5f35ab4781444eaf48243bf8b792bb433 | 1,154 | py | Python | django_india/conf.py | k-mullapudi/django-india | 662a5fb363ac4360b573f5864df65619f2794dc8 | [
"MIT"
] | null | null | null | django_india/conf.py | k-mullapudi/django-india | 662a5fb363ac4360b573f5864df65619f2794dc8 | [
"MIT"
] | null | null | null | django_india/conf.py | k-mullapudi/django-india | 662a5fb363ac4360b573f5864df65619f2794dc8 | [
"MIT"
] | null | null | null | import django.conf
url_bases = {
'geonames': {
'dump': 'http://download.geonames.org/export/dump/',
'zip': 'http://download.geonames.org/export/zip/',
},
}
india_country_code = 'IN'
files = {
'state': {
'filename': '',
'urls': [
url_bases['geonames']['dump'] + ... | 19.233333 | 73 | 0.47747 |
d29f77fa5fac3eb65fe044b9f6c664cd6a9d69a3 | 1,588 | py | Python | src/dao/evaluation_dao.py | Asconius/trading-bot | df544f058d12c5378a0f8c110e28d49d983e0393 | [
"Apache-2.0"
] | 2 | 2021-06-04T11:27:02.000Z | 2021-12-19T03:24:51.000Z | src/dao/evaluation_dao.py | Asconius/trading-bot | df544f058d12c5378a0f8c110e28d49d983e0393 | [
"Apache-2.0"
] | 22 | 2020-08-24T05:16:11.000Z | 2021-12-13T20:51:25.000Z | src/dao/evaluation_dao.py | Asconius/trading-bot | df544f058d12c5378a0f8c110e28d49d983e0393 | [
"Apache-2.0"
] | null | null | null | from decimal import Decimal
from typing import List
from src.dao.dao import DAO
from src.dto.attempt_dto import AttemptDTO
from src.entity.evaluation_entity import EvaluationEntity
from src.utils.utils import Utils
| 40.717949 | 116 | 0.706549 |
d29fef12d764089bdcfe8679c802e9724d8f9325 | 1,031 | py | Python | src/lib/others/info_gathering/finder/finding_comment.py | nahuelhm17/vault_scanner | 574da226db5d274794d751d9d7959cd785bc9990 | [
"MIT"
] | 230 | 2019-01-10T07:43:01.000Z | 2022-03-25T03:16:07.000Z | src/lib/others/info_gathering/finder/finding_comment.py | nahuelhm17/vault_scanner | 574da226db5d274794d751d9d7959cd785bc9990 | [
"MIT"
] | 65 | 2018-11-18T12:48:27.000Z | 2019-01-05T22:40:07.000Z | src/lib/others/info_gathering/finder/finding_comment.py | nahuelhm17/vault_scanner | 574da226db5d274794d751d9d7959cd785bc9990 | [
"MIT"
] | 64 | 2019-01-16T11:56:18.000Z | 2022-01-12T17:28:37.000Z | #! /usr/bin/python
import requests
import re
from bs4 import BeautifulSoup
import colors
| 27.131579 | 69 | 0.596508 |
d2a2c147c06d327188733c71e9a83b70f75131b1 | 27 | py | Python | micro-benchmark-key-errs/snippets/dicts/type_coercion/main.py | WenJinfeng/PyCG | b45e8e04fe697d8301cf27222a8f37646d69f168 | [
"Apache-2.0"
] | 121 | 2020-12-16T20:31:37.000Z | 2022-03-21T20:32:43.000Z | micro-benchmark-key-errs/snippets/dicts/type_coercion/main.py | WenJinfeng/PyCG | b45e8e04fe697d8301cf27222a8f37646d69f168 | [
"Apache-2.0"
] | 24 | 2021-03-13T00:04:00.000Z | 2022-03-21T17:28:11.000Z | micro-benchmark-key-errs/snippets/dicts/type_coercion/main.py | WenJinfeng/PyCG | b45e8e04fe697d8301cf27222a8f37646d69f168 | [
"Apache-2.0"
] | 19 | 2021-03-23T10:58:47.000Z | 2022-03-24T19:46:50.000Z | d = {"1": "a"}
d[1]
d["1"]
| 6.75 | 14 | 0.259259 |
d2a35e41a7de7ed1c211d10b17e2843c3afc87ce | 2,753 | py | Python | scripts/link_assignment.py | metagenomics/antibio | ac79c64417c749ed40263fc97d22498097f2e9b9 | [
"MIT"
] | 4 | 2015-11-03T22:00:33.000Z | 2017-10-21T06:57:35.000Z | scripts/link_assignment.py | metagenomics/antibio | ac79c64417c749ed40263fc97d22498097f2e9b9 | [
"MIT"
] | 49 | 2015-09-28T11:32:38.000Z | 2016-04-11T14:05:00.000Z | scripts/link_assignment.py | metagenomics/antibio | ac79c64417c749ed40263fc97d22498097f2e9b9 | [
"MIT"
] | 2 | 2018-08-27T15:15:45.000Z | 2020-03-31T01:50:48.000Z | #!/usr/bin/python
# This program revises the existing overview file.
# If a keyword is found in an Abstract of an accession of a gene, the url of the abstract is added to the overview file
# The revised overview.txt is created in the same directory of the old one and named overview_new.txt
"""
Usage: link_assignment.py... | 35.753247 | 119 | 0.694878 |
d2a6ca53031a949367ecbf3f9d3bfdb61563f697 | 5,421 | py | Python | app/views.py | LauretteMongina/Instagram-clone | 617135bcebcf6b73f2de7af73a66c177718d338c | [
"MIT"
] | null | null | null | app/views.py | LauretteMongina/Instagram-clone | 617135bcebcf6b73f2de7af73a66c177718d338c | [
"MIT"
] | null | null | null | app/views.py | LauretteMongina/Instagram-clone | 617135bcebcf6b73f2de7af73a66c177718d338c | [
"MIT"
] | null | null | null | from django.shortcuts import render,redirect,get_object_or_404
from django.contrib.auth.decorators import login_required
from .models import *
import cloudinary
import cloudinary.uploader
import cloudinary.api
from django.http import HttpResponseRedirect, JsonResponse
from .forms import RegistrationForm, UpdateUserForm... | 34.310127 | 98 | 0.642317 |
d2a7333fba6a0b271b7f3ddd6746591c934cb750 | 1,557 | py | Python | at_export_config.py | Fmstrat/FreeCAD-ArchTextures | e3af6198ea5e07848602a3b8ba01ebab2335d6b1 | [
"MIT"
] | 21 | 2018-11-16T05:56:31.000Z | 2021-11-09T13:21:53.000Z | at_export_config.py | Fmstrat/FreeCAD-ArchTextures | e3af6198ea5e07848602a3b8ba01ebab2335d6b1 | [
"MIT"
] | 39 | 2018-10-02T18:16:18.000Z | 2022-02-11T13:45:50.000Z | at_export_config.py | Fmstrat/FreeCAD-ArchTextures | e3af6198ea5e07848602a3b8ba01ebab2335d6b1 | [
"MIT"
] | 10 | 2019-07-15T16:34:51.000Z | 2022-01-25T23:57:03.000Z | import FreeCAD, FreeCADGui
from arch_texture_utils.resource_utils import iconPath
import arch_texture_utils.qtutils as qtutils
from arch_texture_utils.selection_utils import findSelectedTextureConfig
if __name__ == "__main__":
command = ExportTextureConfigCommand();
if command.IsActive():
command... | 33.12766 | 122 | 0.689788 |
d2a75f44feb7064f817bce0160b3db28ad77852c | 597 | py | Python | barcode/charsets/ean.py | Azd325/python-barcode | b41b1d5d479fb0ad3290a0a6235a8d3203d34ee9 | [
"MIT"
] | null | null | null | barcode/charsets/ean.py | Azd325/python-barcode | b41b1d5d479fb0ad3290a0a6235a8d3203d34ee9 | [
"MIT"
] | null | null | null | barcode/charsets/ean.py | Azd325/python-barcode | b41b1d5d479fb0ad3290a0a6235a8d3203d34ee9 | [
"MIT"
] | null | null | null | EDGE = '101'
MIDDLE = '01010'
CODES = {
'A': (
'0001101', '0011001', '0010011', '0111101', '0100011', '0110001',
'0101111', '0111011', '0110111', '0001011'
),
'B': (
'0100111', '0110011', '0011011', '0100001', '0011101', '0111001',
'0000101', '0010001', '0001001', '0010111'
... | 28.428571 | 73 | 0.515913 |
d2a835bc55a30790d6234339c5e466df15a50aed | 2,787 | py | Python | Sushant_Boosting/code.py | sushant-bahekar/ga-learner-dsmp-repo | 1087bec60382c2b3156f26cb87629a3b931fc41f | [
"MIT"
] | null | null | null | Sushant_Boosting/code.py | sushant-bahekar/ga-learner-dsmp-repo | 1087bec60382c2b3156f26cb87629a3b931fc41f | [
"MIT"
] | null | null | null | Sushant_Boosting/code.py | sushant-bahekar/ga-learner-dsmp-repo | 1087bec60382c2b3156f26cb87629a3b931fc41f | [
"MIT"
] | null | null | null | # --------------
import pandas as pd
from sklearn.model_selection import train_test_split
#path - Path of file
# Code starts here
df = pd.read_csv(path)
df.head(5)
X = df.drop(['customerID','Churn'],1)
y = df['Churn']
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)
# --... | 24.663717 | 89 | 0.742375 |
d2a9213337ceeb22964f6608d3d20eb1d939ae74 | 16,566 | py | Python | slsgd.py | xcgoner/ecml2019-slsgd | e4856b2015d4c7c39e28743dab2222ef8e0131fa | [
"MIT"
] | 3 | 2019-09-10T15:46:04.000Z | 2020-09-21T17:53:10.000Z | slsgd.py | xcgoner/ecml2019-slsgd | e4856b2015d4c7c39e28743dab2222ef8e0131fa | [
"MIT"
] | null | null | null | slsgd.py | xcgoner/ecml2019-slsgd | e4856b2015d4c7c39e28743dab2222ef8e0131fa | [
"MIT"
] | null | null | null | import argparse, time, logging, os, math, random
os.environ["MXNET_USE_OPERATOR_TUNING"] = "0"
import numpy as np
from scipy import stats
import mxnet as mx
from mxnet import gluon, nd
from mxnet import autograd as ag
from mxnet.gluon import nn
from mxnet.gluon.data.vision import transforms
from gluoncv.model_zoo im... | 40.306569 | 253 | 0.650247 |
d2a9e60639815c6fa23b7d5054d4eac994971146 | 59,644 | py | Python | predictor.py | MIC-DKFZ/DetectionAndRegression | 40f3cb92ec6447767bd85b62a015b0d50e32ad26 | [
"Apache-2.0"
] | 40 | 2019-09-24T08:11:35.000Z | 2022-02-23T13:49:01.000Z | predictor.py | MIC-DKFZ/MedicalDetectionRegression | 40f3cb92ec6447767bd85b62a015b0d50e32ad26 | [
"Apache-2.0"
] | 13 | 2019-11-04T10:52:40.000Z | 2022-03-11T23:57:14.000Z | predictor.py | MIC-DKFZ/MedicalDetectionRegression | 40f3cb92ec6447767bd85b62a015b0d50e32ad26 | [
"Apache-2.0"
] | 22 | 2019-08-28T15:32:25.000Z | 2022-02-18T11:27:30.000Z | #!/usr/bin/env python
# Copyright 2019 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
#
# 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... | 59.229394 | 192 | 0.598803 |
d2aa2e4deaca6a1a85b89b1e9c89d89fa5c4d8f5 | 424 | py | Python | archive/jonesboro/__init__.py | jayktee/scrapers-us-municipal | ff52a331e91cb590a3eda7db6c688d75b77acacb | [
"MIT"
] | 67 | 2015-04-28T19:28:18.000Z | 2022-01-31T03:27:17.000Z | archive/jonesboro/__init__.py | jayktee/scrapers-us-municipal | ff52a331e91cb590a3eda7db6c688d75b77acacb | [
"MIT"
] | 202 | 2015-01-15T18:43:12.000Z | 2021-11-23T15:09:10.000Z | archive/jonesboro/__init__.py | jayktee/scrapers-us-municipal | ff52a331e91cb590a3eda7db6c688d75b77acacb | [
"MIT"
] | 54 | 2015-01-27T03:15:45.000Z | 2021-09-10T19:35:32.000Z | from pupa.scrape import Jurisdiction
from legistar.ext.pupa import LegistarPeopleScraper
| 28.266667 | 87 | 0.735849 |
d2aa498a5dc13b5e44bb5a53742aa0908d8d79da | 2,766 | py | Python | src/config.py | La-tale/MessyTable | 42ae08294f1a576d2477a4b4c12b2aec047c2ba9 | [
"MIT"
] | 32 | 2020-07-13T04:30:00.000Z | 2022-03-17T12:04:32.000Z | src/config.py | La-tale/MessyTable | 42ae08294f1a576d2477a4b4c12b2aec047c2ba9 | [
"MIT"
] | 12 | 2020-08-31T02:58:37.000Z | 2022-03-26T04:05:27.000Z | src/config.py | La-tale/MessyTable | 42ae08294f1a576d2477a4b4c12b2aec047c2ba9 | [
"MIT"
] | 8 | 2020-07-27T05:20:33.000Z | 2022-02-04T06:58:37.000Z | import yaml
import os
def parse_config(args):
"""
prepare configs
"""
file_dir = os.path.dirname(os.path.realpath('__file__'))
messytable_dir = os.path.realpath(os.path.join(file_dir, '..'))
config_pathname = os.path.join(messytable_dir,'models',args.config_dir,'train.yaml')
config = yaml.... | 56.44898 | 181 | 0.713304 |
d2ab49c4b3562bad12874570d0c5751dda4cf3e6 | 1,194 | py | Python | tests/settings.py | josemarimanio/django-adminlte2-templates | d39ab5eaec674c4725015fe43fc93e74dce78a6e | [
"MIT"
] | 10 | 2020-03-21T10:50:11.000Z | 2022-03-04T08:36:43.000Z | tests/settings.py | josemarimanio/django-adminlte2-templates | d39ab5eaec674c4725015fe43fc93e74dce78a6e | [
"MIT"
] | 6 | 2020-06-06T08:48:29.000Z | 2021-06-10T18:49:35.000Z | tests/settings.py | josemarimanio/django-adminlte2-templates | d39ab5eaec674c4725015fe43fc93e74dce78a6e | [
"MIT"
] | 1 | 2021-09-14T02:00:43.000Z | 2021-09-14T02:00:43.000Z | import os
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
SECRET_KEY = '!t_(11ght0&nmb&$tf4to=gdg&u$!hsm3@)c6dzp=zdc*c9zci' # nosec
INSTALLED_APPS = [
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.sites',
'adminlte2_templ... | 23.88 | 74 | 0.629816 |
d2ae04ea58cc84694d33370988510f0b8bdcadb9 | 2,658 | py | Python | two-variables-function-fitting/fxy_gen.py | ettoremessina/fitting-with-mlp-using-tensorflow | 50303c7161521f690c37b80a72a281129052365b | [
"MIT"
] | 9 | 2020-03-21T08:45:28.000Z | 2021-11-30T02:49:41.000Z | two-variables-function-fitting/fxy_gen.py | ettoremessina/fitting-with-mlp-using-tensorflow | 50303c7161521f690c37b80a72a281129052365b | [
"MIT"
] | null | null | null | two-variables-function-fitting/fxy_gen.py | ettoremessina/fitting-with-mlp-using-tensorflow | 50303c7161521f690c37b80a72a281129052365b | [
"MIT"
] | 3 | 2020-04-08T15:35:03.000Z | 2022-03-22T02:19:02.000Z | import argparse
import numpy as np
import csv
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='fxy_gen.py generates a synthetic dataset file calling a two-variables real function on a rectangle')
parser.add_argument('--dsout',
type=str,
d... | 37.971429 | 150 | 0.482318 |
d2aee573a11ac0e4ec731ba7feda47d776f90ea2 | 995 | py | Python | custom/icds_reports/dashboard_utils.py | tstalka/commcare-hq | 902412b0f97ba0daac173fe284f3adc4c01bcd76 | [
"BSD-3-Clause"
] | null | null | null | custom/icds_reports/dashboard_utils.py | tstalka/commcare-hq | 902412b0f97ba0daac173fe284f3adc4c01bcd76 | [
"BSD-3-Clause"
] | null | null | null | custom/icds_reports/dashboard_utils.py | tstalka/commcare-hq | 902412b0f97ba0daac173fe284f3adc4c01bcd76 | [
"BSD-3-Clause"
] | null | null | null | from corehq.apps.locations.util import location_hierarchy_config
from custom.icds_reports.utils import icds_pre_release_features
| 34.310345 | 78 | 0.729648 |
d2af35f5ecd1284185b97cd7fd48a1dabdbf319d | 1,714 | py | Python | data_input.py | zpcore/OnePass | fc102fae172c617535d4661bfa99a0302cbe09db | [
"MIT"
] | null | null | null | data_input.py | zpcore/OnePass | fc102fae172c617535d4661bfa99a0302cbe09db | [
"MIT"
] | null | null | null | data_input.py | zpcore/OnePass | fc102fae172c617535d4661bfa99a0302cbe09db | [
"MIT"
] | null | null | null | import json
import string, sys
from random import *
# tok = Token()
# tok.get_input()
# print(json.dumps(tok, cls=MyEncoder)) | 32.339623 | 101 | 0.656943 |
d2af5783fc08617f08a4edb9dc33a39579f11d65 | 1,401 | py | Python | examples/python/test_dict.py | SmartEconomyWorkshop/workshop | 5961dcc8832f60b3a0407cb9a8361ba5485ac280 | [
"MIT"
] | 79 | 2017-10-22T03:35:06.000Z | 2021-12-02T10:28:06.000Z | examples/python/test_dict.py | SmartEconomyWorkshop/workshop | 5961dcc8832f60b3a0407cb9a8361ba5485ac280 | [
"MIT"
] | 122 | 2017-10-19T12:34:08.000Z | 2020-08-20T12:38:17.000Z | examples/python/test_dict.py | SmartEconomyWorkshop/workshop | 5961dcc8832f60b3a0407cb9a8361ba5485ac280 | [
"MIT"
] | 76 | 2017-10-19T05:09:55.000Z | 2020-12-08T12:03:59.000Z | from boa_test.tests.boa_test import BoaTest
from boa.compiler import Compiler
from neo.Settings import settings
from neo.Prompt.Commands.BuildNRun import TestBuild
| 36.868421 | 108 | 0.666667 |
d2b08bd5689396a0415385c35a4d92cedae61e22 | 520 | py | Python | deployment_classifier/setup.py | m-santh/VayuAnukulani | d1b881ac6268c24761dc0ef6db296d7e5ee1a22e | [
"MIT"
] | 1 | 2021-04-19T17:04:03.000Z | 2021-04-19T17:04:03.000Z | deployment_classifier/setup.py | m-santh/VayuAnukulani | d1b881ac6268c24761dc0ef6db296d7e5ee1a22e | [
"MIT"
] | 18 | 2020-01-28T22:36:26.000Z | 2020-07-28T17:01:35.000Z | deployment_classifier/setup.py | m-santh/VayuAnukulani | d1b881ac6268c24761dc0ef6db296d7e5ee1a22e | [
"MIT"
] | 3 | 2019-04-01T10:33:20.000Z | 2020-10-23T23:29:09.000Z | from setuptools import find_packages
from setuptools import setup
REQUIRED_PACKAGES = ['tensorflow==1.8.0','pandas==0.23.1','setuptools==38.7.0','numpy==1.14.1','Keras==2.1.4','scikit_learn==0.19.1','h5py']
setup(
name='classifier',
version='0.1',
install_requires=REQUIRED_PACKAGES,
packages=find_pack... | 28.888889 | 140 | 0.701923 |
d2b08ef7b1d20d9d85caa8e8727b92065aef39a2 | 1,023 | py | Python | day5.py | zsmoore/Advent-Of-Code-2017 | 895a7fbaa8b8b82a338dac967bccbf97b2092b20 | [
"MIT"
] | null | null | null | day5.py | zsmoore/Advent-Of-Code-2017 | 895a7fbaa8b8b82a338dac967bccbf97b2092b20 | [
"MIT"
] | null | null | null | day5.py | zsmoore/Advent-Of-Code-2017 | 895a7fbaa8b8b82a338dac967bccbf97b2092b20 | [
"MIT"
] | null | null | null | import sys
import copy
if __name__ == "__main__":
main()
| 22.23913 | 60 | 0.567937 |
d2b26b4fc46e989fc34f786c463f49d76b84289c | 4,949 | py | Python | pycudasirecon/_recon_params.py | tlambert03/pycudasirecon | 17ca242b1cfed14216d97df480ca2c7f3471d770 | [
"MIT"
] | 2 | 2021-06-09T15:35:50.000Z | 2021-06-10T05:33:11.000Z | pycudasirecon/_recon_params.py | tlambert03/pycudasirecon | 17ca242b1cfed14216d97df480ca2c7f3471d770 | [
"MIT"
] | null | null | null | pycudasirecon/_recon_params.py | tlambert03/pycudasirecon | 17ca242b1cfed14216d97df480ca2c7f3471d770 | [
"MIT"
] | null | null | null | import os
from contextlib import contextmanager
from tempfile import NamedTemporaryFile
from typing import Optional, Sequence
from pydantic import BaseModel, Field, FilePath
| 40.235772 | 88 | 0.658921 |
d2b2f379a4dedf2bd69de6e708c00763f4c5952f | 4,098 | py | Python | tesseract_converters/tesseract_to_sa_converter.py | superannotateai/annotateonline-input-converters | 753211f48676d06718bb2d32501ba1df3ace9121 | [
"Apache-2.0"
] | 10 | 2020-04-30T08:36:08.000Z | 2021-02-27T21:46:45.000Z | tesseract_converters/tesseract_to_sa_converter.py | superannotateai/input_converters | 753211f48676d06718bb2d32501ba1df3ace9121 | [
"Apache-2.0"
] | 5 | 2020-03-27T07:16:36.000Z | 2020-07-06T04:45:47.000Z | tesseract_converters/tesseract_to_sa_converter.py | superannotateai/annotateonline-input-converters | 753211f48676d06718bb2d32501ba1df3ace9121 | [
"Apache-2.0"
] | 2 | 2020-06-26T20:02:10.000Z | 2020-06-30T20:56:04.000Z | import os
import json
import argparse
if __name__ == '__main__':
main() | 32.784 | 80 | 0.476086 |
d2b3079900df546aeac436f737e69c681f72b12c | 24,525 | py | Python | fhirclient/r4models/contract_tests.py | cspears-mitre/CapStatement | 2390566ed75d420e0615e3a0aacb77e8c030fdcc | [
"Apache-2.0"
] | 1 | 2021-12-24T11:14:38.000Z | 2021-12-24T11:14:38.000Z | fhirclient/r4models/contract_tests.py | cspears-mitre/CapStatement | 2390566ed75d420e0615e3a0aacb77e8c030fdcc | [
"Apache-2.0"
] | null | null | null | fhirclient/r4models/contract_tests.py | cspears-mitre/CapStatement | 2390566ed75d420e0615e3a0aacb77e8c030fdcc | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Generated from FHIR 3.6.0-bd605d07 on 2018-12-20.
# 2018, SMART Health IT.
import os
import io
import unittest
import json
from . import contract
from .fhirdate import FHIRDate
| 69.279661 | 152 | 0.685219 |
d2b34796cb7b21344e2370533fa5aa6227ece2be | 9,978 | py | Python | evaluation/evaluation.py | Ennosigaeon/xautoml | 6e49ee8b2ffb6d19dcfd9cbe8b3397416c9b5ded | [
"BSD-3-Clause"
] | 4 | 2022-02-27T08:54:08.000Z | 2022-03-30T21:19:29.000Z | evaluation/evaluation.py | Ennosigaeon/xautoml | 6e49ee8b2ffb6d19dcfd9cbe8b3397416c9b5ded | [
"BSD-3-Clause"
] | 1 | 2022-02-28T09:41:00.000Z | 2022-03-02T07:44:17.000Z | evaluation/evaluation.py | Ennosigaeon/xautoml | 6e49ee8b2ffb6d19dcfd9cbe8b3397416c9b5ded | [
"BSD-3-Clause"
] | 2 | 2022-03-01T00:38:09.000Z | 2022-03-21T09:38:49.000Z | import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import ttest_ind
from sklearn.preprocessing import LabelEncoder
questionnaire, requirements, tasks = load_data()
print_visual_design(index(questionnaire, slice(27, 32)))
print_previous_k... | 35.763441 | 173 | 0.538785 |
d2b462f25f6094199e7adc2a1e6de5c3e66fd2f5 | 4,941 | py | Python | matplotlib/tutorials_python/colors/colors.py | gottaegbert/penter | 8cbb6be3c4bf67c7c69fa70e597bfbc3be4f0a2d | [
"MIT"
] | 13 | 2020-01-04T07:37:38.000Z | 2021-08-31T05:19:58.000Z | matplotlib/tutorials_python/colors/colors.py | gottaegbert/penter | 8cbb6be3c4bf67c7c69fa70e597bfbc3be4f0a2d | [
"MIT"
] | 3 | 2020-06-05T22:42:53.000Z | 2020-08-24T07:18:54.000Z | matplotlib/tutorials_python/colors/colors.py | gottaegbert/penter | 8cbb6be3c4bf67c7c69fa70e597bfbc3be4f0a2d | [
"MIT"
] | 9 | 2020-10-19T04:53:06.000Z | 2021-08-31T05:20:01.000Z | """
*****************
Specifying Colors
*****************
Matplotlib recognizes the following formats to specify a color:
* an RGB or RGBA (red, green, blue, alpha) tuple of float values in closed
interval ``[0, 1]`` (e.g., ``(0.1, 0.2, 0.5)`` or ``(0.1, 0.2, 0.5, 0.3)``);
* a hex RGB or RGBA string (e.g., ``'#0f0f... | 36.330882 | 79 | 0.646225 |
d2b6b250831a7174cf7989d9fc42a91268a025cd | 1,313 | py | Python | 12-listComprehensions.py | pgiardiniere/notes-WhirlwindTourOfPython | 10f483ea4452f0a45f2103886992fd77c2f3ac7c | [
"CC0-1.0"
] | null | null | null | 12-listComprehensions.py | pgiardiniere/notes-WhirlwindTourOfPython | 10f483ea4452f0a45f2103886992fd77c2f3ac7c | [
"CC0-1.0"
] | null | null | null | 12-listComprehensions.py | pgiardiniere/notes-WhirlwindTourOfPython | 10f483ea4452f0a45f2103886992fd77c2f3ac7c | [
"CC0-1.0"
] | null | null | null | # List Comprehensions
#########################
### Basic List Comprehensions
#########################
# allow us to circumvent constructing lists with for loops
l = [] # The Old Way
for n in range(12):
l.append(n**2)
[n ** 2 for n in range(12)] # Comprehension way
# General Syntax:
# [ ... | 26.795918 | 61 | 0.545316 |
d2b6cbdba4cdbf4de3ed032d08f889932f594f92 | 1,515 | py | Python | src/chemical_roles/export/cli.py | bgyori/chemical-roles | 31a917e911075e3be7eea509e143d3ff48e942cc | [
"MIT"
] | 5 | 2021-02-05T01:27:53.000Z | 2021-07-12T15:47:08.000Z | src/chemical_roles/export/cli.py | bgyori/chemical-roles | 31a917e911075e3be7eea509e143d3ff48e942cc | [
"MIT"
] | 8 | 2019-10-10T13:02:18.000Z | 2020-05-11T18:41:56.000Z | src/chemical_roles/export/cli.py | bgyori/chemical-roles | 31a917e911075e3be7eea509e143d3ff48e942cc | [
"MIT"
] | 5 | 2020-06-07T13:11:34.000Z | 2021-07-12T14:24:01.000Z | # -*- coding: utf-8 -*-
"""CLI for Chemical Roles exporters."""
import os
import click
from ..constants import DATA
directory_option = click.option('--directory', default=DATA)
if __name__ == '__main__':
export()
| 21.041667 | 106 | 0.684488 |
d2b7475246a09fa72d42e65c0defb8588ba3890e | 4,681 | py | Python | gdsfactory/geometry/write_drc.py | jorgepadilla19/gdsfactory | 68e1c18257a75d4418279851baea417c8899a165 | [
"MIT"
] | 42 | 2020-05-25T09:33:45.000Z | 2022-03-29T03:41:19.000Z | gdsfactory/geometry/write_drc.py | jorgepadilla19/gdsfactory | 68e1c18257a75d4418279851baea417c8899a165 | [
"MIT"
] | 133 | 2020-05-28T18:29:04.000Z | 2022-03-31T22:21:42.000Z | gdsfactory/geometry/write_drc.py | jorgepadilla19/gdsfactory | 68e1c18257a75d4418279851baea417c8899a165 | [
"MIT"
] | 17 | 2020-06-30T07:07:50.000Z | 2022-03-17T15:45:27.000Z | """Write DRC rule decks in klayout.
TODO:
- add min area
- define derived layers (composed rules)
"""
import pathlib
from dataclasses import asdict, is_dataclass
from typing import List, Optional
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
from gdsfactory.confi... | 26.902299 | 85 | 0.654134 |
d2b75bb3697ff16713aa871c5e493e77fa916f5c | 1,620 | py | Python | virtus/core/migrations/0004_auto_20180417_1625.py | eltonjncorreia/gerenciar-dados-virtus | b8e1b8caa152b18221046f6841761d805b232268 | [
"MIT"
] | null | null | null | virtus/core/migrations/0004_auto_20180417_1625.py | eltonjncorreia/gerenciar-dados-virtus | b8e1b8caa152b18221046f6841761d805b232268 | [
"MIT"
] | null | null | null | virtus/core/migrations/0004_auto_20180417_1625.py | eltonjncorreia/gerenciar-dados-virtus | b8e1b8caa152b18221046f6841761d805b232268 | [
"MIT"
] | null | null | null | # Generated by Django 2.0.4 on 2018-04-17 19:25
from django.db import migrations, models
| 38.571429 | 114 | 0.569753 |
d2b7ebb7c7ccc1338b94c19d7637e3ceac872b46 | 2,173 | py | Python | image_demo.py | a888999a/yolov3fusion1 | 3659898aee34a351e95ea545236b8bc682901498 | [
"MIT"
] | 7 | 2020-09-23T10:37:17.000Z | 2021-12-26T00:23:02.000Z | image_demo.py | a888999a/yolov3fusion1 | 3659898aee34a351e95ea545236b8bc682901498 | [
"MIT"
] | null | null | null | image_demo.py | a888999a/yolov3fusion1 | 3659898aee34a351e95ea545236b8bc682901498 | [
"MIT"
] | null | null | null | #! /usr/bin/env python
# coding=utf-8
#================================================================
# Copyright (C) 2019 * Ltd. All rights reserved.
#
# Editor : VIM
# File name : image_demo.py
# Author : YunYang1994
# Created date: 2019-01-20 16:06:06
# Description :
#
#====================... | 35.048387 | 135 | 0.673263 |
d2b930c9508039d505766f1d70318392c9baf277 | 7,090 | py | Python | Sensor/main.py | mahsahadian/EdgeBenchmarkTool | cafddb2eb66732da0bff8f26107788e3c93fbe2f | [
"MIT"
] | null | null | null | Sensor/main.py | mahsahadian/EdgeBenchmarkTool | cafddb2eb66732da0bff8f26107788e3c93fbe2f | [
"MIT"
] | null | null | null | Sensor/main.py | mahsahadian/EdgeBenchmarkTool | cafddb2eb66732da0bff8f26107788e3c93fbe2f | [
"MIT"
] | 2 | 2022-01-31T01:55:56.000Z | 2022-02-01T01:43:20.000Z |
import cv2
from datetime import *
import time
import logging
import base64
import sys
import os
import shutil
import paho.mqtt.client as mqtt
from influxdb import InfluxDBClient
import datetime
import sys
import re
from typing import NamedTuple
import json
from dotenv import load_dotenv
load_dotenv("sensor-variab... | 33.130841 | 146 | 0.629478 |
d2b975627d7b7c61820ad7bec967dad5b7b1e8aa | 4,511 | py | Python | oxide/plugins/other/StartupItems.py | john-clark/rust-oxide-umod | 56feca04f96d8a43a1b56e080fc81d526f7471c3 | [
"MIT"
] | 13 | 2019-05-13T08:03:50.000Z | 2022-02-06T16:44:35.000Z | oxide/plugins/other/StartupItems.py | john-clark/rust-oxide-umod | 56feca04f96d8a43a1b56e080fc81d526f7471c3 | [
"MIT"
] | null | null | null | oxide/plugins/other/StartupItems.py | john-clark/rust-oxide-umod | 56feca04f96d8a43a1b56e080fc81d526f7471c3 | [
"MIT"
] | 8 | 2019-12-12T15:48:03.000Z | 2021-12-24T17:04:45.000Z | # Note:
# I add an underscore at the biginning of the variable name for example: "_variable" to prevent
# conflicts with build-in variables from Oxide.
# Use to manage the player's inventory.
import ItemManager
# Use to get player's information.
import BasePlayer
# The plug-in name should be the same as the ... | 51.261364 | 153 | 0.570162 |
d2bbabe21477b77848cbfcaba239a66c8fe04262 | 1,043 | py | Python | error_handler.py | jrg1381/sm_asr_console | 47c4090075deaaa7f58e9a092423a58bc7b0a30f | [
"MIT"
] | 2 | 2019-08-07T11:08:06.000Z | 2021-01-20T11:28:37.000Z | error_handler.py | jrg1381/sm_asr_console | 47c4090075deaaa7f58e9a092423a58bc7b0a30f | [
"MIT"
] | null | null | null | error_handler.py | jrg1381/sm_asr_console | 47c4090075deaaa7f58e9a092423a58bc7b0a30f | [
"MIT"
] | null | null | null | # encoding: utf-8
""" Parameterized decorator for catching errors and displaying them in an error popup """
from enum import Enum
import npyscreen
# PythonDecorators/decorator_function_with_arguments.py
def error_handler(title, dialog_type=DialogType.CONFIRM):
"""
Decorator for functions to catch their excep... | 29.8 | 89 | 0.681687 |
d2bbf8bdae1a8922b42a68b17b2aafcf8fd38f67 | 13,043 | py | Python | parlai/tasks/taskmaster2/agents.py | min942773/parlai_wandb | 1d9ba1a0df2199d0247cee8c4929a2598ac7e41a | [
"MIT"
] | 2 | 2017-09-20T21:49:51.000Z | 2018-08-12T06:58:10.000Z | parlai/tasks/taskmaster2/agents.py | min942773/parlai_wandb | 1d9ba1a0df2199d0247cee8c4929a2598ac7e41a | [
"MIT"
] | 7 | 2021-01-12T01:07:03.000Z | 2022-03-12T00:50:45.000Z | parlai/tasks/taskmaster2/agents.py | min942773/parlai_wandb | 1d9ba1a0df2199d0247cee8c4929a2598ac7e41a | [
"MIT"
] | 1 | 2021-01-07T11:45:03.000Z | 2021-01-07T11:45:03.000Z | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Taskmaster-2 implementation for ParlAI.
No official train/valid/test splits are available as of 2020-05-18, so we m... | 35.734247 | 86 | 0.521966 |
d2bc823500d7e835a13076bd5554f0f404893ff4 | 243 | py | Python | jmeter_api/timers/__init__.py | dashawn888/jmeter_api | 1ab5b02f3a7c8ad1b84fc50db4fe1fc2fa7c91bd | [
"Apache-2.0"
] | 11 | 2020-03-22T13:30:21.000Z | 2021-12-25T06:23:44.000Z | jmeter_api/timers/__init__.py | dashawn888/jmeter_api | 1ab5b02f3a7c8ad1b84fc50db4fe1fc2fa7c91bd | [
"Apache-2.0"
] | 2 | 2020-03-23T00:06:42.000Z | 2021-02-24T21:41:40.000Z | jmeter_api/timers/__init__.py | dashawn888/jmeter_api | 1ab5b02f3a7c8ad1b84fc50db4fe1fc2fa7c91bd | [
"Apache-2.0"
] | 3 | 2020-11-09T14:14:25.000Z | 2021-05-27T02:54:38.000Z | from jmeter_api.timers.constant_throughput_timer.elements import ConstantThroughputTimer, BasedOn
from jmeter_api.timers.constant_timer.elements import ConstantTimer
from jmeter_api.timers.uniform_random_timer.elements import UniformRandTimer
| 60.75 | 97 | 0.90535 |
d2bd972bab298994d41d91b8c6a75e48470ccec5 | 2,520 | py | Python | tensorfn/distributed/launch.py | rosinality/tensorfn | cd410c5e6f6906d223f740501e711b9cfae260e4 | [
"Apache-2.0"
] | 13 | 2021-04-08T03:09:42.000Z | 2022-03-18T08:27:17.000Z | tensorfn/distributed/launch.py | rosinality/tensorfn | cd410c5e6f6906d223f740501e711b9cfae260e4 | [
"Apache-2.0"
] | 2 | 2020-08-16T20:25:34.000Z | 2021-07-13T00:35:52.000Z | tensorfn/distributed/launch.py | rosinality/tensorfn | cd410c5e6f6906d223f740501e711b9cfae260e4 | [
"Apache-2.0"
] | null | null | null | import os
import torch
from torch import distributed as dist
from torch import multiprocessing as mp
from tensorfn import distributed as dist_fn
| 27.096774 | 101 | 0.636508 |
d2bffe6b8d76be452fc84a9fa325b868d681f43c | 4,097 | py | Python | VideoStitchingSubsystem/StereoCameraAPIs/MonoLensStream.py | AriaPahlavan/see-through-adas-core | 7cc530243d324aecd9db538883bb77ee2d519661 | [
"Apache-2.0"
] | null | null | null | VideoStitchingSubsystem/StereoCameraAPIs/MonoLensStream.py | AriaPahlavan/see-through-adas-core | 7cc530243d324aecd9db538883bb77ee2d519661 | [
"Apache-2.0"
] | null | null | null | VideoStitchingSubsystem/StereoCameraAPIs/MonoLensStream.py | AriaPahlavan/see-through-adas-core | 7cc530243d324aecd9db538883bb77ee2d519661 | [
"Apache-2.0"
] | null | null | null | from enum import Enum
from threading import Thread
import cv2
import time
| 28.255172 | 107 | 0.573102 |
d2c143baf7ea1e8434d64873e45800bbd43dfe04 | 444 | py | Python | sdk/python/approzium/mysql/connector/pooling.py | UpGado/approzium | 306b40f16a1ba0dfbe3a312e1c40881e98518137 | [
"Apache-2.0"
] | 59 | 2020-07-14T17:18:09.000Z | 2022-02-24T07:39:22.000Z | sdk/python/approzium/mysql/connector/pooling.py | UpGado/approzium | 306b40f16a1ba0dfbe3a312e1c40881e98518137 | [
"Apache-2.0"
] | 66 | 2020-07-09T19:11:55.000Z | 2022-03-15T11:42:55.000Z | sdk/python/approzium/mysql/connector/pooling.py | UpGado/approzium | 306b40f16a1ba0dfbe3a312e1c40881e98518137 | [
"Apache-2.0"
] | 9 | 2020-07-09T19:20:45.000Z | 2022-02-24T07:39:26.000Z | from mysql.connector.pooling import MySQLConnectionPool
from ._connect import _parse_kwargs, _patch_MySQLConnection
| 31.714286 | 61 | 0.75 |
d2c30d506f338f0ad2e0b0a0c5af2f47676aea3a | 267 | py | Python | setup.py | Faust-Wang/vswarm | d18ce643218c18ef1e762f40562104b2a0926ad7 | [
"MIT"
] | 21 | 2021-03-03T10:51:46.000Z | 2022-03-28T11:00:35.000Z | setup.py | Faust-Wang/vswarm | d18ce643218c18ef1e762f40562104b2a0926ad7 | [
"MIT"
] | 2 | 2021-07-21T07:57:16.000Z | 2022-03-17T12:41:51.000Z | setup.py | hvourtsis/vswarm | d18ce643218c18ef1e762f40562104b2a0926ad7 | [
"MIT"
] | 8 | 2021-02-27T14:29:55.000Z | 2022-01-05T19:40:38.000Z | # Do not manually invoke this setup.py, use catkin instead!
from setuptools import setup
from catkin_pkg.python_setup import generate_distutils_setup
setup_args = generate_distutils_setup(
packages=['vswarm'],
package_dir={'': 'src'}
)
setup(**setup_args)
| 22.25 | 60 | 0.764045 |
d2c38a755a40c6e19281f0cc94b831f228ba7f94 | 250 | py | Python | 实例学习Numpy与Matplotlib/创建 numpy.array.py | shao1chuan/pythonbook | cd9877d04e1e11422d38cc051e368d3d9ce2ab45 | [
"MulanPSL-1.0"
] | 95 | 2020-10-11T04:45:46.000Z | 2022-02-25T01:50:40.000Z | 实例学习Numpy与Matplotlib/创建 numpy.array.py | shao1chuan/pythonbook | cd9877d04e1e11422d38cc051e368d3d9ce2ab45 | [
"MulanPSL-1.0"
] | null | null | null | 实例学习Numpy与Matplotlib/创建 numpy.array.py | shao1chuan/pythonbook | cd9877d04e1e11422d38cc051e368d3d9ce2ab45 | [
"MulanPSL-1.0"
] | 30 | 2020-11-05T09:01:00.000Z | 2022-03-08T05:58:55.000Z |
import numpy as np
nparr = np.array([i for i in range(10)])
a = np.zeros(10)
f = np.zeros(10,dtype=float)
n = np.full((3,5),44)
r = np.random.randint(0,100,size=(3,5))
r2 = np.random.random((3,5))
x = np.linspace(0,100,50)
print(nparr,a,f,n,r,r2,x) | 22.727273 | 40 | 0.64 |
d2c38e45f035250f5b56f9b05cf87de9978e93b9 | 4,790 | py | Python | examples/DecryptLoginExamples/crawlers/weibomonitor/weibomonitor.py | hedou/DecryptLogin | ff86a5d378c8a42d1caebbb7482658a95053f716 | [
"Apache-2.0"
] | null | null | null | examples/DecryptLoginExamples/crawlers/weibomonitor/weibomonitor.py | hedou/DecryptLogin | ff86a5d378c8a42d1caebbb7482658a95053f716 | [
"Apache-2.0"
] | null | null | null | examples/DecryptLoginExamples/crawlers/weibomonitor/weibomonitor.py | hedou/DecryptLogin | ff86a5d378c8a42d1caebbb7482658a95053f716 | [
"Apache-2.0"
] | null | null | null | '''
Function:
Author:
Charles
:
Charles
'''
import re
import time
from DecryptLogin import login
'''''' | 43.153153 | 161 | 0.571816 |
d2c3e3e6ef11ddd684a0bcebf23085d7e1d9152c | 1,191 | py | Python | crawlai/items/critter/base_critter.py | apockill/CreepyCrawlAI | 2862c03e686801884ffb579a7be29f3c9d0da610 | [
"MIT"
] | 13 | 2020-05-04T03:11:26.000Z | 2021-12-05T03:57:45.000Z | crawlai/items/critter/base_critter.py | apockill/CreepyCrawlAI | 2862c03e686801884ffb579a7be29f3c9d0da610 | [
"MIT"
] | null | null | null | crawlai/items/critter/base_critter.py | apockill/CreepyCrawlAI | 2862c03e686801884ffb579a7be29f3c9d0da610 | [
"MIT"
] | null | null | null | from godot.bindings import ResourceLoader
from crawlai.grid_item import GridItem
from crawlai.items.food import Food
from crawlai.math_utils import clamp
from crawlai.turn import Turn
from crawlai.position import Position
_critter_resource = ResourceLoader.load("res://Game/Critter/Critter.tscn")
| 24.306122 | 74 | 0.715365 |
d2c4507ff5f2b0e60108a433da49147fd8f6e6c4 | 3,008 | py | Python | exercises/networking_selfpaced/networking-workshop/collections/ansible_collections/community/general/plugins/doc_fragments/nios.py | tr3ck3r/linklight | 5060f624c235ecf46cb62cefcc6bddc6bf8ca3e7 | [
"MIT"
] | 17 | 2017-06-07T23:15:01.000Z | 2021-08-30T14:32:36.000Z | ansible/ansible/plugins/doc_fragments/nios.py | SergeyCherepanov/ansible | 875711cd2fd6b783c812241c2ed7a954bf6f670f | [
"MIT"
] | 9 | 2017-06-25T03:31:52.000Z | 2021-05-17T23:43:12.000Z | ansible/ansible/plugins/doc_fragments/nios.py | SergeyCherepanov/ansible | 875711cd2fd6b783c812241c2ed7a954bf6f670f | [
"MIT"
] | 3 | 2018-05-26T21:31:22.000Z | 2019-09-28T17:00:45.000Z | # -*- coding: utf-8 -*-
# Copyright: (c) 2015, Peter Sprygada <psprygada@ansible.com>
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
| 35.809524 | 104 | 0.635306 |
d2c4dfb8a30f8c36fa075d277e4458a4776a5ca8 | 25,299 | py | Python | torchrec/metrics/rec_metric.py | xing-liu/torchrec | 82ffde7a69fdb9c66b79a753d6f03afa5db3f73e | [
"BSD-3-Clause"
] | 814 | 2022-02-23T17:24:14.000Z | 2022-03-31T16:52:23.000Z | torchrec/metrics/rec_metric.py | xing-liu/torchrec | 82ffde7a69fdb9c66b79a753d6f03afa5db3f73e | [
"BSD-3-Clause"
] | 89 | 2022-02-23T17:29:56.000Z | 2022-03-31T23:44:13.000Z | torchrec/metrics/rec_metric.py | xing-liu/torchrec | 82ffde7a69fdb9c66b79a753d6f03afa5db3f73e | [
"BSD-3-Clause"
] | 68 | 2022-02-23T17:42:17.000Z | 2022-03-28T06:39:55.000Z | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
#!/usr/bin/env python3
import abc
import math
from collections import defaultdict, dequ... | 38.331818 | 117 | 0.616072 |
d2c55dd79284c9bf304a1f86538b6964cbb89f09 | 7,594 | py | Python | alison.py | johanhoiness/SlothBot | 556f9e0f67aa90543bd98889b06a4b939e30450d | [
"MIT"
] | 1 | 2017-06-28T09:24:49.000Z | 2017-06-28T09:24:49.000Z | alison.py | johanhoiness/SlothBot | 556f9e0f67aa90543bd98889b06a4b939e30450d | [
"MIT"
] | null | null | null | alison.py | johanhoiness/SlothBot | 556f9e0f67aa90543bd98889b06a4b939e30450d | [
"MIT"
] | null | null | null | __author__ = 'JohnHiness'
import sys
import os
import random
import time
import string
import connection
from time import strftime
import ceq
import json, urllib2
import thread
args = sys.argv
req_files = ['filegen.py', 'connection.py', 'commands.py', 'general.py', 'automatics.py']
for filename in req_files:
if os.... | 29.095785 | 157 | 0.658019 |
d2c5679b86d58ca48ad37cdef98dbe5e554266cb | 2,364 | py | Python | pyroomacoustics/experimental/tests/test_deconvolution.py | HemaZ/pyroomacoustics | c401f829c71ff03a947f68f9b6b2f48346ae84b2 | [
"MIT"
] | 1 | 2020-02-13T14:39:37.000Z | 2020-02-13T14:39:37.000Z | pyroomacoustics/experimental/tests/test_deconvolution.py | HemaZ/pyroomacoustics | c401f829c71ff03a947f68f9b6b2f48346ae84b2 | [
"MIT"
] | null | null | null | pyroomacoustics/experimental/tests/test_deconvolution.py | HemaZ/pyroomacoustics | c401f829c71ff03a947f68f9b6b2f48346ae84b2 | [
"MIT"
] | 1 | 2021-01-14T08:42:47.000Z | 2021-01-14T08:42:47.000Z |
from unittest import TestCase
import numpy as np
from scipy.signal import fftconvolve
import pyroomacoustics as pra
# fix seed for repeatability
np.random.seed(0)
h_len = 30
x_len = 1000
SNR = 1000. # decibels
h_lp = np.fft.irfft(np.ones(5), n=h_len)
h_rand = np.random.randn(h_len)
h_hann = pra.hann(h_len, flag='... | 26.266667 | 115 | 0.630711 |
d2c5ccb03692b30b21e99cbcada633194e147414 | 7,423 | py | Python | pthelper/img_to_txt.py | hkcountryman/veg-scanner | 6b3aa4d0799c901cecdbc0f4b5ca61b0d754ab30 | [
"MIT"
] | null | null | null | pthelper/img_to_txt.py | hkcountryman/veg-scanner | 6b3aa4d0799c901cecdbc0f4b5ca61b0d754ab30 | [
"MIT"
] | null | null | null | pthelper/img_to_txt.py | hkcountryman/veg-scanner | 6b3aa4d0799c901cecdbc0f4b5ca61b0d754ab30 | [
"MIT"
] | null | null | null | import cv2 as cv
from deskew import determine_skew
import numpy as np
from PIL import Image, ImageFilter, ImageOps
from pytesseract import image_to_string
from skimage import io
from skimage.color import rgb2gray
from skimage.transform import rotate
from spellchecker import SpellChecker
import traceback
# On Windows, ... | 33.588235 | 108 | 0.649064 |
d2c5ed1f81d8bfe0be0278969594e7da6dcf2781 | 3,544 | py | Python | scripts/training.py | tobinsouth/privacy-preserving-synthetic-mobility-data | fd4d1851b47e3e7304761a894b460e8345fae5db | [
"MIT"
] | null | null | null | scripts/training.py | tobinsouth/privacy-preserving-synthetic-mobility-data | fd4d1851b47e3e7304761a894b460e8345fae5db | [
"MIT"
] | null | null | null | scripts/training.py | tobinsouth/privacy-preserving-synthetic-mobility-data | fd4d1851b47e3e7304761a894b460e8345fae5db | [
"MIT"
] | null | null | null | # Params
learning_rate = 0.001
k = 0.0025
x0 =2500
epochs = 4
batch_size=16
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import torch, numpy as np
from tqdm import tqdm
# Get the dataloader
from dataloader import get_train_test
trainStays, testStays = get_train_test(train_size=0.95, batch_siz... | 30.290598 | 110 | 0.628668 |