ytzi/the-stack-dedup-python-filtered · Datasets at Hugging Face

c2065e5fc7e61fdabd4ab6fd12c1ead2ad9d477a

78,713

py

Python

htdeblur/acquisition/motion.py

zfphil/htdeblur

ac557284f9913292721a6b9f943ff9b921043978

[ "BSD-3-Clause" ]

2

2020-01-16T18:30:55.000Z

2020-02-06T08:33:51.000Z

htdeblur/acquisition/motion.py

zfphil/htdeblur

ac557284f9913292721a6b9f943ff9b921043978

[ "BSD-3-Clause" ]

null

htdeblur/acquisition/motion.py

zfphil/htdeblur

ac557284f9913292721a6b9f943ff9b921043978

[ "BSD-3-Clause" ]

null

# Copyright 2017 Regents of the University of California # # Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with # the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 'AS IS' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import os, sys, time, copy, collections, math, json import numpy as np import scipy as sp import matplotlib from matplotlib import pyplot as plt import llops as yp # Custom scale bar object from matplotlib_scalebar.scalebar import ScaleBar # Libwallerlab imports from llops import display from llops import Roi

54.284828

757

0.612656

c2094cbd00b0292a602f2ea788a9486c162b5e7e

2,053

py

Python

leetcode/weekly150/last_substring.py

jan25/code_sorted

f405fd0898f72eb3d5428f9e10aefb4a009d5089

[ "Unlicense" ]

2

2018-01-18T11:01:36.000Z

2021-12-20T18:14:48.000Z

leetcode/weekly150/last_substring.py

jan25/code_sorted

f405fd0898f72eb3d5428f9e10aefb4a009d5089

[ "Unlicense" ]

null

leetcode/weekly150/last_substring.py

jan25/code_sorted

f405fd0898f72eb3d5428f9e10aefb4a009d5089

[ "Unlicense" ]

null

''' https://leetcode.com/contest/weekly-contest-150/problems/last-substring-in-lexicographical-order/ SA algorithm mostly copied from https://cp-algorithms.com/string/suffix-array.html Status: tle. probably py3 lists '''

31.584615

97

0.431076

c20c7d9e299f07af3208c0a8aedd483571769bbb

18,555

py

Python

schemagen/schemagen.py

GoZaddy/SchemaGen

c8374382f6b52ad3cec398c77fd5bc90fe891818

[ "MIT" ]

3

2021-03-26T22:51:41.000Z

2021-03-27T15:17:24.000Z

schemagen/schemagen.py

GoZaddy/SchemaGen

c8374382f6b52ad3cec398c77fd5bc90fe891818

[ "MIT" ]

null

schemagen/schemagen.py

GoZaddy/SchemaGen

c8374382f6b52ad3cec398c77fd5bc90fe891818

[ "MIT" ]

null

from antlr4 import * from .antlr import GraphQLLexer, GraphQLListener, GraphQLParser from .codegen import CodegenTool, Class, String, ClassInstance, IfElse, If, Method, Expr, Variable import re from math import floor from datetime import datetime from .utils import strip_string_quotes, camel_case_to_snake_case, process_input_value_definition from .errors import ParsingError GraphQLParser = GraphQLParser.GraphQLParser graphene = 'graphene' built_in_scalars = [ 'Int', 'Float', 'String', 'Boolean', 'ID', 'Date', 'Datetime', 'Time' 'Decimal', 'JSONString', 'Base64', ]

46.156716

136

0.492643

c20cac9dd66122173bfd30ba53957fea5bb5307b

2,231

py

Python

app/api/views.py

rickywang432/flask

c956dee6c7dfbb57a5fcd247d23af37e20b96da7

[ "MIT" ]

null

app/api/views.py

rickywang432/flask

c956dee6c7dfbb57a5fcd247d23af37e20b96da7

[ "MIT" ]

1

2021-06-02T02:01:38.000Z

app/api/views.py

rickywang432/flask

c956dee6c7dfbb57a5fcd247d23af37e20b96da7

[ "MIT" ]

null

from flask import Flask, request, jsonify,Blueprint from flask_marshmallow import Marshmallow from app.models import User, Group, Role from app import ma api = Blueprint('api', __name__) user_schema = UserSchema() users_schema = UserSchema(many=True) group_schema = GroupSchema() groups_schema = GroupSchema(many=True) role_schema = RoleSchema() roles_schema = RoleSchema(many=True) # endpoint to get user detail by id # endpoint to get group detail by id # endpoint to get group detail by id

26.247059

80

0.685791

c20d8ed82808f42c1ce9f7452c5668af8015a2b5

2,335

py

Python

setup.py

maljovec/samply

9364c2f671c02cb7bab484c0e856a0a0ca6ecc40

[ "BSD-3-Clause" ]

null

setup.py

maljovec/samply

9364c2f671c02cb7bab484c0e856a0a0ca6ecc40

[ "BSD-3-Clause" ]

2

2019-02-21T00:28:36.000Z

2019-11-09T04:35:39.000Z

setup.py

maljovec/samplers

9364c2f671c02cb7bab484c0e856a0a0ca6ecc40

[ "BSD-3-Clause" ]

null

""" Setup script for samply """ from setuptools import setup import re extra_args = {} def get_property(prop, project): """ Helper function for retrieving properties from a project's __init__.py file @In, prop, string representing the property to be retrieved @In, project, string representing the project from which we will retrieve the property @Out, string, the value of the found property """ result = re.search( r'{}\s*=\s*[\'"]([^\'"]*)[\'"]'.format(prop), open(project + "/__init__.py").read(), ) return result.group(1) VERSION = get_property("__version__", "samply") def long_description(): """ Reads the README.rst file and extracts the portion tagged between specific LONG_DESCRIPTION comment lines. """ description = "" recording = False with open("README.rst") as f: for line in f: if "END_LONG_DESCRIPTION" in line: return description elif "LONG_DESCRIPTION" in line: recording = True continue if recording: description += line # Consult here: https://packaging.python.org/tutorials/distributing-packages/ setup( name="samply", packages=["samply"], version=VERSION, description="A library for computing samplings in arbitrary dimensions", long_description=long_description(), author="Dan Maljovec", author_email="maljovec002@gmail.com", license="BSD", test_suite="samply.tests", url="https://github.com/maljovec/samply", download_url="https://github.com/maljovec/samply/archive/" + VERSION + ".tar.gz", keywords=[""], # Consult here: https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Programming Language :: C++", "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering :: Mathematics", ], setup_requires=["scipy", "numpy", "sklearn", "pyDOE", "ghalton"], install_requires=["scipy", "numpy", "sklearn", "pyDOE", "ghalton"], python_requires=">=2.7, <4", )

29.935897

77

0.615418

c20db92c5e61a54ef4ff2401b5df9360bca3d9b1

4,353

py

Python

数据结构实践课/实验3/文本格式化.py

TD21forever/hdu-term-project-helper

f42f553efd1d7b59162d3fc793ac14ae30850efd

[ "Apache-2.0" ]

17

2021-01-09T06:49:09.000Z

2022-02-23T01:36:20.000Z

数据结构实践课/实验3/文本格式化.py

TD21forever/hdu-term-project-helper

f42f553efd1d7b59162d3fc793ac14ae30850efd

[ "Apache-2.0" ]

null

数据结构实践课/实验3/文本格式化.py

TD21forever/hdu-term-project-helper

f42f553efd1d7b59162d3fc793ac14ae30850efd

[ "Apache-2.0" ]

1

2021-06-22T12:56:16.000Z

# -*- coding: utf-8 -*- # @Author: TD21forever # @Date: 2018-11-14 15:41:57 # @Last Modified by: TD21forever # @Last Modified time: 2018-11-15 16:50:48 file = open('input.txt','r')# # if __name__ == '__main__': while True: print("") print("\n1.\n2.\n3.\n4.\n5.\n6.\n") ans = "no" ans = input("5,55,2,3,3,1?,yesno:") if ans == 'yes': operate() else: print("\n") a = int(input("1.")) b = int(input("2.")) if b>80: b = int(input("80")) c = int(input("3.")) d = int(input("4.")) e = int(input("5.")) ff = int(input("6.")) operate(a,b,c,d,e,ff) f.close()

34.824

107

0.464278

c210287e380e114135144808518dac8414c8a7fc

1,405

py

Python

authors/apps/authentication/tests/test_models.py

C3real-kill3r/binary-jungle-backend

5333138fbce901e75accf5487b10990979afa571

[ "MIT" ]

null

authors/apps/authentication/tests/test_models.py

C3real-kill3r/binary-jungle-backend

5333138fbce901e75accf5487b10990979afa571

[ "MIT" ]

8

2020-02-12T03:04:07.000Z

2022-03-12T00:07:31.000Z

authors/apps/authentication/tests/test_models.py

C3real-kill3r/binary-jungle-backend

5333138fbce901e75accf5487b10990979afa571

[ "MIT" ]

null

from django.test import TestCase from authors.apps.authentication.models import ( User )

30.543478

112

0.64484

c213671e056f4ccf87fd3dab05b33b16957f6f48

5,908

py

Python

simulations/MonteCarlo_function.py

chengning-zhang/Statistical-methods-for-combining-multiple-tests-a-Wrapper-

42b5aabf6a0619fa7fe18a034926236d133a35d8

[ "MIT" ]

1

2020-11-24T02:58:58.000Z

simulations/MonteCarlo_function.py

chengning-zhang/Combining-multiple-tests-Wrapper-

42b5aabf6a0619fa7fe18a034926236d133a35d8

[ "MIT" ]

null

simulations/MonteCarlo_function.py

chengning-zhang/Combining-multiple-tests-Wrapper-

42b5aabf6a0619fa7fe18a034926236d133a35d8

[ "MIT" ]

null

#!/usr/bin/env python #-*- coding:utf-8 -*- """ Created on Nov 23, 2020 @author: Chengning Zhang """ ## simulation for Scenario A: generate X0 and X1. def MonteCarlo_1(T, n0, n1, u0, u1, sigma0, sigma1, log_bool = False): """simulation for first scenario: multivarite normal with equal variance T: number of simulation n0: sample size of class 0 n1: sample size of class 1 """ AUC = {'suliu':[], 'logistic':[], 'stepwise':[],'min-max':[], 'rf':[], 'svml':[], 'svmr':[]} ## same num as simulation time methods = ['suliu', 'logistic', 'stepwise','min-max', 'rf', 'svml', 'svmr'] for i in range(T): ### one monto carlo simulation of size n0 + n1 #i = 10 np.random.seed(seed= 100*i+ 4*i) X0 = multivariate_normal(u0, sigma0, size = n0) X1 = multivariate_normal(u1, sigma1, size = n1) if log_bool: X0 = np.exp(X0) X1 = np.exp(X1) # X = np.concatenate([X0,X1]) y = [0] * n0 y.extend([1]*n1); y = np.array(y) ## X,y is one simulation X = pd.DataFrame(data = X); y = pd.Series(y) ## within that particular MC simulation, do 10 folds CV cv = StratifiedKFold(n_splits= 10, shuffle=True, random_state=42) AUC_folds = {'suliu':[], 'logistic':[], 'stepwise':[],'min-max':[], 'rf':[], 'svml':[], 'svmr':[]} # same number as folders # for folder, (train_index, val_index) in enumerate(cv.split(X, y)): X_train,X_val = X.iloc[train_index],X.iloc[val_index] y_train,y_val = y.iloc[train_index],y.iloc[val_index] # X0_train, X1_train = helper(X_train, y_train); X0_val, X1_val = helper(X_val, y_val) for method in methods: model = AllMethod(method= method, bool_trans= False).fit(X0_train,X1_train) _,_, auc = model.predict(X0_val,X1_val) AUC_folds[method].append(auc) #print(AUC_folds) for key, val in AUC_folds.items(): AUC[key].append( np.mean(np.array(val) )) print({key: (np.mean(np.array(val)) ,np.std(np.array(val))) for key,val in AUC.items()}) return AUC ## Simulation scenario B: generate X first, then generate bernulli Y via logit(P(Y=1|X)) = ... def MonteCarlo_2(T, n, u, sigma): """simulation for last scenario: generate X first from normal, then generate y via logit(Y|X) = 10* ((sinpi*x1) + ... ) T: number of simulation n: sample size u: mean for X sigma: variance for X """ AUC = {'suliu':[], 'logistic':[], 'stepwise':[],'min-max':[], 'rf':[], 'svml':[], 'svmr':[]} ## same num as simulation time methods = ['suliu', 'logistic', 'stepwise','min-max', 'rf', 'svml', 'svmr'] for i in range(T): ### one monto carlo simulation of size n0 + n1 #i = 10 print(i) np.random.seed(seed= 100*i+ 4*i) X = multivariate_normal(u, sigma, size = n) X_trans = [ 10*sum(list(map(lambda x: np.sin(np.pi*x) , ele))) for ele in X] p = list(map(lambda x: 1 / (1 + np.exp(-x)), X_trans)) y = bernoulli.rvs(p, size= n) X = pd.DataFrame(data = X); y = pd.Series(y) ## within that particular MC simulation, do 10 folds CV cv = StratifiedKFold(n_splits= 10, shuffle=True, random_state=42) AUC_folds = {'suliu':[], 'logistic':[], 'stepwise':[],'min-max':[], 'rf':[], 'svml':[], 'svmr':[]} # same number as folders # for folder, (train_index, val_index) in enumerate(cv.split(X, y)): X_train,X_val = X.iloc[train_index],X.iloc[val_index] y_train,y_val = y.iloc[train_index],y.iloc[val_index] # X0_train, X1_train = helper(X_train, y_train); X0_val, X1_val = helper(X_val, y_val) for method in methods: model = AllMethod(method= method, bool_trans= False).fit(X0_train,X1_train) _,_, auc = model.predict(X0_val,X1_val) AUC_folds[method].append(auc) #print(AUC_folds) for key, val in AUC_folds.items(): AUC[key].append( np.mean(np.array(val) )) print({key: (np.mean(np.array(val)) ,np.std(np.array(val))) for key,val in AUC.items()}) return AUC ## Simulation scenario B: generate X first, then generate bernulli Y via logit(P(Y=1|X)) = ... def MonteCarlo_3(T, n, u, sigma): """simulation for last scenario: generate X first from normal, then generate y via logit(Y|X) = 10* ((sinpi*x1) + ... ) T: number of simulation n: sample size """ AUC = {'suliu':[], 'logistic':[], 'stepwise':[],'min-max':[], 'rf':[], 'svml':[], 'svmr':[]} ## same num as simulation time methods = ['suliu', 'logistic', 'stepwise','min-max', 'rf', 'svml', 'svmr'] for i in range(T): ### one monto carlo simulation of size n0 + n1 np.random.seed(seed= 100*i+ 4*i) X = multivariate_normal(u, sigma, size = n); #X = np.exp(X) X_trans = [ele[0] - ele[1] - ele[2]+ (ele[0] - ele[1])**2 - ele[3]**4 for ele in X] ## x1 - x2 - x3 + (x1-x2)^2 - x4^4 p = list(map(lambda x: 1 / (1 + np.exp(-x)), X_trans)) y = bernoulli.rvs(p, size= n) X = pd.DataFrame(data = X); y = pd.Series(y) ## within that particular MC simulation, do 10 folds CV cv = StratifiedKFold(n_splits= 10, shuffle=True, random_state=42) AUC_folds = {'suliu':[], 'logistic':[], 'stepwise':[],'min-max':[], 'rf':[], 'svml':[], 'svmr':[]} # same number as folders # for folder, (train_index, val_index) in enumerate(cv.split(X, y)): X_train,X_val = X.iloc[train_index],X.iloc[val_index] y_train,y_val = y.iloc[train_index],y.iloc[val_index] # X0_train, X1_train = helper(X_train, y_train); X0_val, X1_val = helper(X_val, y_val) for method in methods: model = AllMethod(method= method, bool_trans= False).fit(X0_train,X1_train) _,_, auc = model.predict(X0_val,X1_val) AUC_folds[method].append(auc) #print(AUC_folds) for key, val in AUC_folds.items(): AUC[key].append( np.mean(np.array(val) )) print({key: (np.mean(np.array(val)) ,np.std(np.array(val))) for key,val in AUC.items()}) return AUC

46.15625

128

0.609682

c213c3cc512fab07ba3d806bd3d3286525745450

389

py

Python

crawler/robo_proxy.py

xliangwu/com.caveup.machine_learn

793131c4767f45d468a813752c07d02f623a7b99

[ "Apache-2.0" ]

1

2018-09-19T06:27:14.000Z

crawler/robo_proxy.py

xliangwu/com.caveup.machine_learn

793131c4767f45d468a813752c07d02f623a7b99

[ "Apache-2.0" ]

null

crawler/robo_proxy.py

xliangwu/com.caveup.machine_learn

793131c4767f45d468a813752c07d02f623a7b99

[ "Apache-2.0" ]

null

import requests if __name__ == '__main__': pages_crawler()

24.3125

135

0.676093

c2145a28b8098d26c67f49818369dff92c2ac06b

11,662

py

Python

apiosintDS/apiosintDS.py

davidonzo/apiosintDS

b5bb1c42e1a3d984a69e8794a4c5da6969dcd917

[ "MIT" ]

13

2019-10-15T06:54:49.000Z

2022-03-28T23:23:29.000Z

apiosintDS/apiosintDS.py

davidonzo/apiosintDS

b5bb1c42e1a3d984a69e8794a4c5da6969dcd917

[ "MIT" ]

1

2019-11-12T15:00:53.000Z

2019-11-14T09:37:46.000Z

apiosintDS/apiosintDS.py

davidonzo/apiosintDS

b5bb1c42e1a3d984a69e8794a4c5da6969dcd917

[ "MIT" ]

4

2019-12-05T05:34:07.000Z

2022-03-24T09:59:26.000Z

import sys import logging import pytz logging.basicConfig(format='%(levelname)s: %(message)s') if (sys.version_info < (3, 0)):#NO MORE PYTHON 2!!! https://pythonclock.org/ logging.error(" ########################### ERROR ###########################") logging.error(" =============================================================") logging.error(" Invalid python version detected: "+str(sys.version_info[0])+"."+str(sys.version_info[1])) logging.error(" =============================================================") logging.error(" It seems your are still using python 2 even if you should") logging.error(" now it will be retire next 2020.") logging.error(" For more info please read https://pythonclock.org/") logging.error(" =============================================================") logging.error(" Try again typing: python3 /path/to/"+sys.argv[0]) logging.error(" =============================================================") logging.error(" ########################### ERROR ###########################") exit(0) import tempfile import argparse import os import requests import re import json italyTZ = pytz.timezone("Europe/Rome") from apiosintDS.modules import listutils, dosearch try: from urllib.parse import urlparse except ImportError as ierror: logging.error(ierror) logging.error("To run this script you need to install the \"urllib\" module") logging.error("Try typing: \"pip3 install urllib3\"") exit(0) try: import validators except ImportError as e: logging.error(e) logging.error("To run this script you need to install the \"validators\" module") logging.error("Try typing: \"pip3 install validators\"") exit(0) import platform if platform.system() not in ['Linux']: logging.warning("Script not testes on "+platform.system()+" systems. Use at your own risks.") scriptinfo = {"scriptname": "DigitalSide-API", "majorversion": "1", "minorversion": "8.3", "license": "MIT", "licenseurl": "https://raw.githubusercontent.com/davidonzo/Threat-Intel/master/LICENSE", "author": "Davide Baglieri", "mail": "info[at]digitalside.it", "pgp": "30B31BDA", "fingerprint": "0B4C F801 E8FF E9A3 A602 D2C7 9C36 93B2 30B3 1BDA", "git": "https://github.com/davidonzo/Threat-Intel/blob/master/tools/DigitalSide-API/v1", "DSProjectHP": "https://osint.digitalside.it", "DSGitHubHP": "https://github.com/davidonzo/Threat-Intel"} if __name__ == '__main__': main()

51.149123

428

0.607271

c2160b83bdfd16bb5fd59f1cfbfcbb7c7d36395f

3,327

py

Python

5-3_stock inventory.py

hkrsmk/python

1ee1b0adc911b62af3911428f441c6c59e1b345f

[ "Unlicense" ]

null

5-3_stock inventory.py

hkrsmk/python

1ee1b0adc911b62af3911428f441c6c59e1b345f

[ "Unlicense" ]

null

5-3_stock inventory.py

hkrsmk/python

1ee1b0adc911b62af3911428f441c6c59e1b345f

[ "Unlicense" ]

null

#Stock inventory control system. #======================================= 1 =========================== #======================================= 2 =========================== #======================================= 3 ============================ #main prog below choice = 0 myStock = {} #empty dictionary for myStock try: infile = open("myStock.txt","r") read1LineStock = infile.readline() #read first line while read1LineStock !=" ": #while the file has not ended, myStock[read1LineStock.split(",")[0]] = int(read1LineStock.split(",")[1]) read1LineStock = infile.readline() print(myStock) #place item 0 in the split up sentence as the name for the item for myStock, #and whatever number you can find in item 1 of the split up sentence (ignore '\n') #as the 'quantity' for myStock. #eg myStock['apple'] = '1' #then, read the next line. infile.close() except: print("Welcome to the stock management system!") while choice != 9: choice = menu() #rmb to return choice to the global choice. #the choice inside menu() is a LOCAL choice. if choice ==1: newStock() elif choice ==2: addVolume() elif choice ==3: sell() #======================================= 8 =========================== elif choice ==8: print(myStock) #======================================= 9 =========================== print("Have a noice day")

30.522936

87

0.479411

c21a45651bf528b945d22bdb962a7e3a45ad0e4d

91

py

Python

backend/gunicorn_config_worker.py

matan-h/futurecoder

5117cbab7ed2bc41fe9d4763038d9c4a0aba064e

[ "MIT" ]

null

backend/gunicorn_config_worker.py

matan-h/futurecoder

5117cbab7ed2bc41fe9d4763038d9c4a0aba064e

[ "MIT" ]

1

2022-02-28T01:35:27.000Z

backend/gunicorn_config_worker.py

matan-h/futurecoder

5117cbab7ed2bc41fe9d4763038d9c4a0aba064e

[ "MIT" ]

null

bind = "0.0.0.0:5000" threads = 10 worker_class = "gthread" accesslog = '-' errorlog = '-'

15.166667

24

0.626374

c21a8492971d5deb4f24b54f0d01b958dad6c817

1,780

py

Python

2017/day23.py

andypymont/adventofcode

912aa48fc5b31ec9202fb9654380991fc62afcd1

[ "MIT" ]

null

2017/day23.py

andypymont/adventofcode

912aa48fc5b31ec9202fb9654380991fc62afcd1

[ "MIT" ]

null

2017/day23.py

andypymont/adventofcode

912aa48fc5b31ec9202fb9654380991fc62afcd1

[ "MIT" ]

null

""" 2017 Day 23 https://adventofcode.com/2017/day/23 """ from typing import Dict import aocd # type: ignore def main() -> None: """ Calculate and output the solutions based on the real puzzle input. """ data = aocd.get_data(year=2017, day=23) program = Program(data) program.run() print(f"Part 1: {program.mul_count}") print(f"Part 2: {run_program()}") if __name__ == "__main__": main()

25.797101

75

0.561798

c21ace7559f52cf54fe988e11522102469f04048

1,641

py

Python

src/simulator/wsn/test.py

liuliuliu0605/Federated-Learning-PyTorch

04169455917ae50a8fea2dabd756a0ca1774e5d5

[ "MIT" ]

null

src/simulator/wsn/test.py

liuliuliu0605/Federated-Learning-PyTorch

04169455917ae50a8fea2dabd756a0ca1774e5d5

[ "MIT" ]

null

src/simulator/wsn/test.py

liuliuliu0605/Federated-Learning-PyTorch

04169455917ae50a8fea2dabd756a0ca1774e5d5

[ "MIT" ]

null

import sys from sklearn.datasets import make_blobs from src.simulator.wsn.network import Network from src.simulator.wsn.utils import * from src.simulator.wsn.fcm import * from src.simulator.wsn.direct_communication import * from src.utils import complete, star seed = 1 np.random.seed(seed ) logging.basicConfig(stream=sys.stderr, level=logging.INFO) traces = {} topo = complete(cf.NB_CLUSTERS) # topo = independent(cf.NB_CLUSTERS) # topo = star(cf.NB_CLUSTERS) # topo = ring(cf.NB_CLUSTERS) centers = [[50, 225], [25, 110], [125, 20], [220, 80], [200, 225]] X, y = make_blobs(n_samples=100, centers=centers, n_features=2, random_state=seed, cluster_std=15) traces = {} network = Network(init_nodes=X, topo=topo) # network = Network(topo=topo) for routing_topology in ['FCM']:#, 'DC']: network.reset() routing_protocol_class = eval(routing_topology) network.init_routing_protocol(routing_protocol_class()) # traces[routing_topology] = network.simulate() for i in range(1000): print("--------Round %d--------"% i) network.activate_mix() traces[routing_topology] = network.simulate_one_round() network.deactivate_mix() if len(network.get_alive_nodes()) == 0 : break # plot_clusters(network) # plot_time_of_death(network) # print(network.energy_dis) # print(network.energy_dis['inter-comm']/ network.energy_dis['intra-comm']) print("All death round: ", i) print("First death round: ", network.first_depletion) print("Energy:", network.energy_dis) plot_traces(traces)

32.176471

80

0.672151

c21c3b472b61858775a3801d8a7ee0aff0f5536a

4,149

py

Python

src/dewloosh/geom/cell.py

dewloosh/dewloosh-geom

5c97fbab4b68f4748bf4309184b9e0e877f94cd6

[ "MIT" ]

2

2021-12-11T17:25:51.000Z

2022-01-06T15:36:27.000Z

src/dewloosh/geom/cell.py

dewloosh/dewloosh-geom

5c97fbab4b68f4748bf4309184b9e0e877f94cd6

[ "MIT" ]

null

src/dewloosh/geom/cell.py

dewloosh/dewloosh-geom

5c97fbab4b68f4748bf4309184b9e0e877f94cd6

[ "MIT" ]

null

# -*- coding: utf-8 -*- try: from collections.abc import Iterable except ImportError: from collections import Iterable import numpy as np from numpy import ndarray from dewloosh.math.array import atleast1d from dewloosh.math.utils import to_range from .celldata import CellData from .utils import jacobian_matrix_bulk, points_of_cells, pcoords_to_coords_1d

33.192

89

0.577489

c2221b8872b6350f052296a7af3215fb075a5795

30

py

Python

src/python/src/rmq/items/__init__.py

halimov-oa/scrapy-boilerplate

fe3c552fed26bedb0618c245ab923aa34a89ac9d

[ "MIT" ]

34

2019-12-13T10:31:39.000Z

2022-03-09T15:59:07.000Z

src/python/src/rmq/items/__init__.py

halimov-oa/scrapy-boilerplate

fe3c552fed26bedb0618c245ab923aa34a89ac9d

[ "MIT" ]

49

2020-02-25T19:41:09.000Z

2022-02-27T12:05:25.000Z

src/python/src/rmq/items/__init__.py

halimov-oa/scrapy-boilerplate

fe3c552fed26bedb0618c245ab923aa34a89ac9d

[ "MIT" ]

23

2019-12-23T15:19:42.000Z

2022-03-09T16:00:15.000Z

from .rmq_item import RMQItem

15

29

0.833333

c22246e42a11a496e2843439e4ad4abd332a1d57

968

py

Python

softlearning/environments/mujoco_safety_gym/envs/fetch/slide.py

anyboby/mbpo

98b75cb4cb13a2640fce1fbe1ddef466b864342e

[ "MIT" ]

5

2020-02-12T17:09:09.000Z

2021-09-29T16:06:40.000Z

softlearning/environments/mujoco_safety_gym/envs/fetch/slide.py

anyboby/mbpo

98b75cb4cb13a2640fce1fbe1ddef466b864342e

[ "MIT" ]

10

2020-08-31T02:50:02.000Z

2022-02-09T23:36:43.000Z

softlearning/environments/mujoco_safety_gym/envs/fetch/slide.py

anyboby/mbpo

98b75cb4cb13a2640fce1fbe1ddef466b864342e

[ "MIT" ]

2

2022-03-15T01:45:26.000Z

2022-03-15T06:46:47.000Z

import os import numpy as np from gym import utils from mujoco_safety_gym.envs import fetch_env # Ensure we get the path separator correct on windows MODEL_XML_PATH = os.path.join('fetch', 'slide.xml')

37.230769

105

0.66219

c222e22c9b1710ce4667ef563dce67f96dc33915

163

py

Python

packages/raspi_ip/setup.py

atoy322/PiDrive

8758f4b5dae4a0187ce0a769c4146628c88015de

[ "MIT" ]

null

packages/raspi_ip/setup.py

atoy322/PiDrive

8758f4b5dae4a0187ce0a769c4146628c88015de

[ "MIT" ]

2

2021-09-21T06:32:58.000Z

2021-09-22T23:15:18.000Z

packages/raspi_ip/setup.py

atoy322/PiDrive

8758f4b5dae4a0187ce0a769c4146628c88015de

[ "MIT" ]

null

from setuptools import setup, find_packages setup( name="raspi_ip", version="1.0.0", author="atoy322", description="", long_description="" )

14.818182

43

0.644172

c223b2854d4053fb4e412891092b11e58745c844

339

py

Python

kiteconnect/__version__.py

AnjayGoel/pykiteconnect

e33833a86d3e6483f2cff1be8bed74f40d5771c5

[ "MIT" ]

1

2022-02-05T08:43:37.000Z

kiteconnect/__version__.py

AnjayGoel/pykiteconnect

e33833a86d3e6483f2cff1be8bed74f40d5771c5

[ "MIT" ]

null

kiteconnect/__version__.py

AnjayGoel/pykiteconnect

e33833a86d3e6483f2cff1be8bed74f40d5771c5

[ "MIT" ]

null

__title__ = "open_kite_connect" __description__ = "Fork of the official Kite Connect python client, allowing free access to the api." __url__ = "https://kite.trade" __download_url__ = "https://github.com/AnjayGoel/pykiteconnect" __version__ = "4.0.0" __author__ = "Anjay Goel" __author_email__ = "anjay.goel@gmail.com" __license__ = "MIT"

37.666667

101

0.766962

c223dd7e30b36ebfa0f41bf3e5a06ae1a6e0b5cd

1,679

py

Python

CrsData/pipelines.py

DivineEnder/CrsData

cd0cf14e79b4a3bbf7347b8612a5b67e2a185208

[ "MIT" ]

null

CrsData/pipelines.py

DivineEnder/CrsData

cd0cf14e79b4a3bbf7347b8612a5b67e2a185208

[ "MIT" ]

null

CrsData/pipelines.py

DivineEnder/CrsData

cd0cf14e79b4a3bbf7347b8612a5b67e2a185208

[ "MIT" ]

null

# @Author: DivineEnder # @Date: 2018-03-08 22:24:45 # @Email: danuta@u.rochester.edu # @Last modified by: DivineEnder # @Last modified time: 2018-03-11 01:25:41 # -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html from dotenv import load_dotenv, find_dotenv from Utils import connection_utils as glc from psycopg2.extensions import AsIs import os

34.265306

214

0.731983

c224e7c1cff16812960fb4cd9afab8ab99e06afc

2,227

py

Python

index_to_csv.py

grenzi/photoindexer

d10b3b6f347168706dc9c2673a29102fd73f31e1

[ "Apache-2.0" ]

null

index_to_csv.py

grenzi/photoindexer

d10b3b6f347168706dc9c2673a29102fd73f31e1

[ "Apache-2.0" ]

null

index_to_csv.py

grenzi/photoindexer

d10b3b6f347168706dc9c2673a29102fd73f31e1

[ "Apache-2.0" ]

null

import os import json from enum import Enum from datetime import datetime,date import logging import pathlib from tqdm import tqdm from datastructures import Volume, IndexedFile,load_index_if_exists, save_index from os import listdir from os.path import isfile, join import itertools import csv logger = logging.getLogger() handler = logging.StreamHandler() formatter = logging.Formatter( '%(asctime)s %(name)-12s %(levelname)-8s %(message)s') handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(logging.INFO) ############################################################################### index_dir = os.path.join(os.getcwd(), 'index') logger.info('finding index files') indexfiles = list([f for f in listdir(index_dir) if isfile(join(index_dir, f)) and f[-4:]=='json']) columns = ['VolumeName', 'VolumeSerialNumber', 'Directory', 'Name', 'InodeNumber', 'Modified On', 'Created On', 'SHA256'] exif_columns=set() logger.info('parsing index files') #Pass 1 = collect keys for index_file in indexfiles: index = load_index_if_exists(os.path.join(index_dir, index_file)) for vol in index: for ixf in vol.files: if ixf.EXIF is not None: for i in ixf.EXIF.keys(): exif_columns.add(i) logger.info('writing csv') #Pass 2 = write header with open(os.path.join(os.getcwd(), 'index.csv'), mode='w', encoding='utf-8', newline='') as f: writer = csv.writer(f) writer.writerow(columns+list(exif_columns)) #and now rows for index_file in indexfiles: index = load_index_if_exists(os.path.join(index_dir, index_file)) for vol in index: for ixf in vol.files: row = [ vol.VolumeName, vol.VolumeSerialNumber, ixf.Directory, ixf.Name, ixf.st_ino, ixf.st_mtime.strftime("%c"), ixf.st_ctime.strftime("%c"), ixf.SHA256 ] for ec in exif_columns: row.append(ixf.EXIF.get(ec, None)) writer.writerow(row)

35.349206

122

0.58599

c2253045dcaa56a5991a62320574be6662b1c519

1,056

py

Python

tests/test_wrapper.py

waysup/Jike-Metro

b8ead80dddd5d695784c5587edfd8df87c55a4e6

[ "MIT" ]

193

2018-04-04T02:27:51.000Z

2022-03-14T03:26:44.000Z

tests/test_wrapper.py

BeiFenKu/Jike-Metro

e97fd0a751dca28a39d0e9fb94fbd696d5ee07b3

[ "MIT" ]

16

2018-04-04T05:58:15.000Z

2021-01-08T02:56:57.000Z

tests/test_wrapper.py

BeiFenKu/Jike-Metro

e97fd0a751dca28a39d0e9fb94fbd696d5ee07b3

[ "MIT" ]

24

2018-04-06T09:34:58.000Z

2021-03-02T02:10:07.000Z

import unittest from collections import namedtuple from jike.objects.wrapper import * if __name__ == '__main__': unittest.main()

34.064516

83

0.61553

c225d7cd38555d8a71f34fd96c413aa41e8e84be

10,125

py

Python

storm_control/hal4000/illumination/illuminationChannelUI.py

shiwei23/STORM6

669067503ebd164b575ce529fcc4a9a3f576b3d7

[ "MIT" ]

47

2015-02-11T16:05:54.000Z

2022-03-26T14:13:12.000Z

storm_control/hal4000/illumination/illuminationChannelUI.py

shiwei23/STORM6

669067503ebd164b575ce529fcc4a9a3f576b3d7

[ "MIT" ]

110

2015-01-30T03:53:41.000Z

2021-11-03T15:58:44.000Z

storm_control/hal4000/illumination/illuminationChannelUI.py

shiwei23/STORM6

669067503ebd164b575ce529fcc4a9a3f576b3d7

[ "MIT" ]

61

2015-01-09T18:31:27.000Z

2021-12-21T13:07:51.000Z

#!/usr/bin/env python """ The various ChannelUI classes. Hazen 04/17 """ import os from PyQt5 import QtCore, QtWidgets # # The MIT License # # Copyright (c) 2017 Zhuang Lab, Harvard University # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. #

32.041139

100

0.645136

c229b5ef4f038beb67f6eb13b6306e08e27df0c9

4,051

py

Python

3-compressor/compress.py

JamesG3/Search-Engine

790f32c7833eb82d9b01b84af634e650ef7a9e75

[ "BSD-2-Clause" ]

null

3-compressor/compress.py

JamesG3/Search-Engine

790f32c7833eb82d9b01b84af634e650ef7a9e75

[ "BSD-2-Clause" ]

null

3-compressor/compress.py

JamesG3/Search-Engine

790f32c7833eb82d9b01b84af634e650ef7a9e75

[ "BSD-2-Clause" ]

null

import sys import struct from io import FileIO, BufferedWriter import S9Compressor as S9 BLOCKSIZE = (64*1024) / 4 # number of Int LexiPos = 0 # record the current position for new lexicon writing lexiconBuffer = [] IIBuffer = [] WriteThreshold = 0 del IIBuffer[:] newII.close() return main()

26.827815

96

0.674155

c22ad6cee4570624757675e6c7ad19a18a8249f2

5,028

py

Python

DataProcess/ultimate_subimage.py

EmmaAlexander/possum-tools

051ebca682cd97b68fa2a89c9d67e99cf85b09c7

[ "MIT" ]

5

2021-11-18T13:27:30.000Z

2021-12-05T00:15:33.000Z

DataProcess/ultimate_subimage.py

EmmaAlexander/possum-tools

051ebca682cd97b68fa2a89c9d67e99cf85b09c7

[ "MIT" ]

null

DataProcess/ultimate_subimage.py

EmmaAlexander/possum-tools

051ebca682cd97b68fa2a89c9d67e99cf85b09c7

[ "MIT" ]

null

#CASA script to create cutouts of fits cubes directoryA = '/Volumes/TARDIS/Work/askap/' directoryB = '/Volumes/NARNIA/pilot_cutouts/' import numpy as np sources=np.loadtxt('/Users/emma/GitHub/possum-tools/DataProcess/pilot_sources.txt',dtype='str') for i in range(0,sources.shape[0]): objectname=sources[i,0] POSSUMSB=sources[i,3] EMUSB=sources[i,4] ra=sources[i,1] dec=sources[i,2] sourcecentre=ra+','+dec fov=sources[i,6]#arcsec print(objectname) region='centerbox[['+sourcecentre+'], ['+fov+'arcsec, '+fov+'arcsec]]' possum_outfile=directoryB+objectname+'/'+objectname+'_POSSUM.fits' emu_outfile=directoryB+objectname+'/'+objectname+'_EMU.fits' #POSSUM if POSSUMSB == '5038': #this is the Early Science data possum_cont_filename = '/Volumes/NARNIA/PawseySync/DRAGN_1_0p8_A/DRAGN_1_0p8_A/image.i.SB5038.cont.restored.fits' else: possum_cont_filename = directoryA +'fullfields/image.i.SB'+POSSUMSB+'.cont.taylor.0.restored.fits' if POSSUMSB == '10035': print('Skipping POSSUM: bad SB10035') else: imsubimage(imagename=possum_cont_filename,outfile='possum_cont_temp',region=region,overwrite=True,dropdeg=True) exportfits(imagename='possum_cont_temp',fitsimage=possum_outfile,overwrite=True) #cubes i_filename = '/Volumes/NARNIA/leakage_corrected/image.restored.i.SB'+POSSUMSB+'.contcube.linmos.13arcsec.leakage.zernike.holoI.fits' q_filename = '/Volumes/NARNIA/leakage_corrected/image.restored.q.SB'+POSSUMSB+'.contcube.linmos.13arcsec.leakage.zernike.holoI.fits' u_filename = '/Volumes/NARNIA/leakage_corrected/image.restored.u.SB'+POSSUMSB+'.contcube.linmos.13arcsec.leakage.zernike.holoI.fits' imsubimage(imagename=i_filename,outfile='i_im_temp',region=region,overwrite=True,dropdeg=True) imsubimage(imagename=q_filename,outfile='q_im_temp',region=region,overwrite=True,dropdeg=True) imsubimage(imagename=u_filename,outfile='u_im_temp',region=region,overwrite=True,dropdeg=True) exportfits(imagename='i_im_temp',fitsimage=objectname+'_POSSUM_i.fits',overwrite=True) exportfits(imagename='q_im_temp',fitsimage=objectname+'_POSSUM_q.fits',overwrite=True) exportfits(imagename='u_im_temp',fitsimage=objectname+'_POSSUM_u.fits',overwrite=True) #EMU if EMUSB != 'NaN': if EMUSB=='10083': i_EMU_filename = '/Volumes/NARNIA/fullfields/image.restored.i.SB10083.contcube.conv.fits' q_EMU_filename = '/Volumes/NARNIA/fullfields/image.restored.q.SB10083.contcube.conv.fits' u_EMU_filename = '/Volumes/NARNIA/fullfields/image.restored.u.SB10083.contcube.conv.fits' cont_EMU_filename= '/Volumes/NARNIA/fullfields/image.i.SB10083.cont.taylor.0.restored.conv.fits' imsubimage(imagename=i_EMU_filename,outfile='i_EMU_im_temp',region=region,overwrite=True,dropdeg=True) imsubimage(imagename=q_EMU_filename,outfile='q_EMU_im_temp',region=region,overwrite=True,dropdeg=True) imsubimage(imagename=u_EMU_filename,outfile='u_EMU_im_temp',region=region,overwrite=True,dropdeg=True) imsubimage(imagename=cont_EMU_filename,outfile='EMU_cont_im_temp',region=region,overwrite=True,dropdeg=True) exportfits(imagename='i_EMU_im_temp',fitsimage=objectname+'_EMU_i.fits',overwrite=True) exportfits(imagename='q_EMU_im_temp',fitsimage=objectname+'_EMU_q.fits',overwrite=True) exportfits(imagename='u_EMU_im_temp',fitsimage=objectname+'_EMU_u.fits',overwrite=True) exportfits(imagename='EMU_cont_im_temp',fitsimage=emu_outfile,overwrite=True) elif EMUSB=='10635': i_EMU_filename = '/Volumes/NARNIA/fullfields/image.restored.i.SB10635.contcube.v2.conv.fits' q_EMU_filename = '/Volumes/NARNIA/fullfields/image.restored.q.SB10635.contcube.v2.conv.fits' u_EMU_filename = '/Volumes/NARNIA/fullfields/image.restored.u.SB10635.contcube.v2.conv.fits' cont_EMU_filename= '/Volumes/NARNIA/fullfields/image.i.SB10635.cont.taylor.0.restored.fits' imsubimage(imagename=i_EMU_filename,outfile='i_EMU_im_temp',region=region,overwrite=True,dropdeg=True) imsubimage(imagename=q_EMU_filename,outfile='q_EMU_im_temp',region=region,overwrite=True,dropdeg=True) imsubimage(imagename=u_EMU_filename,outfile='u_EMU_im_temp',region=region,overwrite=True,dropdeg=True) imsubimage(imagename=cont_EMU_filename,outfile='EMU_cont_im_temp',region=region,overwrite=True,dropdeg=True) exportfits(imagename='i_EMU_im_temp',fitsimage=objectname+'_EMU_i.fits',overwrite=True) exportfits(imagename='q_EMU_im_temp',fitsimage=objectname+'_EMU_q.fits',overwrite=True) exportfits(imagename='u_EMU_im_temp',fitsimage=objectname+'_EMU_u.fits',overwrite=True) exportfits(imagename='EMU_cont_im_temp',fitsimage=emu_outfile,overwrite=True) else: #no cubes emu_filename= directoryA +'fullfields/image.i.SB'+EMUSB+'.cont.taylor.0.restored.fits' imsubimage(imagename=emu_filename,outfile='emu_cont_temp',region=region,overwrite=True,dropdeg=True) exportfits(imagename='emu_cont_temp',fitsimage=emu_outfile,overwrite=True) os.system("rm -r emu_cont_temp") #tidy up os.system("rm -r *_temp") os.system("mv *{}* {}/".format(objectname,objectname))

57.136364

134

0.793755

c22b8b9f23f5fc7b3cfeba52a978e7ba6441ed61

92

py

Python

cv_comparison_slider_window/__init__.py

Kazuhito00/cv-comparison-slider-window

215cd91c1832b419af9fb99b484ce8c2a9e79a37

[ "MIT" ]

2

2020-11-14T09:09:02.000Z

2020-11-14T10:54:57.000Z

cv_comparison_slider_window/__init__.py

Kazuhito00/cv-comparison-slider-window

215cd91c1832b419af9fb99b484ce8c2a9e79a37

[ "MIT" ]

null

cv_comparison_slider_window/__init__.py

Kazuhito00/cv-comparison-slider-window

215cd91c1832b419af9fb99b484ce8c2a9e79a37

[ "MIT" ]

null

from cv_comparison_slider_window.cv_comparison_slider_window import CvComparisonSliderWindow

92

0.956522

c22bb60421f79ce3a2d29c04e35af61e67fb09d3

1,207

py

Python

telegram_ecommerce/filters/decorators.py

Anonylions/telegram_ecommerce

f5382886bbebf607c735e2f451774c56df8d6011

[ "MIT" ]

10

2020-11-20T20:55:52.000Z

2022-02-10T20:25:45.000Z

telegram_ecommerce/filters/decorators.py

Anonylions/telegram_ecommerce

f5382886bbebf607c735e2f451774c56df8d6011

[ "MIT" ]

1

2022-02-16T10:28:18.000Z

2022-02-16T10:35:31.000Z

telegram_ecommerce/filters/decorators.py

Anonylions/telegram_ecommerce

f5382886bbebf607c735e2f451774c56df8d6011

[ "MIT" ]

8

2021-05-01T01:13:09.000Z

2022-03-13T14:00:01.000Z

from ..language import get_text from ..database.query import ( user_exist, is_admin) END = -1

26.822222

67

0.71831

c22fbe148dfbc37e36952003c17c1b1180d11337

63

py

Python

albow/demo/openGL/__init__.py

hasii2011/albow-python-3

04b9d42705b370b62f0e49d10274eebf3ac54bc1

[ "MIT" ]

6

2019-04-30T23:50:39.000Z

2019-11-04T06:15:02.000Z

albow/demo/openGL/__init__.py

hasii2011/albow-python-3

04b9d42705b370b62f0e49d10274eebf3ac54bc1

[ "MIT" ]

73

2019-05-12T18:43:14.000Z

2021-04-13T19:19:03.000Z

albow/demo/openGL/__init__.py

hasii2011/albow-python-3

04b9d42705b370b62f0e49d10274eebf3ac54bc1

[ "MIT" ]

null

"""" This package contains the OpenGL demonstration classes """

21

54

0.761905

c2306615617cec84564c5dcb8ee8a144809be27e

1,640

py

Python

openhab2/scripts/readNilan.py

starze/openhab2

e4eeeecd829cdf286372067bd61561e63fed6e1a

[ "MIT" ]

10

2017-04-04T08:28:54.000Z

2021-02-24T04:36:07.000Z

openhab2/scripts/readNilan.py

starze/openhab2

e4eeeecd829cdf286372067bd61561e63fed6e1a

[ "MIT" ]

2

2017-04-18T13:33:12.000Z

2018-06-05T21:27:18.000Z

openhab2/scripts/readNilan.py

starze/openhab2

e4eeeecd829cdf286372067bd61561e63fed6e1a

[ "MIT" ]

7

2017-04-17T18:02:19.000Z

2020-09-25T21:28:08.000Z

#!/usr/bin/env python3 # -*- coding: ISO-8859-1 -*- # https://github.com/starze/openhab2 # https://github.com/roggmaeh/nilan-openhab import minimalmodbus import serial import os, sys import csv import httplib2 minimalmodbus.CLOSE_PORT_AFTER_EACH_CALL = True instrument = minimalmodbus.Instrument('/dev/ttyUSB0', 30, mode='rtu') # port name, slave address (in decimal) instrument.serial.port instrument.serial.baudrate = 19200 # Baud instrument.serial.bytesize = 8 instrument.serial.parity = serial.PARITY_EVEN instrument.serial.stopbits = 1 instrument.serial.timeout = 2 # seconds #instrument.debug = True h = httplib2.Http() with open('nilan_modbus.csv') as csvfile: reader = csv.DictReader(csvfile, delimiter=',') for row in reader: if row['Register Type'] == "Input": fc = 4 elif row['Register Type'] == "Holding": fc = 3 if row['Unit'] == "text" or row['Unit'] == "ascii": strRet = instrument.read_string(int(row['Address']), numberOfRegisters=1, functioncode=fc) lst = list(strRet) strRet = lst[1] + lst[0] elif row['Scale'] == "100": strRet = instrument.read_register(int(row['Address']), numberOfDecimals=2, functioncode=fc) else: strRet = instrument.read_register(int(row['Address']), numberOfDecimals=0, functioncode=fc) if row['Unit'] == "%" or row['Unit'] == "C": print("%s: %s %s" % (row['Name'], strRet, row['Unit'])) h.request("http://localhost:8080/rest/items/" + row['Name'] + "/state", "PUT", body=str(strRet)) else: print("%s: %s" % (row['Name'], strRet)) h.request("http://localhost:8080/rest/items/" + row['Name'] + "/state", "PUT", body=str(strRet))

34.166667

109

0.675

c230b7732d9a3dd108e45e13abd94ad053baac7e

2,316

py

Python

face_signin/prepare_training.py

sribs/FaceRecognition

68284173195d55f32a353fe3d78a53c25fbf1363

[ "Apache-2.0" ]

null

face_signin/prepare_training.py

sribs/FaceRecognition

68284173195d55f32a353fe3d78a53c25fbf1363

[ "Apache-2.0" ]

null

face_signin/prepare_training.py

sribs/FaceRecognition

68284173195d55f32a353fe3d78a53c25fbf1363

[ "Apache-2.0" ]

null

import cv2 import numpy as np import os

31.297297

85

0.593264

c230e009f1c0351446c02fccceb30b7ead29e784

138

py

Python

randomselection.py

Ristinoa/cs257

e7c31f995d08661114d868a55448c628b4cc9327

[ "MIT" ]

null

randomselection.py

Ristinoa/cs257

e7c31f995d08661114d868a55448c628b4cc9327

[ "MIT" ]

null

randomselection.py

Ristinoa/cs257

e7c31f995d08661114d868a55448c628b4cc9327

[ "MIT" ]

null

"randompicker.py" import random "A very short practice program designed to spit out a random, user-determined sample of input names"

13.8

39

0.775362

c2318081600b41f253e54a78d1001f4ddb857e30

15,873

py

Python

fisspy/analysis/tdmap.py

SNU-sunday/FISS-PYTHON

f79420debef476a904356d42542cb6472990bb2f

[ "BSD-2-Clause" ]

3

2017-02-18T06:42:08.000Z

2021-01-05T04:15:08.000Z

fisspy/analysis/tdmap.py

SNU-sunday/fisspy

f79420debef476a904356d42542cb6472990bb2f

[ "BSD-2-Clause" ]

1

2019-06-30T10:35:27.000Z

fisspy/analysis/tdmap.py

SNU-sunday/FISS-PYTHON

f79420debef476a904356d42542cb6472990bb2f

[ "BSD-2-Clause" ]

1

2017-02-23T05:24:13.000Z

from __future__ import absolute_import, division import numpy as np import matplotlib.pyplot as plt from matplotlib import gridspec from fisspy.analysis.filter import FourierFilter from interpolation.splines import LinearSpline from matplotlib.animation import FuncAnimation import astropy.units as u from astropy.time import Time __author__= "Juhyung Kang" __email__ = "jhkang@astro.snu.ac.kr"

41.015504

86

0.449001

c231926cf5107bb89588302bb3bc85d7ec967624

2,806

py

Python

Graphy/setup.py

andrepbento/OpenTracingProcessor

9e4b01cb59cecbfa04af8d5d93e3b7deb76d9ee6

[ "MIT" ]

4

2021-03-06T13:50:58.000Z

2022-03-28T15:17:07.000Z

Graphy/setup.py

andrepbento/OpenTracingProcessor

9e4b01cb59cecbfa04af8d5d93e3b7deb76d9ee6

[ "MIT" ]

null

Graphy/setup.py

andrepbento/OpenTracingProcessor

9e4b01cb59cecbfa04af8d5d93e3b7deb76d9ee6

[ "MIT" ]

null

""" Author: Andr Bento Date last modified: 26-02-2019 """ import subprocess import sys from os.path import dirname, abspath, join from setuptools import find_packages, Command, setup from setuptools.command.test import test as TestCommand this_dir = abspath(dirname(__file__)) NAME = 'graphy' VERSION = '0.0.1' # Readme with open(join(this_dir, 'README.md'), encoding='utf-8') as file: readme = file.read() # License with open(join(this_dir, 'LICENSE'), encoding='utf-8') as file: license_file = file.read() # Requirements with open(join(this_dir, 'requirements.txt')) as file: requirements = file.read().splitlines() setup( name=NAME, version=VERSION, description='A micro-services system monitor command line program in Python.', long_description=readme, # long_description_content_type='text/markdown', url='https://github.com/andrepbento/MScThesis/tree/master/Graphy', author='Andr Bento', author_email='apbento@student.dei.uc.pt', license=license_file, classifiers=[ # How mature is this project? Common values are # 1 - Project setup # 2 - Prototype # 3 - Alpha # 4 - Beta # 5 - Production/Stable 'Development Status :: 2 - Prototype', 'Intended Audience :: Developers', 'Topic :: Observing and Controlling Performance in Micro-services', 'License :: MIT License', 'Programming Language :: Python :: 3.6', ], keywords='cli', packages=find_packages(exclude=('tests*', 'docs')), install_requires=requirements, tests_require=['pytest'], extras_require={ 'test': ['coverage', 'pytest', 'pytest-cov'], }, cmdclass={ 'install': Install, 'run': Run, 'test': Test }, )

24.189655

82

0.62794

c232029579d8b288e2ac9ed43b03f0690df1e9c2

1,317

py

Python

polaris/polaris/sep24/tzinfo.py

yuriescl/django-polaris

8806d0e4e8baaddbffbceb3609786d2436b8abe1

[ "Apache-2.0" ]

81

2019-11-16T21:47:22.000Z

2022-02-17T07:35:02.000Z

polaris/polaris/sep24/tzinfo.py

yuriescl/django-polaris

8806d0e4e8baaddbffbceb3609786d2436b8abe1

[ "Apache-2.0" ]

491

2019-11-10T23:44:30.000Z

2022-03-20T00:25:02.000Z

polaris/polaris/sep24/tzinfo.py

yuriescl/django-polaris

8806d0e4e8baaddbffbceb3609786d2436b8abe1

[ "Apache-2.0" ]

89

2019-11-18T21:31:01.000Z

2022-03-28T13:47:41.000Z

import pytz from datetime import datetime, timedelta, timezone from rest_framework.decorators import api_view, parser_classes, renderer_classes from rest_framework.parsers import JSONParser from rest_framework.renderers import JSONRenderer from rest_framework.request import Request from rest_framework.response import Response from django.contrib.sessions.backends.db import SessionStore from polaris.utils import render_error_response, getLogger logger = getLogger(__name__)

34.657895

87

0.741838

c2321c74ae596a68d5084730c6df5fe1a40a8090

1,615

py

Python

utils/fundoptutils.py

joshualee155/FundOptimizer

da842de6c99f89c767d03c9ef1b392237b726a3f

[ "MIT" ]

2

2021-01-03T00:46:51.000Z

2021-09-01T02:48:51.000Z

utils/fundoptutils.py

joshualee155/FundOptimizer

da842de6c99f89c767d03c9ef1b392237b726a3f

[ "MIT" ]

null

utils/fundoptutils.py

joshualee155/FundOptimizer

da842de6c99f89c767d03c9ef1b392237b726a3f

[ "MIT" ]

1

2021-08-28T11:04:00.000Z

import pandas as pd import datetime as dt def str2date( sDate ): """ Convert a string date to datetime.date """ try: dateTime = dt.datetime.strptime( sDate, "%Y%m%d" ) except ValueError: dateTime = dt.datetime.strptime( sDate, "%Y-%m-%d" ) return dateTime.date() def getHolidays( startDate, endDate ): """ Return China exchange holidays ( non-trading days ) from `startDate` to `endDate` """ with open( 'refData/holidays.txt', 'r' ) as f: holidays = f.read().strip().split('\n') holidays = [ date for date in map( str2date, holidays ) if date >= startDate and date <= endDate ] return holidays

30.471698

102

0.596285

c2329a7b9e06911a1ed82d81214f1385b352823d

398

py

Python

Http-api-auth0-jwt/src/list-all-unicorns.py

JimmyDqv/blogs-and-sessions-code

737a2e88f3fd84bd8426be609f7474374d4ac4d6

[ "MIT" ]

2

2021-07-08T10:31:11.000Z

2022-01-07T23:04:31.000Z

Http-api-auth0-jwt/src/list-all-unicorns.py

JimmyDqv/blogs-and-sessions-code

737a2e88f3fd84bd8426be609f7474374d4ac4d6

[ "MIT" ]

null

Http-api-auth0-jwt/src/list-all-unicorns.py

JimmyDqv/blogs-and-sessions-code

737a2e88f3fd84bd8426be609f7474374d4ac4d6

[ "MIT" ]

null

import json

15.92

36

0.371859

c232b3257fa969c4deba44d282906664d6091820

253

py

Python

examples/http_server.py

srossross/uvio

f4d55ad5ea5900a2a8b9c1249484ed621dc30055

[ "MIT" ]

3

2016-03-23T08:12:03.000Z

2018-10-06T02:46:54.000Z

examples/http_server.py

srossross/uvio

f4d55ad5ea5900a2a8b9c1249484ed621dc30055

[ "MIT" ]

null

examples/http_server.py

srossross/uvio

f4d55ad5ea5900a2a8b9c1249484ed621dc30055

[ "MIT" ]

null

import uvio

14.882353

71

0.632411

c2334b533c25c85bcfe5823b2fbd3fe0b9cec5f6

7,933

py

Python

ui/django_site_v2/data_cube_ui/utils/dc_mosaic.py

ceos-seo/Data_Cube_v2

81c3be66153ea123b5d21cf9ec7f59ccb7a2050a

[ "Apache-2.0" ]

27

2016-08-16T18:22:47.000Z

2018-08-25T17:18:15.000Z

ui/django_site_v2/data_cube_ui/utils/dc_mosaic.py

data-cube/CEOS-cube

31baeba08d8e8470c4663c18aaf9056431d9c49f

[ "Apache-2.0" ]

null

ui/django_site_v2/data_cube_ui/utils/dc_mosaic.py

data-cube/CEOS-cube

31baeba08d8e8470c4663c18aaf9056431d9c49f

[ "Apache-2.0" ]

27

2016-08-26T18:14:40.000Z

2021-12-24T08:41:29.000Z

# Copyright 2016 United States Government as represented by the Administrator # of the National Aeronautics and Space Administration. All Rights Reserved. # # Portion of this code is Copyright Geoscience Australia, 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 # # The CEOS 2 platform is licensed under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0. # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import gdal, osr import collections import gc import numpy as np import xarray as xr from datetime import datetime import collections from collections import OrderedDict import datacube from . import dc_utilities as utilities # Author: KMF # Creation date: 2016-06-14 # Modified by: AHDS # Last modified date: def create_mosaic_iterative(dataset_in, clean_mask=None, no_data=-9999, intermediate_product=None): """ Description: Creates a most recent - oldest mosaic of the input dataset. If no clean mask is given, the 'cf_mask' variable must be included in the input dataset, as it will be used to create a clean mask ----- Inputs: dataset_in (xarray.Dataset) - dataset retrieved from the Data Cube; should contain coordinates: time, latitude, longitude variables: variables to be mosaicked If user does not provide a clean_mask, dataset_in must also include the cf_mask variable Optional Inputs: clean_mask (nd numpy array with dtype boolean) - true for values user considers clean; if user does not provide a clean mask, one will be created using cfmask no_data (int/float) - no data pixel value; default: -9999 Output: dataset_out (xarray.Dataset) - mosaicked data with coordinates: latitude, longitude variables: same as dataset_in """ # Create clean_mask from cfmask if none given if clean_mask is None: cfmask = dataset_in.cf_mask clean_mask = utilities.create_cfmask_clean_mask(cfmask) dataset_in = dataset_in.drop('cf_mask') #masks data with clean_mask. all values that are clean_mask==False are set to nodata. for key in list(dataset_in.data_vars): dataset_in[key].values[np.invert(clean_mask)] = no_data if intermediate_product is not None: dataset_out = intermediate_product.copy(deep=True) else: dataset_out = None for index in reversed(range(len(clean_mask))): dataset_slice = dataset_in.isel(time=index).astype("int16").drop('time') if dataset_out is None: dataset_out = dataset_slice.copy(deep=True) #clear out the params as they can't be written to nc. dataset_out.attrs = OrderedDict() else: for key in list(dataset_in.data_vars): dataset_out[key].values[dataset_out[key].values==-9999] = dataset_slice[key].values[dataset_out[key].values==-9999] return dataset_out def create_median_mosaic(dataset_in, clean_mask=None, no_data=-9999, intermediate_product=None): """ Description: Method for calculating the median pixel value for a given dataset. ----- Input: dataset_in (xarray dataset) - the set of data with clouds and no data removed. Optional Inputs: no_data (int/float) - no data value. """ # Create clean_mask from cfmask if none given if clean_mask is None: cfmask = dataset_in.cf_mask clean_mask = utilities.create_cfmask_clean_mask(cfmask) dataset_in = dataset_in.drop('cf_mask') #required for np.nan dataset_in = dataset_in.astype("float64") for key in list(dataset_in.data_vars): dataset_in[key].values[np.invert(clean_mask)] = no_data dataset_out = dataset_in.isel(time=0).drop('time').copy(deep=True) dataset_out.attrs = OrderedDict() # Loop over every key. for key in list(dataset_in.data_vars): dataset_in[key].values[dataset_in[key].values==no_data] = np.nan dataset_out[key].values = np.nanmedian(dataset_in[key].values, axis=0) dataset_out[key].values[dataset_out[key].values==np.nan] = no_data return dataset_out.astype('int16') def create_max_ndvi_mosaic(dataset_in, clean_mask=None, no_data=-9999, intermediate_product=None): """ Description: Method for calculating the pixel value for the max ndvi value. ----- Input: dataset_in (xarray dataset) - the set of data with clouds and no data removed. Optional Inputs: no_data (int/float) - no data value. """ # Create clean_mask from cfmask if none given if clean_mask is None: cfmask = dataset_in.cf_mask clean_mask = utilities.create_cfmask_clean_mask(cfmask) dataset_in = dataset_in.drop('cf_mask') for key in list(dataset_in.data_vars): dataset_in[key].values[np.invert(clean_mask)] = no_data if intermediate_product is not None: dataset_out = intermediate_product.copy(deep=True) else: dataset_out = None for timeslice in range(clean_mask.shape[0]): dataset_slice = dataset_in.isel(time=timeslice).astype("float64").drop('time') ndvi = (dataset_slice.nir - dataset_slice.red) / (dataset_slice.nir + dataset_slice.red) ndvi.values[np.invert(clean_mask)[timeslice,::]] = -1000000000 dataset_slice['ndvi'] = ndvi if dataset_out is None: dataset_out = dataset_slice.copy(deep=True) #clear out the params as they can't be written to nc. dataset_out.attrs = OrderedDict() else: for key in list(dataset_slice.data_vars): dataset_out[key].values[dataset_slice.ndvi.values > dataset_out.ndvi.values] = dataset_slice[key].values[dataset_slice.ndvi.values > dataset_out.ndvi.values] return dataset_out def create_min_ndvi_mosaic(dataset_in, clean_mask=None, no_data=-9999, intermediate_product=None): """ Description: Method for calculating the pixel value for the min ndvi value. ----- Input: dataset_in (xarray dataset) - the set of data with clouds and no data removed. Optional Inputs: no_data (int/float) - no data value. """ # Create clean_mask from cfmask if none given if clean_mask is None: cfmask = dataset_in.cf_mask clean_mask = utilities.create_cfmask_clean_mask(cfmask) dataset_in = dataset_in.drop('cf_mask') for key in list(dataset_in.data_vars): dataset_in[key].values[np.invert(clean_mask)] = no_data if intermediate_product is not None: dataset_out = intermediate_product.copy(deep=True) else: dataset_out = None for timeslice in range(clean_mask.shape[0]): dataset_slice = dataset_in.isel(time=timeslice).astype("float64").drop('time') ndvi = (dataset_slice.nir - dataset_slice.red) / (dataset_slice.nir + dataset_slice.red) ndvi.values[np.invert(clean_mask)[timeslice,::]] = 1000000000 dataset_slice['ndvi'] = ndvi if dataset_out is None: dataset_out = dataset_slice.copy(deep=True) #clear out the params as they can't be written to nc. dataset_out.attrs = OrderedDict() else: for key in list(dataset_slice.data_vars): dataset_out[key].values[dataset_slice.ndvi.values < dataset_out.ndvi.values] = dataset_slice[key].values[dataset_slice.ndvi.values < dataset_out.ndvi.values] return dataset_out

40.682051

173

0.704399

c234a2bf9d847b0178d0e12fe82918d472e89c91

2,014

py

Python

plotter.py

keshavbantu/covclass

e27cfb4ff8e7e6f076c3429aa1c4696e173bc3a4

[ "MIT" ]

null

plotter.py

keshavbantu/covclass

e27cfb4ff8e7e6f076c3429aa1c4696e173bc3a4

[ "MIT" ]

null

plotter.py

keshavbantu/covclass

e27cfb4ff8e7e6f076c3429aa1c4696e173bc3a4

[ "MIT" ]

null

import cleaner as dataStream import plotly.graph_objects as go import plotly.io as pio #DONUT PLOT - CONDITIONS ----------------------------------------- labels = ['Diabetes','Hypertension','Coronary Heart(D)','Chronic Kidney(D)','No Conditions','Obstructive Pulmonary(D)'] values = dataStream.PIEList fig_cond = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)]) #fig_cond.show() pio.write_html(fig_cond, file="templates/cond.html") #GROUP BAR PLOT - SYMPTOMS --------------------------------------- symplabel=['Symptoms'] fig_symp = go.Figure(data=[ go.Bar(name='Fever', x=symplabel, y=dataStream.Fever), go.Bar(name='Cough', x=symplabel, y=dataStream.Cough), go.Bar(name='Breathlessness', x=symplabel, y=dataStream.Breathlessness), go.Bar(name='Severe Acute Respiratory Syndrome', x=symplabel, y=dataStream.SARI), go.Bar(name='Influenza-like Illness', x=symplabel, y=dataStream.ILI), go.Bar(name='Asymptomatic', x=symplabel, y=dataStream.NONE_sym) ]) fig_symp.update_layout(barmode='group') #fig_symp.show() pio.write_html(fig_symp, file="templates/symp.html") #STACK BAR PLOT - AGE DATA ------------------------------------------ fig_age = go.Figure() fig_age.add_trace(go.Bar( y=['0 to 10', '10 to 20', '20 to 30','30 to 40', '40 to 50', '50 to 60','60 to 70', '70 to 80', '80 to 90','90 to 100'], x=dataStream.maleAgeList, name='Male Deaths', orientation='h', marker=dict( color='rgba(61, 112, 242, 0.6)', line=dict(color='rgba(61, 112, 242, 1.0)', width=2) ) )) fig_age.add_trace(go.Bar( y=['0 to 10', '10 to 20', '20 to 30','30 to 40', '40 to 50', '50 to 60','60 to 70', '70 to 80', '80 to 90','90 to 100'], x=dataStream.femaleAgeList, name='Female Deaths', orientation='h', marker=dict( color='rgba(242, 61, 221, 0.6)', line=dict(color='rgba(242, 61, 221, 1.0)', width=2) ) )) fig_age.update_layout(barmode='stack') #fig_age.show() pio.write_html(fig_age, file="templates/age.html")

38

124

0.627607

c235b37d33733193984303077c70e9f3d941faa4

1,847

py

Python

pyglare/scene/objects.py

keyvank/pyglare

9e26ae444ff4481f0f50d7344d2a5a881d04fe64

[ "MIT" ]

6

2017-01-13T22:32:55.000Z

2022-03-27T22:19:49.000Z

pyglare/scene/objects.py

keyvank/pyglare

9e26ae444ff4481f0f50d7344d2a5a881d04fe64

[ "MIT" ]

1

2016-09-13T17:59:41.000Z

2016-09-13T18:05:20.000Z

pyglare/scene/objects.py

keyvank/pyglare

9e26ae444ff4481f0f50d7344d2a5a881d04fe64

[ "MIT" ]

null

from ..math import geometry as geo from ..image.color import Color import math

24.959459

102

0.7634

c235c83aedce86f0591eb9d244db1ef5424b59b5

1,401

py

Python

Led.py

Zico56/raspberry-gt500

85e29ec8bb604fab9c0eb37b63e85b8058baf2b2

[ "Xnet", "X11" ]

null

Led.py

Zico56/raspberry-gt500

85e29ec8bb604fab9c0eb37b63e85b8058baf2b2

[ "Xnet", "X11" ]

null

Led.py

Zico56/raspberry-gt500

85e29ec8bb604fab9c0eb37b63e85b8058baf2b2

[ "Xnet", "X11" ]

null

import time from tkinter import * from PIL import Image, ImageTk from Configuration import config

31.840909

67

0.615989

c236c320912188a01c92278b510292a2d1855a42

249

py

Python

tests/accounts/model/test_social_security_number.py

Hyaxia/Bank-DDD-CQRS-ES

116e3eb3e93d549c1da53e6d506ab47667d77445

[ "MIT" ]

8

2020-10-27T09:46:20.000Z

2022-01-27T12:16:48.000Z

tests/accounts/model/test_social_security_number.py

Hyaxia/Bank-DDD-CQRS-ES

116e3eb3e93d549c1da53e6d506ab47667d77445

[ "MIT" ]

null

tests/accounts/model/test_social_security_number.py

Hyaxia/Bank-DDD-CQRS-ES

116e3eb3e93d549c1da53e6d506ab47667d77445

[ "MIT" ]

2

2021-05-29T08:11:48.000Z

2021-07-26T04:44:53.000Z

import pytest from bank_ddd_es_cqrs.accounts import SocialSecurityNumber

27.666667

90

0.84739

c236d3b1e5bb73ed1d08dc25325aad2b8f8b0b9e

358

py

Python

setup.py

jjakimoto/rl_traders.py

d5411c96d49ba6a54751d12cdd11974e5cc1a8aa

[ "MIT" ]

2

2018-10-07T14:16:32.000Z

2019-01-28T00:14:29.000Z

setup.py

jjakimoto/rl_traders.py

d5411c96d49ba6a54751d12cdd11974e5cc1a8aa

[ "MIT" ]

null

setup.py

jjakimoto/rl_traders.py

d5411c96d49ba6a54751d12cdd11974e5cc1a8aa

[ "MIT" ]

1

2019-11-05T00:51:20.000Z

from setuptools import setup from setuptools import find_packages setup(name='rl_traders', version='0.1.0', description='Reinforcement Learning for Trading', url='https://github.com/jjakimoto/rl_traders.git', author='jjakimoto', author_email='f.j.akimoto@gmail.com', license='MIT', packages=find_packages() )

27.538462

56

0.678771

c239846032333fb5d26b1c1eb5b5c8a5cf233d15

2,219

py

Python

Music/__init__.py

izazkhan8293/Musicheu

9cd33a71868b8b850d6fd78eaac05dda0713b7cc

[ "Apache-2.0" ]

null

Music/__init__.py

izazkhan8293/Musicheu

9cd33a71868b8b850d6fd78eaac05dda0713b7cc

[ "Apache-2.0" ]

null

Music/__init__.py

izazkhan8293/Musicheu

9cd33a71868b8b850d6fd78eaac05dda0713b7cc

[ "Apache-2.0" ]

null

from pyrogram import Client import asyncio from Music.config import API_ID, API_HASH, BOT_TOKEN, MONGO_DB_URI, SUDO_USERS from motor.motor_asyncio import AsyncIOMotorClient as MongoClient import time import uvloop from Music import config import importlib from pyrogram import Client as Bot from Music.config import API_ID, API_HASH, BOT_TOKEN, MONGO_DB_URI, SUDO_USERS, LOG_GROUP_ID, OWNER_ID from pyrogram import Client from aiohttp import ClientSession from motor.motor_asyncio import AsyncIOMotorClient as MongoClient import time initialize() MONGODB_CLI = MongoClient(MONGO_DB_URI) db = MONGODB_CLI.wbb SUDOERS = SUDO_USERS OWNER = OWNER_ID loop = asyncio.get_event_loop() loop.run_until_complete(load_sudoers()) Music_START_TIME = time.time() loop = asyncio.get_event_loop() BOT_ID = 0 BOT_NAME = "" BOT_USERNAME = "" ASSID = 0 ASSNAME = "" ASSUSERNAME = "" ASSMENTION = "" app = Client( 'MusicBot', API_ID, API_HASH, bot_token=BOT_TOKEN, ) aiohttpsession = ClientSession() client = Client(config.SESSION_NAME, config.API_ID, config.API_HASH) app.start() client.start() all_info(app, client)

28.448718

102

0.708878

c23a870064fefb4e740984ad848e886ea4aa0cd9

9,372

py

Python

test.py

ZJianjin/Traffic4cast2020_lds

6cb76e885a9539e485c055222be77f41a559c507

[ "Apache-2.0" ]

3

2020-12-10T13:43:08.000Z

2021-01-17T04:36:34.000Z

test.py

ZJianjin/Traffic4cast2020_lds

6cb76e885a9539e485c055222be77f41a559c507

[ "Apache-2.0" ]

null

test.py

ZJianjin/Traffic4cast2020_lds

6cb76e885a9539e485c055222be77f41a559c507

[ "Apache-2.0" ]

null

import random from random import shuffle import numpy as np import tensorflow as tf from tensorflow.python.tools import freeze_graph import datetime import time import queue import threading import logging from PIL import Image import itertools import yaml import re import os import glob import shutil import sys import copy import h5py from net_all import * from trainer_all import * season = None use_mask = True use_flip = False use_time = True model_name = 'neta' train_winter = ['-01-', '-02-', '-03-'] train_summer = ['-05-', '-04-', '-06-'] test_winter = ['-11-', '-12-'] test_summer = ['-07-', '-08-', '-09-', '-10-'] SEED = 0 num_train_file = 285 num_frame_per_day = 288 num_frame_before = 12 num_frame_sequence = 24 target_frames = [0, 1, 2, 5, 8, 11] num_sequence_per_day = num_frame_per_day - num_frame_sequence + 1 height = 495 width = 436 num_channel = 9 num_channel_discretized = 8 # 4 * 2 visual_input_channels = 115 # 12 * 8 visual_output_channels = 6 * 8 # 6 * 8 vector_input_channels = 1 # start time point import json # n = 1 s = 255 e = 85 w = 170 tv = 16 ##############################Set the path############################################## data_root = './data' model_root = './jianjzhmodelstest' log_root = './output' ##############################Set the path############################################## # target_city = 'ISTANBUL' # ['BERLIN', 'MOSCOW', 'ISTANBUL'] # test_start_index_list = np.array([ 18, 57, 114, 174, 222], np.int32) # 'BERLIN' # test_start_index_list = np.array([ 45, 102, 162, 210, 246], np.int32) # 'Moscow' # 'Istanbul' input_static_data_path = data_root + '/' + target_city + '/' + target_city + '_static_2019.h5' input_mask_data_path = data_root + '/maskdata/' input_train_data_folder_path = data_root + '/' + target_city + '/training' input_val_data_folder_path = data_root + '/' + target_city + '/validation' input_test_data_folder_path = data_root + '/' + target_city + '/testing' save_model_path = model_root + '/' + target_city + str(season) + str(use_flip) + str(use_mask) summary_path = log_root + '/' + target_city + str(season) + str(use_flip) + str(use_mask) # batch_size_test = 5 learning_rate = 3e-4 load_model_path = model_root + '/' + 'ISTANBULneta' # load_model_path = '' is_training = False # premodel = os.path.join(model_root, 'BERLINneta', 'model-58000.cptk') global_step = 60000 if __name__ == '__main__': random.seed(SEED) np.random.seed(SEED) tf.set_random_seed(SEED) trainer = Trainer(height, width, visual_input_channels, visual_output_channels, vector_input_channels, learning_rate, save_model_path, load_model_path, summary_path, is_training, use_mask, model_name) tf.reset_default_graph() test_data_filepath_list = get_data_filepath_list(input_test_data_folder_path) if season == 'winter': tmp = [] for i in test_data_filepath_list: if any([j in i for j in test_winter]): tmp.append(i) data_filepath_list = tmp elif season == 'summer': tmp = [] for i in test_data_filepath_list: if any([j in i for j in test_summer]): tmp.append(i) data_filepath_list = tmp print('test_data_filepath_list\t', len(test_data_filepath_list), ) test_output_filepath_list = list() for test_data_filepath in test_data_filepath_list: filename = test_data_filepath.split('/')[-1] test_output_filepath_list.append('output/' + target_city + '/' + target_city + '_test' + '/' + filename) static_data = get_static_data(input_static_data_path) mask_data = get_mask_data(input_mask_data_path, target_city) try: if not os.path.exists('output'): os.makedirs('output') if not os.path.exists('output/' + target_city): os.makedirs('output/' + target_city) if not os.path.exists('output/' + target_city + '/' + target_city + '_test'): os.makedirs('output/' + target_city + '/' + target_city + '_test') except Exception: print('output path not made') exit(-1) with open('test_data.json') as f: test_json = json.load(f) for i in range(len(test_data_filepath_list)): file_path = test_data_filepath_list[i] out_file_path = test_output_filepath_list[i] fr = h5py.File(file_path, 'r') a_group_key = list(fr.keys())[0] data = fr[a_group_key] # assert data.shape[0] == num_frame_per_day data = np.array(data, np.uint8) test_data_batch_list = [] test_data_time_list = [] test_data_mask_list = [] batch_size_test = data.shape[0] for j in range(batch_size_test): test_data_time_list.append(float(j) / float(num_frame_per_day)) data_sliced = data[:, :, :, :, :num_channel] if use_time: for time_dict in test_json: time_data = list(time_dict.keys())[0] if time_data in file_path: time_data = time_dict[time_data] break time_id = np.ones_like(data_sliced)[:, :, :, :, :1] for m in range(len(time_data)): for n in range(num_frame_before): time_id[m, n] = time_id[m, n] * (time_data[m] + n) / 288.0 * 255.0 data_sliced = np.concatenate([data_sliced, time_id], axis=-1) data_mask = (np.max(data_sliced, axis=4) == 0) test_data_mask_list = data_mask[:, :, :, :] test_data_batch_list.append(data_sliced) test_data_time_list = np.asarray(test_data_time_list, np.float32) input_time = np.reshape(test_data_time_list, (batch_size_test, 1)) test_data_mask = test_data_mask_list input_data = np.concatenate(test_data_batch_list, axis=0).astype(np.float32) input_data[:, :, :, :, :] = input_data[:, :, :, :, :] / 255.0 input_data = np.moveaxis(input_data, 1, -1).reshape((batch_size_test, height, width, -1)) static_data_tmp = np.tile(static_data, [batch_size_test, 1, 1, 1]) input_data = np.concatenate([input_data, static_data_tmp], axis=-1) # input_data_mask = np.zeros((batch_size_test, num_frame_before, height, width, num_channel_discretized), np.bool) # input_data_mask[test_data_mask[:, :num_frame_before, :, :], :] = True # input_data_mask = np.moveaxis(input_data_mask, 1, -1).reshape((batch_size_test, height, width, -1)) # input_data[input_data_mask] = -1.0 true_label_mask = np.ones((batch_size_test, height, width, visual_output_channels), dtype=np.float32) if use_mask: orig_label_mask = np.tile(mask_data, [1, 1, 1, len(target_frames)]) else: orig_label_mask = np.ones((batch_size_test, height, width, visual_output_channels), dtype=np.float32) prediction_list = [] # print(input_data.shape) # assert 0 import scipy.misc as misc # trainer.load_model(premodel) # print('load model') for b in range(batch_size_test): run_out_one = trainer.infer(input_data[b, :, :, :][np.newaxis, :, :, :], input_time[b, :][np.newaxis, :], true_label_mask[b, :, :, :][np.newaxis, :, :, :], global_step) prediction_one = run_out_one['predict'] prediction_list.append(prediction_one) # print(input_data[b,:,:,:].shape) # for t in range(3): # misc.imsave('output_'+str(b)+'_'+str(t)+'.png', np.reshape(prediction_one, [495, 436, 3, 8])[:, :, t, 0]) # assert 0 prediction = np.concatenate(prediction_list, axis=0) prediction = np.moveaxis(np.reshape(prediction, ( batch_size_test, height, width, num_channel_discretized, len(target_frames),)), -1, 1) prediction = prediction.astype(np.float32) * 255.0 prediction = np.rint(prediction) prediction = np.clip(prediction, 0.0, 255.0).astype(np.uint8) assert prediction.shape == (batch_size_test, len(target_frames), height, width, num_channel_discretized) write_data(prediction, out_file_path)

37.94332

122

0.636364

c23bc080151d66518c85923b1ce1c8be7c0ff949

3,037

py

Python

python/python-010/rds.py

suzuxander/suzuxander_samples

736224dae91b432ef3ec796f5eda23417865f142

[ "MIT" ]

null

python/python-010/rds.py

suzuxander/suzuxander_samples

736224dae91b432ef3ec796f5eda23417865f142

[ "MIT" ]

null

python/python-010/rds.py

suzuxander/suzuxander_samples

736224dae91b432ef3ec796f5eda23417865f142

[ "MIT" ]

null

from troposphere import Template, Ref, Parameter, GetAtt from troposphere.ec2 import SecurityGroup from troposphere.rds import DBSubnetGroup, DBInstance if __name__ == '__main__': create_rds_template()

25.957265

66

0.55186

c23f39dcaa9bc21fb37ef18d6de38e47058d0da3

514

py

Python

hlwtadmin/migrations/0044_gigfinderurl_ignore_periods.py

Kunstenpunt/havelovewilltravel

6a27824b4d3d8b1bf19e0bc0d0648f0f4e8abc83

[ "Apache-2.0" ]

1

2020-10-16T16:29:01.000Z

hlwtadmin/migrations/0044_gigfinderurl_ignore_periods.py

Kunstenpunt/havelovewilltravel

6a27824b4d3d8b1bf19e0bc0d0648f0f4e8abc83

[ "Apache-2.0" ]

365

2020-02-03T12:46:53.000Z

2022-02-27T17:20:46.000Z

hlwtadmin/migrations/0044_gigfinderurl_ignore_periods.py

Kunstenpunt/havelovewilltravel

6a27824b4d3d8b1bf19e0bc0d0648f0f4e8abc83

[ "Apache-2.0" ]

null

# Generated by Django 3.0.7 on 2021-01-26 09:57 import django.contrib.postgres.fields from django.db import migrations, models

25.7

126

0.659533

c23fbfd17a95b6fdf7b229efd815b011116431cc

1,855

py

Python

conanfile.py

ltjax/nativefiledialog-extended

2dc958b98f41e081189e84b56f9f250e1b30f92e

[ "Zlib" ]

null

conanfile.py

ltjax/nativefiledialog-extended

2dc958b98f41e081189e84b56f9f250e1b30f92e

[ "Zlib" ]

null

conanfile.py

ltjax/nativefiledialog-extended

2dc958b98f41e081189e84b56f9f250e1b30f92e

[ "Zlib" ]

null

from conans import ConanFile, CMake, tools

40.326087

116

0.654447

c24130645b33d6b4c145bae50da7d266149801e6

960

py

Python

hackerearth/Algorithms/New World/solution.py

ATrain951/01.python-com_Qproject

c164dd093954d006538020bdf2e59e716b24d67c

[ "MIT" ]

4

2020-07-24T01:59:50.000Z

2021-07-24T15:14:08.000Z

hackerearth/Algorithms/New World/solution.py

ATrain951/01.python-com_Qproject

c164dd093954d006538020bdf2e59e716b24d67c

[ "MIT" ]

null

hackerearth/Algorithms/New World/solution.py

ATrain951/01.python-com_Qproject

c164dd093954d006538020bdf2e59e716b24d67c

[ "MIT" ]

null

""" # Sample code to perform I/O: name = input() # Reading input from STDIN print('Hi, %s.' % name) # Writing output to STDOUT # Warning: Printing unwanted or ill-formatted data to output will cause the test cases to fail """ # Write your code here import bisect t = int(input()) for _ in range(t): n, k = map(int, input().strip().split()) stones = list(map(int, input().strip().split())) low = 1 high = stones[-1] - stones[0] # Location of all stones are given in ascending order. while low <= high: mid = (low + high) // 2 if check(stones, mid, n, k): high = mid - 1 else: low = mid + 1 print(low)

24

94

0.527083

c2416fc1e551162c64c074b04f41a960aa792575

712

py

Python

core/departments/urls.py

IvanRch/bsuphys

105715cde8fc8e9a42019ed4b650fe00b94fa132

[ "Apache-2.0" ]

1

2022-01-04T07:04:46.000Z

core/departments/urls.py

IvanRch/bsuphys

105715cde8fc8e9a42019ed4b650fe00b94fa132

[ "Apache-2.0" ]

1

2020-02-17T19:06:03.000Z

core/departments/urls.py

IvanRch/bsuphys

105715cde8fc8e9a42019ed4b650fe00b94fa132

[ "Apache-2.0" ]

1

2021-07-08T13:21:04.000Z

from django.urls import path, re_path from . import views app_name = "departments" urlpatterns = [path("", views.departmentList, name="energy department"), path( "<slug:department>/", views.department_detail, name="department_detail", ), path( "<slug:department>/staff/", views.department_detail_staff, name="department_detail", ), path( "<slug:department>/thesis/", views.department_detail_thesis, name="department_detail", ), path( "<slug:department>/directions/", views.department_detail_directions, name="department_detail", ), ]

25.428571

72

0.573034

c2456834188f5aaff78f04e88343303b398c8b26

432

py

Python

18th May Assignments/case study 1/question_5.py

JangirSumit/data_science

a1957122f8a4c66e3b4c7b7c93a74c53a2db1fe4

[ "MIT" ]

15

2019-05-05T04:48:42.000Z

2022-02-15T12:08:33.000Z

18th May Assignments/case study 1/question_5.py

JangirSumit/data_science

a1957122f8a4c66e3b4c7b7c93a74c53a2db1fe4

[ "MIT" ]

null

18th May Assignments/case study 1/question_5.py

JangirSumit/data_science

a1957122f8a4c66e3b4c7b7c93a74c53a2db1fe4

[ "MIT" ]

53

2019-11-10T05:09:25.000Z

2022-03-28T01:26:32.000Z

# 5. How do you Count The Number Of Times Each Value Appears In An Array Of Integers? # [0, 5, 4, 0, 4, 4, 3, 0, 0, 5, 2, 1, 1, 9] # Answer should be array([4, 2, 1, 1, 3, 2, 0, 0, 0, 1]) which means 0 comes 4 times, 1 comes 2 times, 2 comes 1 time, 3 comes 1 time and so on. array = [0, 5, 4, 0, 4, 4, 3, 0, 0, 5, 2, 1, 1, 9] count_array_elements = [array.count(a) for a in set(array)] print(count_array_elements)

43.2

144

0.601852

c246213af42c94a04ec5e4409ced7cd800cf77ef

4,957

py

Python

pvp-tic-tac-toe.py

lsfidelis/pvp-tic-tac-toe

6ed73e5a053cc22a092c7b56d774503f57a859ab

[ "MIT" ]

null

pvp-tic-tac-toe.py

lsfidelis/pvp-tic-tac-toe

6ed73e5a053cc22a092c7b56d774503f57a859ab

[ "MIT" ]

null

pvp-tic-tac-toe.py

lsfidelis/pvp-tic-tac-toe

6ed73e5a053cc22a092c7b56d774503f57a859ab

[ "MIT" ]

null

from time import sleep print("Welcome to Tic Tac Toe! \nWe'll be playing in a sec, but, first..") general_board = {'7': ' ', '8': ' ', '9': ' ', '4': ' ', '5': ' ', '6': ' ', '1': ' ', '2': ' ', '3': ' '} # prints board structure # Choose which player goes first # Clear the board and reset the game if __name__ == "__main__": game() restart()

35.92029

89

0.435747

c24663b502469b48e008fb30a563fba0b901fd18

7,119

py

Python

total_tolles_ferleihsystem/auth_providers/ldap_auth_provider.py

spethso/Verleihsystem-TTF

39179f9ac5b07f5106e555f82f3c9011d33805bd

[ "MIT" ]

1

2019-03-17T08:11:14.000Z

total_tolles_ferleihsystem/auth_providers/ldap_auth_provider.py

spethso/Verleihsystem-TTF

39179f9ac5b07f5106e555f82f3c9011d33805bd

[ "MIT" ]

60

2018-06-12T14:46:50.000Z

2020-11-16T00:50:37.000Z

total_tolles_ferleihsystem/auth_providers/ldap_auth_provider.py

FIUS/ttf-backend

39179f9ac5b07f5106e555f82f3c9011d33805bd

[ "MIT" ]

1

2019-12-02T19:25:59.000Z

""" Auth Providers which provides LDAP login """ from typing import List, Dict from ldap3 import Connection, Server, AUTO_BIND_TLS_BEFORE_BIND, SUBTREE from ldap3.core.exceptions import LDAPSocketOpenError, LDAPBindError from ..login import LoginProvider from .. import APP, AUTH_LOGGER

47.46

118

0.61975

c247338889dd4aef3193b428e74aac5424652e3f

4,117

py

Python

md2html.py

osfans/yancheng

1f5cec75c8d97006f8b2ee4b1b36b7dc78930ef0

[ "Apache-2.0" ]

4

2017-01-26T03:25:24.000Z

2019-04-15T14:11:46.000Z

md2html.py

osfans/yancheng

1f5cec75c8d97006f8b2ee4b1b36b7dc78930ef0

[ "Apache-2.0" ]

1

2016-12-02T04:26:31.000Z

2016-12-05T05:02:39.000Z

md2html.py

osfans/xu

1f5cec75c8d97006f8b2ee4b1b36b7dc78930ef0

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import re, os, glob template = """ <!doctype html> <html> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes"> <style> body { font-family: PMingLiu, HanaMinA, HanaMinB, Helvetica, arial, sans-serif; writing-mode: vertical-rl; -webkit-writing-mode: vertical-rl; } .sm { margin: 20px 0 10px; padding: 0; font-weight: bold; font-size: 30px; border-left: 1px solid #cccccc; margin: 0 5px; cursor: text; position: static; clear: both; text-align: right; } .sd, .sd2, .zy, .zi, .zi1, .yi { font-size: 10px; text-align: center; cursor: text; float: left; margin-left: 10px; margin-right: 10px; line-height: 10px; letter-spacing: 0.35em; } .sd, .sd2 { margin-right: 25px; clear: both; } .sd2 { margin-right: 20px; } .zi, .zi1 { padding-top: 20px; padding-bottom: 10px; font-size: 20px; line-height: 20px; } .zi1 { padding-top: 10px; } .yi { min-height: 40px; text-align: left; line-height: 12px; margin-right: 8px; } .clear { clear: both; } </style> <title></title> </head> <body> %s </body> </html> """ lines = list() copy_readme() for filename in glob.glob("wiki/??.md"): md2html(filename)

24.360947

120

0.459072

c2478e02ca506e0323d992197336faa1570d5c97

2,460

py

Python

plugin_hide_run_panel/__init__.py

Holt59/modorganizer-python_plugins

f3404b1c3d9b8f5a6aa2133b47f7fc0218c18dc9

[ "MIT" ]

null

plugin_hide_run_panel/__init__.py

Holt59/modorganizer-python_plugins

f3404b1c3d9b8f5a6aa2133b47f7fc0218c18dc9

[ "MIT" ]

null

plugin_hide_run_panel/__init__.py

Holt59/modorganizer-python_plugins

f3404b1c3d9b8f5a6aa2133b47f7fc0218c18dc9

[ "MIT" ]

null

# -*- encoding: utf-8 -*- from PyQt5.QtCore import QCoreApplication from PyQt5.QtWidgets import QMainWindow, QFrame import mobase

31.948052

85

0.660976

c24ab458d07762596a9a0b958ea5ceac2489021a

164

py

Python

nnet/learning_rate_func/__init__.py

zhaoyan1117/NeuralNet

a0343dd469e981bf9b4f18db0209ca9bfaf58c4f

[ "BSD-2-Clause" ]

null

nnet/learning_rate_func/__init__.py

zhaoyan1117/NeuralNet

a0343dd469e981bf9b4f18db0209ca9bfaf58c4f

[ "BSD-2-Clause" ]

null

nnet/learning_rate_func/__init__.py

zhaoyan1117/NeuralNet

a0343dd469e981bf9b4f18db0209ca9bfaf58c4f

[ "BSD-2-Clause" ]

null

from ._inv_prop_lr import InvPropLR from ._constant_lr import ConstantLR from ._step_size_lr import StepSizeLR from ._dynamic_step_size_lr import DynamicStepSizeLR

32.8

52

0.878049

c24d4c5a8f9125c9ef834c785c10d1d380869f30

8,645

py

Python

src/utils/strava.py

adrigrillo/endomondo-strava-migrator

398ff4a0db4a8a5a3a4f0d8fb53157ffeeb88079

[ "MIT" ]

2

2020-12-08T20:51:38.000Z

2021-01-03T20:42:10.000Z

src/utils/strava.py

adrigrillo/endomondo-strava-migrator

398ff4a0db4a8a5a3a4f0d8fb53157ffeeb88079

[ "MIT" ]

1

2020-12-08T21:09:50.000Z

2020-12-08T21:30:35.000Z

src/utils/strava.py

adrigrillo/endomondo-strava-migrator

398ff4a0db4a8a5a3a4f0d8fb53157ffeeb88079

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ utils/strava.py ================= Utility class to Strava API """ import json import time from configparser import ConfigParser, NoOptionError from datetime import datetime from pathlib import Path from typing import Tuple from loguru import logger from stravalib import Client, exc from utils.parameters import SECRET from utils.constants import CONFIG_PATH, CODE_ID_FILE_NAME, TOKEN_FILE_NAME from utils.files_handler import check_folder from utils.parameters import STRAVA, CLIENT_ID def get_client_id(app_config: ConfigParser) -> int: """ Obtains the client ID from the configuration file. Args: app_config (ConfigParser): app configuration. Returns: int: client id from the configuration file. Raises: NoOptionError: If the `client_id` key is not present in the configuration. ValueError: If the client id is not an integer. """ try: client_id = app_config.getint(STRAVA, CLIENT_ID) except NoOptionError: raise ValueError('The client id has not been set in the configuration.') except ValueError: logger.exception('Invalid client id format.') raise return client_id def get_secret(app_config: ConfigParser) -> str: """ Obtains the secret from the configuration file. Args: app_config (ConfigParser): app configuration. Returns: str: secret from the configuration file. Raises: NoOptionError: If the `secret` key is not present in the configuration. """ try: secret = app_config.get(STRAVA, SECRET) except NoOptionError: raise ValueError('The client id has not been set in the configuration.') return secret def get_strava_token_from_code_id(config: ConfigParser) -> str: """ Method that interchange the temporary authentication code obtained when `src/request_auth.py` is executed. The method reads the file `config/code_id.txt` that contains the temporal authentication and generates the POST request to obtain the final access token which is saved in `config/token.json`. This method requires the Strava application `client_id` and `secret` that has to be set in the configuration file (`config/config.ini`). Args: config (ConfigParser): app configuration. Returns: str: Strava access token. Raises: ValueError: If no token is found in the configuration. """ code_id_path = Path(CONFIG_PATH, CODE_ID_FILE_NAME) if not code_id_path.is_file(): raise ValueError('The file with the temporal authentication code (`config/code_id.txt`)' 'was NOT found. Execute `request_auth.py` to obtain the temporal access.') with open(code_id_path, 'r') as file: logger.debug('The file with the temporal authentication code (`config/code_id.txt`)' 'was found.') code_id = file.read() if not code_id: raise ValueError('No valid temporal code access found. Rerun `request_auth.py` ' 'to obtain the temporal access.') client = Client() token = client.exchange_code_for_token(client_id=get_client_id(config), client_secret=get_secret(config), code=code_id) logger.debug('Obtained access until {}:\n' '- token: {}.' '- refresh token: {}.', datetime.utcfromtimestamp(int(token['expires_at'])).strftime('%d-%m-%Y %H:%M:%S'), token['access_token'], token['refresh_token']) # Save JSON with the response save_path = Path(check_folder(CONFIG_PATH), TOKEN_FILE_NAME) with open(save_path, 'w') as file: logger.info('Writing token information to `{}`.', save_path) json.dump(token, file, indent=4) return token['access_token'] def get_strava_client(config: ConfigParser) -> Client: """ Checks the authentication token and generates the Strava client. Args: config (ConfigParser): app configuration. Returns: if exist, strava client configured with the authentication token. """ token_file_path = Path(check_folder(CONFIG_PATH), TOKEN_FILE_NAME) if token_file_path.is_file(): logger.debug('The token info file (`config/token.json`) was found.') with open(token_file_path, 'r') as file: token_data = json.load(file) token = token_data.get('access_token') # If the file exists but no access token found, check against the temporary auth if not token: logger.warning('The token info file (`config/token.json`) was found' ' but the access token could not be read.') token = get_strava_token_from_code_id(config) else: logger.info('The token info file (`config/token.json`) was NOT found. ' 'Retrieving from the temporal authentication code.') token = get_strava_token_from_code_id(config) client = Client(access_token=token) return client def upload_activity(client: Client, activity_type: str, file_path: Path) -> bool: """ Helper method to upload the activity to Strava. This method will handle the different possibilities when uploading an activity. Args: client (Client): configured Strava client. activity_type (str): Strava activity string. file_path (Path): Path to the `*.tcx` activity file. Returns: bool: True if the activity have been uploaded successfully. False otherwise. Raises: RateLimitExceeded: When the API limits have been reached. Generally when more than 1000 petitions have been done during the day. ConnectionError: When it has been impossible to connect the Strava servers. Exception: Unknown exceptions that will be logged in detail. """ try: activity_file = open(file_path, 'r') client.upload_activity( activity_file=activity_file, data_type='tcx', activity_type=activity_type, private=False ) except exc.ActivityUploadFailed: logger.exception('Error uploading the activity `{}`.', file_path.stem) return False except exc.RateLimitExceeded: logger.exception('Exceeded the API rate limit.') raise except ConnectionError: logger.exception('No internet connection.') raise except Exception: logger.exception('Unknown exception') raise # If no error return true logger.debug('Activity `{}` uploaded sucessfully.', file_path.stem) return True def handle_rate_limit(start_time: float, requests: int) -> Tuple[float, int]: """ Method to handle the 15 minutes API limit. This method will check the elapsed time since the first request and the number of them. Three cases are possible: - Less than 15 minutes elapsed from the first request and less than 100 requests -> continue. - More than 15 minutes elapsed from the first request and less than 100 requests -> reset timer and request number to count from 0 again. - Less than 15 minutes elapsed from the first request but more than 100 requests -> sleep until the 15 minutes block is over and reset timer and request number to count from 0 again. Args: start_time (float): timestamp of the first request of the block. requests (int): number of request done in the block. Returns: float, int: updated start time and number of requests following the possible cases. """ requests += 1 elapsed_time = time.time() - start_time if elapsed_time <= 60 * 15: if requests >= 100: remaining_time_stopped = 60 * 15 - elapsed_time mins, secs = divmod(remaining_time_stopped, 60) logger.warning('The number of allowed request per 15 minutes have' 'been reached. Sleeping for {:0.0f} minutes, {:0.1f} seconds.', mins, secs) time.sleep(remaining_time_stopped) # Reset values. Include petition to be processed logger.info('Waiting time elapsed. Continuing with the process.') requests = 1 start_time = time.time() else: logger.debug('15 minutes have been elapsed. Resetting requests and time.') # Reset values. Include petition to be processed requests = 1 start_time = time.time() return start_time, requests

36.020833

99

0.65587

df9a160281e97721997326dd0b0903a52cd73273

5,293

py

Python

train_synthText.py

skyatmoon/Detailed-Handwriting-detection

1eb7ba8087290cbdd3fbc2c092fbdbc2b715fc9c

[ "MIT" ]

1

2020-12-08T01:24:34.000Z

train_synthText.py

skyatmoon/Detailed-Handwriting-detection

1eb7ba8087290cbdd3fbc2c092fbdbc2b715fc9c

[ "MIT" ]

null

train_synthText.py

skyatmoon/Detailed-Handwriting-detection

1eb7ba8087290cbdd3fbc2c092fbdbc2b715fc9c

[ "MIT" ]

null

""" Author: brooklyn train with synthText """ import torch import torch.nn as nn import torch.optim as optim import torchvision.transforms as transforms import os from net.craft import CRAFT import sys from utils.cal_loss import cal_synthText_loss from dataset.synthDataset import SynthDataset import argparse from eval import eval_net device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') parser = argparse.ArgumentParser(description='CRAFT Train Fine-Tuning') parser.add_argument('--gt_path', default='/media/brooklyn/EEEEE142EEE10425/SynthText/gt.mat', type=str, help='SynthText gt.mat') parser.add_argument('--synth_dir', default='/media/brooklyn/EEEEE142EEE10425/SynthText', type=str, help='SynthText image dir') parser.add_argument('--label_size', default=96, type=int, help='target label size') parser.add_argument('--batch_size', default=16, type=int, help='training data batch size') parser.add_argument('--test_batch_size', default=16, type=int, help='test data batch size') parser.add_argument('--test_interval', default=40, type=int, help='test interval') parser.add_argument('--max_iter', default=50000, type=int, help='max iteration') parser.add_argument('--lr', default=0.0001, type=float, help='initial learning rate') parser.add_argument('--epochs', default=500, type=int, help='training epochs') parser.add_argument('--test_iter', default=10, type=int, help='test iteration') args = parser.parse_args() image_transform = transforms.Compose([ transforms.Resize((args.label_size * 2, args.label_size * 2)), transforms.ToTensor() ]) label_transform = transforms.Compose([ transforms.Resize((args.label_size,args.label_size)), transforms.ToTensor() ]) if __name__ == "__main__": batch_size = args.batch_size test_batch_size = args.test_batch_size epochs = args.epochs # lr = args.lr # test_interval = args.test_interval # max_iter = args.max_iter net = CRAFT(pretrained=True) # craft net = net.to(device) model_save_prefix = 'checkpoints/craft_netparam_' try: train(net=net, batch_size=batch_size, test_batch_size=test_batch_size, lr=lr, test_interval=test_interval, max_iter=max_iter, epochs=epochs, model_save_path=model_save_prefix) except KeyboardInterrupt: torch.save(net.state_dict(), 'INTERRUPTED1.pth') print('Saved interrupt') try: sys.exit(0) except SystemExit: os._exit(0)

37.807143

128

0.642736

df9b4ebedd02514962424a1cc0a1b5aae502b670

1,896

py

Python

friendcircle/models.py

jossafossa/Project24_backend

bb5cc91d21c9f93034b85b3e94e829f7ab33c565

[ "MIT" ]

null

friendcircle/models.py

jossafossa/Project24_backend

bb5cc91d21c9f93034b85b3e94e829f7ab33c565

[ "MIT" ]

9

2019-12-04T23:15:59.000Z

2022-02-10T09:08:38.000Z

friendcircle/models.py

jossafossa/Project24_backend

bb5cc91d21c9f93034b85b3e94e829f7ab33c565

[ "MIT" ]

null

from django.db import models # Keeps track of FriendCircle memberships MATCH_STATUS = ( ('O', 'Not swiped',), ('V', 'Swiped Right',), ('X', 'Swiped Left',), ) # Keeps track of matches. If both parties swiped right, the user can be added to FriendCircleMembership

35.773585

103

0.68038

df9d6d03fbed45db8f46a22336474ebb4831783c

474

py

Python

components/collector/tests/source_collectors/jira/test_issues.py

m-zakeri/quality-time

531931f0d8d4f5d262ea98445868158e41d268da

[ "Apache-2.0" ]

null

components/collector/tests/source_collectors/jira/test_issues.py

m-zakeri/quality-time

531931f0d8d4f5d262ea98445868158e41d268da

[ "Apache-2.0" ]

null

components/collector/tests/source_collectors/jira/test_issues.py

m-zakeri/quality-time

531931f0d8d4f5d262ea98445868158e41d268da

[ "Apache-2.0" ]

null

"""Unit tests for the Jira issues collector.""" from .base import JiraTestCase

29.625

78

0.679325

df9e429f72ebf0471ad51a2d2296ecb2934b944d

1,485

py

Python

cf_xarray/tests/test_coding.py

rcaneill/cf-xarray

210e997ab5e550e411ec1a4e789aac28e77bacff

[ "Apache-2.0" ]

null

cf_xarray/tests/test_coding.py

rcaneill/cf-xarray

210e997ab5e550e411ec1a4e789aac28e77bacff

[ "Apache-2.0" ]

null

cf_xarray/tests/test_coding.py

rcaneill/cf-xarray

210e997ab5e550e411ec1a4e789aac28e77bacff

[ "Apache-2.0" ]

null

import numpy as np import pandas as pd import pytest import xarray as xr import cf_xarray as cfxr

34.534884

80

0.60404

dfa1fd750ebe41f9f5e5dbc785b717257dc70d9d

1,908

py

Python

slack_bolt/adapter/socket_mode/base_handler.py

hirosassa/bolt-python

befc3a1463f3ac8dbb780d66decc304e2bdf3e7a

[ "MIT" ]

504

2020-08-07T05:02:57.000Z

2022-03-31T14:32:46.000Z

slack_bolt/adapter/socket_mode/base_handler.py

hirosassa/bolt-python

befc3a1463f3ac8dbb780d66decc304e2bdf3e7a

[ "MIT" ]

560

2020-08-07T01:16:06.000Z

2022-03-30T00:40:56.000Z

slack_bolt/adapter/socket_mode/base_handler.py

hirosassa/bolt-python

befc3a1463f3ac8dbb780d66decc304e2bdf3e7a

[ "MIT" ]

150

2020-08-07T09:41:14.000Z

2022-03-30T04:54:51.000Z

"""The base class of Socket Mode client implementation. If you want to build asyncio-based ones, use `AsyncBaseSocketModeHandler` instead. """ import logging import signal import sys from threading import Event from slack_sdk.socket_mode.client import BaseSocketModeClient from slack_sdk.socket_mode.request import SocketModeRequest from slack_bolt import App from slack_bolt.util.utils import get_boot_message

32.896552

101

0.678197

dfa2ba545c720071817fb0691cb4e7c5aad3c2a5

8,344

py

Python

project/pfasst/transfer_tools.py

amit17133129/pyMG-2016

b82a60811bb0a8b91d8793c47177a240221f9176

[ "BSD-2-Clause" ]

2

2016-04-04T15:20:50.000Z

2020-08-01T19:28:55.000Z

project/pfasst/transfer_tools.py

amit17133129/pyMG-2016

b82a60811bb0a8b91d8793c47177a240221f9176

[ "BSD-2-Clause" ]

1

2020-10-02T05:44:45.000Z

project/pfasst/transfer_tools.py

amit17133129/pyMG-2016

b82a60811bb0a8b91d8793c47177a240221f9176

[ "BSD-2-Clause" ]

11

2016-03-26T18:37:06.000Z

2020-10-01T19:44:55.000Z

# coding=utf-8 import numpy as np import scipy.interpolate as intpl import scipy.sparse as sprs def to_sparse(D, format="csc"): """ Transform dense matrix to sparse matrix of return_type bsr_matrix(arg1[, shape, dtype, copy, blocksize]) Block Sparse Row matrix coo_matrix(arg1[, shape, dtype, copy]) A sparse matrix in COOrdinate format. csc_matrix(arg1[, shape, dtype, copy]) Compressed Sparse Column matrix csr_matrix(arg1[, shape, dtype, copy]) Compressed Sparse Row matrix dia_matrix(arg1[, shape, dtype, copy]) Sparse matrix with DIAgonal storage dok_matrix(arg1[, shape, dtype, copy]) Dictionary Of Keys based sparse matrix. lil_matrix(arg1[, shape, dtype, copy]) Row-based linked list sparse matrix :param D: Dense matrix :param format: how to save the sparse matrix :return: sparse version """ if format == "bsr": return sprs.bsr_matrix(D) elif format == "coo": return sprs.coo_matrix(D) elif format == "csc": return sprs.csc_matrix(D) elif format == "csr": return sprs.csr_matrix(D) elif format == "dia": return sprs.dia_matrix(D) elif format == "dok": return sprs.dok_matrix(D) elif format == "lil": return sprs.lil_matrix(D) else: return to_dense(D) def next_neighbors_periodic(p, ps, k, T=None): """ This function gives for a value p the k points next to it which are found in in the vector ps and the points which are found periodically. :param p: value :param ps: ndarray, vector where to find the next neighbors :param k: integer, number of neighbours :return: ndarray, with the k next neighbors and an array containing the """ if T is None: T = ps[-1]-2*ps[0]+ps[1] p_bar = p - np.floor(p/T)*T ps = ps - ps[0] distance_to_p = [] for tk in ps: d1 = tk+T-p_bar d2 = tk-p_bar d3 = tk-T-p_bar min_d = min([np.abs(d1), np.abs(d2), np.abs(d3)]) if np.abs(d1) == min_d: distance_to_p.append(d1) elif np.abs(d2) == min_d: distance_to_p.append(d2) else: distance_to_p.append(d3) distance_to_p = np.asarray(distance_to_p) value_index = [] for d,i in zip(distance_to_p, range(distance_to_p.size)): value_index.append((d, i)) # sort by distance value_index_sorted_by_abs = sorted(value_index,cmp=lambda x,y:cmp(np.abs(x),np.abs(y)), key=lambda s: s[0]) if k % 2 == 1: value_index_sorted_by_sign =sorted(value_index_sorted_by_abs[0:k+1], key=lambda s: s[0])[:k] else: value_index_sorted_by_sign =sorted(value_index_sorted_by_abs[0:k], key=lambda s: s[0]) return map(lambda s: s[1], value_index_sorted_by_sign), map(lambda s: s[0]+p, value_index_sorted_by_sign) def next_neighbors(p, ps, k): """ This function gives for a value p the k points next to it which are found in in the vector ps :param p: value :param ps: ndarray, vector where to find the next neighbors :param k: integer, number of neighbours :return: ndarray, with the k next neighbors """ distance_to_p = np.abs(ps-p) # zip it value_index = [] for d,i in zip(distance_to_p, range(distance_to_p.size)): value_index.append((d,i)) # sort by distance value_index_sorted = sorted(value_index, key=lambda s: s[0]) # take first k indices with least distance and sort them return sorted(map(lambda s: s[1], value_index_sorted[0:k])) def restriction_matrix_1d(fine_grid, coarse_grid, k=2, return_type="csc", periodic=False, T=1.0): """ We construct the restriction matrix between two 1d grids, using lagrange interpolation. :param fine_grid: a one dimensional 1d array containing the nodes of the fine grid :param coarse_grid: a one dimensional 1d array containing the nodes of the coarse grid :param k: order of the restriction :return: a restriction matrix """ M = np.zeros((coarse_grid.size, fine_grid.size)) n_g = coarse_grid.size for i, p in zip(range(n_g), coarse_grid): if periodic: nn, cont_arr = next_neighbors_periodic(p, fine_grid, k, T) circulating_one = np.asarray([1.0]+[0.0]*(k-1)) lag_pol = [] for l in range(k): lag_pol.append(intpl.lagrange(cont_arr, np.roll(circulating_one, l))) M[i, nn] = np.asarray(map(lambda x: x(p), lag_pol)) else: nn = next_neighbors(p, fine_grid, k) # construct the lagrange polynomials for the k neighbors circulating_one = np.asarray([1.0]+[0.0]*(k-1)) lag_pol = [] for l in range(k): lag_pol.append(intpl.lagrange(fine_grid[nn], np.roll(circulating_one, l))) M[i, nn] = np.asarray(map(lambda x: x(p), lag_pol)) return to_sparse(M, return_type) def interpolation_matrix_1d(fine_grid, coarse_grid, k=2, return_type="csc", periodic=False, T=1.0): """ We construct the interpolation matrix between two 1d grids, using lagrange interpolation. :param fine_grid: a one dimensional 1d array containing the nodes of the fine grid :param coarse_grid: a one dimensional 1d array containing the nodes of the coarse grid :param k: order of the restriction :return: a interpolation matrix """ M = np.zeros((fine_grid.size, coarse_grid.size)) n_f = fine_grid.size for i, p in zip(range(n_f), fine_grid): if periodic: nn,cont_arr = next_neighbors_periodic(p, coarse_grid, k, T) circulating_one = np.asarray([1.0]+[0.0]*(k-1)) lag_pol = [] for l in range(k): lag_pol.append(intpl.lagrange(cont_arr, np.roll(circulating_one, l))) M[i, nn] = np.asarray(map(lambda x: x(p), lag_pol)) else: nn = next_neighbors(p, coarse_grid, k) # construct the lagrange polynomials for the k neighbors circulating_one = np.asarray([1.0]+[0.0]*(k-1)) lag_pol = [] for l in range(k): lag_pol.append(intpl.lagrange(coarse_grid[nn], np.roll(circulating_one, l))) M[i, nn] = np.asarray(map(lambda x: x(p), lag_pol)) return to_sparse(M, return_type) def kron_on_list(matrix_list): """ :param matrix_list: a list of sparse matrices :return: a matrix """ if len(matrix_list) == 2: return sprs.kron(matrix_list[0], matrix_list[1]) elif len(matrix_list) == 1: return matrix_list[0] else: return sprs.kron(matrix_list[0], kron_on_list(matrix_list[1:])) def interpolate_to_t_end(nodes_on_unit, values): """ Assume a GaussLegendre nodes, we are interested in the value at the end of the interval, but we now only the values in the interior of the interval. We compute the value by legendre interpolation. :param nodes_on_unit: nodes transformed to the unit interval :param values: values on those nodes :return: interpolation to the end of the interval """ n = nodes_on_unit.shape[0] circulating_one = np.asarray([1.0]+[0.0]*(n-1)) lag_pol = [] result = np.zeros(values[0].shape) for i in range(n): lag_pol.append(intpl.lagrange(nodes_on_unit, np.roll(circulating_one, i))) result += values[i]*lag_pol[-1](1.0) return result

36.920354

111

0.628715

dfa3a2fa2289a9c892b09c29ede2ebe39a3dd0c8

7,266

py

Python

python/trees/rbtree_graphviz.py

rcanepa/cs-fundamentals

b362fc206417501e53a5739df1edf7568901eef8

[ "MIT" ]

null

python/trees/rbtree_graphviz.py

rcanepa/cs-fundamentals

b362fc206417501e53a5739df1edf7568901eef8

[ "MIT" ]

null

python/trees/rbtree_graphviz.py

rcanepa/cs-fundamentals

b362fc206417501e53a5739df1edf7568901eef8

[ "MIT" ]

null

"""rbtree_graphviz.py - create a graphviz representation of a LLRBT. The purpose of this module is to visually show how the shape of a LLRBT changes when keys are inserted in it. For every insert, sub graph (tree) is added to the main graph. `initialization_list` holds the values that are inserted in the tree. This list can be changed for a list of anything that can be compared with > == <. For example, with `initialization_list = range(50)` keys from 0 to 49 will be inserted in the tree. Consider that for every key, a graph is going to be generated. """ from graphviz import Digraph from trees.rbtree import LLRBT, is_red NODE_SHAPE = "circle" NONE_NODE_SHAPE = "point" TITLE_SHAPE = "box" RED_COLOR = "#b8000f" DEFAULT_GRAPH_NODE_ATTR = { "shape": NODE_SHAPE, "color": "black", "style": "filled", "fillcolor": "#cfd3d6", } RED_NODE_ATTR = { "fontcolor": "white", "fillcolor": RED_COLOR } DEFAULT_GRAPH_EDGE_ATTR = { "color": "black", "arrowhead": "vee", "style": "solid", } def add_node(graph, node): """Add `node` to `graph`. `node` is a tuple with the following shape: (node_id, {<node attributes>}, {<graph's node attributes>}) ^ ^ ^ string see graphviz documentation""" node_id, node_attr, graph_node_attr = node graph.node(node_id, **node_attr, **graph_node_attr) return graph def add_edge(graph, edge): """Add edge from `edge[0]` to `edge[1]` to `graph`. `edge` is a tuple with the following shape: (source_node_id, destiny_node_id, {<graph's edge attributes>}) ^ ^ ^ string string see graphviz documentation""" source_node_id, destiny_node_id, graph_edge_attr = edge graph.edge(source_node_id, destiny_node_id, **graph_edge_attr) return graph if __name__ == "__main__": initialization_list = ["Z", "W", "F", "D", "S", "E", "A", "R", "C", "H", "X", "M", "P", "L"] # initialization_list = ["A", "B", "C", "D"] tree = LLRBT() # graph = generate_graph(tree, initialization_list) graph = generate_graph_per_insert(tree, initialization_list) print(graph.source) graph.render("trees/rbtree.gv", view=True)

38.648936

100

0.597991

dfa5cb1adcaf33702f7f2c3cd145a0c36382a865

97

py

Python

students/K33421/Samoshchenkov_Alexei/lr_2/hw_system/apps.py

Vivasus/ITMO_ICT_WebDevelopment_2020-2021

833d7ac1d40d9f7456a3c6b104a0e53c652d0035

[ "MIT" ]

null

students/K33421/Samoshchenkov_Alexei/lr_2/hw_system/apps.py

Vivasus/ITMO_ICT_WebDevelopment_2020-2021

833d7ac1d40d9f7456a3c6b104a0e53c652d0035

[ "MIT" ]

null

students/K33421/Samoshchenkov_Alexei/lr_2/hw_system/apps.py

Vivasus/ITMO_ICT_WebDevelopment_2020-2021

833d7ac1d40d9f7456a3c6b104a0e53c652d0035

[ "MIT" ]

null

from django.apps import AppConfig

16.166667

34

0.721649

dfa771b70b06ebdb06698f8a6ef450643663f3e9

5,721

py

Python

azure_sftp_plugin/hooks/adls_gen2_hook.py

christo-olivier/airflow_azure_sftp_plugin

1d483be6419744909af2fa940cd50880ed8d5890

[ "Apache-2.0" ]

null

azure_sftp_plugin/hooks/adls_gen2_hook.py

christo-olivier/airflow_azure_sftp_plugin

1d483be6419744909af2fa940cd50880ed8d5890

[ "Apache-2.0" ]

null

azure_sftp_plugin/hooks/adls_gen2_hook.py

christo-olivier/airflow_azure_sftp_plugin

1d483be6419744909af2fa940cd50880ed8d5890

[ "Apache-2.0" ]

null

from pathlib import Path from typing import Generator from airflow.hooks.base_hook import BaseHook from azure.storage.filedatalake import FileSystemClient from azure.storage.filedatalake._generated.models._models_py3 import ( StorageErrorException, )

39.729167

86

0.632582

dfa9f05edc79136d5654d284a464ccb459169f40

536

py

Python

phr/ciudadano/migrations/0044_ciudadano_codigo_asegurado.py

richardqa/django-ex

e5b8585f28a97477150ac5daf5e55c74b70d87da

[ "CC0-1.0" ]

null

phr/ciudadano/migrations/0044_ciudadano_codigo_asegurado.py

richardqa/django-ex

e5b8585f28a97477150ac5daf5e55c74b70d87da

[ "CC0-1.0" ]

null

phr/ciudadano/migrations/0044_ciudadano_codigo_asegurado.py

richardqa/django-ex

e5b8585f28a97477150ac5daf5e55c74b70d87da

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.3 on 2017-07-14 17:10 from __future__ import unicode_literals from django.db import migrations, models

26.8

109

0.654851

dfad270ef93b37ed6df9bcf779f6cf41ac7ec78e

2,499

py

Python

graphtiny/service.py

Canicio/pyqtgraph-tiny

b88ebe8a2e6ad860ca4857b527adccbbde14851d

[ "MIT" ]

1

2018-03-17T12:36:56.000Z

graphtiny/service.py

Canicio/pyqtgraph-tiny

b88ebe8a2e6ad860ca4857b527adccbbde14851d

[ "MIT" ]

1

2017-08-08T18:31:31.000Z

graphtiny/service.py

Canicio/graphtiny

b88ebe8a2e6ad860ca4857b527adccbbde14851d

[ "MIT" ]

null

from time import sleep import pyqtgraph as pg import threading from graphtiny.api import IChart, IDataStreamWindow from graphtiny.domain import DataStreamWindow, Chart

34.232877

95

0.612645

dfad2ce40cf4b3e7c6bdab613bdf207aa9161bc1

2,576

py

Python

backend/app/app/api/deps.py

totalhack/zillion-web

e567c04d3564aec8105d54533d318b79d943c9c6

[ "MIT" ]

3

2020-10-01T11:28:02.000Z

2020-10-31T15:35:51.000Z

backend/app/app/api/deps.py

totalhack/zillion-web

e567c04d3564aec8105d54533d318b79d943c9c6

[ "MIT" ]

1

2022-02-09T04:19:20.000Z

2022-02-09T13:56:40.000Z

backend/app/app/api/deps.py

totalhack/zillion-web

e567c04d3564aec8105d54533d318b79d943c9c6

[ "MIT" ]

null

from typing import Generator, Dict, Any from fastapi import Depends, HTTPException, status from fastapi.security import OAuth2PasswordBearer from jose import jwt from pydantic import ValidationError from sqlalchemy.orm import Session from tlbx import json, pp from zillion.configs import load_warehouse_config, zillion_config from zillion.model import Warehouses from zillion.warehouse import Warehouse from app import app from app import crud, models, schemas from app.core import security from app.core.config import settings from app.db.session import SessionLocal reusable_oauth2 = OAuth2PasswordBearer( tokenUrl=f"{settings.API_V1_STR}/login/access-token" ) warehouses = {} def get_warehouses() -> Dict[str, Any]: """NOTE: this assumes Zillion Web DB is same as Zillion DB""" global warehouses if warehouses: # TODO: cache control? return warehouses print("Building warehouses...") db = SessionLocal() try: result = db.query(Warehouses).all() for row in result: warehouses[row.id] = Warehouse.load(row.id) pp(warehouses) return warehouses finally: db.close() def get_current_user( db: Session = Depends(get_db), token: str = Depends(reusable_oauth2) ) -> models.User: try: payload = jwt.decode( token, settings.SECRET_KEY, algorithms=[security.ALGORITHM] ) token_data = schemas.TokenPayload(**payload) except (jwt.JWTError, ValidationError): raise HTTPException( status_code=status.HTTP_403_FORBIDDEN, detail="Could not validate credentials", ) user = crud.user.get(db, id=token_data.sub) if not user: raise HTTPException(status_code=404, detail="User not found") return user def get_current_active_user( current_user: models.User = Depends(get_current_user), ) -> models.User: if not crud.user.is_active(current_user): raise HTTPException(status_code=400, detail="Inactive user") return current_user def get_current_active_superuser( current_user: models.User = Depends(get_current_user), ) -> models.User: if not crud.user.is_superuser(current_user): raise HTTPException( status_code=400, detail="The user doesn't have enough privileges" ) return current_user

27.115789

77

0.69604

dfb0e4025d32f8743112eeea3ef16b5393035552

212

py

Python

BufferStockModel/run.py

bbardoczy/ConsumptionSavingNotebooks

91811f784ec61fe2f11f8c9e0e172d085574f57c

[ "MIT" ]

1

2022-03-09T14:43:29.000Z

BufferStockModel/run.py

bbardoczy/ConsumptionSavingNotebooks

91811f784ec61fe2f11f8c9e0e172d085574f57c

[ "MIT" ]

null

BufferStockModel/run.py

bbardoczy/ConsumptionSavingNotebooks

91811f784ec61fe2f11f8c9e0e172d085574f57c

[ "MIT" ]

null

from BufferStockModel import BufferStockModelClass updpar = dict() updpar["Np"] = 1500 updpar["Nm"] = 1500 updpar["Na"] = 1500 model = BufferStockModelClass(name="baseline",solmethod="egm",**updpar) model.test()

26.5

71

0.745283

dfb2125e655f351b14d7a2e313cfea92c5b3d51d

4,629

py

Python

pcie_bw.py

pcie-bench/pcie-model

5bb1a71684c51f4bbbab2b9673c6bbc3dcf57b11

[ "Apache-2.0" ]

30

2018-12-05T22:02:26.000Z

2022-03-13T17:09:51.000Z

pcie_bw.py

pcie-bench/pcie-model

5bb1a71684c51f4bbbab2b9673c6bbc3dcf57b11

[ "Apache-2.0" ]

null

pcie_bw.py

pcie-bench/pcie-model

5bb1a71684c51f4bbbab2b9673c6bbc3dcf57b11

[ "Apache-2.0" ]

13

2018-12-28T14:31:48.000Z

2022-02-25T11:24:36.000Z

#! /usr/bin/env python3 # ## Copyright (C) 2015-2018 Rolf Neugebauer. All rights reserved. ## Copyright (C) 2015 Netronome Systems, Inc. All rights reserved. ## ## Licensed under the Apache License, Version 2.0 (the "License"); ## you may not use this file except in compliance with the License. ## You may obtain a copy of the License at ## ## http://www.apache.org/licenses/LICENSE-2.0 ## ## Unless required by applicable law or agreed to in writing, software ## distributed under the License is distributed on an "AS IS" BASIS, ## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ## See the License for the specific language governing permissions and ## limitations under the License. """A simple script to generate data for PCIe and ethernet bandwidth estimates""" import sys from optparse import OptionParser from model import pcie, eth, mem_bw # pylint: disable=too-many-locals OUT_FILE = "pcie_bw.dat" def main(): """Main""" usage = """usage: %prog [options]""" parser = OptionParser(usage) parser.add_option('--mps', dest='MPS', type="int", action='store', default=256, help='Set the maximum payload size of the link') parser.add_option('--mrrs', dest='MRRS', type="int", action='store', default=512, help='Set the maximum read request size of the link') parser.add_option('--rcb', dest='RCB', type="int", action='store', default=64, help='Set the read completion boundary of the link') parser.add_option('--lanes', dest='lanes', type="string", action='store', default='x8', help='Set num lanes (x2, x4, x8, x16, or x32)') parser.add_option('--gen', dest='gen', type="string", action='store', default='gen3', help='Set PCIe version (gen1, gen2, gen3, gen4, or gen5)') parser.add_option('--addr', dest='addr', type="int", action='store', default=64, help='Set the number of address bits (32 or 64)') parser.add_option('--ecrc', dest='ecrc', type="int", action='store', default=0, help='Use ECRC (0 or 1)') parser.add_option('-o', '--outfile', dest='FILE', default=OUT_FILE, action='store', help='File where to write the data to') (options, _) = parser.parse_args() pciecfg = pcie.Cfg(version=options.gen, lanes=options.lanes, addr=options.addr, ecrc=options.ecrc, mps=options.MPS, mrrs=options.MRRS, rcb=options.RCB) print("PCIe Config:") pciecfg.pp() ethcfg = eth.Cfg('40GigE') tlp_bw = pciecfg.TLP_bw bw_spec = pcie.BW_Spec(tlp_bw, tlp_bw, pcie.BW_Spec.BW_RAW) dat = open(options.FILE, "w") dat.write("\"Payload(Bytes)\" " "\"PCIe Write BW\" " "\"PCIe Write Trans/s\" " "\"PCIe Read BW\" " "\"PCIe Read Trans/s\" " "\"PCIe Read/Write BW\" " "\"PCIe Read/Write Trans/s\" " "\"40G Ethernet BW\" " "\"40G Ethernet PPS\" " "\"40G Ethernet Frame time (ns)\" " "\n") for size in range(1, 1500 + 1): wr_bw = mem_bw.write(pciecfg, bw_spec, size) rd_bw = mem_bw.read(pciecfg, bw_spec, size) rdwr_bw = mem_bw.read_write(pciecfg, bw_spec, size) wr_trans = (wr_bw.tx_eff * 1000 * 1000 * 1000 / 8) / size rd_trans = (rd_bw.rx_eff * 1000 * 1000 * 1000 / 8) / size rdwr_trans = (rdwr_bw.tx_eff * 1000 * 1000 * 1000 / 8) / size if size >= 64: eth_bw = ethcfg.bps_ex(size) / (1000 * 1000 * 1000.0) eth_pps = ethcfg.pps_ex(size) eth_lat = 1.0 * 1000 * 1000 * 1000 / eth_pps dat.write("%d %.2f %.1f %.2f %.1f %.2f %.1f %.2f %d %.2f\n" % (size, wr_bw.tx_eff, wr_trans, rd_bw.rx_eff, rd_trans, rdwr_bw.tx_eff, rdwr_trans, eth_bw, eth_pps, eth_lat)) else: dat.write("%d %.2f %.1f %.2f %.1f %.2f %.1f\n" % (size, wr_bw.tx_eff, wr_trans, rd_bw.rx_eff, rd_trans, rdwr_bw.tx_eff, rdwr_trans)) dat.close() if __name__ == '__main__': sys.exit(main())

38.575

80

0.534241

dfb68a5201db3b2abf55a2e729e1d1531d27950c

77

py

Python

src/buildercore/external.py

elifesciences/builder

161829686f777f7ac7f97bd970395886ba5089c1

[ "MIT" ]

11

2017-03-01T18:00:30.000Z

2021-12-10T05:11:02.000Z

src/buildercore/external.py

elifesciences/builder

161829686f777f7ac7f97bd970395886ba5089c1

[ "MIT" ]

397

2016-07-08T14:39:46.000Z

2022-03-30T12:45:09.000Z

src/buildercore/external.py

elifesciences/builder

161829686f777f7ac7f97bd970395886ba5089c1

[ "MIT" ]

14

2016-07-13T08:33:28.000Z

2020-04-22T21:42:21.000Z

import subprocess

15.4

39

0.779221

dfb696c1a314cee61ccd51a38771b72300f8407a

648

py

Python

Round 2/data_packing.py

kamyu104/GoogleCodeJam-2014

ff29a677f502168eb0b92d6928ad6983d2622017

[ "MIT" ]

10

2016-04-10T22:50:54.000Z

2021-04-17T18:17:02.000Z

Round 2/data_packing.py

kamyu104/GoogleCodeJam-2014

ff29a677f502168eb0b92d6928ad6983d2622017

[ "MIT" ]

null

Round 2/data_packing.py

kamyu104/GoogleCodeJam-2014

ff29a677f502168eb0b92d6928ad6983d2622017

[ "MIT" ]

10

2016-07-19T08:43:38.000Z

2021-07-22T22:38:44.000Z

# Copyright (c) 2021 kamyu. All rights reserved. # # Google Code Jam 2014 Round 2 - Problem A. Data Packing # https://codingcompetitions.withgoogle.com/codejam/round/0000000000432fed/0000000000432b8d # # Time: O(NlogN) # Space: O(1) # for case in xrange(input()): print 'Case #%d: %s' % (case+1, data_packing())

24.923077

91

0.598765

dfb71ae9c49c8ec75050dd6031ca98dd54f66f9f

18,950

py

Python

BiModNeuroCNN/training/bimodal_classification.py

cfcooney/BiModNeuroCNN

f79da6150b4186bcbc15d876394f4af8a47076d0

[ "MIT" ]

4

2020-10-31T21:20:12.000Z

2022-01-05T16:13:07.000Z

BiModNeuroCNN/training/bimodal_classification.py

cfcooney/BiModNeuroCNN

f79da6150b4186bcbc15d876394f4af8a47076d0

[ "MIT" ]

null

BiModNeuroCNN/training/bimodal_classification.py

cfcooney/BiModNeuroCNN

f79da6150b4186bcbc15d876394f4af8a47076d0

[ "MIT" ]

null

""" Description: Class for training CNNs using a nested cross-validation method. Train on the inner_fold to obtain optimized hyperparameters. Train outer_fold to obtain classification performance. """ from braindecode.datautil.iterators import BalancedBatchSizeIterator from braindecode.experiments.stopcriteria import MaxEpochs, NoDecrease, Or from braindecode.torch_ext.util import set_random_seeds, np_to_var, var_to_np from braindecode.datautil.signal_target import SignalAndTarget from braindecode.torch_ext.functions import square, safe_log import torch as th from sklearn.model_selection import train_test_split from BiModNeuroCNN.training.training_utils import current_acc, current_loss from BiModNeuroCNN.data_loader.data_utils import smote_augmentation, multi_SignalAndTarget from BiModNeuroCNN.results.results import Results as res from torch.nn.functional import nll_loss, cross_entropy from BiModNeuroCNN.training.bimodal_training import Experiment import numpy as np import itertools as it import torch from torch import optim import logging from ast import literal_eval from BiModNeuroCNN.results.metrics import cross_entropy import warnings warnings.filterwarnings("ignore", category=UserWarning) log = logging.getLogger(__name__) torch.backends.cudnn.deterministic = True

57.95107

154

0.644063

dfb822e8f7cafa7cb423cc71ade94b740d42328b

7,462

py

Python

simple_task_repeater/str_app.py

lavrpetrov/simple-task-repeater

cd56ed52143ac31171fc757c6e1f7740bebe1ed4

[ "MIT" ]

null

simple_task_repeater/str_app.py

lavrpetrov/simple-task-repeater

cd56ed52143ac31171fc757c6e1f7740bebe1ed4

[ "MIT" ]

null

simple_task_repeater/str_app.py

lavrpetrov/simple-task-repeater

cd56ed52143ac31171fc757c6e1f7740bebe1ed4

[ "MIT" ]

1

2021-04-20T15:38:44.000Z

import datetime from collections import Counter from functools import wraps from dateparser import parse as parse_date from calmlib import get_current_date, get_current_datetime, to_date, trim from .base import Task from .str_database import STRDatabase from .telegram_bot import TelegramBot, command, catch_errors DEFAULT_PERIOD = 4 TASK_PER_DAY_LIMIT = 3 def run(self): with self.db: super().run() def actualize_tasks(self): if self._last_actualize_date < get_current_date(): self._actualize_tasks() self._last_actualize_date = get_current_date() def _actualize_tasks(self): """ Go over all tasks and update date/reschedule """ for user in self.db.user_names: for task in self.db.get_users_tasks(user): today = get_current_datetime() while to_date(task.date) < to_date(today): if task.reschedule: # if task is past due and to be rescheduled - reschedule it on today task.date = today else: task.date += datetime.timedelta(days=task.period) self.db.update_task(task)

32.163793

107

0.590592

dfb8674c6f7746d9692d1c11fcd1c8fdb24ebb98

258

py

Python

Strings/conversion-operation.py

tverma332/python3

544c4ec9c726c37293c8da5799f50575cc50852d

[ "MIT" ]

3

2022-03-28T09:10:08.000Z

2022-03-29T10:47:56.000Z

Strings/conversion-operation.py

tverma332/python3

544c4ec9c726c37293c8da5799f50575cc50852d

[ "MIT" ]

1

2022-03-27T11:52:58.000Z

Strings/conversion-operation.py

tverma332/python3

544c4ec9c726c37293c8da5799f50575cc50852d

[ "MIT" ]

null

# lower , title , upper operations on string x = "spider" y = "MAN" v=x.upper() # all letters will become uppercase w=y.lower() # all letters will become lowercase z=y.title() # only first letter will become upper and rest of all lowercase print(v,w,z)

19.846154

75

0.705426

dfb9067db6876e985a83eb3d9d6219b06ce32b30

1,197

py

Python

setup.py

adadesions/sfcpy

d395218ae9f72fed378c30ad604923373b7fbf3f

[ "MIT" ]

2

2019-08-28T19:30:32.000Z

2020-03-28T16:17:01.000Z

setup.py

adadesions/sfcpy

d395218ae9f72fed378c30ad604923373b7fbf3f

[ "MIT" ]

5

2021-03-18T22:53:57.000Z

2022-03-11T23:42:38.000Z

setup.py

adadesions/sfcpy

d395218ae9f72fed378c30ad604923373b7fbf3f

[ "MIT" ]

null

"""Setup script for sfcpy""" import os.path from setuptools import setup # The directory containing this file HERE = os.path.abspath(os.path.dirname(__file__)) # The text of the README file with open(os.path.join(HERE, "README.md"), encoding='utf-8') as fid: README = fid.read() # This call to setup() does all the work setup( name="sfcpy", version="1.2.3", description="Space-Filling Curve library for image-processing tasks", long_description=README, long_description_content_type="text/markdown", url="https://github.com/adadesions/sfcpy", author="adadesions", author_email="adadesions@gmail.com", license="MIT", classifiers=[ "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", ], packages=["sfcpy"], include_package_data=True, tests_require=['pytest'], install_requires=[ "numpy", "matplotlib", "Pillow" ], entry_points={"console_scripts": ["sfcpy=sfcpy.__main__:main"]}, )

29.925

73

0.652464

dfb94390c72e2b9eb210dfba78b3240cd00784e2

7,921

py

Python

make_DigitalCommons_spreadsheet.py

lsulibraries/CWBR_DigitalCommons

6eb994d08d6de088075cde82f6dc2b3aed15bdda

[ "Apache-2.0" ]

null

make_DigitalCommons_spreadsheet.py

lsulibraries/CWBR_DigitalCommons

6eb994d08d6de088075cde82f6dc2b3aed15bdda

[ "Apache-2.0" ]

null

make_DigitalCommons_spreadsheet.py

lsulibraries/CWBR_DigitalCommons

6eb994d08d6de088075cde82f6dc2b3aed15bdda

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import csv import os from collections import namedtuple import string from nameparser import HumanName if __name__ == '__main__': issues_dict = csv_to_dict('3rdStageSourceCSVs/Interviews.csv') make_csv_data(issues_dict)

36.004545

112

0.550562

dfb9a74f5e09588db5c20e479a0c85f0735ce76b

7,524

py

Python

pip_services3_redis/cache/RedisCache.py

pip-services-python/pip-services-redis-python

ecb2e667ab266af0274b0891a19e802cb256766a

[ "MIT" ]

null

pip_services3_redis/cache/RedisCache.py

pip-services-python/pip-services-redis-python

ecb2e667ab266af0274b0891a19e802cb256766a

[ "MIT" ]

null

pip_services3_redis/cache/RedisCache.py

pip-services-python/pip-services-redis-python

ecb2e667ab266af0274b0891a19e802cb256766a

[ "MIT" ]

null

# -*- coding: utf-8 -*- from typing import Optional, Any import redis from pip_services3_commons.config import IConfigurable, ConfigParams from pip_services3_commons.errors import ConfigException, InvalidStateException from pip_services3_commons.refer import IReferenceable, IReferences from pip_services3_commons.run import IOpenable from pip_services3_components.auth import CredentialResolver from pip_services3_components.cache import ICache from pip_services3_components.connect import ConnectionResolver

37.064039

158

0.636895

dfbade8328cd7332030b49fd40ed470582f05c91

7,392

py

Python

main/model/property.py

lipis/gae-init-magic

6b1e0b50f8e5200cb2dacebca9ac65e796b241a9

[ "MIT" ]

1

2018-10-26T13:33:20.000Z

main/model/property.py

lipis/gae-init-magic

6b1e0b50f8e5200cb2dacebca9ac65e796b241a9

[ "MIT" ]

652

2018-10-26T12:28:08.000Z

2021-08-02T09:13:48.000Z

main/model/property.py

lipis/gae-init-magic

6b1e0b50f8e5200cb2dacebca9ac65e796b241a9

[ "MIT" ]

null

# coding: utf-8 from __future__ import absolute_import from google.appengine.ext import ndb from api import fields import model import util FIELDS = { 'auto_now': fields.Boolean, 'auto_now_add': fields.Boolean, 'autofocus': fields.Boolean, 'choices': fields.String, 'default': fields.String, 'description': fields.String, 'email_filter': fields.Boolean, 'field_property': fields.String, 'forms_property': fields.String, 'kind': fields.String, 'name': fields.String, 'ndb_property': fields.String, 'placeholder': fields.String, 'rank': fields.Integer, 'readonly': fields.Boolean, 'repeated': fields.Boolean, 'required': fields.Boolean, 'sort_filter': fields.Boolean, 'strip_filter': fields.Boolean, 'verbose_name': fields.String, 'wtf_property': fields.String, } FIELDS.update(model.Base.FIELDS)

38.701571

123

0.652056

dfbc302b59b318fa83066ffc6aa91c4caa2533da

1,189

py

Python

tests/test_request.py

pauleveritt/wired_components

a9072d5fc48680d5ff895887842ffd0f06bc0081

[ "MIT" ]

1

2019-09-15T12:30:44.000Z

tests/test_request.py

pauleveritt/wired_components

a9072d5fc48680d5ff895887842ffd0f06bc0081

[ "MIT" ]

null

tests/test_request.py

pauleveritt/wired_components

a9072d5fc48680d5ff895887842ffd0f06bc0081

[ "MIT" ]

null

import pytest from wired import ServiceContainer

31.289474

69

0.764508

dfbd03cf9bf0d42acbc4621a1653916d133bdb8e

958

py

Python

Charts and Graphs/LollipopCharts.py

aprakash7/Buildyourown

58f0530ea84bf9e91f258d947610ea1e93d7d456

[ "MIT" ]

null

Charts and Graphs/LollipopCharts.py

aprakash7/Buildyourown

58f0530ea84bf9e91f258d947610ea1e93d7d456

[ "MIT" ]

null

Charts and Graphs/LollipopCharts.py

aprakash7/Buildyourown

58f0530ea84bf9e91f258d947610ea1e93d7d456

[ "MIT" ]

1

2021-05-31T04:20:54.000Z

# -*- coding: utf-8 -*- """ Created on Mon May 17 21:24:53 2021 @author: Akshay Prakash """ import pandas as pd import numpy as np import matplotlib.pyplot as plt table = pd.read_csv(r'\1617table.csv') table.head() plt.hlines(y= np.arange(1, 21), xmin = 0, xmax = table['Pts'], color = 'skyblue') plt.plot(table['Pts'], np.arange(1,21), "o") plt.yticks(np.arange(1,21), table['team']) plt.show() teamColours = ['#034694','#001C58','#5CBFEB','#D00027', '#EF0107','#DA020E','#274488','#ED1A3B', '#000000','#091453','#60223B','#0053A0', '#E03A3E','#1B458F','#000000','#53162f', '#FBEE23','#EF6610','#C92520','#BA1F1A'] plt.hlines(y= np.arange(1, 21), xmin = 0, xmax = table['Pts'], color = teamColours) plt.plot(table['Pts'], np.arange(1,21), "o") plt.yticks(np.arange(1,21), table['team']) plt.xlabel('Points') plt.ylabel('Teams') plt.title("Premier league 16/17")

30.903226

84

0.583507

dfbf2ca5c949daa624f3881dc6dcb4567701067b

1,126

py

Python

python/merge-kml-files/merge-kml-files.py

bmaupin/graveyard

71d52fe6589ce13dfe7433906d1aa50df48c9f94

[ "MIT" ]

1

2019-11-23T10:44:58.000Z

python/merge-kml-files/merge-kml-files.py

bmaupin/graveyard

71d52fe6589ce13dfe7433906d1aa50df48c9f94

[ "MIT" ]

8

2020-07-16T07:14:12.000Z

2020-10-14T17:25:33.000Z

python/merge-kml-files/merge-kml-files.py

bmaupin/graveyard

71d52fe6589ce13dfe7433906d1aa50df48c9f94

[ "MIT" ]

1

2019-11-23T10:45:00.000Z

#!/usr/bin/env python import sys import lxml.etree if __name__ == '__main__': main()

31.277778

79

0.599467

dfbf59c5b26596753447f4f968efc9068d24fa0b

3,829

py

Python

tccli/services/partners/v20180321/help.py

tarnover/tencentcloud-cli

5b0537913a33884a20d7663405a8aa1c2276b41a

[ "Apache-2.0" ]

null

tccli/services/partners/v20180321/help.py

tarnover/tencentcloud-cli

5b0537913a33884a20d7663405a8aa1c2276b41a

[ "Apache-2.0" ]

null

tccli/services/partners/v20180321/help.py

tarnover/tencentcloud-cli

5b0537913a33884a20d7663405a8aa1c2276b41a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- DESC = "partners-2018-03-21" INFO = { "AgentPayDeals": { "params": [ { "name": "OwnerUin", "desc": "uin" }, { "name": "AgentPay", "desc": "10" }, { "name": "DealNames", "desc": "" } ], "desc": "/" }, "DescribeAgentBills": { "params": [ { "name": "SettleMonth", "desc": "2018-02" }, { "name": "ClientUin", "desc": "ID" }, { "name": "PayMode", "desc": "prepay/postpay" }, { "name": "OrderId", "desc": "" }, { "name": "ClientRemark", "desc": "" }, { "name": "Offset", "desc": "" }, { "name": "Limit", "desc": "" } ], "desc": "" }, "AgentTransferMoney": { "params": [ { "name": "ClientUin", "desc": "ID" }, { "name": "Amount", "desc": "" } ], "desc": "" }, "DescribeRebateInfos": { "params": [ { "name": "RebateMonth", "desc": "2018-02" }, { "name": "Offset", "desc": "" }, { "name": "Limit", "desc": "" } ], "desc": "" }, "ModifyClientRemark": { "params": [ { "name": "ClientRemark", "desc": "" }, { "name": "ClientUin", "desc": "ID" } ], "desc": "" }, "DescribeAgentClients": { "params": [ { "name": "ClientUin", "desc": "ID" }, { "name": "ClientName", "desc": "" }, { "name": "ClientFlag", "desc": "a/b" }, { "name": "OrderDirection", "desc": "ASC/DESC " }, { "name": "Offset", "desc": "" }, { "name": "Limit", "desc": "" } ], "desc": "" }, "DescribeClientBalance": { "params": [ { "name": "ClientUin", "desc": "()ID" } ], "desc": "" }, "DescribeAgentAuditedClients": { "params": [ { "name": "ClientUin", "desc": "ID" }, { "name": "ClientName", "desc": "" }, { "name": "ClientFlag", "desc": "a/b" }, { "name": "OrderDirection", "desc": "ASC/DESC " }, { "name": "ClientUins", "desc": "ID" }, { "name": "HasOverdueBill", "desc": "01" }, { "name": "ClientRemark", "desc": "" }, { "name": "Offset", "desc": "" }, { "name": "Limit", "desc": "" }, { "name": "ClientType", "desc": "new()/assign()/old()/" }, { "name": "ProjectType", "desc": "self()/platform()/repeat( )/" } ], "desc": "" }, "AuditApplyClient": { "params": [ { "name": "ClientUin", "desc": "ID" }, { "name": "AuditResult", "desc": "accept/reject" }, { "name": "Note", "desc": "B" } ], "desc": "" } }

19.049751

68

0.404022

dfbfef0fe41686291ae36ae72197b63006cb0f9c

83,387

py

Python

src/main/python/lib/default/__init__.py

emilybache/texttest-runner

2d5c42b8d37699a2cbcb8f19af7c271d6ad1024a

[ "MIT" ]

null

src/main/python/lib/default/__init__.py

emilybache/texttest-runner

2d5c42b8d37699a2cbcb8f19af7c271d6ad1024a

[ "MIT" ]

null

src/main/python/lib/default/__init__.py

emilybache/texttest-runner

2d5c42b8d37699a2cbcb8f19af7c271d6ad1024a

[ "MIT" ]

null

""" The default configuration, from which all others should be derived """ import os, plugins, sandbox, console, rundependent, comparetest, batch, performance, subprocess, operator, logging from copy import copy from string import Template from fnmatch import fnmatch from threading import Thread # For back-compatibility from runtest import RunTest, Running, Killed from scripts import *

52.876982

316

0.651193

dfc3450cc6a455bca7329de3130cbc552b8baa62

747

py

Python

2019/10 October/dp10302019.py

vishrutkmr7/DailyPracticeProblemsDIP

d1bfbc75f2024736c22c05385f753a90ddcfa0f5

[ "MIT" ]

5

2019-08-06T02:34:41.000Z

2022-01-08T03:03:16.000Z

2019/10 October/dp10302019.py

ourangzeb/DailyPracticeProblemsDIP

66c07af88754e5d59b243e3ee9f02db69f7c0a77

[ "MIT" ]

15

2021-06-01T14:04:16.000Z

2022-03-08T21:17:22.000Z

2019/10 October/dp10302019.py

ourangzeb/DailyPracticeProblemsDIP

66c07af88754e5d59b243e3ee9f02db69f7c0a77

[ "MIT" ]

4

2019-09-19T20:00:05.000Z

2021-08-16T11:31:51.000Z

# This problem was recently asked by LinkedIn: # Given a non-empty array where each element represents a digit of a non-negative integer, add one to the integer. # The most significant digit is at the front of the array and each element in the array contains only one digit. # Furthermore, the integer does not have leading zeros, except in the case of the number '0'. num = [2, 9, 9] print(Solution().plusOne(num)) # [3, 0, 0]

28.730769

114

0.619813

dfc40c993839966190091cb6ae4333cb9d7b2cc3

1,122

py

Python

kbr/run_utils.py

brugger/kbr-tools

95c8f8274e28b986e7fd91c8404026433488c940

[ "MIT" ]

1

2021-02-02T09:47:40.000Z

kbr/run_utils.py

brugger/kbr-tools

95c8f8274e28b986e7fd91c8404026433488c940

[ "MIT" ]

1

2021-08-04T13:00:00.000Z

kbr/run_utils.py

brugger/kbr-tools

95c8f8274e28b986e7fd91c8404026433488c940

[ "MIT" ]

null

import subprocess import sys import os

23.375

123

0.632799

dfc40d4989f8ef494b36888ba91588827d76ffc5

2,614

py

Python

tests/client/test_files.py

philopon/datapane

d7d69865d4def0cbe6eb334acd9edeb829dd67e6

[ "Apache-2.0" ]

481

2020-04-25T05:40:21.000Z

2022-03-30T22:04:35.000Z

tests/client/test_files.py

tig/datapane

defae6776e73b07191c0a5804a50b284ec3c9a63

[ "Apache-2.0" ]

74

2020-04-28T10:47:35.000Z

2022-03-14T15:50:55.000Z

tests/client/test_files.py

admariner/datapane

c440eaf07bd1c1f2de3ff952e0fd8c78d636aa8f

[ "Apache-2.0" ]

41

2020-07-21T16:30:21.000Z

2022-02-21T22:50:27.000Z

from pathlib import Path import altair as alt import folium import matplotlib.pyplot as plt import numpy as np import pandas as pd import plotly.graph_objects as p_go import pytest from bokeh.layouts import column from bokeh.models import ColumnDataSource from bokeh.plotting import figure from pandas.io.formats.style import Styler from datapane.client.api.files import save data = pd.DataFrame({"x": np.random.randn(20), "y": np.random.randn(20)}) # NOTE - test disabled until pip release of altair_pandas - however should work if altair test passes # NOTE - test disabled updated pip release of pdvega that tracks git upstream - however should work if altair test passes def test_save_table(tmp_path: Path): # tests saving a DF directly to a html file save(data) # save styled table save(Styler(data))

26.948454

121

0.694721

dfc53823fb3adccd40e9762c665f5bb3deecbf27

95

py

Python

instance/config.py

antomuli/News_Highlight

9feb33c0a32fa78cd93f5ab2c74942a8ca281701

[ "Unlicense" ]

2

2020-03-23T23:16:51.000Z

2020-04-26T21:15:11.000Z

instance/config.py

antomuli/News_Highlight

9feb33c0a32fa78cd93f5ab2c74942a8ca281701

[ "Unlicense" ]

null

instance/config.py

antomuli/News_Highlight

9feb33c0a32fa78cd93f5ab2c74942a8ca281701

[ "Unlicense" ]

null

NEWS_API_KEY= '138b22df68394ecbaa9c9af0d0377adb' SECRET_KEY= 'f9bf78b9a18ce6d46a0cd2b0b86df9da'

47.5

48

0.905263

dfc5ea1ec35f681b24bc22174c17b45b8de95235

1,417

py

Python

twirp/logging.py

batchar2/twirpy

e5940a2a038926844098def09748953287071747

[ "Unlicense" ]

51

2020-05-23T22:31:53.000Z

2022-03-08T19:14:04.000Z

twirp/logging.py

batchar2/twirpy

e5940a2a038926844098def09748953287071747

[ "Unlicense" ]

20

2020-05-15T10:20:38.000Z

2022-02-06T23:21:56.000Z

twirp/logging.py

batchar2/twirpy

e5940a2a038926844098def09748953287071747

[ "Unlicense" ]

10

2020-05-29T09:55:49.000Z

2021-10-16T00:14:04.000Z

import os import logging import sys import structlog from structlog.stdlib import LoggerFactory, add_log_level _configured = False def configure(force = False): """ Configures logging & structlog modules Keyword Arguments: force: Force to reconfigure logging. """ global _configured if _configured and not force: return # Check whether debug flag is set debug = os.environ.get('DEBUG_MODE', False) # Set appropriate log level if debug: log_level = logging.DEBUG else: log_level = logging.INFO # Set logging config logging.basicConfig( level = log_level, format = "%(message)s", ) # Configure structlog structlog.configure( logger_factory = LoggerFactory(), processors = [ add_log_level, # Add timestamp structlog.processors.TimeStamper('iso'), # Add stack information structlog.processors.StackInfoRenderer(), # Set exception field using exec info structlog.processors.format_exc_info, # Render event_dict as JSON structlog.processors.JSONRenderer() ] ) _configured = True def get_logger(**kwargs): """ Get the structlog logger """ # Configure logging modules configure() # Return structlog return structlog.get_logger(**kwargs)

22.140625

57

0.628088

dfc68640fe94c25498745f6373d4a8f15e6f9a5f

878

py

Python

setup.py

Arkq/pyexec

ec90b0aaff80996155d033bd722ff59c9259460e

[ "MIT" ]

null

setup.py

Arkq/pyexec

ec90b0aaff80996155d033bd722ff59c9259460e

[ "MIT" ]

null

setup.py

Arkq/pyexec

ec90b0aaff80996155d033bd722ff59c9259460e

[ "MIT" ]

null

# setup.py # Copyright (c) 2015-2017 Arkadiusz Bokowy # # This file is a part of pyexec. # # This project is licensed under the terms of the MIT license. from setuptools import setup import pyexec with open("README.rst") as f: long_description = f.read() setup( name="pyexec", version=pyexec.__version__, author="Arkadiusz Bokowy", author_email="arkadiusz.bokowy@gmail.com", url="https://github.com/Arkq/pyexec", description="Signal-triggered process reloader", long_description=long_description, license="MIT", py_modules=["pyexec"], classifiers=[ "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", "Topic :: Software Development :: Libraries", "Topic :: Utilities", ], )

25.085714

62

0.654897

dfc68f46a49d56c4e9d2e4ea5761354ae3746b5b

323

py

Python

tests/func_eqconstr.py

andyjost/Sprite

7ecd6fc7d48d7f62da644e48c12c7b882e1a2929

[ "MIT" ]

1

2022-03-16T16:37:11.000Z

tests/func_eqconstr.py

andyjost/Sprite

7ecd6fc7d48d7f62da644e48c12c7b882e1a2929

[ "MIT" ]

null

tests/func_eqconstr.py

andyjost/Sprite

7ecd6fc7d48d7f62da644e48c12c7b882e1a2929

[ "MIT" ]

null

'''Functional tests for the equational constraint.''' import cytest # from ./lib; must be first

32.3

96

0.693498

dfc7144f2268699316911b76b5597b6509452a54

4,898

py

Python

data-sources/kbr/authority-persons-marc-to-csv.py

kbrbe/beltrans-data-integration

951ae3941b22a6fe0a8d30079bdf6f4f0a55f092

[ "MIT" ]

null

data-sources/kbr/authority-persons-marc-to-csv.py

kbrbe/beltrans-data-integration

951ae3941b22a6fe0a8d30079bdf6f4f0a55f092

[ "MIT" ]

21

2022-02-14T10:58:52.000Z

2022-03-28T14:04:40.000Z

data-sources/kbr/authority-persons-marc-to-csv.py

kbrbe/beltrans-data-integration

951ae3941b22a6fe0a8d30079bdf6f4f0a55f092

[ "MIT" ]

null

# # (c) 2022 Sven Lieber # KBR Brussels # #import xml.etree.ElementTree as ET import lxml.etree as ET import os import json import itertools import enchant import hashlib import csv from optparse import OptionParser import utils import stdnum NS_MARCSLIM = 'http://www.loc.gov/MARC21/slim' ALL_NS = {'marc': NS_MARCSLIM} # ----------------------------------------------------------------------------- def addAuthorityFieldsToCSV(elem, writer, natWriter, stats): """This function extracts authority relevant data from the given XML element 'elem' and writes it to the given CSV file writer.""" # # extract relevant data from the current record # authorityID = utils.getElementValue(elem.find('./marc:controlfield[@tag="001"]', ALL_NS)) namePerson = utils.getElementValue(elem.find('./marc:datafield[@tag="100"]/marc:subfield[@code="a"]', ALL_NS)) nameOrg = utils.getElementValue(elem.find('./marc:datafield[@tag="110"]/marc:subfield[@code="a"]', ALL_NS)) nationalities = utils.getElementValue(elem.findall('./marc:datafield[@tag="370"]/marc:subfield[@code="c"]', ALL_NS)) gender = utils.getElementValue(elem.find('./marc:datafield[@tag="375"]/marc:subfield[@code="a"]', ALL_NS)) birthDateRaw = utils.getElementValue(elem.find('./marc:datafield[@tag="046"]/marc:subfield[@code="f"]', ALL_NS)) deathDateRaw = utils.getElementValue(elem.find('./marc:datafield[@tag="046"]/marc:subfield[@code="g"]', ALL_NS)) isniRaw = utils.getElementValue(elem.xpath('./marc:datafield[@tag="024"]/marc:subfield[@code="2" and (text()="isni" or text()="ISNI")]/../marc:subfield[@code="a"]', namespaces=ALL_NS)) viafRaw = utils.getElementValue(elem.xpath('./marc:datafield[@tag="024"]/marc:subfield[@code="2" and text()="viaf"]/../marc:subfield[@code="a"]', namespaces=ALL_NS)) countryCode = utils.getElementValue(elem.find('./marc:datafield[@tag="043"]/marc:subfield[@code="c"]', ALL_NS)) (familyName, givenName) = utils.extractNameComponents(namePerson) birthDate = '' deathDate = '' datePatterns = ['%Y', '(%Y)', '[%Y]', '%Y-%m-%d', '%d/%m/%Y', '%Y%m%d'] if birthDateRaw: birthDate = utils.parseDate(birthDateRaw, datePatterns) if deathDateRaw: deathDate = utils.parseDate(deathDateRaw, datePatterns) name = f'{namePerson} {nameOrg}'.strip() if nationalities: nationalityURIString = utils.createURIString(nationalities, ';', 'http://id.loc.gov/vocabulary/countries/') for n in nationalityURIString.split(';'): natWriter.writerow({'authorityID': authorityID, 'nationality': n}) newRecord = { 'authorityID': authorityID, 'name': name, 'family_name': familyName, 'given_name': givenName, 'gender': gender, 'birth_date': birthDate, 'death_date': deathDate, 'isni_id': utils.extractIdentifier(authorityID, f'ISNI {isniRaw}', pattern='ISNI'), 'viaf_id': utils.extractIdentifier(authorityID, f'VIAF {viafRaw}', pattern='VIAF'), 'country_code': countryCode } writer.writerow(newRecord) # ----------------------------------------------------------------------------- def main(): """This script reads an XML file in MARC slim format and extracts several fields to create a CSV file.""" parser = OptionParser(usage="usage: %prog [options]") parser.add_option('-i', '--input-file', action='store', help='The input file containing MARC SLIM XML records') parser.add_option('-o', '--output-file', action='store', help='The output CSV file containing selected MARC fields') parser.add_option('-n', '--nationality-csv', action='store', help='The output CSV file containing the IDs of authorities and their nationality') (options, args) = parser.parse_args() # # Check if we got all required arguments # if( (not options.input_file) or (not options.output_file) or (not options.nationality_csv) ): parser.print_help() exit(1) # # Instead of loading everything to main memory, stream over the XML using iterparse # with open(options.output_file, 'w') as outFile, \ open(options.nationality_csv, 'w') as natFile: stats = {} outputFields = ['authorityID', 'name', 'family_name', 'given_name', 'gender', 'birth_date', 'death_date', 'isni_id', 'viaf_id', 'country_code'] outputWriter = csv.DictWriter(outFile, fieldnames=outputFields, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) outputWriter.writeheader() nationalityWriter = csv.DictWriter(natFile, fieldnames=['authorityID', 'nationality'], delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) nationalityWriter.writeheader() for event, elem in ET.iterparse(options.input_file, events=('start', 'end')): # The parser finished reading one authority record, get information and then discard the record if event == 'end' and elem.tag == ET.QName(NS_MARCSLIM, 'record'): addAuthorityFieldsToCSV(elem, outputWriter, nationalityWriter, stats) main()

43.732143

186

0.682115

dfc7e5a8bbc57e53f20590d631fe2b87c31a1671

3,886

py

Python

promoterz/evaluationPool.py

emillj/gekkoJaponicus

d77c8c7a303b97a3643eb3f3c8b995b8b393f3f7

[ "MIT" ]

null

promoterz/evaluationPool.py

emillj/gekkoJaponicus

d77c8c7a303b97a3643eb3f3c8b995b8b393f3f7

[ "MIT" ]

null

promoterz/evaluationPool.py

emillj/gekkoJaponicus

d77c8c7a303b97a3643eb3f3c8b995b8b393f3f7

[ "MIT" ]

1

2021-11-29T20:18:25.000Z

#!/bin/python import time import random from multiprocessing import Pool, Process, Pipe, TimeoutError from multiprocessing.pool import ThreadPool

33.213675

97

0.587494

dfc9bea7af7becf02c3cd0e4f00d6640fee9f247

3,001

py

Python

website/drawquest/apps/following/models.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

19

2015-11-10T17:36:20.000Z

2021-04-12T07:36:00.000Z

website/drawquest/apps/following/models.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

1

2021-06-09T03:45:34.000Z

website/drawquest/apps/following/models.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

6

2015-11-11T00:38:38.000Z

2020-07-25T20:10:08.000Z

from canvas.cache_patterns import CachedCall from drawquest import knobs from drawquest.apps.drawquest_auth.models import User from drawquest.apps.drawquest_auth.details_models import UserDetails from drawquest.pagination import FakePaginator def _paginate(redis_obj, offset, request=None): ''' items should already start at the proper offset. ''' if offset == 'top': items = redis_obj.zrevrange(0, knobs.FOLLOWERS_PER_PAGE, withscores=True) else: items = redis_obj.zrevrangebyscore('({}'.format(offset), '-inf', start=0, num=knobs.FOLLOWERS_PER_PAGE, withscores=True) try: next_offset = items[-1][1] next_offset = next_offset.__repr__() except IndexError: next_offset = None items = [item for item, ts in items] pagination = FakePaginator(items, offset=offset, next_offset=next_offset) return items, pagination def followers(user, viewer=None, offset='top', direction='next', request=None): """ The users who are following `user`. """ if direction != 'next': raise ValueError("Follwers only supports 'next' - scrolling in one direction.") if request is None or (request.idiom == 'iPad' and request.app_version_tuple <= (3, 1)): user_ids = user.redis.new_followers.zrevrange(0, -1) pagination = None else: user_ids, pagination = _paginate(user.redis.new_followers, offset, request=request) users = UserDetails.from_ids(user_ids) if request is None or request.app_version_tuple < (3, 0): users = _sorted(users) return _for_viewer(users, viewer=viewer), pagination def following(user, viewer=None, offset='top', direction='next', request=None): """ The users that `user` is following. """ if direction != 'next': raise ValueError("Following only supports 'next' - scrolling in one direction.") if request is None or (request.idiom == 'iPad' and request.app_version_tuple <= (3, 1)): user_ids = user.redis.new_following.zrange(0, -1) pagination = None else: user_ids, pagination = _paginate(user.redis.new_following, offset, request=request) users = UserDetails.from_ids(user_ids) if request is None or request.app_version_tuple < (3, 0): users = _sorted(users) return _for_viewer(users, viewer=viewer), pagination

34.102273

92

0.65978

dfcaf8188821bfe0448579c92b86161cf07a8cb5

3,674

py

Python

Python 3/PyGame/Matrix_based_3D/entities.py

DarkShadow4/python

4cd94e0cf53ee06c9c31e9272572ca9656697c30

[ "MIT" ]

null

Python 3/PyGame/Matrix_based_3D/entities.py

DarkShadow4/python

4cd94e0cf53ee06c9c31e9272572ca9656697c30

[ "MIT" ]

null

Python 3/PyGame/Matrix_based_3D/entities.py

DarkShadow4/python

4cd94e0cf53ee06c9c31e9272572ca9656697c30

[ "MIT" ]

1

2020-08-19T17:25:22.000Z

import numpy as np

33.099099

106

0.459717

dfcb6f77810c69d9413f60d8f54a4f595fd87395

65

py

Python

pytreearray/multiply.py

PhilipVinc/netket_dynamics

6e8009098c279271cb0f289ba9e85c039bb284e4

[ "Apache-2.0" ]

2

2021-10-02T20:29:44.000Z

2021-10-02T20:38:28.000Z

pytreearray/multiply.py

PhilipVinc/netket_dynamics

6e8009098c279271cb0f289ba9e85c039bb284e4

[ "Apache-2.0" ]

11

2021-10-01T09:15:06.000Z

2022-03-21T09:19:23.000Z

pytreearray/multiply.py

PhilipVinc/netket_dynamics

6e8009098c279271cb0f289ba9e85c039bb284e4

[ "Apache-2.0" ]

null

from ._src.multiply import multiply_outer as outer # noqa: F401

32.5

64

0.784615

dfcda9e0f1ad0a543490dfbdc63f6f36b102ec00

1,258

py

Python

setup.py

utix/django-json-api

938f78f664a4ecbabb9e678595926d1a580f9d0c

[ "MIT" ]

7

2021-02-26T14:35:17.000Z

2021-02-26T21:21:58.000Z

setup.py

utix/django-json-api

938f78f664a4ecbabb9e678595926d1a580f9d0c

[ "MIT" ]

7

2021-02-26T14:44:30.000Z

2021-06-02T14:27:17.000Z

setup.py

utix/django-json-api

938f78f664a4ecbabb9e678595926d1a580f9d0c

[ "MIT" ]

1

2021-02-26T20:10:42.000Z

#!/usr/bin/env python from os.path import join from setuptools import find_packages, setup # DEPENDENCIES core_deps = requirements_from_pip("requirements.txt") dev_deps = requirements_from_pip("requirements_dev.txt") # DESCRIPTION with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() setup( author="Sharework", author_email="root@sharework.co", description="JSON API specification for Django services", extras_require={"all": dev_deps, "dev": dev_deps}, install_requires=core_deps, long_description=long_description, long_description_content_type="text/markdown", name="django-json-api", package_data={"django_json_api": ["resources/VERSION"]}, packages=find_packages(), python_requires=">=3.8", url="https://github.com/share-work/django-json-api", version=open(join("django_json_api", "resources", "VERSION")).read().strip(), classifiers=[ "Programming Language :: Python :: 3.8", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], )

29.952381

90

0.692369

dfcf9bc6b50b9274d2e45ff7e0b6d1af9920cab0

1,632

py

Python

youtube_dl/extractor/businessinsider.py

MOODesign/Youtube-videos-Download

730c0d12a06f349907481570f1f2890251f7a181

[ "Unlicense" ]

16

2020-12-01T15:26:58.000Z

2022-02-24T23:12:14.000Z

youtube_dl/extractor/businessinsider.py

MOODesign/Youtube-videos-Download

730c0d12a06f349907481570f1f2890251f7a181

[ "Unlicense" ]

5

2021-02-20T10:30:00.000Z

2021-06-01T21:12:31.000Z

youtube_dl/extractor/businessinsider.py

MOODesign/Youtube-videos-Download

730c0d12a06f349907481570f1f2890251f7a181

[ "Unlicense" ]

7

2020-12-01T15:27:04.000Z

2022-01-09T23:21:53.000Z

# coding: utf-8 from __future__ import unicode_literals from .common import InfoExtractor from .jwplatform import JWPlatformIE

37.953488

112

0.571078

dfcfb445e47c75ccbf0dd0f1527b09b9571a8702

578

py

Python

map_house.py

renankalfa/python-0-ao-Data_Scientist

2f61e1cbb1c5565da53cc1cd9aa5c3f5d1cacc88

[ "MIT" ]

1

2022-03-27T23:55:37.000Z

map_house.py

renankalfa/python-0-ao-Data_Scientist

2f61e1cbb1c5565da53cc1cd9aa5c3f5d1cacc88

[ "MIT" ]

null

map_house.py

renankalfa/python-0-ao-Data_Scientist

2f61e1cbb1c5565da53cc1cd9aa5c3f5d1cacc88

[ "MIT" ]

null

import plotly.express as px import pandas as pd data = pd.read_csv('kc_house_data.csv') data_mapa = data[['id', 'lat', 'long', 'price']] grafico1 = px.scatter_mapbox(data_mapa, lat='lat', lon='long', hover_name='id', hover_data=['price'], color_discrete_sequence=['fuchsia'], zoom=3, height=300) grafico1.update_layout(mapbox_style='open-street-map') grafico1.update_layout(height=600, margin={'r': 0, 't': 0, 'l': 0, 'b': 0}) grafico1.show() grafico1.write_html('map_house_rocket.html')

36.125

75

0.610727