anton-l/python_sample_10k · Datasets at Hugging Face

zhaopingsun/RadarGUI

10,943,576,716,043

42f0f788efd42236758e6a1a2946d1d92bcee6c9

06e877c541be92a0ecaeb1ece7fd8556ad99a664

/PyRadar.py

da329631a234e47e5d424fca80add1f627839d29

[]

no_license

https://github.com/zhaopingsun/RadarGUI

ded352e4e000dd0c3d3e2fdaac48b3f4221c5aba

06fbf90aa89f3ed8325337248268859b76634973

refs/heads/master

2020-04-08T11:17:47.778466

2018-08-01T22:56:56

null

0

null

import numpy as np import matplotlib.pyplot as plt from mayavi import mlab class Radar: def __init__(self, path, name): self.AbsolutePath = path + name file = open(self.AbsolutePath, 'rb') file.seek(0) self.RawData = np.array([int(i) for i in file.read()]) self.Name = name[:-4] self.Count = int(len(self.RawData) / 2432) self.RawArray = self.RawData.reshape(self.Count, 2432) self.NumberOfElevation = [self.RawArray[i][44] + self.RawArray[i][45] * 256 for i in range(0, self.Count)] # 层数 self.StartOfReflectivity = [self.RawArray[i][46] + self.RawArray[i][47] * 256 for i in range(0, self.Count)] # 起始距离 self.StartOfSpeed = [self.RawArray[i][48] + self.RawArray[i][49] * 256 for i in range(0, self.Count)] self.StepOfReflectivity = [self.RawArray[i][50] + self.RawArray[i][51] * 256 for i in range(0, self.Count)] # 库长 self.StepOfSpeed = [self.RawArray[i][52] + self.RawArray[i][53] * 256 for i in range(0, self.Count)] self.NumberOfReflectivity = [self.RawArray[i][54] + self.RawArray[i][55] * 256 for i in range(0, self.Count)] # 库数 self.NumberOfSpeed = [self.RawArray[i][56] + self.RawArray[i][57] * 256 for i in range(0, self.Count)] self.PointerOfReflectivity = [self.RawArray[i][64] + self.RawArray[i][65] * 256 for i in range(0, self.Count)] # 数据位置指针 self.PointerOfSpeed = [self.RawArray[i][66] + self.RawArray[i][67] * 256 for i in range(0, self.Count)] self.PointerOfSpectralWidth = [self.RawArray[i][66] + self.RawArray[i][67] * 256 for i in range(0, self.Count)] self.ResolutionOfSpeed = [self.RawArray[i][70] + self.RawArray[i][71] * 256 for i in range(0, self.Count)] # 速度分辨率 self.vcp = [self.RawArray[i][72] + self.RawArray[i][73] * 256 for i in range(0, self.Count)] # 11:降水，16；21：降水，14；31：晴空，8；32：晴空，7. self.Elevation = [(self.RawArray[i][42] + 256 * self.RawArray[i][43]) / 8 * 180 / 4096 for i in range(0, self.Count)] # 仰角 self.Azimuth = [(self.RawArray[i][36] + 256 * self.RawArray[i][37]) / 8 * 180 / 4096 for i in range(0, self.Count)] # 方位角 self.Storage = self.getStorage() self.AllInfo = self.getAllInfo() self.x, self.y, self.z, self.r = self.getXyzr() self.AllInfo = self.getAllInfo() self.space_info = self.get_space_info() self.elevation_list = self.get_elevation_list() def getStorage(self): Storage = [[ [0, 0, [], []], # 反射率，距离 [0, 0, [], []], # 速度，距离 [0, 0, [], []] # 谱宽，距离 ] for i in range(0, self.Count)] for i in range(0, self.Count): Storage[i][0][0] = self.Elevation[i] Storage[i][1][0] = self.Elevation[i] Storage[i][2][0] = self.Elevation[i] Storage[i][0][1] = self.Azimuth[i] Storage[i][1][1] = self.Azimuth[i] Storage[i][2][1] = self.Azimuth[i] for j in range(0, self.NumberOfReflectivity[i]): if self.RawArray[i][self.PointerOfReflectivity[i] + j] != 0 and self.RawArray[i][ self.PointerOfReflectivity[i] + j] != 1 and ( self.RawArray[i][self.PointerOfReflectivity[i] + j] - 2) / 2 - 32 >= 0: Storage[i][0][2].append((self.RawArray[i][self.PointerOfReflectivity[i] + j] - 2) / 2 - 32) else: Storage[i][0][2].append(0) Storage[i][0][3].append(self.StartOfReflectivity[i] + j * self.StepOfReflectivity[i]) for j in range(0, self.NumberOfSpeed[i]): if self.ResolutionOfSpeed[i] == 2: if self.RawArray[i][self.PointerOfSpeed[i] + j] != 0 and self.RawArray[i][ self.PointerOfSpeed[i] + j]: Storage[i][1][2].append((self.RawArray[i][self.PointerOfSpeed[i] + j] - 2) / 2 - 63.5) else: Storage[i][1][2].append(0) if self.ResolutionOfSpeed[i] == 4: if self.RawArray[i][self.PointerOfSpeed[i] + j] != 0 and self.RawArray[i][ self.PointerOfSpeed[i] + j]: Storage[i][1][2].append(self.RawArray[i][self.PointerOfSpeed[i] + j] - 2 - 127) else: Storage[i][1][2].append(0) Storage[i][1][3].append(self.StartOfSpeed[i] + j * self.StepOfSpeed[i]) for j in range(0, self.NumberOfSpeed[i]): if self.RawArray[i][self.PointerOfSpectralWidth[i] + j] != 0 and self.RawArray[i][ self.PointerOfSpectralWidth[i] + j] != 1: Storage[i][2][2].append((self.RawArray[i][self.PointerOfSpectralWidth[i] + j] - 2) / 2 - 63.5) else: Storage[i][2][2].append(0) Storage[i][2][3].append(self.StartOfSpeed[i] + j * self.StepOfSpeed[i]) return Storage def get_space_info(self): AllInfo_ = [[], [], [], []] # 仰角方位角距离反射率 for i in self.Storage: for j in range(0, int(len(i[0][2]))): if 1: # 剔除反射率零点，以[0,0,0,0]代替以不影响矩阵形状 i[0][2][j] > 0 AllInfo_[0].append(i[0][0]) # 仰角 AllInfo_[1].append(i[0][1]) # 方位角 AllInfo_[3].append(i[0][2][j]) # 反射率因子 AllInfo_[2].append(i[0][3][j]) # 距离 AllInfo_[0].append(0) AllInfo_[1].append(0) AllInfo_[2].append(0) AllInfo_[3].append(75) while (len(AllInfo_[0])) % 460 != 0: # 标准化为460倍数（补[0，0，0，0]法） AllInfo_[0].append(0) AllInfo_[1].append(0) AllInfo_[2].append(0) AllInfo_[3].append(0) return AllInfo_ def getAllInfo(self): AllInfo_ = [[], [], [], []] # 仰角方位角距离反射率 for i in self.Storage: # if i[0][0] <= 1 and i[0][0] >= 0: # 设定仰角范围 if 1: for j in range(0, int(len(i[0][2]))): if 1: # 剔除反射率零点，以[0,0,0,0]代替以不影响矩阵形状 i[0][2][j] > 0 AllInfo_[0].append(i[0][0]) # 仰角 AllInfo_[1].append(i[0][1]) # 方位角 AllInfo_[3].append(i[0][2][j]) # 反射率因子 AllInfo_[2].append(i[0][3][j]) # 距离 AllInfo_[0].append(0) AllInfo_[1].append(0) AllInfo_[2].append(0) AllInfo_[3].append(75) while (len(AllInfo_[0])) % 460 != 0: # 标准化为460倍数（补[0，0，0，0]法） AllInfo_[0].append(0) AllInfo_[1].append(0) AllInfo_[2].append(0) AllInfo_[3].append(0) return AllInfo_ def getXyzr(self): Info_1 = np.array(self.AllInfo) x = Info_1[2] * np.cos(np.deg2rad(Info_1[0])) * np.cos(np.deg2rad(Info_1[1])) y = Info_1[2] * np.cos(np.deg2rad(Info_1[0])) * np.sin(np.deg2rad(Info_1[1])) z = Info_1[2] * np.sin(np.deg2rad(Info_1[0])) r = Info_1[3] return x, y, z, r def draw(self): x, y, z, r = self.x, self.y, self.z, self.r plt.style.use('dark_background') plt.subplot(1, 1, 1) plt.title(self.Name) plt.contourf(x.reshape(int(len(x) / 460), 460), y.reshape(int(len(y) / 460), 460), r.reshape(int(len(z) / 460), 460), cmap='jet') # contourf jet gray plt.colorbar() plt.savefig('C:/data/gui/temp/' + self.Name, dpi=300) plt.close() def grey(self): x, y, z, r = self.x, self.y, self.z, self.r plt.style.use('dark_background') plt.subplot(1, 1, 1) plt.title(self.Name) plt.contourf(x.reshape(int(len(x) / 460), 460), y.reshape(int(len(y) / 460), 460), r.reshape(int(len(z) / 460), 460), cmap='gist_gray') # contourf jet gray plt.colorbar() plt.savefig('C:/data/img/Z9592' + self.Name, dpi=300) plt.close() def get_elevation_list(self): if self.vcp[0] == 11: return [0.5, 1.45, 2.4, 3.35, 4.3, 5.2, 6.2, 7.5, 8.7, 10.0, 12.0, 14.0, 16.7, 19.5] if self.vcp[0] == 12: return [0.5, 0.9, 1.3, 1.8, 2.4, 3.1, 4.0, 5.1, 6.4, 8.0, 10.0, 12.5, 15.6, 19.5] if self.vcp[0] == 21: return [0.5, 1.45, 2.4, 3.35, 4.3, 6.0, 9.9, 14.6, 19.5] if self.vcp[0] == 31: return [0.5, 1.5, 2.5, 3.5, 3.5] # 按仰角绘制PPI def ppi(self, elevation): AllInfo = [[], [], [], []] # 仰角方位角距离反射率 for i in self.Storage: if elevation - 0.5 <= i[0][0] <= elevation + 0.5: # 设定仰角范围 for j in range(0, int(len(i[0][2]))): if 1: # 剔除反射率零点，以[0,0,0,0]代替以不影响矩阵形状 i[0][2][j] > 0 AllInfo[0].append(i[0][0]) # 仰角 # print(i[0][0]) AllInfo[1].append(i[0][1]) # 方位角 AllInfo[3].append(i[0][2][j]) # 反射率因子 AllInfo[2].append(i[0][3][j]) # 距离 AllInfo[0].append(0) AllInfo[1].append(0) AllInfo[2].append(0) AllInfo[3].append(75) while (len(AllInfo[0])) % 460 != 0: # 标准化为460倍数（补[0，0，0，0]法） AllInfo[0].append(0) AllInfo[1].append(0) AllInfo[2].append(0) AllInfo[3].append(0) Info_1 = np.array(AllInfo) x = Info_1[2] * np.cos(np.deg2rad(Info_1[0])) * np.cos(np.deg2rad(Info_1[1])) y = Info_1[2] * np.cos(np.deg2rad(Info_1[0])) * np.sin(np.deg2rad(Info_1[1])) z = Info_1[2] * np.sin(np.deg2rad(Info_1[0])) r = Info_1[3] plt.style.use('dark_background') plt.subplot(1, 1, 1) plt.title(self.Name) plt.tricontourf(x, y, r, cmap='jet') # contourf jet gray plt.colorbar() plt.savefig('C:/data/gui/temp/ppi_ref/' + self.Name + '_ppi_' + str(elevation) + '.png', dpi=300) plt.close() def rhi(self, azimuth): AllInfo = [[], [], [], []] # 仰角方位角距离反射率 for i in self.Storage: if azimuth - 0.5 <= i[0][1] <= azimuth + 0.5: # 设定仰角范围 for j in range(0, int(len(i[0][2]))): if 1: # 剔除反射率零点，以[0,0,0,0]代替以不影响矩阵形状 i[0][2][j] > 0 AllInfo[0].append(i[0][0]) # 仰角 # print(i[0][0]) AllInfo[1].append(i[0][1]) # 方位角 AllInfo[3].append(i[0][2][j]) # 反射率因子 AllInfo[2].append(i[0][3][j]) # 距离 AllInfo[0].append(0) AllInfo[1].append(0) AllInfo[2].append(0) AllInfo[3].append(75) while (len(AllInfo[0])) % 460 != 0: # 标准化为460倍数（补[0，0，0，0]法） AllInfo[0].append(0) AllInfo[1].append(0) AllInfo[2].append(0) AllInfo[3].append(0) Info_1 = np.array(AllInfo) y = Info_1[2] * np.cos(np.deg2rad(Info_1[0])) z = Info_1[2] * np.sin(np.deg2rad(Info_1[0])) r = Info_1[3] plt.style.use('dark_background') plt.subplot(1, 1, 1) plt.title(self.Name) plt.tricontourf(y, z, r, cmap='jet') # contourf jet gray plt.colorbar() plt.savefig('C:/data/gui/temp/rhi_ref/' + self.Name + '_rhi_' + str(azimuth) + '.png', dpi=300) plt.close() def points(self): x = [] y = [] z = [] r = [] for i in range(len(self.r)): if 70 > self.r[i] > 0: x.append(np.sqrt(self.x[i])) y.append(np.sqrt(self.y[i])) z.append(np.sqrt(self.z[i])) r.append(self.r[i]) points = mlab.points3d(x, y, z, r, colormap='jet', scale_factor=.25) mlab.show() # 0.5° 仰角速绘 def ppi(absolute_path): Name = absolute_path[-46:-4] file = open(absolute_path, 'rb') file.seek(0) RawData = np.array([int(i) for i in file.read()]) Count = int(len(RawData) / 2432) RawArray = RawData.reshape(Count, 2432) Elevation = [(RawArray[i][42] + 256 * RawArray[i][43]) / 8 * 180 / 4096 for i in range(0, Count)] # 仰角 Azimuth = [(RawArray[i][36] + 256 * RawArray[i][37]) / 8 * 180 / 4096 for i in range(0, Count)] # 方位角 PointerOfReflectivity = [RawArray[i][64] + RawArray[i][65] * 256 for i in range(0, Count)] # 数据位置指针 StartOfReflectivity = [RawArray[i][46] + RawArray[i][47] * 256 for i in range(0, Count)] # 起始距离 StepOfReflectivity = [RawArray[i][50] + RawArray[i][51] * 256 for i in range(0, Count)] # 库长 AllInfo = [[], [], [], []] # 仰角方位角反射率距离 NumberOfReflectivity = [] for i in range(Count): if 0 < Elevation[i] < 1: NumberOfReflectivity = int(RawArray[i][54] + RawArray[i][55] * 256) for j in range(NumberOfReflectivity): AllInfo[0].append(Elevation[i]) AllInfo[1].append(Azimuth[i]) reflectivity = (RawArray[i][PointerOfReflectivity[i] + j] - 2) / 2 - 32 if reflectivity != 0 and reflectivity != 1 and reflectivity >= 0: AllInfo[2].append(reflectivity) else: AllInfo[2].append(0) AllInfo[3].append(StartOfReflectivity[i] + j * StepOfReflectivity[i]) AllInfo[0].append(0) AllInfo[1].append(0) AllInfo[2].append(75) AllInfo[3].append(0) while (len(AllInfo[0])) % 460 != 0: # 标准化为460倍数（补[0，0，0，0]法） AllInfo[0].append(0) AllInfo[1].append(0) AllInfo[2].append(0) AllInfo[3].append(0) Info_1 = np.array(AllInfo) x = Info_1[3] * np.cos(np.deg2rad(Info_1[0])) * np.cos(np.deg2rad(Info_1[1])) y = Info_1[3] * np.cos(np.deg2rad(Info_1[0])) * np.sin(np.deg2rad(Info_1[1])) r = Info_1[2] plt.style.use('dark_background') plt.subplot(1, 1, 1) plt.title(Name) plt.contourf(x.reshape(int(len(x) / 460), 460), y.reshape(int(len(y) / 460), 460), r.reshape(int(len(r) / 460), 460), cmap='jet') # contourf jet gray plt.colorbar() # plt.show() plt.savefig('C:/data/gui/temp/animation/' + Name, dpi=300) plt.close() def test(absolute_path): Name = absolute_path[-46:-4] file = open(absolute_path, 'rb') file.seek(0) RawData = np.array([int(i) for i in file.read()]) Count = int(len(RawData) / 2432) RawArray = RawData.reshape(Count, 2432) Elevation = [(RawArray[i][42] + 256 * RawArray[i][43]) / 8 * 180 / 4096 for i in range(0, Count)] # 仰角 Azimuth = [(RawArray[i][36] + 256 * RawArray[i][37]) / 8 * 180 / 4096 for i in range(0, Count)] # 方位角 PointerOfReflectivity = [RawArray[i][64] + RawArray[i][65] * 256 for i in range(0, Count)] # 数据位置指针 StartOfReflectivity = [RawArray[i][46] + RawArray[i][47] * 256 for i in range(0, Count)] # 起始距离 StepOfReflectivity = [RawArray[i][50] + RawArray[i][51] * 256 for i in range(0, Count)] # 库长 AllInfo = [[], [], [], []] # 仰角方位角反射率距离 NumberOfReflectivity = [] for i in range(Count): if 5 < Elevation[i] < 8: print(Elevation[i]) NumberOfReflectivity = int(RawArray[i][54] + RawArray[i][55] * 256) for j in range(NumberOfReflectivity): AllInfo[0].append(Elevation[i]) AllInfo[1].append(Azimuth[i]) reflectivity = (RawArray[i][PointerOfReflectivity[i] + j] - 2) / 2 - 32 if reflectivity != 0 and reflectivity != 1 and reflectivity >= 0: AllInfo[2].append(reflectivity) else: AllInfo[2].append(0) AllInfo[3].append(StartOfReflectivity[i] + j * StepOfReflectivity[i]) AllInfo[0].append(0) AllInfo[1].append(0) AllInfo[2].append(75) AllInfo[3].append(0) while (len(AllInfo[0])) % 460 != 0: # 标准化为460倍数（补[0，0，0，0]法） AllInfo[0].append(0) AllInfo[1].append(0) AllInfo[2].append(0) AllInfo[3].append(0) Info_1 = np.array(AllInfo) x = Info_1[3] * np.cos(np.deg2rad(Info_1[0])) * np.cos(np.deg2rad(Info_1[1])) y = Info_1[3] * np.cos(np.deg2rad(Info_1[0])) * np.sin(np.deg2rad(Info_1[1])) r = Info_1[2] plt.style.use('dark_background') plt.subplot(1, 1, 1) plt.title(Name) plt.tricontourf(x, y, r, cmap='jet') # tripcolor plt.colorbar() # plt.show() plt.savefig('C:/data/gui/temp/animation/' + Name, dpi=300) plt.close()

UTF-8

Python

false

17,434

py

4

PyRadar.py

2

0.50957

0.448791

0

359

45.420613

120

Faiyaz42/Resume-Projects

7,799,660,656,349

af0031013a45d2e26b944075ed9f751ef90151d1

f07914af8d8abef96f851691477cd242db495434

/Hangman/WordGuess.py

0e2401f6e9a88534d4913dab5957132b70bc2324

[ "Apache-2.0" ]

permissive

https://github.com/Faiyaz42/Resume-Projects

a126e9cd8e1c9859689426076dd6bae21f54c846

9d6e5597aba6672ad7bd20d02e4b7cf7d4654fcd

refs/heads/main

2023-03-28T17:38:37.819818

2021-03-30T18:54:16

330,059,753

1

null

false

2021-02-13T23:29:28

2021-01-16T01:27:21

2021-02-07T17:56:35

2021-02-13T23:29:28

38,896

0

Python

false

import random from SecretWord import SecretWord class WordGuess: def __init__(self, wordDic): self.words_dict = wordDic self.guess_words = [] #constructor,initiation self.guesses = 0 self.random_word = '' self.current_guess = '' def play(self): """ Plays out a single full game of Word Guess """ #play game self.random_word = self.chooseSecretWord() #choose random word print('A secret word has been randomly chosen!') acontainer = SecretWord() #container(instance) to hold random word sorted_container = SecretWord() #sorted container or instance acontainer.setWord(self.random_word) #make linked list sorted_container.setWord(self.random_word) # '' '' string1 = str(acontainer) #str of original random word string2 = sorted_container.sort() string2 = str(sorted_container) #str of sorted word find_distance = self.editDistance(string1,string2,len(string1),len(string2)) #find edit distance alloted_guesses = 2*find_distance #find the alloted number of guesse (*2) if alloted_guesses < 5: alloted_guesses = 5 elif alloted_guesses > 15: alloted_guesses = 15 self.guesses = int(alloted_guesses) while self.guesses > 0 and acontainer.isSolved() == False: print('You have %d guesses remaining' % (self.guesses)) #if user hasnt guessed it yet loop acontainer.printProgress() #print progress self.getGuess() #get guess acontainer.update(self.current_guess) #update if self.current_guess not in string1 and self.current_guess != '*': #if wrong guess self.guesses = self.guesses - 1 #deduct if self.guesses > 0 and acontainer.isSolved() == True: #if successfully solved print('You solved the puzzle!') print('The secret word was: %s ' % (str(acontainer))) elif self.guesses == 0: #if failed print('You have run out of guesses\nGame Over') print('The secret word was: %s ' % (str(acontainer))) self.guess_words = [] self.guesses = 0 #reset self.random_word = '' self.current_guess = '' def chooseSecretWord(self): """ Chooses the secret word that will be guessed """ #choose a random word from the dict item = random.choice(list(self.words_dict)) return str(item ) def editDistance(self, s1, s2,length1,length2): # edit distance with length1 of string1 and length2 of string 2 ,for later recursion """ Recursively returns the total number of insertions and deletions required to convert S1 into S2 """ if length1 == 0: #if first string is empty,return second string value since its being totally transferred return length2 if length2 == 0: #vice versa return length1 if s1[length1-1]==s2[length2-1]: return self.editDistance(s1,s2,length1-1,length2-1) #recursively find the distance for eahc operation and find the minimum return 1 + min(self.editDistance(s1, s2, length1, length2-1), # Insert self.editDistance(s1, s2, length1-1, length2)) # Remove def getGuess(self): """ Queries the user to guess a character in the secret word """ ask = True #ask loop while ask: user_input = input('Enter a character that has not been guessed or * for a hint: ') self.current_guess = str(user_input) if user_input == '*': #if asked for hint hint = self.words_dict[self.random_word] print('Hint: %s' % (hint)) #show hint and deduct 1 self.guesses = self.guesses - 1 ask = False elif self.current_guess not in self.guess_words: #if guess is not repeated self.guess_words.append(self.current_guess) #add to guesses list ask = False elif user_input in self.guess_words: #if guess is repeated print('Invalid guess. You have already guessed this letter.')

UTF-8

Python

false

5,378

py

42

WordGuess.py

13

0.477501

0.465787

0

91

57.967033

165

kingbar1990/labs

7,533,372,671,335

3d5177889b1d9e13f27a17e566775ede24106596

3bd7c46a7bb2da9bfaf4a7dc3e34591e54211f8d

/Lab2.py

55b5377421becfa51aeaa42269bbd6a7c1673779

[]

no_license

https://github.com/kingbar1990/labs

a5d997aa06c59a608e1913990abf1c87b70311b8

c1b4a55b76fbe1d927fd3ac7f779a5cb8e4ac681

refs/heads/master

2020-04-06T18:43:15.427704

2018-10-24T16:30:59

null

0

null

from math import exp, cos, sin, pi import random import time import sys n = int(raw_input('Enter n(10000): ') or 10000) mu = float(raw_input('Enter mu(5): ') or 5) k = float(raw_input('Enter k(5): ') or 5) m = 50 maximum = 0 def fx(x): return (1 - x) * x def fy(y, mu): return exp(-mu * y) def fz(z, k): return 1 + cos(2 * pi * k * z) def func(x, y, z, mu, k): return fx(x) * fy(y, mu) * fz(z, k) def analytical(): """Analytical method""" i_x = 1 / 6.0 i_y = (-1 / float(mu)) * (exp(-mu) - 1) i_z = (1 + 1/(2*pi*k) * sin(2*pi*k)) print '\n' + "Analytical value: {}".format(i_x * i_y * i_z) def rectangle(): """Rectangle method""" start = time.time() h = 1/float(n) x, y, z, i_x, i_y, i_z = h/2, h/2, h/2, 0, 0, 0 for _ in xrange(n): i_x += fx(x) x += h i_y += fy(y, mu) y += h i_z += fz(z, k) z += h delta_time = time.time() - start print '\n' + "Rectangle value: {}".format((i_x * h) * (i_y * h) * (i_z * h)) +\ '\n' + "time processing: {}".format(delta_time) def simple(): """Simple calculation of integral""" start = time.time() arr = [0]*m mi, d, s = 0, 0, 0 for i in xrange(m): for _ in xrange(n): s += func(random.random(), random.random(), random.random(), mu, k) arr[i] = s/n mi += arr[i] s = 0 for j in xrange(m): d += (arr[j] - mi/m)**2 delta_time = time.time() - start print '\n' + "Simple method: {}".format(mi / m) + \ '\n' + "time processing: {}".format(delta_time) + \ '\n' + "dispersion: {}".format(d / m) + \ '\n' + "laboriousness: {}".format(d / m * delta_time) def find_min_max(): """Finding function maximum""" def f(x, y, z): return (1 - x) * x * exp(-mu * y) * (1 + cos(2 * pi * k * z)) max_func = - sys.maxint - 1 min_func = sys.maxint maximal_x, maximal_y, maximal_z = None, None, None minimal_x, minimal_y, minimal_z = None, None, None for i in xrange(1000000): randx, randy, randz = random.random(), random.random(), random.random() result = f(randx, randy, randz) max_func = max(max_func, result) if max_func == result: maximal_x, maximal_y, maximal_z = randx, randy, randz min_func = min(min_func, result) if min_func == result: minimal_x, minimal_y, minimal_z = randx, randy, randz global maximum maximum = max_func print '\n' + "Maximal (x, y):", (maximal_x, maximal_y, maximal_z) print "Max func value:", max_func, '\n' print "Minimal (x, y):", (minimal_x, minimal_y, minimal_z) print "Min func value:", min_func def neyman(): """Neyman calculation of integral""" start = time.time() arr = [0]*m mi, d, s = 0, 0, 0 for i in xrange(m): for _ in xrange(n): if func(random.random(), random.random(), random.random(), mu, k) > random.random()*maximum: s += 1 arr[i] = (s/float(n))*maximum*1 mi += arr[i] s = 0 for j in xrange(m): d += (arr[j] - mi/m)**2 delta_time = time.time() - start print '\n' + "Neyman method: {}".format(mi / m) + \ '\n' + "time processing: {}".format(delta_time) + \ '\n' + "dispersion: {}".format(d / m) + \ '\n' + "laboriousness: {}".format(d / m * delta_time) analytical(), rectangle(), simple(), find_min_max(), neyman()

UTF-8

Python

false

3,501

py

4

Lab2.py

3

0.502142

0.484719

0

126

26.785714

104

ralf-meyer/RLSVRD

19,353,122,663,798

66e48d9b66db49e44b7bbc54184956f1bdac321e

6f81cc8e67475b23b5b343dc14c55b6227ee49c8

/__init__.py

68a9d0e4d6a382c68824b2ef371f961a8601c04d

[]

no_license

https://github.com/ralf-meyer/RLSVRD

232778c185c3e460f81f5c0c7f97512b5ef19026

10ce8567ec521c71d8ab4dcdc5685ef1840b6c4d

refs/heads/master

2021-04-27T17:56:12.614967

2018-05-15T14:23:47

122,330,560

0

1

null

from RLSVRD import RLSVRD from IRWLS_SVR import IRWLS_SVR

UTF-8

Python

false

58

py

6

__init__.py

4

0.827586

0

2

28

31

LouPlus/jobplus3-12

17,257,178,633,714

b69c31159bb3e39526506030e8808f92138a6890

556b48bb805a1be3609c844fc4a251b83a893817

/app/config.py

29fc0fd77ff5e0babf04fd2b018879bf9b35a500

[]

no_license

https://github.com/LouPlus/jobplus3-12

3a48fea7001dda32f2756113b51215c1e14f9a9f

bce7eec354187d0ce69621d5cb16cd8a3012e00d

refs/heads/master

2021-05-13T20:43:28.162737

2018-01-30T16:20:57

116,917,555

0

3

null

false

2018-01-30T16:20:58

2018-01-10T06:29:39

2018-01-15T14:26:06

2018-01-30T16:20:58

28

0

3

2

Python

false

null

# 域名和端口可根据个人环境改变 DOMAIN_NAME = 'root@localhost' PORT = '3306' class BaseConfig(object): SECRET_KEY = 'wubba lubba dub dub' INDEX_PER_PAGE = 9 class DevelopementConfig(BaseConfig): DEBUG = True SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://' + DOMAIN_NAME + ':' + PORT + '/jobplus?charset=utf8' class ProductionConfig(BaseConfig): DEBUG = False class TestingConfig(BaseConfig): DEBUG = False configs = { 'development':DevelopementConfig, 'production': ProductionConfig, 'testing': TestingConfig }

UTF-8

Python

false

542

py

13

config.py

8

0.723735

0.712062

0

28

17.392857

98

seokzin/Algorithm_Python

5,703,716,607,205

84b0a76f0a736c6b8ad362db4c4883575495f81a

d17522373f7c82d7f16807a34aa90369dca85621

/Code/SWEA/1966-숫자를 정렬하자.py

814e0e7b0bbf90da78eec681270da22c742d7358

[]

no_license

https://github.com/seokzin/Algorithm_Python

30f15d120a73132ebfc0d55629eab6db2e24eaec

8a6dbe19dd3613fa2e5db544db69183053dc1f5b

refs/heads/master

2022-11-15T00:18:11.063138

2021-12-27T16:52:30

2021-12-27T16:56:20

276,327,838

0

null

def selection_sort(s): if s: x = min(s) s.remove(x) return [x] + selection_sort(s) else: return [] for tc in range(1, int(input())+1): n = int(input()) arr = list(map(int, input().split())) print(f'#{tc}', *selection_sort(arr)) # 재귀적 선택정렬 직접 구현해봄

UTF-8

Python

false

328

py

356

1966-숫자를 정렬하자.py

355

0.513245

0.506623

0

16

17.9375

41

FabienCharmet/ImpactEval

807,453,879,054

18ab0b5080dff7c527a56a46afc93be683f44700

ab551cad61f922203918367b681afa3624ac4af5

/ImpactEval.py

84c1e818c1ee954b69256557df07576a87847cab

[]

no_license

https://github.com/FabienCharmet/ImpactEval

115a1a7f6b845e801be66e72d69947be8c020f73

e976c1acd2bcf4159f9a9dd667f38b39745174f6

refs/heads/master

2023-01-15T18:35:46.226873

2020-11-21T08:46:13

297,141,005

0

null

# -*- coding: utf-8 -*- """ Éditeur de Spyder """ import networkx as nx import matplotlib.pyplot as plt import numpy as np import itertools import random G=nx.DiGraph() Gfunc=nx.DiGraph() """ IMPORT FUNCTION OF THE RESOURCE GRAPH """ # Rarray = [[0,1,0.1],[1,0,0.7],[0,2,0.4],[2,0,0.1],[0,3,0.2],[3,0,0.1], # [1,3,0.6],[3,1,0.9],[2,3,0.9],[3,2,0.5]] # SEarray = [[4,1,0.3],[5,2,0.9]] # BFarray = [[0,6,0.9],[0,7,0.1],[1,6,0.6],[6,8,0.8],[7,8,0.7]] Rarray = [[0,1,0.8],[1,0,0.6]] SEarray = [[2,0,0.8],[3,0,0.8]] BFarray = [[0,4,0.7],[1,4,0.6]] Rnodes = set() for g in Rarray: Rnodes.add(g[0]) SEnodes = set() for se in SEarray: SEnodes.add(se[0]) # Calculating which nodes are Business Resource nodes # i.e. nodes from Rarray (resource graph) connected to BFarray (business graph) set_infranodes = set((x[0] for x in Rarray)).union((x[1] for x in Rarray)) BRnodes = set_infranodes.intersection((x[0] for x in BFarray)) # print(BRnodes) BFnodes = set() for bf in BFarray: BFnodes.add(bf[1]) BFnodes = sorted(BFnodes) Gtemp=Rarray Gtemp+=SEarray Gtemp+=BFarray # print(Gnodes) # print(SEnodes) # print(BFnodes) # print(BRnodes) for a in Gtemp: G.add_edge(a[0],a[1],weight=a[2]) for a in BFarray: Gfunc.add_edge(a[0],a[1],weight=a[2]) """ IMPORT FUNCTION OF THE TRANSITION MATRICES """ # np.random.seed() """ COMPUTING THE IMPACT ON A TARGET NODE """ def compute_impact_proba(ntimes): counter_array=[0]*G.number_of_nodes() # np.random.seed(42) for i in range(ntimes): var_sampling = [] for i in Gtemp: var_sampling.append([i[0],i[1],np.random.rand()]) # print(var_sampling) number_of_ticks=0 tick_array=[0]*G.number_of_nodes() # Checking if business resources are impacted for brsource in BRnodes: varcont=True # print("\n\n") for sesource in SEnodes: paths = nx.all_simple_paths(G, source=sesource, target=brsource) pathlist = list(paths) random.shuffle(pathlist) # print(type(pathlist)) for p in pathlist: # print(p) ind=0 path_array=[0] * len(p) path_array[0]=1 while(ind<len(p)-1): proba = np.random.rand() proba = (x for x in var_sampling if x[0]==p[ind] and x[1]==p[ind+1]) proba = list(proba) proba = proba[0][2] # print(G[p[ind]][p[ind+1]]["weight"]) # print(str(proba) + " " + str(G[p[ind]][p[ind+1]]["weight"]) + " " + str(proba<=G[p[ind]][p[ind+1]]["weight"])) if(proba<=G[p[ind]][p[ind+1]]["weight"]): path_array[ind+1]=1 # else: # print(str(proba) + " " + str(G[p[ind]][p[ind+1]]["weight"]) + " " + str(proba<=G[p[ind]][p[ind+1]]["weight"])) # print(ind) # print(G[p[ind]][p[ind+1]]["weight"]) ind+=1 if(0 not in path_array): # print(p) # print(path_array) # print("success for br: " + str(brsource) + " and se: " + str(sesource)) varcont=False if(tick_array[brsource]==0): # if(brsource==1): # print(path_array) tick_array[brsource]=1 counter_array[brsource]+=1 number_of_ticks+=1 break else: print("error") if(varcont==False): break for bfsource in BFnodes: varcont=True for brsource in BRnodes: if(tick_array[brsource]==1): paths = nx.all_simple_paths(G, source=brsource, target=bfsource) pathlist=list(paths) random.shuffle(pathlist) gen = (p for p in pathlist if len(p)==2) # gen = (p for p in list(paths)) for p in gen: ind=0 path_array=[0] * len(p) path_array[0]=tick_array[brsource] # print(p) while(ind<len(p)-1): proba = np.random.rand() proba = (x for x in var_sampling if x[0]==p[ind] and x[1]==p[ind+1]) proba = list(proba) proba = proba[0][2] if(proba<=G[p[ind]][p[ind+1]]["weight"]): path_array[ind+1]=1 # print(ind) # print(G[p[ind]][p[ind+1]]["weight"]) ind+=1 if(0 not in path_array): # print(path_array) # print(p) # print("success for bf: " + str(bfsource) + " and br: " + str(brsource)) varcont=False if(tick_array[bfsource]==0): tick_array[bfsource]=1 counter_array[bfsource]+=1 number_of_ticks+=1 else: print("error") break if(varcont==False): break if(number_of_ticks>len(BFnodes + list(BRnodes))): print("error") for bftarget in BFnodes: if(tick_array[bfsource]==1): break varcont=True for bfsource in BFnodes: if(tick_array[bfsource]==1) and (bfsource != bftarget): paths = nx.all_simple_paths(G, source=bfsource, target=bftarget ) pathlist=list(paths) random.shuffle(pathlist) gen = (p for p in pathlist if len(p)==2) for p in gen: path_array=[0] * len(p) path_array[0]=tick_array[bfsource] # print(p) proba = np.random.rand() proba = (x for x in var_sampling if x[0]==p[ind] and x[1]==p[ind+1]) proba = list(proba) proba = proba[0][2] if(proba<=G[p[0]][p[1]]["weight"]): path_array[1]=1 # print(ind) # print(G[p[ind]][p[ind+1]]["weight"]) if(0 not in path_array): # print(path_array) # print(p) # print("success for bf: " + str(bfsource) + " and br: " + str(brsource)) varcont=False if(tick_array[bftarget]==0): tick_array[bftarget]=1 counter_array[bftarget]+=1 number_of_ticks+=1 else: print("error") break if(varcont==False): break proba_array = [x / ntimes for x in counter_array] for i in SEnodes: proba_array[i]=1.0 print(proba_array) def verbose_compute_impact_proba(): ntimes=1 counter_array=[0]*G.number_of_nodes() # np.random.seed(42) print("Evaluating each random variable\n") for i in range(ntimes): var_sampling = [] for i in Gtemp: var_sampling.append([i[0],i[1],np.random.rand()]) print("Current state: \n") print(var_sampling) # print(var_sampling) number_of_ticks=0 tick_array=[0]*G.number_of_nodes() # Checking if business resources are impacted print("\nChecking if resource nodes are impacted\n") for brsource in BRnodes: print("Evaluating resource node: " + str(brsource) + "\n") varcont=True # print("\n\n") for sesource in SEnodes: print("Evaluating the impact of shock event " + str(sesource) + " on node " + str(brsource)) paths = nx.all_simple_paths(G, source=sesource, target=brsource) pathlist = list(paths) print("List of paths between shock event" + str(sesource)+ " and node " + str(brsource)) print(pathlist) random.shuffle(pathlist) # print(type(pathlist)) for p in pathlist: print("\nEvaluating impact of SE: " + str(sesource) + " on node: " + str(brsource) + " via path: " + str(p)) # print(p) ind=0 path_array=[0] * len(p) path_array[0]=1 while(ind<len(p)-1): proba = np.random.rand() proba = (x for x in var_sampling if x[0]==p[ind] and x[1]==p[ind+1]) proba = list(proba) proba = proba[0][2] # print(G[p[ind]][p[ind+1]]["weight"]) # print(str(proba) + " " + str(G[p[ind]][p[ind+1]]["weight"]) + " " + str(proba<=G[p[ind]][p[ind+1]]["weight"])) if(proba<=G[p[ind]][p[ind+1]]["weight"]): path_array[ind+1]=1 # else: # print(str(proba) + " " + str(G[p[ind]][p[ind+1]]["weight"]) + " " + str(proba<=G[p[ind]][p[ind+1]]["weight"])) # print(ind) # print(G[p[ind]][p[ind+1]]["weight"]) ind+=1 print("Instantiation of random variables in path: " + str(p)) print(path_array) if(0 not in path_array): print("SE: " + str(sesource) + " has impacted node: " + str(brsource) + ". No need for further checks.\n") # print(p) # print(path_array) # print("success for br: " + str(brsource) + " and se: " + str(sesource)) varcont=False if(tick_array[brsource]==0): # if(brsource==1): # print(path_array) tick_array[brsource]=1 counter_array[brsource]+=1 number_of_ticks+=1 break else: print("error") if(varcont==False): break print("SE: " + str(sesource) + " has not impacted node: " + str(brsource) + ". Continuing checks for next SE.\n") for bfsource in BFnodes: print("Evaluating Business Function node: " + str(bfsource) + "\n") varcont=True print("For each resource nodes connected to "+ str(bfsource)) for brsource in BRnodes: if(tick_array[brsource]==1): print("Evaluating impact of resource node " + str(brsource) + " on node " + str(bfsource)) paths = nx.all_simple_paths(G, source=brsource, target=bfsource) pathlist=list(paths) random.shuffle(pathlist) gen = (p for p in pathlist if len(p)==2) # gen = (p for p in list(paths)) for p in gen: ind=0 path_array=[0] * len(p) path_array[0]=tick_array[brsource] # print(p) while(ind<len(p)-1): proba = np.random.rand() proba = (x for x in var_sampling if x[0]==p[ind] and x[1]==p[ind+1]) proba = list(proba) proba = proba[0][2] if(proba<=G[p[ind]][p[ind+1]]["weight"]): path_array[ind+1]=1 # print(ind) # print(G[p[ind]][p[ind+1]]["weight"]) ind+=1 print("Instantiation of random variables in path: " + str(p)) print(path_array) if(0 not in path_array): print("Node " + str(brsource) + " has impacted node: " + str(bfsource) + ". No need for further checks.\n") # print(path_array) # print(p) # print("success for bf: " + str(bfsource) + " and br: " + str(brsource)) varcont=False if(tick_array[bfsource]==0): tick_array[bfsource]=1 counter_array[bfsource]+=1 number_of_ticks+=1 else: print("error") break if(varcont==False): break print("Node " + str(brsource) + " has not impacted node: " + str(bfsource) + ". Continuing checks for next SE.\n") if(number_of_ticks>len(BFnodes + list(BRnodes))): print("error") for bftarget in BFnodes: if(tick_array[bfsource]==1): break print("Evaluating Business Function node " + str(bftarget) + "\n") varcont=True for bfsource in BFnodes: if(tick_array[bfsource]==1) and (bfsource != bftarget): paths = nx.all_simple_paths(G, source=bfsource, target=bftarget ) pathlist=list(paths) if(len(pathlist)>0): print("Evaluating impact of business node " + str(bfsource) + " on node " + str(bftarget)) else: print("There are no business function nodes impacting node " + str(bftarget)) break gen = (p for p in pathlist if len(p)==2) for p in gen: print(p) path_array=[0] * len(p) path_array[0]=tick_array[bfsource] # print(p) proba = np.random.rand() proba = (x for x in var_sampling if x[0]==p[ind] and x[1]==p[ind+1]) proba = list(proba) proba = proba[0][2] if(proba<=G[p[0]][p[1]]["weight"]): path_array[1]=1 # print(ind) # print(G[p[ind]][p[ind+1]]["weight"]) print("Instantiation of random variables in path: " + str(p)) print(path_array) if(0 not in path_array): print("BF: " + str(bfsource) + " has impacted node: " + str(bftarget) + ". No need for further checks.\n") # print(path_array) # print(p) # print("success for bf: " + str(bfsource) + " and br: " + str(brsource)) varcont=False if(tick_array[bftarget]==0): tick_array[bftarget]=1 counter_array[bftarget]+=1 number_of_ticks+=1 else: print("error") break if(varcont==False): break print("BF: " + str(bfsource) + " has not impacted node: " + str(bftarget) + ". Continuing checks for next SE.\n") proba_array = [x / ntimes for x in counter_array] for i in SEnodes: proba_array[i]=1.0 print("Final array after one iteration:") print(proba_array) def verbose_inclusion_exclusion(): proba_array=[0]*G.number_of_nodes() print("Evaluating all possible target nodes\n") for bf in BFnodes + list(BRnodes): print("Evaluating node: " + str(bf)) # print("\n\n\n") list_of_paths = [] list_of_proba = [] bfproba=0 sbfproba="" print("Evaluating all possible shock events\n") for se in SEnodes: print("Evaluating shock event: " + str(se)) paths = nx.all_simple_paths(G, source=se, target=bf) temp_paths = nx.all_simple_paths(G, source=se, target=bf) print("List of paths between shock event " + str(se)+ " and node " + str(bf)) print(list(temp_paths)) for p in list(paths): list_of_paths.append(list(p)) probaset = set() for ind in range(0,len(p)-1): probaset=probaset.union({tuple([p[ind],p[ind+1],G[p[ind]][p[ind+1]]["weight"]])}) # print([p[ind],p[ind+1],G[p[ind]][p[ind+1]]["weight"]]) # print(p) # print(probaset) list_of_proba+=[probaset] # print(list_of_proba) # print("\n\n") # for i in range(len(list_of_paths)): # print(list_of_paths[i]) # print(list_of_proba[i]) # print("\n\n") print("\n Aggregating all paths between all shock events and node " + str(bf)) print(list_of_paths) print("\n For each path, generating a set containing all probabilities of variables in the path") print(list_of_proba) print("Generating all possible probability sets related to path combinations of size 1 to " + str(len(list_of_proba))) for i in range(1,len(list_of_proba)+1): proof_combination = list(itertools.combinations(list_of_proba,i)) # print(len(proof_combination)) # print(len(proof_combination[0])) # print(proof_combination) # temp = proof_combination[0] for comb in proof_combination: combset = set() for elem in comb: # print(set(elem)) combset = combset.union(set(elem)) print("\nCurrent combination : " +str(combset)) # print("Source: " + str(bf)) # print(combset) proba=1 sproba="" print("\n Computing path probabilities by multiplying all probabilities in " + str(combset)) for probaelem in combset: # print(probaelem[2]) proba*=probaelem[2] sproba+=" * " + str(probaelem[2]) # print("\n Path probability: " + str(sproba[2:]) + " = " + str(round(proba,3))) # print(elem) # print(proba) # print(bfproba) bfproba+=((-1)**(i+1))*proba sbfproba+=" + ((-1)**("+str(i+1)+"))*"+str(round(proba,3)) print("\n Probability for node " + str(bf) + ": \n" + sbfproba[3:] + " = " + str(round(bfproba,3))) # print(len(list_of_proba)) # print(len(list_of_paths)) # print("\n\n") proba_array[bf]=bfproba for i in SEnodes: proba_array[i]=1.0 print("\nProbability array:") print(proba_array) def inclusion_exclusion(): proba_array=[0]*G.number_of_nodes() for bf in BFnodes + list(BRnodes): # print("\n\n\n") list_of_paths = [] list_of_proba = [] bfproba=0 for se in SEnodes: paths = nx.all_simple_paths(G, source=se, target=bf ) for p in list(paths): list_of_paths.append(list(p)) probaset = set() for ind in range(0,len(p)-1): probaset=probaset.union({tuple([p[ind],p[ind+1],G[p[ind]][p[ind+1]]["weight"]])}) # print([p[ind],p[ind+1],G[p[ind]][p[ind+1]]["weight"]]) # print(p) # print(probaset) list_of_proba+=[probaset] # print(list_of_proba) # print("\n\n") # for i in range(len(list_of_paths)): # print(list_of_paths[i]) # print(list_of_proba[i]) # print("\n\n") for i in range(1,len(list_of_proba)+1): proof_combination = list(itertools.combinations(list_of_proba,i)) # print(len(proof_combination)) # print(len(proof_combination[0])) # temp = proof_combination[0] for comb in proof_combination: combset = set() for elem in comb: # print(set(elem)) combset = combset.union(set(elem)) # print("Source: " + str(bf)) # print(combset) proba=1 for probaelem in combset: # print(probaelem[2]) proba*=probaelem[2] # print(elem) # print(proba) # print(bfproba) bfproba+=((-1)**(i+1))*proba # print(len(list_of_proba)) # print(len(list_of_paths)) # print("\n\n") proba_array[bf]=bfproba for i in SEnodes: proba_array[i]=1.0 print(proba_array) # nx.draw(G) labeldict={} for i in range(G.number_of_nodes()): labeldict[i]=str(i) # nx.draw(G,labels=labeldict,with_labels=True) # plt.show() sampling = [10**x for x in range(3,4)] # sampling = [1] for i in sampling: compute_impact_proba(i) verbose_compute_impact_proba() verbose_inclusion_exclusion() #inclusion_exclusion() # print(BFnodes) # print(BRnodes) # a = set({tuple([0,1]),tuple([0,4]),tuple([0,3]),tuple([0,2]),tuple([0,2])}) # b = set({tuple([1,1]),tuple([1,4]),tuple([1,3]),tuple([1,2]),tuple([0,2])}) # c = set({tuple([2,1]),tuple([2,4]),tuple([2,3]),tuple([2,2]),tuple([1,2])}) # a=a.union({tuple([0,5])}) # for x in list(itertools.combinations(a,2)): # print(x) # d = set.union(a,b,c) # print(d) # for x in list(itertools.combinations(d,2)): # print(x) # nx.draw(G) # plt.savefig("simple_path.png") # save as png # plt.draw() # display # print("Nodes of graph: ") # print(G.nodes()) # print("Edges of graph: ") # # [print(G.get_edge_data(*e)) for e in G.edges] # for e in G.edges: # print(G.get_edge_data(*e)['weight']) # print(e)

UTF-8

Python

false

23,316

py

1

ImpactEval.py

1

0.437916

0.423118

0

566

40.190813

148

Mumbaikar007/Code

12,816,182,420,545

09aedb3c3130d28e63b4f9ef5bdc447f343af387

dee4f176db476b621a208e1e9c8562740228c84c

/CoursesCodes/PythonGUI/basicsOfTkinter.py

dbf7f5b90266d9e9e37ed8ca7ccad4cb5d641a11

[]

no_license

https://github.com/Mumbaikar007/Code

7a62f2bcb9e01c06d3d83370f78298a76f94ee87

b55ee260f3e4e33fb59ba6ed35c2ee29443bbc11

refs/heads/master

2021-09-27T05:31:00.973615

2018-11-06T10:42:31

104,623,021

6

8

null

false

2018-10-31T15:55:05

2017-09-24T06:10:27

2018-10-30T17:15:53

2018-10-31T15:41:01

8,054

4

7

0

Makefile

false

null

from tkinter import * a = Tk() a.title("My First Window") a.mainloop()

UTF-8

Python

false

75

py

206

basicsOfTkinter.py

174

0.64

0

7

9.571429

26

rahulmahato46/leetcode-june20

2,645,699,899,507

9a8b242b4651e10e6b319f7e6e36eb675d1fab27

f67447f374c4a4e8889e02b4614eba0e05ea9544

/queue-construction-by-height-part2.py

3273b5a955bc61f7d630871a88f2252550df9c72

[]

no_license

https://github.com/rahulmahato46/leetcode-june20

6519cd55ae12f8e699797588a0652da3c747e5ab

2f378037e5c234cf4a806082d6192485338cd97c

refs/heads/master

2022-10-02T16:31:30.334070

2020-06-06T14:34:54

268,527,999

0

null

class Solution: def reconstructQueue(self, people: List[List[int]]) -> List[List[int]]: new_arr = [] people = sorted(people, key= lambda x:(-x[0],x[1])) for var in people: new_arr.insert(var[1],var) return new_arr

UTF-8

Python

false

261

py

3

queue-construction-by-height-part2.py

3

0.563218

0.551724

0

7

36.428571

75

lanzhou2012/Object-detection

7,060,926,280,841

e174df36f21abe5971bcd0d7d8d4e36341e6745e

9fd4467dcd715b6b33c4e0cb24656c82a592ac1d

/main/my_method/validation.py

201aaf3d5fbfd60f3bdbb6d10a388f0bbf57a03e

[]

no_license

https://github.com/lanzhou2012/Object-detection

911711c17070c2a6d677099bc4796bb2446b7aa4

11d506051c274484bea31335a1ec5e12569f9719

refs/heads/master

2020-03-05T22:59:31.088966

2017-03-27T07:15:36

null

0

null

import cv2 import keras from keras.applications.imagenet_utils import preprocess_input from keras.backend.tensorflow_backend import set_session from keras.models import Model from keras.preprocessing import image import matplotlib.pyplot as plt import numpy as np from scipy.misc import imread import tensorflow as tf import os import operator import itertools from collections import Counter import pickle from ssd import SSD300 from ssd_utils import BBoxUtility import pascal_VOC def numDups(a, b): if len(a)>len(b): a,b = b,a a_count = Counter(a) b_count = Counter(b) return sum(min(b_count[ak], av) for ak,av in a_count.items()) def load_model(): # matplotlib inline plt.rcParams['figure.figsize'] = (8, 8) plt.rcParams['image.interpolation'] = 'nearest' np.set_printoptions(suppress=True) config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.45 set_session(tf.Session(config=config)) voc_classes = ['Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle', 'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable', 'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant', 'Sheep', 'Sofa', 'Train', 'Tvmonitor'] NUM_CLASSES = len(voc_classes) + 1 input_shape=(300, 300, 3) model = SSD300(input_shape, num_classes=NUM_CLASSES) model.load_weights('../data/weights_SSD300.hdf5', by_name=True) bbox_util = BBoxUtility(NUM_CLASSES) return model, bbox_util def validates_images(model, bbox_util): inputs = [] images = [] files = [] for filename in os.listdir('../data/VOC2007/JPEGImages'): if filename.endswith('.jpg'): files.append(filename) b =0 for filename in sorted(files): if b < 3: img_path = '../data/VOC2007/JPEGImages/' + filename img = image.load_img(img_path, target_size=(300, 300)) img = image.img_to_array(img) images.append(imread(img_path)) inputs.append(img.copy()) b += 1 inputs = preprocess_input(np.array(inputs)) preds = model.predict(inputs, batch_size=1, verbose=1) results = bbox_util.detection_out(preds) return results, img def process_images(results): image_list = [] for i in range(len(results)): a_list = [] # Parse the outputs. det_label = results[i][:, 0] det_conf = results[i][:, 1] det_xmin = results[i][:, 2] det_ymin = results[i][:, 3] det_xmax = results[i][:, 4] det_ymax = results[i][:, 5] # Get detections with confidence higher than 0.4, as it gives the highest % accuracy of labels. top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.4] # Format of a_list: confidence, labels, xmin, ymin, xmax, ymax a_list.append(det_conf[top_indices]) a_list.append(det_label[top_indices].tolist()) a_list.append(det_xmin[top_indices]) a_list.append(det_ymin[top_indices]) a_list.append(det_xmax[top_indices]) a_list.append(det_ymax[top_indices]) image_list.append(a_list) return image_list def checker(image_list, img): for i in range(len(image_list)): for j in range(image_list[i][0].shape[0]): if j < 1: xmin = int(round(image_list[i][2][j] * img.shape[1])) ymin = int(round(image_list[i][3][j] * img.shape[0])) xmax = int(round(image_list[i][4][j] * img.shape[1])) ymax = int(round(image_list[i][5][j] * img.shape[0])) print(xmin, ymin, xmax, ymax) print(image_list[i][2][j], image_list[i][3][j], image_list[i][4][j], image_list[i][5][j]) input('1') with open('../data/VOC2007.pkl', 'rb') as read: x = pickle.load(read) sorted_x = sorted(x.items(), key=operator.itemgetter(0)) number_correct = 0 total = 0 extras = 0 a = 0 for i, j in enumerate(sorted_x): if a < 3: print(j[1]) list_of_one_hot = [[k for k, int1 in enumerate(a_list[3:]) if int1 == 1.0] for a_list in j[1]] list_of_one_hot = list(itertools.chain.from_iterable(list_of_one_hot)) # This counts how many labels there are in total total += len(list_of_one_hot) # This counts how many labels are correct similarity = numDups(image_list[i][1], list_of_one_hot) number_correct +=similarity # This counts how many extra labels are identified extras += len(image_list[1][i]) a+=1 return number_correct, total, extras if __name__ == '__main__': model, bbox_util = load_model() results, img = validates_images(model, bbox_util) image_list = process_images(results) number_correct, total, extras = checker(image_list, img) if extras > total: extras -= total else: extras = 0 percentage = (number_correct - extras)/total*100 print('Total: {}\nCorrect: {}\nExtras: {}\nPercentage: {}%'.format(total, number_correct, extras, percentage))

UTF-8

Python

false

5,234

py

3

validation.py

1

0.588269

0.570501

0

151

33.662252

114

lewbenj/qbb2018-answers

19,292,993,120,608

9e6cd6aac38cebf5e32f2bb8fc3ef76cd800561e

7809ad3224f25b41b5be2c3bf572eb69701b1a17

/week8-lab/motif.py

a4460f6586853a93655691ef88474dac6f7753d2

[]

no_license

https://github.com/lewbenj/qbb2018-answers

3346ca5d2785c987d8fbba0740ace224d7a2be39

e845c928b22450cd8ed26c211420bb293e7043b6

refs/heads/master

2020-03-27T09:06:04.647369

2018-12-24T21:40:29

146,313,771

0

null

#!/usr/bin/env python3 import sys import matplotlib.pyplot as plt #Usage: ./motif.py ER4_peaks.narrowPeak.bed Bed = open(sys.argv[1]) Percentage = [] for count, line in enumerate(Bed): "Skip the header" if line.startswith("#"): continue else: fields = line.rstrip("\r\n").split("\t") #print(fields) sm = (fields[3]) em = float(fields[4]) sp = float(fields[10]) ep = float(fields[11]) #print(sm,em,sp,ep) percentage = abs((sm-sp)/(ep-sp)) Percentage.append(percentage) fig, ax = plt.subplots() fig.set_size_inches(12, 9) plt.hist(Percentage, bins=60, color="red") ax.set_xlabel("Relative position in the sequences") ax.set_ylabel("How often the motifs are detected") fig.suptitle("Top 100 motifs - Week 8 - Density plot") fig.savefig("Motifs.png") plt.tight_layout() plt.close(fig)

UTF-8

Python

false

862

py

37

motif.py

35

0.642691

0.62181

0

36

22.972222

54

n-kimberly/Playground_Python

34,359,763,233

9bbb388ecbf25e2d0c4c6ce889878a271e58111a

6db5887ee3f7236c5ec3f55c32db887a061f8060

/hb/w1/d4_markov-ffs/n-grams.py

623b9fdff8cd99d7cc1f5b51e0f68c0a16adc315

[]

no_license

https://github.com/n-kimberly/Playground_Python

f43851410071f2e5bb3256495d6398e478b358ab

fad99fdb690e0eeb3f2c9141e70bf4a9a33a2aef

refs/heads/master

2020-03-29T14:14:53.271813

2018-10-02T04:39:03

141,058,022

0

null

""" Further Study Do any of the following: Replace our dummy file with your own. Pass in this file from the command line, using sys.argv. The longer the sequence of words to the left of the arrow, the closer it becomes to the original text, as well as valid English, because there are fewer and fewer random successors. Here, we use n_grams (word pairs) and a successor, but we could use trigrams or n-grams (sequences of n words). The longer the n-gram, the closer you get to the source text. Modify the program to allow any number of words to use as keys so you can easily choose the size of your n-gram used in your chain rather than always using bi-grams. Begin on a capital letter and end only at an instance of sentence punctuation. See what happens when you mix two different authors together as a single source. This often works best when they have somewhat similar writing styles; trying to combine Dr. Seuss and the Bible probably wouldn’t work as well as combining two Jane Austen books. i.e. >> python3 n-grams.py pe.txt sorority-speech.txt kant.txt """ import sys import random import markov_helpers as mh def make_chains(text_string, n): chains = {} words = text_string.split() current_index = 0 remaining_index = len(words)-1 while current_index < len(words)-(n+1): n_gram = tuple(words[current_index:current_index+n]) current_index += 1 remaining_index -= 1 subsequent_word = words[current_index+n] if n_gram in chains: chains[n_gram].append(subsequent_word) else: chains[n_gram] = [subsequent_word] return chains n_gram = 3 input_text = "" for i in range(len(sys.argv[1:])): print(sys.argv[i+1]) input_text += mh.open_and_read_file(sys.argv[i+1]) print(input_text) chains = make_chains(input_text, n_gram) for chain in chains: print(chain,":",chains[chain]) random_text = mh.make_text(chains, n_gram) print(random_text)

UTF-8

Python

false

1,978

py

241

n-grams.py

206

0.702935

0.697874

0

58

33.068966

355

foxreymann/Violent-Python-Examples

9,285,719,335,126

7345c6f5a2500994b348de9369dea92b01bf4ced

6c4824d50ec1d302a37edaafc2abc82b3a7153df

/Chapter-1/iteration.py

f8ddffbc25bac66b25597978b124a074d89ad73c

[]

no_license

https://github.com/foxreymann/Violent-Python-Examples

223414bd410af54d3987898188c4365ca0682549

1f74792f1ec55f91569981c24cf44384ef396518

refs/heads/master

2021-08-06T06:25:09.375291

2017-11-03T16:53:06

109,045,939

0

null

true

2017-10-31T19:57:01

2017-10-31T19:56:59

2017-10-26T22:38:30

2012-12-23T19:08:49

844

0

null

false

null

import socket def retBanner(ip, port): try: socket.setdefaulttimeout(2) s = socket.socket() s.connect((ip,port)) banner = s.recv(1024) return banner.decode().strip('\n') except: return def checkVulns(banner): if "vsFTPd 3.0.3" in banner: return "vsFTPd is vulnerable" else: return "FTP Server is not vulnerable" def main(): ports = [21, 22, 80] for host in range(109, 112): ip = '100.109.237.' + str(host) for port in ports: banner = retBanner(ip, port) if banner: print(ip + ':' + str(port) + ': ' + banner) print(checkVulns(banner)) if __name__ == '__main__': main()

UTF-8

Python

false

737

py

4

iteration.py

3

0.522388

0.483039

0

30

23.566667

59

nekazino/pythonlab1

17,446,157,175,005

8345b90b387b177f4554bbaf8886e59a0a6b06db

9130af7f40de20a5fcf9eecfed747df1e4f84a0d

/src/program.py

b13a9b768647865255b830b0b91c07107032f2e7

[]

no_license

https://github.com/nekazino/pythonlab1

9e222081ecc393e75a117a7bea37d3cac7b896ba

a094fe24d91b9c521bb45a4e77be3b84bf2f5ab3

refs/heads/master

2019-04-09T14:50:28.232033

2017-05-06T19:00:32

89,696,201

1

0

null

false

2017-05-06T19:00:33

2017-04-28T10:34:23

2017-05-04T20:30:30

2017-05-06T19:00:33

10

0

Python

null

''' program ======= Provides a kivy.App implementation which create main program window. ''' import kivy kivy.require('1.6.0') from kivy.app import App from kivy.uix.boxlayout import BoxLayout from kivy.uix.button import Button from kivy.uix.label import Label from kivy.uix.popup import Popup from kivy.uix.textinput import TextInput from abstractdeptreefactory import * from treewidget import TreeWidget import threading class Program(App): '''kivy.App implementation which create main program window. ''' def __init__(self, dep_tree_factory): if not isinstance(dep_tree_factory, AbstractDepTreeFactory): raise TypeError("dep_tree_factory must be an AbstractDepTreeFactory implementation.") super(Program, self).__init__() self.factory = dep_tree_factory; def build(self): '''Initializes gui components. ''' parent = BoxLayout(padding=10, orientation="vertical") tb_package_name = TextInput(size_hint=(1, 0.05), multiline=False, hint_text="package name") btn_get_deps = Button(text="Build", size_hint=(1, 0.05)) trv_deps = TreeWidget(size_hint=(1, 0.85), hide_root=True) lbl_status = Label(text="ready", size_hint=(1, 0.05)) def load_dep(): lbl_status.text = "building..." try: trv_deps.load(self.factory.create(tb_package_name.text)) except Exception as e: btn = Button(text="OK") popup = Popup( title=str(e), content=btn, auto_dismiss=False, size_hint=(None, None), size=(350, 100) ) btn.bind(on_press=popup.dismiss) popup.open() lbl_status.text = "ready" btn_get_deps.bind(on_press=lambda instance: threading.Thread(target=load_dep).start()) parent.add_widget(tb_package_name) parent.add_widget(btn_get_deps) parent.add_widget(trv_deps) parent.add_widget(lbl_status) return parent

UTF-8

Python

false

2,129

py

10

program.py

6

0.596524

0.583842

0

65

31.753846

99

motyliak/motor

6,004,364,311,599

d1132f6b5bcf618c7a21c68db0911f66fdb1b21e

cae3f975267a5f410daede1394ca8a0558c94b2c

/main.py

3467eb2f82db0efc9e17f6d2dd58b58caf88887d

[]

no_license

https://github.com/motyliak/motor

c0bfecb4a9e62c7d7e3224686b2b649bd38da3d0

e64fcbcddcffe26e0aaadde3183b2be2a6cffa03

refs/heads/master

2022-11-07T20:37:43.839236

2020-06-23T21:23:18

273,765,453

0

null

def motor_off(): pins.digital_write_pin(DigitalPin.P0, 0) basic.show_icon(IconNames.NO) def on_button_pressed_a(): global remote_control remote_control = False motor_on() strip.rotate(1) strip.show() input.on_button_pressed(Button.A, on_button_pressed_a) def on_button_pressed_b(): global remote_control motor_off() remote_control = True input.on_button_pressed(Button.B, on_button_pressed_b) def motor_on(): pins.digital_write_pin(DigitalPin.P0, 1) basic.show_icon(IconNames.YES) strip: neopixel.Strip = None remote_control = False pins.digital_write_pin(DigitalPin.P0, 1) pins.set_pull(DigitalPin.P1, PinPullMode.PULL_UP) basic.show_icon(IconNames.HAPPY) remote_control = True strip = neopixel.create(DigitalPin.P2, 8, NeoPixelMode.RGB) strip.show_rainbow(1, 360) strip.show() def on_forever(): strip.show() if remote_control: if pins.digital_read_pin(DigitalPin.P1) == 0: motor_on() else: motor_off() basic.forever(on_forever)

UTF-8

Python

false

1,034

py

2

main.py

2

0.68472

0.669246

0

41

24.219512

59

flashiam/wodo-backend

1,348,619,737,609

e3b8a0fcda0e199e08cbf270bdb3f6cb0fcd67ce

43c61d5186ffe1e0ca1fba27ac71a860af3f1712

/wodo/migrations/0016_auto_20201127_0929.py

68bfb172e738c74a78b0ea858cfdbc49d294012a

[]

no_license

https://github.com/flashiam/wodo-backend

00e4b7b15dc60458419a86c04c4fff30bb0028a0

a020f7ab91410a03b921488d990d8013970b862f

refs/heads/master

2023-06-05T17:12:05.944944

2020-12-04T06:49:52

383,648,984

0

null

# Generated by Django 3.1.2 on 2020-11-27 09:29 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('wodo', '0015_auto_20201127_0928'), ] operations = [ migrations.AlterField( model_name='dutydenials', name='user', field=models.ForeignKey(default='shiva12', on_delete=django.db.models.deletion.SET_DEFAULT, to='wodo.appuser', to_field='username', verbose_name='username'), ), migrations.AlterField( model_name='filtercache', name='userF', field=models.ForeignKey(default='shiva12', on_delete=django.db.models.deletion.SET_DEFAULT, to='wodo.appuser', verbose_name='User'), ), migrations.AlterField( model_name='saved', name='userS', field=models.ForeignKey(default='shiva12', on_delete=django.db.models.deletion.SET_DEFAULT, to='wodo.appuser', to_field='username', verbose_name='User'), ), migrations.AlterField( model_name='transaction', name='userT', field=models.ForeignKey(default='shiva12', on_delete=django.db.models.deletion.SET_DEFAULT, to='wodo.appuser', to_field='username', verbose_name='User'), ), migrations.AlterField( model_name='workrating', name='userR', field=models.ForeignKey(default='shiva12', on_delete=django.db.models.deletion.SET_DEFAULT, to='wodo.appuser', to_field='username', verbose_name='User'), ), ]

UTF-8

Python

false

1,598

py

54

0016_auto_20201127_0929.py

34

0.62015

0.594493

0

39

39.974359

169

LsxnH/ATLAS

10,892,037,068,817

32c6ec1b144bde546142b7b664057afcd5892ce6

6b83126f9d0507129e732d559c63de21d7e3da6e

/smartScripts/checkEntries.py

73c9d35c68109200acf9d38408f25b209313ce04

[]

no_license

https://github.com/LsxnH/ATLAS

51773ee6496417eae0e211b443d31e3882003581

b1573bc0f4456c330749570ec7881e3f812615e6

refs/heads/master

2020-05-27T13:27:03.574661

2019-06-24T07:59:16

31,648,326

0

null

#!/usr/bin/env python import os, fnmatch import re import sys import ROOT def main(): listOfFiles = os.listdir(sys.argv[1]) pattern = "*.root" ntotal = 0 for ifile in listOfFiles: if fnmatch.fnmatch(ifile, pattern): inputfile = ROOT.TFile(sys.argv[1]+"/"+ifile,"READ") nominal = inputfile.Get("nominal") print ifile, " entries: ", nominal.GetEntries() ntotal += nominal.GetEntries() print "total entries: ", ntotal if __name__ == "__main__": main()

UTF-8

Python

false

540

py

8

checkEntries.py

3

0.581481

0.575926

0

21

24.714286

64

matibilkis/cartpole-tf2

7,662,221,676,487

14a043409180d2f101ba192a6231b7a94943cf35

1d2650d3e0e295635ed9e82e9decf0b34dc319be

/main.py

291f73b77e7f3d2367894c87ff6a5dd3109a12e7

[]

no_license

https://github.com/matibilkis/cartpole-tf2

1a62a14ddf2f167ae50f296de967a7a18b236733

c26457f1a18e4c1f614cec99800124d1506de9f2

refs/heads/master

2020-09-03T17:37:46.658348

2019-11-04T14:37:17

219,523,231

0

null

import gym import keras import datetime as dt import tensorflow as tf import random import numpy as np import math from tensorflow.keras.layers import Dense from tqdm import tqdm from gym import wrappers STORE_PATH = '/run' MAX_EPSILON = 1 MIN_EPSILON = 0.01 LAMBDA = 0.0005 GAMMA = 0.95 BATCH_SIZE = 40 TAU = 0.08 RANDOM_REWARD_STD = 1.0 # env = gym.make("MountainCar-v0") state_size = 4 env = gym.make("CartPole-v1") num_actions = env.action_space.n class Memory(): def __init__(self, max_memory): self._max_memory = max_memory self._samples = [] def add_sample(self, sample): self._samples.append(sample) if len(self._samples) > self._max_memory: self._samples.pop(0) def sample(self, no_samples): if no_samples > len(self._samples): return random.sample(self._samples, len(self._samples)) else: return random.sample(self._samples, no_samples) @property def num_samples(self): return len(self._samples) memory = Memory(50000) class QN(tf.keras.Model): def __init__(self): super(QN,self).__init__() self.l1 = Dense(30, input_shape=(4,), kernel_initializer='random_uniform', bias_initializer='random_uniform') self.l2 = Dense(35, kernel_initializer='random_uniform', bias_initializer='random_uniform') # self.l21 = Dense(90, kernel_initializer='random_uniform', # bias_initializer='random_uniform') self.l3 = Dense(num_actions, kernel_initializer='random_uniform', bias_initializer='random_uniform') def call(self, input): feat = tf.nn.relu(self.l1(input)) feat = tf.nn.relu(self.l2(feat)) # feat = tf.nn.relu(self.l21(feat)) value = self.l3(feat) return value def choose_action(state, primary_network, eps): if random.random() < eps: return random.randint(0, num_actions - 1) else: state = np.expand_dims(np.array(state),axis=0) #otherwise throuhg eerror.. return np.argmax(primary_network(state)) def train(primary_network, memory, tarket_network): if memory.num_samples < BATCH_SIZE*3: return 0 else: batch = memory.sample(BATCH_SIZE) states = np.array([val[0] for val in batch]) actions = np.array([val[1] for val in batch]) rewards = np.array([val[2] for val in batch]) next_states = np.array([(np.zeros(state_size) if val[3] is None else val[3]) for val in batch]) prim_qt = primary_network(np.expand_dims(states,axis=0)) # Q_t[s,a] prim_qtp1 = primary_network(np.expand_dims(next_states,axis=0)) #Q_{t+1}[s_{t+1},a_{t+1}] updates = rewards valid_idxs = np.array(next_states).sum(axis=1) != 0 batch_idxs = np.arange(BATCH_SIZE) opt_q_tp1_eachS = np.argmax(np.squeeze(prim_qtp1.numpy()), axis=1) # Argmax a_{t+1} Q_{t+1} [ s_{t+1}, a_{t+1}] q_from_target = target_network(np.expand_dims(next_states, axis=0)) #Q^{target} [ s, a] updates[valid_idxs] += GAMMA*np.squeeze(q_from_target.numpy())[valid_idxs, opt_q_tp1_eachS[valid_idxs]] # update = r + \gamma Q[s_{t+1}, a^{*}_{t+1}]; with a^{*}_{t+1} = ArgMax Q_{s_t+1, a_t+1} ###### In the disc case... a_t = \beta_1 .... > Q[\beta_1] -> Q^{target}[n_1, \beta_1; \beta_2^{*}] with \beta_2^{*} = ArgMax Q[n_1, \beta1, \BB2] #consequences: for each state in the first layer, the action will be someone. target_q = np.squeeze(prim_qt.numpy()) target_q[batch_idxs, actions] = updates with tf.device("/cpu:0"): with tf.GradientTape() as tape: tape.watch(primary_network.trainable_variables) predicted_q = primary_network(states) target_q = np.expand_dims(target_q,axis=0) loss = tf.keras.losses.MSE(predicted_q, target_q) loss = tf.reduce_mean(loss) grads = tape.gradient(loss, primary_network.trainable_variables) optimizer.apply_gradients(zip(grads, primary_network.trainable_variables)) for t, e in zip(target_network.trainable_variables, primary_network.trainable_variables): t.assign(t*(1-TAU) + e*TAU) return loss save=True # for agent in range(1): env = gym.make("CartPole-v0") env = wrappers.Monitor(env, './videos/' + str(2) + '/') primary_network = QN() target_network = QN() optimizer = tf.keras.optimizers.Adam(lr=0.01) num_episodes = 200 eps = 1 render = False # train_writer = tf.summary.create_file_writer("summarie/1") steps = 0 rews=[] times=[] for i in range(num_episodes): state = env.reset() cnt=0 avg_loss=0 while True: # env.render() action = choose_action(state, primary_network, eps) next_state, reward, done, info = env.step(action) reward = np.random.normal(1.0, RANDOM_REWARD_STD) if cnt==300: done = True if done: next_state = None memory.add_sample((state, action, reward, next_state)) loss = train(primary_network, memory, target_network) avg_loss += loss state = next_state steps +=1 eps = MIN_EPSILON + (MAX_EPSILON - MIN_EPSILON)*np.exp(- LAMBDA*steps) if done: avg_loss /= cnt print(f"Episode: {i}, Reward: {cnt}, avg loss: {avg_loss:.3f}, eps: {eps:.3f}") rews.append(cnt) times.append(i+1) # with train_writer.as_default(): # tf.summary.scalar('reward', cnt, step=i) # tf.summary.scalar('avg loss', avg_loss, step=i) break cnt += 1 if save: np.save("data"+str(agent),np.array([times, rews]), allow_pickle=True)

UTF-8

Python

false

6,001

py

22

main.py

9

0.575571

0.556741

0

173

33.687861

201

luoluo/TestDataGenerator

6,270,652,252,250

0cb4962eb344d4dbc4498cf7d89dbe83b3960e83

d56cc09d758149cbaef30e59908df5e509209daf

/exectutor.py

c9f6f1519d925f21845b9856b2d9017e6a0b9a36

[]

no_license

https://github.com/luoluo/TestDataGenerator

e4a4cd6ba5edca3a639fc681b8d747a365f1a753

fec7ab70ca9f3b32dd582712c644508cd587b72a

refs/heads/master

2016-09-06T09:39:04.364948

2014-08-01T08:05:30

null

0

null

from myConfig import MyConfig from generator import * class Exectutor(): def __init__(self): pass def run(self): testDataGenerator = TestDataGenerator() testDataGenerator.loadDescriptionFromFIle("generate.cfg") testDataGenerator.generate()

UTF-8

Python

false

280

py

12

exectutor.py

8

0.692857

0

9

30.111111

65

github/codeql

13,348,758,363,030

89d5197ecbae5dacfee8b96debd685a55a645db0

167c6226bc77c5daaedab007dfdad4377f588ef4

/python/ql/test/library-tests/frameworks/django-orm/testapp/tests.py

6b1bcb7e83c37e8ff2ddcfad617790a1663589ee

[ "MIT", "LicenseRef-scancode-python-cwi", "LicenseRef-scancode-other-copyleft", "GPL-1.0-or-later", "LicenseRef-scancode-free-unknown", "Python-2.0" ]

permissive

https://github.com/github/codeql

1eebb449a34f774db9e881b52cb8f7a1b1a53612

d109637e2d7ab3b819812eb960c05cb31d9d2168

refs/heads/main

2023-08-20T11:32:39.162059

2023-08-18T14:33:32

143,040,428

5,987

1,363

MIT

false

2023-09-14T19:36:50

2018-07-31T16:35:51

2023-09-14T08:53:44

2023-09-14T18:02:59

281,478

6,371

1,465

964

CodeQL

false

import importlib import re import pytest # Create your tests here. def discover_save_tests(): mod = importlib.import_module("testapp.orm_tests") test_names = [] for name in dir(mod): m = re.match("test_(save.*)_load", name) if not m: continue name = m.group(1) test_names.append(name) return test_names def discover_load_tests(): mod = importlib.import_module("testapp.orm_tests") test_names = [] for name in dir(mod): m = re.match("test_(load.*)", name) if not m: continue name = m.group(1) if name == "load_init": continue test_names.append(name) return test_names @pytest.mark.django_db @pytest.mark.parametrize("name", discover_save_tests()) def test_run_save_tests(name): mod = importlib.import_module("testapp.orm_tests") init_func = getattr(mod, f"test_{name}_init", None) store_func = getattr(mod, f"test_{name}_store", None) load_func = getattr(mod, f"test_{name}_load", None) if init_func: init_func() store_func() load_func() has_run_load_init = False @pytest.fixture def load_test_init(): from .orm_tests import test_load_init test_load_init() @pytest.mark.django_db @pytest.mark.parametrize("name", discover_load_tests()) def test_run_load_tests(load_test_init, name): mod = importlib.import_module("testapp.orm_tests") load_func = getattr(mod, f"test_{name}", None) load_func() assert getattr(mod, "TestLoad").objects.count() == 10 @pytest.mark.django_db def test_mymodel_form_save(): from .orm_form_test import MyModel, MyModelForm import uuid text = str(uuid.uuid4()) form = MyModelForm(data={"text": text}) form.save() obj = MyModel.objects.last() assert obj.text == text @pytest.mark.django_db def test_none_all(): from .orm_form_test import MyModel MyModel.objects.create(text="foo") assert len(MyModel.objects.all()) == 1 assert len(MyModel.objects.none().all()) == 0 assert len(MyModel.objects.all().none()) == 0 @pytest.mark.django_db def test_orm_inheritance(): from .orm_inheritance import (save_physical_book, save_ebook, save_base_book, fetch_book, fetch_physical_book, fetch_ebook, PhysicalBook, EBook, ) base = save_base_book() physical = save_physical_book() ebook = save_ebook() fetch_book(base.id) fetch_book(physical.id) fetch_book(ebook.id) fetch_physical_book(physical.id) fetch_ebook(ebook.id) try: fetch_physical_book(base.id) except PhysicalBook.DoesNotExist: pass try: fetch_ebook(ebook.id) except EBook.DoesNotExist: pass @pytest.mark.django_db def test_poly_orm_inheritance(): from .orm_inheritance import (poly_save_physical_book, poly_save_ebook, poly_save_base_book, poly_fetch_book, poly_fetch_physical_book, poly_fetch_ebook, PolyPhysicalBook, PolyEBook, ) base = poly_save_base_book() physical = poly_save_physical_book() ebook = poly_save_ebook() poly_fetch_book(base.id, test_for_subclass=False) poly_fetch_book(physical.id) poly_fetch_book(ebook.id) poly_fetch_physical_book(physical.id) poly_fetch_ebook(ebook.id) try: poly_fetch_physical_book(base.id) except PolyPhysicalBook.DoesNotExist: pass try: poly_fetch_ebook(ebook.id) except PolyEBook.DoesNotExist: pass

UTF-8

Python

false

3,504

py

27,332

tests.py

11,432

0.63984

0.637557

0

143

23.503497

96

Parkyunhwan/BaekJoon

19,524,921,348,316

0f6e4aa765ab25c701382bef01c34b3c3c096659

c3432a248c8a7a43425c0fe1691557c0936ab380

/21_04/13/1197_최소스패닝트리.py

2559f831fbdfe6172a7933360822f395f5faebba

[]

no_license

https://github.com/Parkyunhwan/BaekJoon

13cb3af1f45212d7c418ecc4b927f42615b14a74

9a882c568f991c9fed3df45277f091626fcc2c94

refs/heads/master

2022-12-24T21:47:47.052967

2022-12-20T16:16:59

232,264,447

3

0

null

V, E = map(int, input().split()) parent = [x for x in range(V + 1)] def find_parent(parent, x): if parent[x] != x: parent[x] = find_parent(parent, parent[x]) return parent[x] def union(parent, a, b): a = find_parent(parent, a) b = find_parent(parent, b) if a > b: parent[a] = b else: parent[b] = a graph = [] result = 0 for _ in range(E): a, b, cost = map(int, input().split()) graph.append([cost, a, b]) graph.sort() for cost, a, b in graph: if find_parent(parent, a) != find_parent(parent, b): union(parent, a, b) result += cost print(result)

UTF-8

Python

false

627

py

425

1197_최소스패닝트리.py

413

0.548644

0.545455

0

31

19.258065

56

k6project/scene3d

19,164,144,076,984

cef70f8324564892a738a17695a54d7ff1a2d9f5

af8c31763223f3634ef329ec5725d9f881e4f649

/python/s3dexport.py

94e6944a66a7e12dea2d09d4eb938de2c038ad6f

[]

no_license

https://github.com/k6project/scene3d

af2d4236d44bf6b31bfdb82e808b7ed7a29632c5

f5ac65d8d7004a795879f3e191e9fe8efb9526c9

refs/heads/master

2020-03-26T14:42:57.837876

2019-03-18T14:17:37

145,001,289

0

null

bl_info = { "name": "Scene3D binary format", "category": "Import-Export", "support": "TESTING" } import bpy import bmesh import s3dconv from bpy.props import StringProperty from bpy_extras.io_utils import ExportHelper class S3DExport(bpy.types.Operator, ExportHelper): bl_idname = "export_scene.s3d" bl_label = "Export for Scene3D" bl_options = {'PRESET'} filename_ext = ".s3d" filter_glob = StringProperty(default="*.s3d", options={"HIDDEN"}) def execute(self, context): s3dconv.begin(self.filepath) scene = context.scene for obj in scene.objects: if obj.type == "MESH": mesh = obj.to_mesh(context.scene, True, settings='RENDER', calc_tessface=False) tmp = bmesh.new() tmp.from_mesh(mesh) bmesh.ops.triangulate(tmp, faces=tmp.faces) tmp.to_mesh(mesh) tmp.free() mesh.calc_normals_split() for face in mesh.polygons: indices = [] for li in face.loop_indices: loop = mesh.loops[li] v = mesh.vertices[loop.vertex_index] vdata = [ v.co.x, v.co.y, v.co.z ] + [loop.normal.x, loop.normal.y, loop.normal.z] indices.append(s3dconv.add_vertex(vdata)) s3dconv.add_face(indices) s3dconv.end() return {"FINISHED"} def menu_func_export(self, context): self.layout.operator("export_scene.s3d", text="Scene3D (.s3d)") def register(): bpy.utils.register_class(S3DExport) bpy.types.INFO_MT_file_export.append(menu_func_export) def unregister(): bpy.utils.unregister_class(S3DExport) bpy.types.INFO_MT_file_export.remove(menu_func_export) if __name__ == "__main__": register()

UTF-8

Python

false

1,864

py

62

s3dexport.py

23

0.575644

0.56706

0

54

33.518519

106

MichaelT2828/Unit_1

16,372,415,370,225

01f57d57569d9134bc8c2dbde33e857efeaac3e3

c70e91646853a5e6732fcb56f5238cd5da840cee

/least common multiple.py

e224d673a4a8ea7f05b82b38af599a71be11239a

[]

no_license

https://github.com/MichaelT2828/Unit_1

48bbdcc7b80e53372bbb0ea7350a2c37c87ea564

d47e06972b4a2b43306be8f1fa85d1af76d1afaa

refs/heads/main

2023-09-05T22:38:25.696152

2021-10-28T05:44:51

398,943,742

0

null

def lcm(a, b, c): #find biggest value biggest = 0 if a > b and a > c: biggest = a elif b > a and b > c: biggest = b elif c > a and c > a: biggest = c while(True): if (biggest % a == 0) and (biggest % b == 0) and (biggest % c == 0): lcm = biggest break biggest += 1 return biggest print(lcm(18, 4, 7))

UTF-8

Python

false

395

py

14

least common multiple.py

5

0.455696

0.432911

0

18

20.944444

76

google-research/language

15,453,292,334,085

8d2de083ec705cba9d43eea3398d9d3abcceff5b

ab40571d5051ad53c0f205fa797ba36eac516d06

/language/bert_extraction/steal_bert_classifier/data_generation/build_aux_membership.py

7b7c0907a3afc445d961d81595eda52df4c44c9f

[ "Apache-2.0", "LicenseRef-scancode-generic-cla" ]

permissive

https://github.com/google-research/language

e941b1a92ab46d40d8d03bb0c314905cb6902ce2

ac9447064195e06de48cc91ff642f7fffa28ffe8

refs/heads/master

2023-08-24T23:10:13.207294

2023-05-25T20:47:18

2023-05-25T22:29:27

153,201,352

1,567

371

Apache-2.0

false

2023-07-06T23:03:15

2018-10-16T00:58:14

2023-07-05T08:17:26

2023-07-06T23:03:14

6,047

1,468

339

85

Python

false

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in 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. """Construct auxiliary membership classification test sets (RANDOM, SHUFFLE) to check the generalization of the classifier (Section 6.1).""" import random import tensorflow.compat.v1 as tf app = tf.app flags = tf.flags gfile = tf.gfile logging = tf.logging flags.DEFINE_string("membership_dev_data", None, "File with original membership classification dev data") flags.DEFINE_string( "random_membership_dev_data", None, "membership classification dev data built from RANDOM scheme") flags.DEFINE_string("aux_path", None, "Path to output the auxiliary membership datasets") FLAGS = flags.FLAGS def main(_): with gfile.Open(FLAGS.membership_dev_data, "r") as f: orig_dev_data = f.read().strip().split("\n") orig_dev_header = orig_dev_data[0] orig_dev_data = orig_dev_data[1:] true_data_membership = [] for point in orig_dev_data: if point.split("\t")[-1] == "true": true_data_membership.append(point.split("\t")[1:]) random.shuffle(true_data_membership) combined_data = [] # shuffle both premise and hypothesis of the original dev data to create # "fake" examples for point in true_data_membership: combined_data.append(point) premise_tokens = point[0].split() hypo_tokens = point[1].split() random.shuffle(premise_tokens) random.shuffle(hypo_tokens) fake_point = [" ".join(premise_tokens), " ".join(hypo_tokens), "fake"] combined_data.append(fake_point) random.shuffle(combined_data) final_split = "\n".join( [orig_dev_header] + ["%d\t%s" % (i, "\t".join(x)) for i, x in enumerate(combined_data)]) gfile.MakeDirs(FLAGS.aux_path + "/shuffle") with gfile.Open(FLAGS.aux_path + "/shuffle/dev.tsv", "w") as f: f.write(final_split) with gfile.Open(FLAGS.random_membership_dev_data, "r") as f: random_dev_data = f.read().strip().split("\n") random_dev_data = random_dev_data[1:] fake_data_membership = [] for point in random_dev_data: if point.split("\t")[-1] == "fake": fake_data_membership.append(point.split("\t")[1:]) # combine the "true" examples from the original membership dev set with "fake" # examples from the RANDOM dev set combined_data = true_data_membership + fake_data_membership random.shuffle(combined_data) final_split = "\n".join( [orig_dev_header] + ["%d\t%s" % (i, "\t".join(x)) for i, x in enumerate(combined_data)]) gfile.MakeDirs(FLAGS.aux_path + "/random") with gfile.Open(FLAGS.aux_path + "/random/dev.tsv", "w") as f: f.write(final_split) if __name__ == "__main__": app.run(main)

UTF-8

Python

false

3,237

py

956

build_aux_membership.py

809

0.676552

0.670065

0

103

30.427184

140

ekorkut/hackerrank

7,129,645,743,679

efce8bf0c1c720df441f68f370240048c36c3e2c

4ded0f0206710baa717f4e538e60800bc5cea857

/algorithms/strings/sherlock_and_valid_strings/main.py

f22cff156e6e90a6e8ba6a25336da07a9107a5e4

[]

no_license

https://github.com/ekorkut/hackerrank

c8f7456bf2f7a0812c60016ca82200380de93a77

fdcde54a421ebc5c8a007c5f050971fafb727b33

refs/heads/master

2018-10-10T18:33:22.900269

2018-06-24T14:10:02

82,475,484

0

null

#!/opt/bb/bin/bbpy2.7 input_str = raw_input().strip() char_to_freq = {} for c in input_str: if c in char_to_freq: char_to_freq[c] += 1 else: char_to_freq[c] = 1 freqs = [char_to_freq[x] for x in char_to_freq] distinct_freqs = set(freqs) print freqs print distinct_freqs if len(distinct_freqs) == 1: print "YES" elif len(distinct_freqs) > 2: print "NO" else: small_freq = min(distinct_freqs) small_count = freqs.count(small_freq) large_freq = max(distinct_freqs) large_count = freqs.count(large_freq) if (small_freq == 1) and (small_count == 1): print "YES" elif (large_freq-small_freq == 1) and (large_count == 1): print "YES" else: print "NO"

UTF-8

Python

false

744

py

47

main.py

35

0.591398

0.577957

0

30

23.633333

61

cal-poly-dxhub/familycaresurveytool

17,738,214,945,505

96a381909fae26be5f25c4ac76dec449009d5dca

e8442d6fe7b77880f532e882c9d80b324683e3fc

/lambda/PostResult/db_config.py

6d5534b224192e4e002d2ea7252d1d0731acbf69

[ "Apache-2.0" ]

permissive

https://github.com/cal-poly-dxhub/familycaresurveytool

12b86ccbc6074af33e360df7f80338572f503cf3

2adac2af91abdc3d6bd7dc5b85e1801ca4071687

refs/heads/master

2023-01-10T19:31:00.452654

2020-08-03T21:53:05

240,388,053

0

null

false

2023-01-06T02:32:59

2020-02-13T23:25:03

2020-08-03T21:53:08

2023-01-06T02:32:58

6,891

0

21

Python

false

RDS_HOST='' RDS_USER='' RDS_PW='' RDS_DB=''

UTF-8

Python

false

44

py

16

db_config.py

12

0.545455

0

4

10

11

mylekiller/NLPProject

13,237,089,231,672

370ff87b8a55f7b1c3557ef90be199c3451e8bdd

d3194b506bde8b33a331e99348fd8c488b0cbe66

/align.py

bedd7bad3226e9d7422eebda78fe64f8336123e7

[]

no_license

https://github.com/mylekiller/NLPProject

a014aec336ed852036dccd703a739aef5b39b812

226a8cbeab9bbf3cdcbe26c19f15c325386b4288

refs/heads/master

2020-04-11T10:05:57.322113

2018-12-14T08:26:30

161,703,223

0

null

import numpy import sys # -*- coding: utf-8 -*- params = dict() ewordtypes = set() fwordtypes = set() counts = dict() outputfile = 'align.out' def EM(trainfile): log_likelihood = 0 counts = dict() #E with open(trainfile) as file: for line in file: e = line.strip().split('\t')[1] f = line.strip().split('\t')[0] ewords = e.strip().split(' ') fwords = f.strip().split(' ') log_product = 1 for j in range(len(fwords)): summation = 0 for i in range(len(ewords) + 1): eword = '' fword = fwords[j] if i == 0: eword = 'NULL' else: eword = ewords[i - 1] summation += params[eword][fword] log_product *= (1.0/(len(ewords) + 1))*(summation) log_product *= (1./100.) log_likelihood += numpy.log(log_product) for j in range(len(fwords)): total = 0.0 for word in ewords: total += params[word][fwords[j]] for i in range(len(ewords) + 1): eword = '' fword = fwords[j] if i == 0: eword = 'NULL' else: eword = ewords[i - 1] if fword not in counts: counts[fword] = dict() if eword not in counts[fword]: counts[fword][eword] = params[eword][fword] / total else: counts[fword][eword] += params[eword][fword] / total for e in ewordtypes: total = 0 for fp in params[e]: total += counts[fp][e] for f in params[e]: params[e][f] = counts[f][e]/total return log_likelihood if __name__ == "__main__": args = sys.argv[1:] if len(args) != 1: print("Wrong number of arguments. Expected: 1") exit(1) trainfile = args[0] #read in file with open(trainfile) as file: for line in file: e = line.strip().split('\t')[1] f = line.strip().split('\t')[0] ewords = e.strip().split(' ') ewords.append('NULL') for eword in ewords: for fword in f.strip().split(' '): ewordtypes.add(eword) fwordtypes.add(fword) if eword not in params: params[eword] = dict() if fword not in params[eword]: params[eword][fword] = 0.0 #initialize to uniform for e in params: total = 0.0 for f in params[e]: total += 1.0 for f in params[e]: params[e][f] = 1.0/total print("Training model") for iteration in range(10): print("Starting iteration: {}".format(iteration)) log_likelihood = EM(trainfile) print("The log_likelihood was: {}".format(log_likelihood)) print("Testing model from train.zh-en and writing to align.out") with open(outputfile, 'w') as outfile, open(trainfile) as infile: for line in infile: e = line.strip().split('\t')[1] f = line.strip().split('\t')[0] ewords = e.strip().split(' ') ewords.append('NULL') fwords = f.strip().split(' ') for j in range(len(fwords)): best = None for i in range(len(ewords)): if not best or params[ewords[i]][fwords[j]] > best[2]: best = (j, i, params[ewords[i]][fwords[j]]) if not best[1] == len(ewords) - 1: outfile.write('{}-{} '.format(best[0],best[1])) else: outfile.write('{}-{} '.format(best[0], '_' )) outfile.write('\n')

UTF-8

Python

false

3,062

py

11

align.py

7

0.583932

0.568583

0

123

23.886179

66

Sudarsan-Sridharan/shift-python

360,777,299,039

4b1c2002561ba3dc82e50bb2c101fc56275d3960

08727db800af02da3d7b0f3ec6921b0670bed898

/demo/demo.py

221cd64c062285dc3387a425509e4d98c1f8f8af

[]

no_license

https://github.com/Sudarsan-Sridharan/shift-python

bc65510656a9f03d960f98d158ad5a93b5248dc5

0eadb1d2b6c1dd108f2387355d1af7405bc057ff

refs/heads/master

2020-06-03T08:10:18.376394

2019-06-08T03:40:42

null

0

null

import shift import sys import time def demo01(trader): """ This method submits a limit buy order by indicating order type, symbol, size, and limit price. :param trader: :return: """ limit_buy = shift.Order(shift.Order.LIMIT_BUY, "AAPL", 1, 10.00) trader.submitOrder(limit_buy) return def demo02(trader): """ This method submits 2 limit buy orders by indicating order type, symbol, size, and limit price. :param trader: :return: """ aapl_limit_buy = shift.Order(shift.Order.LIMIT_BUY, "AAPL", 10, 10.00) trader.submitOrder(aapl_limit_buy) xom_limit_buy = shift.Order(shift.Order.LIMIT_BUY, "XOM", 10, 10.00) trader.submitOrder(xom_limit_buy) return def demo03(trader): """ This method prints the local bid order book for corresponding symbols. :param trader: :return: """ print("AAPL:") print(" Price\t\tSize\t Dest\t\tTime") for order in trader.getOrderBook("AAPL", shift.OrderBookType.LOCAL_BID): print("%7.2f\t\t%4d\t%6s\t\t%19s" % (order.price, order.size, order.destination, order.time)) print() print("XOM:") print(" Price\t\tSize\t Dest\t\tTime") for order in trader.getOrderBook("XOM", shift.OrderBookType.LOCAL_BID): print("%7.2f\t\t%4d\t%6s\t\t%19s" % (order.price, order.size, order.destination, order.time)) def demo04(trader): """ This method prints all current waiting orders information. :param trader: :return: """ print("Symbol\t\t\t\tType\t Price\t\tSize\tExecuted\tID\t\t\t\t\t\t\t\t\t\t\t\t\t\t Status\t\tTimestamp") for order in trader.getWaitingList(): print("%6s\t%16s\t%7.2f\t\t%4d\t\t%4d\t%36s\t%23s\t\t%26s" % (order.symbol, order.type, order.price, order.size, order.executed_size, order.id, order.status, order.timestamp)) return def demo05(trader): """ This method cancels all the orders in the waiting list. :param trader: :return: """ print("Symbol\t\t\t\tType\t Price\t\tSize\tExecuted\tID\t\t\t\t\t\t\t\t\t\t\t\t\t\t Status\t\tTimestamp") for order in trader.getWaitingList(): print("%6s\t%16s\t%7.2f\t\t%4d\t\t%4d\t%36s\t%23s\t\t%26s" % (order.symbol, order.type, order.price, order.size, order.executed_size, order.id, order.status, order.timestamp)) print() print("Waiting list size: " + str(trader.getWaitingListSize())) print("Canceling all pending orders...", end=" ") # trader.cancelAllPendingOrders() also works for order in trader.getWaitingList(): trader.submitCancellation(order) i = 0 while trader.getWaitingListSize() > 0: i += 1 print(i, end=" ") time.sleep(1) print() print("Waiting list size: " + str(trader.getWaitingListSize())) return def demo06(trader): """ This method shows how to submit market buy orders. :param trader: :return: """ aapl_market_buy = shift.Order(shift.Order.MARKET_BUY, "AAPL", 1) trader.submitOrder(aapl_market_buy) xom_market_buy = shift.Order(shift.Order.MARKET_BUY, "XOM", 1) trader.submitOrder(xom_market_buy) return def demo07(trader): """ This method provides information on the structure of PortfolioSummary and PortfolioItem objects: getPortfolioSummary() returns a PortfolioSummary object with the following data: 1. Total Buying Power (totalBP) 2. Total Shares (totalShares) 3. Total Realized Profit/Loss (totalRealizedPL) 4. Timestamp of Last Update (timestamp) getPortfolioItems() returns a dictionary with "symbol" as keys and PortfolioItem as values, with each providing the following information: 1. Symbol (getSymbol()) 2. Shares (getShares()) 3. Price (getPrice()) 4. Realized Profit/Loss (getRealizedPL()) 5. Timestamp of Last Update (getTimestamp()) :param trader: :return: """ print("Buying Power\tTotal Shares\tTotal P&L\tTimestamp") print("%12.2f\t%12d\t%9.2f\t%26s" % (trader.getPortfolioSummary().getTotalBP(), trader.getPortfolioSummary().getTotalShares(), trader.getPortfolioSummary().getTotalRealizedPL(), trader.getPortfolioSummary().getTimestamp())) print() print("Symbol\t\tShares\t\tPrice\t\t P&L\tTimestamp") for item in trader.getPortfolioItems().values(): print("%6s\t\t%6d\t%9.2f\t%9.2f\t%26s" % (item.getSymbol(), item.getShares(), item.getPrice(), item.getRealizedPL(), item.getTimestamp())) return def demo08(trader): """ This method shows how to submit market sell orders. :param trader: :return: """ aapl_market_sell = shift.Order(shift.Order.MARKET_SELL, "AAPL", 1) trader.submitOrder(aapl_market_sell) xom_market_sell = shift.Order(shift.Order.MARKET_SELL, "XOM", 1) trader.submitOrder(xom_market_sell) return def demo09(trader): """ This method prints all submitted orders information. :param trader: :return: """ print("Symbol\t\t\t\tType\t Price\t\tSize\tExecuted\tID\t\t\t\t\t\t\t\t\t\t\t\t\t\t Status\t\tTimestamp") for order in trader.getSubmittedOrders(): if order.executed_size == order.size: price = order.executed_price else: price = order.price print("%6s\t%16s\t%7.2f\t\t%4d\t\t%4d\t%36s\t%23s\t\t%26s" % (order.symbol, order.type, price, order.size, order.executed_size, order.id, order.status, order.timestamp)) return def demo10(trader): """ This method prints the global bid order book for a corresponding symbol and type. :param trader: :return: """ print(" Price\t\tSize\t Dest\t\tTime") for order in trader.getOrderBook("AAPL", shift.OrderBookType.GLOBAL_BID, 5): print("%7.2f\t\t%4d\t%6s\t\t%19s" % (order.price, order.size, order.destination, order.time)) def main(argv): # create trader object trader = shift.Trader("democlient") # connect and subscribe to all available order books try: trader.connect("initiator.cfg", "password") trader.subAllOrderBook() except shift.IncorrectPassword as e: print(e) except shift.ConnectionTimeout as e: print(e) # demo01(trader) # demo02(trader) # demo03(trader) # demo04(trader) # demo05(trader) # demo06(trader) # demo07(trader) # demo08(trader) # demo09(trader) # demo10(trader) # disconnect trader.disconnect() return if __name__ == "__main__": main(sys.argv)

UTF-8

Python

false

6,781

py

17

demo.py

11

0.624982

0.602861

0

241

27.136929

111

zcZhangCheng/3d_vision

7,318,624,307,881

fcf35fadafbe104cbe6e5b22f13538c3360d8b40

13c587886fc2b8398ed67f96fd6cf3c668e55410

/visual_map/output_re.py

fa25442a19fd1078e8630c2b1cbd3e9ef19919c3

[]

no_license

https://github.com/zcZhangCheng/3d_vision

99288f177e75b7259ec710cf4cf9d469c6657904

3555c4d8ddfec6574f1d94bc81e3a443dcee67d1

refs/heads/master

2023-03-17T16:06:25.909697

2019-07-26T09:43:31

null

0

null

import os import sys d = sys.argv[1] out_file=os.path.join(d, 'final_reuslt.txt') with open(out_file,'w') as fw: fw.seek(0,0) file_list = os.listdir(d) for folder in file_list: full_addr = os.path.join(d, folder) if os.path.isdir(full_addr): if ".bag" in folder: map_name=folder file_list1 = os.listdir(full_addr) for folder1 in file_list1: full_addr1 = os.path.join(full_addr, folder1) if os.path.isdir(full_addr1): if ".bag" in folder1: loc_name=folder1 print(full_addr1) if not os.path.isfile(os.path.join(full_addr1, 'raw_match.txt')): fw.write(map_name+' + '+loc_name+' : '+'failed!!!\n') continue os.system('python ./assessLoc.py '+full_addr1+' ./assessConfig.yaml') with open(os.path.join(full_addr1, 'scale_match.txt'),'r') as fr: fr.seek(0,0) re_str = fr.readline() fw.write(map_name+' + '+loc_name+' : '+re_str+'\n')

UTF-8

Python

false

1,318

py

143

output_re.py

96

0.430956

0.418058

0

27

46.407407

97

BomberDim/Python-practice

16,106,127,401,228

66423a5ebd3a8ea657ff70efe986de3159bde8ec

f2348887dbffbd2124376b97c942939e78093e8f

/examples№1/example1.py

95b7606a0521983aea855b0e92db38fcbd728966

[]

no_license

https://github.com/BomberDim/Python-practice

f2d0c9051b67a0269b04861c205d268611ed259b

b55baabaed088449dc70cda9f09e706f3614fbf3

refs/heads/master

2020-05-19T09:57:42.117307

2015-12-13T16:17:06

42,327,071

0

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from math import cos, pi x = 1.426 y = -1.220 z = 3.5 a = 2 * cos(x - pi/6)/0.5 + ((1 - cos(2*y))/2) b = 1 + (2 / (3 + (z**2/5))) print("a =", a) print("b =", b) input()

UTF-8

Python

false

224

py

84

example1.py

84

0.455357

0.348214

0

18

11.444444

46

itsolutionscorp/AutoStyle-Clustering

2,989,297,251,869

f1bb4d1f9b770ca29b16988737155debc2777e26

781e2692049e87a4256320c76e82a19be257a05d

/all_data/exercism_data/python/difference-of-squares/d5d1107acb684775a1fb8d66887d2658.py

601132579eb0f44eeb770164dbdfbd1e2736bae4

[]

no_license

https://github.com/itsolutionscorp/AutoStyle-Clustering

54bde86fe6dbad35b568b38cfcb14c5ffaab51b0

be0e2f635a7558f56c61bc0b36c6146b01d1e6e6

refs/heads/master

2020-12-11T07:27:19.291038

2016-03-16T03:18:00

2016-03-16T03:18:42

59,454,921

4

0

null

true

2016-05-23T05:40:56

2016-05-19T22:14:37

2016-05-19T22:35:40

133,854

0

null

def square_of_sum(n): return (sum(range(1, n + 1)) ** 2) def sum_of_squares(n): return (sum([i ** 2 for i in range(1, n + 1)])) def difference(n): return (square_of_sum(n) - sum_of_squares(n))

UTF-8

Python

false

209

py

54,209

d5d1107acb684775a1fb8d66887d2658.py

21,653

0.569378

0.54067

0

10

19.9

51

korJAEYOUNGYUN/django-youtube_clone

4,896,262,734,569

e98eaea03db7d6145d0bead55c4c9b35d23054b4

b73762c15cf6b8a835400fbd2a8aeaef35ad50ab

/django_youtube_clone/apps/video/urls.py

80c29861f912b3ef7d3bdfdb9fa1bc9e86090be0

[]

no_license

https://github.com/korJAEYOUNGYUN/django-youtube_clone

b1c7df1656e2effa1d6ef0a8f38407a962e1d646

0e13a81286cceb578372ecb751accdeab395e9d7

refs/heads/master

2022-11-27T21:45:24.257145

2020-06-10T03:35:08

242,132,236

1

0

null

false

2022-11-22T05:24:05

2020-02-21T12:17:01

2020-11-27T14:37:13

2022-11-22T05:24:01

74

1

0

2

Python

false

from django.urls import path from django_youtube_clone.apps.video import views urlpatterns = [ path('', views.Home.as_view(), name='home'), path('videos/upload/', views.Upload.as_view(), name='upload'), path('search/', views.Search.as_view(), name='search'), path('videos/<int:id>/edit', views.EditVideo.as_view(), name='edit_video'), path('videos/<int:pk>/', views.VideoDetail.as_view(), name='video_detail'), path('videos/<int:pk>/delete', views.DeleteVideo.as_view(), name='delete_video'), ]

UTF-8

Python

false

521

py

21

urls.py

11

0.664107

0

12

42.416667

85

taichi-dev/taichi-nerfs

10,161,892,625,963

261618bd915febe62817ce73c06786f3fb7d0fd3

59a3d1c5739d27df28a7904ec32f908575427641

/modules/utils.py

d7bac1579736d9e69b018f6ba07eecf54242ac90

[ "Apache-2.0" ]

permissive

https://github.com/taichi-dev/taichi-nerfs

8d91e66b29aa9f0712e177c38ee6f257c4288e77

50bac170584628df6a7017b77d76e780296f0e6c

refs/heads/main

2023-07-10T07:01:29.423283

2023-05-29T05:53:38

610,072,946

548

37

Apache-2.0

false

2023-05-29T05:53:40

2023-03-06T02:58:21

2023-05-29T05:43:43

2023-05-29T05:53:39

157,331

459

32

8

Python

false

import os import cv2 import torch import numpy as np import taichi as ti from taichi.math import uvec3 data_type = ti.f32 torch_type = torch.float32 MAX_SAMPLES = 1024 NEAR_DISTANCE = 0.01 SQRT3 = 1.7320508075688772 SQRT3_MAX_SAMPLES = SQRT3 / 1024 SQRT3_2 = 1.7320508075688772 * 2 def res_in_level_np( level_i, base_res, log_per_level_scale ): result = np.ceil( float(base_res) * np.exp( float(level_i) * log_per_level_scale ) - 1.0 ) return float(result + 1) def scale_in_level_np( base_res, max_res, levels, ): result = np.log( float(max_res) / float(base_res) ) / float(levels - 1) return result def align_to(x, y): return int((x+y-1)/y)*y @ti.kernel def random_initialize(data: ti.types.ndarray()): for I in ti.grouped(data): data[I] = (ti.random() * 2.0 - 1.0) * 1e-4 @ti.func def scalbn(x, exponent): return x * ti.math.pow(2, exponent) @ti.func def calc_dt(t, exp_step_factor, grid_size, scale): return ti.math.clamp(t * exp_step_factor, SQRT3_MAX_SAMPLES, SQRT3_2 * scale / grid_size) @ti.func def frexp_bit(x): exponent = 0 if x != 0.0: # frac = ti.abs(x) bits = ti.bit_cast(x, ti.u32) exponent = ti.i32((bits & ti.u32(0x7f800000)) >> 23) - 127 # exponent = (ti.i32(bits & ti.u32(0x7f800000)) >> 23) - 127 bits &= ti.u32(0x7fffff) bits |= ti.u32(0x3f800000) frac = ti.bit_cast(bits, ti.f32) if frac < 0.5: exponent -= 1 elif frac > 1.0: exponent += 1 return exponent @ti.func def mip_from_pos(xyz, cascades): mx = ti.abs(xyz).max() # _, exponent = _frexp(mx) exponent = frexp_bit(ti.f32(mx)) + 1 # frac, exponent = ti.frexp(ti.f32(mx)) return ti.min(cascades - 1, ti.max(0, exponent)) @ti.func def mip_from_dt(dt, grid_size, cascades): # _, exponent = _frexp(dt*grid_size) exponent = frexp_bit(ti.f32(dt * grid_size)) # frac, exponent = ti.frexp(ti.f32(dt*grid_size)) return ti.min(cascades - 1, ti.max(0, exponent)) @ti.func def __expand_bits(v): v = (v * ti.uint32(0x00010001)) & ti.uint32(0xFF0000FF) v = (v * ti.uint32(0x00000101)) & ti.uint32(0x0F00F00F) v = (v * ti.uint32(0x00000011)) & ti.uint32(0xC30C30C3) v = (v * ti.uint32(0x00000005)) & ti.uint32(0x49249249) return v @ti.func def __morton3D(xyz): xyz = __expand_bits(xyz) return xyz[0] | (xyz[1] << 1) | (xyz[2] << 2) @ti.func def __morton3D_invert(x): x = x & (0x49249249) x = (x | (x >> 2)) & ti.uint32(0xc30c30c3) x = (x | (x >> 4)) & ti.uint32(0x0f00f00f) x = (x | (x >> 8)) & ti.uint32(0xff0000ff) x = (x | (x >> 16)) & ti.uint32(0x0000ffff) return ti.int32(x) @ti.kernel def morton3D_invert_kernel(indices: ti.types.ndarray(ndim=1), coords: ti.types.ndarray(ndim=2)): for i in indices: ind = ti.uint32(indices[i]) coords[i, 0] = __morton3D_invert(ind >> 0) coords[i, 1] = __morton3D_invert(ind >> 1) coords[i, 2] = __morton3D_invert(ind >> 2) def morton3D_invert(indices): coords = torch.zeros(indices.size(0), 3, device=indices.device, dtype=torch.int32) morton3D_invert_kernel(indices.contiguous(), coords) ti.sync() return coords @ti.kernel def morton3D_kernel(xyzs: ti.types.ndarray(ndim=2), indices: ti.types.ndarray(ndim=1)): for s in indices: xyz = uvec3([xyzs[s, 0], xyzs[s, 1], xyzs[s, 2]]) indices[s] = ti.cast(__morton3D(xyz), ti.int32) def morton3D(coords1): indices = torch.zeros(coords1.size(0), device=coords1.device, dtype=torch.int32) morton3D_kernel(coords1.contiguous(), indices) ti.sync() return indices @ti.kernel def packbits(density_grid: ti.types.ndarray(ndim=1), density_threshold: float, density_bitfield: ti.types.ndarray(ndim=1)): for n in density_bitfield: bits = ti.uint8(0) for i in ti.static(range(8)): bits |= (ti.uint8(1) << i) if ( density_grid[8 * n + i] > density_threshold) else ti.uint8(0) density_bitfield[n] = bits @ti.kernel def torch2ti(field: ti.template(), data: ti.types.ndarray()): for I in ti.grouped(data): field[I] = data[I] @ti.kernel def ti2torch(field: ti.template(), data: ti.types.ndarray()): for I in ti.grouped(data): data[I] = field[I] @ti.kernel def ti2torch_grad(field: ti.template(), grad: ti.types.ndarray()): for I in ti.grouped(grad): grad[I] = field.grad[I] @ti.kernel def torch2ti_grad(field: ti.template(), grad: ti.types.ndarray()): for I in ti.grouped(grad): field.grad[I] = grad[I] @ti.kernel def torch2ti_vec(field: ti.template(), data: ti.types.ndarray()): for I in range(data.shape[0] // 2): field[I] = ti.Vector([data[I * 2], data[I * 2 + 1]]) @ti.kernel def ti2torch_vec(field: ti.template(), data: ti.types.ndarray()): for i, j in ti.ndrange(data.shape[0], data.shape[1] // 2): data[i, j * 2] = field[i, j][0] data[i, j * 2 + 1] = field[i, j][1] @ti.kernel def ti2torch_grad_vec(field: ti.template(), grad: ti.types.ndarray()): for I in range(grad.shape[0] // 2): grad[I * 2] = field.grad[I][0] grad[I * 2 + 1] = field.grad[I][1] @ti.kernel def torch2ti_grad_vec(field: ti.template(), grad: ti.types.ndarray()): for i, j in ti.ndrange(grad.shape[0], grad.shape[1] // 2): field.grad[i, j][0] = grad[i, j * 2] field.grad[i, j][1] = grad[i, j * 2 + 1] def depth2img(depth): depth = (depth - depth.min()) / (depth.max() - depth.min()) depth_img = cv2.applyColorMap((depth * 255).astype(np.uint8), cv2.COLORMAP_TURBO) return depth_img def save_deployment_model(model, dataset, save_dir): padding = torch.zeros(13, 16) rgb_out = model.rgb_net.output_layer.weight.detach().cpu() rgb_out = torch.cat([rgb_out, padding], dim=0) new_dict = { 'poses': dataset.poses.cpu().numpy(), 'model.density_bitfield': model.density_bitfield.cpu().numpy(), 'model.hash_encoder.params': model.pos_encoder.hash_table.detach().cpu().numpy(), 'model.per_level_scale': model.pos_encoder.log_b, 'model.xyz_encoder.params': torch.cat( [model.xyz_encoder.hidden_layers[0].weight.detach().cpu().reshape(-1), model.xyz_encoder.output_layer.weight.detach().cpu().reshape(-1)] ).numpy(), 'model.rgb_net.params': torch.cat( [model.rgb_net.hidden_layers[0].weight.detach().cpu().reshape(-1), rgb_out.reshape(-1)] ).numpy(), } np.save( os.path.join(f'{save_dir}', 'deployment.npy'), new_dict )

UTF-8

Python

false

7,049

py

59

utils.py

41

0.567598

0.51369

0

253

26.86166

89

thibaudlemaire/autolights

5,557,687,681,384

3471e5b1fe827c271918afcc336df139d0806330

7cf7592bca7b20627ec4c23d0d43c748367fa74f

/soft/sys_expert/energy_detection.py

01ea956c4163abeba9d608dfccf11eb077f4d1b0

[]

no_license

https://github.com/thibaudlemaire/autolights

4b64d5a2ef1885c21b8a2d2e0e145dd7b067dd97

3b6d331efdec8b08c12c6503870704e8fe4addd7

refs/heads/master

2021-06-18T09:08:37.924753

2017-06-29T09:14:11

94,530,591

1

0

null

#!/usr/bin/python3 # -*- coding: utf-8 -*- """ @author: thibaud """ import logging import librosa import math import time import numpy as np from threading import Thread from .bibliotheque import energie # Constants BUFFER_SIZE = 10 # Number of frames to store in the buffer (10 -> 0,25s) SAMPLE_PER_FRAME = 1024 # See audio module SAMPLE_RATE = 44100 # See audio module ENERGY_SILENCE_THRESHOLD = 10 # Absolute RMS Energy threshold under which sound is concidered as silence ENERGY_CHANGE_THRESHOLD = 5 # Delta BASS_THRESHOLD = 2 # Relative to mean SWEEP_THRESHOLD = 1.5 # Relative to mean BREAK_THRESHOLD = 2.2 # Relative INTER_STATES_TIME = 3 # Time beteween states in state machine MEAN_NUMBER = 30 # Number of value in slincing means # States _STATE_WAITING = 0 _STATE_SWEEP = 1 _STATE_BREAK = 2 _STATE_DROP = 3 # This class provide a thread for the SE module class EnergyDetector(Thread): def __init__(self, audio_frames, manager): Thread.__init__(self) self.terminated = False # Stop flag self.audio_frames = audio_frames # Contain 5ms frames self.last_energy = 0 # Energy register self.last_bass_energy = 0 self.last_high_energy = 0 self.bass_mean = 30 # Means self.high_mean = 15 self.counter = 0 # State counter self.frames = None # Frames buffer self.manager = manager # Pointer to manager self.state = 0 # State machine : 0 waiting for sweep, 1 waiting for silence, 2 waiting for bass self.state_timestamp = 0 # Time since last state change # Thread processing BPM Detection def run(self): logging.info("Starting RMS Energy detector") # This loop condition have to be checked frequently, so the code inside may not be blocking while not self.terminated: if time.time() - self.state_timestamp > INTER_STATES_TIME: self.state = _STATE_WAITING new_frame = self.audio_frames.get() # Get new frame (blocking) if self.counter == 0: self.frames = new_frame self.counter += 1 elif self.counter >= BUFFER_SIZE: self.frames = np.append(self.frames, new_frame) # Global Energy energy_raw = librosa.feature.rmse(y=self.frames) # RMS Energy calculation on full spectrum new_energy = np.mean(energy_raw) # Mean energy if math.isnan(new_energy): logging.warning("Volume trop fort !") else: new_energy = int(new_energy) # Round energy if np.abs(self.last_energy - new_energy) > ENERGY_CHANGE_THRESHOLD: # Detect a change self.last_energy = new_energy self.manager.new_energy(new_energy) if new_energy < ENERGY_SILENCE_THRESHOLD: # Detect a silence self.manager.silence() # High frequency energy new_high_energy = np.mean(energie.high_freq_energie(self.frames, SAMPLE_RATE)) # RMS Energy on high freq if math.isnan(new_high_energy): logging.warning("Volume trop fort !") else: new_high_energy = int(new_high_energy) self.high_mean = (self.high_mean * MEAN_NUMBER + new_high_energy) / (1 + MEAN_NUMBER) # Slicing mean calculation if np.abs(self.last_high_energy - new_high_energy) > ENERGY_CHANGE_THRESHOLD: # Detect high energy change self.last_high_energy = new_high_energy self.manager.new_high_energy(new_high_energy) if new_high_energy > self.high_mean * SWEEP_THRESHOLD: # Detect a sweep (high energy on high freq) self.manager.sweep() if self.state == _STATE_SWEEP: # Change machine state self.state_timestamp = time.time() if self.state == _STATE_WAITING: self.state_timestamp = time.time() self.state = _STATE_SWEEP # Bass frequency energy new_bass_energy = np.mean(energie.low_freq_energie(self.frames, SAMPLE_RATE)) # RMS Energy on low freq if math.isnan(new_bass_energy): logging.warning("Volume trop fort !") else: new_bass_energy = int(new_bass_energy) self.bass_mean = (self.bass_mean * MEAN_NUMBER + new_bass_energy) / (1 + MEAN_NUMBER) # Slicing mean calculation if np.abs(self.last_bass_energy - new_bass_energy) > ENERGY_CHANGE_THRESHOLD: # Detect low energy change self.last_bass_energy = new_bass_energy self.manager.new_bass_energy(new_bass_energy) if new_bass_energy > self.bass_mean * BASS_THRESHOLD: # Detect high bass self.manager.bass() if self.state == _STATE_BREAK: # Change machine state self.state_timestamp = time.time() self.state = _STATE_DROP self.manager.drop() if new_bass_energy < self.bass_mean / BREAK_THRESHOLD: # Detect break (low energy on low freq) self.manager.bass_break() if self.state == _STATE_BREAK: # Change machine state self.state_timestamp = time.time() if self.state == _STATE_SWEEP: self.state_timestamp = time.time() self.state = _STATE_BREAK self.counter = 0 else: self.frames = np.append(self.frames, new_frame) self.counter += 1 # Method called to stop the thread def stop(self): self.terminated = True self.audio_frames.put(np.empty(SAMPLE_PER_FRAME, dtype=np.int16)) # Release blocking getter

UTF-8

Python

false

6,534

py

49

energy_detection.py

37

0.530762

0.522345

0

122

52.557377

135

whccx/ccxshop

8,134,668,079,717

72a266367d2a2ea46a0de3952249b00bc9120f70

28ffaedb0d91e8c8316f958e002132843f41dcfd

/apps/shop/urls.py

ffec2281a9ad5dc45e2d926bddee110f6dfd1a5b

[]

no_license

https://github.com/whccx/ccxshop

31d58cc5098f61318486a57cb927552be1074949

9d7ff9b49c975002dc46497df30471ba1585c962

refs/heads/master

2022-12-20T05:52:53.477857

2018-09-21T08:47:32

147,655,448

0

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.conf.urls import url from shop import views #导入py文件 app_name = 'shop' urlpatterns = [ url(r'^$', views.shop),#默认shop页面 ]

UTF-8

Python

false

216

py

23

urls.py

18

0.68

0.675

0

11

17.272727

39

bh2smith/advent

9,715,216,029,971

cd54221ece46ac6693dda0eb957f08d8f0ee9b4f

da43f29a091ee81e93c9a91fb94ebae76ddcfa73

/2015/day08/day08.py

e4790143fd2d509b7af724a9e1aad938837b1dba

[]

no_license

https://github.com/bh2smith/advent

c243e77992af5ea88fd77df9313afd6a90e87843

6b94bdd97ad4d15d9f68048f65f6db8a3173e841

refs/heads/master

2020-04-11T19:03:36.972230

2019-01-01T16:32:50

162,020,640

0

null

import codecs if __name__ == '__main__': strings = list(map(lambda t: t.strip(), open('input').readlines())) l, r, q = 0, 0, 0 for s in strings: l += len(s) t = codecs.getdecoder("unicode_escape")(s)[0] r += len(t) - 2 m = s.encode("UTF-8") q += len(str(m)) + s.count('"') - 1 print("part 1:", l - r) print("part 2:", q - l)

UTF-8

Python

false

387

py

36

day08.py

35

0.465116

0.44186

0

14

26.642857

71

mittgaurav/Pietone

19,567,871,030,748

57c8b6f7955530afaf0bce4c7741893b4a8cd0fe

41862e79ab6eb99ea2dee9b5f9258a2102d29b18

/longest_palin_subsequence.py

f08c939f25f46c453ad80f8cbd2c146a3901ced1

[]

no_license

https://github.com/mittgaurav/Pietone

688427226a9ed3287b5ea4136042d4d475c83ebc

859a2d4d80e11e2b4474580a423626a0e08ac245

refs/heads/master

2021-08-16T08:29:42.083499

2021-06-17T20:13:19

156,988,154

1

0

null

# -*- coding: utf-8 -*- """ Created on Mon Jan 21 00:54:52 2019 @author: gaurav """ def longest_palin_seq(arr) -> 'int': """longest palindromic subsequence""" if not arr: return 0 if len(arr) == 1: return 1 if arr[0] == arr[-1]: # The two extremes match, # try after removing them return 2 + longest_palin_seq(arr[1:-1]) # see by removing either first or last return max(longest_palin_seq(arr[1:]), longest_palin_seq(arr[:-1])) def longest_palin_dp(arr, i, j) -> 'int': """memoization: matrix of start and end indices""" if i < 0 or j >= len(arr) or i > j: return 0 if matrix[i][j] != -1: # already set return matrix[i][j] if i == j: matrix[i][j] = 1 elif arr[i] == arr[j]: matrix[i][j] = 2 + longest_palin_dp(arr, i+1, j-1) else: matrix[i][j] = max(longest_palin_dp(arr, i+1, j), longest_palin_dp(arr, i, j-1)) return matrix[i][j] print(longest_palin_seq.__name__) for A in ["abdbca", "cddpd"]: matrix = [[-1 for _ in A] for _ in A] print(A, longest_palin_seq(A), longest_palin_dp(A, 0, len(A)-1)) print("--------------------") def l_p_string(arr): """contiguous string that's a palindrome""" ### NOT CORRECT ### Need MANACHER if not arr: return (0, True) if len(arr) == 1: return (1, True) if len(arr) == 2: return (2, True) if arr[0] == arr[1] else (0, False) # chars don't match, # so check internal. # Is not continuous if arr[0] != arr[-1]: return((max(l_p_string(arr[:-1])[0], l_p_string(arr[1:])[0]), False)) val, club = l_p_string(arr[1:-1]) if club: # inside is continuous # Club with the outer match val += 2 # now, it may happen that sub-str # has longer match than continuos without = max(l_p_string(arr[1:]), l_p_string(arr[:-1])) if without[0] > val: return without return (val, club) print(l_p_string.__name__) print("abab", l_p_string("abab")) print("babad", l_p_string("babad")) print("abbccbba", l_p_string("abbccbba")) print("abbccbballabbccbbal", l_p_string("abbccbballabbccbbal")) print("abbccbballxabbccbbal", l_p_string("abbccbballxabbccbbal")) print("rrar", l_p_string("rrar")) print("--------------------") def longest_paren(arr): """parenthesis match longest""" # not working if not arr: return 0 if len(arr) == 1: return 0 if len(arr) == 2: return 2 if arr[0] == '(' and arr[1] == ')' else 0 if arr[0] == '(' and arr[1] == ')': return 2 + longest_paren(arr[1:-1]) elif arr[0] != '(': return longest_paren(arr[1:]) elif arr[-1] != ')': return longest_paren(arr[:-1]) print(longest_paren.__name__) print(longest_paren("()(")) print(longest_paren("(")) print(longest_paren(")()())")) print(longest_paren(")())())"))

UTF-8

Python

false

2,988

py

70

longest_palin_subsequence.py

70

0.533802

0.509705

0

120

23.9

68

EdmundOgban/music-experiments

19,567,871,002,396

f2216110ec35a229101f2dba16e6ae05dfb9d3e9

2ed536d11a712e701830652c1388d2f040739422

/scores/ezio/ezio0.py

88eb2a88c5fd9c83f8d724b3f95aacd5a6e46ccf

[ "MIT" ]

permissive

https://github.com/EdmundOgban/music-experiments

ba3b1601ef93694db072e76da58b7e046277cbb3

c340deb335eb37e6e0ca8ee20a3fe4fac7409ed3

refs/heads/master

2023-08-20T06:13:39.316250

2020-05-09T05:42:41

259,487,465

0

MIT

true

2020-04-28T00:15:47

2020-04-28T00:15:46

2020-04-27T19:24:34

2020-04-27T19:24:32

22

0

null

false

import random import itertools from music import tone, play_sequence def gen_rhythm(beats): while True: res = random.choices([1,2,4,8], (32,8,2,1), k=random.choice([4,8,16,32])) if sum(res) == beats: return res def gen_rhythm2(beats): if beats == 1: return [1] if random.random() < 0.9: return [*gen_rhythm2(int(beats/2)), *gen_rhythm2(int(beats/2))] else: return [beats] def make_music(synth): MUL = 4 I = (0, 3, 5, 7, 10) # A C D E G IV = (9, 12, 14, 16, 19) # F# A B C# E V = (16, 19, 21, 23, 26) # C# E F# G# B scale_I = [tone(x, 440) for x in I] scale_IV = [tone(x, 440) for x in IV] scale_V = [tone(x, 440) for x in V] for scale in itertools.cycle([scale_I, scale_IV, scale_V, scale_V]): TEMPO = random.choice([300, 400, 600]) BASE = 60 / TEMPO bass_scale = [x / 4 for x in scale] beat_duration = random.choice([4,8,16,32]) durations = [BASE*d for d in gen_rhythm2(beat_duration)] hdlen = len(durations) notes = [*random.choices(scale, (5,5,3,1,1), k=hdlen)*2, *random.choices(scale, (1,1,3,5,5), k=hdlen)*2] durations *= MUL print(len(durations), len(notes)) sequence = list(zip(notes, durations)) #print(*[f'{round(freq,1):5}/{d:2}' for (freq, d) in sequence]) sequence2 = [(random.choice(scale) if x % 2 == 0 else 0, BASE*int(MUL/2)) for x in range(beat_duration*int(MUL/2))] sequence3 = [(bass_scale[0], BASE*MUL) for x in range(beat_duration)] synth.play_mix( play_sequence(seq) for seq in [sequence, sequence2, sequence3] )

UTF-8

Python

false

1,746

py

10

ezio0.py

8

0.538373

0.479381

0

51

33.235294

81

bojangles-m/cli-clock

9,405,978,401,642

ca50b97f0bdd12226f4f697f9615cf1b9d1ef2f2

cf5b403544ae2bc664c3a529a7e6fa18b7c9f691

/lib/apps.py

677c3e96a60fc8c333a4d7575f757b8e69c83527

[]

no_license

https://github.com/bojangles-m/cli-clock

11e8a319029db651a8c7aad911f5dac7d9439e63

f2bfcbe5f19aa557cc20579d8aff2ba76b16c2fa

refs/heads/master

2021-06-10T06:13:15.742592

2016-12-26T11:24:31

null

0

null

import subprocess import time from datetime import datetime from lib.notification import notify from lib.exceptions import PastTimeEnetredError class timer: @staticmethod def run(sec, msg=None): while (sec > 0): sec -= 1 time.sleep(1) notify("It's time", msg, sound=True) @staticmethod def init(sec, msg=None): msg = msg if msg else "This message should appear instantly, with a sound" subprocess.Popen(['nohup', './apps/timer.py', sec, msg]) class alarm: @staticmethod def run(at, msg=None): at = datetime.strptime(at, "%Y-%m-%d %H:%M") while(at > datetime.now()): time.sleep(0.200) notify("Wake up!", msg, sound=True) @staticmethod def init(at, msg=None): msg = msg if msg else "This message should appear instantly, with a sound" try: al = datetime.strptime(at, "%Y-%m-%d %H:%M") now = datetime.now() if now >= al: raise PastTimeEnetredError(at) subprocess.Popen(['nohup', './apps/alarm.py', at, msg]) except ValueError as err: print "Wrong input!" print err except PastTimeEnetredError as ex: print "Time is up!" print "Entered '%s' time has to be in the future." % (ex.value)

UTF-8

Python

false

1,362

py

7

apps.py

6

0.571219

0.566079

0

51

25.705882

82

ec500-software-engineering/exercise-1-modularity-sunithapriya

2,671,469,675,547

43357bc2d71998c68106c19c6a1bbc1bcc5ecfd8

06c6969c148e205200b912f73e16c878081892f9

/main.py

69334efef7d6a01981ba9378abd8b130b0fd6f8f

[]

no_license

https://github.com/ec500-software-engineering/exercise-1-modularity-sunithapriya

ace829f125cfff3d4e165115cd1214af50de8284

050e023693478c72cc5b54614c150cb08932a973

refs/heads/master

2020-04-22T01:24:45.301419

2019-02-19T22:09:05

170,013,476

0

null

from input import getPatientInfo, readSensorData from storage import searchPerson, insert from alert_system import alertCheck from output import patient if __name__ == "__main__": #Input Module# patientInfo = getPatientInfo() patientInfo.encode("ascii","replace") sensorData = readSensorData() sensorData.encode("ascii","replace") #Storage Module# #Insert paitent and sensor data into mongodb insert(patientInfo, sensorData) #Search for Patient Details using PatientId patientDetails = searchPerson("1234") #Alert Module# #Check sensorData for alerts# alert = alertCheck(sensorData) #Output Module# patient = patient() #Recieve message from Alert system patient.recieveFromAlert(alert) #Display alert to UI patient.send_alert_to_UI()

UTF-8

Python

false

761

py

6

main.py

4

0.764783

0.759527

0

28

26.071429

48

CodeEnvironment/django-rest-framework-deploy-heroku

10,196,252,379,339

f1496cd1f56cfbf088a11b9cc4b17e3beabca0c5

7e205af8825a41c48d7f8fb3f9988a8746564f36

/racing/admin.py

a8b11aecb7cc2ebaa82ffbc9f5cf0329748337a4

[ "MIT" ]

permissive

https://github.com/CodeEnvironment/django-rest-framework-deploy-heroku

6135a9f9837842933c958683b8f0f5c6bc555046

c6ffb20961c193b0f4dc1289de904b5d6750f335

refs/heads/main

2023-04-24T16:03:18.606340

2021-04-29T20:47:42

354,850,998

4

1

null

from django.contrib import admin from .models import Driver admin.site.register(Driver)

UTF-8

Python

false

89

py

53

admin.py

50

0.820225

0

4

21.25

32

bfpimentel/dotfiles

11,897,059,443,582

a62027c62f0b29a9c7ee12c9ab09edd336811691

73e68928271ef728dd9c25979248b578a699a1bb

/.config/qtile/settings/keys.py

ee551c47d81e1d9f4a19a5def7e44b034ad29029

[]

no_license

https://github.com/bfpimentel/dotfiles

9461397ebf7947bb3bda69e9f12d26d53d28e871

d786d584651acffcd6b5d4e270d8334da07fb7c3

refs/heads/master

2023-02-23T18:58:14.035005

2021-01-29T18:23:58

323,170,869

0

null

from libqtile.config import Key from libqtile.lazy import lazy from settings.groups import groups mod = "mod4" keys = [ # Qtile Key([mod, "mod1"], "r", lazy.restart()), Key([mod, "mod1"], "q", lazy.shutdown()), # Switch between windows Key([mod], "Down", lazy.layout.down()), Key([mod], "Up", lazy.layout.up()), Key([mod], "Left", lazy.layout.left()), Key([mod], "Right", lazy.layout.right()), # Toggle floating Key([mod], "f", lazy.window.toggle_floating()), # Move windows Key([mod, "shift"], "Down", lazy.layout.shuffe_down()), Key([mod, "shift"], "Up", lazy.layout.shuffe_up()), Key([mod, "shift"], "Left", lazy.layout.shuffle_left()), Key([mod, "shift"], "Right", lazy.layout.shuffle_right()), # Toggle layouts Key([mod], "Tab", lazy.next_layout()), # Switch Screens Key([mod, "mod1"], "Tab", lazy.next_screen()), # Kill Window Key([mod], "w", lazy.window.kill()), # Alacritty Key([mod], "Return", lazy.spawn("kitty")), # Rofi Key([mod], "space", lazy.spawn("rofi -show drun")), Key([mod, "shift"], "space", lazy.spawn("rofi -show")), # Flameshot Key([mod], "p", lazy.spawn("flameshot gui")), Key([mod, "shift"], "p", lazy.spawn("flameshot screen -r -c")), Key([mod, "mod1"], "p", lazy.spawn("flameshot screen -r -p ~/Pictures")), ] for key, group in enumerate(groups, 1): keys.append(Key([mod], str(key), lazy.group[group.name].toscreen())) keys.append(Key([mod, "shift"], str(key), lazy.window.togroup(group.name)))

UTF-8

Python

false

1,566

py

32

keys.py

17

0.584291

0.58046

0

51

29.686275

79

l0kihardt/time-analyse-bot

4,836,133,212,911

c52f41ea4762c9ba7caa95abe2b7887343f53f3f

ffcdf925083be0b9ec7bab177aef4abd90936dc8

/plugins/Test.py

ed3c837d603d3979c6f8640b60c8436463e887a1

[]

no_license

https://github.com/l0kihardt/time-analyse-bot

8f46dc63752c3e2a0b256357e66ec302c5a85453

b33900043b952bce7bc302a28f89a26620522d60

refs/heads/master

2021-05-05T23:48:37.259783

2018-01-10T02:15:57

116,893,041

2

0

null

#!/usr/bin/env python # -*- coding:utf-8 -*- # # This file defines a class that eases the connection to iCload for caldav manipulation # Is uses the credentials supplied in the constructor to discver the users principal and calendar-home-set urls then uses # these as inputs to the CALDAV library to add a caledndar, and create an event # If the example is re-run - an Authorisation error will occur as the example will try to re-add the same event which will be rejected due to the duplicate ID # from __future__ import print_function from datetime import datetime import sys from bs4 import BeautifulSoup import caldav from caldav.elements import dav, cdav from lxml import etree import requests from requests.auth import HTTPBasicAuth class iCloudConnector(object): icloud_url = "http://cal.trumind.net:8008" username = None password = None propfind_principal = ( u'''<?xml version="1.0" encoding="utf-8"?><propfind xmlns='DAV:'>''' u'''<prop><current-user-principal/></prop></propfind>''' ) propfind_calendar_home_set = ( u'''<?xml version="1.0" encoding="utf-8"?><propfind xmlns='DAV:' ''' u'''xmlns:cd='urn:ietf:params:xml:ns:caldav'><prop>''' u'''<cd:calendar-home-set/></prop></propfind>''' ) def __init__(self, username, password, **kwargs): self.username = username self.password = password if 'icloud_url' in kwargs: self.icloud_url = kwargs['icloud_url'] self.discover() self.get_calendars() # discover: connect to icloud using the provided credentials and discover # # 1. The principal URL # 2 The calendar home URL # # These URL's vary from user to user # once doscivered, these can then be used to manage calendars def discover(self): # Build and dispatch a request to discover the prncipal us for the # given credentials headers = { 'Depth': '1', } auth = HTTPBasicAuth(self.username, self.password) principal_response = requests.request( 'PROPFIND', self.icloud_url, auth=auth, headers=headers, data=self.propfind_principal.encode('utf-8') ) if principal_response.status_code != 207: print('Failed to retrieve Principal: ', principal_response.status_code) exit(-1) # Parse the resulting XML response soup = BeautifulSoup(principal_response.content, 'lxml') self.principal_path = soup.find( 'current-user-principal' ).find('href').get_text() discovery_url = self.icloud_url + self.principal_path # Next use the discovery URL to get more detailed properties - such as # the calendar-home-set home_set_response = requests.request( 'PROPFIND', discovery_url, auth=auth, headers=headers, data=self.propfind_calendar_home_set.encode('utf-8') ) if home_set_response.status_code != 207: print('Failed to retrieve calendar-home-set', home_set_response.status_code) exit(-1) # And then extract the calendar-home-set URL soup = BeautifulSoup(home_set_response.content, 'lxml') self.calendar_home_set_url = 'http://cal.trumind.net:8008'+soup.find( 'href', attrs={'xmlns':'DAV:'} ).get_text() # get_calendars # Having discovered the calendar-home-set url # we can create a local object to control calendars (thin wrapper around # CALDAV library) def get_calendars(self): self.caldav = caldav.DAVClient(self.calendar_home_set_url, username=self.username, password=self.password) self.principal = self.caldav.principal() self.calendars = self.principal.calendars() def get_named_calendar(self, name): if len(self.calendars) > 0: for calendar in self.calendars: properties = calendar.get_properties([dav.DisplayName(), ]) display_name = properties['{DAV:}displayname'] if display_name == name: return calendar return None def create_calendar(self,name): return self.principal.make_calendar(name=name) def delete_all_events(self,calendar): for event in calendar.events(): event.delete() return True def create_events_from_ical(self, ical): # to do pass def create_simple_timed_event(self,start_datetime, end_datetime, summary, description): # to do pass def create_simple_dated_event(self,start_datetime, end_datetime, summary, description): # to do pass # Simple example code # 格式化时间字串 # DTSTAMP = time.strftime('%Y%m%dT%H%M%SZ', time.localtime()) vcal = """BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Example Corp.//CalDAV Client//EN BEGIN:VEVENT UID:0000000008 DTSTAMP:20180104T111016Z DTSTART:20180104T111016Z DTEND:20180104T131016Z SUMMARY:This is an event END:VEVENT END:VCALENDAR """ username = 'user01' password = 'user01' # The above is an 'application password' any app must now have its own # password in iCloud. For info refer to # https://www.imore.com/how-generate-app-specific-passwords-iphone-ipad-mac icx = iCloudConnector(username, password) # 获取所有日历 cal = icx.get_named_calendar('MyCalendar') # 新建日历 if not cal: cal = icx.create_calendar('MyCalendar') #新建事件 try: cal.add_event(vcal) except AuthorisationError as ae: print('Couldn\'t add event', ae.reason) #获取 2018/1/1 ~ 2018/6/1 间的所有事件 results = cal.date_search(datetime(2018, 1, 1), datetime(2018, 6, 1)) for event in results: print("Found", event) print(event.data) # 打印事件的数据 #event.delete() //删除服务器上的事件 print("----------")

UTF-8

Python

false

6,118

py

6

Test.py

4

0.621599

0.603019

0

187

31.229947

158

thomastu/CaReCur

2,482,491,111,974

de4e21bf7fda86d0e6337df63f5133328d94b26a

d94a8f8e512093a49a8cb0a6c246c2431b94e7ff

/src/data/geography/ca_counties.py

cf0dad2b0cd465bf8e7b05da1438f9cd85a43a69

[ "MIT" ]

permissive

https://github.com/thomastu/CaReCur

e66a4be7f009ed36eea5fd9adaa3b9afbe0d3987

bd057ea8c20d403beedc5eec2af353fcffaa304a

refs/heads/master

2022-07-16T12:43:37.395740

2020-05-15T18:58:05

240,231,758

3

0

MIT

false

2020-03-26T05:49:10

2020-02-13T10:18:12

2020-03-26T05:48:32

2020-03-26T05:48:28

6,254

1

0

Jupyter Notebook

false

"""California county shape files. https://data.ca.gov/dataset/ca-geographic-boundaries/resource/b0007416-a325-4777-9295-368ea6b710e6 """ import zipfile from invoke import run from loguru import logger from src.conf import settings RAW_DIR = settings.DATA_DIR / "raw/geography/" PROCESSED_DIR = settings.DATA_DIR / "processed/geography/" url = "https://data.ca.gov/dataset/e212e397-1277-4df3-8c22-40721b095f33/resource/b0007416-a325-4777-9295-368ea6b710e6/download/ca-county-boundaries.zip" fn = "ca-county-boundaries.zip" if __name__ == "__main__": # Get data RAW_DIR.mkdir(exist_ok=True) PROCESSED_DIR.mkdir(exist_ok=True) fp = RAW_DIR/fn # Download data cmd = f"curl -L {url} -o {fp}" run(cmd) # Unzip it! with zipfile.ZipFile(fp, "r") as fh: fh.extractall(PROCESSED_DIR)

UTF-8

Python

false

827

py

54

ca_counties.py

18

0.70133

0.608222

0

32

24.875

152

rowenama/Ma

3,109,556,363,992

f7c38d94b6919cba3d3d274b11d3c1f295702cb4

fca04e7621f77dd05ce2f7d7eb8e609342c12022

/Question4/question4.py

dbaae7b88f2c59f2b913da9a398c06ad5b75c530

[]

no_license

https://github.com/rowenama/Ma

897ac6d8196a3d2cb6e81eb56b9e745691ed17e6

3b4073ece142750e73946e1470209910516d641a

refs/heads/main

2023-03-21T15:07:23.454259

2021-03-09T12:46:07

345,871,038

0

null

# Given Inputs L1 = 20 L2 = 10 InputImage= [ [0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1], [1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1], [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1], [1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1], [0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0], [1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1] ] OutputMatrix = [[0 for x in range(L1)] for y in range(L2)] # 4-connectivity CountNum = 1 # frist determine the connection of each pixel with its upper and left neighour for i, data_i in enumerate(InputImage): for j, data_j in enumerate(data_i): if data_j>0: Con = [CountNum] if j!=0: Con.append(OutputMatrix[i][j-1]) if j<L1-1: Con.append(OutputMatrix[i][j+1]) if data_i[j+1] >= 1 and i!=0: Con.append(OutputMatrix[i-1][j+1]) if i!=0: Con.append(OutputMatrix[i-1][j]) if i<L2-1: Con.append(OutputMatrix[i+1][j]) flag = min(list(filter(lambda a: a != 0, Con))) if len(list(filter(lambda a: a != 0, Con)))==1: CountNum = CountNum + 1 OutputMatrix[i][j] = flag Seq=[0] # then determine the connection of each pixel with its lower and right neighour for i, data_i in reversed(list(enumerate(InputImage))): for j, data_j in reversed(list(enumerate(data_i))): if data_j>0: Con = [OutputMatrix[i][j]] if j!=0: Con.append(OutputMatrix[i][j-1]) if j<L1-1: Con.append(OutputMatrix[i][j+1]) if i!=0: Con.append(OutputMatrix[i-1][j]) if i<L2-1: Con.append(OutputMatrix[i+1][j]) Con = list(filter(lambda a: a != 0, Con)) if len(Con)>=1: flag = min(Con) OutputMatrix[i][j] = flag Seq.append(flag) #sort all the existing connection and order them Seq = list(set(Seq)) #Output the result for i, data in enumerate(OutputMatrix): for j, data_j in enumerate(data): print(Seq.index(data_j), end =" ") print()

UTF-8

Python

false

2,509

py

8

question4.py

7

0.474293

0.376644

0

69

35.362319

79

keiji/region_cropper

15,187,004,360,964

5962289d308a019ff3288f0b72ebdfa388b64ea2

c8bb15cabe090d1c49cff017420868b5bbf213d9

/tools/src/entity/rect.py

971be521dfd98b9a4e2f3bb84ec7114876cb8be2

[ "Apache-2.0" ]

permissive

https://github.com/keiji/region_cropper

c47930ec60a175d5801e6d976f1f158e88e12a51

b0a6838fb52569a08f48954785a36f31762f263c

refs/heads/master

2020-12-25T16:24:57.843518

2020-11-04T16:32:40

68,176,688

10

0

null

#!/bin/python3 # -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function class Rect(object): left = 0 top = 0 right = 0 bottom = 0 def width(self): return (self.right - self.left) def height(self): return (self.bottom - self.top) def center(self): cX = round(self.left + (self.width() / 2)) cY = round(self.top + (self.height() / 2)) return (cX, cY) def __init__(self, left, top, right, bottom): self.left = left self.top = top self.right = right self.bottom = bottom def __eq__(self, other): if isinstance(other, Rect): return ( (self.left == other.left) and (self.top == other.top) and (self.right == other.right) and (self.bottom == other.bottom) ) else: return False def __ne__(self, other): return (not self.__eq__(other)) def __repr__(self): return "Entry(%f, %f, %f, %f)" % ( self.left, self.top, self.right, self.bottom) def __hash__(self): return hash(self.__repr__()) def copy(self): return Rect(self.left, self.top, self.right, self.bottom) def tostring(self): return '(%f, %f, %f, %f)' % (self.left, self.top, self.right, self.bottom)

UTF-8

Python

false

1,284

py

30

rect.py

5

0.575545

0.569315

0

58

21.137931

78

epam/Indigo

17,961,553,245,071

e86a2857d82af12fdede4cce83c02468c9a6c5f2

182bbadb0ee7f59f1abd154d06484e555a30c6d8

/api/tests/integration/tests/basic/basic_load.py

4997a4a4c405056155d6748462e0cfd803bf4829

[ "Apache-2.0" ]

permissive

https://github.com/epam/Indigo

08559861adf474122366b6e2e499ed3aa56272d1

8e473e69f393c3a57ff75b7728999c5fb4cbf1a3

refs/heads/master

2023-09-02T10:14:46.843829

2023-08-25T08:39:24

37,536,320

265

106

Apache-2.0

false

2023-09-14T17:34:00

2015-06-16T14:45:56

2023-09-06T21:50:50

2023-09-14T17:33:59

225,517

246

88

336

C++

false

import os import sys sys.path.append( os.path.normpath( os.path.join(os.path.abspath(__file__), "..", "..", "..", "common") ) ) from env_indigo import * # noqa indigo = Indigo() indigo.setOption("molfile-saving-skip-date", "1") mol = indigo.loadMolecule( "OCC1C(O)C(O)C(O)C(OC2C(O)C(O)C(OCC3CCCCC3)OC2CO)O1 |ha:0,1,2,3,4,5,6,7,8,9,10,29,hb:0,1,2,3,4,5,6,7,8,9,30,31|" ) print("****** Molfile 2000 ********") indigo.setOption("molfile-saving-mode", "2000") print(mol.molfile()) print("****** Molfile 3000 ********") indigo.setOption("molfile-saving-mode", "3000") print(mol.molfile()) print("****** CML ********") print(mol.cml()) print("****** SMILES ********") print(mol.smiles()) print("****** Canonical SMILES ********") mol.unhighlight() print(mol.canonicalSmiles()) print("****** Loading SDF with multiline properties ********") for item in indigo.iterateSDFile( joinPathPy("molecules/multiline_properties.sdf", __file__) ): for prop in item.iterateProperties(): print(prop.name() + " : " + prop.rawData()) print("****** CurlySMILES ********") m = indigo.loadMolecule("PC{-}{+n}N") print(m.smiles()) m = indigo.loadMolecule("PC{-}O{+n}N") print(m.smiles()) print("****** Finding invalid stereocenters ********") for item in indigo.iterateSDFile( joinPathPy("molecules/invalid_3d_stereocenters.sdf", __file__) ): try: print(item.molfile()) except IndigoException as e: print(getIndigoExceptionText(e)) try: print(item.smiles()) except IndigoException as e: print(getIndigoExceptionText(e)) print("****** Extended aromatic SMILES ********") m = indigo.loadMolecule("NC(Cc1c[nH]c2cc[te]c12)C(O)=O") print(m.smiles()) m.dearomatize() print(m.smiles()) print("****** Skip BOM flag ********") m = indigo.loadMoleculeFromFile( joinPathPy("molecules/mol-utf8-bom.mol", __file__) ) print(m.name()) print("****** Incomplete stereo in SMILES/SMARTS ********") print("[*@]") m = indigo.loadQueryMolecule("[*@]") print(m.smiles()) print("[*@H]") m = indigo.loadQueryMolecule("[*@H]") print(m.smiles()) print("[*@H](~*)~*") m = indigo.loadQueryMolecule("[*@H](~*)~*") print(m.smiles()) print("****** H2 molecule ********") m = indigo.loadMoleculeFromFile(joinPathPy("molecules/H2.mol", __file__)) indigo.setOption("molfile-saving-mode", "2000") print(m.smiles()) print(m.canonicalSmiles()) print(m.molfile()) print(m.grossFormula()) print("****** S-group's SCL (CLASS) support ********") m = indigo.loadMoleculeFromFile( joinPathPy("molecules/sa-class-v2000.mol", __file__) ) indigo.setOption("molfile-saving-mode", "2000") print(m.canonicalSmiles()) print(m.molfile()) indigo.setOption("molfile-saving-mode", "3000") m = indigo.loadMoleculeFromFile( joinPathPy("molecules/sa-class-v3000.mol", __file__) ) print(m.canonicalSmiles()) print(m.molfile()) print("****** S-group's SPL (PARENT) support ********") m = indigo.loadMoleculeFromFile( dataPath("molecules/sgroups/sgroups-V2000.mol") ) indigo.setOption("molfile-saving-mode", "2000") print(m.canonicalSmiles()) print(m.molfile()) indigo.setOption("molfile-saving-mode", "3000") m = indigo.loadMoleculeFromFile( dataPath("molecules/sgroups/sgroups-V3000.mol") ) print(m.canonicalSmiles()) print(m.molfile()) print("****** Load custom collection ********") m = indigo.loadMoleculeFromFile( joinPathPy("molecules/custom_collection.mol", __file__) ) print(m.molfile()) print("****** Load TEMPLATE (SCSR) structure ********") m = indigo.loadMoleculeFromFile( joinPathPy("molecules/SCSR_test.mol", __file__) ) print(m.molfile()) print("****** Alias handling (V2000) ********") m = indigo.loadMoleculeFromFile( joinPathPy("molecules/alias_marvin_v2000.mol", __file__) ) indigo.setOption("molfile-saving-mode", "2000") print(m.molfile()) print("****** Alias handling (V3000) ********") indigo.setOption("molfile-saving-mode", "3000") print(m.molfile()) print("****** Alias handling (CML) ********") print(m.cml()) m = indigo.loadMoleculeFromFile( joinPathPy("molecules/alias_marvin.cml", __file__) ) indigo.setOption("molfile-saving-mode", "2000") print(m.molfile()) print("****** Alias handling (SMILES) ********") print(m.canonicalSmiles()) print("****** Test load from gzip buffer ********") with open(joinPathPy("molecules/benzene.mol.gz", __file__), "rb") as gz_mol: buf = gz_mol.read() if isIronPython(): from System import Array, Byte buf_arr = bytearray(buf) buf = Array[Byte]([Byte(b) for b in buf_arr]) m = indigo.loadMoleculeFromBuffer(buf) print(m.canonicalSmiles()) print("****** Load V3000 with DISP keyword ********") m = indigo.loadMoleculeFromFile( joinPathPy("molecules/V3000_disp.mol", __file__) ) indigo.setOption("molfile-saving-mode", "3000") print(m.molfile()) print("****** Load V3000 with unknown keyword ********") try: mol = indigo.loadMoleculeFromFile( joinPathPy("molecules/V3000_unknown.mol", __file__) ) except IndigoException as e: print(getIndigoExceptionText(e)) try: mol = indigo.loadMoleculeFromFile( joinPathPy("molecules/V3000_unknown_atom_key.mol", __file__) ) except IndigoException as e: print(getIndigoExceptionText(e)) print("****** Name is skeletal prefix ********") try: m = indigo.loadMolecule("sil") except IndigoException as e: print(getIndigoExceptionText(e))

UTF-8

Python

false

5,390

py

2,282

basic_load.py

1,260

0.642115

0.615584

0

189

27.518519

116

jasonchoww/tradingview-automation

5,909,875,038,555

9986872ae1bfeae4e9eea9aef503742835b74019

837e81a18dbe597cf389b34ffe3bca3a28b6ee93

/launch_tradingview.py

c12dda425c8e27ab95cedb85ca181e77224acb6a

[]

no_license

https://github.com/jasonchoww/tradingview-automation

1c5de353edbcec94cae4bb9dce230839adcb2c01

b4c7bd56f746c90dbe1c633f8bb4bbd75b3f5b52

refs/heads/master

2020-05-07T12:31:40.372230

2019-04-14T19:55:10

180,507,855

0

3

null

import time def launch(driver): # launches tradingview driver.get("https://www.tradingview.com/") # login driver.find_element_by_xpath('/html/body/div[2]/div[2]/div[1]/div[4]/span[2]/a').click() time.sleep(2) # google+ login driver.find_element_by_xpath( '//*[@id="overlap-manager-root"]/div/div[2]/div/div/div/div/div/div[1]/div[2]/span[2]').click() time.sleep(2) # switches to second window driver.switch_to.window(driver.window_handles[1]) # send_keys: email account driver.find_element_by_xpath('//*[@id="identifierId"]').send_keys("example@email.com") driver.find_element_by_xpath('//*[@id="identifierNext"]/content/span').click() time.sleep(2) # send_keys: password driver.find_element_by_xpath('//*[@id="password"]/div[1]/div/div[1]/input').send_keys("password_goes_here") driver.find_element_by_xpath('//*[@id="passwordNext"]/content/span').click() time.sleep(2) # switches back to original window after logging in driver.switch_to.window(driver.window_handles[0]) time.sleep(3) # opens chart driver.find_element_by_xpath('/html/body/div[3]/div[2]/div[2]/ul/li[6]').click()

UTF-8

Python

false

1,191

py

3

launch_tradingview.py

3

0.649034

0.630563

0

35

33.028571

111

muhlik20033/muhlik20033

12,773,232,758,766

2e2835bfe307c9fcaa7a5a20e27974b7369003c1

a2419e48f7a8ea87f71a96775eff2faa087e1603

/TSIS 5/17.py

a9fc92a4c2fa87e5693b646fc382bbefcd1a80f4

[]

no_license

https://github.com/muhlik20033/muhlik20033

6bc7211074a97aed25229a22e8e729b29df494c6

60bcdda9eeb360de6c233ed45597e48a874c349f

refs/heads/main

2023-06-24T01:43:44.032740

2021-07-29T08:45:04

380,452,848

0

null

with open('text.txt') as f: file = f.read().splitlines() print([s.rstrip('\n ') for s in file])

UTF-8

Python

false

105

py

48

17.py

48

0.571429

0

3

33.666667

42

AlexandruGodri/pygame-tooltkit

8,512,625,184,477

40a7642f74671d5e7457025bd994d5d3b8c87711

1abd3c2dd22c04fb291907989c7b4475cf1709b1

/game/game.py

203464ad43dac6484c41f8353fe03046dec7e81a

[]

no_license

https://github.com/AlexandruGodri/pygame-tooltkit

76b4d428541cb7ab5b34dd6328c11be816285117

611ef986e520ed95205d4926e512a2a8c2770057

refs/heads/master

2021-01-20T07:57:20.031582

2017-06-08T13:23:52

90,072,762

0

null

import time import pygame class Game(): def __init__(self): self._size = None self._background = None self.screen = None self.sprites = pygame.sprite.Group() self.events = {} self._ready = False def init(self, size, background): self._size = size self._background = background pygame.init() self.screen = pygame.display.set_mode(size) self.screen.fill(background) def ready(self): self._ready = True def create_image_sprite(self, img_path, position, angle, size): try: img = pygame.sprite.Sprite() img.image = pygame.Surface(size) img.image = pygame.image.load(img_path).convert_alpha() img.image = pygame.transform.rotate(img.image, angle) img.rect = img.image.get_rect() img.rect.x = position[0] img.rect.y = position[1] self.sprites.add(img) return img except Exception as e: print 'Error Adding Image', e, img_path, position, size, angle return None def create_rectangle(self, color, position, size): try: img = pygame.sprite.Sprite() img.image = pygame.Surface(size) img.image.fill(color) img.rect = img.image.get_rect() img.rect.x = position[0] img.rect.y = position[1] self.sprites.add(img) return img except Exception as e: print 'Error Adding Rectangle', e, position, size return None def move_sprite(self, sprite, position=None, angle=None): if sprite in self.sprites: if angle is not None: orig_rect = sprite.image.get_rect() rot_image = pygame.transform.rotate(sprite.image, angle) rot_rect = orig_rect.copy() rot_rect.center = rot_image.get_rect().center sprite.image = rot_image.subsurface(rot_rect).copy() if position is not None: sprite.rect.x = position[0] sprite.rect.y = position[1] def render(self): self.screen.fill(self._background) self.sprites.update() self.sprites.draw(self.screen) pygame.display.flip() def on(self, event, cb): if event not in self.events: self.events[event] = [] self.events[event].append(cb) def run(self): while not self._ready: time.sleep(0.01) clock = pygame.time.Clock() while True: for event in pygame.event.get(): if self._ready: if event.type in self.events: for cb in self.events[event.type]: cb(event) if self._ready: try: self.render() except Exception as e: pass clock.tick(60)

UTF-8

Python

false

3,003

py

11

game.py

10

0.525475

0.521812

0

97

29.958763

74

mir-am/Mir-Repo

12,515,534,709,857

773c73bebcbd9c993227828abde46a0576c8d816

f40f2c84b3063eee6404422fdc3ed33b413f9503

/src/iknntsvm.py

53f96ce04cbdda50d8d9b452a5c931968b121cf2

[]

no_license

https://github.com/mir-am/Mir-Repo

ec3607b9fcf6de727f7548a6bacdb39a174a1694

8edf848592a0111d541c5d311303ad1b2a58fd03

refs/heads/master

2020-03-20T20:09:56.505968

2019-06-11T15:33:37

137,673,294

0

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sat Mar 17 12:09:33 2018 @author: Mir, A. """ # Implementation of Improved KNN-based twin support vector machine # IKNN-TSVM import clippSolverv3 from dataproc import read_data from twinsvm import train_tsvm, predict_tsvm from clipp import clipp_dcd from weight import w_compute_mir #from playground import hyperp_eq from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold from sklearn.metrics import accuracy_score from ls_wtsvm import accuracy from scipy import sparse from itertools import product import numpy as np import matplotlib.pyplot as plt import pandas as pd import time # Train IKNN-TSVM - Linear case def train_IKTSVM(X_train, X_train_label, c, k, method='FSA'): # Matrix A or class 1 data mat_A = X_train[X_train_label == 1] # Matrix B or class -1 data mat_B = X_train[X_train_label == -1] # Compute weight matrices - KNN finding weight = w_compute_mir(X_train, X_train_label, k, method) # Define margin points of class +1 and -1 mat_M_1 = mat_A[weight[3] == 1] # Class + 1 mat_M_2 = mat_B[weight[1] == 1] # Class -1 # Prepare D_1, D_2 - Diag. matrices mat_D_1 = np.diag(weight[0].reshape(weight[0].shape[0],)) mat_D_2 = np.diag(weight[2].reshape(weight[2].shape[0],)) # Scipy sparse matrix #mat_D_1_s = sparse.diags(weight[0].reshape(weight[0].shape[0],)) #mat_D_2_s = sparse.diags(weight[2].reshape(weight[2].shape[0],)) # Define H=[A e] & G=[M2 e] matrix mat_H = np.column_stack((mat_A, np.ones((mat_A.shape[0], 1), \ dtype=np.float64))) # Class +1 mat_G = np.column_stack((mat_M_2, np.ones((mat_M_2.shape[0], 1), \ dtype=np.float64))) # Margin points of class -1 # Define Q=[B e] & P=[M1 e] mat_Q = np.column_stack((mat_B, np.ones((mat_B.shape[0], 1), \ dtype=np.float64))) # Class -1 mat_P = np.column_stack((mat_M_1, np.ones((mat_M_1.shape[0], 1), \ dtype=np.float64))) # Margin points of class +1 # Transpose of H, G, Q & P mat_H_t = np.transpose(mat_H) mat_G_t = np.transpose(mat_G) mat_Q_t = np.transpose(mat_Q) mat_P_t = np.transpose(mat_P) # regulariztion term- for overcoming ill-possible condition reg = 2 ** float(-7) # Sparse matrix multiplication #mat_H_D_1 = sparse.csr_matrix.dot(mat_H_t, mat_D_1) #mat_Q_D_2 = sparse.csr_matrix.dot(mat_Q_t, mat_D_2) # Compute matrix inverses mat_H_D_H = np.linalg.inv(np.dot(np.dot(mat_H_t, mat_D_1), mat_H) + \ (reg * np.identity(mat_H.shape[1]))) mat_Q_D_Q = np.linalg.inv(np.dot(np.dot(mat_Q_t, mat_D_2), mat_Q) + \ (reg * np.identity(mat_Q.shape[1]))) # Wolfe dual of class 1 mat_dual1 = np.dot(np.dot(mat_G, mat_H_D_H), mat_G_t) # Wofle dual of class 2 mat_dual2 = np.dot(np.dot(mat_P, mat_Q_D_Q), mat_P_t) # Solving dual problem 1 - obtaining hyperplane of class 1 #alpha_d1 = clipp_dcd(mat_dual1, c) alpha_d1 = np.array(clippSolverv3.clippDCD_V3(mat_dual1, c)).reshape(mat_dual1.shape[0], 1) # Solving dual problem 2 - obtaining hyperplane of class 2 #alpha_d2 = clipp_dcd(mat_dual2, c) alpha_d2 = np.array(clippSolverv3.clippDCD_V3(mat_dual2, c)).reshape(mat_dual2.shape[0], 1) # Obtain hyperplane 1 & 2 hyper_p_1 = -1 * np.dot(np.dot(mat_H_D_H, mat_G_t), alpha_d1) w_1 = hyper_p_1[:hyper_p_1.shape[0] - 1, :] b_1 = hyper_p_1[-1, :] hyper_p_2 = np.dot(np.dot(mat_Q_D_Q, mat_P_t), alpha_d2) w_2 = hyper_p_2[:hyper_p_2.shape[0] - 1, :] b_2 = hyper_p_2[-1, :] return w_1, b_1, w_2, b_2 # Predict IKNN-TSVM - Linear case def pre_IKTSVM(X_test, w_1, b_1, w_2, b_2): prepen_distance = np.zeros((X_test.shape[0], 2)) for i in range(X_test.shape[0]): # Prependicular distance of data pint i from hyperplane 2(class 1) prepen_distance[i, 1] = np.abs(np.dot(X_test[i, :], w_1) + b_1) # Prependicular distance of data pint i from hyperplane 1 (class -1) prepen_distance[i, 0] = np.abs(np.dot(X_test[i, :], w_2) + b_2) # Step 5: Assign data points to class +1 or -1 based on distance from hyperplanes output = 2 * np.argmin(prepen_distance, axis=1) - 1 return output # Linear case - IKNN-TSVM - Cross validation def cv_lin_IKTSVM(data_train, data_labels, k_fold, k, c, method='FSA'): # K-Fold Cross validation, divide data into K subsets k_fold = KFold(k_fold) # Store result after each run mean_accuracy = [] # Postive class mean_recall_p, mean_precision_p, mean_f1_p = [], [], [] # Negative class mean_recall_n, mean_precision_n, mean_f1_n = [], [], [] # Count elements of confusion matrix tp, tn, fp, fn = 0, 0, 0, 0 k_time = 1 # Train and test IKNN-TSVM K times for train_index, test_index in k_fold.split(data_train): # Extract data based on index created by k_fold X_train = np.take(data_train, train_index, axis=0) X_test = np.take(data_train, test_index, axis=0) X_train_label = np.take(data_labels, train_index, axis=0) X_test_label = np.take(data_labels, test_index, axis=0) # Train Classifier - obtain two non-parallel hyperplane hyper_p = train_IKTSVM(X_train, X_train_label, c, k, method) # Parameters of two hyperplanes w_1 = hyper_p[0] b_1 = hyper_p[1] w_2 = hyper_p[2] b_2 = hyper_p[3] # Predict output = pre_IKTSVM(X_test, w_1, b_1, w_2, b_2) # Compute evaluation metrics accuracy_test = accuracy(X_test_label, output) mean_accuracy.append(accuracy_test[4]) # Positive cass mean_recall_p.append(accuracy_test[5]) mean_precision_p.append(accuracy_test[6]) mean_f1_p.append(accuracy_test[7]) # Negative class mean_recall_n.append(accuracy_test[8]) mean_precision_n.append(accuracy_test[9]) mean_f1_n.append(accuracy_test[10]) # Count tp = tp + accuracy_test[0] tn = tn + accuracy_test[1] fp = fp + accuracy_test[2] fn = fn + accuracy_test[3] #print("K_fold %d finished..." % k_time) k_time = k_time + 1 # m_a=0, m_r_p=1, m_p_p=2, m_f1_p=3, k_nn=4, c_1=5, k=6, w_1=7, b_1=8, w_2=9, b_2=10 # m_r_n=11, m_p_n=12, m_f1_n=13, tp=14, tn=15, fp=16, fn=17 return mean_accuracy, mean_recall_p, mean_precision_p, mean_f1_p, k_fold.get_n_splits(), \ c, k, w_1, b_1, w_2, b_2, mean_recall_n, mean_precision_n, mean_f1_n, \ tp, tn, fp, fn # Grid search - IKNN-TSVM- Linear def gs_lin_IKTSVM(data, k_fold, k_l, k_u, l_bound, u_bound, step, \ method ,file_name): train_data = data[0] labels = data[1] # Store result_list = [] # Max accuracy max_acc, acc_std = 0, 0 # Create an excel file for excel_write = pd.ExcelWriter(file_name, engine='xlsxwriter') # Search space - C parameter c_range = np.arange(l_bound, u_bound, step) # Search space - neighborhood size - k k_range = np.arange(k_l, k_u, 1) search_space = list(product(*[c_range ] + [k_range])) # Total number of search elements search_total = len(search_space) # Count run = 1 for element in search_space: c = 2 ** float(element[0]) k = element[1] start = time.time() result = cv_lin_IKTSVM(train_data, labels, k_fold, k, c, method) end = time.time() acc = np.mean(result[0]) # Add results to the list result_list.append([acc, np.std(result[0]), np.mean(result[1]), np.std(result[1]), np.mean(result[2]), \ np.std(result[2]), np.mean(result[3]), np.std(result[3]), np.mean(result[11]), np.std(result[11]), \ np.mean(result[12]), np.std(result[12]), np.mean(result[13]), np.std(result[13]), result[14], result[15], \ result[16], result[17], result[5], result[6], result[4], run]) # Save best accuracy if acc > max_acc: max_acc = acc acc_std = np.std(result[0]) print("IKNN-TSVM(%s)| Run: %d | %d |Data:%s | K: %d | C: 2^%d |B-Acc:%.2f+-%.2f |Acc: %.2f+-%.2f | Time: %.2f Sec." % (method, run, search_total, data[2], k, element[0], \ max_acc, acc_std, acc ,np.std(result[0]) , end - start)) run = run + 1 print("Best Accuracy: %.2f-+%.2f" % (max_acc, acc_std)) # Create a panda data frame result_frame = pd.DataFrame(result_list, columns=['accuracy', 'acc_std', 'recall_p', 'r_p_std', 'precision_p', 'p_p_std', \ 'f1_p', 'f1_p_std', 'recall_n', 'r_n_std', 'precision_n', 'p_n_std', 'f1_n',\ 'f1_n_std', 'tp', 'tn', 'fp', 'fn', 'c', 'knn', 'k_fold', 'run']) # Write result to excel result_frame.to_excel(excel_write, sheet_name='Sheet1') excel_write.save() return result_frame, max_acc # Plot hyperplanes obtained by IKNN-TSVM def plot_IKNNTSVM(X_train, y_train, c, k): # Split train data into separate class X_t_c1 = X_train[y_train == 1] X_t_c2 = X_train[y_train == -1] # Train a classifier with toy data model = train_IKTSVM(X_train, y_train, c, k, 'FSA') # Class1 hyper plane w_1 = model[0] b_1 = model[1] # Class 2 hyperplane w_2 = model[2] b_2 = model[3] # Line Equation hyperplane 1 slope1, intercept1 = hyperp_eq(w_1, b_1) # Line Equation hyperplane 2 slope2, intercept2 = hyperp_eq(w_2, b_2) # Min and Max of feature X1 and creating X values for creating line xx_1 = np.linspace(np.min(X_train[:, 0]), np.max(X_train[:, 0])) # y values yy_1 = slope1 * xx_1 + intercept1 yy_2 = slope2 * xx_1 + intercept2 fig = plt.figure(1) # Plot Training data plt.scatter(X_t_c1[:, 0], X_t_c1[:, 1], marker='o', cmap=plt.cm.Paired) plt.scatter(X_t_c2[:, 0], X_t_c2[:, 1], marker='+', cmap=plt.cm.Paired) # Plot two hyperplanes plt.plot(xx_1, yy_1, 'k--', label='Hyperplane +1') # Hyperplane of class 1 plt.plot(xx_1, yy_2, 'k-', label='Hyperplane -1') # Hyperplane of class 2 plt.ylim(-0.7, 1.5) plt.legend() plt.show() # Test if __name__ == '__main__': # Address of datasets data_add = 'Dataset/Synthetic/' # Read a dataset data = read_data(data_add + '/ripley.csv') # Split dataset X_train, X_test, y_train, y_test = train_test_split(data[0], data[1], \ test_size=0.1) start = time.time() # Parameters c = 2 ** -2 k = 5 # k_fold = 10 # fold-CV # k_l = 2 # k_u = 11 # c_l_b = -8 # c_u_b = 9 # rbf_l_b = -8 # rbf_u_b = 0 # # gs_lin_IKTSVM(data, k_fold, k_l, k_u, c_l_b, c_u_b, 1, \ # 'ld', 'Result/IKNN-TSVM-Lin-titanic12.xlsx') #test = train_IKTSVM(X_train, y_train, c, k) #test = cv_lin_IKTSVM(data[0], data[1], 10, k, c) plot_IKNNTSVM(X_train, y_train, c, k) print('IKNN-TSVM-Finished: %.2f ms.' % ((time.time() - start) * 1000)) #pre_1 = pre_IKNN_TSVM(X_test, test[0], test[1], test[2], test[3]) #acc_1 = accuracy_score(y_test, pre_1) * 100 #acc_1 = np.mean(test[0]) #print("IKNN-TSVM-Acc: %.2f+-%.2f" % (acc_1, np.std(test[0]))) ## Becnmark TSVM vs. IKNN-TSVM #start = time.time() # #test_tsvm = train_tsvm(X_train, y_train, c, c, 'cpp') # #print('TSVM-Finished: %.2f ms' % ((time.time() - start) * 1000)) # #pre_2 = predict_tsvm(X_test, test_tsvm[0], test_tsvm[1], test_tsvm[2], test_tsvm[3]) # #acc_2 = accuracy_score(y_test, pre_2) * 100 # #print("TSVM-Acc: %.2f" % acc_2)

UTF-8

Python

false

12,594

py

108

iknntsvm.py

48

0.539146

0.506432

0

382

31.968586

179

azuline/cryptopals

17,154,099,403,293

1c291746ae98fffc4f198173b5c9f845ad0c5dc9

1046257be7fede8e197cb90a5a855a748bde78ce

/set4/c32.py

9502887de3f52298ea5e45f9668c99beffc8cf4d

[ "Apache-2.0" ]

permissive

https://github.com/azuline/cryptopals

d051f86f7c4d1b7090110f96eaf04e007707127e

22280022fffad3bfb44bfc443abea0bad1125c8b

refs/heads/master

2023-01-28T01:26:31.360723

2020-12-14T03:58:11

209,361,493

0

null

""" Break HMAC-SHA1 with a slightly less artificial timing leak """ import sys # isort:skip from pathlib import Path # isort:skip sys.path.append(str(Path(__file__).parent.resolve().parent)) import logging from secrets import token_bytes from threading import Thread from time import sleep, time import requests from set4.c31 import hmac_sha1, start_webserver # Shut Flask and Werkzeug up. wzlogger = logging.getLogger("werkzeug") wzlogger.disabled = True def insecure_compare(sig1, sig2): if len(sig1) != len(sig2): return False for c1, c2 in zip(sig1, sig2): sleep(0.005) if c1 != c2: return False return True def crack_mac_for_any_file(file): print("\nCracking MAC...") mac = b"" for _ in range(20): times = [] for byte in [bytes([i]) for i in range(256)]: padding = b"\x00" * (20 - (len(mac) + 1)) total_time = 0 for _ in range(10): start_time = time() r = requests.post( "http://localhost:5000/test", params={ "file": file.hex(), "signature": (mac + byte + padding).hex(), }, ) end_time = time() total_time += end_time - start_time times.append((byte, total_time)) byte, longest_time = sorted(times, key=lambda v: v[1], reverse=True)[0] assert longest_time > (len(mac) + 1.5) * 0.05 print(f"Found a byte of the mac: {byte.hex()}") mac += byte assert r.status_code == 200 # Assert that the last MAC was valid. return mac if __name__ == "__main__": secret_key = token_bytes(64) print("Starting webserver.") Thread(target=start_webserver(insecure_compare, secret_key)).start() sleep(1) # Give the webserver time to spin up... file = token_bytes(24) print("\nThe file is:") print(file) print("\nThe secret key is:") print(secret_key.hex()) print("\nThe MAC is:") print(hmac_sha1(secret_key, file).hex()) mac = crack_mac_for_any_file(file) print("\nFound full MAC:") print(mac.hex())

UTF-8

Python

false

2,224

py

43

c32.py

39

0.556655

0.533273

0.00045

92

23.173913

79

bmyerz/iowa-computer-science-methods

15,135,464,779,421

67c7bf6d87fc170ce4928667e6a7730c54b91262

d6b1a72f2726397f5fc7f8493362b2f0a5c20b5d

/jes-code/horndup.py

57be016a3eabd94684c21f58d670cd95182304cf

[]

no_license

https://github.com/bmyerz/iowa-computer-science-methods

a9d8e42cff9323d0d8419a000278498e46ba8f3a

bd76a2f65c1cf291ca0255e7c03e06feba6231b3

refs/heads/master

2021-06-20T03:22:57.555524

2021-02-22T17:29:18

189,730,334

0

null

s = makeSound("Ensoniq-SQ-1-French-Horn-C4.wav") openSoundTool(s)

UTF-8

Python

false

65

py

19

horndup.py

19

0.753846

0.723077

0

2

32

48

WDB40/CIS189

9,878,424,794,517

339b4b34c6ab795327c3578f7298b46338943799

59f4388d9c3816a3af6b6d27b14e086509656cf9

/Module8/src/get_test_scores.py

e6c47cd9e871106dcdd68abbfd38c56cf4a9259f

[]

no_license

https://github.com/WDB40/CIS189

93f6152f3b152d038182243d73830fb823dee339

3d51ba4d060c532c140ae317a825fd648ea1c7f7

refs/heads/master

2020-07-17T03:18:58.817312

2019-12-09T02:39:50

205,929,980

0

null

""" Program: get_test_scores.py Author: Wes Brown Last date modified: 10/15/19 Purpose: """ def valid_number(value, min, max): INVALID_INPUT = -1 if value < min or value > max or value == INVALID_INPUT: return False else: return True def get_test_score(): INVALID_INPUT = -1 MAX_SCORE = 100 MIN_SCORE = 0 user_input = INVALID_INPUT while not valid_number(user_input, MIN_SCORE, MAX_SCORE): try: user_input = int(input("Enter a test score: ")) except ValueError: user_input = INVALID_INPUT return user_input def average_scores(the_dict): total = 0 for key in the_dict: total = total + the_dict[key] return total / len(the_dict) def get_num_tests(): INVALID_INPUT = -1 MAX_SCORE = 10 MIN_SCORE = 1 user_input = INVALID_INPUT while not valid_number(user_input, MIN_SCORE, MAX_SCORE): try: user_input = int(input("Enter the number of tests: ")) except ValueError: user_input = INVALID_INPUT return user_input if __name__ == '__main__': num_scores = get_num_tests() scores_dict = dict() for i in range(1, num_scores + 1): score = get_test_score() scores_dict.update({i: score}) print("Average Score: %.2f" % average_scores(scores_dict))

UTF-8

Python

false

1,360

py

122

get_test_scores.py

118

0.590441

0.575735

0

66

19.606061

66

django-group/python-itvdn

4,080,218,965,983

8ad07f5ecfeb5ec69a28f5ab39bed50a35b04632

8a1ab23b056886965fec2a3e4064c5ed55e22bfb

/домашка/essential/lesson 3/Dmytro Marianchenko/t_3.py

827afb0f46e7a992dee4d19b588939d6e08a49f0

[]

no_license

https://github.com/django-group/python-itvdn

5d8a59f06618f993d20d2f60374c36aae4ae8ab0

62ef87dfac947ed4bf1f5b6b890461f56814d893

refs/heads/master

2021-01-07T11:16:37.996524

2020-02-14T13:53:47

null

0

null

def checker(name, year, company_bd): if year > company_bd: print(f"{name} is not an employee of the company") else: print(f"{name} works in the company since {year}") def name_form(x): while x is None: x = input(f"Please enter:\n>> ") if x.isalpha(): y = x.capitalize() return y else: print("should not contain a numbers") pass def year_form(x): while x is None: try: x = int(input(f"Please enter:\n>> ")) return x except ValueError: print("year should not contain a letter or any symbols except numbers") def validation(x, company_bd): for i in x: if company_bd >= i[3]: print(f"{i[0]} {i[1]} is not а company member") else: print(f"{i[0]} {i[1]} work in {i[2]} department sins {i[3]} year") def main(): name = None surname = None year = None company_bd = 1991 print("Enter a name of worker") name = name_form(name) print("Enter a surname of worker") surname = name_form(surname) force = input("Enter a department of company:\n>> ") print("Enter a year of start working in company") year = year_form(year) pers = [name, surname, force, int(year)] personal.append(pers) while True: sw = input("Do you wont to add an another person? y/n\n>> ") if sw == "y": break elif sw == "n": validation(personal, company_bd) input("Pres 'Enter' to exit...") exit() main() if __name__ == '__main__': personal = [] main()

UTF-8

Python

false

1,658

py

706

t_3.py

679

0.532287

0.525649

0

63

25.301587

83

loek-tonnaer/UnsupervisedActionEstimation

12,412,455,504,012

f5ac5d406068dc7ba1b3691ca8d4d6be24feafd0

c5bdee1e0209cf74ba70aad7e954028f0fcecae0

/metrics/utils.py

09624714a75eb354d85186c472499254a738017c

[]

no_license

https://github.com/loek-tonnaer/UnsupervisedActionEstimation

e856b32ccc5e01cdbcae50a9eead16d75548f115

1c951bf65abb8e5a8189cc67102bda6e1834168b

refs/heads/master

2022-12-26T20:13:22.956433

2020-10-06T16:23:40

null

0

null

import numpy as np import sklearn import torch def _get_random_latent(ds): f = [] for factor in ds.latents_sizes: f.append(np.random.randint(0, factor)) return np.array(f) def _sample_one_representation(rep_fn, ds, paired=False): latent_1 = ds.sample_latent() img1 = ds.get_img_by_latent(latent_1)[0] if not torch.is_tensor(img1): img1 = img1[0] z = rep_fn(img1.to('cuda').unsqueeze(0)) return z.detach().cpu(), latent_1 def sample_batch(model, num_points, ds, paired=False): reps, factors = None, None for i in range(num_points): rep, fac = _sample_one_representation(model, ds, paired=paired) # fac = fac[1:] if i == 0: reps, factors = rep, fac else: factors = np.vstack((factors, fac)) reps = np.vstack((reps, rep)) return np.transpose(reps), np.transpose(factors) def histogram_discretize(target, num_bins=20): discretized = np.zeros_like(target) for i in range(target.shape[0]): discretized[i, :] = np.digitize(target[i, :], np.histogram(target[i, :], num_bins)[1][:-1]) return discretized def discrete_mutual_info(mus, ys): """Compute discrete mutual information.""" num_codes = mus.shape[0] num_factors = ys.shape[0] m = np.zeros([num_codes, num_factors]) for i in range(num_codes): for j in range(num_factors): m[i, j] = sklearn.metrics.mutual_info_score(ys[j, :], mus[i, :]) return m def discrete_entropy(ys): """Compute discrete mutual information.""" num_factors = ys.shape[0] h = np.zeros(num_factors) for j in range(num_factors): h[j] = sklearn.metrics.mutual_info_score(ys[j, :], ys[j, :]) return h def normalize_data(data, mean=None, stddev=None): if mean is None: mean = np.mean(data, axis=1) if stddev is None: stddev = np.std(data, axis=1) return (data - mean[:, np.newaxis]) / stddev[:, np.newaxis], mean, stddev

UTF-8

Python

false

1,997

py

34

utils.py

33

0.607912

0.595894

0

70

27.528571

99

jshrall/pm_tools

4,217,657,896,767

2384c8a438dfcf1decd67b3e67686f65170a47c3

c6e967dca129fabae2ca3f54e880805c7b0ee9df

/plugins/mermaid/mermaid.py

99e861ac0f71f6309be805f171314877f3d8f353

[ "MIT" ]

permissive

https://github.com/jshrall/pm_tools

d3354b5c868ddfd7dfb5a138209401129c964b23

2207f1f145172c3016b059b60b89c83ece184c72

refs/heads/master

2021-09-09T12:04:03.086767

2018-03-15T21:21:41

113,763,387

0

null

true

2017-12-10T16:00:04

2017-12-10T16:00:02

2017-11-18T19:02:41

2017-11-18T19:42:15

61,739

0

null

false

null

import os class MermaidPlugin(object): def __init__(self, preprocessor): self.mermaid_js = preprocessor.toolpath("plugins/mermaid/mermaid.cli/index.bundle.js") # Config and style are both currently unused #self.mermaid_cfg = preprocessor.toolpath("plugins/mermaid/mermaid_config.json") #self.mermaid_css = preprocessor.toolpath("plugins/mermaid/mermaid.css") self.pp = preprocessor self.token = "mermaid" self.pp.register_plugin(self) def process(self, code, filename_or_title, title=None, div_style=None): """ Process mermaid code and return the proper insertion string """ mmdfile, outfile, update, title = self.pp.get_source(code, filename_or_title, ".mmd", ".svg", title) if update: self.mermaid2img(mmdfile, outfile) return self.pp.img2md(outfile, title, div_style) def mermaid2img(self, infile, outfile): """Convert mermaid file to image output file. Args: infile (str): [description] outfile (str, optional): Defaults to None. Image will be written to this file. The outfile extension describes the type, either png or svg. """ try: if outfile and os.path.exists(outfile): os.unlink(outfile) self.pp._call(r'"%s" -i "%s" -o "%s"' % (self.mermaid_js, infile, outfile)) except SystemExit: # If mermaid failed, but generated output SVG, that SVG contains error description # so should be good enough to continue if outfile and os.path.exists(outfile): print "Ignoring the error above. See mermaid output diagram for detailed error description" else: raise new = MermaidPlugin

UTF-8

Python

false

1,826

py

68

mermaid.py

33

0.617196

0.615553

0

47

37.851064

132

tomaszmartin/stocks

10,823,317,619,212

b3ef49faf7afc423762745c82020f65391ee7581

fd2b3452d1cc5539282c5861ba188bc48f5d7912

/app/scrapers/coinapi.py

c484a97651e684dbfb0902d2793286ab57bc1512

[]

no_license

https://github.com/tomaszmartin/stocks

9bc568769406abd207eb4447a7029707a0bd0af3

4e97dd1f69e59b9643c52340bd052fd173693a4d

refs/heads/main

2023-08-19T20:52:17.946003

2021-12-30T18:48:02

365,441,638

0

null

false

2021-10-09T18:19:03

2021-05-08T06:51:33

2021-07-24T20:22:02

2021-10-09T18:19:02

232

0

2

HTML

false

"""Extracts data from a coinbase API.""" import datetime as dt import json import pandas as pd from airflow.providers.http.hooks.http import HttpHook COINS = [ "ADA", "BTC", "BTT", "BNB", "DASH", "DOGE", "ETH", "ETC", "LTC", "LUNA", "XLM", ] def download_realtime(for_date: dt.datetime, coin_symbol: str) -> bytes: """Downloads current prices for a specified coin. Args: coin_symbol: what coin should be downloaded, for example BTC for_date: unused in this context Returns: bytes: result """ hook = HttpHook("GET", http_conn_id="coinapi") endpoint = f"v1/quotes/BINANCE_SPOT_{coin_symbol.upper()}_USDT/current" resp = hook.run(endpoint) data = resp.content return data def download_data(for_date: dt.datetime, coin_symbol: str) -> bytes: """Downloads file with appropriate data from the CoinAPI. Args: coin_symbol: what coin should be downloaded, for example BTC for_date: for what day Returns: bytes: result """ next_day = for_date + dt.timedelta(days=1) hook = HttpHook("GET", http_conn_id="coinapi") endpoint = "v1/exchangerate/{coin}/USD/history?period_id=1DAY&time_start={start}&time_end={end}" endpoint = endpoint.format( coin=coin_symbol.upper(), start=for_date.date(), end=next_day.date() ) resp = hook.run(endpoint) data = resp.content return data def parse_data(data: bytes, for_date: dt.datetime, coin_symbol: str = ""): """Extracts data from file into correct format. Args: data: data from file for_date: for what day data was downloaded coin_symbol: what coin this data holds. It's not present in the file data. Raises: ValueError: when no coin is passed Returns: final data """ if not coin_symbol: raise ValueError("Need to specify coin!") frame = pd.read_json(data) frame = frame.rename( columns={ "date": "time_close", "rate_open": "open", "rate_high": "high", "rate_low": "low", "rate_close": "close", } ) frame = frame.drop( columns=["time_period_start", "time_period_end", "time_open", "time_close"] ) frame["date"] = for_date.date() frame["coin"] = coin_symbol.upper() frame["base"] = "USD" return frame.to_dict("records") def parse_realtime(data: bytes, for_date: dt.datetime, coin_symbol: str = ""): """Extracts realtime data from file into correct format. Args: data: data from file for_date: not used in this context coin_symbol: what coin this data holds. It's not present in the file data. Raises: ValueError: when no coin is passed Returns: final data """ if not coin_symbol: raise ValueError("Need to specify coin!") json_data = json.loads(data) frame = pd.json_normalize(json_data) frame["coin"] = coin_symbol.upper() frame["base"] = "USD" frame = frame.drop( columns=[ "symbol_id", "last_trade.time_exchange", "last_trade.time_coinapi", "last_trade.uuid", "last_trade.price", "last_trade.size", "last_trade.taker_side", "time_exchange", "time_coinapi", ] ) return frame.to_dict("records")

UTF-8

Python

false

3,440

py

62

coinapi.py

34

0.586919

0.585756

0

130

25.461538

100

peuic/pokemon

7,481,833,038,659

0fcb5a8b8c306d92a1e347d52b21e7f543742025

00fd18e5bf1ea0b209d0e4033c7007b0a3ba6d30

/poketest.py

bb8128532530e54413a2feaa53ce9fedf4ee0856

[]

no_license

https://github.com/peuic/pokemon

aefcdd334cdf29528028c6673c1aaec855dc08df

c3741f7d700f1469ccf0549fb5b1128a04880962

refs/heads/master

2022-12-09T17:15:38.555538

2018-06-21T02:20:26

138,101,337

0

null

false

2022-12-08T02:09:35

2018-06-21T00:50:32

2018-06-21T14:39:22

2022-12-08T02:09:33

4,831

0

4

Python

false

from flask import Flask, render_template, request import requests app = Flask(__name__) @app.route('/pokeresult', methods=['POST']) def pokedata(): num = request.form['pokeq'] r = requests.get('https://pokeapi.co/api/v2/pokemon/'+num+'/') json_object = r.json() poke_id = json_object ['id'] poke_name = json_object ['name'] poke_pic = json_object ['sprites'] ['front_default'] poke_peso = json_object ['weight'] return render_template('pokeresult.html', pokeid=poke_id, pokename=poke_name, pokepic=poke_pic, pokepeso=poke_peso) @app.route('/') def index(): return render_template('index.html') if __name__ == '__main__': app.run(debug=True)

UTF-8

Python

false

681

py

4

poketest.py

1

0.651982

0.650514

0

22

30

119

bitbybitsth/django_deploy

14,817,637,200,521

a6496ade3f09c77d59d3b2342c350113db5aced8

ec60b96d8ed11b750ea91a64196ecc3a6d8b299a

/ecom/product/migrations/0002_auto_20211012_0924.py

a260695ce655eafb129b87dd0acbda5afd246d98

[]

no_license

https://github.com/bitbybitsth/django_deploy

1d6fe99989ea7a4ab8961dee70b3612e4bc2b589

670e716c4cf8a605c1ca2ffdfbed000a13f17bf7

refs/heads/main

2023-08-25T06:42:39.424848

2021-10-27T03:54:38

421,664,267

0

null

# Generated by Django 3.2.8 on 2021-10-12 03:54 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("product", "0001_initial"), ] operations = [ migrations.AlterModelOptions( name="product", options={"verbose_name": "Product", "verbose_name_plural": "Products"}, ), migrations.AddField( model_name="product", name="delivery", field=models.CharField( default="India", max_length=40, verbose_name="Delivery Country" ), ), migrations.AlterField( model_name="product", name="warranty", field=models.IntegerField(default=1, verbose_name="Warranty in Year"), ), ]

UTF-8

Python

false

805

py

3

0002_auto_20211012_0924.py

3

0.556522

0.529193

0

29

26.758621

83

pyro-ppl/pyro

627,065,241,847

f952fcbce3e5a98e2b6c2d3711cd19445676f828

edc1134436a79ca883a0d25f3c8dfffc4235c514

/tests/infer/test_svgd.py

d90efb0c8c1c8d1c7051fa944d13b930238448db

[ "Apache-2.0" ]

permissive

https://github.com/pyro-ppl/pyro

2283d8ca528fc090c724a3a6e0f344e505ebbf77

0e82cad30f75b892a07e6c9a5f9e24f2cb5d0d81

refs/heads/dev

2023-08-18T00:35:28.014919

2023-08-06T21:01:36

94,506,832

3,647

606

Apache-2.0

false

2023-09-14T13:52:14

2017-06-16T05:03:47

2023-09-14T05:15:11

2023-09-14T12:55:38

95,825

8,118

973

243

Python

false

# Copyright (c) 2017-2019 Uber Technologies, Inc. # SPDX-License-Identifier: Apache-2.0 import pytest import torch import pyro import pyro.distributions as dist from pyro.infer import SVGD, IMQSteinKernel, RBFSteinKernel from pyro.infer.autoguide.utils import _product from pyro.optim import Adam from tests.common import assert_equal @pytest.mark.parametrize( "latent_dist", [ dist.Normal(torch.zeros(2), torch.ones(2)).to_event(1), dist.LogNormal(torch.tensor([-1.0]), torch.tensor([0.7])).to_event(1), dist.LogNormal(torch.tensor(-1.0), torch.tensor(0.7)), dist.Beta(torch.tensor([0.3]), torch.tensor([0.7])).to_event(1), ], ) @pytest.mark.parametrize("mode", ["univariate", "multivariate"]) @pytest.mark.parametrize("stein_kernel", [RBFSteinKernel, IMQSteinKernel]) def test_mean_variance(latent_dist, mode, stein_kernel, verbose=True): pyro.clear_param_store() def model(): pyro.sample("z", latent_dist) kernel = stein_kernel() adam = Adam({"lr": 0.05}) svgd = SVGD(model, kernel, adam, 200, 0, mode=mode) bandwidth_start = 1.0 bandwidth_end = 5.0 n_steps = 301 # scramble initial particles svgd.step() pyro.param("svgd_particles").unconstrained().data *= 1.3 pyro.param("svgd_particles").unconstrained().data += 0.7 for step in range(n_steps): kernel.bandwidth_factor = bandwidth_start + (step / n_steps) * ( bandwidth_end - bandwidth_start ) squared_gradients = svgd.step() if step % 125 == 0: print("[step %03d] " % step, squared_gradients) final_particles = svgd.get_named_particles()["z"] if verbose: print( "[mean]: actual, expected = ", final_particles.mean(0).data.numpy(), latent_dist.mean.data.numpy(), ) print( "[var]: actual, expected = ", final_particles.var(0).data.numpy(), latent_dist.variance.data.numpy(), ) assert_equal(final_particles.mean(0), latent_dist.mean, prec=0.01) prec = 0.05 if mode == "multivariate" else 0.02 assert_equal(final_particles.var(0), latent_dist.variance, prec=prec) @pytest.mark.parametrize("shape", [(1, 1), (2, 1, 3), (4, 2), (1, 2, 1, 3)]) @pytest.mark.parametrize("stein_kernel", [RBFSteinKernel, IMQSteinKernel]) def test_shapes(shape, stein_kernel): pyro.clear_param_store() shape1, shape2 = (5,) + shape, shape + (6,) mean_init1 = torch.arange(_product(shape1)).double().reshape(shape1) / 100.0 mean_init2 = torch.arange(_product(shape2)).double().reshape(shape2) def model(): pyro.sample("z1", dist.LogNormal(mean_init1, 1.0e-8).to_event(len(shape1))) pyro.sample("scalar", dist.Normal(0.0, 1.0)) pyro.sample("z2", dist.Normal(mean_init2, 1.0e-8).to_event(len(shape2))) num_particles = 7 svgd = SVGD(model, stein_kernel(), Adam({"lr": 0.0}), num_particles, 0) for step in range(2): svgd.step() particles = svgd.get_named_particles() assert particles["z1"].shape == (num_particles,) + shape1 assert particles["z2"].shape == (num_particles,) + shape2 for particle in range(num_particles): assert_equal(particles["z1"][particle, ...], mean_init1.exp(), prec=1.0e-6) assert_equal(particles["z2"][particle, ...], mean_init2, prec=1.0e-6) @pytest.mark.parametrize("mode", ["univariate", "multivariate"]) @pytest.mark.parametrize("stein_kernel", [RBFSteinKernel, IMQSteinKernel]) def test_conjugate(mode, stein_kernel, verbose=False): data = torch.tensor([1.0, 2.0, 3.0, 3.0, 5.0]).unsqueeze(-1).expand(5, 3) alpha0 = torch.tensor([1.0, 1.8, 2.3]) beta0 = torch.tensor([2.3, 1.5, 1.2]) alpha_n = alpha0 + data.sum(0) # posterior alpha beta_n = beta0 + data.size(0) # posterior beta def model(): with pyro.plate("rates", alpha0.size(0)): latent = pyro.sample("latent", dist.Gamma(alpha0, beta0)) with pyro.plate("data", data.size(0)): pyro.sample("obs", dist.Poisson(latent), obs=data) kernel = stein_kernel() adam = Adam({"lr": 0.05}) svgd = SVGD(model, kernel, adam, 200, 2, mode=mode) bandwidth_start = 1.0 bandwidth_end = 5.0 n_steps = 451 for step in range(n_steps): kernel.bandwidth_factor = bandwidth_start + (step / n_steps) * ( bandwidth_end - bandwidth_start ) squared_gradients = svgd.step() if step % 150 == 0: print("[step %03d] " % step, squared_gradients) final_particles = svgd.get_named_particles()["latent"] posterior_dist = dist.Gamma(alpha_n, beta_n) if verbose: print( "[mean]: actual, expected = ", final_particles.mean(0).data.numpy(), posterior_dist.mean.data.numpy(), ) print( "[var]: actual, expected = ", final_particles.var(0).data.numpy(), posterior_dist.variance.data.numpy(), ) assert_equal(final_particles.mean(0)[0], posterior_dist.mean, prec=0.02) prec = 0.05 if mode == "multivariate" else 0.02 assert_equal(final_particles.var(0)[0], posterior_dist.variance, prec=prec)

UTF-8

Python

false

5,251

py

639

test_svgd.py

525

0.613217

0.576081

0

149

34.241611

83

okotaku/kaggle_statoil

4,020,089,423,558

5a51f2cd307c627487617e94263b984e75b0ecbd

ed7ffb471f80f8aed29c50b1b5b1187cbd3b8a8d

/model/vgg16.py

54b98e01004b9e5e586635ea0d8d30ab38ecdd25

[]

no_license

https://github.com/okotaku/kaggle_statoil

f9ddc3396663eed26e533b4844cb7f57b3a39be5

3c3225bb4eeaf32ae9109614eda4af102394c667

refs/heads/master

2021-09-05T18:11:32.016595

2018-01-30T05:55:44

115,371,866

3

0

null

# -*- coding: utf-8 -*- from keras.applications.vgg16 import VGG16 from keras.layers import Input, Flatten, Dense from keras.layers.core import Activation from keras.models import Model def Vgg16(freeze_leyer): input_tensor = Input(shape=(75, 75, 3)) vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=input_tensor) x = Flatten()(vgg16.output) x = Dense(256)(x) x = Activation('relu')(x) x = Dense(1, activation='sigmoid')(x) model = Model(input=vgg16.input, output=x) if freeze_leyer > 0: for layer in model.layers[:freeze_leyer]: layer.trainable = False return model

UTF-8

Python

false

663

py

15

vgg16.py

14

0.650075

0.612368

0

23

27.826087

56

manishbisoi/Jinee

10,273,561,793,635

c14d928a2818615015cac7c4c9ceac859596960e

05db88673dd09c36406faeb9b9d0afcb40b5fa26

/tasks/fortune.py

ee62b4464c694dc43fda8317d964bba14b0f3c9d

[]

no_license

https://github.com/manishbisoi/Jinee

67f9d40fbad4231ba605b890e36500140c6bf69d

5676cd0fc4c4df4237fabb132dcfa9dec6578d5f

refs/heads/master

2021-01-20T22:09:45.160364

2016-08-12T14:14:07

65,555,800

0

null

import os os.system('fortune | xcowsay')

UTF-8

Python

false

40

py

19

fortune.py

18

0.75

0

2

19.5

30

gacanepa/CursoBashPython

5,042,291,648,916

0ed53325fa1d72c4c2500bfca51be343adde7ad2

4a292a4d66451b323952d565c438d4a65d9408aa

/clase2/clase2.py

a9efa124a62c9546b0ee9c3d14fcdf67e8e376ca

[]

no_license

https://github.com/gacanepa/CursoBashPython

c5963d042aabbe64e9f182be2f14ba477401152f

2cc83be2609aad9656594942b34a57020baf4b82

refs/heads/master

2019-01-25T00:28:03.621524

2017-12-13T20:11:05

86,010,949

0

null

# EJERCICIOS CLASE 2 # ------------------------------------------ # FUNCIONES AUXILIARES # TipoDeDato: recibe una variable (nombreVariable) y su valor (dato) como entrada y devuelve el tipo de dato def TipoDeDato(nombreVariable, dato): print('La variable', nombreVariable, 'es igual a', dato, 'y es del tipo', type(dato)) # ------------------------------------------ # EJEMPLOS VISTOS EN CLASE # ------------------------------------------ # TIPOS DE DATOS # Enteros: numero1=5 numero2=-9 TipoDeDato('numero1', numero1) TipoDeDato('numero2', numero2) # Flotantes numero3=1.32 numero4=1.2 TipoDeDato('numero3', numero3) TipoDeDato('numero4', numero4) # Strings. Se incluyen ejemplos de la concatenación y de la replicación de strings. nombre='Gabriel' apellido='Cánepa' nombreCompleto=nombre + ' ' + apellido # Concatenación subrayado='-' * len(nombre) # Replicación TipoDeDato('nombre', nombre) TipoDeDato('apellido', apellido) TipoDeDato('nombreCompleto', nombreCompleto) TipoDeDato('subrayado', subrayado) # Booleanos (y parecidos) # Recordemos que Python interpreta (además de False) a cualquier objeto vacío como False, y al resto como True. a1=True a2=False TipoDeDato('a1', a1) TipoDeDato('a2', a2) # Listas paises=['Argentina', 'Uruguay', 'Paraguay', 'Chile'] calificaciones=[7.5, 9, 10, 6.75] TipoDeDato('paises', paises) TipoDeDato('calificaciones', calificaciones) # ------------------------------------------ # CONVERSIÓN DE DATOS # Entero a flotante intFloat1=float(1) intFloat2=float(-5) TipoDeDato('intFloat1', intFloat1) TipoDeDato('intFloat2', intFloat2) # Flotante a entero. ¡CUIDADO! Se truncará el valor floatInt1=int(1.32) floatInt2=int(2.0) TipoDeDato('floatInt1', floatInt1) TipoDeDato('floatInt2', floatInt2) # Entero o flotante a string. Necesario para concatenar strings con el operador + num1Str=str(14) num2Str=str(2.32) TipoDeDato('num1Str', num1Str) TipoDeDato('num2Str', num2Str) # La siguiente asignación produciría un error: # otraVariable='Hoy es ' + 29 + ' de marzo' # Pero esta funcionaría correctamente: # otraVariable='Hoy es ' + str(29) + ' de marzo' # String a flotante o entero strInt=int('84') strFloat=float('2.345') TipoDeDato('strInt', strInt) TipoDeDato('strFloat', strFloat) # Varios tipos a booleano. Al realizar la conversión, Python considera a cualquier objeto vacío o a None como False. # El resto será True. Este procedimiento es útil al evaluar condiciones en un control de flujo. intBool1=bool(0) intBool2=bool(1) intBool3=bool(120) floatBool1=bool(0.0) floatBool2=bool(2.32) stringBool1=bool('') stringBool2=bool('Hola a todos') noneBool=bool(None) listaBool1=bool([]) listaBool2=bool(paises) TipoDeDato('intBool1', intBool1) TipoDeDato('intBool2', intBool2) TipoDeDato('intBool3', intBool3) TipoDeDato('floatBool1', floatBool1) TipoDeDato('floatBool2', floatBool2) TipoDeDato('stringBool1', stringBool1) TipoDeDato('stringBool2', stringBool2) TipoDeDato('noneBool', noneBool) TipoDeDato('listaBool1', listaBool1) TipoDeDato('listaBool2', listaBool2) # ------------------------------------------ # INTERACCIÓN # Solicitar la entrada de un valor y asignarlo a una variable. Descomentar para testear. # respuesta=input() # print(respuesta) # ------------------------------------------ # DIFERENCIA ENTRE = Y == # = se utiliza para asignar un valor a una variable # == se emplea para chequear que una variable posea tal o cual valor miVar=6 # Asignación del valor 6 a la variable miVar print(miVar==6) # Como miVar contiene el valor 6, esta sentencia devolverá True. print(miVar==7) # Como miVar contiene el valor 6, esta sentencia devolverá False. # ------------------------------------------ # OPERACIONES CON LISTAS # Agregar los números del 1 al 15 en una lista llamada numerosConFor usando un for loop numerosConFor=[] for i in range(1,16): numerosConFor.append(i) # Mostrar la lista y verificar su tipo: TipoDeDato('numerosConFor', numerosConFor) # Eliminar de la lista los elementos que se encuentren en las posiciones 0, 3, y 9: numerosConFor.pop(0) numerosConFor.pop(3) numerosConFor.pop(9) # Insertar el número -5 en la posición 4: numerosConFor.insert(4, -5) # Cambiar el valor del elemento que se encuentra en la primera y en la última posición de la lista. # Los nuevos valores deben ser 11 y -30, respectivamente. numerosConFor[0] = 11 maxIndex = numerosConFor.index(max(numerosConFor)) numerosConFor[maxIndex] = -30 # Nuestra lista tiene los siguientes elementos luego de haber realizado los cambios anteriores: TipoDeDato('numerosConFor', numerosConFor) # ------------------------------------------ # TAREA # Ejercicio 1: Reemplazar el bucle while de la diapositiva correspondiente con un bucle for. Utilizar la función range(). listaNumeros=[] for i in range(0,5): listaNumeros.append(i) print(listaNumeros) # Ejercicio 2: Escribir un módulo que incluya funciones para sumar, restar, multiplicar, y dividir dos números. Guardarlo # con el nombre operaciones.py en el directorio actual e invocarlo desde la consola interactiva de Python. A continuación, # ejemplificar el uso de cada una de las funciones. def suma(a, b): '''Devuelve el resultado de la suma de dos números a y b''' return a + b def resta(a, b): '''Devuelve el resultado de la resta de a menos b''' return a - b def multiplicacion(a, b): '''Devuelve el resultado del producto de números a y b''' return a * b def division(a, b): '''Devuelve el resultado de la división entre dos a y b''' return a / b # Ejercicio 3: Escribir una función que tome 3 números como entrada y calcule el promedio, redondeando el resultado a dos # decimales. Si el resultado es mayor o igual que 7, mostrar el mensaje 'Aprobado'. De lo contrario, mostrar 'No aprobado’. def promedio(a, b, c): resultado = (a + b + c) / 3 if resultado >= 7: print('Aprobado') else: print('No aprobado')

UTF-8

Python

false

6,149

py

13

clase2.py

11

0.677683

0.652978

0

196

29.183673

123

il-dionigi/180DB-GradCaps

790,274,004,428

144888937cd0e60660cbf55279a6181ff5ed8e2c

ede143f1801ab6dcc1ad67626980032257a0e936

/HWGroup/pixelPi/encoder.py

574bc53d9eb7cae06cac4bb5c5aac15a70892e41

[]

no_license

https://github.com/il-dionigi/180DB-GradCaps

1349f0c829ee43cc7a89d01fb31ef631a8df6b9b

7330ec6d0a4bb1d1737ce634686ca463a8de1829

refs/heads/master

2020-12-13T12:57:25.377614

2020-03-12T21:13:57

234,423,121

0

1

null

from bitstring import BitArray import math import Node xpos_len = 4 # 4 bits -> 0-15 xpos_start = 0 # x starts at bit 0 in x+y bits ypos_len = 4 # 4 bits -> 0-15 ypos_start = xpos_len # y starts at bit len(xpos in bits) in x+y bits xy_len = xpos_len + ypos_len # total lengh of x+y bits message_length = 20 * 8 # in bits group_id_len = 4 # bits used for group id group_size = 2^4 # currently 19 def encodeMessage(map): ''' Function to encode seat positions for sending via message_length BLE broadcasts Args: @map (Array of Tuples or Arrays): Format of tuples/arrays is [(int)id, (int)x, (int)y] Returns: @ (string): String of encoded ids with their coords ''' sorted_map = sorted(map, key=lambda x: x[0]) message_ba = BitArray('') for i in range(len(sorted_map)): message_ba.append(hex(sorted_map[i][0]>>4)) message_ba.append(hex(sorted_map[i][0]&0b1111)) message_ba.append(hex(sorted_map[i][1])) message_ba.append(hex(sorted_map[i][2])) return message_ba.tobytes().decode('cp437') def decodeMessage(message): ''' Function to decode seat positions for sending via message_length BLE broadcasts Args: @messages (string): Should be the full output from encodeMessage Returns: @map (array of tuples): format of tuples is ((int)id, (int)x, (int)y) ''' seat_map = [] message_bytes = BitArray(message.encode('cp437')).tobytes() for i in range(0, len(message_bytes),2): byte0 = message_bytes[i] byte1 = message_bytes[i+1] seat_map.append((int(byte0), int(byte1)&0b1111, int(byte1)>>4)) return seat_map

UTF-8

Python

false

1,542

py

51

encoder.py

36

0.690013

0.656939

0

47

31.829787

87

slightlynybbled/manufacturing

18,614,388,295,929

8bbc2cb674f970ebc423e83189072f218c6447e8

18742a0ecbf392328d8df994abf937e0e72d89f3

/examples/ppk_plot.py

475d772870ff5c8e74adc0c024ef88cc66100a44

[ "MIT" ]

permissive

https://github.com/slightlynybbled/manufacturing

1ab9c3dd46b168d8be1b98472d818f5265483def

4d172c7a12a649259ad3bc0e3f6993b1dcd79e0c

refs/heads/master

2023-06-09T00:18:31.143098

2023-06-06T18:23:29

224,505,774

45

16

MIT

false

2023-06-06T15:45:03

2019-11-27T19:47:39

2023-05-12T05:52:55

2023-06-06T15:44:15

3,068

40

13

3

Python

false

import logging import matplotlib.pyplot as plt from manufacturing import import_excel, ppk_plot logging.basicConfig(level=logging.INFO) data = import_excel('data/example_data_with_faults.xlsx', columnname='value') fig, ax = plt.subplots() # creating a figure to provide to the ppk_plot as a parameter ppk_plot(**data, parameter_name='Current', upper_specification_limit=10.1, lower_specification_limit=5.5, show_dppm=True, figure=fig) plt.show()

UTF-8

Python

false

517

py

54

ppk_plot.py

33

0.673114

0.663443

0

19

26.210526

87

rlee287/secure-notes-client

14,740,327,766,827

d10cb831011f988208c9f56177100bad3bcb0095

347059357e2ca1db87e6e0ceb2722c243a93e6c7

/secure_notes_client/gui_editor.py

665b1572285911c2092b21c809715c0582ffb093

[ "MIT" ]

permissive

https://github.com/rlee287/secure-notes-client

b669b6ee400262ff23cc5ece35e7091edb7135af

56d5fcce1d2eeb46de22aac63131fe7214b6f185

refs/heads/master

2022-12-09T02:51:26.149435

2019-10-14T01:56:37

193,761,929

0

MIT

false

2022-12-08T06:36:18

2019-06-25T18:24:58

2019-10-14T01:56:45

2022-12-08T06:36:17

39

0

5

Python

false

from PySide2 import QtWidgets from PySide2.QtCore import Signal, Qt import filesystem import networking class EditorClass(QtWidgets.QMainWindow): close_signal = Signal(QtWidgets.QMainWindow) def __init__(self, ui_obj, config_obj, note_id): # type: (EditorClass, Ui_NoteEditWindow, ConfigObj, str) -> None super().__init__() self.ui_obj = ui_obj self.config_obj = config_obj self.note_id = note_id self.edited_since_last_save = False self.allow_edits = False self.note_obj=filesystem.read_noteobj(config_obj,note_id) ui_obj.setupUi(self) self.setAttribute(Qt.WA_DeleteOnClose,True) self.setWindowTitle(self.note_obj["note"]["title"]) ui_obj.titleLineEdit.setText(self.note_obj["note"]["title"]) ui_obj.noteTextEdit.setPlainText(self.note_obj["note"]["text"]) ui_obj.titleLineEdit.editingFinished.connect(self.mark_edited) ui_obj.noteTextEdit.textChanged.connect(self.mark_edited) ui_obj.actionSave.triggered.connect(self.save_file) self.update_editor_enabled_status() def set_editing_enabled(self, enable_editing): if self.allow_edits == enable_editing: return if self.allow_edits: # T -> F, throw warning if unsaved changes if self.edited_since_last_save: raise ValueError("Unsaved changes present") else: # F -> T, just do enabling self.allow_edits = True self.edited_since_last_save = False self.update_editor_enabled_status() def update_editor_enabled_status(self): self.ui_obj.titleLineEdit.setReadOnly(not self.allow_edits) self.ui_obj.noteTextEdit.setReadOnly(not self.allow_edits) def mark_edited(self): self.edited_since_last_save = True def save_file(self): note_obj_copy = self.note_obj.copy() note_obj_copy["note"]["title"] = ui_obj.titleLineEdit.getText() note_obj_copy["note"]["text"] = ui_obj.noteTextEdit.getText() self.edited_since_last_save = False def closeEvent(self, event): # TODO: confirmation dialog stuff self.close_signal.emit(self) event.accept()

UTF-8

Python

false

2,241

py

12

gui_editor.py

8

0.643909

0.643017

0

61

35.754098

72

ZackJorquera/ScaleLiquidRemainingIOT

4,363,686,782,744

20a8b98e95aeef1bb5269b21b512d9720c49ef7d

e3210970da44105e392c6e3abbd7b1e12c0e755f

/RaspberryPiCode/ScaleAggregator/ScaleAggregator.py

5e5058542f382aef660833981a133211a9caf99b

[ "MIT" ]

permissive

https://github.com/ZackJorquera/ScaleLiquidRemainingIOT

dedf6d1faa17dfe186cbf6db2b68761a84e9da06

57a349b95a3205c7dd5ad98337a9e5b01f384022

refs/heads/master

2018-10-22T06:41:20.700818

2018-07-25T03:40:57

114,922,290

2

0

null

false

2018-02-07T13:59:36

2017-12-20T19:14:25

2018-02-07T13:32:49

2018-02-07T13:58:52

56

0

1

Python

false

null

import time import sys import logging import os sys.path.append('../Tools/') import ScaleInfoReaderWriter as ScaleIRW import DatabaseReaderWriter as DBRW import ConfigReaderWriter as CfgRW def LoadDB(): if CfgRW.cfgVars["dbToUse"] == "Mongo": db = DBRW.MongoDBProfile() else: db = DBRW.MongoDBProfile() # db = DBRW.MySQLDBProfile() db.Connect() return db def printAndLog(msg, loglevel): print msg logger.log(loglevel, msg) def createLogger(): logDir = "../Log" if not os.path.exists(logDir): os.makedirs(logDir) logPath = "../Log/Log.txt" file_handler = logging.FileHandler(logPath) file_handler.setLevel(logging.DEBUG) formatter = logging.Formatter('%(levelname)s\t%(asctime)s \t%(message)s') file_handler.setFormatter(formatter) tmplogger = logging.getLogger() tmplogger.setLevel(logging.DEBUG) tmplogger.addHandler(file_handler) return tmplogger ScaleDataDB = LoadDB() logger = createLogger() scaleInfoList = None loopOn = 0 secsPerPersist = int(CfgRW.cfgVars["aggregatorSecsPerPersist"],10) # try catch loopsOfPersists = int(CfgRW.cfgVars["aggregatorLoopsOfPersists"],10) timeOfLastUpdate = None printAndLog("Starting Aggregation every " + str(secsPerPersist) + " Second.", logging.INFO) if ScaleDataDB.Client != None: printAndLog("Outputting to " + CfgRW.cfgVars["dbToUse"] + " database " + ScaleDataDB.DBName + " at: " + str(ScaleDataDB.Client.address), logging.INFO) while True: if CfgRW.cfgVars["uselatestFromMongoAsCurrent"].upper() == "TRUE": break timeOfLastUpdate = time.time() if scaleInfoList is None or loopOn > loopsOfPersists or len(scaleInfoList) != ScaleIRW.GetNumOfScales(): try: scaleInfoList = ScaleIRW.GetListOfScaleInfos() except Exception as error: printAndLog(str(error), logging.ERROR) break if ScaleDataDB.Connected: successfulPushes = 0 failedPushes = 0 for si in scaleInfoList: try: if si.Failed: raise Exception() ScaleDataDB.Write(si, (si.GetValue() * 100)) # There is a Write Function for both the MySQLRW and MongoRW classes successfulPushes += 1 except: failedPushes += 1 if CfgRW.cfgVars["aggregatorPrintPushes"].upper() == "TRUE": printAndLog(str(successfulPushes) + " documents successfully added to database" \ " with " + str(failedPushes) + " fails. " \ "Waiting " + str(secsPerPersist) + " seconds before next update.", logging.INFO) else: if failedPushes > 0: printAndLog(str(failedPushes) + " documents failed to push to database.", logging.ERROR) if successfulPushes == 0 and len(scaleInfoList) != 0: printAndLog("DB failed to push, attempting Reconnect.", logging.ERROR) if ScaleDataDB.Reconnect(): printAndLog("Successfully reconnected to " + CfgRW.cfgVars["dbToUse"] + " database " + ScaleDataDB.DBName + " at: " + str(ScaleDataDB.Client.address), logging.INFO) else: printAndLog("DB failed to connect, attempting Reconnect.", logging.ERROR) ScaleDataDB.Reconnect() if ScaleDataDB.Client != None: printAndLog("Outputting to " + CfgRW.cfgVars["dbToUse"] + " database " + ScaleDataDB.DBName + " at: " + str(ScaleDataDB.Client.address), logging.INFO) while time.time() - timeOfLastUpdate < secsPerPersist: time.sleep(1) loopOn += 1

UTF-8

Python

false

3,607

py

18

ScaleAggregator.py

10

0.648461

0.643748

0

102

34.372549

184

eQTL-Catalogue/eQTL-SumStats

5,076,651,386,447

09a87f5f83f060c8e65d6b6d1cf524a1392a9db9

91bc0c7c9ee8db759444d430c100ce88bf242f8a

/sumstats/main.py

f7ffdb1ed75181eb336d8c5b394d702e065cbbb9

[]

no_license

https://github.com/eQTL-Catalogue/eQTL-SumStats

7bc7e9e9e8df2865aa946eba21bf248a79f21d4c

84751d782139cb0f84c41607c9e8ee4e98ff072e

refs/heads/master

2023-06-23T03:33:17.794710

2023-06-13T10:46:29

234,100,303

2

1

null

false

2022-12-08T01:50:52

2020-01-15T14:38:50

2021-06-04T15:46:03

2022-12-08T01:50:51

8,853

2

1

18

Python

false

from fastapi import FastAPI, Request from fastapi.responses import JSONResponse, ORJSONResponse from fastapi.middleware.cors import CORSMiddleware import logging from sumstats.config import (API_BASE, APP_VERSION, API_DESCRIPTION, TAGS_METADATA) from sumstats.dependencies.error_classes import APIException import sumstats.api_v1.routers.routes as routes_v1 import sumstats.api_v2.routers.eqtl as routes_v2 logging.config.fileConfig("sumstats/log_conf.ini", disable_existing_loggers=False) logger = logging.getLogger(__name__) app = FastAPI(title="eQTL Catalogue Summary Statistics API Documentation", openapi_tags=TAGS_METADATA, description=API_DESCRIPTION, docs_url=f"{API_BASE}/docs", redoc_url=None, openapi_url=f"{API_BASE}/openapi.json", version=APP_VERSION ) @app.exception_handler(ValueError) async def value_error_exception_handler(request: Request, exc: ValueError): return JSONResponse( status_code=400, content={"message": str(exc)}, ) @app.exception_handler(APIException) async def handle_custom_api_exception(request: Request, exc: APIException): return JSONResponse( status_code=exc.status_code, content={"message": exc.message}, ) # configure CORS app.add_middleware(CORSMiddleware, allow_origins=['*']) # v1 API (default) app.include_router(routes_v1.router, prefix=API_BASE, include_in_schema=False, default_response_class=ORJSONResponse) app.include_router(routes_v1.router, prefix=f"{API_BASE}/v1", default_response_class=ORJSONResponse, deprecated=True, tags=["eQTL API v1"]) # v2 API app.include_router(routes_v2.router, prefix=f"{API_BASE}/v2", default_response_class=ORJSONResponse, tags=["eQTL API v2"])

UTF-8

Python

false

2,148

py

163

main.py

135

0.601024

0.593575

0

65

32.046154

75

ForeverZyh/ASCC

10,505,490,011,325

2a661ca6c9abc4667133de3169f4c55847aed8c2

4820859e8d6f68678dd212d6ad185b7b2914597e

/PWWS/get_NE_list.py

3d8dd3a9dec1cc2da5c009a187eec93814bff31a

[ "MIT" ]

permissive

https://github.com/ForeverZyh/ASCC

c44d88e58b4a1e39bb61abcb7aaf1641c1a14164

2d76d679889953501c469221a37d486e7ee42ded

refs/heads/main

2023-04-27T16:45:26.575805

2021-05-07T01:36:41

364,674,852

0

MIT

true

2021-05-05T18:44:13

2021-05-05T18:44:12

2021-05-05T18:44:10

2021-05-03T05:27:15

41,614

0

null

false

# coding: utf-8 import os import numpy as np from .config import config import copy import sys from .read_files import split_imdb_files, split_yahoo_files, split_agnews_files import spacy import argparse import re from collections import Counter, defaultdict nlp = spacy.load('en') parser = argparse.ArgumentParser('named entity recognition') parser.add_argument('-d', '--dataset', help='Data set', choices=['imdb', 'agnews', 'yahoo'], default='yahoo') NE_type_dict = { 'PERSON': defaultdict(int), # People, including fictional. 'NORP': defaultdict(int), # Nationalities or religious or political groups. 'FAC': defaultdict(int), # Buildings, airports, highways, bridges, etc. 'ORG': defaultdict(int), # Companies, agencies, institutions, etc. 'GPE': defaultdict(int), # Countries, cities, states. 'LOC': defaultdict(int), # Non-GPE locations, mountain ranges, bodies of water. 'PRODUCT': defaultdict(int), # Object, vehicles, foods, etc.(Not services) 'EVENT': defaultdict(int), # Named hurricanes, battles, wars, sports events, etc. 'WORK_OF_ART': defaultdict(int), # Titles of books, songs, etc. 'LAW': defaultdict(int), # Named documents made into laws. 'LANGUAGE': defaultdict(int), # Any named language. 'DATE': defaultdict(int), # Absolute or relative dates or periods. 'TIME': defaultdict(int), # Times smaller than a day. 'PERCENT': defaultdict(int), # Percentage, including "%". 'MONEY': defaultdict(int), # Monetary values, including unit. 'QUANTITY': defaultdict(int), # Measurements, as of weight or distance. 'ORDINAL': defaultdict(int), # "first", "second", etc. 'CARDINAL': defaultdict(int), # Numerals that do not fall under another type. } def recognize_named_entity(texts): ''' Returns all NEs in the input texts and their corresponding types ''' NE_freq_dict = copy.deepcopy(NE_type_dict) for text in texts: doc = nlp(text) for word in doc.ents: NE_freq_dict[word.label_][word.text] += 1 return NE_freq_dict def find_adv_NE(D_true, D_other): ''' find NE_adv in D-D_y_true which is defined in the end of section 3.1 ''' # adv_NE_list = [] for type in NE_type_dict.keys(): # find the most frequent true and other NEs of the same type true_NE_list = [NE_tuple[0] for (i, NE_tuple) in enumerate(D_true[type]) if i < 15] other_NE_list = [NE_tuple[0] for (i, NE_tuple) in enumerate(D_other[type]) if i < 30] for other_NE in other_NE_list: if other_NE not in true_NE_list and len(other_NE.split()) == 1: # adv_NE_list.append((type, other_NE)) print("'" + type + "': '" + other_NE + "',") with open('./{}.txt'.format(args.dataset), 'a', encoding='utf-8') as f: f.write("'" + type + "': '" + other_NE + "',\n") break class NameEntityList(object): # If the original input in IMDB belongs to class 0 (negative) imdb_0 = {'PERSON': 'David', 'NORP': 'Australian', 'FAC': 'Hound', 'ORG': 'Ford', 'GPE': 'India', 'LOC': 'Atlantic', 'PRODUCT': 'Highly', 'EVENT': 'Depression', 'WORK_OF_ART': 'Casablanca', 'LAW': 'Constitution', 'LANGUAGE': 'Portuguese', 'DATE': '2001', 'TIME': 'hours', 'PERCENT': '98%', 'MONEY': '4', 'QUANTITY': '70mm', 'ORDINAL': '5th', 'CARDINAL': '7', } # If the original input in IMDB belongs to class 1 (positive) imdb_1 = {'PERSON': 'Lee', 'NORP': 'Christian', 'FAC': 'Shannon', 'ORG': 'BAD', 'GPE': 'Seagal', 'LOC': 'Malta', 'PRODUCT': 'Cat', 'EVENT': 'Hugo', 'WORK_OF_ART': 'Jaws', 'LAW': 'RICO', 'LANGUAGE': 'Sebastian', 'DATE': 'Friday', 'TIME': 'minutes', 'PERCENT': '75%', 'MONEY': '$', 'QUANTITY': '9mm', 'ORDINAL': 'sixth', 'CARDINAL': 'zero', } imdb = [imdb_0, imdb_1] agnews_0 = {'PERSON': 'Williams', 'NORP': 'European', 'FAC': 'Olympic', 'ORG': 'Microsoft', 'GPE': 'Australia', 'LOC': 'Earth', 'PRODUCT': '#', 'EVENT': 'Cup', 'WORK_OF_ART': 'PowerBook', 'LAW': 'Pacers-Pistons', 'LANGUAGE': 'Chinese', 'DATE': 'third-quarter', 'TIME': 'Tonight', 'MONEY': '#39;t', 'QUANTITY': '#39;t', 'ORDINAL': '11th', 'CARDINAL': '1', } agnews_1 = {'PERSON': 'Bush', 'NORP': 'Iraqi', 'FAC': 'Outlook', 'ORG': 'Microsoft', 'GPE': 'Iraq', 'LOC': 'Asia', 'PRODUCT': '#', 'EVENT': 'Series', 'WORK_OF_ART': 'Nobel', 'LAW': 'Constitution', 'LANGUAGE': 'French', 'DATE': 'third-quarter', 'TIME': 'hours', 'MONEY': '39;Keefe', 'ORDINAL': '2nd', 'CARDINAL': 'Two', } agnews_2 = {'PERSON': 'Arafat', 'NORP': 'Iraqi', 'FAC': 'Olympic', 'ORG': 'AFP', 'GPE': 'Baghdad', 'LOC': 'Earth', 'PRODUCT': 'Soyuz', 'EVENT': 'Cup', 'WORK_OF_ART': 'PowerBook', 'LAW': 'Constitution', 'LANGUAGE': 'Filipino', 'DATE': 'Sunday', 'TIME': 'evening', 'MONEY': '39;m', 'QUANTITY': '20km', 'ORDINAL': 'eighth', 'CARDINAL': '6', } agnews_3 = {'PERSON': 'Arafat', 'NORP': 'Iraqi', 'FAC': 'Olympic', 'ORG': 'AFP', 'GPE': 'Iraq', 'LOC': 'Kashmir', 'PRODUCT': 'Yukos', 'EVENT': 'Cup', 'WORK_OF_ART': 'Gazprom', 'LAW': 'Pacers-Pistons', 'LANGUAGE': 'Hebrew', 'DATE': 'Saturday', 'TIME': 'overnight', 'MONEY': '39;m', 'QUANTITY': '#39;t', 'ORDINAL': '11th', 'CARDINAL': '6', } agnews = [agnews_0, agnews_1, agnews_2, agnews_3] yahoo_0 = {'PERSON': 'Fantasy', 'NORP': 'Russian', 'FAC': 'Taxation', 'ORG': 'Congress', 'GPE': 'U.S.', 'LOC': 'Sea', 'PRODUCT': 'Variable', 'EVENT': 'Series', 'WORK_OF_ART': 'Stopping', 'LAW': 'Constitution', 'LANGUAGE': 'Hebrew', 'DATE': '2004-05', 'TIME': 'morning', 'MONEY': '$ale', 'QUANTITY': 'Hiberno-English', 'ORDINAL': 'Tertiary', 'CARDINAL': 'three', } yahoo_1 = {'PERSON': 'Equine', 'NORP': 'Japanese', 'FAC': 'Music', 'ORG': 'Congress', 'GPE': 'UK', 'LOC': 'Sea', 'PRODUCT': 'RuneScape', 'EVENT': 'Series', 'WORK_OF_ART': 'Stopping', 'LAW': 'Strap-', 'LANGUAGE': 'Spanish', 'DATE': '2004-05', 'TIME': 'night', 'PERCENT': '100%', 'MONEY': 'five-dollar', 'QUANTITY': 'Hiberno-English', 'ORDINAL': 'Sixth', 'CARDINAL': '5', } yahoo_2 = {'PERSON': 'Equine', 'NORP': 'Canadian', 'FAC': 'Music', 'ORG': 'Congress', 'GPE': 'California', 'LOC': 'Atlantic', 'PRODUCT': 'Variable', 'EVENT': 'Series', 'WORK_OF_ART': 'Weight', 'LANGUAGE': 'Filipino', 'DATE': '2004-05', 'TIME': 'night', 'PERCENT': '100%', 'MONEY': 'ten-dollar', 'QUANTITY': '$ale', 'ORDINAL': 'Tertiary', 'CARDINAL': 'two', } yahoo_3 = {'PERSON': 'Equine', 'NORP': 'Irish', 'FAC': 'Music', 'ORG': 'Congress', 'GPE': 'California', 'LOC': 'Sea', 'PRODUCT': 'RuneScape', 'EVENT': 'Series', 'WORK_OF_ART': 'Weight', 'LAW': 'Strap-', 'LANGUAGE': 'Spanish', 'DATE': '2004-05', 'TIME': 'tonight', 'PERCENT': '100%', 'MONEY': 'five-dollar', 'QUANTITY': 'Hiberno-English', 'ORDINAL': 'Sixth', 'CARDINAL': '5', } yahoo_4 = {'PERSON': 'Equine', 'NORP': 'Irish', 'FAC': 'Music', 'ORG': 'Congress', 'GPE': 'Canada', 'LOC': 'Sea', 'PRODUCT': 'Variable', 'WORK_OF_ART': 'Stopping', 'LAW': 'Constitution', 'LANGUAGE': 'Spanish', 'DATE': '2004-05', 'TIME': 'seconds', 'PERCENT': '100%', 'MONEY': 'hundred-dollar', 'QUANTITY': 'Hiberno-English', 'ORDINAL': 'Tertiary', 'CARDINAL': '100', } yahoo_5 = {'PERSON': 'Equine', 'NORP': 'English', 'FAC': 'Music', 'ORG': 'Congress', 'GPE': 'Australia', 'LOC': 'Sea', 'PRODUCT': 'Variable', 'EVENT': 'Series', 'WORK_OF_ART': 'Weight', 'LAW': 'Strap-', 'LANGUAGE': 'Filipino', 'DATE': '2004-05', 'TIME': 'seconds', 'MONEY': 'hundred-dollar', 'ORDINAL': 'Tertiary', 'CARDINAL': '2000', } yahoo_6 = {'PERSON': 'Fantasy', 'NORP': 'Islamic', 'FAC': 'Music', 'ORG': 'Congress', 'GPE': 'California', 'LOC': 'Sea', 'PRODUCT': 'Variable', 'EVENT': 'Series', 'WORK_OF_ART': 'Stopping', 'LANGUAGE': 'Filipino', 'DATE': '2004-05', 'TIME': 'seconds', 'PERCENT': '100%', 'MONEY': '$ale', 'QUANTITY': '$ale', 'ORDINAL': 'Tertiary', 'CARDINAL': '100', } yahoo_7 = {'PERSON': 'Fantasy', 'NORP': 'Canadian', 'FAC': 'Music', 'ORG': 'Congress', 'GPE': 'UK', 'LOC': 'West', 'PRODUCT': 'Variable', 'EVENT': 'Watergate', 'WORK_OF_ART': 'Stopping', 'LAW': 'Constitution', 'LANGUAGE': 'Filipino', 'DATE': '2004-05', 'TIME': 'tonight', 'PERCENT': '100%', 'MONEY': '$ale', 'QUANTITY': '$ale', 'ORDINAL': 'Tertiary', 'CARDINAL': '2000', } yahoo_8 = {'PERSON': 'Equine', 'NORP': 'Japanese', 'FAC': 'Music', 'ORG': 'Congress', 'GPE': 'Chicago', 'LOC': 'Sea', 'PRODUCT': 'Variable', 'EVENT': 'Series', 'WORK_OF_ART': 'Stopping', 'LAW': 'Strap-', 'LANGUAGE': 'Spanish', 'DATE': '2004-05', 'TIME': 'night', 'PERCENT': '100%', 'QUANTITY': '$ale', 'ORDINAL': 'Sixth', 'CARDINAL': '2', } yahoo_9 = {'PERSON': 'Equine', 'NORP': 'Chinese', 'FAC': 'Music', 'ORG': 'Digital', 'GPE': 'U.S.', 'LOC': 'Atlantic', 'PRODUCT': 'Variable', 'EVENT': 'Series', 'WORK_OF_ART': 'Weight', 'LAW': 'Constitution', 'LANGUAGE': 'Spanish', 'DATE': '1918-1945', 'TIME': 'night', 'PERCENT': '100%', 'MONEY': 'ten-dollar', 'QUANTITY': 'Hiberno-English', 'ORDINAL': 'Tertiary', 'CARDINAL': '5' } yahoo = [yahoo_0, yahoo_1, yahoo_2, yahoo_3, yahoo_4, yahoo_5, yahoo_6, yahoo_7, yahoo_8, yahoo_9] L = {'imdb': imdb, 'agnews': agnews, 'yahoo': yahoo} NE_list = NameEntityList() if __name__ == '__main__': args = parser.parse_args() print('dataset:', args.dataset) class_num = config.num_classes[args.dataset] if args.dataset == 'imdb': train_texts, train_labels, dev_texts, dev_labels, test_texts, test_labels = split_imdb_files(opt) # get input texts in different classes pos_texts = train_texts[:12500] pos_texts.extend(test_texts[:12500]) neg_texts = train_texts[12500:] neg_texts.extend(test_texts[12500:]) texts = [neg_texts, pos_texts] elif args.dataset == 'agnews': texts = [[] for i in range(class_num)] train_texts, train_labels, test_texts, test_labels = split_agnews_files() for i, label in enumerate(train_labels): texts[np.argmax(label)].append(train_texts[i]) for i, label in enumerate(test_labels): texts[np.argmax(label)].append(test_texts[i]) elif args.dataset == 'yahoo': train_texts, train_labels, test_texts, test_labels = split_yahoo_files() texts = [[] for i in range(class_num)] for i, label in enumerate(train_labels): texts[np.argmax(label)].append(train_texts[i]) for i, label in enumerate(test_labels): texts[np.argmax(label)].append(test_texts[i]) D_true_list = [] for i in range(class_num): D_true = recognize_named_entity(texts[i]) # D_true contains the NEs in input texts with the label y_true D_true_list.append(D_true) for i in range(class_num): D_true = copy.deepcopy(D_true_list[i]) D_other = copy.deepcopy(NE_type_dict) for j in range(class_num): if i == j: continue for type in NE_type_dict.keys(): # combine D_other[type] and D_true_list[j][type] for key in D_true_list[j][type].keys(): D_other[type][key] += D_true_list[j][type][key] for type in NE_type_dict.keys(): D_other[type] = sorted(D_other[type].items(), key=lambda k_v: k_v[1], reverse=True) D_true[type] = sorted(D_true[type].items(), key=lambda k_v: k_v[1], reverse=True) print('\nfind adv_NE_list in class', i) with open('./{}.txt'.format(args.dataset), 'a', encoding='utf-8') as f: f.write('\nfind adv_NE_list in class' + str(i)) find_adv_NE(D_true, D_other)

UTF-8

Python

false

15,737

py

23

get_NE_list.py

19

0.432166

0.418885

0

428

35.768692

113

igor-barsukov/hurst-calc

4,844,723,132,812

66cf9ad6f0409854330b339393af7e0f3f76edbb

8519cc340770d1d05842dd75a1ce0e2cb47ac705

/basic_rs/parse/parse_tbp.py

4557402a59a25d7ec826bd7c428230c3981ad612

[]

no_license

https://github.com/igor-barsukov/hurst-calc

25c6194d4a98cb191d233f0d99dc33e8ce0c90f9

03d3ec160af780029b7fed4a5a916c044388ac5d

refs/heads/master

2023-03-23T14:22:22.513762

2019-11-20T20:34:32

221,783,518

0

null

#!/usr/bin/python # -*- encoding:utf-8 -*- """ Для работы требует модуль dpkt, достуаный через pip. В качестве первого аргумента коммандной строки требует имя файла для обработки, результат записывается в текущий каталог в файл с таким же именем, но расширением csv. РАССЧЕТ ВРЕМЕНИ МЕЖДУ ПАКЕТАМИ - tbp - TIME BETWEEN PACKETS """ import dpkt import sys from os import path def run(pcapfile): # открываем файл с данными - первый параметр коммандной строки infile = open(pcapfile,'rb') # открываем для записи файл для сохранения статистики # получаем имя 1-ого файла без расширения и добавляем .csv outfileName = path.splitext(path.basename(pcapfile))[0]+'_tbp.csv' outfile = open(outfileName,'w') # заголовки столбцов данных # outfile.write('deltaTime\n') # инициализаия счётчиков deltaTime = 0 previousTime = 0 for ts, buf in dpkt.pcap.Reader(infile): # print "ts - ", ts if previousTime > 0: deltaTime = ts - previousTime previousTime = ts outfile.write(str(deltaTime)+'\n') else: outfile.write(str(0)+'\n') previousTime = ts infile.close() outfile.close() return outfileName

UTF-8

Python

false

1,613

py

8

parse_tbp.py

7

0.663961

0.658279

0

46

25.782609

70

nagyist/ParaView

11,338,713,702,683

63c423be57d97a90e908f3ea849e406830a9c0ec

09d1138225f295ec2e5f3e700b44acedcf73f383

/Web/Python/paraview/web/data_converter.py

ed094f364e1f01b5a4781c7a4e7692e467a8d74a

[ "BSD-3-Clause" ]

permissive

https://github.com/nagyist/ParaView

e86d1ed88a805aecb13f707684103e43d5f6b09f

6810d701c44b2097baace5ad2c05f81c6d0fd310

refs/heads/master

2023-09-04T07:34:57.251637

2023-09-03T00:34:36

2023-09-03T00:34:57

85,244,343

0

BSD-3-Clause

true

2023-09-11T15:57:25

2017-03-16T21:44:59

2022-12-16T20:27:24

2023-09-11T15:57:22

238,433

0

C++

false

from vtkmodules.vtkIOImage import vtkPNGReader from vtkmodules.vtkCommonCore import vtkFloatArray, vtkUnsignedCharArray from vtkmodules.vtkCommonDataModel import vtkImageData from vtkmodules.vtkIOLegacy import vtkDataSetWriter from vtkmodules.web.camera import normalize, vectProduct, dotProduct from vtkmodules.web import iteritems import json, os, math, array # ----------------------------------------------------------------------------- # Helper function # ----------------------------------------------------------------------------- def getScalarFromRGB(rgb, scalarRange=[-1.0, 1.0]): delta = (scalarRange[1] - scalarRange[0]) / 16777215.0 # 2^24 - 1 => 16,777,215 if rgb[0] != 0 or rgb[1] != 0 or rgb[2] != 0: # Decode encoded value return scalarRange[0] + delta * float( rgb[0] * 65536 + rgb[1] * 256 + rgb[2] - 1 ) else: # No value return float("NaN") def convertImageToFloat(srcPngImage, destFile, scalarRange=[0.0, 1.0]): reader = vtkPNGReader() reader.SetFileName(srcPngImage) reader.Update() rgbArray = reader.GetOutput().GetPointData().GetArray(0) stackSize = rgbArray.GetNumberOfTuples() size = reader.GetOutput().GetDimensions() outputArray = vtkFloatArray() outputArray.SetNumberOfComponents(1) outputArray.SetNumberOfTuples(stackSize) for idx in range(stackSize): outputArray.SetTuple1( idx, getScalarFromRGB(rgbArray.GetTuple(idx), scalarRange) ) # Write float file with open(destFile, "wb") as f: f.write(memoryview(outputArray)) return size def convertRGBArrayToFloatArray(rgbArray, scalarRange=[0.0, 1.0]): linearSize = rgbArray.GetNumberOfTuples() outputArray = vtkFloatArray() outputArray.SetNumberOfComponents(1) outputArray.SetNumberOfTuples(linearSize) for idx in range(linearSize): outputArray.SetTuple1( idx, getScalarFromRGB(rgbArray.GetTuple(idx), scalarRange) ) return outputArray # ----------------------------------------------------------------------------- # Composite.json To order.array # ----------------------------------------------------------------------------- class CompositeJSON(object): def __init__(self, numberOfLayers): self.nbLayers = numberOfLayers self.encoding = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" def load(self, file): with open(file, "r") as f: composite = json.load(f) self.width = composite["dimensions"][0] self.height = composite["dimensions"][1] self.pixels = composite["pixel-order"].split("+") self.imageSize = self.width * self.height self.stackSize = self.imageSize * self.nbLayers def getImageSize(self): return self.imageSize def getStackSize(self): return self.stackSize def writeOrderSprite(self, path): ds = vtkImageData() ds.SetDimensions(self.width, self.height, self.nbLayers) ds.GetPointData().AddArray(self.getSortedOrderArray()) writer = vtkDataSetWriter() writer.SetInputData(ds) writer.SetFileName(path) writer.Update() def getSortedOrderArray(self): sortedOrder = vtkUnsignedCharArray() sortedOrder.SetName("layerIdx") sortedOrder.SetNumberOfTuples(self.stackSize) # Reset content for idx in range(self.stackSize): sortedOrder.SetValue(idx, 255) idx = 0 for pixel in self.pixels: x = idx % self.width y = idx / self.width flipYIdx = self.width * (self.height - y - 1) + x if "@" in pixel: idx += int(pixel[1:]) else: # Need to decode the order layerIdx = 0 for layer in pixel: sortedOrder.SetValue( flipYIdx + self.imageSize * layerIdx, self.encoding.index(layer) ) layerIdx += 1 # Move to next pixel idx += 1 return sortedOrder # ----------------------------------------------------------------------------- # Composite Sprite to Sorted Composite Dataset Builder # ----------------------------------------------------------------------------- class ConvertCompositeSpriteToSortedStack(object): def __init__(self, directory): self.basePath = directory self.layers = [] self.data = [] self.imageReader = vtkPNGReader() # Load JSON metadata with open(os.path.join(directory, "config.json"), "r") as f: self.config = json.load(f) self.nbLayers = len(self.config["scene"]) while len(self.layers) < self.nbLayers: self.layers.append({}) with open(os.path.join(directory, "index.json"), "r") as f: self.info = json.load(f) with open(os.path.join(directory, "offset.json"), "r") as f: offsets = json.load(f) for key, value in iteritems(offsets): meta = key.split("|") if len(meta) == 2: self.layers[int(meta[0])][meta[1]] = value elif meta[1] in self.layers[int(meta[0])]: self.layers[int(meta[0])][meta[1]][int(meta[2])] = value else: self.layers[int(meta[0])][meta[1]] = [value, value, value] self.composite = CompositeJSON(len(self.layers)) def listData(self): return self.data def convert(self): for root, dirs, files in os.walk(self.basePath): if "rgb.png" in files: print("Process", root) self.processDirectory(root) def processDirectory(self, directory): self.imageReader.SetFileName(os.path.join(directory, "rgb.png")) self.imageReader.Update() rgbArray = self.imageReader.GetOutput().GetPointData().GetArray(0) self.composite.load(os.path.join(directory, "composite.json")) orderArray = self.composite.getSortedOrderArray() imageSize = self.composite.getImageSize() stackSize = self.composite.getStackSize() # Write order (sorted order way) with open(os.path.join(directory, "order.uint8"), "wb") as f: f.write(memoryview(orderArray)) self.data.append( {"name": "order", "type": "array", "fileName": "/order.uint8"} ) # Encode Normals (sorted order way) if "normal" in self.layers[0]: sortedNormal = vtkUnsignedCharArray() sortedNormal.SetNumberOfComponents(3) # x,y,z sortedNormal.SetNumberOfTuples(stackSize) # Get Camera orientation and rotation information camDir = [0, 0, 0] worldUp = [0, 0, 0] with open(os.path.join(directory, "camera.json"), "r") as f: camera = json.load(f) camDir = normalize( [camera["position"][i] - camera["focalPoint"][i] for i in range(3)] ) worldUp = normalize(camera["viewUp"]) # [ camRight, camUp, camDir ] will be our new orthonormal basis for normals camRight = vectProduct(camDir, worldUp) camUp = vectProduct(camRight, camDir) # Tmp structure to capture (x,y,z) normal normalByLayer = vtkFloatArray() normalByLayer.SetNumberOfComponents(3) normalByLayer.SetNumberOfTuples(stackSize) # Capture all layer normals layerIdx = 0 zPosCount = 0 zNegCount = 0 for layer in self.layers: normalOffset = layer["normal"] for idx in range(imageSize): normalByLayer.SetTuple3( layerIdx * imageSize + idx, getScalarFromRGB( rgbArray.GetTuple(idx + normalOffset[0] * imageSize) ), getScalarFromRGB( rgbArray.GetTuple(idx + normalOffset[1] * imageSize) ), getScalarFromRGB( rgbArray.GetTuple(idx + normalOffset[2] * imageSize) ), ) # Re-orient normal to be view based vect = normalByLayer.GetTuple3(layerIdx * imageSize + idx) if not math.isnan(vect[0]): # Express normal in new basis we computed above rVect = normalize( [ -dotProduct(vect, camRight), dotProduct(vect, camUp), dotProduct(vect, camDir), ] ) # Need to reverse vector ? if rVect[2] < 0: normalByLayer.SetTuple3( layerIdx * imageSize + idx, -rVect[0], -rVect[1], -rVect[2], ) else: normalByLayer.SetTuple3( layerIdx * imageSize + idx, rVect[0], rVect[1], rVect[2] ) layerIdx += 1 # Sort normals and encode them as 3 bytes ( -1 < xy < 1 | 0 < z < 1) for idx in range(stackSize): layerIdx = int(orderArray.GetValue(idx)) if layerIdx == 255: # No normal => same as view direction sortedNormal.SetTuple3(idx, 128, 128, 255) else: offset = layerIdx * imageSize imageIdx = idx % imageSize vect = normalByLayer.GetTuple3(imageIdx + offset) if ( not math.isnan(vect[0]) and not math.isnan(vect[1]) and not math.isnan(vect[2]) ): sortedNormal.SetTuple3( idx, int(127.5 * (vect[0] + 1)), int(127.5 * (vect[1] + 1)), int(255 * vect[2]), ) else: print( "WARNING: encountered NaN in normal of layer ", layerIdx, ": [", vect[0], ",", vect[1], ",", vect[2], "]", ) sortedNormal.SetTuple3(idx, 128, 128, 255) # Write the sorted data with open(os.path.join(directory, "normal.uint8"), "wb") as f: f.write(memoryview(sortedNormal)) self.data.append( { "name": "normal", "type": "array", "fileName": "/normal.uint8", "categories": ["normal"], } ) # Encode Intensity (sorted order way) if "intensity" in self.layers[0]: intensityOffsets = [] sortedIntensity = vtkUnsignedCharArray() sortedIntensity.SetNumberOfTuples(stackSize) for layer in self.layers: intensityOffsets.append(layer["intensity"]) for idx in range(stackSize): layerIdx = int(orderArray.GetValue(idx)) if layerIdx == 255: sortedIntensity.SetValue(idx, 255) else: offset = 3 * intensityOffsets[layerIdx] * imageSize imageIdx = idx % imageSize sortedIntensity.SetValue( idx, rgbArray.GetValue(imageIdx * 3 + offset) ) with open(os.path.join(directory, "intensity.uint8"), "wb") as f: f.write(memoryview(sortedIntensity)) self.data.append( { "name": "intensity", "type": "array", "fileName": "/intensity.uint8", "categories": ["intensity"], } ) # Encode Each layer Scalar layerIdx = 0 for layer in self.layers: for scalar in layer: if scalar not in ["intensity", "normal"]: offset = imageSize * layer[scalar] scalarRange = self.config["scene"][layerIdx]["colors"][scalar][ "range" ] delta = ( scalarRange[1] - scalarRange[0] ) / 16777215.0 # 2^24 - 1 => 16,777,215 scalarArray = vtkFloatArray() scalarArray.SetNumberOfTuples(imageSize) for idx in range(imageSize): rgb = rgbArray.GetTuple(idx + offset) if rgb[0] != 0 or rgb[1] != 0 or rgb[2] != 0: # Decode encoded value value = scalarRange[0] + delta * float( rgb[0] * 65536 + rgb[1] * 256 + rgb[2] - 1 ) scalarArray.SetValue(idx, value) else: # No value scalarArray.SetValue(idx, float("NaN")) with open( os.path.join(directory, "%d_%s.float32" % (layerIdx, scalar)), "wb", ) as f: f.write(memoryview(scalarArray)) self.data.append( { "name": "%d_%s" % (layerIdx, scalar), "type": "array", "fileName": "/%d_%s.float32" % (layerIdx, scalar), "categories": ["%d_%s" % (layerIdx, scalar)], } ) layerIdx += 1 # ----------------------------------------------------------------------------- # Composite Sprite to Sorted Composite Dataset Builder # ----------------------------------------------------------------------------- class ConvertCompositeDataToSortedStack(object): def __init__(self, directory): self.basePath = directory self.layers = [] self.data = [] self.imageReader = vtkPNGReader() # Load JSON metadata with open(os.path.join(directory, "config.json"), "r") as f: self.config = json.load(f) self.nbLayers = len(self.config["scene"]) while len(self.layers) < self.nbLayers: self.layers.append({}) with open(os.path.join(directory, "index.json"), "r") as f: self.info = json.load(f) def listData(self): return self.data def convert(self): for root, dirs, files in os.walk(self.basePath): if "depth_0.float32" in files: print("Process", root) self.processDirectory(root) def processDirectory(self, directory): # Load depth depthStack = [] imageSize = self.config["size"] linearSize = imageSize[0] * imageSize[1] nbLayers = len(self.layers) stackSize = nbLayers * linearSize layerList = range(nbLayers) for layerIdx in layerList: with open( os.path.join(directory, "depth_%d.float32" % layerIdx), "rb" ) as f: a = array.array("f") a.fromfile(f, linearSize) depthStack.append(a) orderArray = vtkUnsignedCharArray() orderArray.SetName("layerIdx") orderArray.SetNumberOfComponents(1) orderArray.SetNumberOfTuples(stackSize) pixelSorter = [(i, i) for i in layerList] for pixelId in range(linearSize): # Fill pixelSorter for layerIdx in layerList: if depthStack[layerIdx][pixelId] < 1.0: pixelSorter[layerIdx] = (layerIdx, depthStack[layerIdx][pixelId]) else: pixelSorter[layerIdx] = (255, 1.0) # Sort pixel layers pixelSorter.sort(key=lambda tup: tup[1]) # Fill sortedOrder array for layerIdx in layerList: orderArray.SetValue( layerIdx * linearSize + pixelId, pixelSorter[layerIdx][0] ) # Write order (sorted order way) with open(os.path.join(directory, "order.uint8"), "wb") as f: f.write(memoryview(orderArray)) self.data.append( {"name": "order", "type": "array", "fileName": "/order.uint8"} ) # Remove depth files for layerIdx in layerList: os.remove(os.path.join(directory, "depth_%d.float32" % layerIdx)) # Encode Normals (sorted order way) if "normal" in self.config["light"]: sortedNormal = vtkUnsignedCharArray() sortedNormal.SetNumberOfComponents(3) # x,y,z sortedNormal.SetNumberOfTuples(stackSize) # Get Camera orientation and rotation information camDir = [0, 0, 0] worldUp = [0, 0, 0] with open(os.path.join(directory, "camera.json"), "r") as f: camera = json.load(f) camDir = normalize( [camera["position"][i] - camera["focalPoint"][i] for i in range(3)] ) worldUp = normalize(camera["viewUp"]) # [ camRight, camUp, camDir ] will be our new orthonormal basis for normals camRight = vectProduct(camDir, worldUp) camUp = vectProduct(camRight, camDir) # Tmp structure to capture (x,y,z) normal normalByLayer = vtkFloatArray() normalByLayer.SetNumberOfComponents(3) normalByLayer.SetNumberOfTuples(stackSize) # Capture all layer normals zPosCount = 0 zNegCount = 0 for layerIdx in layerList: # Load normal(x,y,z) from current layer normalLayer = [] for comp in [0, 1, 2]: with open( os.path.join( directory, "normal_%d_%d.float32" % (layerIdx, comp) ), "rb", ) as f: a = array.array("f") a.fromfile(f, linearSize) normalLayer.append(a) # Store normal inside vtkArray offset = layerIdx * linearSize for idx in range(linearSize): normalByLayer.SetTuple3( idx + offset, normalLayer[0][idx], normalLayer[1][idx], normalLayer[2][idx], ) # Re-orient normal to be view based vect = normalByLayer.GetTuple3(layerIdx * linearSize + idx) if not math.isnan(vect[0]): # Express normal in new basis we computed above rVect = normalize( [ -dotProduct(vect, camRight), dotProduct(vect, camUp), dotProduct(vect, camDir), ] ) # Need to reverse vector ? if rVect[2] < 0: normalByLayer.SetTuple3( layerIdx * linearSize + idx, -rVect[0], -rVect[1], -rVect[2], ) else: normalByLayer.SetTuple3( layerIdx * linearSize + idx, rVect[0], rVect[1], rVect[2], ) # Sort normals and encode them as 3 bytes ( -1 < xy < 1 | 0 < z < 1) for idx in range(stackSize): layerIdx = int(orderArray.GetValue(idx)) if layerIdx == 255: # No normal => same as view direction sortedNormal.SetTuple3(idx, 128, 128, 255) else: offset = layerIdx * linearSize imageIdx = idx % linearSize vect = normalByLayer.GetTuple3(imageIdx + offset) if ( not math.isnan(vect[0]) and not math.isnan(vect[1]) and not math.isnan(vect[2]) ): sortedNormal.SetTuple3( idx, int(127.5 * (vect[0] + 1)), int(127.5 * (vect[1] + 1)), int(255 * vect[2]), ) else: print( "WARNING: encountered NaN in normal of layer ", layerIdx, ": [", vect[0], ",", vect[1], ",", vect[2], "]", ) sortedNormal.SetTuple3(idx, 128, 128, 255) # Write the sorted data with open(os.path.join(directory, "normal.uint8"), "wb") as f: f.write(memoryview(sortedNormal)) self.data.append( { "name": "normal", "type": "array", "fileName": "/normal.uint8", "categories": ["normal"], } ) # Remove depth files for layerIdx in layerList: os.remove( os.path.join(directory, "normal_%d_%d.float32" % (layerIdx, 0)) ) os.remove( os.path.join(directory, "normal_%d_%d.float32" % (layerIdx, 1)) ) os.remove( os.path.join(directory, "normal_%d_%d.float32" % (layerIdx, 2)) ) # Encode Intensity (sorted order way) if "intensity" in self.config["light"]: sortedIntensity = vtkUnsignedCharArray() sortedIntensity.SetNumberOfTuples(stackSize) intensityLayers = [] for layerIdx in layerList: with open( os.path.join(directory, "intensity_%d.uint8" % layerIdx), "rb" ) as f: a = array.array("B") a.fromfile(f, linearSize) intensityLayers.append(a) for idx in range(stackSize): layerIdx = int(orderArray.GetValue(idx)) if layerIdx == 255: sortedIntensity.SetValue(idx, 255) else: imageIdx = idx % linearSize sortedIntensity.SetValue(idx, intensityLayers[layerIdx][imageIdx]) with open(os.path.join(directory, "intensity.uint8"), "wb") as f: f.write(memoryview(sortedIntensity)) self.data.append( { "name": "intensity", "type": "array", "fileName": "/intensity.uint8", "categories": ["intensity"], } ) # Remove depth files for layerIdx in layerList: os.remove(os.path.join(directory, "intensity_%d.uint8" % layerIdx))

UTF-8

Python

false

24,782

py

5,743

data_converter.py

3,836

0.453071

0.438867

0

644

37.481366

88

17tangs/CPP

7,851,200,260,332

bde8891980a4633e512b665696bdb027e86bd0a0

2bd90ca0875148b19bc273d423f2b4bdee451841

/Main.py

b714328ee79c3788190987c2807d4650372878e9

[]

no_license

https://github.com/17tangs/CPP

af54884fda274f77cdf326c17180d31e938587b1

0b0f996f5728c59043e30cf03a55c7fd320dc5e5

refs/heads/master

2021-01-13T00:36:47.867158

2016-04-13T05:08:34

52,191,204

0

null

false

2016-03-07T04:35:55

2016-02-21T04:49:50

2016-02-21T05:03:44

2016-03-07T04:35:55

3,522

0

Python

null

import time import numpy as np import sys import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap from Population import * from R import * from random import * start_time = time.time() #the best solution found on the internet at this time uses annealing #after 400,000 iterations, the creator of that found the optimal distance to be 10,618 miles = 17,088 km #http://toddwschneider.com/posts/traveling-salesman-with-simulated-annealing-r-and-shiny/ class CPP: #probabilities of weave methods wp1 = 0.5 wp2 = 1-wp1 #size of two weave functions ws1 = 24 ws2 = 8 #probabilities of mutations rp = 0.3 #reverse sp = 0.2 #shift #different greedy alg greedy = 10 #number of iterations I = 5000 def main(self): ##create a population class using Population.py #p = Population() ##generate the population based on the greedy algorithm ## p.greedy(0) #for i in range(10): #p.greedy(i) ##generate a certain number of random solutions #p.add_random(950) ##call and print the statistics of the iteration #averages = [] #bests = [] #worsts = [] #for i in range(CPP.I): #averages.append(p.average) #bests.append(p.best) ##worsts.append(p.worst) #p.breed(1)#wp1, wp2, ws1, ws2, rp, sp) size = [2,4,6,8,12,24] A = [] B = [] for j in range(1): p1 = Population() for i in range(10): p1.greedy(i) p1.add_random(950) a1 = [] b1 = [] for i in range(CPP.I): a1.append(p1.average) b1.append(p1.best) p1.breed(size[j]) A.append(a1) B.append(b1) print p1.stat() self.stat(A,B,size) self.draw(p.pop) ##DISPLAY #plots the cross-iteration trend of averages def stat(self, A, B, size): x = [i for i in range(CPP.I)] c = ["b","g","r","c","m","y","k","w"] l = [] for i in range(len(A)): l1, = plt.plot(x, A[i], color = c[i], label = str(size[i])) l.append(l1,) plt.axis([0,CPP.I,0,100000]) plt.legend(handles=l) plt.show() #draws a map of the US and displays the solutions graphically def draw(self, pop): fig=plt.figure() ax=fig.add_axes([0.1,0.1,0.8,0.8]) m = Basemap(llcrnrlon=-125.,llcrnrlat=25.,urcrnrlon=-65.,urcrnrlat=52., rsphere=(6378137.00,6356752.3142), resolution='l',projection='merc', lat_0=40.,lon_0=-20.,lat_ts=20.) l = pop[0] for i in range(len(l.sol)): lat1 = l.sol[i].lat lon1 = l.sol[i].lon m.drawgreatcircle(lon1,lat1,lon1,lat1, linewidth=4, color = 'r') if i == len(l.sol) - 1: lat2 = l.sol[0].lat lon2 = l.sol[0].lon else: lat2 = l.sol[i+1].lat lon2 = l.sol[i+1].lon m.drawgreatcircle(lon1,lat1,lon2,lat2, color = 'b') m.drawcoastlines() m.drawstates() m.drawcountries() m.fillcontinents() ax.set_title('GREEDY') plt.show() ##RECYCLING BIN #Methods that read data.txt and generate the lists C, CCOR, CDIS and CS. #The data is exported to R.py where it can be referenced upon later. #Once they run, there's no need to run it again. """ def shortest(self, c, l): m = sys.maxint ind = 0 for i in range(len(CDIS)): if C[i] not in l: if self.getDist(c, C[i]) != 0: if self.getDist(c, C[i]) < m: m = self.getDist(c, C[i]) ind = i return C[ind] def init(self): f = open("data.txt", "r") for i in range(48): e = [] for k in range(8): s = f.readline() if k % 2 == 0: if(s[-2] == "\r"): s = s[:-2] else: s = s[:-1] #excluding empty lines and name of state if(s[:4] != "Name"): #slicing string for only the name of capital and longtitude/latitude e.append(s.split(":")[1][1:]) #append each small list city list for 2D array CCOR.append(e) for y in range(len(CCOR)): dis = [] for x in range(len(CCOR)): lat1 = radians(float(CCOR[x][1])) lon1 = radians(float(CCOR[x][2])) lat2 = radians(float(CCOR[y][1])) lon2 = radians(float(CCOR[y][2])) dlon = lon2 - lon1 dlat = lat2 - lat1 a = (sin(dlat/2))**2 + cos(lat1) * cos(lat2) * (sin(dlon/2))**2 n = 2 * atan2( sqrt(a), sqrt(1-a) ) d = R * n dis.append(int(d)) CDIS.append(dis) for z in range(len(CCOR)): C.append(CCOR[z][0]) f1 = open("R.py", "w") f1.write("R = 6371\n") f1.write("C = " + str(C) + "\n" ) f1.write("CCOR = " + str(CCOR) + "\n") f1.write("CDIS = " + str(CDIS) + "\n") f1.close() def seed_greedy(self, l,i): if i == len(C) - 1: return else: lis = [n.name for n in l] k = 0 while l[i].s[k] in lis: k += 1 l.append(CPP.CO[C.index(l[i].s[k])]) self.seed_greedy(l,i + 1) def ss(self): CS = [] for c in C: CS.append(self.s(c)) f = open("R.py", "a") f.write("CS = " + str(CS)) f.close() return def s(self, c): l = [c] for x in range(len(C)-1): m = sys.maxint ind = 0 for i in range(len(CDIS)): if C[i] not in l: if self.getDist(c, C[i]) != 0: if self.getDist(c, C[i]) < m: m = self.getDist(c, C[i]) ind = i l.append(C[ind]) return l """ ##CALLING MAIN FUNCTION x = CPP() x.main() #printing the elapsed time to complete I iterations print("--- %s seconds ---" % (time.time() - start_time))

UTF-8

Python

false

7,104

py

6

Main.py

5

0.427083

0.398367

0

212

31.528302

104

drc-ima/hospital_project

7,791,070,701,113

9ddf463f84e7f7f0c592edbf585ef63b07124c27

29a7f665d20880db00a8b6cd567244fe6dab1872

/apps/user/models.py

a3d53b5a33959b36da724b3dc77dad56924cb088

[]

no_license

https://github.com/drc-ima/hospital_project

ab7b1a9178ab58517229dcbe8f8dc57046491015

8abbdf569578fb6d44dcb90fb6bc28ba97237ebc

refs/heads/main

2023-01-15T12:36:16.185323

2020-11-24T16:18:07

303,461,051

0

1

null

from django.contrib.auth.base_user import BaseUserManager, AbstractBaseUser from django.contrib.auth.models import PermissionsMixin from django.db import models from django.utils import timezone class UserManager(BaseUserManager): def create_user(self, staff_id, first_name, last_name, password=None, **extra_fields): if not staff_id: raise ValueError('Staff Id is required') user = self.model( staff_id=staff_id, first_name=first_name, username=staff_id, last_name=last_name, ) user.set_password(password) user.save(using=self._db) return user def create_superuser(self, staff_id, first_name, last_name, password): user = self.create_user(staff_id=staff_id, first_name=first_name, last_name=last_name, password=password) user.is_superuser = True user.is_active = True user.is_staff = True user.username = user.staff_id user.save(using=self._db) return user USERTYPE = { ('MU', 'Manager'), ('SU', 'Support'), ('NU', 'Normal Staff'), ('AD', 'Administrator') } ROLES = { ('HR', 'Human Resource'), ('ACC', 'Accountant'), ('NRS', 'Nurse'), ('DR', 'Doctor'), ('CH', 'Cashier'), ('PHM', 'Pharmacist'), ('IT', 'IT'), ('HM', 'Hospital Manager') } STATUS = { ('On Leave', 'On Leave'), ('Active', 'Active'), ('Suspended', 'Suspended'), ('Dismissed', 'Dismissed'), } class User(AbstractBaseUser, PermissionsMixin): username = models.CharField( max_length=255, blank=True, null=True ) staff_id = models.CharField( max_length=255, unique=True, ) user_type = models.CharField( choices=USERTYPE, max_length=255, blank=True, null=True, ) status = models.CharField(max_length=100, blank=True, null=True, choices=STATUS) role = models.CharField( choices=ROLES, max_length=255, blank=True, null=True ) first_name = models.CharField( max_length=100, blank=True, null=True ) last_name = models.CharField( max_length=100, blank=True, null=True ) is_active = models.BooleanField(default=True) is_superuser = models.BooleanField(default=False) is_staff = models.BooleanField(default=False) date_joined = models.DateTimeField(default=timezone.now) objects = UserManager() USERNAME_FIELD = 'staff_id' REQUIRED_FIELDS = ['first_name', 'last_name'] def full_name(self): return self.first_name + ' ' + self.last_name def user_kind(self): return self.get_user_type_display() + ' - ' + self.get_role_display() class Meta: verbose_name_plural = 'Users' verbose_name = 'user' ordering = ('staff_id', 'date_joined') db_table = 'user' def __str__(self): return str(self.staff_id)

UTF-8

Python

false

3,014

py

81

models.py

50

0.586928

0.57996

0

128

22.554688

113

summygupta/hackerrank_python

9,199,819,949,767

03b4a81ca23233c9fdf43d5dd382f0186fc16502

7d416ec7872f632f66d1062ee5f606152dfb3ee8

/String Split and Join.py

59bd53173f332f9bad1e4bc493533f4fb3672cdf

[]

no_license

https://github.com/summygupta/hackerrank_python

66e193bdd5e6526bbc0a095d94f70ae3218305a9

85638548e33baf87278e59dcfe2954491f70db3c

refs/heads/master

2022-12-12T23:26:41.453896

2020-08-31T10:09:47

286,259,481

0

null

def split_and_join(line): t = line.split(" ") return ("-".join(t))

UTF-8

Python

false

75

py

18

String Split and Join.py

18

0.533333

0

3

24

25

salesforce/provis

18,519,898,992,301

b7548d02673f7d422dd996f2f3d5dc62753c480e

5e206b314c058b9b89a15a9c4fd3cee49bf3bd8e

/protein_attention/attention_analysis/compute_edge_features.py

262ce2ae7248ea941662858b088132a354864df5

[ "BSD-3-Clause" ]

permissive

https://github.com/salesforce/provis

80abd0b98173849c7dcc4c765a28d39eb255bb87

051fe89190d9ac74865a6f49a3c25fd7b0fcca57

refs/heads/master

2023-08-29T00:53:31.325955

2023-05-01T19:22:27

272,459,149

292

49

NOASSERTION

false

2023-06-12T21:27:47

2020-06-15T14:22:26

2023-06-12T01:04:40

2023-06-12T21:27:47

7,535

273

46

3

Python

false

"""Compute aggregate statistics of attention edge features over a dataset Copyright (c) 2020, salesforce.com, inc. All rights reserved. SPDX-License-Identifier: BSD-3-Clause For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause """ import re from collections import defaultdict import numpy as np import torch from tqdm import tqdm def compute_mean_attention(model, n_layers, n_heads, items, features, tokenizer, model_name, model_version, cuda=True, max_seq_len=None, min_attn=0): model.eval() with torch.no_grad(): # Dictionary that maps feature_name to array of shape (n_layers, n_heads), containing # weighted sum of feature values for each layer/head over all examples feature_to_weighted_sum = defaultdict(lambda: torch.zeros((n_layers, n_heads), dtype=torch.double)) # Sum of attention_analysis weights in each layer/head over all examples weight_total = torch.zeros((n_layers, n_heads), dtype=torch.double) for item in tqdm(items): # Get attention weights, shape is (num_layers, num_heads, seq_len, seq_len) attns = get_attention(model, item, tokenizer, model_name, model_version, cuda, max_seq_len) if attns is None: print('Skipping due to not returning attention') continue # Update total attention_analysis weights per head. Sum over from_index (dim 2), to_index (dim 3) mask = attns >= min_attn weight_total += mask.long().sum((2, 3)) # Update weighted sum of feature values per head seq_len = attns.size(2) for to_index in range(seq_len): for from_index in range(seq_len): for feature in features: # Compute feature values feature_dict = feature.get_values(item, from_index, to_index) for feature_name, value in feature_dict.items(): # Update weighted sum of feature values across layers and heads mask = attns[:, :, from_index, to_index] >= min_attn feature_to_weighted_sum[feature_name] += mask * value return feature_to_weighted_sum, weight_total def get_attention(model, item, tokenizer, model_name, model_version, cuda, max_seq_len): tokens = item['primary'] if model_name == 'bert': if max_seq_len: tokens = tokens[:max_seq_len - 2] # Account for SEP, CLS tokens (added in next step) if model_version in ('prot_bert', 'prot_bert_bfd', 'prot_albert'): formatted_tokens = ' '.join(list(tokens)) formatted_tokens = re.sub(r"[UZOB]", "X", formatted_tokens) token_idxs = tokenizer.encode(formatted_tokens) else: token_idxs = tokenizer.encode(tokens) if isinstance(token_idxs, np.ndarray): token_idxs = token_idxs.tolist() if max_seq_len: assert len(token_idxs) == min(len(tokens) + 2, max_seq_len), (tokens, token_idxs, max_seq_len) else: assert len(token_idxs) == len(tokens) + 2 elif model_name == 'xlnet': if max_seq_len: tokens = tokens[:max_seq_len - 2] # Account for SEP, CLS tokens (added in next step) formatted_tokens = ' '.join(list(tokens)) formatted_tokens = re.sub(r"[UZOB]", "X", formatted_tokens) token_idxs = tokenizer.encode(formatted_tokens) if isinstance(token_idxs, np.ndarray): token_idxs = token_idxs.tolist() if max_seq_len: # Skip rare sequence with this issue if len(token_idxs) != min(len(tokens) + 2, max_seq_len): print('Warning: the length of the sequence changed through tokenization, skipping') return None else: assert len(token_idxs) == len(tokens) + 2 else: raise ValueError inputs = torch.tensor(token_idxs).unsqueeze(0) if cuda: inputs = inputs.cuda() attns = model(inputs)[-1] if model_name == 'bert': # Remove attention from <CLS> (first) and <SEP> (last) token attns = [attn[:, :, 1:-1, 1:-1] for attn in attns] elif model_name == 'xlnet': # Remove attention from <CLS> (last) and <SEP> (second to last) token attns = [attn[:, :, :-2, :-2] for attn in attns] else: raise NotImplementedError if 'contact_map' in item: assert (item['contact_map'].shape == attns[0][0, 0].shape) or (attns[0][0, 0].shape[0] == max_seq_len - 2), \ (item['id'], item['contact_map'].shape, attns[0][0, 0].shape) if 'site_indic' in item: assert (item['site_indic'].shape == attns[0][0, 0, 0].shape) or (attns[0][0, 0].shape[0] == max_seq_len - 2), \ item['id'] if 'modification_indic' in item: assert (item['modification_indic'].shape == attns[0][0, 0, 0].shape) or ( attns[0][0, 0].shape[0] == max_seq_len - 2), \ item['id'] attns = torch.stack([attn.squeeze(0) for attn in attns]) return attns.cpu() def convert_item(dataset_name, x, data, model_name, features): item = {} try: item['id'] = data['id'] except ValueError: item['id'] = data['id'].decode('utf8') item['primary'] = data['primary'] if dataset_name == 'proteinnet': if 'contact_map' in features: token_ids, input_mask, contact_map, protein_length = x item['contact_map'] = contact_map elif dataset_name == 'secondary': if 'ss4' in features: ss8_blank_index = 7 ss4_blank_index = 3 item['secondary'] = [ss4_blank_index if ss8 == ss8_blank_index else ss3 for ss3, ss8 in \ zip(data['ss3'], data['ss8'])] elif dataset_name == 'binding_sites': if 'binding_sites' in features: token_ids, input_mask, site_indic = x item['site_indic'] = site_indic elif dataset_name == 'protein_modifications': if 'protein_modifications' in features: token_ids, input_mask, modification_indic = x item['modification_indic'] = modification_indic else: raise ValueError if model_name == 'bert': # Remove label values from <CLS> (first) and <SEP> (last) token if 'site_indic' in item: item['site_indic'] = item['site_indic'][1:-1] if 'modification_indic' in item: item['modification_indic'] = item['modification_indic'][1:-1] elif model_name == 'xlnet': # Remove label values from <CLS> (last) and <SEP> (second to last) token if 'site_indic' in item: item['site_indic'] = item['site_indic'][:-2] if 'modification_indic' in item: item['modification_indic'] = item['modification_indic'][:-2] else: raise NotImplementedError return item if __name__ == "__main__": import pickle import pathlib from transformers import BertModel, AutoTokenizer, XLNetModel, XLNetTokenizer, AlbertModel, AlbertTokenizer from tape import TAPETokenizer, ProteinBertModel from tape.datasets import ProteinnetDataset, SecondaryStructureDataset from protein_attention.datasets import BindingSiteDataset, ProteinModificationDataset from protein_attention.utils import get_cache_path, get_data_path from protein_attention.attention_analysis.features import AminoAcidFeature, SecStructFeature, BindingSiteFeature, \ ContactMapFeature, ProteinModificationFeature import argparse parser = argparse.ArgumentParser() parser.add_argument('--exp-name', required=True, help='Name of experiment. Used to create unique filename.') parser.add_argument('--features', nargs='+', required=True, help='list of features') parser.add_argument('--dataset', required=True, help='Dataset id') parser.add_argument('--num-sequences', type=int, required=True, help='Number of sequences to analyze') parser.add_argument('--model', default='bert', help='Name of model.') parser.add_argument('--model-version', help='Name of model version.') parser.add_argument('--model_dir', help='Optional directory where pretrained model is located') parser.add_argument('--shuffle', action='store_true', help='Whether to randomly shuffle data') parser.add_argument('--max-seq-len', type=int, required=True, help='Max sequence length') parser.add_argument('--seed', type=int, default=123, help='PyTorch seed') parser.add_argument('--min-attn', type=float, help='min attention value for inclusion in analysis') parser.add_argument('--no_cuda', action='store_true', help='CPU only') args = parser.parse_args() print(args) if args.model_version and args.model_dir: raise ValueError('Cannot specify both model version and directory') if args.num_sequences is not None and not args.shuffle: print('WARNING: You are using a subset of sequences and you are not shuffling the data. This may result ' 'in a skewed sample.') cuda = not args.no_cuda torch.manual_seed(args.seed) if args.dataset == 'proteinnet': dataset = ProteinnetDataset(get_data_path(), 'train') elif args.dataset == 'secondary': dataset = SecondaryStructureDataset(get_data_path(), 'train') elif args.dataset == 'binding_sites': dataset = BindingSiteDataset(get_data_path(), 'train') elif args.dataset == 'protein_modifications': dataset = ProteinModificationDataset(get_data_path(), 'train') else: raise ValueError(f"Invalid dataset id: {args.dataset}") if not args.num_sequences: raise NotImplementedError if args.model == 'bert': if args.model_dir: model_version = args.model_dir else: model_version = args.model_version or 'bert-base' if model_version == 'prot_bert_bfd': model = BertModel.from_pretrained("Rostlab/prot_bert_bfd", output_attentions=True) tokenizer = AutoTokenizer.from_pretrained("Rostlab/prot_bert_bfd", do_lower_case=False) elif model_version == 'prot_bert': model = BertModel.from_pretrained("Rostlab/prot_bert", output_attentions=True) tokenizer = AutoTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False) elif model_version == 'prot_albert': model = AlbertModel.from_pretrained("Rostlab/prot_albert", output_attentions=True) tokenizer = AlbertTokenizer.from_pretrained("Rostlab/prot_albert", do_lower_case=False) else: model = ProteinBertModel.from_pretrained(model_version, output_attentions=True) tokenizer = TAPETokenizer() num_layers = model.config.num_hidden_layers num_heads = model.config.num_attention_heads elif args.model == 'xlnet': model_version = args.model_version if model_version == 'prot_xlnet': model = XLNetModel.from_pretrained("Rostlab/prot_xlnet", output_attentions=True) tokenizer = XLNetTokenizer.from_pretrained("Rostlab/prot_xlnet", do_lower_case=False) else: raise ValueError('Invalid model version') num_layers = model.config.n_layer num_heads = model.config.n_head else: raise ValueError(f"Invalid model: {args.model}") print('Layers:', num_layers) print('Heads:', num_heads) if cuda: model.to('cuda') if args.shuffle: random_indices = torch.randperm(len(dataset))[:args.num_sequences].tolist() items = [] print('Loading dataset') for i in tqdm(random_indices): item = convert_item(args.dataset, dataset[i], dataset.data[i], args.model, args.features) items.append(item) else: raise NotImplementedError features = [] for feature_name in args.features: if feature_name == 'aa': features.append(AminoAcidFeature()) elif feature_name == 'ss4': features.append(SecStructFeature()) elif feature_name == 'binding_sites': features.append(BindingSiteFeature()) elif feature_name == 'protein_modifications': features.append(ProteinModificationFeature()) elif feature_name == 'contact_map': features.append(ContactMapFeature()) else: raise ValueError(f"Invalid feature name: {feature_name}") feature_to_weighted_sum, weight_total = compute_mean_attention( model, num_layers, num_heads, items, features, tokenizer, args.model, model_version, cuda, max_seq_len=args.max_seq_len, min_attn=args.min_attn) cache_dir = get_cache_path() pathlib.Path(cache_dir).mkdir(parents=True, exist_ok=True) path = cache_dir / f'{args.exp_name}.pickle' pickle.dump((args, dict(feature_to_weighted_sum), weight_total), open(path, 'wb')) print('Wrote to', path)

UTF-8

Python

false

13,708

py

32

compute_edge_features.py

26

0.59447

0.588634

0

320

41.8375

119

A8IK/Python-2

5,360,119,212,848

35a4b734bb85edbae4737173c825f7414974d208

54d3a1558a4bd38888d4d51f1ae2d2699965087c

/array.py

df7a56f511a10410349ec8c940b4aebe768e9bbe

[]

no_license

https://github.com/A8IK/Python-2

a86843c6ccfe23d42faebb020307351a108075bd

538aee64bac73110cd0a8ac74747c9d2fa485149

refs/heads/main

2023-01-21T12:42:51.226144

2020-12-04T18:14:32

null

0

null

from array import* vals=array('i',[1,2,3,4,-5,9,10]) print (vals)

UTF-8

Python

false

67

py

82

array.py

82

0.626866

0.507463

0

3

21

33

brjagger/mmvt_seekr

566,935,708,805

cb29c68e4832357cdad37a0db083468a033190f7

c885c0fdf8ff049d71f12fa7e06e56b892243826

/mmvt_seekr/apbs.py

7697fc2b6aafd37d2d8d50ecf6b94329b6124339

[ "MIT" ]

permissive

https://github.com/brjagger/mmvt_seekr

dc0d4a82a44c004b802afbe79336678d7f1be943

9ee0f43716e64e16239bbf14e3eca6df6534f758

refs/heads/master

2023-07-03T10:22:05.976144

2021-08-11T16:06:24

207,873,958

1

MIT

false

2021-08-11T16:06:25

2019-09-11T17:56:36

2020-09-02T22:46:55

2021-08-11T16:06:25

13,107

1

Jupyter Notebook

false

#!/usr/bin/python ''' apbs.py creates the necessary files to run an electrostatic simulation using APBS ''' import os, sys, shutil #, math, subprocess #, make_fxd #import numpy as np import pdb2 as pdb #from copy import deepcopy # needed to keep track of separate structure objects import unittest import re #import datetime from adv_template import * verbose = True parser = pdb.Big_PDBParser() self_path = os.path.dirname(os.path.realpath(__file__)) # get the path to this script apbs_input_template_location = os.path.join(self_path, 'apbs_input.template') test_inputgen_location = "./inputgen.py" test_apbs_location = "apbs" test_pqr_filename = "../test/1cbj.pqr" default_apbs_params = { 'pqr':'', 'dimx':'65', 'dimy':'65', 'dimz':'65', 'cglenx':'100.0000', 'cgleny':'100.0000', 'cglenz':'100.0000', 'fglenx':'65.0000', 'fgleny':'65.0000', 'fglenz':'65.0000', 'boundary_condition':'sdh', 'lpbe_npbe':'lpbe', 'solute_dielec':'2.0', 'solvent_dielec':'78.5400', #'ion1crg':'-1.00', #'ion1conc':'0.150', #'ion1rad':'1.8150', #'ion2crg':'1.00', #'ion2conc':'0.150', #'ion2rad':'1.8750', 'temp':'310.0', 'stem':'pot', } default_inputgen_settings = { 'fadd':'60', 'gmemceil':'64000', 'resolution':'0.5', 'ionic_str':'0.15', 'cfac':'4.0', } def make_apbs_input_using_inputgen(inputgen_filename, pqr_filename, fadd=60, cfac=4.0, gmemceil=64000, resolution=0.5, ionic_str=0.15): """makes an apbs input file given a pqr file & other parameters. See Inputgen.py in PDB2PQR documentation for descriptions of other arguments""" pqr_basename= os.path.basename(pqr_filename) #pre_ext = '.'.join(pqr_filename.split('.')[0:-1]) # the part of the filename before the extension pqr_abspath=os.path.dirname(os.path.abspath(pqr_filename)) #print 'pqr abspath', pqr_abspath pre_ext = (pqr_basename.split('.'))[0] #print "pre_ext", pre_ext assert os.path.exists(inputgen_filename) runstring = "python %s --potdx --fadd=%s --cfac=%s --space=%s --gmemceil=%s --istrng=%s %s" % (inputgen_filename, fadd, cfac, resolution, gmemceil, ionic_str, pqr_basename, ) olddir = os.path.abspath(os.curdir) print("oldir", olddir) #print 'curdir', os.curdir os.chdir(os.path.dirname(pqr_filename)) print('curdir', os.path.abspath(os.curdir)) print("Now creating APBS input file using command:", runstring) #print 'PWD', os.getcwd() os.system(runstring) os.chdir(olddir) print("return to inputgen olddir", os.path.abspath(os.curdir)) #print 'pqr filename', pqr_filename #pqr_basename= os.path.basename(pqr_filename) #pre_ext = '.'.join(pqr_filename.split('.')[0:-1]) # the part of the filename before the extension #pqr_abspath=os.path.dirname(os.path.abspath(pqr_filename)) #print 'pqr abspath', pqr_abspath #pre_ext = (pqr_basename.split('.'))[0] #print "pre_ext", pre_ext input_filename = os.path.join(pqr_abspath+'/'+ pre_ext+'.in') print("APBS input_filename", input_filename) return input_filename #def make_apbs_input_using_inputgen(inputgen_filename, pqr_filename, fadd=60, cfac=4.0, gmemceil=64000, resolution=0.5, ionic_str=0.15): # """makes an apbs input file given a pqr file & other parameters. See Inputgen.py in PDB2PQR documentation for descriptions of other arguments""" # runstring = "python %s --potdx --fadd=%s --cfac=%s --space=%s --gmemceil=%s --istrng=%s %s" % (inputgen_filename, fadd, cfac, resolution, gmemceil, ionic_str, pqr_filename, ) # print "Now creating APBS input file using command:", runstring # os.system(runstring) # #pre_ext = '.'.join(pqr_filename.split('.')[0:-1]) # the part of the filename before the extension # pre_ext = pqr_filename # input_filename = os.path.join(pre_ext+'.in') # print "APBS input_filename", input_filename # return input_filename def scrape_inputfile(input_filename): '''NOTE: only takes out the dime, pdime, cglen, and fglen parameters from an APBS input file.''' dimestring = pdimestring = cglenstring = fglenstring = None infile = open(input_filename,'r') for line in infile: if re.search(' dime', line) and not dimestring: dimestring = line if re.search('pdime', line) and not pdimestring: pdimestring = line if re.search('cglen', line) and not cglenstring: cglenstring = line if re.search('fglen', line) and not fglenstring: fglenstring = line infile.close() if pdimestring: raise Exception("Parallel-run dx files not yet implemented...") apbs_params = {} # a dictionary containing what we scraped outta here dime_list = dimestring.strip().split() cglen_list = cglenstring.strip().split() fglen_list = fglenstring.strip().split() apbs_params['dimx'],apbs_params['dimy'],apbs_params['dimz'] = dime_list[1:] apbs_params['cglenx'],apbs_params['cgleny'],apbs_params['cglenz'] = cglen_list[1:] apbs_params['fglenx'],apbs_params['fgleny'],apbs_params['fglenz'] = fglen_list[1:] return apbs_params def make_apbs_input_using_template (new_apbs_params, apbs_file_location="apbs.in"): apbs_params = {} # create empty directory apbs_params.update(default_apbs_params) # populate with default values apbs_params.update(new_apbs_params) # populate with new parameters apbs_input = File_template(apbs_input_template_location, apbs_params) # fill parameters into the template to make apbs file print('APBS file loc', apbs_file_location) apbs_input.save(apbs_file_location) # save the apbs input file return def run_apbs (apbs_filename, input_filename, pqr_filename, std_out="apbs.out"): """runs apbs using a given input file "input_filename" and writes all standard output to 'std_out'.""" rundir = os.path.dirname(input_filename) print("copying file: %s to directory: %s" % (pqr_filename, os.path.join(rundir,os.path.basename(pqr_filename)))) if os.path.abspath(os.path.dirname(pqr_filename)) != os.path.abspath(rundir): shutil.copyfile(pqr_filename, os.path.join(rundir,os.path.basename(pqr_filename))) pqr_filename = os.path.basename(pqr_filename) #print "pqr filename:", pqr_filename #print "input_filename1", input_filename input_filename = os.path.basename(input_filename) #print "input_fielname2", input_filename std_out = os.path.basename(std_out) runstring = "%s %s > %s" % (apbs_filename, input_filename, std_out) # string to run apbs print("running command:", runstring) curdir = os.getcwd() print("curdir", curdir) os.chdir(rundir) # we want to run APBS in the directory print("rundir", os.getcwd()) result = os.system(runstring) # execute the string if result != 0: raise Exception("There was a problem running APBS") # then an error occured dx_filename = pqr_filename + '.dx' # inputgen will automatically make this .dx file os.chdir(curdir) print("back to dir", os.getcwd()) return dx_filename # return the name of the dx file def get_debye_length(apbs_std_outfilename): """Will parse an apbs stdout file to look for the Debye length.""" debye_string = re.compile("Debye length") debye_list = [] # a list of numbers that will be returned for line in open(apbs_std_outfilename, 'r'): m = re.search(debye_string, line) if m: # then we've found a line number_obj = re.search("[0-9.]+", line).group() debye_list.append(number_obj) if number_obj == "0": print("ALERT: Debye length of zero found. This may mean that your PQR file has a net charge that is NOT zero, or that your ion concentration was zero...") assert len(debye_list) > 0, "Debye length not found in APBS output: %s. Please ensure that APBS calculation was completed properly and that the correct output file was specified." return debye_list[0] # take the first member of it by default. There may be a better way for this but all outputs seem to be the same def flatten_ion_list(apbs_settings): if 'ions' not in apbs_settings.keys(): return apbs_settings ion_list = apbs_settings.pop('ions') for ion in ion_list: key = ion['key'] apbs_settings['%sconc' % key] = ion['concentration'] apbs_settings['%scrg' % key] = ion['charge'] apbs_settings['%srad' % key] = ion['radius'] return apbs_settings def main(pqr_filename,inputgen_settings={},apbs_settings={},): user_settings = {} user_settings.update(default_inputgen_settings) user_settings.update(inputgen_settings) apbs_settings = flatten_ion_list(apbs_settings) if apbs_settings['ion1conc']: user_settings['ionic_str'] = apbs_settings['ion1conc'] # make APBS input file using inputgen (enabled) inputgen_location = inputgen_settings['inputgen_executable'] apbs_location = apbs_settings['apbs_executable'] input_filename = make_apbs_input_using_inputgen(inputgen_location, pqr_filename, fadd=user_settings['fadd'], cfac=user_settings['cfac'], gmemceil=user_settings['gmemceil'], resolution=user_settings['resolution'], ionic_str=user_settings['ionic_str']) # make APBS input file using template (disabled) #input_filename # make DX grids pqr_filename = os.path.abspath((pqr_filename)) print('INPUT FILENAME', input_filename) print('PQR filename', pqr_filename) apbs_out=pqr_filename+'.out' # make a default apbs output file # use the inputgen-generated file to make our own, more customized file apbs_params = scrape_inputfile(input_filename) #if fhpd_mode: apbs_params['pqr'] = apbs_params['stem'] = os.path.abspath(pqr_filename) #else: #apbs_params['pqr'] = apbs_params['stem'] = pqr_filename apbs_params.update(apbs_settings) new_input_filename = os.path.abspath(pqr_filename) + '.in' dx = pqr_filename + '.dx' print(os.getcwd()) print('new_inp', new_input_filename) make_apbs_input_using_template(apbs_params, new_input_filename) #if not fhpd_mode: run_apbs(apbs_location, new_input_filename, pqr_filename, std_out=apbs_out) # save the electrostatic grid # find the Debye length debye = get_debye_length(apbs_out) return dx, debye def is_number(s): '''returns True if the string 's' can be converted to a float/int, False otherwise''' try: float(s) return True except ValueError: return False class Test_apbs_functions(unittest.TestCase): # several test cases to ensure the functions in this module are working properly def test_make_apbs_input_using_inputgen(self): # test whether the apbs input file has been created properly #print 'test pqr filename', test_pqr_filename self.APBS_inp = make_apbs_input_using_inputgen(test_inputgen_location, test_pqr_filename) # get file location #print "self.APBS_inp", self.APBS_inp fileexists = os.path.exists(self.APBS_inp) self.assertTrue(fileexists) def test_make_apbs_input_using_template(self):# test whether the apbs input file has been created properly make_apbs_input_using_template({},'/tmp/test_apbs.in') fileexists = os.path.exists('/tmp/test_apbs.in') self.assertTrue(fileexists) # if it exists, then that's good def test_run_apbs(self): # test whether apbs is running properly self.APBS_inp = make_apbs_input_using_inputgen(test_inputgen_location, os.path.abspath(test_pqr_filename)) # get file location self.APBS_inp2 = '/tmp/input2.in' self.inp_dict = scrape_inputfile(self.APBS_inp) self.inp_dict['pqr'] = self.inp_dict['stem'] = os.path.abspath(test_pqr_filename) make_apbs_input_using_template(self.inp_dict, self.APBS_inp2) run_apbs(test_apbs_location, self.APBS_inp2, test_pqr_filename) self.APBS_dx = test_pqr_filename + '.dx' fileexists = os.path.exists(self.APBS_dx) self.assertTrue(fileexists) def test_is_number(self): # test the is_number function self.assertTrue(is_number('0')) self.assertTrue(is_number('3.14')) self.assertTrue(is_number('2.0e-8')) self.assertFalse(is_number('foobar')) def test_get_debye_length(self): testfile1 = open('/tmp/debye_test1','w') # file with numbers testfile1.writelines(['CALCULATION #1: MULTIGRID\n', 'Setting up problem...\n', 'Vpbe_ctor: Using max ion radius (0 A) for exclusion function\n', 'Debye length: 99.23 A\n', 'Current memory usage: 731.506 MB total, 731.506 MB high water\n', 'Using cubic spline charge discretization.\n',]) testfile1.close() testfile2 = open('/tmp/debye_test2','w') # file with nothing testfile2.close() result1 = get_debye_length('/tmp/debye_test1') # should return the numeric value 99.32 self.assertEqual(result1, '99.23') self.assertRaises(AssertionError, get_debye_length, '/tmp/debye_test2') # this is an empty file, so the function should throw an error def test_scrape_inputfile(self): testfile1 = open('/tmp/scrape_test1','w') # file with numbers testfile1.writelines([''' dime 129 129 193 cglen 80.2842 77.5999 116.9345 fglen 67.2260 65.6470 88.7850 ''',]) testfile1.close() test_params = {'dimx':'129', 'dimy':'129', 'dimz':'193','cglenx':'80.2842', 'cgleny':'77.5999', 'cglenz':'116.9345', 'fglenx':'67.2260', 'fgleny':'65.6470', 'fglenz':'88.7850'} self.assertEqual(test_params, scrape_inputfile('/tmp/scrape_test1')) if __name__=='__main__': print("Running unit tests for apbs.py") unittest.main() # run tests of all functions

UTF-8

Python

false

13,105

py

29

apbs.py

15

0.700038

0.677222

0

291

44.034364

252

abhishekjha2468/HackerRank-Python

970,662,637,369

fcb2e58d58e572ab5a9a95b873bc9a43f43b35cd

e8854329efa49df5a9038b2decc1f037540b2ef0

/Validating Credit Card Number.py

8fc56f27613bcbcca267b679f93d202d240f1e58

[]

no_license

https://github.com/abhishekjha2468/HackerRank-Python

2a6f217ccfa9805629d5af698bf8838b95a77eac

3d5ff2e51b228e633a8250b97a6dbb4e7e115b42

refs/heads/main

2023-03-28T16:26:06.878931

2021-03-30T06:07:51

352,885,018

0

null

def count(string): string=string Count=1 list=[] for i in range(len(string)): try: if i<(len(string)-1): if string[i]==string[i+1]: Count=Count+1 else: list.append(Count) Count=1 else: list.append(Count) except: list.append(Count) return max(list) #---------------------------------------- if __name__=='__main__': n=int(input()) for m in range(n): num=list(input()) #print(num) N=[] flag='Invalid' c=True while(c): try: if int(num[0]) not in [4,5,6]: #print('Goes Wrong in line no.. 10') c=False break #print("First Condition in passed That first number is either 4,5 or 6") if len(num)==16: new=[ int(i) for i in num ] #print("While Checking integer ") #print("new num list is: ",new) #set_list=list(set(new)) #print("All Good Till line finding unique number in num list i.e : ",set_list) if count(''.join(num))>3: c=False break #for i in set_list: #print("Checking the count of ",i) #if new.count(i)>=4: #N.append(new.count(i)) #print("Error on counting") # c=False #break #print("The Highest Count is ",N) if len(new)==16 and N==[]: flag='Valid' #print("Flag is set to Valid") #print("Breaking the while loop now ") c=False break #------------------------------------------------- elif len(num)==19 and num.count('-')==3: #print("we are on secound case of hyphen '-' ") h=[] h.append(num.pop(4)) h.append(num.pop(8)) h.append(num.pop(12)) #print("hyphen list is: ",h) if h!=['-','-','-']: c=False break else: continue #print("All hyphen are on right position ") new=[ int(i) for i in num ] #print("While Checking integer ") #print("new num list is: ",new) #set_list=list(set(new)) #print("All Good Till line finding unique number in num list i.e : ",set_list) if count(''.join(num))>3: c=False break #for i in set_list: #print("Checking the count of ",i) #if new.count(i)>=4: #N.append(new.count(i)) #print("Error on counting") #c=False #break #print("The Highest Count is ",N) if len(new)==16 and N==[]: flag='Valid' #print("Flag is set to Valid") #print("Breaking the while loop now ") break except: flag='Invalid' break #print("Now we are out of the While Loop") print(flag)

UTF-8

Python

false

2,518

py

108

Validating Credit Card Number.py

107

0.527403

0.515091

0

96

25.239583

83

Jisup-lim/academy-study

2,774,548,916,142

7546105c1f4625270eabb34d8c8416a1c9f3cd50

52c4a91abba094cefe93a1a6cc837eac564bd332

/ml/m09_SelectModel_2_cancer.py

c77fc708face697326a7db6303b93551652c5b80

[]

no_license

https://github.com/Jisup-lim/academy-study

9805a7d1809b51781f341b3b0eceb46f2da956df

74e07370ac697b01a026aaaf95437300dc9913fd

refs/heads/master

2023-04-07T19:49:35.605546

2021-05-31T15:15:47

325,250,894

0

null

from sklearn.model_selection import train_test_split from sklearn.datasets import load_breast_cancer from sklearn.metrics import accuracy_score from sklearn.utils.testing import all_estimators # from sklearn.utils import all_estimators import warnings warnings.filterwarnings('ignore') dataset = load_breast_cancer() x = dataset.data y = dataset.target x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=104) allAlgorithms = all_estimators(type_filter='classifier') for (name,algorithm) in allAlgorithms: try: model = algorithm() model.fit(x_train, y_train) y_pred = model.predict(x_test) print(name,'의 정답률 : ', accuracy_score(y_test,y_pred)) except: # continue print(name,'없는 모델') import sklearn print(sklearn.__version__) # 0.23.2 # AdaBoostClassifier 의 정답률 : 0.9736842105263158 # BaggingClassifier 의 정답률 : 0.9473684210526315 # BernoulliNB 의 정답률 : 0.6052631578947368 # CalibratedClassifierCV 의 정답률 : 0.9298245614035088 # CategoricalNB 없는 모델 # CheckingClassifier 의 정답률 : 0.39473684210526316 # ClassifierChain 없는 모델 # ComplementNB 의 정답률 : 0.8947368421052632 # DecisionTreeClassifier 의 정답률 : 0.9298245614035088 # DummyClassifier 의 정답률 : 0.5263157894736842 # ExtraTreeClassifier 의 정답률 : 0.9035087719298246 # ExtraTreesClassifier 의 정답률 : 0.956140350877193 # GaussianNB 의 정답률 : 0.9210526315789473 # GaussianProcessClassifier 의 정답률 : 0.9298245614035088 # GradientBoostingClassifier 의 정답률 : 0.9385964912280702 # HistGradientBoostingClassifier 의 정답률 : 0.9736842105263158 # KNeighborsClassifier 의 정답률 : 0.9473684210526315 # LabelPropagation 의 정답률 : 0.42105263157894735 # LabelSpreading 의 정답률 : 0.42105263157894735 # LinearDiscriminantAnalysis 의 정답률 : 0.9736842105263158 # LinearSVC 의 정답률 : 0.8859649122807017 # LogisticRegression 의 정답률 : 0.9298245614035088 # LogisticRegressionCV 의 정답률 : 0.956140350877193 # MLPClassifier 의 정답률 : 0.9210526315789473 # MultiOutputClassifier 없는 모델 # MultinomialNB 의 정답률 : 0.8947368421052632 # NearestCentroid 의 정답률 : 0.868421052631579 # NuSVC 의 정답률 : 0.8508771929824561 # OneVsOneClassifier 없는 모델 # OneVsRestClassifier 없는 모델 # OutputCodeClassifier 없는 모델 # PassiveAggressiveClassifier 의 정답률 : 0.9298245614035088 # Perceptron 의 정답률 : 0.9385964912280702 # QuadraticDiscriminantAnalysis 의 정답률 : 0.9736842105263158 # RadiusNeighborsClassifier 없는 모델 # RandomForestClassifier 의 정답률 : 0.956140350877193 # RidgeClassifier 의 정답률 : 0.9736842105263158 # RidgeClassifierCV 의 정답률 : 0.9649122807017544 # SGDClassifier 의 정답률 : 0.8333333333333334 # SVC 의 정답률 : 0.9210526315789473 # StackingClassifier 없는 모델 # VotingClassifier 없는 모델

UTF-8

Python

false

3,025

py

242

m09_SelectModel_2_cancer.py

237

0.767677

0.554807

0

76

34.184211

91

satyam-seth-learnings/ds_algo_learning

9,491,877,749,891

430c51fc8a240cbfbcc1cb24ef612138b92e031e

48b7c5c6d4576c06f157abb317ced99dae64f5c5

/Applied Course/4.Problem Solving/5.Problems on Trees/30.All Elements in Two Binary Search Trees.py

9f7d772881a6ac6e58fc114466eb2ec5edfe0a72

[]

no_license

https://github.com/satyam-seth-learnings/ds_algo_learning

38cc5e6545ec8a5fbabefc797aee486c98cfb314

761cba2cd2dd7d7cdaf5a3c41503fdfc6dfe73ad

refs/heads/master

2023-08-31T00:48:30.707533

2021-10-16T09:37:45

415,274,888

0

null

# https://leetcode.com/problems/all-elements-in-two-binary-search-trees/ # Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right # Logic-1 class Solution: def getAllElements(self, root1: TreeNode, root2: TreeNode) -> List[int]: def inorder(root): if root: return inorder(root.left)+[root.val]+inorder(root.right) else: return [] tree1=inorder(root1) tree2=inorder(root2) result=[] i,j=0,0 while i<len(tree1) and j<len(tree2): if tree1[i]<tree2[j]: result.append(tree1[i]) i+=1 else: result.append(tree2[j]) j+=1 if i<len(tree1): result+=tree1[i:] if j<len(tree2): result+=tree2[j:] return result # Logic-2 class Solution: def getAllElements(self, root1: TreeNode, root2: TreeNode) -> List[int]: stack1,stack2,result=[],[],[] while root1 or root2 or stack1 or stack2: while root1: stack1.append(root1) root1=root1.left while root2: stack2.append(root2) root2=root2.left if not stack2 or stack1 and stack1[-1].val<=stack2[-1].val: root1=stack1.pop() result.append(root1.val) root1=root1.right else: root2=stack2.pop() result.append(root2.val) root2=root2.right return result

UTF-8

Python

false

1,688

py

429

30.All Elements in Two Binary Search Trees.py

404

0.510071

0.476303

0

51

32.117647

76

thraddash/python_tut

4,071,629,026,525

a11e43baef8f5b4aa1c61f139255e0d36979c052

799a90344c4e2e367bd79fff063ede765f816549

/16_exception_handling/pass.py

4ba63f53cc74b1e3bc65938668b988ac5e12fbef

[]

no_license

https://github.com/thraddash/python_tut

a49765801882003b6d3b1d6958bee476f5768470

5bd359f2021bb26953bcd955018dfbf6b2b6395f

refs/heads/master

2023-03-16T10:28:56.581004

2021-03-05T00:08:06

325,146,371

0

null

#!/usr/bin/env python # Ignore exception by pass def div(x, y): return x / y try: div_result = div(2, 1) #div_result = div(2, 0) except: pass # do nothing else: print("Div result is: " + str(div_result)) def div2(x, y): try: result = x / y except: pass # do nothing else: return "Div result is: " + str(result) print() print("v2 passing arguments") print(div2(2, 1)) print(div2(2, 0))

UTF-8

Python

false

448

py

178

pass.py

171

0.564732

0.537946

0

25

16.92

46

karthikpappu/pyc_source

18,408,229,860,346

86038a4900b15b777972893507c5333fc984ab84

91fa095f423a3bf47eba7178a355aab3ca22cf7f

/pypi_install_script/yadage-service-cli-0.1.11.tar/setup.py

810ad4f9a67120c49934ddeecb6ff8e50666a027

[]

no_license

https://github.com/karthikpappu/pyc_source

0ff4d03e6d7f88c1aca7263cc294d3fa17145c9f

739e7e73180f2c3da5fd25bd1304a3fecfff8d6e

refs/heads/master

2023-02-04T11:27:19.098827

2020-12-27T04:51:17

null

0

null

from setuptools import setup, find_packages setup( name = 'yadage-service-cli', version = '0.1.11', description = 'yadage service command line tools', url = 'http://github.com/yadage/yadage-service-cli', author = 'Kyle Cranmer, Lukas Heinrich', author_email = 'cranmer@cern.ch, lukas.heinrich@cern.ch', packages = find_packages(), entry_points = { 'console_scripts': [ 'yad = yadagesvccli.cli:yad', ] }, install_requires = [ 'click', 'requests', 'pyyaml', 'requests_toolbelt', 'clint' ], extras_require = { 'local' : [ 'yadage-schemas' ] } )

UTF-8

Python

false

628

py

114,545

setup.py

111,506

0.598726

0.592357

0

28

21.428571

59

zgreat/transient

15,324,443,336,750

51270bef51b2ff9dc35131945962a06ce9e963d8

77da6217bf83d41b2fe479d6e414a1df4f997b3c

/runserver.py

14dfed33dbd933b5ae057aa7b4b1e8bb9ddb0f19

[ "MIT" ]

permissive

https://github.com/zgreat/transient

e4deb14951dc05692bc1ccb624c66cf394bc9664

1cfc1fe65079ef3c75754eaa0cd97f7ebb55664a

refs/heads/master

2021-05-30T10:49:40.529829

2015-12-20T03:46:39

null

0

null

#!/usr/bin/python from transient import api if __name__ == "__main__": api.run()

UTF-8

Python

false

86

py

30

runserver.py

26

0.581395

0

5

16.2

26

jgubert/Ensemble

11,132,555,279,782

9ac2e1464c7dde54db2cdfa8ebbeeb74856f69a9

c890014818f638d9c6f512689c515b545c21f84e

/main_teste.py

cf814623a77eae4c0095548619de0d4ade40459a

[]

no_license

https://github.com/jgubert/Ensemble

23689853fbaf8ba5a0f7deb0a1c7dab6d5c194a1

d1e9d33864357f4adaf9bc8c52209f3d0e6d433b

refs/heads/master

2020-04-06T16:57:20.383220

2018-11-15T02:33:04

146,955,467

0

null

# -*- coding: utf-8 -*- """ Função main() """ import fileinput import csv import math import sys import bootstrap import decisionTree import errorMeasures import header import kFoldStratified import preProcessing import tree import voting import random import sampling """ Como executar: > python3 main_teste.py <datafile.format> <num_trees> """ def main(filename, n_trees): # coletando os argumentos #filename = str(sys.argv[1]) #n_trees = int(sys.argv[2]) n_folds = 10 list_forest = [] # definindo seed random.seed(1) # abrindo o arquivo #datafile = preProcessing.openDataFile(filename) # TO DO: FIX THIS PIECE OF CODE with open(filename) as csvfile: csv_reader = csv.reader(csvfile, delimiter=',') datafile = list(csv_reader) #print("\n============= DATA FILE =============") #print (*datafile,sep="\n") m = math.ceil(math.sqrt(len(datafile[1]))) # Fazendo amostragem para as m colunas com maior ganho if m > len(datafile[0]): print("valor m é maior que quantidade de atributos") return -1 datafile = sampling.sampleAttributes(datafile, m) # Setando o cabeçalho dataheader = header.Header() dataheader.setHeader(datafile) # Lista que vai armazenar os folds fold_list = [] fold_list = kFoldStratified.kFoldStratified(datafile, n_folds) # Quantidade de entradas testadas é o tamanho de um fold # vezes a quantidade de testes que sera feito tam_testfold = len(fold_list[0]) * n_folds ''' print("\n============= FOLD lIST =============") for i in range(n_folds): print("\nFold N " + str(i)) print(*fold_list[i], sep="\n") ''' # inicializa a matriz de confusão value_classes = kFoldStratified.countPossibleAttributes(datafile) errorMeasures.initConfusionMatrix(len(value_classes)) # chamando o bootstrap (K-Fold posteriormente) for i in range(n_folds): aux_fold_list = [] test_fold = [] training_folds = [] # copia a lista de folds para uma lista auxiliar aux_fold_list = list(map(list, fold_list)) # pega o fold de teste test_fold = aux_fold_list[i] # DEBUG #print(*test_fold,sep="\n") #print("\n") # #print (*aux_fold_list,sep="\n") # pega os folds de treinamento aux_fold_list.remove(test_fold) # transforma lista de listas em uma lista só, para facilitar implementação for j in aux_fold_list: training_folds += j list_forest.append(decisionTree.makeForest(training_folds, n_trees, dataheader)) final_votes = decisionTree.startClassification(test_fold, list_forest[i], dataheader, value_classes) # DEBUG: impressão das medidas de erro errorMeasures.compactConfusionMatrix(value_classes) print("\n\n ===========================================") print("Num Folds: " + str(n_folds)) print("Num Trees: " + str(n_trees)) print("RESULT MATRIX:") errorMeasures.printResultMatrix() print("CONFUSION MATRIX:") errorMeasures.printConfusionMatrix() print("Accuracy: ") print(errorMeasures.accuracy(tam_testfold,value_classes)) print("Error: ") print(errorMeasures.error(tam_testfold,value_classes)) print("Recall: ") print(errorMeasures.recall(value_classes)) print("Precision: ") print(errorMeasures.precision(value_classes)) print("FMeasure: ") print(errorMeasures.FMeasure(errorMeasures.precision(value_classes), errorMeasures.recall(value_classes), 1)) print("===========================================") # Limpando Matriz de Confusão errorMeasures.resetConfusionMatrix(len(value_classes)) ''' #(*) # DEBUG: impressão das florestas for i in range(len(list_forest)): for j in range(len(list_forest[i])): #for k in range(len(list_forest[i][j])): #tree.printTree(list_forest[i][j][k]) tree.printTree(list_forest[i][j]) ''' ''' Executando a main() ''' #main()

UTF-8

Python

false

4,125

py

14

main_teste.py

12

0.617311

0.614393

0

155

25.535484

113

sandeepgoyal194/CustomerChoice

7,370,163,881,444

713ac129cd60bf41c7187f29065bb2cafbeafbe3

0a52c30680948e74349105edcd3b424c44df4b20

/services/TopSiteGratis.py

edf39f80a92323da2618c748032fe8a96cbafdb0

[]

no_license

https://github.com/sandeepgoyal194/CustomerChoice

e47cedf4e9b0214e949cd29532ce4b77f305abd5

755e182900d94a82de0b3a5d803630db6b21fedf

refs/heads/master

2018-09-03T04:46:21.870455

2018-06-29T05:00:38

128,005,895

0

1

null

false

2018-07-18T06:30:40

2018-04-04T04:14:48

2018-06-29T05:01:02

2018-07-17T06:05:31

21,686

0

1

Python

false

null

from model.Servicemodel import ServiceRecord from scrapy import Spider, Request class TopSiteGratis(Spider): def __init__(self): pass def parsing(self, response): return self.crawl(response,self.category,self.servicename) def crawl(self, response, category, servicename): reviews = [] reviews1 = [] self.category = category self.servicename = servicename for node in response.xpath( "//div[@class='reviews product-reviews']/div[@class='item']/p[@class='excerpt']"): reviews.append(node.xpath('string()').extract()); ratings = response.xpath("//div[@class='reviews product-reviews']/div[@class='item']/div[@class='right-block']/div[@class='ratings']/span[@class='rate_False']/span").extract() dates = response.xpath("//div[@class='reviews product-reviews']/div[@class='item']/meta[@itemprop='datePublished']/@content").extract() authors = response.xpath("//div[@class='reviews product-reviews']/div[@class='item']/div[@class='author-info']/a/text()").extract() img_src = response.xpath( "//div[@class='reviews product-reviews']/div[@class='item']/div[@class='left-block']/div[@class='product-info']/div[@class='img pull-left']/img/@src").extract() # headings = response.xpath("//div[@class='pr-review-wrap']/div[@class='pr-review-rating-wrapper']/div[@class='pr-review-rating']/p[@class='pr-review-rating-headline']/text()").extract() website_name1 = response.xpath("//div[@class='footer']/div[@class='row']/div[@class='col-md-7 text-right']/text()").extract() website_name = [] i = 0 while(i< len(website_name1)): c = website_name1[1].split(" ") website_name.append(c[12]) break i = i+1 print("Reviews ", len(reviews), reviews) print("Authors ", len(authors), authors) print("Rating ", len(ratings), ratings) print("Dates ", len(dates), dates) print("img_src ", len(img_src), img_src) print("websites ", len(website_name), website_name) for item in range(0, len(reviews)): servicename1 = ServiceRecord(response.url, ratings[item], None, dates[item], authors[item], category, servicename, reviews[item], img_src, website_name) servicename1.save()

UTF-8

Python

false

2,407

py

91

TopSiteGratis.py

84

0.606149

0.600748

0

46

51.217391

194

bellyfat/etherscan-python

9,019,431,338,699

26e8d4fa3b7a294e4100dce618b751849a0cc8d1

6b902b5fe1dbdbde22047fe503ce46a33040ce7e

/build/lib/etherscan/enums/actions_enum.py

2ca5d53e64874ac3b93ceaed1ae31d90d1cd246d

[ "Python-2.0", "MIT" ]

permissive

https://github.com/bellyfat/etherscan-python

fc6cfa9ce9639febd9b069c466827a3c1ea915c0

0254144fc2db38c897ff843069ba3c945e19b866

refs/heads/master

2022-12-28T06:59:05.873455

2020-10-16T11:01:52

null

0

null

from dataclasses import dataclass @dataclass(frozen=True) class ActionsEnum: BALANCE_HISTORY: str = "balancehistory" BALANCE_MULTI: str = "balancemulti" BALANCE: str = "balance" CHAIN_SIZE: str = "chainsize" ETH_BLOCK_NUMBER: str = "eth_blockNumber" ETH_CALL: str = "eth_call" ETH_ESTIMATE_GAS: str = "eth_estimateGas" ETH_GAS_PRICE: str = "eth_gasPrice" ETH_GET_BLOCK_BY_NUMBER: str = "eth_getBlockByNumber" ETH_GET_BLOCK_TRANSACTION_COUNT_BY_NUMBER: str = "eth_getBlockTransactionCountByNumber" ETH_GET_TRANSACTION_BY_BLOCK_NUMBER_AND_INDEX: str = "eth_getTransactionByBlockNumberAndIndex" ETH_GET_CODE: str = "eth_getCode" ETH_GET_STORAGE_AT: str = "eth_getStorageAt" ETH_GET_TRANSACTION_BY_HASH: str = "eth_getTransactionByHash" ETH_GET_TRANSACTION_COUNT: str = "eth_getTransactionCount" ETH_GET_TRANSACTION_RECEIPT: str = "eth_getTransactionReceipt" ETH_GET_UNCLE_BY_BLOCK_NUMBER_AND_INDEX: str = "eth_getUncleByBlockNumberAndIndex" ETH_PRICE: str = "ethprice" ETH_SUPPLY: str = "ethsupply" GAS_ESTIMATE: str = "gasestimate" GAS_ORACLE: str = "gasoracle" GET_ABI: str = "getabi" GET_BLOCK_COUNTDOWN: str = "getblockcountdown" GET_BLOCK_NUMBER_BY_TIME: str = "getblocknobytime" GET_BLOCK_REWARD: str = "getblockreward" GET_MINED_BLOCKS: str = "getminedblocks" GET_SOURCE_CODE: str = "getsourcecode" GET_STATUS: str = "getstatus" GET_TX_RECEIPT_STATUS: str = "gettxreceiptstatus" TOKEN_BALANCE: str = "tokenbalance" TOKEN_SUPPLY: str = "tokensupply" TOKENNFTTX: str = "tokennfttx" TOKENTX: str = "tokentx" TXLIST_INTERNAL: str = "txlistinternal" TXLIST: str = "txlist"

UTF-8

Python

false

1,715

py

20

actions_enum.py

1

0.69621

0

40

41.85

98

aloscc/Ambulance-Dispatching

13,065,290,545,068

0be62f9214bd80b49085c425e3b8a4977468e65a

1cd37beb04515d22a185624e5a304a9de0923801

/src/gt/plot/DispatcherPlot.py

d07b56860af6950089cb2820758aca0827ee0f57

[]

no_license

https://github.com/aloscc/Ambulance-Dispatching

fac3585096b1c3f6c5d2c9ab23cc6be5e57e44dd

429538a08f8580043f2c72aeb289527477ec4949

refs/heads/master

2020-05-22T00:31:39.958716

2019-05-11T17:45:20

null

0

null

from gt.core.HospitalsAndDispatcherModel import DispatcherAndHospitalsModel from neng.game import Game from common import * def find_nash(la, mu, n, print_matrix=False): model = DispatcherAndHospitalsModel(la, mu, n, 3, 10) matrix = model.game_matrix() if print_matrix: print(matrix) text = "NFG 1 R \"Ambulance Dispatching Game\" { \"Dispatcher\" \"Hospital1\" \"Hospital2\" }\n\n" text += "{ { \"N2\" \"N1\" \"BE\" }\n" text += "{ \"A\" \"R\" }\n" text += "{ \"A\" \"R\" }\n" text += "}\n\"\"\n\n" text += "{\n" for k in range(2): for j in range(2): for i in range(3): text += "{ \"\" %f, %f, %f }\n" % (matrix[i][j][k][0], matrix[i][j][k][2], matrix[i][j][k][2]) text += "}\n" text += "1 2 3 4 5 6 7 8 9 10 11 12" game = Game(text) sol = game.findEquilibria('pne') return extract_strategies_from_solutions(sol) def extract_strategies_from_solutions(solutions): strategies = set() if not solutions: return strategies for sol in solutions: cur_stra = '' if sol[0][0] == 1: cur_stra += 'N2' elif sol[0][1] == 1: cur_stra += 'N1' else: cur_stra += 'BE' strategies.add(cur_stra) continue cur_stra += ';' if sol[1][0] == 1: cur_stra += 'A' else: cur_stra += 'R' if sol[2][0] == 1: cur_stra += 'A' else: cur_stra += 'R' strategies.add(cur_stra) return strategies def solution_plot(n, ax=None, legend=False): print('Computing for n = {}'.format(n)) data = { 'Lambda': [], 'Mu': [], 'Nash Equilibrium': [] } for mu in np.linspace(0.5, 3, 30): # print('Computing for mu={}'.format(mu)) for l in np.linspace(0.5, 3, 30): data['Lambda'].append(l) data['Mu'].append(mu) data['Nash Equilibrium'].append(','.join(find_nash(l, mu, n))) data = pd.DataFrame(data) if ax is not None: sns.scatterplot(x='Lambda', y='Mu', hue='Nash Equilibrium', data=data, ax=ax, legend=legend, marker='s', s=1000) ax.set_title('N = ' + str(n)) else: sns.scatterplot(x='Lambda', y='Mu', hue='Nash Equilibrium', data=data, legend=legend, marker='s', s=1000) if __name__ == '__main__': _, axs = plt.subplots(nrows=3, ncols=3, figsize=(15, 15)) solution_plot([1, 1], ax=axs[0][0], legend='brief') solution_plot([1, 2], ax=axs[0][1], legend='brief') solution_plot([1, 3], ax=axs[0][2], legend='brief') solution_plot([2, 1], ax=axs[1][0], legend='brief') solution_plot([2, 2], ax=axs[1][1], legend='brief') solution_plot([2, 3], ax=axs[1][2], legend='brief') solution_plot([3, 1], ax=axs[2][0], legend='brief') solution_plot([3, 2], ax=axs[2][1], legend='brief') solution_plot([3, 3], ax=axs[2][2], legend='brief') plt.savefig('../../images/Dispatcher/Dispatcher Nash Equilibrium') plt.show()

UTF-8

Python

false

3,060

py

8

DispatcherPlot.py

7

0.524837

0.491176

0

93

31.903226

120

DenisCarriere/gpsimage

2,576,980,414,240

b92a3038f63f8230030312f1a3f8a29d56c78fb4

1eb44f45c7972def3b78d783582d1fe51a01c2ed

/gpsimage/base.py

654ef0a7804c32723f757c40cd0a7b8313784ecf

[ "Apache-2.0" ]

permissive

https://github.com/DenisCarriere/gpsimage

3796adc92d9b8d3e7c718801b3b904f520d6238b

4f2f6c0acb7c6bb173299f86932d894c553e6c5c

refs/heads/master

2022-11-30T23:19:06.771136

2014-12-01T01:22:02

22,666,022

7

6

Apache-2.0

false

2022-11-22T00:30:49

2014-08-06T01:53:16

2022-07-05T12:13:46

2022-11-22T00:30:46

6,177

6

7

5

Python

false

import os import time import datetime import dateutil.parser import exifread class GPSImage(object): """ """ _exclude = ['lat', 'lng','debug','json','ok', 'help', 'x', 'y', 'path','exif', 'image'] exif = {} def __init__(self, image): if isinstance(image, str): self.path = os.path.abspath(image) self.filename = os.path.basename(self.path) self.image = open(self.path) else: self.image = image # Initial Functions self._read_exif() def __repr__(self): if self.ok: return '<GPSImage - {0} [{1}, {2} ({3})]>'.format(self.filename, self.lat, self.lng, self.datum) else: return '<GPSImage [{1}]>'.format(self.status) def _read_exif(self): self.exif = exifread.process_file(self.image) def _dms_to_dd(self, dms, ref): if len(dms) == 3: degrees = dms[0].num minutes = dms[1].num / 60.0 seconds = float(dms[2].num) / float(dms[2].den) / 60.0 / 60.0 dd = degrees + minutes + seconds # South & West returns Negative values if ref in ['S', 'W']: dd *= -1 return dd def _pretty(self, key, value, special=''): if special: key = special.get(key) if key: extra_spaces = ' ' * (20 - len(key)) return '{0}{1}: {2}'.format(key, extra_spaces, value) def debug(self): # JSON Results print('## JSON Results') for key, value in self.json.items(): print(self._pretty(key, value)) print('') # Camera Raw if self._exif: print('## Camera Raw') for key, value in self._exif.items(): print(self._pretty(key, value, TAGS)) print('') # GPS Raw if self._GPSInfo: print('## GPS Raw') for key, value in self._GPSInfo.items(): print(self._pretty(key, value, GPSTAGS)) @property def status(self): if not self.exif: return 'ERROR - Exif not found' elif not self.ok: return 'ERROR - No Geometry' else: return 'OK' """ @property def dpi(self): value = self._image.info.get('dpi') if value: if len(value) == 2: if bool(value[0] and value[1]): return value # If both values are (0, 0) then change it to the standard 72DPI else: return (72, 72) else: # Retrieves X & Y resolution from Exif instead of PIL Image x = self._divide(self.XResolution) y = self._divide(self.YResolution) if bool(x and y): return (int(x), int(y)) """ @property def ok(self): if bool(self.lat and self.lng): return True else: return False """ @property def model(self): return self.Model @property def make(self): return self.Make """ @property def datum(self): datum = self.exif.get('GPS GPSMapDatum') if datum: return datum.values else: return 'WGS-84' @property def lng(self): lng_dms = self.exif.get('GPS GPSLongitude') lng_ref = self.exif.get('GPS GPSLongitudeRef') if bool(lng_dms and lng_ref): return self._dms_to_dd(lng_dms.values, lng_ref.values) @property def x(self): return self.lng @property def lat(self): lat_dms = self.exif.get('GPS GPSLatitude') lat_ref = self.exif.get('GPS GPSLatitudeRef') if bool(lat_dms and lat_ref): return self._dms_to_dd(lat_dms.values, lat_ref.values) @property def y(self): return self.lat @property def altitude(self): altitude = self.exif.get('GPS GPSAltitude') if altitude: return altitude.values @property def direction(self): direction = self.exif.get('GPS GPSImgDirection') if direction: return direction.values @property def timestamp(self): # For GoPro timestamp = self.exif.get('Image DateTime') if timestamp: timestamp = timestamp.values.replace(':','-',2) return dateutil.parser.parse(timestamp) """ @property def width(self): return self._image.size[0] @property def height(self): return self._image.size[1] @property def size(self): if bool(self.height and self.width): return (self.width, self.height) """ @property def geometry(self): if self.ok: return {'type':'POINT', 'coordinates':[self.lng, self.lat]} @property def satellites(self): satellites = self.exif.get('GPS GPSSatellites').values if satellites: return int(satellites) @property def json(self): container = {} for key in dir(self): if bool(not key.startswith('_') and key not in self._exclude): value = getattr(self, key) if value: container[key] = value return container if __name__ == '__main__': img = GPSImage('/home/denis/Github/gpsimage/gpsimage/images/nikon_coolpix_aw100.jpg') print img.json

UTF-8

Python

false

5,484

py

6

base.py

4

0.519329

0.511306

0

207

25.497585

108

jingshisun/Sentiment-Analysis

1,348,619,732,610

fb1a0bfad2910a66feabcae47c7cb00aa426681d

f8857b6a70c38d55056457047beeed79d9381504

/Project Part 3/projectpart3.py

b308dc2780057f5d561354ea74d5fed8c5708034

[]

no_license

https://github.com/jingshisun/Sentiment-Analysis

122e90cba465bffdceba176f52befdab9bb30b7a

8d062138e9c3379eb942a18b0940c9ec126979ed

refs/heads/master

2021-09-01T08:29:41.101618

2017-12-26T02:02:18

null

0

null

# -*- coding: utf-8 -*- """ Created on Tue Nov 22 11:31:20 2016 @author: tomec """ import urllib import pandas as pd from datetime import timedelta import datetime import csv import re import unicodedata import nltk from nltk.sentiment.util import mark_negation from sklearn.cluster import KMeans from sklearn.cluster import AgglomerativeClustering from sklearn.metrics import silhouette_score from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import matplotlib.cm as cm import numpy as np from selenium import webdriver from bs4 import BeautifulSoup import time from selenium.webdriver.common.keys import Keys from statsmodels.tsa.stattools import grangercausalitytests from statsmodels.tsa.stattools import adfuller from sklearn.preprocessing import StandardScaler from sklearn.neural_network import MLPRegressor import sys from bokeh.io import output_file, show, vplot from bokeh.plotting import figure from bokeh.models import Span ### Create function to break apart contractions to its derivative words ### A text file containing this('contractions.txt') should be located at the ### working directory along with this script. def break_contractions(text): #### Import dictionary of contractions: contractions.txt with open('contractions.txt','r') as inf: contractions = eval(inf.read()) pattern = re.compile(r'\b(' + '|'.join(contractions.keys()) + r')\b') result = pattern.sub(lambda x: contractions[x.group()], text) return(result) ### Create function to lemmatize (stem) words to their root ### This requires the NLTK wordnet dataset. def lemmatize_words(text): # Create a lemmatizer object wordnet_lemmatizer = nltk.stem.WordNetLemmatizer() out = [] for word in text: word = ''.join(w.lower() for w in word if w.isalpha()) out.append(wordnet_lemmatizer.lemmatize(word)) return(out) #### Create function to remove stopwords (e.g., and, if, to) #### Removes stopwords from a list of words (i.e., to be used on lyrics after splitting). #### This requires the NLTK stopwords dataset. def remove_stopwords(text): # Create set of all stopwords stopword_set = set(w.lower() for w in nltk.corpus.stopwords.words()) out = [] for word in text: # Convert words to lower case alphabetical letters only # word = ''.join(w.lower() for w in word if w.isalpha()) if word not in stopword_set: out.append(word) # Return only words that are not stopwords return(out) #### Create a class that stores the NRC Word-Emotion Assocations dataset as a #### a dictionary (once the word_association object is constructed), then #### provides the 'count_emotions' method to count the number occasions for #### emotion. class word_assocations: def __init__(self): # Import NRC Word-Emotion Association data with open("NRC-emotion-lexicon-wordlevel-alphabetized-v0.92.txt", "r", newline = '', encoding = 'utf-8') as f: file = f.readlines() file = file[46:] # First 45 lines are comments # Create dictionary with words and their associated emotions associations = {} for line in file: elements = line.split() if elements[2] == '1': if elements[0] in associations: associations[elements[0]].append(elements[1]) else: associations[elements[0]] = [elements[1]] # Initializes associations dictionary (so not to repeat it) self.associations = associations def count_emotions(self, text): # Clean up the string of characters temp0 = break_contractions(text) # Break up contractions temp1 = lemmatize_words(temp0.split()) # Split string to words, then lemmatize temp2 = mark_negation(temp1, double_neg_flip = True) # Account for negations temp3 = remove_stopwords(temp2) # Remove any stopwords # check_spelling(temp2) # Function is no longer useful # Count number of emotional associations for each valid word bank = [] wordcount = 0 for word in temp3: if word in self.associations: bank.extend(self.associations[word]) wordcount += 1 # Returns a tuple of integers for negative, positive, anger, fear, anticipation, # surprise, trust, sadness, joy, disgust, and total word count, respectively. return((bank.count('negative'), bank.count('positive'), bank.count('anger'), bank.count('fear'), bank.count('anticipation'), bank.count('surprise'), bank.count('trust'), bank.count('sadness'), bank.count('joy'), bank.count('disgust'), wordcount)) # This function removes parentheses and also the contents of the parentheses # for the purposes of improving search matches when finding lyrics. def remove_parenth(text): patt = re.compile('\s*$.*?$\s*') out = re.findall(patt, text) if len(out) > 0: text = text.replace(out[0], "") return(text) # This function converts characters (byte string) that are otherwise # not caught by the replace_accents normalization function. def replace_special(text): temp1 = text.encode('utf-8') temp2 = temp1.replace(b"\xc3\x98", b"O") temp3 = temp2.replace(b"|", b"L") temp4 = temp3.decode() return(temp4) # This function uses unicodedata to attempt to convert exotic characters, such # as accents, to a byte-friendly alternative that can be used in a url. def replace_accents(text): temp1 = unicodedata.normalize('NFKD', text) temp2 = temp1.encode('ASCII', 'ignore') temp3 = temp2.decode() return(temp3) # This function removes html comment text embedded inside the lyric text. def remove_comments(text): patt = re.compile('()') out = re.findall(patt, text) if len(out) > 0: temp = text.replace(out[0], "") else: temp = text return(temp) # This function produces decimal text based on their integer code. This is # needed to decode the lyrics during webscraping (which is in coded in decimal). def decode_decimal(letters): iletters = [] for i in letters: if len(i) < 4: iletters.append(int(i)) lyrics = "" for i in iletters: lyrics = lyrics + chr(i) return(lyrics) def getlyrics(track, artist): # Main regex search pattern Pattern = re.compile('lyricbox..>(.+?)<div class=..lyricsbreak') # Attempt initial search using the raw song and artist name url = "http://lyrics.wikia.com/wiki/" + artist + ":" + track url = remove_parenth(url) # url: remove parentheses and its contents url = url.strip().replace(" ", "_") # url: replace spaces with underscores url = replace_special(url) # url: replace non-convertible special characters url = replace_accents(url) # url: remove accents on characters req = urllib.request.Request(url) # create Request object print(req.get_full_url()) # print full url passed to urlopen try: data = urllib.request.urlopen(req) # open site and pull html getdata = str(data.read()) # convert html to byte string output = re.findall(Pattern, getdata) # search suing main regex pattern # If the search fails, but there is a recommended url: if len(output) == 0: patt = re.compile('Did you mean <a href=.(.+?)..title=') output = re.findall(patt, getdata) # If search still fails, but a redirect exists: if len(output) == 0: patt = re.compile('redirectText.><li><a href=.(.+?)..title=') output = re.findall(patt, getdata) url = "http://lyrics.wikia.com" url = url + str(output[0]) # url: create new url url = url.strip().replace(" ", "_") # url: replace spaces with underscores url = replace_special(url) # url: replace non-convertible special characters url = replace_accents(url) # url: remove accents on characters req = urllib.request.Request(url) # url: create Request object print(req.get_full_url()) # print full url passed to urlopen data = urllib.request.urlopen(req) # open site and pull html getdata = str(data.read()) # convert html to byte string output = re.findall(Pattern, getdata) # search using main regex pattern text = remove_comments(output[0]) # data: remove html comments text = text.replace("<br />", " ") # data: replace breaks with spaces text = text.replace("<i>", "") # data: remove italic formatting text = text.replace("</i>", "") # data: remove italic formatting text = text.replace("&#", "") # data: remove throwaway characters letters = text.split(sep = ";") # data: split data based on semicolon letters.pop() # data: remove last element (always blank) lyrics = decode_decimal(letters) # data: convert integers to decimal characters # Write to output file return(lyrics) # This is the last-resort case where there are no reasonable matches except Exception: return('Not found') pass # This function creates a string list of all days between the start and end dates # including the start date, but excluding the end date def days_between(start, end): # Start and end must be date objects delta = end - start out = [] for i in range(0, delta.days): out.append(str(start + timedelta(i))) return(out) # This function combines streaming data from spotifycharts.com based on the # requested start and end dates (output is written to "spotifycharts.csv") def spotify_charts(start, end): headers = ['Position', 'Track Name', 'Artist', 'Streams', 'URL', 'Date'] # Write headers into output CSV file with open("spotifycharts.csv", "w", newline = '', encoding='utf-8') as f: writer = csv.DictWriter(f, fieldnames = headers) writer.writeheader() # Create string list of days between requested start and end dates datelist = days_between(start, end) # Collect CSV file for each date, and write to output file for i in datelist: # Open connection to URL url = 'https://spotifycharts.com/regional/us/daily/' + i + '/download' f = urllib.request.urlopen(url) output = pd.read_csv(f) for line in output.iterrows(): with open("spotifycharts.csv", "a", newline = '', encoding = 'utf-8') as f: writer = csv.DictWriter(f, fieldnames = headers) writer.writerow({'Position': line[1][0], 'Track Name': line[1][1], 'Artist': line[1][2], 'Streams': line[1][3], 'URL': line[1][4], 'Date': i}) f.close() # Close connection def spotify_charts_emotions(): # Read the data df = pd.read_csv('spotifycharts.csv') # Create track name and artist concatenation (to determine unique tracks) df['name'] = df[['Track Name', 'Artist']].apply(lambda x: '--'.join(x), axis = 1) # Create flag variable for unique tracks bank = [] # Create a bank of unique uid's duplicates = [] # This will become a Boolean list: 0=First instance, 1=Duplicate for i in df['name']: if i not in bank: duplicates.append(0) bank.append(i) else: duplicates.append(1) df['Duplicates'] = duplicates # Create data frame of only unique tracks uniquetracks = df[['Track Name', 'Artist', 'name']].loc[df['Duplicates'] == 0] associator = word_assocations() headers = ['Track Name', 'Artist', 'name', 'negative', 'positive', 'anger', 'fear', 'anticipation', 'surprise', 'trust', 'sadness', 'joy', 'disgust', 'wordcount', 'lyrics', 'negative_percent', 'positive_percent', 'anger_percent', 'fear_percent', 'anticipation_percent', 'surprise_percent', 'trust_percent', 'sadness_percent', 'joy_percent', 'disgust_percent'] # Write headers into output CSV file with open("spotifychartsemotions.csv", "w", newline = '', encoding='utf-8') as f: writer = csv.DictWriter(f, fieldnames = headers) writer.writeheader() for line in uniquetracks.iterrows(): temp_track = line[1][0] temp_artist = line[1][1] temp_name = line[1][2] temp_lyrics = getlyrics(temp_track, temp_artist) temp_emotions = associator.count_emotions(temp_lyrics) if temp_emotions[0] > 0: negative_percent = temp_emotions[0] / temp_emotions[10] positive_percent = temp_emotions[1] / temp_emotions[10] anger_percent = temp_emotions[2] / temp_emotions[10] fear_percent = temp_emotions[3] / temp_emotions[10] anticipation_percent= temp_emotions[4] / temp_emotions[10] surprise_percent = temp_emotions[5] / temp_emotions[10] trust_percent = temp_emotions[6] / temp_emotions[10] sadness_percent = temp_emotions[7] / temp_emotions[10] joy_percent = temp_emotions[8] / temp_emotions[10] disgust_percent = temp_emotions[9] / temp_emotions[10] with open("spotifychartsemotions.csv", "a", newline = '', encoding = 'utf-8') as f: writer = csv.DictWriter(f, fieldnames = headers) writer.writerow({'Track Name': temp_track, 'Artist': temp_artist, 'name': temp_name, 'negative': temp_emotions[0], 'positive': temp_emotions[1], 'anger': temp_emotions[2], 'fear': temp_emotions[3], 'anticipation': temp_emotions[4], 'surprise': temp_emotions[5], 'trust': temp_emotions[6], 'sadness': temp_emotions[7], 'joy': temp_emotions[8], 'disgust': temp_emotions[9], 'wordcount': temp_emotions[10], 'lyrics': temp_lyrics, 'negative_percent': negative_percent, 'positive_percent': positive_percent, 'anger_percent': anger_percent, 'fear_percent': fear_percent, 'anticipation_percent': anticipation_percent, 'surprise_percent': surprise_percent, 'trust_percent': trust_percent, 'sadness_percent': sadness_percent, 'joy_percent': joy_percent, 'disgust_percent': disgust_percent}) def articles_emotions(): # Read the data df = pd.read_csv('getdayarticles.csv') associator = word_assocations() headers = ['id', 'header', 'date', 'location', 'categories', 'description', 'socialmediascore','negative', 'positive', 'anger', 'fear', 'anticipation', 'surprise', 'trust', 'sadness', 'joy', 'disgust', 'wordcount', 'negative_percent', 'positive_percent', 'anger_percent', 'fear_percent', 'anticipation_percent', 'surprise_percent', 'trust_percent', 'sadness_percent', 'joy_percent', 'disgust_percent'] # Write headers into output CSV file with open("getdayarticlesemotions.csv", "w", newline = '', encoding='utf-8') as f: writer = csv.DictWriter(f, fieldnames = headers) writer.writeheader() for line in df.iterrows(): temp_emotions = associator.count_emotions(line[1][1] + ' ' + line[1][5]) if temp_emotions[0] > 0: negative_percent = temp_emotions[0] / temp_emotions[10] positive_percent = temp_emotions[1] / temp_emotions[10] anger_percent = temp_emotions[2] / temp_emotions[10] fear_percent = temp_emotions[3] / temp_emotions[10] anticipation_percent= temp_emotions[4] / temp_emotions[10] surprise_percent = temp_emotions[5] / temp_emotions[10] trust_percent = temp_emotions[6] / temp_emotions[10] sadness_percent = temp_emotions[7] / temp_emotions[10] joy_percent = temp_emotions[8] / temp_emotions[10] disgust_percent = temp_emotions[9] / temp_emotions[10] with open("getdayarticlesemotions.csv", "a", newline = '', encoding = 'utf-8') as f: writer = csv.DictWriter(f, fieldnames = headers) writer.writerow({'id': line[1][0], 'header': line[1][1], 'date': line[1][2], 'location': line[1][3], 'categories': line[1][4], 'description': line[1][5], 'socialmediascore': line[1][6], 'negative': temp_emotions[0], 'positive': temp_emotions[1], 'anger': temp_emotions[2], 'fear': temp_emotions[3], 'anticipation': temp_emotions[4], 'surprise': temp_emotions[5], 'trust': temp_emotions[6], 'sadness': temp_emotions[7], 'joy': temp_emotions[8], 'disgust': temp_emotions[9], 'wordcount': temp_emotions[10], 'negative_percent': negative_percent, 'positive_percent': positive_percent, 'anger_percent': anger_percent, 'fear_percent': fear_percent, 'anticipation_percent': anticipation_percent, 'surprise_percent': surprise_percent, 'trust_percent': trust_percent, 'sadness_percent': sadness_percent, 'joy_percent': joy_percent, 'disgust_percent': disgust_percent}) def articles_emotions_perday(): # Read the data df = pd.read_csv('getdayarticlesemotions.csv') headers = ['date', 'anger_percent_weighted', 'sadness_percent_weighted', 'joy_percent_weighted'] # Convert string to integer df['socialmediascore'] = [int(x.replace(',', '')) for x in list((df['socialmediascore']))] df['anger_percent_weighted'] = np.multiply(list(df['socialmediascore']), list(df['anger_percent'])) df['sadness_percent_weighted'] = np.multiply(list(df['socialmediascore']), list(df['sadness_percent'])) df['joy_percent_weighted'] = np.multiply(list(df['socialmediascore']), list(df['joy_percent'])) sums = df['socialmediascore'].groupby(df['date']).sum() anger_percent_weighted = df['anger_percent_weighted'].groupby(df['date']).sum() sadness_percent_weighted = df['sadness_percent_weighted'].groupby(df['date']).sum() joy_percent_weighted = df['joy_percent_weighted'].groupby(df['date']).sum() out = pd.concat([sums, anger_percent_weighted, sadness_percent_weighted, joy_percent_weighted], axis = 1) out['anger_percent_weighted'] = np.divide(list(out['anger_percent_weighted']), list(out['socialmediascore'])) out['sadness_percent_weighted'] = np.divide(list(out['sadness_percent_weighted']), list(out['socialmediascore'])) out['joy_percent_weighted'] = np.divide(list(out['joy_percent_weighted']), list(out['socialmediascore'])) # Write headers into output CSV file with open("getdayarticlesemotionsperday.csv", "w", newline = '', encoding='utf-8') as f: writer = csv.DictWriter(f, fieldnames = headers) writer.writeheader() for line in out.iterrows(): with open("getdayarticlesemotionsperday.csv", "a", newline = '', encoding = 'utf-8') as f: writer = csv.DictWriter(f, fieldnames = headers) writer.writerow({'date': line[0][5:], 'anger_percent_weighted': line[1][1], 'sadness_percent_weighted': line[1][2], 'joy_percent_weighted': line[1][3]}) #### Generates K-means centroids (as a CSV file) and also returns the labels def kmeans_centroids(df, var, k, name): kmeans = KMeans(n_clusters = k) kmeans.fit(df[var]) labels = kmeans.labels_ # Save labels for use later centroids = kmeans.cluster_centers_ kmeansout = pd.DataFrame(centroids, columns = var) # Create dataframe of centroids kmeanscounts = pd.Series(labels, name = "Counts").value_counts() # Create number of points in each cluster kmeansout = pd.concat([kmeansout, kmeanscounts], axis = 1) kmeansout.to_csv(name, sep=',', index = True, header = True) return(labels) # Return labels to be used later #### Generates silhouette scores for K-means using a list of the number of clusters def kmeans_silhouette(df, var, k, name): with open(name, "w", newline = None, encoding = 'utf-8') as file: file.write("\n\nThe following are silhouette scores for K-means with varying number of K clusters: \n\n") with open(name, "a", newline = None, encoding = 'utf-8') as file: for c in k: kmeans = KMeans(n_clusters = c) kmeans.fit(df[var]) labels = kmeans.labels_ file.write("For K=" + str(c) + ", the silhouette score is: " + str(silhouette_score(df[var], labels)) + "\n") #### Generates Ward group means (as a CSV file) and also returns the labels def ward_groupmeans(df, var, k, name): ward = AgglomerativeClustering(n_clusters = k, linkage = 'ward') ward.fit(df[var]) labels = ward.labels_ # Save labels for use later wardout = df[var].groupby(labels).mean() # Create grouped means wardcounts = pd.Series(labels, name = "Counts").value_counts() # Create number of points in each cluster wardout = pd.concat([wardout, wardcounts], axis = 1) wardout.to_csv(name, sep=',', index = True, header = True) # Save to file return(labels) # Return labels to be used later #### Generates silhouette scores for Ward using a list of the number of clusters def ward_silhouette(df, var, k, name): with open(name, "w", newline = None, encoding = 'utf-8') as file: file.write("\n\nThe following are silhouette scores for Ward's method with varying number of K clusters: \n\n") with open(name, "a", newline = None, encoding = 'utf-8') as file: for c in k: ward = AgglomerativeClustering(n_clusters = c, linkage = 'ward') ward.fit(df[var]) labels = ward.labels_ file.write("For K=" + str(c) + ", the silhouette score is: " + str(silhouette_score(df[var], labels)) + "\n") #### Generates 3D scatterplots def scatterplotclusters(df, var, labels, title, savename): fig = plt.figure(figsize = (12, 12)) ax = fig.add_subplot(111, projection = '3d') colors = cm.rainbow(np.linspace(0, 1, len(set(labels)))) # Use automatic color selection based on cluster count for name, group in df[var].groupby(labels): ax.scatter(group[var[0]], group[var[1]], group[var[2]], alpha = 0.8, c = colors[name], label = name) ax.set_xlabel(var[0]) ax.set_ylabel(var[1]) ax.set_zlabel(var[2]) plt.title(title) ax.legend() plt.savefig(savename) plt.clf() plt.close() #### Pulls news articles from EventRegisty.org for each day within the specified range def getdayarticles(start, end, directory, login_email, login_password): # Create CSV file with appropriate headers with open("getdayarticles.csv", "w", newline = '', encoding='utf-8') as f: fieldnames = ['id', 'header', 'date', 'location', 'categories', 'description', 'socialmediascore'] writer = csv.DictWriter(f, fieldnames = fieldnames) writer.writeheader() # Open new browser and login to eventregistry.org browser = webdriver.Firefox(firefox_binary = directory) browser.get("http://eventregistry.org/login?redirectUrl=%2FsearchEvents") time.sleep(5) username = browser.find_element_by_id("email") password = browser.find_element_by_id("pass") username.send_keys(login_email) # Enter email password.send_keys(login_password) # Enter password browser.find_element_by_xpath('//*[@id="form-id"]/button').click() # Click submit time.sleep(5) for day in days_between(start, end): # Open new tab browser.find_element_by_tag_name('body').send_keys(Keys.CONTROL + 't') # Create URL based on day url = "http://eventregistry.org/searchEvents?query=%7B%22" + \ "locations%22:%5B%7B%22label%22:%22United%20States" + \ "%22,%22uri%22:%22http:%2F%2Fen.wikipedia.org%2Fwiki%2F" + \ "United_States%22,%22negate%22:false%7D%5D,%22dateStart%22:%22" + \ day + "%22,%22dateEnd%22:%22" + \ day + "%22,%22lang%22:%22eng%22,%22minArticles%22:50,%22" + \ "preferredLang%22:%22eng%22%7D&tab=events" browser.get(url) # Open URL time.sleep(50) # Wait 20 seconds for page to load # Click "sort events by social media hotness" to get most popular events browser.find_element_by_xpath('//*[@id="tab-events"]/div/div/div[3]/div[2]/div/div[2]/button[4]').click() time.sleep(5) # Wait 5 seconds for page to reload out = browser.page_source.encode("utf-8") # Save source code # Save social media score for each news event temp1 = BeautifulSoup(out, "lxml").findAll("span", {'class': "score ng-binding"}) socialmedia = [] for i in temp1: socialmedia.append(i.contents[0]) # Save header for each news event temp2 = BeautifulSoup(out, "lxml").findAll("h4", {'class': "media-heading"}) articleheader = [] for i in temp2: articleheader.append(i.contents[0].contents[0]) # Save time and date and location for each news event temp3 = BeautifulSoup(out, "lxml").findAll("span", {'class': "info-val ng-binding"}) timedate = [] for i in temp3: timedate.append(i.contents[0]) dates = timedate[::2] location = timedate[1::2] # Save categories for each news event temp4 = BeautifulSoup(out, "lxml").findAll("div", {'class': "categories"}) categories = [] for i in temp4: k = i.findAll("span", {'class': "ng-binding"}) t = [] for j in k: t.append(j.contents[0].replace('→',', ')) categories.append(t) # Save description of each news event temp5 = BeautifulSoup(out, "lxml").findAll("div", {'class': "lighter smaller ng-binding"}) description = [] for i in temp5: description.append(i.contents[0]) # Save news event ID temp6 = BeautifulSoup(out, "lxml").find_all("a", {'target': "_blank", 'class': "ng-binding"}, href=True) eventids = [] for i in temp6: eventids.append(i['href']) eventids = eventids[1:] # Remove first element (contains no information) ids = [] for i in eventids: ids.append(re.findall('/event/(.......).lang=eng', i)[0]) articles = pd.DataFrame([ids, articleheader, dates, location, categories, description, socialmedia]) for j in range(0, articles.shape[1]): # Write to output file with open("getdayarticles.csv", "a", newline = '', encoding='utf-8') as file: writer = csv.DictWriter(file, fieldnames = fieldnames) writer.writerow({'id': articles[j][0], 'header': articles[j][1], 'date': articles[j][2], 'location': articles[j][3], 'categories': articles[j][4], 'description': articles[j][5], 'socialmediascore': articles[j][6]}) browser.quit() #### Applies the longitudinal multi-layer perceptron model with 100 hidden layers, number of lags of the #### predictor value and returns the predicted values. It also plots the results as 'name.png'. def nn_tester(df, predictor, predicted, lag, name): length = lag start = 0 temp = df[[predictor, predicted]] iterations = len(temp[predicted]) X = pd.DataFrame(np.zeros(length)).T y = [0] for i in range(length, iterations): temp_y = temp[predicted][i] temp_X = pd.DataFrame(temp[predictor][start:(i)]).T.reset_index(drop = True) temp_X.columns = [x for x in range(0, lag)] y.extend([temp_y]) X = pd.concat([X, temp_X]) start = start + 1 X.reset_index(inplace = True, drop = True) X.drop(X.index[[0]], inplace = True) X.reset_index(inplace = True, drop = True) y = y[1:] X_train = X[0:100] # Training set X_test = X[100:] # Test set y_train = y[0:100] # Training set y_test = y[100:] # Test set scaler = StandardScaler() scaler.fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) mlp = MLPRegressor(activation = 'logistic', solver = 'lbfgs', max_iter = 10000, tol = 1e-5, alpha = .01, hidden_layer_sizes = (100,), random_state = 1) mlp.fit(X_train, y_train) print(mlp.score(X_test, df[predicted][-len(y_test):])) plt.figure(figsize = (20, 12)) plot_pred = plt.plot(df['date'][lag:], mlp.predict(scaler.transform(X))) plot_ytest = plt.plot(df['date'][lag:], y) plt.setp(plot_pred, color = 'black', linestyle = '--', linewidth = 1.0) plt.setp(plot_ytest, color = 'black', linewidth = 1.0) plt.figtext(.8, .85, "R-Squared = " + str(mlp.score(X_test, df[predicted][-len(y_test):]).round(3)), fontsize = 12) plt.axvline(df['date'][len(df['date']) - len(y_test) - 1], color = 'r', linewidth = 1.0) locs, labels = plt.xticks() plt.setp(labels, rotation = 90) plt.savefig(name) plt.clf() plt.close() return(mlp.predict(scaler.transform(X))) def main(): ################################################################################## # The following lines of code can be uncommented and run for test purposes, but # we recommend running them with a smaller date window. It should also be noted # EventRegistry.org (the site where the news article data were pulled) is currently # undergoing restructuring due to Google's move to fund their project -- so it is # possible that the results will incomplete if run at the current time. # # IMPORTANT: The getdayarticles() function requires the installation of the # selenium Python package (through pip), the geckodriver application (which is # included in the with this code for Windows), and a valid installation of Firefox # along with the directory to its application (which needs to be placed in the # 'directory' argument). ################################################################################## # # start_date = date(2016, 6, 1) # end_date = date(2016, 11, 21) # spotify_charts(start_date, end_date) # spotify_charts_emotions() # getdayarticles(start = start_date, # end = end_date, # directory = r'C:\Program Files (x86)\Mozilla Firefox\firefox.exe', # login_email = "jmc511@georgetown.edu", # login_password = "password123") # articles_emotions() # articles_emotions_perday() # ################################################################################## ##### Clustering for Songs ##### # Import data df = pd.read_csv('spotifychartsemotions.csv') # Silhouette scores kmeans_silhouette(df, var = ['anger_percent', 'sadness_percent', 'joy_percent'], k = [2,3,4,5,6,7,8,9,10], name = "kmeans_silhouettescores_songs.txt") ward_silhouette(df, var = ['anger_percent', 'sadness_percent', 'joy_percent'], k = [2,3,4,5,6,7,8,9,10], name = "ward_silhouettescores_songs.txt") # Get labels for K-means and Ward labels1 = kmeans_centroids(df, var = ['anger_percent', 'sadness_percent', 'joy_percent'], k = 3, name = 'kmeans_centroids_songs.csv') labels2 = ward_groupmeans(df, var = ['anger_percent', 'sadness_percent', 'joy_percent'], k = 3, name = 'ward_groupedmeans_songs.csv') # Plot 3D scatterplot scatterplotclusters(df = df, var = ['anger_percent', 'sadness_percent', 'joy_percent'], labels = labels1, title = 'K-Means Scatterplot by Cluster', savename = "kmeans_3Dscatterplot_songs") scatterplotclusters(df = df, var = ['anger_percent', 'sadness_percent', 'joy_percent'], labels = labels2, title = 'Ward Scatterplot by Cluster', savename = "ward_3Dscatterplot_songs") ##### Group Song longitudinal by Clusters ##### # Import data df2 = pd.read_csv('spotifycharts.csv') df2['name'] = df2[['Track Name', 'Artist']].apply(lambda x: '--'.join(x), axis = 1) # Use labels and group longitudinal data by clusters positive_tracks = df['name'].loc[labels1 == 1] negative_tracks = df['name'].loc[labels1 == 0] null_tracks = df['name'].loc[labels1 == 2] positive_tracks_labels = df2.loc[df2['name'].isin(positive_tracks)] negative_tracks_labels = df2.loc[df2['name'].isin(negative_tracks)] null_tracks_labels = df2.loc[df2['name'].isin(null_tracks)] ##### Get average stream counts for each of emotion class ##### positive_grouped = positive_tracks_labels.groupby('Date').mean() negative_grouped = negative_tracks_labels.groupby('Date').mean() null_grouped = null_tracks_labels.groupby('Date').mean() emotion_grouped = pd.concat([positive_grouped['Streams'], negative_grouped['Streams'], null_grouped['Streams']], axis = 1, keys = ['positive_grouped', 'negative_grouped', 'null_grouped']) emotion_grouped['date'] = emotion_grouped.index.values emotion_grouped = emotion_grouped.reset_index(drop = True) # Get emotion percentages article_emotions = pd.read_csv('getdayarticlesemotionsperday.csv') article_emotions['date'] = [datetime.datetime.strptime(x, '%B %d, %Y') for x in article_emotions['date']] article_emotions = article_emotions.sort_values('date') article_emotions['date'] = [str(x)[:10] for x in article_emotions['date']] article_emotions = article_emotions.reset_index(drop = True) # Merge data for plotting plotdata = pd.merge(emotion_grouped, article_emotions[['anger_percent_weighted', 'sadness_percent_weighted', 'joy_percent_weighted', 'date']], on = 'date', how = 'left') plotdata.fillna(0, inplace = True) # Some article percentages are NaN, so convert these to zero plotdata['date'] = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in plotdata['date']] plotdata.to_csv('emotion_analysis.csv', sep=',', index = True, header = True) # Write to CSV ##### Line Plots (matplotlib images) ##### # Import data plotdata = pd.read_csv('emotion_analysis.csv') plotdata['date'] = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in plotdata['date']] # Plot of news article emotion percentages plt.figure(figsize = (10, 8)) anger_article = plt.plot(plotdata['date'], plotdata['anger_percent_weighted']) sadness_article = plt.plot(plotdata['date'], plotdata['sadness_percent_weighted']) joy_article = plt.plot(plotdata['date'], plotdata['joy_percent_weighted']) x1, x2, y1, y2 = plt.axis() plt.axis((x1, x2, 0, .6)) plt.setp(anger_article, color = 'r', linewidth = 1.0) plt.setp(sadness_article, color = 'b', linewidth = 1.0) plt.setp(joy_article, color = 'g', linewidth = 1.0) plt.tick_params(axis = 'y', which = 'major', labelsize = 10) plt.tick_params(axis = 'y', which = 'minor', labelsize = 10) plt.tick_params(axis = 'x', which = 'major', labelsize = 9) plt.tick_params(axis = 'x', which = 'minor', labelsize = 9) locs, labels = plt.xticks() plt.setp(labels, rotation = 90) plt.legend() plt.savefig('emotion_articles') plt.clf() plt.close() # Plot of average song streaming counts by emotion plt.figure(figsize = (10, 8)) positive_streamed = plt.plot(plotdata['date'], plotdata['positive_grouped']) negative_streamed = plt.plot(plotdata['date'], plotdata['negative_grouped']) null_streamed = plt.plot(plotdata['date'], plotdata['null_grouped']) x1, x2, y1, y2 = plt.axis() plt.axis((x1, x2, 0, 500000)) plt.setp(positive_streamed, color = 'g', linewidth = 1.0) plt.setp(negative_streamed, color = 'r', linewidth = 1.0) plt.setp(null_streamed, color = 'black', linewidth = 1.0) plt.tick_params(axis = 'y', which = 'major', labelsize = 10) plt.tick_params(axis = 'y', which = 'minor', labelsize = 10) plt.tick_params(axis = 'x', which = 'major', labelsize = 9) plt.tick_params(axis = 'x', which = 'minor', labelsize = 9) locs, labels = plt.xticks() plt.setp(labels, rotation = 90) plt.legend() plt.savefig('emotion_streamed') plt.clf() plt.close() ##### Hypothesis Tests ##### # Perform Granger Causality Tests (use sys.stdout to capture printed outputs) plotdata.set_index(keys = plotdata['date'], inplace = True) # Set date as index # Article percentages are all stationary (ADF should be significant) print(adfuller(plotdata['anger_percent_weighted'], autolag = 'bic', regression = 'ct', maxlag = 10)) print(adfuller(plotdata['sadness_percent_weighted'], autolag = 'bic', regression = 'ct', maxlag = 10)) print(adfuller(plotdata['joy_percent_weighted'], autolag = 'bic', regression = 'ct', maxlag = 10)) # Make positive_grouped stationary via moving average moving_avg = pd.rolling_mean(plotdata['positive_grouped'], 6) plotdata['positive_grouped_ma'] = plotdata['positive_grouped'] - moving_avg print(adfuller(plotdata['positive_grouped_ma'].dropna(), autolag = 'bic', regression = 'ct', maxlag = 10)) # Make positive_grouped stationary via moving average moving_avg = pd.rolling_mean(plotdata['negative_grouped'], 6) plotdata['negative_grouped_ma'] = plotdata['negative_grouped'] - moving_avg print(adfuller(plotdata['negative_grouped_ma'].dropna(), autolag = 'bic', regression = 'ct', maxlag = 10)) # Make null_grouped stationary via moving average moving_avg = pd.rolling_mean(plotdata['null_grouped'], 6) plotdata['null_grouped_ma'] = plotdata['null_grouped'] - moving_avg print(adfuller(plotdata['null_grouped_ma'].dropna(), autolag = 'bic', regression = 'ct', maxlag = 10)) # Perform Granger tests using ma variables (and save t0 grangertests.txt) former, sys.stdout = sys.stdout, open('grangertests.txt', 'w') print('\n\nOutput for Granger: anger_percent_weighted Granger causes positive_grouped_ma\n') grangercausalitytests(plotdata[['positive_grouped_ma', 'anger_percent_weighted']].dropna(), maxlag = 7) print('\n\nOutput for Granger: sadness_percent_weighted Granger causes positive_grouped_ma\n') grangercausalitytests(plotdata[['positive_grouped_ma', 'sadness_percent_weighted']].dropna(), maxlag = 7) print('\n\nOutput for Granger: joy_percent_weighted Granger causes positive_grouped_ma\n') grangercausalitytests(plotdata[['positive_grouped_ma', 'joy_percent_weighted']].dropna(), maxlag = 7) print('\n\nOutput for Granger: anger_percent_weighted Granger causes negative_grouped_ma\n') grangercausalitytests(plotdata[['negative_grouped_ma', 'anger_percent_weighted']].dropna(), maxlag = 7) print('\n\nOutput for Granger: sadness_percent_weighted Granger causes negative_grouped_ma\n') grangercausalitytests(plotdata[['negative_grouped_ma', 'sadness_percent_weighted']].dropna(), maxlag = 7) print('\n\nOutput for Granger: joy_percent_weighted Granger causes negative_grouped_ma\n') grangercausalitytests(plotdata[['negative_grouped_ma', 'joy_percent_weighted']].dropna(), maxlag = 7) print('\n\nOutput for Granger: anger_percent_weighted Granger causes null_grouped_ma\n') grangercausalitytests(plotdata[['null_grouped_ma', 'anger_percent_weighted']].dropna(), maxlag = 7) print('\n\nOutput for Granger: sadness_percent_weighted Granger causes null_grouped_ma\n') grangercausalitytests(plotdata[['null_grouped_ma', 'sadness_percent_weighted']].dropna(), maxlag = 7) print('\n\nOutput for Granger: joy_percent_weighted Granger causes null_grouped_ma\n') grangercausalitytests(plotdata[['null_grouped_ma', 'joy_percent_weighted']].dropna(), maxlag = 7) results, sys.stdout = sys.stdout, former results.close() ##### Prediction using Neural Networks ##### # Run multi-layer perceptron with 100 hidden units, alpha = .01, lbfgs optimizer, and # logistic activation function. Functions return predicted values. lag = 7 nn_positive_anger = nn_tester(df = plotdata, predictor = 'anger_percent_weighted', predicted = 'positive_grouped', lag = lag, name = 'nn_positive_anger') nn_positive_sadness = nn_tester(df = plotdata, predictor = 'sadness_percent_weighted', predicted = 'positive_grouped', lag = lag, name = 'nn_positive_sadness') nn_positive_joy = nn_tester(df = plotdata, predictor = 'joy_percent_weighted', predicted = 'positive_grouped', lag = lag, name = 'nn_positive_joy') nn_negative_anger = nn_tester(df = plotdata, predictor = 'anger_percent_weighted', predicted = 'negative_grouped', lag = lag, name = 'nn_negative_anger') nn_negative_sadness = nn_tester(df = plotdata, predictor = 'sadness_percent_weighted', predicted = 'negative_grouped', lag = lag, name = 'nn_negative_sadness') nn_negative_joy = nn_tester(df = plotdata, predictor = 'joy_percent_weighted', predicted = 'negative_grouped', lag = lag, name = 'nn_negative_joy') nn_null_anger = nn_tester(df = plotdata, predictor = 'anger_percent_weighted', predicted = 'null_grouped', lag = lag, name = 'nn_null_anger') nn_null_sadness = nn_tester(df = plotdata, predictor = 'sadness_percent_weighted', predicted = 'null_grouped', lag = lag, name = 'nn_null_sadness') nn_null_joy = nn_tester(df = plotdata, predictor = 'joy_percent_weighted', predicted = 'null_grouped', lag = lag, name = 'nn_null_joy') ##### Interactive plot of findings (bokeh) ##### # x-axis x = plotdata['date'][lag:] # Different y-axes y1_1 = plotdata['positive_grouped'][lag:] y1_2 = nn_positive_anger y2_1 = plotdata['positive_grouped'][lag:] y2_2 = nn_positive_sadness y3_1 = plotdata['positive_grouped'][lag:] y3_2 = nn_positive_joy y4_1 = plotdata['negative_grouped'][lag:] y4_2 = nn_negative_anger y5_1 = plotdata['negative_grouped'][lag:] y5_2 = nn_negative_sadness y6_1 = plotdata['negative_grouped'][lag:] y6_2 = nn_negative_joy y7_1 = plotdata['null_grouped'][lag:] y7_2 = nn_null_anger y8_1 = plotdata['null_grouped'][lag:] y8_2 = nn_null_sadness y9_1 = plotdata['null_grouped'][lag:] y9_2 = nn_null_joy # Plot predictions for Average Positive Stream output_file("plots1.html", title = "Prediction of Song Playcounts") s1 = figure(width = 900, plot_height = 300, title = "Positive Streams by Article Anger", x_axis_type = "datetime", tools="pan,wheel_zoom,box_zoom,reset") s1.line(x, y1_1, color = 'green', legend = "Average Positive Stream Count", line_width = 2, line_alpha = 0.7) s1.line(x, y1_2, color = 'black', line_alpha = 0.7, legend = "Predicted by Percent Anger", line_width = 2, line_dash = 'dotted') s1.left[0].formatter.use_scientific = False vline1 = Span(location = plotdata['date'][lag + 100 - 1].timestamp()*1000, dimension = 'height', line_color = 'red', line_width = 1) s1.renderers.extend([vline1]) s2 = figure(width = 900, plot_height = 300, title = "Positive Streams by Article Sadness", x_axis_type = "datetime", tools="pan,wheel_zoom,box_zoom,reset") s2.line(x, y2_1, color = 'green', legend = "Average Positive Stream Count", line_width = 2, line_alpha = 0.7) s2.line(x, y2_2, color = 'black', line_alpha = 0.7, legend = "Predicted by Percent Sadness", line_width = 2, line_dash = 'dotted') s2.left[0].formatter.use_scientific = False vline2 = Span(location = plotdata['date'][lag + 100 - 1].timestamp()*1000, dimension = 'height', line_color = 'red', line_width = 1) s2.renderers.extend([vline2]) s3 = figure(width = 900, plot_height = 300, title = "Positive Streams by Article Joy", x_axis_type = "datetime", tools="pan,wheel_zoom,box_zoom,reset") s3.line(x, y3_1, color = 'green', legend = "Average Positive Stream Count", line_width = 2, line_alpha = 0.7) s3.line(x, y3_2, color = 'black', line_alpha = 0.7, legend = "Predicted by Percent Joy", line_width = 2, line_dash = 'dotted') s3.left[0].formatter.use_scientific = False vline3 = Span(location = plotdata['date'][lag + 100 - 1].timestamp()*1000, dimension = 'height', line_color = 'red', line_width = 1) s3.renderers.extend([vline3]) p = vplot(s1, s2, s3) show(p) # Plot predictions for Average Negative Stream output_file("plots2.html", title = "Prediction of Song Playcounts") s4 = figure(width = 900, plot_height = 300, title = "Negative Streams by Article Anger", x_axis_type = "datetime", tools="pan,wheel_zoom,box_zoom,reset") s4.line(x, y4_1, color = 'red', legend = "Average Negative Stream Count", line_width = 2, line_alpha = 0.7) s4.line(x, y4_2, color = 'black', line_alpha = 0.7, legend = "Predicted by Percent Anger", line_width = 2, line_dash = 'dotted') s4.left[0].formatter.use_scientific = False vline4 = Span(location = plotdata['date'][lag + 100 - 1].timestamp()*1000, dimension = 'height', line_color = 'red', line_width = 1) s4.renderers.extend([vline4]) s5 = figure(width = 900, plot_height = 300, title = "Negative Streams by Article Sadness", x_axis_type = "datetime", tools="pan,wheel_zoom,box_zoom,reset") s5.line(x, y5_1, color = 'red', legend = "Average Negative Stream Count", line_width = 2, line_alpha = 0.7) s5.line(x, y5_2, color = 'black', line_alpha = 0.7, legend = "Predicted by Percent Sadness", line_width = 2, line_dash = 'dotted') s5.left[0].formatter.use_scientific = False vline5 = Span(location = plotdata['date'][lag + 100 - 1].timestamp()*1000, dimension = 'height', line_color = 'red', line_width = 1) s5.renderers.extend([vline5]) s6 = figure(width = 900, plot_height = 300, title = "Negative Streams by Article Joy", x_axis_type = "datetime", tools="pan,wheel_zoom,box_zoom,reset") s6.line(x, y6_1, color = 'red', legend = "Average Negative Stream Count", line_width = 2, line_alpha = 0.7) s6.line(x, y6_2, color = 'black', line_alpha = 0.7, legend = "Predicted by Percent Joy", line_width = 2, line_dash = 'dotted') s6.left[0].formatter.use_scientific = False vline6 = Span(location = plotdata['date'][lag + 100 - 1].timestamp()*1000, dimension = 'height', line_color = 'red', line_width = 1) s6.renderers.extend([vline6]) p = vplot(s4, s5, s6) show(p) # Plot predictions for Average Null Stream output_file("plots3.html", title = "Prediction of Song Playcounts") s7 = figure(width = 900, plot_height = 300, title = "Null Streams by Article Anger", x_axis_type = "datetime", tools="pan,wheel_zoom,box_zoom,reset") s7.line(x, y7_1, color = 'black', legend = "Average Null Stream Count", line_width = 2, line_alpha = 0.7) s7.line(x, y7_2, color = 'black', line_alpha = 0.7, legend = "Predicted by Percent Anger", line_width = 2, line_dash = 'dotted') s7.left[0].formatter.use_scientific = False vline7 = Span(location = plotdata['date'][lag + 100 - 1].timestamp()*1000, dimension = 'height', line_color = 'red', line_width = 1) s7.renderers.extend([vline7]) s8 = figure(width = 900, plot_height = 300, title = "Null Streams by Article Sadness", x_axis_type = "datetime", tools="pan,wheel_zoom,box_zoom,reset") s8.line(x, y8_1, color = 'black', legend = "Average Null Stream Count", line_width = 2, line_alpha = 0.7) s8.line(x, y8_2, color = 'black', line_alpha = 0.7, legend = "Predicted by Percent Sadness", line_width = 2, line_dash = 'dotted') s8.left[0].formatter.use_scientific = False vline8 = Span(location = plotdata['date'][lag + 100 - 1].timestamp()*1000, dimension = 'height', line_color = 'red', line_width = 1) s8.renderers.extend([vline8]) s9 = figure(width = 900, plot_height = 300, title = "Null Streams by Article Joy", x_axis_type = "datetime", tools="pan,wheel_zoom,box_zoom,reset") s9.line(x, y9_1, color = 'black', legend = "Average Null Stream Count", line_width = 2, line_alpha = 0.7) s9.line(x, y9_2, color = 'black', line_alpha = 0.7, legend = "Predicted by Percent Joy", line_width = 2, line_dash = 'dotted') s9.left[0].formatter.use_scientific = False vline9 = Span(location = plotdata['date'][lag + 100 - 1].timestamp()*1000, dimension = 'height', line_color = 'red', line_width = 1) s9.renderers.extend([vline9]) p = vplot(s7, s8, s9) show(p) if __name__ == "__main__": main()

UTF-8

Python

false

54,521

py

37

projectpart3.py

3

0.564705

0.547792

0.000037

1,098

47.653005

121

glennneiger/magicmirror

6,992,206,759,743

140faa740f9a491c35b78e915857b50f23331f36

82acb938feb7482b07d72114342e9f29663611f2

/python scripts/motion_sensor_updated.py

7175556fbfaa63a4b6a01d298b71242167eff6e3

[]

no_license

https://github.com/glennneiger/magicmirror

80c677fc467d6b1946fd390b9b20a715e4396428

606f7801d57c613c23696a988d0d435f7e46cda4

refs/heads/master

2020-04-19T06:00:14.523986

2018-10-08T08:01:59

null

0

null

#!/usr/bin/python # -*- coding: utf-8 -*- import RPi.GPIO as GPIO import time import os import glob import serial import subprocess from subprocess import Popen, PIPE, STDOUT GPIO.setmode(GPIO.BOARD) LED_OUT = 10 MOTION_IN = 8 device_file = "" def initialize_all(): #ser = serial.Serial('/dev/ttyACM0', 115200) GPIO.setup(LED_OUT, GPIO.OUT) GPIO.setup(MOTION_IN, GPIO.IN) os.system('modprobe w1-gpio') os.system('modprobe w1-therm') base_dir = '/sys/bus/w1/devices/' device_folder = glob.glob(base_dir + '28-01be4007010c')[0] device_file = device_folder + '/w1_slave' print ("Sensor initializing............") time.sleep(1); print ('Sensor Ready....') return 0 def motion_detect(): while True: if GPIO.input(MOTION_IN) == True: print ('Motion Detected!!!!\n') GPIO.output(LED_OUT, True) command = ('vcgencmd display_power 1') subprocess.call(command, shell=True) else: command = 'vcgencmd display_power 0' subprocess.call(command, shell=True) print ('No one Here\n') GPIO.output(LED_OUT, False) return 0 def temperature_read(): f = open(device_file, 'r') lines = f.readlines() print(lines) f.close() return lines def read_temp(): lines = temperature_read() while lines[0].strip()[-3:] != 'YES': time.sleep(0.2) lines = temperature_read() equals_pos = lines[1].find('t=') if equals_pos != -1: temp_string = lines[1][equals_pos+2:] temp_c = float(temp_string) / 1000.0 temp_f = temp_c * 9.0 / 5.0 + 32.0 return temp_c, temp_f def write_temp_to_serial(): print(read_temp()) return 0 #initializes all sensors and variables initialize_all() #reads to check if motion has been detected from the sensor motion_detect() #reads temperature values from the temp* sensor temperature_read() #writes the read temperature value to the serial port #which will be then read from NodeJs write_temp_to_serial()

UTF-8

Python

false

2,121

py

12

motion_sensor_updated.py

10

0.603017

0.578029

0

137

14.481752

62

nikolajjakubets/bypass_utility

12,128,987,650,458

e89e415f95a34b6f11edc1fcf3a410e3622d7f43

b3e063f035f97f90d1305b148ea16fa37d320db2

/src/exploit.py

c8b51f44723af9520de11460aad0c2eba36e3c20

[ "MIT" ]

permissive

https://github.com/nikolajjakubets/bypass_utility

31f9b36f5ee18a16fb179857424ed09356ad6ed5

08f860671813951934900031a358598bc032f162

refs/heads/master

2023-02-21T11:02:02.976401

2021-01-23T17:18:13

2021-01-23T17:28:31

null

0

null

from src.common import to_bytes import usb def exploit(device, watchdog_address, var_0, var_1, payload): addr = watchdog_address + 0x50 device.write32(addr, [0xA1000]) # 0x00100A00 if var_0: readl = var_0 + 0x4 device.read32(addr - var_0, readl // 4) else: cnt = 15 for i in range(cnt): device.read32(addr - (cnt - i) * 4, cnt - i + 1) device.echo(0xE0) payload = payload.read() while len(payload) % 4 != 0: payload += to_bytes(0) device.echo(len(payload), 4) # clear 2 bytes device.read(2) if len(payload) >= 0xA00: raise RuntimeError("payload too large") device.write(payload) # clear 4 bytes device.read(4) udev = usb.core.find(idVendor=0x0E8D, idProduct=0x3) try: # noinspection PyProtectedMember udev._ctx.managed_claim_interface = lambda *args, **kwargs: None except AttributeError as e: raise RuntimeError("libusb is not installed for port {}".format(device.dev.port)) from e try: udev.ctrl_transfer(0xA1, 0, 0, var_1, 0) except usb.core.USBError as e: print(e) pattern = device.read(4) if pattern != to_bytes(0xA1A2A3A4, 4): raise RuntimeError("received {} instead of expected pattern".format(pattern.hex()))

UTF-8

Python

false

1,325

py

2

exploit.py

2

0.610566

0.561509

0

52

24.480769

96

azulnaturalbz/chillbout_django

18,511,309,077,209

e734bbaed0d71052d95718addd07f376260b2ca4

675b512308cea8b96c42a1ad27d373355fcf7ebb

/establishments/migrations/0003_establishment_creation_date.py

faa7388c6707986212db5986d05511df4741c85c

[]

no_license

https://github.com/azulnaturalbz/chillbout_django

2e7d8a959fb9d44801995a7dc8bbd1e21e91d45e

0948c291f146368bf6ab0f74e1b9383d6e2655f8

refs/heads/master

2018-09-10T23:48:35.423582

2018-09-04T03:15:33

136,164,108

0

null

# Generated by Django 2.0.4 on 2018-05-02 12:39 import datetime from django.db import migrations, models from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('establishments', '0002_auto_20180426_1458'), ] operations = [ migrations.AddField( model_name='establishment', name='creation_date', field=models.DateField(default=datetime.datetime(2018, 5, 2, 12, 39, 55, 10623, tzinfo=utc)), ), ]

UTF-8

Python

false

514

py

62

0003_establishment_creation_date.py

43

0.638132

0.544747

0

20

24.7

105

encse/adventofcode-2015-python

8,581,344,699,049

1b9496792bf7e45582e9ac76e97dba2a821e9eb2

47e082011ce03c55f3e92ef172233ea7bcc7c9a8

/day20/solution.py

81ed5c26f2a46e68af4c87f97e8b862b1608d099

[]

no_license

https://github.com/encse/adventofcode-2015-python

1138ade1e46ee5b37762054e7fa67d8c27e49e2b

3e222b682430ae5570626ea9acf2ffe40f1e6264

refs/heads/master

2021-06-09T05:37:04.129547

2016-12-28T13:33:39

47,879,224

1

0

null

dsum1 = [1] * 3600000 dsum2 = [1] * 3600000 i=2 res1 = None res2 = None while True: d = i while d < len(dsum1): dsum1[d] += i if d < i*50: dsum2[d] += i d += i if not res1 and dsum1[i]*10 >= 36000000: res1 = i if not res2 and dsum2[i]*11 >= 36000000: res2 = i if res1 and res2: break i+=1 print res1 print res2

UTF-8

Python

false

355

py

26

solution.py

26

0.549296

0.388732

0

23

13.521739

41

gvnsai/python_practice

15,796,889,717,697

1f2d2b4d7e8c3362ed55c0260f8dbbf910b9cb08

d6a31cfac7f6c899399c492f450ff832f2f2f4f5

/assignment-3/numpy_food/numpy_food.py

6c1910338645249bed5074d41727e486257d8189

[]

no_license

https://github.com/gvnsai/python_practice

c2e51e9c73bc901375b1fb397b80a28802d85754

dffe4b6bb4a991d2caa790415c1e5b041ed95ee3

refs/heads/master

2020-03-26T17:55:25.713217

2018-11-10T05:56:48

145,187,300

0

null

''' # Numpy in Python # Create a Python script (numpy_food.py) to perform the following tasks: • Query the database and retrieve data to calculate the average violations for every month between July 2015 and December 2017 for each postcode. • Use MatPlotLib to plot the follow data over time: o The violations per month for the postcode(s) with the highest total violations o The violations per month for the postcode(s) with the greatest variance (difference) between the lowest and highest number of violations for all months. o The average violations per month for ALL of California (all postcodes combined) o The violations per month for all McDonalds and Burger Kings. This will require a new query as it is not grouped by postal code.''' import pandas as pd import sqlite3 import matplotlib.pyplot as plt pdlist=[] pd1list=[] mclist=[] burlist=[] try: conn = sqlite3.connect('food_violations.db') print ("Database co0nnected successfully"); #selecting distinct bussiness with atlest one violation sql query sql = """SELECT \ VIOLATIONS.serial_number as v_code, \ INSPECTIONS.activity_date as date,\ INSPECTIONS.facility_zip as postcode\ FROM INSPECTIONS\ INNER JOIN VIOLATIONS ON INSPECTIONS.serial_number=VIOLATIONS.serial_number WHERE INSPECTIONS.activity_date BETWEEN "2015-07-01" AND "2017-12-01";""" #select inspections.activity_date,violations.violation_description from inspections,violations where inspections.serial_number=violations.serial_number WHERE INSPECTIONS.activity_date BETWEEN "2015-07-01" AND "2017-12-01"; cursor = conn.execute(sql) #appending to a list inside the for for row in cursor: templist=[] templist.append(row[0]) templist.append(row[1]) templist.append(row[2]) pdlist.append(templist) print ("Operation done successfully"); sql_mc = """ SELECT facility_name, violations.serial_number as v_code, inspections.activity_date as date FROM inspections INNER JOIN violations ON inspections.serial_number=violations.serial_number WHERE inspections.facility_name like '%McDonalds%' OR '%BURGER KING%';""" cursor = conn.execute(sql_mc) #appending to a list inside the for for row in cursor: templist=[] templist.append(row[0]) templist.append(row[1]) templist.append(row[2]) mclist.append(templist) print ("mcd Operation done successfully"); sql_bur = """ SELECT facility_name, violations.serial_number as v_code, inspections.activity_date as date FROM inspections INNER JOIN violations ON inspections.serial_number=violations.serial_number WHERE inspections.facility_name like '%BURGER KING%';""" cursor = conn.execute(sql_bur) #appending to a list inside the for for row in cursor: templist=[] templist.append(row[0]) templist.append(row[1]) templist.append(row[2]) burlist.append(templist) print ("burger Operation done successfully"); except Exception as e: print(e) clos=["v_code","date","postcodes"] df=pd.DataFrame(pdlist,columns=clos) df3=pd.DataFrame() df4=pd.DataFrame() y1=pd.DataFrame() df['date'] = pd.to_datetime(df['date']) df2=df.groupby(['v_code', 'date']).size().reset_index(name='counts') df3['avg_counts']=df2.groupby(df['date'].dt.strftime('%B'))['counts'].mean().sort_values() df3['month']=df3.index #avg on month bases print(df3.head()) print(df.head()) #df to plot highest violations df4=df.groupby(['postcodes','date']).size().reset_index(name='post_counts') df4['count_max'] = df4.groupby(df4['date'].dt.strftime('%B'))['post_counts'].transform(max) df4['count_min'] = df4.groupby(df4['date'].dt.strftime('%B'))['post_counts'].transform(min) df4['diff_count'] = df4['count_max']-df4['count_min'] # bk dfs mcclos=["name","v_code","date"] mc=pd.DataFrame(mclist,columns=mcclos) print(mc.head()) # bk dfs bkclos=["name","v_code","date"] bkdf=pd.DataFrame(burlist,columns=bkclos) print(bkdf.head()) framelist=[mc,bkdf] mcbkdf = pd.concat(framelist) print(mcbkdf.head(30)) mcbkdf=mcbkdf.groupby(['name', 'date']).size().reset_index(name='counts') #print(mcbkdf.head()) y1['avg_counts']=mcbkdf.groupby(df['date'].dt.strftime('%B'))['counts'].mean() print(type(y1)) y1['month']=y1.index print(y1.head()) #graphs x=df4['date'].dt.strftime('%B').head(30) y=df4['diff_count'].head(30) plt.bar(x,y,color = "red" ) plt.title("violations difference") plt.xlabel("dates") plt.ylabel("differences of max and min postcodes") plt.legend() plt.show() x2=df4['date'].dt.strftime('%B') y3=df3['avg_counts']=df2.groupby(df['date'].dt.strftime('%B'))['counts'].mean().sort_values() plt.plot(y3) plt.title("averages") plt.xlabel("dates") plt.ylabel("monthely avreage violations") plt.show() y2=mcbkdf.groupby(df['date'].dt.strftime('%B'))['counts'].mean() plt.plot(y2) plt.title("McD and BK average per month") plt.xlabel("months") plt.ylabel("averages per month") plt.show() y4=df4['count_max'] plt.plot(y4) plt.title("max number of violation based on postcodes") plt.xlabel("month") plt.ylabel("max violations") plt.show()

UTF-8

Python

false

5,075

py

25

numpy_food.py

20

0.714455

0.695524

0

153

32.124183

291

rachittoshniwal/opencv-projects

10,995,116,320,360

186105fc3745345bae18be60c2a876fc977ca5a1

00102adf23cfec94df86bab5663f4cbc7a5dc2a9

/face_blurring_image_haar.py

809362b651ec8dcb1308b1b68ee536110665ca00

[]

no_license

https://github.com/rachittoshniwal/opencv-projects

10236d5107c3e53fe8a2fa72a1ab01e02d548995

7c1378ba719bc79f17668b40bc86596aaf0c62fa

refs/heads/master

2023-06-25T10:49:44.040554

2021-07-21T16:09:52

386,977,089

0

null

import cv2 as cv haar_face = cv.CascadeClassifier('haarcascade_frontalface_default.xml') img = cv.imread('./test images/friends1.jpg') img = cv.resize(img, (640,480)) cv.imshow("original image", img) gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) face_rect = haar_face.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5) for face in face_rect: (x, y, w, h) = face face_roi = img[y:y+h, x:x+w] face_roi = cv.GaussianBlur(face_roi, (29,29), 15) img[y:y+h, x:x+w, :] = face_roi cv.imshow("blurred image", img) cv.waitKey(0)

UTF-8

Python

false

545

py

4

face_blurring_image_haar.py

2

0.67156

0.636697

0

19

27.736842

77

edublancas/sklearn-evaluation

11,433,202,956,858

c9c7e8c6cfbeb849ad522ec3b86e0a845a98e9e6

4b2d797b26bc919b91ce4b91479237c1ffe417a0

/src/sklearn_evaluation/plot/_matrix.py

cb6c27e01268c33a464f66bbaf7f6027a6609b3d

[ "MIT" ]

permissive

https://github.com/edublancas/sklearn-evaluation

c0c4a0948889dab6b8987775b82672dfc5fb5787

91b547e7705c4096f0d7ebee5aaf7e34af795dd3

refs/heads/master

2023-05-28T14:46:32.627880

2023-01-13T21:57:34

589,342,851

3

0

MIT

true

2023-01-15T21:18:53

2023-01-15T21:18:52

2023-01-15T08:53:00

2023-01-15T17:14:45

13,596

0

null

false

import numpy as np from matplotlib.tri import Triangulation from sklearn_evaluation.util import default_heatmap def add(first, second, ax, invert_axis=False, max_=None): # Adapted from: https://stackoverflow.com/a/63531813/709975 # TODO: validate first and second have the same shape M, N = first.shape if not invert_axis else first.shape[::-1] x = np.arange(M + 1) y = np.arange(N + 1) xs, ys = np.meshgrid(x, y) zs = (xs * ys) % 10 zs = zs[:-1, :-1].ravel() if max_ is None: max_ = np.max([first.max(), second.max()]) triangles1 = [ (i + j * (M + 1), i + 1 + j * (M + 1), i + (j + 1) * (M + 1)) for j in range(N) for i in range(M) ] triangles2 = [ (i + 1 + j * (M + 1), i + 1 + (j + 1) * (M + 1), i + (j + 1) * (M + 1)) for j in range(N) for i in range(M) ] triang1 = Triangulation(xs.ravel() - 0.5, ys.ravel() - 0.5, triangles1) triang2 = Triangulation(xs.ravel() - 0.5, ys.ravel() - 0.5, triangles2) cmap = default_heatmap() ax.tripcolor(triang1, first.ravel(), cmap=cmap, vmax=max_) ax.tripcolor(triang2, second.ravel(), cmap=cmap, vmax=max_) ax.set_xlim(x[0] - 0.5, x[-1] - 0.5) ax.set_ylim(y[-1] - 0.5, y[0] - 0.5) for pad, arr in ((-1 / 5, first), (1 / 5, second)): for (y, x), v in np.ndenumerate(arr): try: label = "{:.2}".format(v) except Exception: label = v ax.text( x + pad, y + pad, label, horizontalalignment="center", verticalalignment="center", )

UTF-8

Python

false

1,686

py

158

_matrix.py

109

0.502966

0.46382

0

57

28.578947

79

Lorry1123/logging

10,075,993,290,554

786c9052b1314d14b863d53f126b4bfdb24fdfc2

626d8d39c4864b8086b8aed3ecfed62ed4c52a7e

/logging_demo.py

ac2f977975d790f6e3c6b2873a5677307747905c

[]

no_license

https://github.com/Lorry1123/logging

6f9424e8394e4916f2ea2ba43b4d981dcfe2365c

2a26e1d7b0a53c3fad51bedcc2110dcbc323e7f2

refs/heads/master

2020-04-08T19:07:30.904086

2019-03-29T02:36:51

159,641,002

0

null

# coding: utf8 import sys from exceptions import Exception CRITICAL = 50 FATAL = CRITICAL ERROR = 40 WARNING = 30 WARN = WARNING INFO = 20 DEBUG = 10 NOTSET = 0 _levelNames = { CRITICAL : 'CRITICAL', ERROR : 'ERROR', WARNING : 'WARNING', INFO : 'INFO', DEBUG : 'DEBUG', NOTSET : 'NOTSET', 'CRITICAL' : CRITICAL, 'ERROR' : ERROR, 'WARN' : WARNING, 'WARNING' : WARNING, 'INFO' : INFO, 'DEBUG' : DEBUG, 'NOTSET' : NOTSET, } class Logger(): def __init__(self, name, propagate=True): self.name = name self.handlers = [] self.level = NOTSET self.parent = None self.propagate = propagate def _log(self, level, msg, args): record = LogRecord(self.name, level, msg, args) self.callHandlers(record) def callHandlers(self, record): c = self found = 0 while c: for handler in c.handlers: found += 1 if record.levelno >= handler.level: handler.emit(record) if not c.propagate: c = None else: c = c.parent if found == 0: raise Exception('No handlers could be found for logger "%s"' % self.name) def addHandler(self, handler): if not isinstance(handler, BaseHandler): raise Exception('addHandler only receive a instance of BaseHandler') self.handlers.append(handler) def setLevel(self, level): self.level = level def isEnabledFor(self, level): return level >= self.level def debug(self, msg, *args): if self.isEnabledFor(DEBUG): self._log(DEBUG, msg, args) def info(self, msg, *args): if self.isEnabledFor(INFO): self._log(INFO, msg, args) def warning(self, msg, *args): if self.isEnabledFor(WARN): self._log(WARN, msg, args) warn = warning def error(self, msg, *args): if self.isEnabledFor(ERROR): self._log(ERROR, msg, args) class BaseHandler(): def __init__(self): self.formatter = Formatter('%(message)s') self.level = NOTSET def emit(self, record): pass def setFormatter(self, formatter): self.formatter = formatter def setLevel(self, level): self.level = level class MyStreamHandler(BaseHandler): def __init__(self): BaseHandler.__init__(self) self.stream = sys.stdout def emit(self, record): msg = self.formatter.format(record) self.stream.write('%s\n' % msg) class LogRecord(): def __init__(self, name, level, msg, args): self.name = name self.level = _levelNames[level] self.levelno = level self.msg = msg self.args = args def getMessage(self): return self.msg % self.args class Formatter(): def __init__(self, fmt): self.fmt = fmt def format(self, record): record.message = record.getMessage() ret = self.fmt % record.__dict__ return ret # test code logger = Logger('my_logger') sh = MyStreamHandler() sh.setFormatter(Formatter('[%(level)s][%(message)s]')) logger.addHandler(sh) # logger.addHandler(MyStreamHandler()) # logger.setLevel(WARN) # logger.info('hello world') # logger.warn('hello %s', 'lorry') # sh.setLevel(ERROR) # logger.info('hello info') # logger.warn('hello warning') parent = Logger('parent logger') parent_hdl = MyStreamHandler() parent_hdl.setFormatter(Formatter('[PARENT][%(level)s][%(message)s]')) parent.addHandler(parent_hdl) # 父节点 logger.parent = parent logger.info('hello parent') logger.propagate = False logger.info('hello parent 2')

UTF-8

Python

false

3,738

py

4

logging_demo.py

2

0.587085

0.582797

0

167

21.347305

85

ii0/algorithms-6

7,859,790,181,301

9a1317a89a2eb0c8a2e2a76bb53f55d370f43457

1c7b5b866b505b7b8c47dce504c5bd27a34d5992

/Leetcode/BinarySearch/69_Sqrt(x).py

64813d0bf503b1347e64f80a499a7d92942f40b7

[]

no_license

https://github.com/ii0/algorithms-6

2dbcb3df504810ea52b41e5129b334f62136d70a

3eddc77d2f3dafffd177f2a9ee28e9850da2f020

refs/heads/master

2022-04-25T23:17:53.332297

2019-09-19T14:52:04

null

0

null

""" author: buppter datetime: 2019/8/8 12:45 题目描述：实现 int sqrt(int x) 函数 Implement int sqrt(int x). Compute and return the square root of x, where x is guaranteed to be a non-negative integer. Since the return type is an integer, the decimal digits are truncated and only the integer part of the result is returned. 示例： Input: 4 Output: 2 Input: 8 Output: 2 Explanation: The square root of 8 is 2.82842..., and since the decimal part is truncated, 2 is returned. 解题思路：二叉查找 """ class Solution: def mySqrt(self, x: int) -> int: if x == 0 or x == 1: return x l = 0 r = x while l <= r: mid = (l + r) // 2 if mid * mid <= x < (mid + 1) * (mid + 1): return mid elif x < mid * mid: r = mid else: l = mid

UTF-8

Python

false

907

py

100

69_Sqrt(x).py

98

0.544509

0.512139

0

43

19.116279

92

murrayrm/BioCRNPyler

11,493,332,488,539

a28076cc7ca78b0a63d58bdb0395a40ebb14ec54

5e6a21328057f91d489319533e1927b8107b9e0c

/biocrnpyler/dcas9.py

980a4ab55d7178053a7eb4c642994dee9e5cb524

[ "BSD-3-Clause" ]

permissive

https://github.com/murrayrm/BioCRNPyler

d84437326742a04ac508a7e068c19a8c8816d7d7

2e7d4c521b1ebdf7cff6867b25cbee014e0ee1a3

refs/heads/master

2020-07-05T10:12:56.382470

2020-04-23T20:03:01

202,620,151

1

0

BSD-3-Clause

true

2019-08-15T22:35:16

2019-08-14T16:57:46

2019-08-14T10:32:31

5,189

0

null

false

# Copyright (c) 2019, Build-A-Cell. All rights reserved. # See LICENSE file in the project root directory for details. from .component import Component, RNA from .mechanism import Reversible_Bimolecular_Binding from .chemical_reaction_network import Species class guideRNA(RNA): def __init__(self, guide_name, dCas9 = "dCas9", **keywords): if isinstance(dCas9, Species): self.dCas = dCas9 elif isinstance(dCas9, str): self.dCas = Species(dCas9, material_type ="protein") elif isinstance(dCas9, Component) and dCas9.get_species()!= None: self.dCas = dCas9.get_species() else: raise ValueError("dCas9 parameter must be a " "chemical_reaction_network.species, Component " "with get_species(), or a string") self.default_mechanisms = { "dCas9_binding" : Reversible_Bimolecular_Binding(name = "dCas9_binding", mechanism_type = "bimolecular binding") } RNA.__init__(self, name = guide_name, **keywords) self.gRNA = self.get_species() def get_dCasComplex(self): binding_species = \ self.mechanisms['dCas9_binding'].update_species(self.gRNA, self.dCas) if len(binding_species) > 1: raise ValueError("dCas9_binding mechanisms " f"{self.mechanisms['dCas9_binding'].name} returned " "multiple complexes. Unclear which is active." ) else: return binding_species[0] def update_species(self): species = [self.gRNA, self.dCas] species += self.mechanisms['dCas9_binding'].update_species(self.gRNA, self.dCas) return species def update_reactions(self): ku = self.get_parameter("ku", part_id = self.gRNA.name, mechanism = self.mechanisms['dCas9_binding']) kb = self.get_parameter("kb", part_id = self.gRNA.name, mechanism = self.mechanisms['dCas9_binding']) rxns = self.mechanisms['dCas9_binding'].update_reactions(self.gRNA, self.dCas, kb = kb, ku = ku) return rxns

UTF-8

Python

false

2,369

py

29

dcas9.py

20

0.552976

0.542423

0

56

41.303571

80