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How to use current_path method in localstack
Best Python code snippet using localstack_python
DAE_AFE_lossweight.py
Source:DAE_AFE_lossweight.py 
1import os, sys2import numpy as np3print 'Read Multi Data Start ...'4current_path = '/home/ding/Forgithub/'5baseDir = current_path6trainingSet = 'multi/'7subDir = [ 'clean1' , 'clean2' , 'clean3' , 'clean4' , 8 'N1_SNR5' , 'N2_SNR5' , 'N3_SNR5' , 'N4_SNR5',9 'N1_SNR10', 'N2_SNR10', 'N3_SNR10', 'N4_SNR10',10 'N1_SNR15', 'N2_SNR15', 'N3_SNR15', 'N4_SNR15',11 'N1_SNR20', 'N2_SNR20', 'N3_SNR20', 'N4_SNR20']12training_list = []13multiTR_path = []14num_frame_group = 1115multi_length = []16multi_clean = []17multi_SNR20 = []18multi_SNR15 = []19multi_SNR10 = []20multi_SNR5 = []21for h in range(len(subDir)):22 dirs = os.listdir(baseDir + trainingSet + subDir[h])23 for num in range(len(dirs)):24 25 tmp = np.loadtxt(baseDir + trainingSet + subDir[h] + '/' + dirs[num])26 27 multi_length.append(tmp.size/39)28 29 multiTR_path.append(baseDir + trainingSet + '/' + dirs[num])30 zero = np.zeros(((num_frame_group -1)/2,39))31 tmp = np.concatenate((zero, tmp), axis=0)32 tmp = np.concatenate((tmp, zero), axis=0)33 # CMVN34 tmp = np.transpose(tmp)35 36 cur_mean = np.mean(tmp, axis=1)37 cur_std = np.std(tmp, axis=1, ddof=1)38 tmp = tmp - cur_mean[:, None]39 tmp = tmp / cur_std[:, None]40 tmp = np.nan_to_num(tmp)41 42 tmp = np.transpose(tmp)43 44 for num_split_group in range(tmp.size/39-num_frame_group+1): 45 46 temp_data = np.concatenate((tmp[num_split_group], tmp[num_split_group+1], tmp[num_split_group+2], tmp[num_split_group+3], tmp[num_split_group+4], tmp[num_split_group+5], tmp[num_split_group+6], tmp[num_split_group+7], tmp[num_split_group+8], tmp[num_split_group+9], tmp[num_split_group+10]), axis=0)47 training_list.append(temp_data)48 if h<4 :49 multi_clean.append(temp_data)50 elif h>=4 and h<8 :51 multi_SNR5.append(temp_data)52 elif h>=8 and h<12 :53 multi_SNR10.append(temp_data)54 elif h>=12 and h<16 :55 multi_SNR15.append(temp_data)56 elif h>=16 and h<20 :57 multi_SNR20.append(temp_data)58x_train_multi = np.array(training_list)59multi_clean = np.array(multi_clean)60multi_SNR5 = np.array(multi_SNR5)61multi_SNR10 = np.array(multi_SNR10)62multi_SNR15 = np.array(multi_SNR15)63multi_SNR20 = np.array(multi_SNR20)64print 'Read Multi Data End ...'65print 'Read Clean Data Start ...'66baseDir = current_path67trainingSet = 'clean/'68training_list = []69clean_clean = []70clean_SNR20 = []71clean_SNR15 = []72clean_SNR10 = []73clean_SNR5 = []74for h in range(len(subDir)):75 dirs = os.listdir(baseDir + trainingSet + subDir[h])76 for num in range(len(dirs)):77 78 tmp = np.loadtxt(baseDir + trainingSet + subDir[h] + '/' + dirs[num])79 80 tmp = np.transpose(tmp)81 82 cur_mean = np.mean(tmp, axis=1)83 cur_std = np.std(tmp, axis=1, ddof=1)84 tmp = tmp - cur_mean[:, None]85 tmp = tmp / cur_std[:, None]86 tmp = np.nan_to_num(tmp)87 88 tmp = np.transpose(tmp)89 90 for i in range(len(tmp)):91 training_list.append(tmp[i])92 if h<4 :93 clean_clean.append(tmp[i])94 elif h>=4 and h<8 :95 clean_SNR5.append(tmp[i])96 elif h>=8 and h<12 :97 clean_SNR10.append(tmp[i])98 elif h>=12 and h<16 :99 clean_SNR15.append(tmp[i])100 elif h>=16 and h<20 :101 clean_SNR20.append(tmp[i])102 103x_train_clean = np.array(training_list)104clean_clean = np.array(clean_clean)105clean_SNR5 = np.array(clean_SNR5)106clean_SNR10 = np.array(clean_SNR10)107clean_SNR15 = np.array(clean_SNR15)108clean_SNR20 = np.array(clean_SNR20)109print 'Read Clean Data End ...'110print 'Start training ...'111from keras.layers import *112from keras.models import *113import keras.optimizers114cleanw = []115SNR5w = []116SNR10w = []117SNR15w = []118SNR20w = []119for i in range(273713) : #clean120 cleanw.append(1.0)121for i in range(276048) : #5dB122 SNR5w.append(1.0)123for i in range(272158) : #10dB124 SNR10w.append(1.0)125for i in range(271282) : #15dB126 SNR15w.append(1.0)127for i in range(270481) : #20dB128 SNR20w.append(1.0)129cleanw = np.array(cleanw)130SNR5w = np.array(SNR5w)131SNR10w = np.array(SNR10w)132SNR15w = np.array(SNR15w)133SNR20w = np.array(SNR20w)134weightloss = np.concatenate((cleanw,SNR5w,SNR10w,SNR15w,SNR20w) , axis=0)135leakyrelu = keras.layers.LeakyReLU(alpha=0.3)136# optimizer137adam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=1e-5)138# instantiate model139model = Sequential()140model.add(Dense(500, input_dim=429))141model.add(leakyrelu)142model.add(Dense(500))143model.add(leakyrelu)144model.add(Dense(273))145model.add(leakyrelu)146model.add(Dense(300))147model.add(leakyrelu)148model.add(Dense(300))149model.add(leakyrelu)150model.add(Dense(39))151model.compile(loss='mse', optimizer=adam)152rclean=[]153rSNR5=[]154rSNR10=[]155rSNR15=[]156rSNR20=[]157rclean.append(1.0)158rSNR5.append(1.0)159rSNR10.append(1.0)160rSNR15.append(1.0)161rSNR20.append(1.0)162alphac = 0.1163alpha5 = 0.1164alpha10 = 0.1165alpha15 = 0.1166alpha20 = 0.1167for epoch in range(1) :168 model.fit(x_train_multi, x_train_clean, epochs=1, batch_size=100, shuffle=True, sample_weight=weightloss)169 loss_clean = model.evaluate(multi_clean, clean_clean, verbose=0, batch_size=500)170 loss_SNR5 = model.evaluate(multi_SNR5 , clean_SNR5 , verbose=0, batch_size=500)171 loss_SNR10 = model.evaluate(multi_SNR10, clean_SNR10, verbose=0, batch_size=500)172 loss_SNR15 = model.evaluate(multi_SNR15, clean_SNR15, verbose=0, batch_size=500)173 loss_SNR20 = model.evaluate(multi_SNR20, clean_SNR20, verbose=0, batch_size=500)174 175 avgloss = (loss_clean + loss_SNR5 + loss_SNR10 + loss_SNR15 + loss_SNR20)/5176 177 weightloss = []178 cleanw = cleanw + alphac*(loss_clean-avgloss)179 SNR5w = SNR5w + alpha5*(loss_SNR5-avgloss)180 SNR10w = SNR10w + alpha10*(loss_SNR10-avgloss)181 SNR15w = SNR15w + alpha15*(loss_SNR15-avgloss)182 SNR20w = SNR20w + alpha20*(loss_SNR20-avgloss)183 184 weightloss = np.concatenate((cleanw,SNR5w,SNR10w,SNR15w,SNR20w) , axis=0)185 alpha5 = alpha5 * 0.95186 alpha10 = alpha10 * 0.95187 alpha15 = alpha15 * 0.95188 alpha20 = alpha20 * 0.95189 alphac = alphac * 0.95190 rclean.append(cleanw[0])191 rSNR5.append(SNR5w[0])192 rSNR10.append(SNR10w[0])193 rSNR15.append(SNR15w[0])194 rSNR20.append(SNR20w[0])195 196 print 'Epoch = ', epoch+2197print 'training is over.'198rrclean = np.array(rclean)199rrSNR5 = np.array(rSNR5)200rrSNR10 = np.array(rSNR10)201rrSNR15 = np.array(rSNR15)202rrSNR20 = np.array(rSNR20)203np.savetxt('clean.txt', rrclean)204np.savetxt('SNR5.txt' , rrSNR5)205np.savetxt('SNR10.txt', rrSNR10)206np.savetxt('SNR15.txt', rrSNR15)207np.savetxt('SNR20.txt', rrSNR20)208print 'record is over'209cleanw = None210SNR5w = None211SNR10w = None212SNR15w = None213SNR20w = None214weightloss = None215print 'Create Directory ...'216output_dir = 'Output_DAE_AFE_lossweight1'217os.makedirs(current_path + output_dir)218os.makedirs(current_path + output_dir + '/multi')219os.makedirs(current_path + output_dir + '/testA')220os.makedirs(current_path + output_dir + '/testB')221os.makedirs(current_path + output_dir + '/testC')222os.makedirs(current_path + output_dir + '/testA/clean1')223os.makedirs(current_path + output_dir + '/testA/clean2')224os.makedirs(current_path + output_dir + '/testA/clean3')225os.makedirs(current_path + output_dir + '/testA/clean4')226os.makedirs(current_path + output_dir + '/testA/N1_SNR0')227os.makedirs(current_path + output_dir + '/testA/N1_SNR5')228os.makedirs(current_path + output_dir + '/testA/N1_SNR10')229os.makedirs(current_path + output_dir + '/testA/N1_SNR15')230os.makedirs(current_path + output_dir + '/testA/N1_SNR20')231os.makedirs(current_path + output_dir + '/testA/N1_SNR-5')232os.makedirs(current_path + output_dir + '/testA/N2_SNR0')233os.makedirs(current_path + output_dir + '/testA/N2_SNR5')234os.makedirs(current_path + output_dir + '/testA/N2_SNR10')235os.makedirs(current_path + output_dir + '/testA/N2_SNR15')236os.makedirs(current_path + output_dir + '/testA/N2_SNR20')237os.makedirs(current_path + output_dir + '/testA/N2_SNR-5')238os.makedirs(current_path + output_dir + '/testA/N3_SNR0')239os.makedirs(current_path + output_dir + '/testA/N3_SNR5')240os.makedirs(current_path + output_dir + '/testA/N3_SNR10')241os.makedirs(current_path + output_dir + '/testA/N3_SNR15')242os.makedirs(current_path + output_dir + '/testA/N3_SNR20')243os.makedirs(current_path + output_dir + '/testA/N3_SNR-5')244os.makedirs(current_path + output_dir + '/testA/N4_SNR0')245os.makedirs(current_path + output_dir + '/testA/N4_SNR5')246os.makedirs(current_path + output_dir + '/testA/N4_SNR10')247os.makedirs(current_path + output_dir + '/testA/N4_SNR15')248os.makedirs(current_path + output_dir + '/testA/N4_SNR20')249os.makedirs(current_path + output_dir + '/testA/N4_SNR-5')250os.makedirs(current_path + output_dir + '/testB/clean1')251os.makedirs(current_path + output_dir + '/testB/clean2')252os.makedirs(current_path + output_dir + '/testB/clean3')253os.makedirs(current_path + output_dir + '/testB/clean4')254os.makedirs(current_path + output_dir + '/testB/N1_SNR0')255os.makedirs(current_path + output_dir + '/testB/N1_SNR5')256os.makedirs(current_path + output_dir + '/testB/N1_SNR10')257os.makedirs(current_path + output_dir + '/testB/N1_SNR15')258os.makedirs(current_path + output_dir + '/testB/N1_SNR20')259os.makedirs(current_path + output_dir + '/testB/N1_SNR-5')260os.makedirs(current_path + output_dir + '/testB/N2_SNR0')261os.makedirs(current_path + output_dir + '/testB/N2_SNR5')262os.makedirs(current_path + output_dir + '/testB/N2_SNR10')263os.makedirs(current_path + output_dir + '/testB/N2_SNR15')264os.makedirs(current_path + output_dir + '/testB/N2_SNR20')265os.makedirs(current_path + output_dir + '/testB/N2_SNR-5')266os.makedirs(current_path + output_dir + '/testB/N3_SNR0')267os.makedirs(current_path + output_dir + '/testB/N3_SNR5')268os.makedirs(current_path + output_dir + '/testB/N3_SNR10')269os.makedirs(current_path + output_dir + '/testB/N3_SNR15')270os.makedirs(current_path + output_dir + '/testB/N3_SNR20')271os.makedirs(current_path + output_dir + '/testB/N3_SNR-5')272os.makedirs(current_path + output_dir + '/testB/N4_SNR0')273os.makedirs(current_path + output_dir + '/testB/N4_SNR5')274os.makedirs(current_path + output_dir + '/testB/N4_SNR10')275os.makedirs(current_path + output_dir + '/testB/N4_SNR15')276os.makedirs(current_path + output_dir + '/testB/N4_SNR20')277os.makedirs(current_path + output_dir + '/testB/N4_SNR-5')278os.makedirs(current_path + output_dir + '/testC/clean1')279os.makedirs(current_path + output_dir + '/testC/clean2')280os.makedirs(current_path + output_dir + '/testC/N1_SNR0')281os.makedirs(current_path + output_dir + '/testC/N1_SNR5')282os.makedirs(current_path + output_dir + '/testC/N1_SNR10')283os.makedirs(current_path + output_dir + '/testC/N1_SNR15')284os.makedirs(current_path + output_dir + '/testC/N1_SNR20')285os.makedirs(current_path + output_dir + '/testC/N1_SNR-5')286os.makedirs(current_path + output_dir + '/testC/N2_SNR0')287os.makedirs(current_path + output_dir + '/testC/N2_SNR5')288os.makedirs(current_path + output_dir + '/testC/N2_SNR10')289os.makedirs(current_path + output_dir + '/testC/N2_SNR15')290os.makedirs(current_path + output_dir + '/testC/N2_SNR20')291os.makedirs(current_path + output_dir + '/testC/N2_SNR-5')292print 'Start testing ...'293print 'Multi train data put into model...'294x_train_multi_new = model.predict(x_train_multi, batch_size=500)295print 'End Feeding ...'296print 'Write multi train data into directory ...'297index_count = 0298temp = []299for file_num in range(len(multi_length)): 300 for frame_num in range(multi_length[file_num]): 301 temp = np.concatenate((temp, x_train_multi_new[index_count + frame_num]), axis=0) 302 303 temp = temp.reshape(multi_length[file_num],39)304 305 temp = np.transpose(temp)306 savepath = multiTR_path[file_num].replace('multi',output_dir+'/multi')307 savefile = savepath.split('.')308 np.save(savefile[0], temp)309 310 index_count = index_count + multi_length[file_num]311 312 temp=[]313for testindex in ['testA/', 'testB/', 'testC/'] :314 315 baseDir = current_path316 trainingSet = testindex317 if testindex != 'testC/' :318 subDir = ['clean1', 'N1_SNR0', 'N1_SNR5', 'N1_SNR10', 'N1_SNR15', 'N1_SNR20', 'N1_SNR-5',319 'clean2', 'N2_SNR0', 'N2_SNR5', 'N2_SNR10', 'N2_SNR15', 'N2_SNR20', 'N2_SNR-5',320 'clean3', 'N3_SNR0', 'N3_SNR5', 'N3_SNR10', 'N3_SNR15', 'N3_SNR20', 'N3_SNR-5',321 'clean4', 'N4_SNR0', 'N4_SNR5', 'N4_SNR10', 'N4_SNR15', 'N4_SNR20', 'N4_SNR-5',]322 else :323 subDir = ['clean1', 'N1_SNR0', 'N1_SNR5', 'N1_SNR10', 'N1_SNR15', 'N1_SNR20', 'N1_SNR-5',324 'clean2', 'N2_SNR0', 'N2_SNR5', 'N2_SNR10', 'N2_SNR15', 'N2_SNR20', 'N2_SNR-5']325 set_list = []326 set_path = []327 num_frame_group = 11328 set_length = []329 for h in range(len(subDir)):330 dirs = os.listdir(baseDir + trainingSet + subDir[h])331 332 for num in range(len(dirs)):333 tmp = np.loadtxt(baseDir + trainingSet + subDir[h] + '/' + dirs[num])334 set_length.append(tmp.size/39)335 set_path.append(baseDir + trainingSet + subDir[h] +'/' + dirs[num])336 zero = np.zeros(((num_frame_group -1)/2,39))337 tmp = np.concatenate((zero, tmp), axis=0)338 tmp = np.concatenate((tmp, zero), axis=0)339 tmp = np.transpose(tmp)340 341 cur_mean = np.mean(tmp, axis=1)342 cur_std = np.std(tmp, axis=1, ddof=1)343 tmp = tmp - cur_mean[:, None]344 tmp = tmp / cur_std[:, None]345 tmp = np.nan_to_num(tmp)346 347 tmp = np.transpose(tmp)348 349 for num_split_group in range(tmp.size/39-num_frame_group+1):350 351 temp_data = np.concatenate((tmp[num_split_group], tmp[num_split_group+1], tmp[num_split_group+2], tmp[num_split_group+3], tmp[num_split_group+4], tmp[num_split_group+5], tmp[num_split_group+6], tmp[num_split_group+7], tmp[num_split_group+8], tmp[num_split_group+9], tmp[num_split_group+10]), axis=0)352 set_list.append(temp_data)353 354 x_test = np.array(set_list)355 356 x_test_new = model.predict(x_test, batch_size=500)357 358 index_count = 0359 temp = []360 for file_num in range(len(set_length)):361 for frame_num in range(set_length[file_num]):362 temp = np.concatenate((temp, x_test_new[index_count + frame_num]), axis=0)363 364 temp = temp.reshape(set_length[file_num],39)365 366 temp = np.transpose(temp)367 savepath = set_path[file_num].replace(testindex,output_dir+'/'+testindex)368 savefile = savepath.split('.')369 np.save(savefile[0], temp)370 371 index_count = index_count + set_length[file_num]372 373 temp=[]374 set_list = []375 set_path = []...
jumpsolve.py
Source:jumpsolve.py
1#main function receiving data about the puzzle2def main():3 y = int(input("What is the length of the puzzle? "))4 x = int(input("What is the breadth of the puzzle? "))5 x1 = int(input("What is the x position of the start tile? "))6 y1 = int(input("What is the y position of the start tile? "))7 xG = int(input("What is the x position of the goal tile? "))8 yG = int(input("What is the y position of the goal tile? "))9 start = (x1,y1)10 goal = (xG,yG)11 board = list()12 for i in range(x):13 row = list()14 for j in range(y):15 temp = int(input("What is the value at the (" + str(i) + ", " + str(j) + ") tile? "))16 row.append(temp)17 board.append(row)18 astar_solution = jumpsolve_astar(start, goal, board)19 print("This is the A* Solution: ")20 print(astar_solution)21 bfs_solution = jumpsolve_bfs(start, goal, board)22 print("This is the BFS Solution:")23 print(bfs_solution)24 ids_solution = jumpsolve_ids(start, goal, board)25 print("This is the IDS Solution: ")26 print(ids_solution)2728#astar search implementation29def jumpsolve_astar(start, goal, board):30 astar_queue_list = list()31 current = start32 current_path = list()33 current_path.append(start)34 while (1):35 print(current)36 if ((current[1]-board[current[0]][current[1]]) >= 0) and board[current[0]][current[1]] != 0:37 temp_option = (current[0],(current[1]-board[current[0]][current[1]]))38 current_path.append(temp_option)39 if goal in current_path:40 break41 temp_list = current_path[:]42 astar_queue_list.append(temp_list)43 current_path.pop()44 if ((current[0]-board[current[0]][current[1]]) >= 0) and board[current[0]][current[1]] != 0:45 temp_option = ((current[0]-board[current[0]][current[1]]),current[1])46 current_path.append(temp_option)47 if goal in current_path:48 break49 temp_list = current_path[:]50 astar_queue_list.append(temp_list)51 current_path.pop()52 if ((current[1]+board[current[0]][current[1]]) < len(board[0])) and board[current[0]][current[1]] != 0:53 temp_option = (current[0], (current[1] + board[current[0]][current[1]]))54 current_path.append(temp_option)55 if goal in current_path:56 break57 temp_list = current_path[:]58 astar_queue_list.append(temp_list)59 current_path.pop()60 if ((current[0]+board[current[0]][current[1]]) < len(board)) and board[current[0]][current[1]] != 0:61 temp_option = ((current[0]+board[current[0]][current[1]]),current[1])62 current_path.append(temp_option)63 if goal in current_path:64 break65 temp_list = current_path[:]66 astar_queue_list.append(temp_list)67 current_path.pop()68 print(astar_queue_list)69 options_rating = list()70 for i in range(len(astar_queue_list)):71 options_rating.append(f_n(astar_queue_list[i],goal,board))72 print(options_rating)73 min_rating = min(options_rating)74 choice = options_rating.index(min_rating)75 current_path = astar_queue_list.pop(choice)76 current = current_path[-1]77 return current_path7879#heuristic function for astar search80def h_n(option, goal, board):81 return ((max(option[0],goal[0]) - min(option[0],goal[0])) + (max(option[1],goal[1]) - min(option[1],goal[1])))/(len(board))8283#calculating path cost for astar search84def g_n(option_path):85 return (len(option_path)-1)8687#calculating the sum of heuristic function and path cost88def f_n(option_path,goal,board):89 return g_n(option_path) + h_n(option_path[-1],goal,board)9091#implementation of breadth-first search92def jumpsolve_bfs(start, goal, board):93 bfs_queue_list = list()94 current = start95 current_path = list()96 current_path.append(start)97 while (1):98 if ((current[1]-board[current[0]][current[1]]) >= 0) and board[current[0]][current[1]] != 0:99 temp_option = (current[0],(current[1]-board[current[0]][current[1]]))100 current_path.append(temp_option)101 if goal in current_path:102 break103 temp_list = current_path[:]104 bfs_queue_list.append(temp_list)105 current_path.pop()106 if ((current[0]-board[current[0]][current[1]]) >= 0) and board[current[0]][current[1]] != 0:107 temp_option = ((current[0]-board[current[0]][current[1]]),current[1])108 current_path.append(temp_option)109 if goal in current_path:110 break111 temp_list = current_path[:]112 bfs_queue_list.append(temp_list)113 current_path.pop()114 if ((current[1]+board[current[0]][current[1]]) < len(board[0])) and board[current[0]][current[1]] != 0:115 temp_option = (current[0], (current[1] + board[current[0]][current[1]]))116 current_path.append(temp_option)117 if goal in current_path:118 break119 temp_list = current_path[:]120 bfs_queue_list.append(temp_list)121 current_path.pop()122 if ((current[0]+board[current[0]][current[1]]) < len(board)) and board[current[0]][current[1]] != 0:123 temp_option = ((current[0]+board[current[0]][current[1]]),current[1])124 current_path.append(temp_option)125 if goal in current_path:126 break127 temp_list = current_path[:]128 bfs_queue_list.append(temp_list)129 current_path.pop()130 current_path = bfs_queue_list.pop(0)131 current = current_path[-1]132 return current_path133134#implementation of iterative depth search, starting with limit = 1135def jumpsolve_ids(start, goal, board):136 ids_queue_list = list()137 current = start138 current_path = list()139 current_path.append(start)140 limit = 1141 while (1):142 if ((current[1] - board[current[0]][current[1]]) >= 0) and board[current[0]][current[1]] != 0:143 temp_option = (current[0], (current[1] - board[current[0]][current[1]]))144 current_path.append(temp_option)145 if goal in current_path:146 break147 temp_list = current_path[:]148 if len(temp_list)<=(limit+1):149 ids_queue_list.append(temp_list)150 current_path.pop()151 if ((current[0] - board[current[0]][current[1]]) >= 0) and board[current[0]][current[1]] != 0:152 temp_option = ((current[0] - board[current[0]][current[1]]), current[1])153 current_path.append(temp_option)154 if goal in current_path:155 break156 temp_list = current_path[:]157 if len(temp_list)<=(limit+1):158 ids_queue_list.append(temp_list)159 current_path.pop()160 if ((current[1] + board[current[0]][current[1]]) < len(board[0])) and board[current[0]][current[1]] != 0:161 temp_option = (current[0], (current[1] + board[current[0]][current[1]]))162 current_path.append(temp_option)163 if goal in current_path:164 break165 temp_list = current_path[:]166 if len(temp_list)<=(limit+1):167 ids_queue_list.append(temp_list)168 current_path.pop()169 if ((current[0] + board[current[0]][current[1]]) < len(board)) and board[current[0]][current[1]] != 0:170 temp_option = ((current[0] + board[current[0]][current[1]]), current[1])171 current_path.append(temp_option)172 if goal in current_path:173 break174 temp_list = current_path[:]175 if len(temp_list)<=(limit+1):176 ids_queue_list.append(temp_list)177 current_path.pop()178 if(len(ids_queue_list)==0):179 limit+=1180 current = start181 current_path = []182 current_path.append(start)183 else:184 current_path = ids_queue_list.pop()185 current = current_path[-1]186 return current_path187188#calling the main function to activate program
...
reformatDataGEO.py
Source:reformatDataGEO.py
1import os2#function to rename GEO barcodes, genes, and matrix files and prepare appropriate folders for Seurat3def moveGEO():4 current_path = os.getcwd() #change into GEO data folder directory5 folder = "/mouse_heart_GEO_data"6 os.chdir(current_path + folder)7 #prepare folders for 4 zomes of the heart8 SAN_folder = '/SAN_GEO' #ZONE I9 AVN_folder = '/AVN_GEO' #ZONE II10 LPF_folder = '/LPF_GEO' #ZONE III11 RPF_folder = '/RPF_GEO' #ZONE III12 #create folders for each zone13 os.mkdir(current_path + folder + SAN_folder)14 os.mkdir(current_path + folder + AVN_folder)15 os.mkdir(current_path + folder + LPF_folder)16 os.mkdir(current_path + folder + RPF_folder)17 #move files into designated folders18 os.system('mv ' + current_path + folder + '/GSM3885058_SANbarcodes.tsv.gz ' + current_path + folder + SAN_folder + '/barcodes.tsv.gz')19 os.system('mv ' + current_path + folder + '/GSM3885058_SANgenes.tsv.gz ' + current_path + folder + SAN_folder + '/features.tsv.gz')20 os.system('mv ' + current_path + folder + '/GSM3885058_SANmatrix.mtx.gz ' + current_path + folder + SAN_folder + '/matrix.mtx.gz')21 os.system('mv ' + current_path + folder + '/GSM3885059_AVNbarcodes.tsv.gz ' + current_path + folder + AVN_folder + '/barcodes.tsv.gz')22 os.system('mv ' + current_path + folder + '/GSM3885059_AVNgenes.tsv.gz ' + current_path + folder + AVN_folder + '/features.tsv.gz')23 os.system('mv ' + current_path + folder + '/GSM3885059_AVNmatrix.mtx.gz ' + current_path + folder + AVN_folder + '/matrix.mtx.gz')24 os.system('mv ' + current_path + folder + '/GSM3885060_LPFbarcodes.tsv.gz ' + current_path + folder + LPF_folder + '/barcodes.tsv.gz')25 os.system('mv ' + current_path + folder + '/GSM3885060_LPFgenes.tsv.gz ' + current_path + folder + LPF_folder + '/features.tsv.gz')26 os.system('mv ' + current_path + folder + '/GSM3885060_LPFmatrix.mtx.gz ' + current_path + folder + LPF_folder + '/matrix.mtx.gz')27 os.system('mv ' + current_path + folder + '/GSM3885061_RPFbarcodes.tsv.gz ' + current_path + folder + RPF_folder + '/barcodes.tsv.gz')28 os.system('mv ' + current_path + folder + '/GSM3885061_RPFgenes.tsv.gz ' + current_path + folder + RPF_folder + '/features.tsv.gz')29 os.system('mv ' + current_path + folder + '/GSM3885061_RPFmatrix.mtx.gz ' + current_path + folder + RPF_folder + '/matrix.mtx.gz')...
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