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How to use current_path method in localstack

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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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