converted tabs to spaces

waveformCSV.py

@@ -8,121 +8,121 @@ import argparse
 waves = ['delta', 'theta', 'alpha', 'beta', 'gamma']
 
 def getLetter(i):
-	return string.ascii_lowercase[i]
+    return string.ascii_lowercase[i]
 
 def printINFO(v, string):
-	if (v):
-		print("INFO: {}".format(string))
+    if (v):
+        print("INFO: {}".format(string))
 
 def SaveWave2csv(pid, v=False, extension='raw', inOneCSV=False, nfilterCoeff=4001):
-	printINFO(v, "Patient ID: {}".format(pid))
-	
-	p = patient.Patient(pid)
-	
-	printINFO(v, "Extracting waves for season_start!")
-	preprocessing.extractWaves(p.season_start, n=nfilterCoeff, samplingRate=256, wave='all')
-	
-	printINFO(v, "Extracting waves for concussion!")
-	for i in range(len(p.concussions)):
-		preprocessing.extractWaves(p.concussions[i], n=nfilterCoeff, samplingRate=256, wave='all')
-	
-	printINFO(v, "Extracting waves for season_end!")
-	preprocessing.extractWaves(p.season_end, n=nfilterCoeff, samplingRate=256, wave='all')
-	
-	printINFO(v, "Saving extracting waves to files!")
-	if (inOneCSV):
-		# Save season_start to csv
-		fname = "".join([pid,'a_waves.', extension])
-		fpath = os.path.join(path,fname)
-		tmp = list(p.season_start.waves.values())
-		
-		for j in range(len(tmp)-1):
-			tmp[j].drop('time', axis=1, inplace=True)
-		
-		df = pd.concat(tmp, axis=1)
-		printINFO(v,"Saving file: {}".format(fpath))
-		df.to_csv(fpath, index=False)
-
-		# Save concussian to csv
-		for i in range(len(p.concussions)):
-
-			fname = "".join([pid, getLetter(i+1), '_waves.', extension])
-			fpath = os.path.join(path,fname)
-			tmp = list(p.concussions[i].waves.values())
-			
-			for j in range(len(tmp)-1):
-				tmp[j].drop('time', axis=1, inplace=True)
-			
-			df = pd.concat(tmp, axis=1)
-			printINFO(v,"Saving file: {}".format(fpath))
-			df.to_csv(fpath, index=False)
-
-		# Save season_end to csv
-		fname = "".join([pid, getLetter(i+2), '_waves.', extension])
-		fpath = os.path.join(path,fname)
-		tmp = list(p.season_end.waves.values())
-
-		for j in range(len(tmp)-1):
-			tmp[j].drop('time', axis=1, inplace=True)
-		
-		df = pd.concat(tmp, axis=1)
-		printINFO(v,"Saving file: {}".format(fpath))
-		df.to_csv(fpath, index=False)
-
-	else:
-		# Will create one csv file for each waveform
-		for wave in waves:
-			# save season_start to csv
-			fname = "".join([pid,'a_',wave,'.', extension])
-			fpath = os.path.join(path,fname)
-			printINFO(v,"Saving file: {}".format(fpath))
-			p.season_start.waves[wave].to_csv(fpath, index=False)
-
-			# save concussion to csv
-			for i in range(len(p.concussions)):
-
-				fname = "".join([pid, getLetter(i+1), '_', wave,'.', extension])
-				fpath = os.path.join(path,fname)
-				printINFO(v,"Saving file: {}".format(fpath))
-				p.concussions[i].waves[wave].to_csv(fpath, index=False)
-
-			# save season_end to csv
-			fname = "".join([pid, getLetter(i+2), '_', wave,'.', extension])
-			fpath = os.path.join(path,fname)
-			printINFO(v,"Saving file: {}".format(fpath))
-			p.season_end.waves[wave].to_csv(fpath, index=False)
+    printINFO(v, "Patient ID: {}".format(pid))
+    
+    p = patient.Patient(pid)
+    
+    printINFO(v, "Extracting waves for season_start!")
+    preprocessing.extractWaves(p.season_start, n=nfilterCoeff, samplingRate=256, wave='all')
+    
+    printINFO(v, "Extracting waves for concussion!")
+    for i in range(len(p.concussions)):
+        preprocessing.extractWaves(p.concussions[i], n=nfilterCoeff, samplingRate=256, wave='all')
+    
+    printINFO(v, "Extracting waves for season_end!")
+    preprocessing.extractWaves(p.season_end, n=nfilterCoeff, samplingRate=256, wave='all')
+    
+    printINFO(v, "Saving extracting waves to files!")
+    if (inOneCSV):
+        # Save season_start to csv
+        fname = "".join([pid,'a_waves.', extension])
+        fpath = os.path.join(path,fname)
+        tmp = list(p.season_start.waves.values())
+        
+        for j in range(len(tmp)-1):
+            tmp[j].drop('time', axis=1, inplace=True)
+        
+        df = pd.concat(tmp, axis=1)
+        printINFO(v,"Saving file: {}".format(fpath))
+        df.to_csv(fpath, index=False)
+
+        # Save concussian to csv
+        for i in range(len(p.concussions)):
+
+            fname = "".join([pid, getLetter(i+1), '_waves.', extension])
+            fpath = os.path.join(path,fname)
+            tmp = list(p.concussions[i].waves.values())
+            
+            for j in range(len(tmp)-1):
+                tmp[j].drop('time', axis=1, inplace=True)
+            
+            df = pd.concat(tmp, axis=1)
+            printINFO(v,"Saving file: {}".format(fpath))
+            df.to_csv(fpath, index=False)
+
+        # Save season_end to csv
+        fname = "".join([pid, getLetter(i+2), '_waves.', extension])
+        fpath = os.path.join(path,fname)
+        tmp = list(p.season_end.waves.values())
+
+        for j in range(len(tmp)-1):
+            tmp[j].drop('time', axis=1, inplace=True)
+        
+        df = pd.concat(tmp, axis=1)
+        printINFO(v,"Saving file: {}".format(fpath))
+        df.to_csv(fpath, index=False)
+
+    else:
+        # Will create one csv file for each waveform
+        for wave in waves:
+            # save season_start to csv
+            fname = "".join([pid,'a_',wave,'.', extension])
+            fpath = os.path.join(path,fname)
+            printINFO(v,"Saving file: {}".format(fpath))
+            p.season_start.waves[wave].to_csv(fpath, index=False)
+
+            # save concussion to csv
+            for i in range(len(p.concussions)):
+
+                fname = "".join([pid, getLetter(i+1), '_', wave,'.', extension])
+                fpath = os.path.join(path,fname)
+                printINFO(v,"Saving file: {}".format(fpath))
+                p.concussions[i].waves[wave].to_csv(fpath, index=False)
+
+            # save season_end to csv
+            fname = "".join([pid, getLetter(i+2), '_', wave,'.', extension])
+            fpath = os.path.join(path,fname)
+            printINFO(v,"Saving file: {}".format(fpath))
+            p.season_end.waves[wave].to_csv(fpath, index=False)
 
 
 if __name__ == '__main__':
-	parser = argparse.ArgumentParser()
-	parser.add_argument("-path", required=True, type=str, help="the folder path to save the csv's")
-	parser.add_argument("-v", required=False, action="store_true", help="verbose option to print INFO")
-	parser.add_argument("-extension", required=False, type=str, help="the file extension to be used, default is raw")
-	parser.add_argument("-pid", required=False, type=int, help="the pid to be saved, if not given it will save for all the patients id's")
-	parser.add_argument("-nfilterCoeff", required=False, type=int, help="number of filter coefficients")
-	parser.add_argument("-csvPerWave", required=False, action="store_true", help="save each waveform to different csv file")
-
-	args = parser.parse_args()
-	
-	path = args.path
-
-	if args.nfilterCoeff:
-		nfilterCoeff = args.nfilterCoeff
-	else:
-		nfilterCoeff = 4001
-
-	if args.extension:
-		extension = args.extension
-	else:
-		extension = 'raw'
-	
-	if args.nfilterCoeff:
-		nfilterCoeff = args.nfilterCoeff
-
-	if args.pid:
-		pid = [args.pid]
-	else:
-		pid = np.arange(1,99)
-	
-	for i in pid:
-		SaveWave2csv(str(i), v=args.v, inOneCSV=not(args.csvPerWave), extension=extension, nfilterCoeff=nfilterCoeff)
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-path", required=True, type=str, help="the folder path to save the csv's")
+    parser.add_argument("-v", required=False, action="store_true", help="verbose option to print INFO")
+    parser.add_argument("-extension", required=False, type=str, help="the file extension to be used, default is raw")
+    parser.add_argument("-pid", required=False, type=int, help="the pid to be saved, if not given it will save for all the patients id's")
+    parser.add_argument("-nfilterCoeff", required=False, type=int, help="number of filter coefficients")
+    parser.add_argument("-csvPerWave", required=False, action="store_true", help="save each waveform to different csv file")
+
+    args = parser.parse_args()
+    
+    path = args.path
+
+    if args.nfilterCoeff:
+        nfilterCoeff = args.nfilterCoeff
+    else:
+        nfilterCoeff = 4001
+
+    if args.extension:
+        extension = args.extension
+    else:
+        extension = 'raw'
+    
+    if args.nfilterCoeff:
+        nfilterCoeff = args.nfilterCoeff
+
+    if args.pid:
+        pid = [args.pid]
+    else:
+        pid = np.arange(1,99)
+    
+    for i in pid:
+        SaveWave2csv(str(i), v=args.v, inOneCSV=not(args.csvPerWave), extension=extension, nfilterCoeff=nfilterCoeff)