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brain_age_from_eeg
Commit 6a0ec7bb authored by Can Pervane's avatar Can Pervane
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converted tabs to spaces

parent bf3d1d27
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Showing with 111 additions and 111 deletions
waveformCSV.py
View file @ 6a0ec7bb
......@@ -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)
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