def add_lfp_filt(f, fs = 384.384384384):
lfp = f['lfp'].value
nchan, ntrials, nsamp = lfp.shape
lfp_filt = np.empty_like(lfp)
for i in range(nchan):
for j in range(ntrials):
lfp_filt[i, j, :] = EEG.remove_60hz(lfp[i, j, :])
f.create_dataset('lfp_filt', data = lfp_filt, compression = 'gzip')
def combine(epoch = 'Pre', stimtype = 'noise'):
'''
combine left and right ear stimulation blocks into one pandas DataFrame
'''
sesspaths = glob.glob(os.path.join(studydir, 'Sessions', epoch, '*'))
for sesspath in sesspaths:
absol, sessID = os.path.split(sesspath)
print sessID
fpaths = glob.glob(os.path.join(sesspath, 'fileconversion', '*.h5'))
subjIDs = np.unique([os.path.splitext(os.path.split(fpath)[1])[0].split('_')[0] for fpath in fpaths])
hemis = 'rl'
ears = 'rl'
for subjID in subjIDs:
LFP = []; LFPFILT = []; ATTEN = []; EAR = []; HEMI = []; RELHEMI = []; SESSID = [];
for ear in ears:
fpath = glob.glob(os.path.join(sesspath, 'fileconversion', '%s_%s_%s_b*.h5' % (subjID, ear.upper(), stimtype)))[0]
fin = h5py.File(fpath, 'r')
lfp = fin['lfp'].value
stimparams = fin['stimID'].value
fin.close()
nchan, ntrials, nsamp = lfp.shape
for i in xrange(nchan):
[LFP.append(lfp[i, j, :]) for j in xrange(ntrials)]
[LFPFILT.append(EEG.remove_60hz(lfp[i, j, :])) for j in xrange(ntrials)]
ATTEN.extend(stimparams[:, 1].tolist())
EAR.extend([ear]*ntrials)
HEMI.extend([hemis[i]]*ntrials)
if ear==hemis[i]:
relhemi = 'ipsi'
elif ear!=hemis[i]:
relhemi = 'contra'
RELHEMI.extend([relhemi]*ntrials)
SESSID.extend([sessID]*ntrials)
d = dict(lfp = LFP, lfpfilt = LFPFILT, atten = ATTEN, ear = EAR, hemi = HEMI, relhemi = RELHEMI, sess = SESSID)
df = pd.DataFrame(d)
fout = pd.HDFStore(os.path.join(sesspath, 'both', '%s_both.h5' % subjID))
fout['df'] = df
fout.close()
