def demo_4d_rsHRF(input_file, mask_file, output_dir, para, mode='bids'):
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
if mode == 'bids':
name = input_file.filename.split('/')[-1].split('.')[0]
v = spm_dep.spm.spm_vol(mask_file.filename)
elif mode == 'bids w/ atlas':
name = input_file.filename.split('/')[-1].split('.')[0]
v = spm_dep.spm.spm_vol(mask_file)
else:
name = input_file.split('/')[-1].split('.')[0]
v = spm_dep.spm.spm_vol(mask_file)
brain = spm_dep.spm.spm_read_vols(v)
voxel_ind = np.where(brain > 0)[0]
temporal_mask = []
if mode == 'bids' or mode == 'bids w/ atlas':
v1 = spm_dep.spm.spm_vol(input_file.filename)
else:
v1 = spm_dep.spm.spm_vol(input_file)
if v1.header.get_data_shape()[:-1] != v.header.get_data_shape():
print('The dimension of your mask is different than '
'the one of your fMRI data!')
return
else:
data = v1.get_data()
nobs = data.shape[3]
data1 = np.reshape(data, (-1, nobs), order='F').T
bold_sig = stats.zscore(data1[:, voxel_ind], ddof=1)
bold_sig = np.nan_to_num(bold_sig)
bold_sig = processing. \
rest_filter. \
rest_IdealFilter(bold_sig, para['TR'], para['passband'])
data_deconv = np.zeros(bold_sig.shape)
event_number = np.zeros((1, bold_sig.shape[1]))
print('Retrieving HRF ...')
if 'canon' in para['estimation']:
beta_hrf, bf, event_bold = \
canon.canon_hrf2dd.wgr_rshrf_estimation_canonhrf2dd_par2(
bold_sig, para, temporal_mask
)
hrfa = np.dot(bf, beta_hrf[np.arange(0, bf.shape[1]), :])
elif 'FIR' in para['estimation']:
para['T'] = 1
hrfa, event_bold = sFIR. \
smooth_fir. \
wgr_rsHRF_FIR(bold_sig, para, temporal_mask)
nvar = hrfa.shape[1]
PARA = np.zeros((3, nvar))
for voxel_id in range(nvar):
hrf1 = hrfa[:, voxel_id]
PARA[:, voxel_id] = \
parameters.wgr_get_parameters(hrf1, para['TR'] / para['T'])
print('Done')
print('Deconvolving HRF ...')
T = np.around(para['len'] / para['TR'])
if para['T'] > 1:
hrfa_TR = signal.resample_poly(hrfa, 1, para['T'])
else:
hrfa_TR = hrfa
for voxel_id in range(nvar):
hrf = hrfa_TR[:, voxel_id]
H = np.fft.fft(np.append(hrf, np.zeros(
(nobs - max(hrf.shape), 1))),
axis=0)
M = np.fft.fft(bold_sig[:, voxel_id])
data_deconv[:, voxel_id] = \
np.fft.ifft(H.conj() * M / (H * H.conj() + 2))
event_number[:, voxel_id] = np.amax(event_bold[voxel_id].shape)
if mode == 'bids' or mode == 'bids w/ atlas':
try:
sub_save_dir = os.path.join(output_dir,
'sub-' + input_file.subject,
'session-' + input_file.session,
input_file.modality)
except AttributeError as e:
sub_save_dir = os.path.join(output_dir,
'sub-' + input_file.subject,
input_file.modality)
else:
sub_save_dir = output_dir
if not os.path.isdir(sub_save_dir):
os.makedirs(sub_save_dir, exist_ok=True)
sio.savemat(os.path.join(sub_save_dir, name + '_hrf.mat'), {
'para': para,
'hrfa': hrfa,
'event_bold': event_bold,
'PARA': PARA
})
HRF_para_str = ['Height.nii', 'Time2peak.nii', 'FWHM.nii']
data = np.zeros(v.get_data().shape).flatten(order='F')
for i in range(3):
fname = os.path.join(sub_save_dir, name + '_' + HRF_para_str[i])
data[voxel_ind] = PARA[i, :]
data = data.reshape(v.get_data().shape, order='F')
spm_dep.spm.spm_write_vol(v, data, fname)
data = data.flatten(order='F')
fname = os.path.join(sub_save_dir, name + '_event_number.nii')
data[voxel_ind] = event_number
data = data.reshape(v.get_data().shape, order='F')
spm_dep.spm.spm_write_vol(v, data, fname)
data = np.zeros(v1.get_data().shape)
dat3 = np.zeros(v1.header.get_data_shape()[:-1]).flatten(order='F')
for i in range(nobs):
fname = os.path.join(sub_save_dir, name + '_deconv')
dat3[voxel_ind] = data_deconv[i, :]
dat3 = dat3.reshape(v1.header.get_data_shape()[:-1], order='F')
data[:, :, :, i] = dat3
dat3 = dat3.flatten(order='F')
spm_dep.spm.spm_write_vol(v1, data, fname)
event_plot = lil_matrix((1, nobs))
event_plot[:, event_bold[0]] = 1
event_plot = np.ravel(event_plot.toarray())
plt.figure()
plt.plot(para['TR'] * np.arange(1,
np.amax(hrfa[:, 0].shape) + 1),
hrfa[:, 0],
linewidth=1)
plt.xlabel('time (s)')
plt.savefig(os.path.join(sub_save_dir, name + '_plot_1.png'))
plt.figure()
plt.plot(para['TR'] * np.arange(1, nobs + 1),
np.nan_to_num(stats.zscore(bold_sig[:, 0], ddof=1)),
linewidth=1)
plt.plot(para['TR'] * np.arange(1, nobs + 1),
np.nan_to_num(stats.zscore(data_deconv[:, 0], ddof=1)),
color='r',
linewidth=1)
markerline, stemlines, baseline = \
plt.stem(para['TR'] * np.arange(1, nobs + 1), event_plot)
plt.setp(baseline, 'color', 'k', 'markersize', 1)
plt.setp(stemlines, 'color', 'k', 'markersize', 1)
plt.setp(markerline, 'color', 'k', 'markersize', 3, 'marker', 'd')
plt.legend(['BOLD', 'deconvolved', 'events'])
plt.xlabel('time (s)')
plt.savefig(os.path.join(sub_save_dir, name + '_plot_2.png'))
def demo_rsHRF(input_file,
mask_file,
output_dir,
para,
p_jobs,
file_type=".nii",
mode="bids"):
# book-keeping w.r.t parameter values
temporal_mask = []
if 'localK' not in para or para['localK'] == None:
if para['TR'] <= 2:
para['localK'] = 1
else:
para['localK'] = 2
# creating the output-directory if not already present
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
# for four-dimensional input
if mode != 'time-series':
if mode == 'bids' or mode == 'bids w/ atlas':
name = input_file.filename.split('/')[-1].split('.')[0]
v1 = spm_dep.spm.spm_vol(input_file.filename)
else:
name = input_file.split('/')[-1].split('.')[0]
v1 = spm_dep.spm.spm_vol(input_file)
if mask_file != None:
if mode == 'bids':
mask_name = mask_file.filename.split('/')[-1].split('.')[0]
v = spm_dep.spm.spm_vol(mask_file.filename)
else:
mask_name = mask_file.split('/')[-1].split('.')[0]
v = spm_dep.spm.spm_vol(mask_file)
if file_type == ".nii" or file_type == ".nii.gz":
brain = spm_dep.spm.spm_read_vols(v)
else:
brain = v.agg_data().flatten(order='F')
if ((file_type == ".nii" or file_type == ".nii.gz") and \
v1.header.get_data_shape()[:-1] != v.header.get_data_shape()) or \
((file_type == ".gii" or file_type == ".gii.gz") and \
v1.agg_data().shape[0]!= v.agg_data().shape[0]):
raise ValueError('Inconsistency in input-mask dimensions' +
'\n\tinput_file == ' + name + file_type +
'\n\tmask_file == ' + mask_name + file_type)
else:
if file_type == ".nii" or file_type == ".nii.gz":
data = v1.get_data()
else:
data = v1.agg_data()
else:
print('No atlas provided! Generating mask file...')
if file_type == ".nii" or file_type == ".nii.gz":
data = v1.get_data()
brain = np.nanvar(data.reshape(-1, data.shape[3]), -1, ddof=0)
else:
data = v1.agg_data()
brain = np.nanvar(data, -1, ddof=0)
print('Done')
voxel_ind = np.where(brain > 0)[0]
mask_shape = data.shape[:-1]
nobs = data.shape[-1]
data1 = np.reshape(data, (-1, nobs), order='F').T
bold_sig = stats.zscore(data1[:, voxel_ind], ddof=1)
# for time-series input
else:
name = input_file.split('/')[-1].split('.')[0]
data1 = (np.loadtxt(input_file, delimiter=","))
if data1.ndim == 1:
data1 = np.expand_dims(data1, axis=1)
nobs = data1.shape[0]
bold_sig = stats.zscore(data1, ddof=1)
bold_sig = np.nan_to_num(bold_sig)
bold_sig_deconv = processing. \
rest_filter. \
rest_IdealFilter(bold_sig, para['TR'], para['passband_deconvolve'])
bold_sig = processing. \
rest_filter. \
rest_IdealFilter(bold_sig, para['TR'], para['passband'])
data_deconv = np.zeros(bold_sig.shape)
event_number = np.zeros((1, bold_sig.shape[1]))
print('Retrieving HRF ...')
#Estimate HRF for the fourier / hanning / gamma / cannon basis functions
if not (para['estimation'] == 'sFIR' or para['estimation'] == 'FIR'):
bf = basis_functions.basis_functions.get_basis_function(
bold_sig.shape, para)
beta_hrf, event_bold = utils.hrf_estimation.compute_hrf(bold_sig,
para,
temporal_mask,
p_jobs,
bf=bf)
hrfa = np.dot(bf, beta_hrf[np.arange(0, bf.shape[1]), :])
#Estimate HRF for FIR and sFIR
else:
beta_hrf, event_bold = utils.hrf_estimation.compute_hrf(
bold_sig, para, temporal_mask, p_jobs)
hrfa = beta_hrf[:-1, :]
nvar = hrfa.shape[1]
PARA = np.zeros((3, nvar))
for voxel_id in range(nvar):
hrf1 = hrfa[:, voxel_id]
PARA[:, voxel_id] = \
parameters.wgr_get_parameters(hrf1, para['TR'] / para['T'])
print('Done')
print('Deconvolving HRF ...')
if para['T'] > 1:
hrfa_TR = signal.resample_poly(hrfa, 1, para['T'])
else:
hrfa_TR = hrfa
for voxel_id in range(nvar):
hrf = hrfa_TR[:, voxel_id]
H = np.fft.fft(np.append(hrf, np.zeros((nobs - max(hrf.shape), 1))),
axis=0)
M = np.fft.fft(bold_sig_deconv[:, voxel_id])
data_deconv[:, voxel_id] = \
np.fft.ifft(H.conj() * M / (H * H.conj() + .1*np.mean((H * H.conj()))))
event_number[:, voxel_id] = np.amax(event_bold[voxel_id].shape)
# setting the output-path
if mode == 'bids' or mode == 'bids w/ atlas':
try:
sub_save_dir = os.path.join(output_dir,
'sub-' + input_file.subject,
'session-' + input_file.session,
input_file.modality)
except AttributeError as e:
sub_save_dir = os.path.join(output_dir,
'sub-' + input_file.subject,
input_file.modality)
else:
sub_save_dir = output_dir
if not os.path.isdir(sub_save_dir):
os.makedirs(sub_save_dir, exist_ok=True)
dic = {'para': para, 'hrfa': hrfa, 'event_bold': event_bold, 'PARA': PARA}
ext = '_hrf.mat'
if mode == "time-series":
dic["event_number"] = event_number
dic["data_deconv"] = data_deconv
ext = '_hrf_deconv.mat'
sio.savemat(os.path.join(sub_save_dir, name + ext), dic)
HRF_para_str = ['Height', 'Time2peak', 'FWHM']
if mode != "time-series":
mask_data = np.zeros(mask_shape).flatten(order='F')
for i in range(3):
fname = os.path.join(sub_save_dir, name + '_' + HRF_para_str[i])
mask_data[voxel_ind] = PARA[i, :]
mask_data = mask_data.reshape(mask_shape, order='F')
spm_dep.spm.spm_write_vol(v1, mask_data, fname, file_type)
mask_data = mask_data.flatten(order='F')
fname = os.path.join(sub_save_dir, name + '_event_number.nii')
mask_data[voxel_ind] = event_number
mask_data = mask_data.reshape(mask_shape, order='F')
spm_dep.spm.spm_write_vol(v1, mask_data, fname, file_type)
mask_data = np.zeros(data.shape)
dat3 = np.zeros(data.shape[:-1]).flatten(order='F')
for i in range(nobs):
fname = os.path.join(sub_save_dir, name + '_deconv')
dat3[voxel_ind] = data_deconv[i, :]
dat3 = dat3.reshape(data.shape[:-1], order='F')
if file_type == ".nii" or file_type == ".nii.gz":
mask_data[:, :, :, i] = dat3
else:
mask_data[:, i] = dat3
dat3 = dat3.flatten(order='F')
spm_dep.spm.spm_write_vol(v1, mask_data, fname, file_type)
pos = 0
while pos < hrfa.shape[1]:
if np.any(hrfa[:, pos]):
break
pos += 1
event_plot = lil_matrix((1, nobs))
event_plot[:, event_bold[pos]] = 1
event_plot = np.ravel(event_plot.toarray())
plt.figure()
plt.plot(para['TR'] * np.arange(1,
np.amax(hrfa[:, pos].shape) + 1),
hrfa[:, pos],
linewidth=1)
plt.xlabel('time (s)')
plt.savefig(os.path.join(sub_save_dir, name + '_plot_1.png'))
plt.figure()
plt.plot(para['TR'] * np.arange(1, nobs + 1),
np.nan_to_num(stats.zscore(bold_sig[:, pos], ddof=1)),
linewidth=1)
plt.plot(para['TR'] * np.arange(1, nobs + 1),
np.nan_to_num(stats.zscore(data_deconv[:, pos], ddof=1)),
color='r',
linewidth=1)
markerline, stemlines, baseline = \
plt.stem(para['TR'] * np.arange(1, nobs + 1), event_plot)
plt.setp(baseline, 'color', 'k', 'markersize', 1)
plt.setp(stemlines, 'color', 'k')
plt.setp(markerline, 'color', 'k', 'markersize', 3, 'marker', 'd')
plt.legend(['BOLD', 'deconvolved', 'events'])
plt.xlabel('time (s)')
plt.savefig(os.path.join(sub_save_dir, name + '_plot_2.png'))
print('Done')