def _list_outputs(self):
from bids.layout import BIDSLayout
base_dir = self.inputs.base_directory
os.makedirs(base_dir, exist_ok=True)
layout = BIDSLayout(base_dir, validate=False)
path_patterns = self.inputs.path_patterns
if not isdefined(path_patterns):
path_patterns = None
out_files = []
for entities, in_file in zip(self.inputs.entities,
self.inputs.in_file):
ents = {**self.inputs.fixed_entities}
ents.update(entities)
ents = {k: snake_to_camel(str(v)) for k, v in ents.items()}
out_fname = os.path.join(base_dir,
layout.build_path(ents, path_patterns))
makedirs(os.path.dirname(out_fname), exist_ok=True)
_copy_or_convert(in_file, out_fname)
out_files.append(out_fname)
return {'out_file': out_files}
def _list_outputs(self):
from bids.layout import BIDSLayout
base_dir = self.inputs.base_directory
os.makedirs(base_dir, exist_ok=True)
layout = BIDSLayout(base_dir, validate=False)
path_patterns = self.inputs.path_patterns
if not isdefined(path_patterns):
path_patterns = None
out_files = []
for entities, in_file in zip(self.inputs.entities, self.inputs.in_file):
ents = {**self.inputs.fixed_entities}
ents.update(entities)
ext = bids_split_filename(in_file)[2]
ents['extension'] = self._extension_map.get(ext, ext)
# In some instances, name/contrast could have the following
# format (eg: gain.Range, gain.EqualIndifference).
# This prevents issues when creating/searching files for the report
for k, v in ents.items():
if k in ("node", "name", "contrast", "stat"):
ents.update({k: to_alphanum(str(v))})
out_fname = os.path.join(
base_dir, layout.build_path(ents, path_patterns, validate=False)
)
os.makedirs(os.path.dirname(out_fname), exist_ok=True)
_copy_or_convert(in_file, out_fname)
out_files.append(out_fname)
return {'out_file': out_files}
def write_full_report(report_dict, run_context, deriv_dir):
fl_layout = BIDSLayout(deriv_dir, config=['bids', 'derivatives', 'fitlins'])
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(searchpath=pkgr.resource_filename('fitlins', '/'))
)
tpl = env.get_template('data/full_report.tpl')
model_name = snake_to_camel(report_dict['model'].get('name') or "untitled")
target_file = op.join(
deriv_dir, fl_layout.build_path({'model': model_name}, PATH_PATTERNS, validate=False)
)
html = tpl.render(deroot({**report_dict, **run_context}, op.dirname(target_file)))
Path(target_file).parent.mkdir(parents=True, exist_ok=True)
Path(target_file).write_text(html)
def write_report(level, report_dicts, run_context, deriv_dir):
fl_layout = BIDSLayout(
deriv_dir, config=['bids', 'derivatives', 'fitlins'])
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(
searchpath=pkgr.resource_filename('fitlins', '/')))
tpl = env.get_template('data/report.tpl')
for context in report_dicts:
ents = context['ents'].copy()
ents['model'] = snake_to_camel(context['model_name'])
target_file = op.join(deriv_dir, fl_layout.build_path(ents, PATH_PATTERNS))
html = tpl.render(deroot({'level': level, **context, **run_context},
op.dirname(target_file)))
with open(target_file, 'w') as fobj:
fobj.write(html)
def demo_rsHRF(input_file,
mask_file,
output_dir,
para,
p_jobs,
file_type=".nii",
mode="bids",
wiener=False,
temporal_mask=[]):
# book-keeping w.r.t parameter values
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.split('/')[-1].split('.')[0]
v1 = spm_dep.spm.spm_vol(input_file)
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.split('/')[-1].split('.')[0]
v = spm_dep.spm.spm_vol(mask_file)
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)
if len(temporal_mask) > 0 and len(temporal_mask) != nobs:
raise ValueError('Inconsistency in temporal_mask dimensions.\n' +
'Size of mask: ' + str(len(temporal_mask)) + '\n' +
'Size of time-series: ' + str(nobs))
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:
para['T'] = 1
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]
if not wiener:
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()))))
else:
data_deconv[:,
voxel_id] = iterative_wiener_deconv.rsHRF_iterative_wiener_deconv(
bold_sig_deconv[:, voxel_id], hrf)
event_number[:, voxel_id] = np.amax(event_bold[voxel_id].shape)
print('Done')
print('Saving Output ...')
# setting the output-path
if mode == 'bids' or mode == 'bids w/ atlas':
layout_output = BIDSLayout(output_dir)
entities = parse_file_entities(input_file)
sub_save_dir = layout_output.build_path(entities).rsplit('/', 1)[0]
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'
name = name.rsplit('_bold', 1)[0]
sio.savemat(os.path.join(sub_save_dir, name + ext), dic)
HRF_para_str = ['height', 'T2P', '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 + '_eventnumber')
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_TR.shape[1]:
if np.any(hrfa_TR[:, pos]):
break
pos += 1
event_plot = lil_matrix((1, nobs))
if event_bold.size:
event_plot[:, event_bold[pos]] = 1
else:
print("No Events Detected!")
return 0
event_plot = np.ravel(event_plot.toarray())
plt.figure()
plt.plot(para['TR'] * np.arange(1,
np.amax(hrfa_TR[:, pos].shape) + 1),
hrfa_TR[:, pos],
linewidth=1)
plt.xlabel('time (s)')
plt.savefig(os.path.join(sub_save_dir, name + '_hrf_plot.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 BOLD', 'Events'], loc='best')
plt.xlabel('time (s)')
plt.savefig(os.path.join(sub_save_dir, name + '_deconvolution_plot.png'))
print('Done')
return 0
# Creating the layout object for this BIDS data set
layout = BIDSLayout(dataDir)
# new subjects
newSubj = list(range(10, 19))
# other information for the directory organization
listSes = ['test', 'retest']
listMod = ['anat', 'func', 'dwi']
# first, a list of new subject directories
pattern = "sub-{subject}"
for iSubj in newSubj:
# dictionary listing entitied
entities = {'subject': '%02d' % iSubj}
newDir = layout.build_path(entities, path_patterns=[pattern])
print(newDir)
# session directories for each subject
pattern = "sub-{subject}/ses-{session}"
for iSubj in newSubj:
for iSes in listSes:
# dictionary listing entitied
entities = {'subject': '%02d' % iSubj, 'session': iSes}
newDir = layout.build_path(entities, path_patterns=[pattern])
print(newDir)
# image modality directories for each subject
pattern = "sub-{subject}/ses-{session}/{modality}"
for iSubj in newSubj:
for iSes in listSes:
