def save_time_series(k):
if simulation.has_key(k):
fn_stim_induced = add_prefix(
op.join(output_dir, k + '.nii'), prefix)
logger.info('%s flat shape %s', k, str(simulation[k].shape))
vol = expand_array_in_mask(simulation[k], mask_vol, flat_axis=1)
write_volume(np.rollaxis(vol, 0, 4), fn_stim_induced)
def split_big_parcels(parcel_file, output_file, max_size=400):
print 'split_big_parcels ...'
roiMask, roiHeader = read_volume(parcel_file)
roiIds = np.unique(roiMask)
background = roiIds.min()
labels = roiMask[np.where(roiMask > background)].astype(int)
if (np.bincount(labels) <= max_size).all():
logger.info('no parcel to split')
return
graphs = parcels_to_graphs(roiMask, kerMask3D_6n)
for roiId in roiIds:
if roiId != background:
roi_size = (roiMask == roiId).sum()
if roi_size > max_size:
print 'roi %d, size = %d' % (roiId, roi_size)
nparcels = int(np.ceil(roi_size * 1. / max_size))
print 'split into %d parcels ...' % (nparcels)
split_parcel(labels,
graphs,
roiId,
nparcels,
inplace=True,
verbosity=1)
final_roi_mask = np.zeros_like(roiMask)
final_roi_mask[np.where(roiMask > background)] = labels
assert (np.bincount(labels) <= max_size).all()
write_volume(final_roi_mask, output_file, roiHeader)
def split_big_parcels(parcel_file, output_file, max_size=400):
print 'split_big_parcels ...'
roiMask, roiHeader = read_volume(parcel_file)
roiIds = np.unique(roiMask)
background = roiIds.min()
labels = roiMask[np.where(roiMask>background)].astype(int)
if (np.bincount(labels) <= max_size).all():
pyhrf.verbose(1, 'no parcel to split')
return
graphs = parcels_to_graphs(roiMask, kerMask3D_6n)
for roiId in roiIds:
if roiId != background:
roi_size = (roiMask==roiId).sum()
if roi_size > max_size:
print 'roi %d, size = %d' %(roiId, roi_size)
nparcels = int(np.ceil(roi_size*1./max_size))
print 'split into %d parcels ...' %(nparcels)
split_parcel(labels, graphs, roiId, nparcels, inplace=True,
verbosity=1)
final_roi_mask = np.zeros_like(roiMask)
final_roi_mask[np.where(roiMask>background)] = labels
#print np.bincount(labels)
assert (np.bincount(labels) <= max_size).all()
write_volume(final_roi_mask, output_file, roiHeader)
def save(self, output_dir):
"""
Save paradigm to output_dir/paradigm.csv,
BOLD to output_dir/bold.nii, mask to output_dir/mask.nii
#TODO: handle multi-session
Return: tuple of file names in this order: (paradigm, bold, mask)
"""
from pyhrf.tools._io import write_volume, write_texture
paradigm_file = op.join(output_dir, 'paradigm.csv')
self.paradigm.save_csv(paradigm_file)
if self.data_type == 'volume':
# unflatten bold
bold_vol = expand_array_in_mask(self.bold, self.roiMask, 1)
bold_vol = np.rollaxis(bold_vol, 0, 4)
bold_file = op.join(output_dir, 'bold.nii')
write_volume(bold_vol, bold_file, self.meta_obj)
mask_file = op.join(output_dir, 'mask.nii')
write_volume(self.roiMask, mask_file, self.meta_obj)
elif self.data_type == 'surface': # TODO surface
bold_file = op.join(output_dir, 'bold.gii')
write_texture(self.bold_vol, bold_file, self.meta_obj)
pass
return paradigm_file, bold_file, mask_file
def save_time_series(k):
if simulation.has_key(k):
fn_stim_induced = add_prefix(op.join(output_dir, k + '.nii'),
prefix)
logger.info('%s flat shape %s', k, str(simulation[k].shape))
vol = expand_array_in_mask(simulation[k], mask_vol, flat_axis=1)
write_volume(np.rollaxis(vol, 0, 4), fn_stim_induced)
def glm_matlab_from_files(bold_file, tr, paradigm_csv_file, output_dir,
mask_file, hf_cut=128, hack_mask=False):
"""
Only mono-session
#TODO: compute mask if mask_file does not exist
#TODO: handle contrasts
"""
# Functional mask
# if not op.exists(mask_file):
# pyhrf.verbose(1, 'Mask file does not exist. Computing mask from '\
# 'BOLD data')
# compute_mask_files(bold_files, mask_file, False, 0.4, 0.9)
#split BOLD into 3D vols:
bold, hbold = read_volume(bold_file)
bold_files = []
tmp_path = tempfile.mkdtemp(dir=pyhrf.cfg['global']['tmp_path'])
for iscan, bscan in enumerate(bold):
f = op.join(tmp_path, 'bold_%06d.nii' %iscan)
write_volume(bscan, f, hbold)
bold_files.append(f)
bold_files = ';'.join(bold_files)
script_path = op.join(op.dirname(pyhrf.__file__),'../../script/SPM')
spm_path = pyhrf.cfg['global']['spm_path']
matlab_code = "cd %s;paradigm_file='%s';TR=%f;mask_file='%s';" \
"bold_files='%s';output_path='%s';" \
"HF_cut=%f;spm_path='%s';api=1;hack_mask=%d;glm_intra_subj;exit" \
%(script_path,paradigm_csv_file,tr,mask_file,bold_files,output_dir,
hf_cut,spm_path,hack_mask)
matlab_cmd = 'matlab -nosplash -nodesktop -r "%s"'%matlab_code
if op.exists(op.join(output_dir,'SPM.mat')):
#remove SPM.mat so that SPM won't ask over ask overwriting
os.remove(op.join(output_dir,'SPM.mat'))
#print 'matlab cmd:'
#print matlab_cmd
os.system(matlab_cmd)
# Fix shape of outputs if necessary
# eg if input data has shape (n,m,1) then SPM will write outputs of
# shape (n,m) so that they are not consistent with their QForm
input_shape = bscan.shape
for foutput in glob.glob(op.join(output_dir, '*.img')):
data, h = read_volume(foutput)
if data.ndim < 3:
sm = ','.join([ [':','np.newaxis'][d==1] \
for d in input_shape ] )
exec('data = data[%s]' %sm)
assert data.shape == input_shape
write_volume(data, foutput, h)
shutil.rmtree(tmp_path)
#TODO: maybe find a better way to grab beta file names
beta_files = sorted(glob.glob(op.join(output_dir,'beta_*.img')))
return beta_files
def save(self, output_dir):
"""
Save paradigm to output_dir/paradigm.csv,
BOLD to output_dir/bold.nii, mask to output_dir/mask.nii
#TODO: handle multi-session
Return: tuple of file names in this order: (paradigm, bold, mask)
"""
from pyhrf.tools._io import write_volume, write_texture
paradigm_file = op.join(output_dir, 'paradigm.csv')
self.paradigm.save_csv(paradigm_file)
if self.data_type == 'volume':
# unflatten bold
bold_vol = expand_array_in_mask(self.bold, self.roiMask, 1)
bold_vol = np.rollaxis(bold_vol, 0, 4)
bold_file = op.join(output_dir, 'bold.nii')
write_volume(bold_vol, bold_file, self.meta_obj)
mask_file = op.join(output_dir, 'mask.nii')
write_volume(self.roiMask, mask_file, self.meta_obj)
elif self.data_type == 'surface': # TODO surface
bold_file = op.join(output_dir, 'bold.gii')
write_texture(self.bold_vol, bold_file, self.meta_obj)
pass
return paradigm_file, bold_file, mask_file
def glm_matlab_from_files(bold_file, tr, paradigm_csv_file, output_dir,
mask_file, hf_cut=128, hack_mask=False):
"""
Only mono-session
#TODO: compute mask if mask_file does not exist
#TODO: handle contrasts
"""
# Functional mask
# if not op.exists(mask_file):
# logger.info('Mask file does not exist. Computing mask from '
# 'BOLD data')
# compute_mask_files(bold_files, mask_file, False, 0.4, 0.9)
# split BOLD into 3D vols:
bold, hbold = read_volume(bold_file)
bold_files = []
tmp_path = tempfile.mkdtemp(dir=pyhrf.cfg['global']['tmp_path'])
for iscan, bscan in enumerate(bold):
f = op.join(tmp_path, 'bold_%06d.nii' % iscan)
write_volume(bscan, f, hbold)
bold_files.append(f)
bold_files = ';'.join(bold_files)
script_path = op.join(op.dirname(pyhrf.__file__), '../../script/SPM')
spm_path = pyhrf.cfg['global']['spm_path']
matlab_code = "cd %s;paradigm_file='%s';TR=%f;mask_file='%s';" \
"bold_files='%s';output_path='%s';" \
"HF_cut=%f;spm_path='%s';api=1;hack_mask=%d;glm_intra_subj;exit" \
% (script_path, paradigm_csv_file, tr, mask_file, bold_files, output_dir,
hf_cut, spm_path, hack_mask)
matlab_cmd = 'matlab -nosplash -nodesktop -r "%s"' % matlab_code
if op.exists(op.join(output_dir, 'SPM.mat')):
# remove SPM.mat so that SPM won't ask over ask overwriting
os.remove(op.join(output_dir, 'SPM.mat'))
# print 'matlab cmd:'
# print matlab_cmd
os.system(matlab_cmd)
# Fix shape of outputs if necessary
# eg if input data has shape (n,m,1) then SPM will write outputs of
# shape (n,m) so that they are not consistent with their QForm
input_shape = bscan.shape
for foutput in glob.glob(op.join(output_dir, '*.img')):
data, h = read_volume(foutput)
if data.ndim < 3:
sm = ','.join([[':', 'np.newaxis'][d == 1]
for d in input_shape])
exec('data = data[%s]' % sm)
assert data.shape == input_shape
write_volume(data, foutput, h)
shutil.rmtree(tmp_path)
# TODO: maybe find a better way to grab beta file names
beta_files = sorted(glob.glob(op.join(output_dir, 'beta_*.img')))
return beta_files
def test_pyhrf_extract_cc_vol(self):
test_mask = np.array(
[[[1, 1, 0, 1, 1], [1, 1, 0, 1, 1], [0, 0, 0, 0, 0],
[1, 0, 1, 1, 0], [0, 0, 1, 1, 0]],
[[1, 1, 0, 1, 1], [1, 1, 0, 1, 1], [0, 0, 0, 0, 0],
[0, 0, 1, 1, 0], [0, 0, 1, 1, 0]]],
dtype=int)
mask_file = op.join(self.tmp_dir, 'test_mask.nii')
write_volume(test_mask, mask_file)
cmd = 'pyhrf_extract_cc_vol -v0 -m 2 %s' % mask_file
if os.system(cmd) != 0:
raise Exception('Command %s failed' % cmd)
assert len(os.listdir(self.tmp_dir)) == 4
def test_pyhrf_extract_cc_vol(self):
test_mask = np.array([[[1, 1, 0, 1, 1],
[1, 1, 0, 1, 1],
[0, 0, 0, 0, 0],
[1, 0, 1, 1, 0],
[0, 0, 1, 1, 0]],
[[1, 1, 0, 1, 1],
[1, 1, 0, 1, 1],
[0, 0, 0, 0, 0],
[0, 0, 1, 1, 0],
[0, 0, 1, 1, 0]]],
dtype=int)
mask_file = op.join(self.tmp_dir, 'test_mask.nii')
write_volume(test_mask, mask_file)
cmd = 'pyhrf_extract_cc_vol -v0 -m 2 %s' % mask_file
if os.system(cmd) != 0:
raise Exception('Command %s failed' % cmd)
assert len(os.listdir(self.tmp_dir)) == 4
def make_parcellation_cubed_blobs_from_file(parcellation_file,
output_path,
roi_ids=None,
bg_parcel=0,
skip_existing=False):
p, mp = read_volume(parcellation_file)
p = p.astype(np.int32)
if bg_parcel == 0 and p.min() == -1:
p += 1 # set background to 0
if roi_ids is None:
roi_ids = np.unique(p)
logger.info('%d rois to extract', (len(roi_ids) - 1))
tmp_dir = pyhrf.get_tmp_path('blob_parcellation')
tmp_parcel_mask_file = op.join(tmp_dir, 'parcel_for_blob.nii')
out_files = []
for roi_id in roi_ids:
if roi_id != bg_parcel: # discard background
output_blob_file = op.join(output_path,
'parcel_%d_cubed_blob.arg' % roi_id)
out_files.append(output_blob_file)
if skip_existing and os.path.exists(output_blob_file):
continue
parcel_mask = (p == roi_id).astype(np.int32)
write_volume(parcel_mask, tmp_parcel_mask_file, mp)
logger.info('Extract ROI %d -> %s', roi_id, output_blob_file)
cmd = 'AimsGraphConvert -i %s -o %s --bucket' \
% (tmp_parcel_mask_file, output_blob_file)
logger.info('Cmd: %s', cmd)
os.system(cmd)
if op.exists(tmp_parcel_mask_file):
os.remove(tmp_parcel_mask_file)
return out_files
def make_parcellation_cubed_blobs_from_file(parcellation_file, output_path,
roi_ids=None, bg_parcel=0,
skip_existing=False):
p,mp = read_volume(parcellation_file)
p = p.astype(np.int32)
if bg_parcel==0 and p.min() == -1:
p += 1 #set background to 0
if roi_ids is None:
roi_ids = np.unique(p)
pyhrf.verbose(1,'%d rois to extract' %(len(roi_ids)-1))
tmp_dir = pyhrf.get_tmp_path('blob_parcellation')
tmp_parcel_mask_file = op.join(tmp_dir, 'parcel_for_blob.nii')
out_files = []
for roi_id in roi_ids:
if roi_id != bg_parcel: #discard background
output_blob_file = op.join(output_path, 'parcel_%d_cubed_blob.arg'\
%roi_id)
out_files.append(output_blob_file)
if skip_existing and os.path.exists(output_blob_file):
continue
parcel_mask = (p==roi_id).astype(np.int32)
write_volume(parcel_mask, tmp_parcel_mask_file, mp)
pyhrf.verbose(3,'Extract ROI %d -> %s' %(roi_id,output_blob_file))
cmd = 'AimsGraphConvert -i %s -o %s --bucket' \
%(tmp_parcel_mask_file, output_blob_file)
pyhrf.verbose(3,'Cmd: %s' %(cmd))
os.system(cmd)
if op.exists(tmp_parcel_mask_file):
os.remove(tmp_parcel_mask_file)
return out_files
def project_fmri_from_kernels(input_mesh, kernels_file, fmri_data_file,
output_tex, bin_threshold=None, ):
pyhrf.verbose(2,'Project data onto mesh using kernels ...')
if 0:
print 'Projecting ...'
print 'func data:', fmri_data_file
print 'Mesh file:', input_mesh
print 'Save as:', output_tex
pyhrf.verbose(2,'Call AimsFunctionProjection -op 1 ...')
data_files = []
output_texs = []
p_ids = None
if bin_threshold is not None:
d,h = read_volume(fmri_data_file)
if np.allclose(d.astype(int), d):
tmp_dir = pyhrf.get_tmp_path()
p_ids = np.unique(d)
pyhrf.verbose(2, 'bin threshold: %f' %bin_threshold)
pyhrf.verbose(2, 'pids(n=%d): %d...%d' \
%(len(p_ids),min(p_ids),max(p_ids)))
for i,p_id in enumerate(p_ids):
if p_id != 0:
new_p = np.zeros_like(d)
new_p[np.where(d==p_id)] = i + 1 #0 is background
ifn = op.join(tmp_dir,'pmask_%d.nii'%p_id)
write_volume(new_p, ifn, h)
data_files.append(ifn)
ofn = op.join(tmp_dir,'ptex_%d.gii'%p_id)
output_texs.append(ofn)
else:
data_files.append(fmri_data_file)
output_texs.append(output_tex)
else:
data_files.append(fmri_data_file)
output_texs.append(output_tex)
pyhrf.verbose(3, 'input data files: %s' %str(data_files))
pyhrf.verbose(3, 'output data files: %s' %str(output_texs))
for data_file, o_tex in zip(data_files, output_texs):
projection = [
'AimsFunctionProjection',
'-op', '1',
'-d', kernels_file,
'-d1', data_file,
'-m', input_mesh,
'-o', o_tex
]
cmd = ' '.join(map(str,projection))
pyhrf.verbose(3, 'cmd: %s' %cmd)
os.system(cmd)
if bin_threshold is not None:
pyhrf.verbose(2, 'Binary threshold of texture at %f' %bin_threshold)
o_tex = output_texs[0]
data,data_gii = read_texture(o_tex)
data = (data>bin_threshold).astype(np.int32)
print 'data:', data.dtype
if p_ids is not None:
for pid, o_tex in zip(p_ids[1:], output_texs[1:]):
pdata,pdata_gii = read_texture(o_tex)
data += (pdata>bin_threshold).astype(np.int32) * pid
#assert (np.unique(data) == p_ids).all()
write_texture(data, output_tex, intent='NIFTI_INTENT_LABEL')
def simulation_save_vol_outputs(simulation,
output_dir,
bold_3D_vols_dir=None,
simulation_graph_output=None,
prefix=None,
vol_meta=None):
""" simulation_graph_output : None, 'simple', 'thumbnails' #TODO
"""
if simulation.has_key('paradigm'):
fn = add_prefix(op.join(output_dir, 'paradigm.csv'), prefix)
simulation['paradigm'].save_csv(fn)
# Save all volumes in nifti format:
if simulation.has_key('labels_vol'):
mask_vol = np.ones_like(simulation['labels_vol'][0])
elif simulation.has_key('mask'):
mask_vol = simulation.get('mask', None)
elif simulation.has_key('labels'):
mask_vol = np.ones_like(simulation['labels'][0])
else:
raise Exception('Dunno where to get mask')
logger.info('Vol mask of shape %s', str(mask_vol.shape))
fn_mask = add_prefix(op.join(output_dir, 'mask.nii'), prefix)
write_volume(mask_vol.astype(np.int32), fn_mask, vol_meta)
if simulation.has_key('hrf_territories'):
fn_h_territories = add_prefix(
op.join(output_dir, 'hrf_territories.nii'), prefix)
ht = expand_array_in_mask(simulation['hrf_territories'] + 1, mask_vol)
write_volume(ht, fn_h_territories, vol_meta)
if simulation.has_key('hrf'):
from pyhrf.ndarray import MRI3Daxes
fn_hrf = add_prefix(op.join(output_dir, 'hrf.nii'), prefix)
logger.info('hrf flat shape %s', str(simulation['hrf'].shape))
if simulation['hrf'].ndim == 1:
hrf = (np.ones(mask_vol.size) * simulation['hrf'][:, np.newaxis])
else:
hrf = simulation['hrf']
hrfs_vol = expand_array_in_mask(hrf, mask_vol, flat_axis=1)
dt = simulation['dt']
chrfs = xndarray(
hrfs_vol,
axes_names=[
'time',
] + MRI3Daxes,
axes_domains={'time': np.arange(hrfs_vol.shape[0]) * dt})
chrfs.save(fn_hrf, vol_meta)
ttp_vol = hrfs_vol.argmax(0)
fn_ttp = add_prefix(op.join(output_dir, 'ttp.nii'), prefix)
write_volume(ttp_vol, fn_ttp, vol_meta)
if simulation.has_key('brf'):
from pyhrf.ndarray import MRI3Daxes
fn_brf = add_prefix(op.join(output_dir, 'brf.nii'), prefix)
logger.info('brf flat shape %s', str(simulation['brf'].shape))
brfs_vol = expand_array_in_mask(simulation['brf'],
mask_vol,
flat_axis=1)
dt = simulation['dt']
cbrfs = xndarray(
brfs_vol,
axes_names=[
'time',
] + MRI3Daxes,
axes_domains={'time': np.arange(brfs_vol.shape[0]) * dt})
cbrfs.save(fn_brf, vol_meta)
if simulation.has_key('prf'):
from pyhrf.ndarray import MRI3Daxes
fn_brf = add_prefix(op.join(output_dir, 'prf.nii'), prefix)
logger.info('prf flat shape %s', str(simulation['prf'].shape))
brfs_vol = expand_array_in_mask(simulation['prf'],
mask_vol,
flat_axis=1)
dt = simulation['dt']
cbrfs = xndarray(
brfs_vol,
axes_names=[
'time',
] + MRI3Daxes,
axes_domains={'time': np.arange(brfs_vol.shape[0]) * dt})
cbrfs.save(fn_brf, vol_meta)
if simulation.has_key('drift'):
fn_drift = add_prefix(op.join(output_dir, 'drift.nii'), prefix)
logger.info('drift flat shape %s', str(simulation['drift'].shape))
drift_vol = expand_array_in_mask(simulation['drift'],
mask_vol,
flat_axis=1)
write_volume(np.rollaxis(drift_vol, 0, 4), fn_drift)
if simulation.has_key('drift_coeffs'):
fn_drift = add_prefix(op.join(output_dir, 'drift_coeffs.nii'), prefix)
logger.info('drift flat shape %s',
str(simulation['drift_coeffs'].shape))
drift_vol = expand_array_in_mask(simulation['drift'],
mask_vol,
flat_axis=1)
write_volume(np.rollaxis(drift_vol, 0, 4), fn_drift)
if simulation.has_key('noise'):
fn_noise = add_prefix(op.join(output_dir, 'noise.nii'), prefix)
logger.info('noise flat shape %s', str(simulation['noise'].shape))
noise_vol = expand_array_in_mask(simulation['noise'],
mask_vol,
flat_axis=1)
write_volume(np.rollaxis(noise_vol, 0, 4), fn_noise, vol_meta)
fn_noise = add_prefix(op.join(output_dir, 'noise_emp_var.nii'), prefix)
noise_vol = expand_array_in_mask(simulation['noise'].var(0), mask_vol)
write_volume(noise_vol, fn_noise, vol_meta)
if simulation.has_key('noise_var'):
fn_noise_var = add_prefix(op.join(output_dir, 'noise_var.nii'), prefix)
logger.info('noise_var flat shape %s',
str(simulation['noise_var'].shape))
noise_var_vol = expand_array_in_mask(simulation['noise_var'], mask_vol)
write_volume(noise_var_vol, fn_noise_var, vol_meta)
if simulation.has_key('stim_induced_signal'):
fn_stim_induced = add_prefix(op.join(output_dir, 'stim_induced.nii'),
prefix)
logger.info('stim_induced flat shape %s',
str(simulation['stim_induced_signal'].shape))
stim_induced_vol = expand_array_in_mask(
simulation['stim_induced_signal'], mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
if simulation.has_key('perf_stim_induced'):
fn_stim_induced = add_prefix(
op.join(output_dir, 'perf_stim_induced.nii'), prefix)
logger.info('asl_stim_induced flat shape %s',
str(simulation['perf_stim_induced'].shape))
stim_induced_vol = expand_array_in_mask(
simulation['perf_stim_induced'], mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
fn_stim_induced = add_prefix(
op.join(output_dir, 'perf_stim_induced_ct.nii'), prefix)
logger.info('asl_stim_induced flat shape %s',
str(simulation['perf_stim_induced'].shape))
dsf = simulation['dsf']
perf = np.dot(simulation['ctrl_tag_mat'],
simulation['perf_stim_induced'][0:-1:dsf])
stim_induced_vol = expand_array_in_mask(perf, mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
if simulation.has_key('perf_baseline'):
fn = add_prefix(op.join(output_dir, 'perf_baseline.nii'), prefix)
pb = np.zeros_like(simulation['bold']) + simulation['perf_baseline']
write_volume(expand_array_in_mask(pb[0], mask_vol), fn, vol_meta)
if simulation.has_key('bold_stim_induced'):
fn_stim_induced = add_prefix(
op.join(output_dir, 'bold_stim_induced.nii'), prefix)
logger.info('asl_stim_induced flat shape %s',
str(simulation['bold_stim_induced'].shape))
stim_induced_vol = expand_array_in_mask(
simulation['bold_stim_induced'], mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
m = np.where(mask_vol)
labels_and_mask = mask_vol.copy()[m]
for ic in xrange(simulation['labels'].shape[0]):
if simulation.has_key('condition_defs'):
c_name = simulation['condition_defs'][ic].name
else:
c_name = 'cond%d' % ic
fn_labels = add_prefix(op.join(output_dir, 'labels_%s.nii' % c_name),
prefix)
if simulation.has_key('labels'):
labels_c = simulation['labels'][ic]
labels_and_mask[np.where(labels_c)] = ic + 2
write_volume(
expand_array_in_mask(labels_c, mask_vol).astype(np.int32),
fn_labels, vol_meta)
elif simulation.has_key('labels_vol'):
labels_c = simulation['labels_vol'][ic]
labels_and_mask[np.where(labels_c[m])] = ic + 2
write_volume(labels_c.astype(np.int32), fn_labels, vol_meta)
if simulation.has_key('nrls'):
nrls_c = simulation['nrls'][ic]
fn = add_prefix(op.join(output_dir, 'nrls_%s.nii' % c_name),
prefix)
write_volume(expand_array_in_mask(nrls_c, mask_vol), fn, vol_meta)
if simulation.has_key('nrls_session'):
nrls_session_c = simulation['nrls_session'][ic]
fn = add_prefix(
op.join(output_dir, 'nrls_session_%s.nii' % (c_name)), prefix)
write_volume(expand_array_in_mask(nrls_session_c, mask_vol), fn,
vol_meta)
if simulation.has_key('brls'):
brls_c = simulation['brls'][ic]
fn = add_prefix(op.join(output_dir, 'brls_%s.nii' % c_name),
prefix)
write_volume(expand_array_in_mask(brls_c, mask_vol), fn, vol_meta)
if simulation.has_key('prls'):
prls_c = simulation['prls'][ic]
fn = add_prefix(op.join(output_dir, 'prls_%s.nii' % c_name),
prefix)
write_volume(expand_array_in_mask(prls_c, mask_vol), fn, vol_meta)
if simulation.has_key('neural_efficacies'):
ne_c = simulation['neural_efficacies'][ic]
fn = add_prefix(
op.join(output_dir, 'neural_efficacies_%s.nii' % c_name),
prefix)
write_volume(expand_array_in_mask(ne_c, mask_vol), fn, vol_meta)
fn_labels_and_mask = add_prefix(op.join(output_dir, 'mask_and_labels.nii'),
prefix)
write_volume(
expand_array_in_mask(labels_and_mask, mask_vol).astype(int),
fn_labels_and_mask, vol_meta)
if simulation.has_key('bold_full_vol') or simulation.has_key('bold'):
fn = add_prefix(op.join(output_dir, 'bold.nii'), prefix)
if simulation.has_key('bold_full_vol'):
bold4D = simulation['bold_full_vol']
else:
bold = simulation['bold']
bold4D = expand_array_in_mask(bold, mask_vol, flat_axis=1)
write_volume(np.rollaxis(bold4D, 0, 4), fn, vol_meta)
def save_time_series(k):
if simulation.has_key(k):
fn_stim_induced = add_prefix(op.join(output_dir, k + '.nii'),
prefix)
logger.info('%s flat shape %s', k, str(simulation[k].shape))
vol = expand_array_in_mask(simulation[k], mask_vol, flat_axis=1)
write_volume(np.rollaxis(vol, 0, 4), fn_stim_induced)
save_time_series('flow_induction')
save_time_series('cbv')
save_time_series('hbr')
save_time_series('bold_stim_induced_rescaled')
if simulation.has_key('asl'):
fn = add_prefix(op.join(output_dir, 'asl.nii'), prefix)
asl4D = expand_array_in_mask(simulation['asl'], mask_vol, flat_axis=1)
write_volume(np.rollaxis(asl4D, 0, 4), fn, vol_meta)
if simulation.has_key('outliers'):
fn = add_prefix(op.join(output_dir, 'outliers.nii'), prefix)
outliers = expand_array_in_mask(simulation['outliers'],
mask_vol,
flat_axis=1)
write_volume(np.rollaxis(outliers, 0, 4), fn, vol_meta)
if simulation.has_key('hrf_group'):
hrfgroup = simulation['hrf_group']
nb_vox = mask_vol.size
fn_hrf = add_prefix(op.join(output_dir, 'hrf_group.nii'), prefix)
logger.info('hrf group shape %s', str(simulation['hrf_group'].shape))
hrfGd = duplicate_hrf(nb_vox, hrfgroup)
hrfs_vol = expand_array_in_mask(hrfGd, mask_vol, flat_axis=1)
dt = simulation['dt']
chrfs = xndarray(
hrfs_vol,
axes_names=[
'time',
] + MRI3Daxes,
axes_domains={'time': np.arange(hrfs_vol.shape[0]) * dt})
chrfs.save(fn_hrf, vol_meta)
if bold_3D_vols_dir is not None:
assert op.exists(bold_3D_vols_dir)
for iscan, bscan in enumerate(bold4D):
fnout = add_prefix('bold_%06d.nii' % (iscan), prefix)
write_volume(bscan, op.join(bold_3D_vols_dir, fnout), vol_meta)
def glm_nipy_from_files(bold_file,
tr,
paradigm_csv_file,
output_dir,
mask_file,
session=0,
contrasts=None,
con_test_baseline=0.0,
hrf_model='Canonical',
drift_model='Cosine',
hfcut=128,
residuals_model='spherical',
fit_method='ols',
fir_delays=[0]):
"""
#TODO: handle surface data
hrf_model : Canonical | Canonical with Derivative | FIR
"""
fdata = FmriData.from_vol_files(mask_file, paradigm_csv_file, [bold_file],
tr)
g, dm, cons = glm_nipy(fdata,
contrasts=contrasts,
hrf_model=hrf_model,
hfcut=hfcut,
drift_model=drift_model,
residuals_model=residuals_model,
fit_method=fit_method,
fir_delays=fir_delays)
ns, nr = dm.matrix.shape
cdesign_matrix = xndarray(dm.matrix,
axes_names=['time', 'regressor'],
axes_domains={
'time': np.arange(ns) * tr,
'regressor': dm.names
})
cdesign_matrix.save(op.join(output_dir, 'design_matrix.nii'))
beta_files = []
beta_values = dict.fromkeys(dm.names)
beta_vars = dict.fromkeys(dm.names)
beta_vars_voxels = dict.fromkeys(dm.names)
for ib, bname in enumerate(dm.names):
# beta values
beta_vol = expand_array_in_mask(g.beta[ib], fdata.roiMask > 0)
beta_fn = op.join(output_dir, 'beta_%s.nii' % bname)
write_volume(beta_vol, beta_fn, fdata.meta_obj)
beta_files.append(beta_fn)
beta_values[bname] = beta_vol
# normalized variance of betas
# variance: diag of cov matrix
beta_vars[bname] = sp.diag(g.nvbeta)[ib]
# sig2 = g.s2 #ResMS
# variance for all voxels, condition ib
var_cond = sp.diag(g.nvbeta)[ib] * g.s2
beta_vars_voxels[bname] = var_cond
#beta_var_fn = op.join(output_dir, 'var_beta_%s.nii' %bname)
#write_volume(beta_var, beta_var_fn, fdata.meta_obj)
# beta_var_files.append(beta_var_fn)
if cons is not None:
con_files = []
pval_files = []
for cname, con in cons.iteritems():
con_vol = expand_array_in_mask(con.effect, fdata.roiMask > 0)
con_fn = op.join(output_dir, 'con_effect_%s.nii' % cname)
write_volume(con_vol, con_fn, fdata.meta_obj)
con_files.append(con_fn)
pval_vol = expand_array_in_mask(con.pvalue(con_test_baseline),
fdata.roiMask > 0)
pval_fn = op.join(output_dir, 'con_pvalue_%s.nii' % cname)
write_volume(pval_vol, pval_fn, fdata.meta_obj)
pval_files.append(pval_fn)
else:
con_files = None
pval_files = None
dof = g.dof
# if do_ppm:
# for
# TODO: FIR stuffs
# , con_files, pval_files
return beta_files, beta_values, beta_vars_voxels, dof
def project_fmri_from_kernels(
input_mesh,
kernels_file,
fmri_data_file,
output_tex,
bin_threshold=None,
):
logger.info('Project data onto mesh using kernels ...')
if 0:
print 'Projecting ...'
print 'func data:', fmri_data_file
print 'Mesh file:', input_mesh
print 'Save as:', output_tex
logger.info('Call AimsFunctionProjection -op 1 ...')
data_files = []
output_texs = []
p_ids = None
if bin_threshold is not None:
d, h = read_volume(fmri_data_file)
if np.allclose(d.astype(int), d):
tmp_dir = pyhrf.get_tmp_path()
p_ids = np.unique(d)
logger.info('bin threshold: %f', bin_threshold)
logger.info('pids(n=%d): %d...%d', len(p_ids), min(p_ids),
max(p_ids))
for i, p_id in enumerate(p_ids):
if p_id != 0:
new_p = np.zeros_like(d)
new_p[np.where(d == p_id)] = i + 1 # 0 is background
ifn = op.join(tmp_dir, 'pmask_%d.nii' % p_id)
write_volume(new_p, ifn, h)
data_files.append(ifn)
ofn = op.join(tmp_dir, 'ptex_%d.gii' % p_id)
output_texs.append(ofn)
else:
data_files.append(fmri_data_file)
output_texs.append(output_tex)
else:
data_files.append(fmri_data_file)
output_texs.append(output_tex)
logger.info('input data files: %s', str(data_files))
logger.info('output data files: %s', str(output_texs))
for data_file, o_tex in zip(data_files, output_texs):
projection = [
'AimsFunctionProjection', '-op', '1', '-d', kernels_file, '-d1',
data_file, '-m', input_mesh, '-o', o_tex
]
cmd = ' '.join(map(str, projection))
logger.info('cmd: %s', cmd)
os.system(cmd)
if bin_threshold is not None:
logger.info('Binary threshold of texture at %f', bin_threshold)
o_tex = output_texs[0]
data, data_gii = read_texture(o_tex)
data = (data > bin_threshold).astype(np.int32)
print 'data:', data.dtype
if p_ids is not None:
for pid, o_tex in zip(p_ids[1:], output_texs[1:]):
pdata, pdata_gii = read_texture(o_tex)
data += (pdata > bin_threshold).astype(np.int32) * pid
#assert (np.unique(data) == p_ids).all()
write_texture(data, output_tex, intent='NIFTI_INTENT_LABEL')
def parcellation_for_jde(fmri_data, avg_parcel_size=250, output_dir=None,
method='gkm', glm_drift='Cosine', glm_hfcut=128):
"""
method: gkm, ward, ward_and_gkm
"""
if output_dir is None:
output_dir = tempfile.mkdtemp(prefix='pyhrf_JDE_parcellation_GLM',
dir=pyhrf.cfg['global']['tmp_path'])
glm_output_dir = op.join(output_dir, 'GLM_for_parcellation')
if not op.exists(glm_output_dir): os.makedirs(glm_output_dir)
pyhrf.verbose(1, 'GLM for parcellation')
# if fmri_data.data_type == 'volume':
# paradigm_file, bold_file, mask_file = fmri_data.save(glm_output_dir)
# beta_files = glm_nipy_from_files(bold_file, fmri_data.tr, paradigm_file,
# glm_output_dir, mask_file,
# drift_model=glm_drift, hfcut=glm_hfcut)
# elif fmri_data.data_type == 'surface':
# beta_files = glm_nipy(fmri_data, glm_output_dir,
# drift_model=glm_drift, hfcut=glm_hfcut)
g, dm, cons = glm_nipy(fmri_data, drift_model=glm_drift, hfcut=glm_hfcut)
pval_files = []
if cons is not None:
func_data = [('con_pval_%s' %cname, con.pvalue()) \
for cname, con in cons.iteritems()]
else:
reg_cst_drift = re.compile(".*constant.*|.*drift.*")
func_data = [('beta_%s' %reg_name, g.beta[ir]) \
for ir,reg_name in enumerate(dm.names) \
if not reg_cst_drift.match(reg_name)]
for name, data in func_data:
val_vol = expand_array_in_mask(data, fmri_data.roiMask>0)
val_fn = op.join(glm_output_dir, '%s.nii' %name)
write_volume(val_vol, val_fn, fmri_data.meta_obj)
pval_files.append(val_fn)
mask_file = op.join(glm_output_dir,'mask.nii')
write_volume(fmri_data.roiMask>0, mask_file, fmri_data.meta_obj)
nvox = fmri_data.get_nb_vox_in_mask()
nparcels = round_nb_parcels(nvox * 1. / avg_parcel_size)
pyhrf.verbose(1, 'Parcellation from GLM outputs, method: %s, ' \
'nb parcels: %d' %(method, nparcels))
if fmri_data.data_type == 'volume':
parcellation_file = op.join(output_dir, 'parcellation_%s_np%d.nii'
%(method, nparcels))
make_parcellation_from_files(pval_files, mask_file, parcellation_file,
nparcels, method)
parcellation,_ = read_volume(parcellation_file)
else:
mesh_file = fmri_data.data_files[-1]
parcellation_file = op.join(output_dir, 'parcellation_%s_np%d.gii'
%(method, nparcels))
make_parcellation_surf_from_files(pval_files, mesh_file,
parcellation_file, nparcels, method,
verbose=1)
parcellation,_ = read_texture(parcellation_file)
#print parcellation_file
pyhrf.verbose(1, parcellation_report(parcellation))
return parcellation, parcellation_file
def BMA_consensus_cluster_parallel(cfg, remote_path, remote_BOLD_fn, remote_mask_fn, Y, nifti_masker, \
num_vox, K_clus, K_clusters, \
parc, alpha, prop, nbItRFIR, onsets, durations,\
output_sub_parc, rescale=True, averg_bold=False):
'''
Performs all steps for one clustering case (Kclus given, number l of the parcellation given)
remote_path: path on the cluster, where results will be stored
'''
import os
import sys
sys.path.append("/home/pc174679/pyhrf/pyhrf-tree_trunk/script/WIP/Scripts_IRMf_BB/Parcellations/")
sys.path.append("/home/pc174679/pyhrf/pyhrf-tree_trunk/script/WIP/Scripts_IRMf_Adultes_Solv/")
sys.path.append("/home/pc174679/pyhrf/pyhrf-tree_trunk/script/WIP/Scripts_IRMf_Adultes_Solv/Scripts_divers_utiles/Scripts_utiles/")
sys.path.append('/home/pc174679/local/installations/consensus-cluster-0.6')
from Random_parcellations import random_parcellations, subsample_data_on_time
from Divers_parcellations_test import *
from RFIR_evaluation_parcellations import JDE_estim, RFIR_estim, clustering_from_RFIR
from Random_parcellations import hrf_roi_to_vox
from pyhrf.tools._io import remote_copy, remote_mkdir
from nisl import io
#nifti_masker.mask=remote_mask_fn
# Creation of the necessary paths --> do not do here
parc_name = 'Subsampled_data_with_' + str(K_clus) + 'clusters'
parc_name_clus = parc_name + 'rnd_number_' + str(parc+1)
remote_sub = os.sep.join((remote_path, parc_name))
#if not os.path.exists(remote_sub):
#os.path.exists(remote_sub)
#print 'remote_sub:', remote_sub
#os.makedirs(remote_sub)
remote_sub_parc = os.sep.join((remote_sub,parc_name_clus))
#if not os.path.exists(remote_sub_parc):
#os.makedirs(remote_sub_parc)
output_RFIR_parc = os.sep.join((output_sub_parc,'RFIR_estim'))
###################################
## 1st STEP: SUBSAMPLING
print '--- Subsample data ---'
Ysub = subsample_data_on_time(Y, remote_mask_fn, K_clus, alpha, prop, \
nifti_masker, rescale=rescale)
print 'Ysub:', Ysub
print 'remote_sub_prc:', remote_sub_parc
Ysub_name = 'Y_sub_'+ str(K_clus) + 'clusters_' + 'rnd_number_' + str(parc+1) +'.nii'
Ysub_fn = os.sep.join((remote_sub_parc, Ysub_name))
Ysub_masked = nifti_masker.inverse_transform(Ysub).get_data()
write_volume(Ysub_masked, Ysub_fn)
###################################
## 2D STEP: RFIR
print '--- Performs RFIR estimation ---'
remote_RFIR_parc_clus = os.sep.join((remote_sub_parc, 'RFIR_estim'))
#if not os.path.exists(remote_RFIR_parc):os.makedirs(remote_RFIR_parc)
#remote_RFIR_parc_clus = os.sep.join((remote_RFIR_parc, parc_name_clus))
#if not os.path.exists(remote_RFIR_parc_clus):os.makedirs(remote_RFIR_parc_clus)
print ' * output path for RFIR ', remote_RFIR_parc_clus
print ' * RFIR for subsampling nb ', str(parc+1), ' with ', K_clus, ' clusters'
RFIR_estim(nbItRFIR, onsets, durations, Ysub_fn, remote_mask_fn, \
remote_RFIR_parc, avg_bold=averg_bold)
hrf_fn = os.sep.join((remote_RFIR_parc_clus, 'rfir_ehrf.nii'))
#remote_copy([hrf_fn], remote_host,
#remote_user, remote_path)[0]
###################################
## 3D STEP: CLUSTERING FROM RFIR RESULTS
name_hrf = 'rfir_ehrf.nii'
from pyhrf.tools._io import write_volume, read_volume
from pyhrf.tools._io import read_volume, write_volume
import nisl.io as ionisl
from sklearn.feature_extraction import image
from sklearn.cluster import WardAgglomeration
from scipy.spatial.distance import cdist, pdist
hrf_fn = os.sep.join((remote_RFIR_parc_clus,name_hrf))
hrf=read_volume(hrf_fn)[0]
hrf_t_fn = add_suffix(hrf_fn, 'transpose')
#taking only 1st condition to parcellate
write_volume(hrf[:,:,:,:,0], hrf_t_fn)
nifti_masker = ionisl.NiftiMasker(remote_mask_fn)
Nm = nifti_masker.fit(hrf_t_fn)
#features: coeff of the HRF
HRF = Nm.fit_transform(hrf_t_fn)
mask, meta_data = read_volume(remote_mask_fn)
shape = mask.shape
connectivity = image.grid_to_graph(n_x=shape[0], n_y=shape[1],
n_z=shape[2], mask=mask)
#features used for clustering
features = HRF.transpose()
ward = WardAgglomeration(n_clusters=K_clus, connectivity=connectivity,
memory='nisl_cache')
ward.fit(HRF)
labels_tot = ward.labels_+1
#Kelbow, Perc_WSS, all_parc_from_RFIR_fns, all_parc_RFIR = \
#clustering_from_RFIR(K_clusters, remote_RFIR_parc_clus, remote_mask_fn, name_hrf, plots=False)
#labels_tot = all_parc_RFIR[str(Kelbow)]
#to retrieve clustering with as many clusters as determined in K_clusters
#labels_tot = all_parc_RFIR[str(K_clus)]
#Parcellation retrieved: for K=Kelbow
#clusters_RFIR_fn = all_parc_from_RFIR[str(Kelbow)]
#clustering_rfir_fn = os.path.join(remote_RFIR_parc_clus, 'output_clustering_elbow.nii')
#write_volume(read_volume(clusters_RFIR_fn)[0], clustering_rfir_fn, meta_bold)
#labels_tot = nifti_masker.fit_transform([clusters_RFIR_fn])[0]
#labels_tot = read_volume(clusters_RFIR_fn)[0]
#labels_name='labels_' + str(int(K_clus)) + '_' + str(parc+1) + '.pck'
#name_f = os.sep.join((remote_sub_parc, labels_name))
#pickle_labels=open(name_f, 'w')
#cPickle.dump(labels_tot,f)
#pickle_labels.close()
#remote_copy(pickle_labels, remote_user,
#remote_host, output_sub_parc)
#################################
## Prepare consensus clustering
print 'Prepare consensus clustering'
clustcount, totalcount = upd_similarity_matrix(labels_tot)
print 'results:', clustcount
return clustcount.astype(np.bool)
def parcellation_for_jde(fmri_data,
avg_parcel_size=250,
output_dir=None,
method='gkm',
glm_drift='Cosine',
glm_hfcut=128):
"""
method: gkm, ward, ward_and_gkm
"""
if output_dir is None:
output_dir = tempfile.mkdtemp(prefix='pyhrf_JDE_parcellation_GLM',
dir=pyhrf.cfg['global']['tmp_path'])
glm_output_dir = op.join(output_dir, 'GLM_for_parcellation')
if not op.exists(glm_output_dir):
os.makedirs(glm_output_dir)
logger.info('GLM for parcellation')
g, dm, cons = glm_nipy(fmri_data, drift_model=glm_drift, hfcut=glm_hfcut)
pval_files = []
if cons is not None:
func_data = [('con_pval_%s' % cname, con.pvalue())
for cname, con in cons.iteritems()]
else:
reg_cst_drift = re.compile(".*constant.*|.*drift.*")
func_data = [('beta_%s' % reg_name, g.beta[ir])
for ir, reg_name in enumerate(dm.names)
if not reg_cst_drift.match(reg_name)]
for name, data in func_data:
val_vol = expand_array_in_mask(data, fmri_data.roiMask > 0)
val_fn = op.join(glm_output_dir, '%s.nii' % name)
write_volume(val_vol, val_fn, fmri_data.meta_obj)
pval_files.append(val_fn)
mask_file = op.join(glm_output_dir, 'mask.nii')
write_volume(fmri_data.roiMask > 0, mask_file, fmri_data.meta_obj)
nvox = fmri_data.get_nb_vox_in_mask()
nparcels = round_nb_parcels(nvox * 1. / avg_parcel_size)
logger.info('Parcellation from GLM outputs, method: %s, nb parcels: %d',
method, nparcels)
if fmri_data.data_type == 'volume':
parcellation_file = op.join(
output_dir, 'parcellation_%s_np%d.nii' % (method, nparcels))
make_parcellation_from_files(pval_files, mask_file, parcellation_file,
nparcels, method)
parcellation, _ = read_volume(parcellation_file)
else:
mesh_file = fmri_data.data_files[-1]
parcellation_file = op.join(
output_dir, 'parcellation_%s_np%d.gii' % (method, nparcels))
make_parcellation_surf_from_files(pval_files,
mesh_file,
parcellation_file,
nparcels,
method,
verbose=1)
parcellation, _ = read_texture(parcellation_file)
logger.info(parcellation_report(parcellation))
return parcellation, parcellation_file
def project_fmri_from_kernels(input_mesh, kernels_file, fmri_data_file, output_tex, bin_threshold=None):
logger.info("Project data onto mesh using kernels ...")
if 0:
print "Projecting ..."
print "func data:", fmri_data_file
print "Mesh file:", input_mesh
print "Save as:", output_tex
logger.info("Call AimsFunctionProjection -op 1 ...")
data_files = []
output_texs = []
p_ids = None
if bin_threshold is not None:
d, h = read_volume(fmri_data_file)
if np.allclose(d.astype(int), d):
tmp_dir = pyhrf.get_tmp_path()
p_ids = np.unique(d)
logger.info("bin threshold: %f", bin_threshold)
logger.info("pids(n=%d): %d...%d", len(p_ids), min(p_ids), max(p_ids))
for i, p_id in enumerate(p_ids):
if p_id != 0:
new_p = np.zeros_like(d)
new_p[np.where(d == p_id)] = i + 1 # 0 is background
ifn = op.join(tmp_dir, "pmask_%d.nii" % p_id)
write_volume(new_p, ifn, h)
data_files.append(ifn)
ofn = op.join(tmp_dir, "ptex_%d.gii" % p_id)
output_texs.append(ofn)
else:
data_files.append(fmri_data_file)
output_texs.append(output_tex)
else:
data_files.append(fmri_data_file)
output_texs.append(output_tex)
logger.info("input data files: %s", str(data_files))
logger.info("output data files: %s", str(output_texs))
for data_file, o_tex in zip(data_files, output_texs):
projection = [
"AimsFunctionProjection",
"-op",
"1",
"-d",
kernels_file,
"-d1",
data_file,
"-m",
input_mesh,
"-o",
o_tex,
]
cmd = " ".join(map(str, projection))
logger.info("cmd: %s", cmd)
os.system(cmd)
if bin_threshold is not None:
logger.info("Binary threshold of texture at %f", bin_threshold)
o_tex = output_texs[0]
data, data_gii = read_texture(o_tex)
data = (data > bin_threshold).astype(np.int32)
print "data:", data.dtype
if p_ids is not None:
for pid, o_tex in zip(p_ids[1:], output_texs[1:]):
pdata, pdata_gii = read_texture(o_tex)
data += (pdata > bin_threshold).astype(np.int32) * pid
# assert (np.unique(data) == p_ids).all()
write_texture(data, output_tex, intent="NIFTI_INTENT_LABEL")
def simulation_save_vol_outputs(
simulation, output_dir, bold_3D_vols_dir=None, simulation_graph_output=None, prefix=None, vol_meta=None
):
""" simulation_graph_output : None, 'simple', 'thumbnails' #TODO
"""
if simulation.has_key("paradigm"):
fn = add_prefix(op.join(output_dir, "paradigm.csv"), prefix)
simulation["paradigm"].save_csv(fn)
# Save all volumes in nifti format:
if simulation.has_key("labels_vol"):
mask_vol = np.ones_like(simulation["labels_vol"][0])
elif simulation.has_key("mask"):
mask_vol = simulation.get("mask", None)
elif simulation.has_key("labels"):
mask_vol = np.ones_like(simulation["labels"][0])
else:
raise Exception("Dunno where to get mask")
logger.info("Vol mask of shape %s", str(mask_vol.shape))
fn_mask = add_prefix(op.join(output_dir, "mask.nii"), prefix)
write_volume(mask_vol.astype(np.int32), fn_mask, vol_meta)
if simulation.has_key("hrf_territories"):
fn_h_territories = add_prefix(op.join(output_dir, "hrf_territories.nii"), prefix)
ht = expand_array_in_mask(simulation["hrf_territories"] + 1, mask_vol)
write_volume(ht, fn_h_territories, vol_meta)
if simulation.has_key("hrf"):
from pyhrf.ndarray import MRI3Daxes
fn_hrf = add_prefix(op.join(output_dir, "hrf.nii"), prefix)
logger.info("hrf flat shape %s", str(simulation["hrf"].shape))
if simulation["hrf"].ndim == 1:
hrf = np.ones(mask_vol.size) * simulation["hrf"][:, np.newaxis]
else:
hrf = simulation["hrf"]
hrfs_vol = expand_array_in_mask(hrf, mask_vol, flat_axis=1)
dt = simulation["dt"]
chrfs = xndarray(
hrfs_vol, axes_names=["time"] + MRI3Daxes, axes_domains={"time": np.arange(hrfs_vol.shape[0]) * dt}
)
chrfs.save(fn_hrf, vol_meta)
ttp_vol = hrfs_vol.argmax(0)
fn_ttp = add_prefix(op.join(output_dir, "ttp.nii"), prefix)
write_volume(ttp_vol, fn_ttp, vol_meta)
if simulation.has_key("brf"):
from pyhrf.ndarray import MRI3Daxes
fn_brf = add_prefix(op.join(output_dir, "brf.nii"), prefix)
logger.info("brf flat shape %s", str(simulation["brf"].shape))
brfs_vol = expand_array_in_mask(simulation["brf"], mask_vol, flat_axis=1)
dt = simulation["dt"]
cbrfs = xndarray(
brfs_vol, axes_names=["time"] + MRI3Daxes, axes_domains={"time": np.arange(brfs_vol.shape[0]) * dt}
)
cbrfs.save(fn_brf, vol_meta)
if simulation.has_key("prf"):
from pyhrf.ndarray import MRI3Daxes
fn_brf = add_prefix(op.join(output_dir, "prf.nii"), prefix)
logger.info("prf flat shape %s", str(simulation["prf"].shape))
brfs_vol = expand_array_in_mask(simulation["prf"], mask_vol, flat_axis=1)
dt = simulation["dt"]
cbrfs = xndarray(
brfs_vol, axes_names=["time"] + MRI3Daxes, axes_domains={"time": np.arange(brfs_vol.shape[0]) * dt}
)
cbrfs.save(fn_brf, vol_meta)
if simulation.has_key("drift"):
fn_drift = add_prefix(op.join(output_dir, "drift.nii"), prefix)
logger.info("drift flat shape %s", str(simulation["drift"].shape))
drift_vol = expand_array_in_mask(simulation["drift"], mask_vol, flat_axis=1)
write_volume(np.rollaxis(drift_vol, 0, 4), fn_drift)
if simulation.has_key("drift_coeffs"):
fn_drift = add_prefix(op.join(output_dir, "drift_coeffs.nii"), prefix)
logger.info("drift flat shape %s", str(simulation["drift_coeffs"].shape))
drift_vol = expand_array_in_mask(simulation["drift"], mask_vol, flat_axis=1)
write_volume(np.rollaxis(drift_vol, 0, 4), fn_drift)
if simulation.has_key("noise"):
fn_noise = add_prefix(op.join(output_dir, "noise.nii"), prefix)
logger.info("noise flat shape %s", str(simulation["noise"].shape))
noise_vol = expand_array_in_mask(simulation["noise"], mask_vol, flat_axis=1)
write_volume(np.rollaxis(noise_vol, 0, 4), fn_noise, vol_meta)
fn_noise = add_prefix(op.join(output_dir, "noise_emp_var.nii"), prefix)
noise_vol = expand_array_in_mask(simulation["noise"].var(0), mask_vol)
write_volume(noise_vol, fn_noise, vol_meta)
if simulation.has_key("noise_var"):
fn_noise_var = add_prefix(op.join(output_dir, "noise_var.nii"), prefix)
logger.info("noise_var flat shape %s", str(simulation["noise_var"].shape))
noise_var_vol = expand_array_in_mask(simulation["noise_var"], mask_vol)
write_volume(noise_var_vol, fn_noise_var, vol_meta)
if simulation.has_key("stim_induced_signal"):
fn_stim_induced = add_prefix(op.join(output_dir, "stim_induced.nii"), prefix)
logger.info("stim_induced flat shape %s", str(simulation["stim_induced_signal"].shape))
stim_induced_vol = expand_array_in_mask(simulation["stim_induced_signal"], mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
if simulation.has_key("perf_stim_induced"):
fn_stim_induced = add_prefix(op.join(output_dir, "perf_stim_induced.nii"), prefix)
logger.info("asl_stim_induced flat shape %s", str(simulation["perf_stim_induced"].shape))
stim_induced_vol = expand_array_in_mask(simulation["perf_stim_induced"], mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
fn_stim_induced = add_prefix(op.join(output_dir, "perf_stim_induced_ct.nii"), prefix)
logger.info("asl_stim_induced flat shape %s", str(simulation["perf_stim_induced"].shape))
dsf = simulation["dsf"]
perf = np.dot(simulation["ctrl_tag_mat"], simulation["perf_stim_induced"][0:-1:dsf])
stim_induced_vol = expand_array_in_mask(perf, mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
if simulation.has_key("perf_baseline"):
fn = add_prefix(op.join(output_dir, "perf_baseline.nii"), prefix)
pb = np.zeros_like(simulation["bold"]) + simulation["perf_baseline"]
write_volume(expand_array_in_mask(pb[0], mask_vol), fn, vol_meta)
if simulation.has_key("bold_stim_induced"):
fn_stim_induced = add_prefix(op.join(output_dir, "bold_stim_induced.nii"), prefix)
logger.info("asl_stim_induced flat shape %s", str(simulation["bold_stim_induced"].shape))
stim_induced_vol = expand_array_in_mask(simulation["bold_stim_induced"], mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
m = np.where(mask_vol)
labels_and_mask = mask_vol.copy()[m]
for ic in xrange(simulation["labels"].shape[0]):
if simulation.has_key("condition_defs"):
c_name = simulation["condition_defs"][ic].name
else:
c_name = "cond%d" % ic
fn_labels = add_prefix(op.join(output_dir, "labels_%s.nii" % c_name), prefix)
if simulation.has_key("labels"):
labels_c = simulation["labels"][ic]
labels_and_mask[np.where(labels_c)] = ic + 2
write_volume(expand_array_in_mask(labels_c, mask_vol).astype(np.int32), fn_labels, vol_meta)
elif simulation.has_key("labels_vol"):
labels_c = simulation["labels_vol"][ic]
labels_and_mask[np.where(labels_c[m])] = ic + 2
write_volume(labels_c.astype(np.int32), fn_labels, vol_meta)
if simulation.has_key("nrls"):
nrls_c = simulation["nrls"][ic]
fn = add_prefix(op.join(output_dir, "nrls_%s.nii" % c_name), prefix)
write_volume(expand_array_in_mask(nrls_c, mask_vol), fn, vol_meta)
if simulation.has_key("nrls_session"):
nrls_session_c = simulation["nrls_session"][ic]
fn = add_prefix(op.join(output_dir, "nrls_session_%s.nii" % (c_name)), prefix)
write_volume(expand_array_in_mask(nrls_session_c, mask_vol), fn, vol_meta)
if simulation.has_key("brls"):
brls_c = simulation["brls"][ic]
fn = add_prefix(op.join(output_dir, "brls_%s.nii" % c_name), prefix)
write_volume(expand_array_in_mask(brls_c, mask_vol), fn, vol_meta)
if simulation.has_key("prls"):
prls_c = simulation["prls"][ic]
fn = add_prefix(op.join(output_dir, "prls_%s.nii" % c_name), prefix)
write_volume(expand_array_in_mask(prls_c, mask_vol), fn, vol_meta)
if simulation.has_key("neural_efficacies"):
ne_c = simulation["neural_efficacies"][ic]
fn = add_prefix(op.join(output_dir, "neural_efficacies_%s.nii" % c_name), prefix)
write_volume(expand_array_in_mask(ne_c, mask_vol), fn, vol_meta)
fn_labels_and_mask = add_prefix(op.join(output_dir, "mask_and_labels.nii"), prefix)
write_volume(expand_array_in_mask(labels_and_mask, mask_vol).astype(int), fn_labels_and_mask, vol_meta)
if simulation.has_key("bold_full_vol") or simulation.has_key("bold"):
fn = add_prefix(op.join(output_dir, "bold.nii"), prefix)
if simulation.has_key("bold_full_vol"):
bold4D = simulation["bold_full_vol"]
else:
bold = simulation["bold"]
bold4D = expand_array_in_mask(bold, mask_vol, flat_axis=1)
write_volume(np.rollaxis(bold4D, 0, 4), fn, vol_meta)
def save_time_series(k):
if simulation.has_key(k):
fn_stim_induced = add_prefix(op.join(output_dir, k + ".nii"), prefix)
logger.info("%s flat shape %s", k, str(simulation[k].shape))
vol = expand_array_in_mask(simulation[k], mask_vol, flat_axis=1)
write_volume(np.rollaxis(vol, 0, 4), fn_stim_induced)
save_time_series("flow_induction")
save_time_series("cbv")
save_time_series("hbr")
save_time_series("bold_stim_induced_rescaled")
if simulation.has_key("asl"):
fn = add_prefix(op.join(output_dir, "asl.nii"), prefix)
asl4D = expand_array_in_mask(simulation["asl"], mask_vol, flat_axis=1)
write_volume(np.rollaxis(asl4D, 0, 4), fn, vol_meta)
if simulation.has_key("outliers"):
fn = add_prefix(op.join(output_dir, "outliers.nii"), prefix)
outliers = expand_array_in_mask(simulation["outliers"], mask_vol, flat_axis=1)
write_volume(np.rollaxis(outliers, 0, 4), fn, vol_meta)
if simulation.has_key("hrf_group"):
hrfgroup = simulation["hrf_group"]
nb_vox = mask_vol.size
fn_hrf = add_prefix(op.join(output_dir, "hrf_group.nii"), prefix)
logger.info("hrf group shape %s", str(simulation["hrf_group"].shape))
hrfGd = duplicate_hrf(nb_vox, hrfgroup)
hrfs_vol = expand_array_in_mask(hrfGd, mask_vol, flat_axis=1)
dt = simulation["dt"]
chrfs = xndarray(
hrfs_vol, axes_names=["time"] + MRI3Daxes, axes_domains={"time": np.arange(hrfs_vol.shape[0]) * dt}
)
chrfs.save(fn_hrf, vol_meta)
if bold_3D_vols_dir is not None:
assert op.exists(bold_3D_vols_dir)
for iscan, bscan in enumerate(bold4D):
fnout = add_prefix("bold_%06d.nii" % (iscan), prefix)
write_volume(bscan, op.join(bold_3D_vols_dir, fnout), vol_meta)
def simulation_save_vol_outputs(simulation, output_dir, bold_3D_vols_dir=None,
simulation_graph_output=None, prefix=None,
vol_meta=None):
""" simulation_graph_output : None, 'simple', 'thumbnails' #TODO
"""
if simulation.has_key('paradigm'):
fn = add_prefix(op.join(output_dir, 'paradigm.csv'), prefix)
simulation['paradigm'].save_csv(fn)
# Save all volumes in nifti format:
if simulation.has_key('labels_vol'):
mask_vol = np.ones_like(simulation['labels_vol'][0])
elif simulation.has_key('mask'):
mask_vol = simulation.get('mask', None)
elif simulation.has_key('labels'):
mask_vol = np.ones_like(simulation['labels'][0])
else:
raise Exception('Dunno where to get mask')
logger.info('Vol mask of shape %s', str(mask_vol.shape))
fn_mask = add_prefix(op.join(output_dir, 'mask.nii'), prefix)
write_volume(mask_vol.astype(np.int32), fn_mask, vol_meta)
if simulation.has_key('hrf_territories'):
fn_h_territories = add_prefix(op.join(output_dir, 'hrf_territories.nii'),
prefix)
ht = expand_array_in_mask(simulation['hrf_territories'] + 1, mask_vol)
write_volume(ht, fn_h_territories, vol_meta)
if simulation.has_key('hrf'):
from pyhrf.ndarray import MRI3Daxes
fn_hrf = add_prefix(op.join(output_dir, 'hrf.nii'), prefix)
logger.info('hrf flat shape %s', str(simulation['hrf'].shape))
if simulation['hrf'].ndim == 1:
hrf = (np.ones(mask_vol.size) * simulation['hrf'][:, np.newaxis])
else:
hrf = simulation['hrf']
hrfs_vol = expand_array_in_mask(hrf, mask_vol, flat_axis=1)
dt = simulation['dt']
chrfs = xndarray(hrfs_vol, axes_names=['time', ] + MRI3Daxes,
axes_domains={'time': np.arange(hrfs_vol.shape[0]) * dt})
chrfs.save(fn_hrf, vol_meta)
ttp_vol = hrfs_vol.argmax(0)
fn_ttp = add_prefix(op.join(output_dir, 'ttp.nii'), prefix)
write_volume(ttp_vol, fn_ttp, vol_meta)
if simulation.has_key('brf'):
from pyhrf.ndarray import MRI3Daxes
fn_brf = add_prefix(op.join(output_dir, 'brf.nii'), prefix)
logger.info('brf flat shape %s', str(simulation['brf'].shape))
brfs_vol = expand_array_in_mask(
simulation['brf'], mask_vol, flat_axis=1)
dt = simulation['dt']
cbrfs = xndarray(brfs_vol, axes_names=['time', ] + MRI3Daxes,
axes_domains={'time': np.arange(brfs_vol.shape[0]) * dt})
cbrfs.save(fn_brf, vol_meta)
if simulation.has_key('prf'):
from pyhrf.ndarray import MRI3Daxes
fn_brf = add_prefix(op.join(output_dir, 'prf.nii'), prefix)
logger.info('prf flat shape %s', str(simulation['prf'].shape))
brfs_vol = expand_array_in_mask(
simulation['prf'], mask_vol, flat_axis=1)
dt = simulation['dt']
cbrfs = xndarray(brfs_vol, axes_names=['time', ] + MRI3Daxes,
axes_domains={'time': np.arange(brfs_vol.shape[0]) * dt})
cbrfs.save(fn_brf, vol_meta)
if simulation.has_key('drift'):
fn_drift = add_prefix(op.join(output_dir, 'drift.nii'), prefix)
logger.info('drift flat shape %s', str(simulation['drift'].shape))
drift_vol = expand_array_in_mask(simulation['drift'], mask_vol,
flat_axis=1)
write_volume(np.rollaxis(drift_vol, 0, 4), fn_drift)
if simulation.has_key('drift_coeffs'):
fn_drift = add_prefix(op.join(output_dir, 'drift_coeffs.nii'), prefix)
logger.info(
'drift flat shape %s', str(simulation['drift_coeffs'].shape))
drift_vol = expand_array_in_mask(simulation['drift'], mask_vol,
flat_axis=1)
write_volume(np.rollaxis(drift_vol, 0, 4), fn_drift)
if simulation.has_key('noise'):
fn_noise = add_prefix(op.join(output_dir, 'noise.nii'), prefix)
logger.info('noise flat shape %s', str(simulation['noise'].shape))
noise_vol = expand_array_in_mask(simulation['noise'], mask_vol,
flat_axis=1)
write_volume(np.rollaxis(noise_vol, 0, 4), fn_noise, vol_meta)
fn_noise = add_prefix(op.join(output_dir, 'noise_emp_var.nii'), prefix)
noise_vol = expand_array_in_mask(simulation['noise'].var(0), mask_vol)
write_volume(noise_vol, fn_noise, vol_meta)
if simulation.has_key('noise_var'):
fn_noise_var = add_prefix(op.join(output_dir, 'noise_var.nii'), prefix)
logger.info(
'noise_var flat shape %s', str(simulation['noise_var'].shape))
noise_var_vol = expand_array_in_mask(simulation['noise_var'], mask_vol)
write_volume(noise_var_vol, fn_noise_var, vol_meta)
if simulation.has_key('stim_induced_signal'):
fn_stim_induced = add_prefix(op.join(output_dir, 'stim_induced.nii'),
prefix)
logger.info('stim_induced flat shape %s',
str(simulation['stim_induced_signal'].shape))
stim_induced_vol = expand_array_in_mask(simulation['stim_induced_signal'],
mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
if simulation.has_key('perf_stim_induced'):
fn_stim_induced = add_prefix(op.join(output_dir, 'perf_stim_induced.nii'),
prefix)
logger.info('asl_stim_induced flat shape %s',
str(simulation['perf_stim_induced'].shape))
stim_induced_vol = expand_array_in_mask(simulation['perf_stim_induced'],
mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
fn_stim_induced = add_prefix(op.join(output_dir,
'perf_stim_induced_ct.nii'),
prefix)
logger.info('asl_stim_induced flat shape %s',
str(simulation['perf_stim_induced'].shape))
dsf = simulation['dsf']
perf = np.dot(simulation['ctrl_tag_mat'],
simulation['perf_stim_induced'][0:-1:dsf])
stim_induced_vol = expand_array_in_mask(perf, mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
if simulation.has_key('perf_baseline'):
fn = add_prefix(op.join(output_dir, 'perf_baseline.nii'), prefix)
pb = np.zeros_like(simulation['bold']) + simulation['perf_baseline']
write_volume(expand_array_in_mask(pb[0], mask_vol), fn, vol_meta)
if simulation.has_key('bold_stim_induced'):
fn_stim_induced = add_prefix(op.join(output_dir, 'bold_stim_induced.nii'),
prefix)
logger.info('asl_stim_induced flat shape %s',
str(simulation['bold_stim_induced'].shape))
stim_induced_vol = expand_array_in_mask(simulation['bold_stim_induced'],
mask_vol, flat_axis=1)
write_volume(np.rollaxis(stim_induced_vol, 0, 4), fn_stim_induced)
m = np.where(mask_vol)
labels_and_mask = mask_vol.copy()[m]
for ic in xrange(simulation['labels'].shape[0]):
if simulation.has_key('condition_defs'):
c_name = simulation['condition_defs'][ic].name
else:
c_name = 'cond%d' % ic
fn_labels = add_prefix(op.join(output_dir, 'labels_%s.nii' % c_name),
prefix)
if simulation.has_key('labels'):
labels_c = simulation['labels'][ic]
labels_and_mask[np.where(labels_c)] = ic + 2
write_volume(expand_array_in_mask(labels_c, mask_vol).astype(np.int32),
fn_labels, vol_meta)
elif simulation.has_key('labels_vol'):
labels_c = simulation['labels_vol'][ic]
labels_and_mask[np.where(labels_c[m])] = ic + 2
write_volume(labels_c.astype(np.int32), fn_labels, vol_meta)
if simulation.has_key('nrls'):
nrls_c = simulation['nrls'][ic]
fn = add_prefix(
op.join(output_dir, 'nrls_%s.nii' % c_name), prefix)
write_volume(expand_array_in_mask(nrls_c, mask_vol), fn, vol_meta)
if simulation.has_key('nrls_session'):
nrls_session_c = simulation['nrls_session'][ic]
fn = add_prefix(op.join(output_dir, 'nrls_session_%s.nii'
% (c_name)), prefix)
write_volume(expand_array_in_mask(nrls_session_c, mask_vol),
fn, vol_meta)
if simulation.has_key('brls'):
brls_c = simulation['brls'][ic]
fn = add_prefix(
op.join(output_dir, 'brls_%s.nii' % c_name), prefix)
write_volume(expand_array_in_mask(brls_c, mask_vol), fn, vol_meta)
if simulation.has_key('prls'):
prls_c = simulation['prls'][ic]
fn = add_prefix(
op.join(output_dir, 'prls_%s.nii' % c_name), prefix)
write_volume(expand_array_in_mask(prls_c, mask_vol), fn, vol_meta)
if simulation.has_key('neural_efficacies'):
ne_c = simulation['neural_efficacies'][ic]
fn = add_prefix(op.join(output_dir, 'neural_efficacies_%s.nii'
% c_name), prefix)
write_volume(expand_array_in_mask(ne_c, mask_vol), fn, vol_meta)
fn_labels_and_mask = add_prefix(op.join(output_dir, 'mask_and_labels.nii'),
prefix)
write_volume(expand_array_in_mask(labels_and_mask, mask_vol).astype(int),
fn_labels_and_mask, vol_meta)
if simulation.has_key('bold_full_vol') or simulation.has_key('bold'):
fn = add_prefix(op.join(output_dir, 'bold.nii'), prefix)
if simulation.has_key('bold_full_vol'):
bold4D = simulation['bold_full_vol']
else:
bold = simulation['bold']
bold4D = expand_array_in_mask(bold, mask_vol, flat_axis=1)
write_volume(np.rollaxis(bold4D, 0, 4), fn, vol_meta)
def save_time_series(k):
if simulation.has_key(k):
fn_stim_induced = add_prefix(
op.join(output_dir, k + '.nii'), prefix)
logger.info('%s flat shape %s', k, str(simulation[k].shape))
vol = expand_array_in_mask(simulation[k], mask_vol, flat_axis=1)
write_volume(np.rollaxis(vol, 0, 4), fn_stim_induced)
save_time_series('flow_induction')
save_time_series('cbv')
save_time_series('hbr')
save_time_series('bold_stim_induced_rescaled')
if simulation.has_key('asl'):
fn = add_prefix(op.join(output_dir, 'asl.nii'), prefix)
asl4D = expand_array_in_mask(simulation['asl'], mask_vol, flat_axis=1)
write_volume(np.rollaxis(asl4D, 0, 4), fn, vol_meta)
if simulation.has_key('outliers'):
fn = add_prefix(op.join(output_dir, 'outliers.nii'), prefix)
outliers = expand_array_in_mask(simulation['outliers'], mask_vol,
flat_axis=1)
write_volume(np.rollaxis(outliers, 0, 4), fn, vol_meta)
if simulation.has_key('hrf_group'):
hrfgroup = simulation['hrf_group']
nb_vox = mask_vol.size
fn_hrf = add_prefix(op.join(output_dir, 'hrf_group.nii'), prefix)
logger.info('hrf group shape %s', str(simulation['hrf_group'].shape))
hrfGd = duplicate_hrf(nb_vox, hrfgroup)
hrfs_vol = expand_array_in_mask(hrfGd, mask_vol, flat_axis=1)
dt = simulation['dt']
chrfs = xndarray(hrfs_vol, axes_names=['time', ] + MRI3Daxes,
axes_domains={'time': np.arange(hrfs_vol.shape[0]) * dt})
chrfs.save(fn_hrf, vol_meta)
if bold_3D_vols_dir is not None:
assert op.exists(bold_3D_vols_dir)
for iscan, bscan in enumerate(bold4D):
fnout = add_prefix('bold_%06d.nii' % (iscan), prefix)
write_volume(bscan, op.join(bold_3D_vols_dir, fnout), vol_meta)
def glm_nipy_from_files(bold_file, tr, paradigm_csv_file, output_dir,
mask_file, session=0, contrasts=None,
con_test_baseline=0.0,
hrf_model='Canonical',
drift_model='Cosine', hfcut=128,
residuals_model='spherical', fit_method='ols',
fir_delays=[0]):
"""
#TODO: handle surface data
hrf_model : Canonical | Canonical with Derivative | FIR
"""
fdata = FmriData.from_vol_files(mask_file, paradigm_csv_file,
[bold_file], tr)
g, dm, cons = glm_nipy(fdata, contrasts=contrasts, hrf_model=hrf_model,
hfcut=hfcut, drift_model=drift_model,
residuals_model=residuals_model,
fit_method=fit_method, fir_delays=fir_delays)
ns, nr = dm.matrix.shape
cdesign_matrix = xndarray(dm.matrix, axes_names=['time', 'regressor'],
axes_domains={'time': np.arange(ns) * tr,
'regressor': dm.names})
cdesign_matrix.save(op.join(output_dir, 'design_matrix.nii'))
beta_files = []
beta_values = dict.fromkeys(dm.names)
beta_vars = dict.fromkeys(dm.names)
beta_vars_voxels = dict.fromkeys(dm.names)
for ib, bname in enumerate(dm.names):
# beta values
beta_vol = expand_array_in_mask(g.beta[ib], fdata.roiMask > 0)
beta_fn = op.join(output_dir, 'beta_%s.nii' % bname)
write_volume(beta_vol, beta_fn, fdata.meta_obj)
beta_files.append(beta_fn)
beta_values[bname] = beta_vol
# normalized variance of betas
# variance: diag of cov matrix
beta_vars[bname] = sp.diag(g.nvbeta)[ib]
# sig2 = g.s2 #ResMS
# variance for all voxels, condition ib
var_cond = sp.diag(g.nvbeta)[ib] * g.s2
beta_vars_voxels[bname] = var_cond
#beta_var_fn = op.join(output_dir, 'var_beta_%s.nii' %bname)
#write_volume(beta_var, beta_var_fn, fdata.meta_obj)
# beta_var_files.append(beta_var_fn)
if cons is not None:
con_files = []
pval_files = []
for cname, con in cons.iteritems():
con_vol = expand_array_in_mask(con.effect, fdata.roiMask > 0)
con_fn = op.join(output_dir, 'con_effect_%s.nii' % cname)
write_volume(con_vol, con_fn, fdata.meta_obj)
con_files.append(con_fn)
pval_vol = expand_array_in_mask(con.pvalue(con_test_baseline),
fdata.roiMask > 0)
pval_fn = op.join(output_dir, 'con_pvalue_%s.nii' % cname)
write_volume(pval_vol, pval_fn, fdata.meta_obj)
pval_files.append(pval_fn)
else:
con_files = None
pval_files = None
dof = g.dof
# if do_ppm:
# for
# TODO: FIR stuffs
# , con_files, pval_files
return beta_files, beta_values, beta_vars_voxels, dof
