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 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)
"""
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 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)
#print os.listdir(self.tmp_dir)
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)
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 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
beta_vars[bname] = sp.diag(g.nvbeta)[ib] #variance: diag of cov matrix
#sig2 = g.s2 #ResMS
var_cond = sp.diag(g.nvbeta)[ib]*g.s2 #variance for all voxels, condition ib
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
return beta_files, beta_values, beta_vars_voxels, dof#, con_files, pval_files
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 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')
pyhrf.verbose(3,'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)
pyhrf.verbose(3,'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})
#write_volume(np.rollaxis(hrfs_vol,0,4), fn_hrf, vol_meta)
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)
pyhrf.verbose(3,'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})
#write_volume(np.rollaxis(hrfs_vol,0,4), fn_hrf, vol_meta)
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)
pyhrf.verbose(3,'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})
#write_volume(np.rollaxis(hrfs_vol,0,4), fn_hrf, vol_meta)
cbrfs.save(fn_brf, vol_meta)
if simulation.has_key('drift'):
fn_drift = add_prefix(op.join(output_dir, 'drift.nii'), prefix)
pyhrf.verbose(3,'drift flat shape %s' %str(simulation['drift'].shape))
drift_vol = expand_array_in_mask(simulation['drift'], mask_vol,
flat_axis=1)
#write_volume(drift_vol, fn_drift, vol_meta)
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)
pyhrf.verbose(3,'drift flat shape %s' %str(simulation['drift_coeffs'].shape))
drift_vol = expand_array_in_mask(simulation['drift'], mask_vol,
flat_axis=1)
#write_volume(drift_vol, fn_drift, vol_meta)
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)
pyhrf.verbose(3,'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)
pyhrf.verbose(3,'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)
pyhrf.verbose(3,'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)
pyhrf.verbose(3,'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)
pyhrf.verbose(3,'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)
pyhrf.verbose(3,'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)
pyhrf.verbose(3,'%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)
pyhrf.verbose(3,'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)
#print fnout
write_volume(bscan, op.join(bold_3D_vols_dir,fnout), vol_meta)
def save_time_series(k):
if simulation.has_key(k):
fn_stim_induced = add_prefix(op.join(output_dir, k+'.nii'), prefix)
pyhrf.verbose(3,'%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 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)
