def load_and_get_fdata_params(self, sessions_data, mask):
params = stack_trees([sd.to_dict() for sd in sessions_data])
fns = params.pop('func_data_file')
pyhrf.verbose(1, 'Load functional data from: %s' %',\n'.join(fns))
fdata = stack_cuboids([xndarray.load(fn) for fn in fns], 'session')
fdata = np.concatenate(fdata.data) #scan sessions along time axis
pio.discard_bad_data(fdata, mask)
pyhrf.verbose(1, 'Functional data shape %s' %str(fdata.shape))
params['func_data'] = fdata
return params
def load_and_get_fdata_params(self, mask):
if op.splitext(self.paradigm_file)[-1] == '.csv':
onsets, durations = pio.load_paradigm_from_csv(self.paradigm_file)
else:
raise Exception('Only CSV file format support for paradigm')
fns = self.func_data_files
pyhrf.verbose(1, 'Load functional data from: %s' %',\n'.join(fns))
fdata = stack_cuboids([xndarray.load(fn) for fn in fns], 'session')
fdata = np.concatenate(fdata.data) #scan sessions along time axis
pio.discard_bad_data(fdata, mask)
pyhrf.verbose(1, 'Functional data shape %s' %str(fdata.shape))
return {'stim_onsets': onsets, 'stim_durations':durations,
'func_data': fdata}
def load_surf_bold_mask(bold_files, mesh_file, mask_file=None):
pyhrf.verbose(1, 'Load mesh: ' + mesh_file)
coords,triangles,coord_sys = read_mesh(mesh_file)
pyhrf.verbose(2, 'Build graph ... ')
fgraph = graph_from_mesh(triangles)
assert graph_is_sane(fgraph)
pyhrf.verbose(1, 'Mesh has %d nodes' %len(fgraph))
pyhrf.verbose(2, 'Compute length of edges ... ')
edges_l = np.array([np.array([distance(coords[i],
coords[n],
coord_sys.xform) \
for n in nl]) \
for i,nl in enumerate(fgraph)],dtype=object)
if mask_file is None or not op.exists(mask_file):
pyhrf.verbose(1,'Mask file %s does not exist. Taking '\
' all nodes ...' %mask_file)
mask = np.ones(len(fgraph))
mask_meta_obj = None
mask_loaded_from_file = False
else:
mask, mask_meta_obj = read_texture(mask_file)
mask_loaded_from_file = True
if not (np.round(mask) == mask).all():
raise Exception("Mask is not n-ary")
if len(mask) != len(fgraph):
raise Exception('Size of mask (%d) is different from size '\
'of graph (%d)' %(len(mask),len(fgraph)))
mask = mask.astype(np.int32)
if mask.min() == -1:
mask += 1
#Split graph into rois:
graphs = {}
edge_lengths = {}
for roiId in np.unique(mask):
mroi = np.where(mask==roiId)
g, nm = sub_graph(fgraph, mroi[0])
edge_lengths[roiId] = edges_l[mroi]
graphs[roiId] = g
#Load BOLD:
lastScan = 0
sessionScans = []
bolds = []
for boldFile in bold_files:
pyhrf.verbose(1, 'load bold: ' + boldFile)
b,_ = read_texture(boldFile)
pyhrf.verbose(1, 'bold shape: ' + str(b.shape))
bolds.append(b)
sessionScans.append(np.arange(lastScan, lastScan+b.shape[0],
dtype=int))
lastScan += b.shape[0]
bold = np.concatenate(tuple(bolds))
if len(fgraph) != bold.shape[1]:
raise Exception('Nb positions not consistent between BOLD (%d) '\
'and mesh (%d)' %(bold.shape[1],len(fgraph)))
# discard bad data (bold with var=0 and nan values):
discard_bad_data(bold, mask, time_axis=0)
return mask, mask_meta_obj, mask_loaded_from_file, bold, sessionScans, \
graphs, edge_lengths
def load_vol_bold_and_mask(bold_files, mask_file):
# Handle mask
if not op.exists(mask_file):
pyhrf.verbose(1,'Mask file %s does not exist. Mask is '\
' computed from BOLD ...' %mask_file)
bf = 'subj0_bold_session0.nii.gz'
# HACK
if bold_files[0] == pyhrf.get_data_file_name(bf):
max_frac = .99999 #be sure to keep non zero voxels
cc = 0 # default BOLD vol has 2 ROIs
else:
max_frac = .9
cc = 1
compute_mask_files(bold_files[0], mask_file, False, .4,
max_frac, cc=cc)
mask_loaded_from_file = False
else:
mask_loaded_from_file = True
pyhrf.verbose(1,'Assuming orientation for mask file: ' + \
string.join(MRI3Daxes, ','))
pyhrf.verbose(2,'Read mask from: %s' %mask_file)
mask, mask_meta_obj = read_volume(mask_file)
if not np.allclose(np.round(mask),mask):
raise Exception("Mask is not n-ary (%s)" %mask_file)
mask = mask.astype(np.int32)
pyhrf.verbose(1,'Mask has shape %s' %str(mask.shape))
pyhrf.verbose(1,'Mask min value: %d' %mask.min())
pyhrf.verbose(1,'Mask max value: %d' %mask.max())
mshape = mask.shape
if mask.min() == -1:
mask += 1
#Load BOLD:
lastScan = 0
sessionScans = []
bolds = []
pyhrf.verbose(1,'Assuming orientation for BOLD files: ' + \
string.join(MRI4Daxes, ','))
#print 'type of bold_files[0]:', type(bold_files[0])
#print 'bold_files:', bold_files
if type(bold_files[0]) is list:
bold_files = bold_files[0]
for bold_file in bold_files:
if not op.exists(bold_file):
raise Exception('File not found: ' + bold_file)
b, bold_meta = read_volume(bold_file)
bolds.append(b)
sessionScans.append(np.arange(lastScan,
lastScan+b.shape[TIME_AXIS],
dtype=int))
lastScan += b.shape[TIME_AXIS]
bold = np.concatenate(tuple(bolds), axis=TIME_AXIS)
pyhrf.verbose(1,'BOLD has shape %s' %str(bold.shape))
discard_bad_data(bold, mask)
# #HACK
# if mask_file != DEFAULT_MASK_VOL_FILE:
# write_volume(mask,'./treated_mask.nii')
# sys.exit(0)
return mask, mask_meta_obj, mask_loaded_from_file, bold, sessionScans