def load_preproc(location, sessions_order=None, **kwargs):
""" Loads the input parameters of the different processing steps of
the SPM preprocessing.
Parameters
----------
location: str
jobs_preproc.mat file path
kwargs: dict
extra args
Note
----
The returned parameters and paths are inputs of the steps, therefore
the bold series in slice_timing will be the original time series,
and the series in realign will be the resulting images from the
slice_timing step.
Returns
-------
A dict with one key per step.
"""
doc = {}
work_dir = os.path.split(os.path.realpath(location))[0]
doc.update(_check_kwargs(work_dir, **kwargs))
mat = load_matfile(location)['jobs']
doc['order'] = ['slice_timing', 'realign', 'preproc', 'normalize', 'final']
def get_images(images, return_motion=False):
if len(images[0].shape) == 0:
data_dir = find_data_dir(work_dir, str(images[0]))
paths = [[os.path.join(
data_dir, os.path.split(str(x))[1]) for x in images]]
fname = glob.glob(os.path.join(data_dir, 'rp_*.txt'))[0]
motion = [fname]
else:
paths = []
motion = []
for session_scans in images:
scans = []
data_dir = find_data_dir(work_dir, str(session_scans[0]))
for x in session_scans:
scans.append(
os.path.join(data_dir, os.path.split(str(x))[1]))
paths.append(scans)
fname = glob.glob(os.path.join(data_dir, 'rp_*.txt'))[0]
motion.append(fname)
if return_motion:
return paths, motion
return paths
def get_path(path):
data_dir = find_data_dir(work_dir, str(path))
return os.path.join(data_dir, os.path.split(str(path))[1])
# doc['mat'] = mat
def save_slice_timing(m):
# slice timing
slice_timing = m
doc['slice_timing'] = {}
doc['slice_timing']['bold'] = []
doc['slice_timing']['n_slices'] = int(slice_timing.nslices)
doc['slice_timing']['ref_slice'] = int(slice_timing.refslice)
doc['slice_timing']['slices_order'] = slice_timing.so.tolist()
doc['slice_timing']['ta'] = float(slice_timing.ta)
doc['slice_timing']['tr'] = float(slice_timing.tr)
doc['slice_timing']['bold'] = get_images(slice_timing.scans)
n_scans = [len(s) for s in doc['slice_timing']['bold']]
doc['n_scans'] = n_scans
def save_realign(m0):
# realign
realign = m0
doc['realign'] = {}
doc['realign']['bold'] = []
doc['realign']['motion'] = []
doc['realign']['bold'], doc['realign']['motion'] = \
get_images(realign.data, return_motion=True)
def save_preproc_normalize(m0, m1):
# preproc / normalize
preproc = m0
normalize = m1
doc['preproc'] = {}
doc['normalize'] = {}
doc['preproc']['anatomy'] = get_path(preproc.data)
doc['normalize']['anatomy'] = get_path(normalize.write.subj.resample)
doc['normalize']['mat_file'] = get_path(normalize.write.subj.matname)
def save_coregistration(m0):
# coregistration
coreg = m0
doc['coregistration'] = {}
doc['coregistration']['realigned'] = [get_path(p)
for p in coreg.estimate.other]
doc['coregistration']['anatomy'] = get_path(coreg.estimate.ref)
doc['coregistration']['realigned_ref'] = get_path(
coreg.estimate.source)
def save_normalize(m0):
# normalize bold
normalize = m0
doc['normalize']['bold'] = []
for session_scans in np.split(
normalize.resample, np.cumsum(doc['n_scans']))[:-1]:
scans = []
data_dir = find_data_dir(work_dir, str(session_scans[0]))
for x in session_scans:
scans.append(os.path.join(data_dir, os.path.split(str(x))[1]))
doc['normalize']['bold'].append(scans)
def save_smooth(m0):
# smooth
smooth = m0
doc['smooth'] = {}
doc['smooth']['bold'] = []
doc['smooth']['fwhm'] = float(smooth.fwhm)
for session_scans in np.split(
smooth.data, np.cumsum(doc['n_scans']))[:-1]:
scans = []
data_dir = find_data_dir(work_dir, str(session_scans[0]))
for x in session_scans:
scans.append(os.path.join(data_dir, os.path.split(str(x))[1]))
doc['smooth']['bold'].append(scans)
for m in mat:
try:
m0 = m.temporal.st
save_slice_timing(m0)
except:
pass
try:
m0 = m.spatial.realign.estwrite
save_realign(m0)
except:
pass
try:
m0 = m.spatial[0].preproc
m1 = m.spatial[1].normalise
save_preproc_normalize(m0, m1)
except:
pass
try:
m0 = m.spatial.coreg
save_coregistration(m0)
except:
pass
try:
m0 = m.spatial.normalise.write.subj
save_normalize(m0)
except:
pass
try:
m0 = m.spatial.smooth
save_smooth(m0)
except:
pass
doc['final'] = {}
doc['final']['anatomy'] = prefix_filename(
doc['normalize']['anatomy'], 'w')
doc['final']['bold'] = []
for session_scans in doc['smooth']['bold']:
scans = []
for x in session_scans:
scans.append(prefix_filename(x, 's'))
doc['final']['bold'].append(scans)
if 'subject_id' not in doc:
doc['subject_id'] = hashlib.md5(work_dir).hexdigest()
if sessions_order is not None:
order = []
for session_pattern in sessions_order:
for i, bold in enumerate(doc['final']['bold']):
if fnmatch.fnmatch(os.path.split(bold[0])[1], session_pattern):
order.append(i)
break
order = np.array(order)
for step in doc.keys():
if 'bold' in doc[step]:
doc[step]['bold'] = np.array(doc[step]['bold'])[order].tolist()
doc['realign']['motion'] = np.array(
doc['realign']['motion'])[order].tolist()
return doc
def load_intra(location, fix=None, **kwargs):
doc = {}
mat = load_matfile(location)['SPM']
work_dir = os.path.split(os.path.realpath(location))[0]
doc.update(_check_kwargs(work_dir, **kwargs))
# doc['mat'] = mat
doc['design_matrices'] = mat.xX.X.tolist() # xX: model
doc['design_conditions'] = [str(i) for i in mat.xX.name]
doc['design_contrasts'] = {}
doc['n_scans'] = mat.nscan.tolist() \
if isinstance(mat.nscan.tolist(), list) else [mat.nscan.tolist()]
doc['n_sessions'] = mat.nscan.size
doc['tr'] = float(mat.xY.RT) # xY: data
doc['mask'] = os.path.join(work_dir, str(mat.VM.fname)) # VM: mask
doc['beta_maps'] = []
doc['c_maps'] = {}
doc['t_maps'] = {}
doc['condition_key'] = []
doc['task_contrasts'] = {}
doc['onsets'] = []
swabold = np.split(mat.xY.P.tolist(), np.cumsum(doc['n_scans'])[:-1])
doc['data'] = {}
for session in swabold:
session_dir = find_data_dir(work_dir, session[0])
scans = [os.path.join(session_dir, os.path.split(s)[1].strip())
for s in session]
doc['data'].setdefault('swabold', []).append(scans)
for s in doc['data']['swabold']:
scans = []
for i in s:
scans.append(strip_prefix_filename(i, 1))
doc['data'].setdefault('wabold', []).append(scans)
for s in doc['data']['swabold']:
scans = []
for i in s:
scans.append(strip_prefix_filename(i, 2))
doc['data'].setdefault('abold', []).append(scans)
for s in doc['data']['swabold']:
scans = []
for i in s:
scans.append(strip_prefix_filename(i, 3))
doc['data'].setdefault('bold', []).append(scans)
doc['motion'] = []
if doc['n_sessions'] > 1:
for session in mat.Sess:
doc['motion'].append(session.C.C.tolist())
else:
doc['motion'].append(mat.Sess.C.C.tolist())
def get_condition_onsets(condition):
onset_time = condition.ons.tolist()
onset_duration = condition.dur.tolist()
if not isinstance(onset_time, list):
onset_time = [onset_time]
onset_duration = [onset_duration]
onset_weight = [1] * len(onset_time)
return zip(onset_time, onset_duration, onset_weight)
if hasattr(mat.Sess, '__iter__'):
for session in mat.Sess:
onsets = {}
condition_key = []
for condition_id, condition in enumerate(session.U):
k = 'cond%03i' % (condition_id + 1)
onsets[k] = get_condition_onsets(condition)
condition_key.append(str(condition.name))
doc['condition_key'].append(condition_key)
doc['onsets'].append(onsets)
else:
onsets = {}
condition_key = []
for condition_id, condition in enumerate(mat.Sess.U):
k = 'cond%03i' % (condition_id + 1)
onsets[k] = get_condition_onsets(condition)
condition_key.append(str(condition.name))
doc['condition_key'].append(condition_key)
doc['onsets'].append(onsets)
for c in mat.xCon:
name = str(c.name)
try:
doc['c_maps'][name] = os.path.join(work_dir, str(c.Vcon.fname))
doc['t_maps'][name] = os.path.join(work_dir, str(c.Vspm.fname))
doc['design_contrasts'][name] = c.c.tolist()
except:
# sometimes c.Vcon is an empty array
pass
for i, b in enumerate(mat.Vbeta):
doc['beta_maps'].append(os.path.join(work_dir, str(b.fname)))
if 'subject_id' not in doc:
doc['subject_id'] = hashlib.md5(work_dir).hexdigest()
def get_condition_index(name):
for i, full_name in enumerate(doc['design_conditions']):
if name in full_name:
return i
# find the indices of the actual experimental conditions in the
# design matrix, not the additional regressors...
ii = []
for session in doc['condition_key']:
ii.append([get_condition_index(name) for name in session])
# redefine the contrasts with the experimental conditions
for k, contrast in doc['design_contrasts'].iteritems():
doc['task_contrasts'][k] = []
for per_session in ii:
doc['task_contrasts'][k].append(
np.array(contrast)[per_session].tolist())
# attempt to guess condition names with the contrast names & values
condition_key = [np.array(ck, dtype='|S32') for ck in doc['condition_key']]
for contrast_name, session_contrasts in doc['task_contrasts'].items():
for ck, contrast in zip(condition_key, session_contrasts):
contrast = np.array(contrast)
if ((contrast < 0).sum() == 0 and len(contrast.shape) == 1
and (contrast == np.abs(contrast).max()).sum() == 1):
ck[np.array(contrast) > 0] = contrast_name
doc['condition_key'] = condition_key
# reformat SPM design per session
(doc['design_matrices'],
doc['design_conditions'],
doc['design_contrasts']) = make_design_from_spm(
doc['n_scans'],
doc['design_matrices'],
doc['design_conditions'],
doc['design_contrasts'])
if fix is not None:
doc = fix_experiment(doc, fix)[0]
return doc
