def parse_spm5_preproc(work_dir, step):
doc = {}
if hasattr(step, 'spatial') and hasattr(step.spatial, 'realign'):
realign = step.spatial.realign.estwrite
motion = []
if len(realign.data[0].shape) == 0:
realign = [realign]
else:
realign = realign.data
for session in realign:
data_dir = find_data_dir(work_dir, check_path(session[0]))
motion.append(glob.glob(os.path.join(data_dir, 'rp_*.txt'))[0])
doc['motion'] = motion
if hasattr(step, 'spatial') and isinstance(step.spatial, np.ndarray):
doc['anatomy'] = makeup_path(work_dir,
check_path(step.spatial[0].preproc.data))
doc['wmanatomy'] = prefix_filename(
makeup_path(
work_dir,
check_path(step.spatial[1].normalise.write.subj.resample)),
'w')
if hasattr(step, 'temporal'):
doc['n_slices'] = int(step.temporal.st.nslices)
doc['ref_slice'] = int(step.temporal.st.refslice)
doc['slice_order'] = step.temporal.st.so.tolist()
doc['ta'] = float(step.temporal.st.ta)
doc['tr'] = float(step.temporal.st.tr)
doc['bold'] = []
doc['swabold'] = []
if len(step.temporal.st.scans[0].shape) == 0:
bold = [step.temporal.st.scans]
else:
bold = step.temporal.st.scans
for session in bold:
data_dir = find_data_dir(work_dir, str(session[0]))
doc['bold'].append(
check_paths([
os.path.join(data_dir,
os.path.split(str(x))[1]) for x in session
]))
doc['swabold'].append(
check_paths([
prefix_filename(
os.path.join(data_dir,
os.path.split(str(x))[1]), 'swa')
for x in session
]))
doc['n_scans'] = [len(s) for s in doc['bold']]
return doc
def parse_spm8_preproc(work_dir, step):
doc = {}
if hasattr(step, 'spatial') and hasattr(step.spatial, 'preproc'):
doc['anatomy'] = makeup_path(
work_dir, check_path(step.spatial.preproc.data))
doc['wmanatomy'] = prefix_filename(doc['anatomy'], 'wm')
if hasattr(step, 'temporal'):
doc['n_slices'] = int(step.temporal.st.nslices)
doc['ref_slice'] = int(step.temporal.st.refslice)
doc['slice_order'] = step.temporal.st.so.tolist()
doc['ta'] = float(step.temporal.st.ta)
doc['tr'] = float(step.temporal.st.tr)
doc['bold'] = []
doc['swabold'] = []
if len(step.temporal.st.scans[0].shape) == 0:
bold = [step.temporal.st.scans]
else:
bold = step.temporal.st.scans
for session in bold:
data_dir = find_data_dir(work_dir, str(session[0]))
doc['bold'].append(check_paths(
[os.path.join(data_dir, os.path.split(str(x))[1])
for x in session]))
doc['swabold'].append(check_paths(
[prefix_filename(os.path.join(
data_dir, os.path.split(str(x))[1]), 'swa')
for x in session]))
doc['n_scans'] = [len(s) for s in doc['bold']]
return doc
def parse_spm5_preproc(work_dir, step):
doc = {}
if hasattr(step, 'spatial') and hasattr(step.spatial, 'realign'):
realign = step.spatial.realign.estwrite
motion = []
if len(realign.data[0].shape) == 0:
realign = [realign]
else:
realign = realign.data
for session in realign:
data_dir = find_data_dir(work_dir, check_path(session[0]))
motion.append(glob.glob(os.path.join(data_dir, 'rp_*.txt'))[0])
doc['motion'] = motion
if hasattr(step, 'spatial') and isinstance(step.spatial, np.ndarray):
doc['anatomy'] = makeup_path(
work_dir, check_path(step.spatial[0].preproc.data))
doc['wmanatomy'] = prefix_filename(makeup_path(
work_dir,
check_path(step.spatial[1].normalise.write.subj.resample)),
'w')
if hasattr(step, 'temporal'):
doc['n_slices'] = int(step.temporal.st.nslices)
doc['ref_slice'] = int(step.temporal.st.refslice)
doc['slice_order'] = step.temporal.st.so.tolist()
doc['ta'] = float(step.temporal.st.ta)
doc['tr'] = float(step.temporal.st.tr)
doc['bold'] = []
doc['swabold'] = []
if len(step.temporal.st.scans[0].shape) == 0:
bold = [step.temporal.st.scans]
else:
bold = step.temporal.st.scans
for session in bold:
data_dir = find_data_dir(work_dir, str(session[0]))
doc['bold'].append(check_paths(
[os.path.join(data_dir, os.path.split(str(x))[1])
for x in session]))
doc['swabold'].append(check_paths(
[prefix_filename(os.path.join(
data_dir, os.path.split(str(x))[1]), 'swa')
for x in session]))
doc['n_scans'] = [len(s) for s in doc['bold']]
return doc
def get_intra_preproc(mat_file, work_dir, n_scans, memory=Memory(None)):
mat = memory.cache(load_matfile)(mat_file)['SPM']
preproc = {}
get_motion_file = False
if len(n_scans) > 1:
preproc['motion'] = []
for session in mat.Sess:
preproc['motion'].append(session.C.C.tolist())
if session.C.C.size == 0:
get_motion_file = True
else:
preproc['motion'] = [mat.Sess.C.C.tolist()]
if mat.Sess.C.C.size == 0:
get_motion_file = True
swabold = check_paths(mat.xY.P)
if len(nb.load(makeup_path(work_dir, swabold[0])).shape) == 4:
swabold = np.unique(swabold)
else:
swabold = np.split(swabold, np.cumsum(n_scans)[:-1])
if get_motion_file:
preproc['motion'] = []
for session in swabold:
session_dir = find_data_dir(work_dir, check_path(session[0]))
if get_motion_file:
motion_file = glob.glob(os.path.join(session_dir, 'rp_*.txt'))[0]
motion = np.fromfile(motion_file, sep=' ')
motion = motion.reshape(motion.shape[0] / 6, 6)
preproc['motion'].append(motion)
if isinstance(session, (list, np.ndarray)):
scans = [
os.path.join(session_dir,
os.path.split(scan)[1].strip())
for scan in session
]
preproc.setdefault('swabold', []).append(scans)
preproc.setdefault('abold', []).append(
[strip_prefix_filename(scan, 2) for scan in scans])
preproc.setdefault('bold', []).append(
[strip_prefix_filename(scan, 3) for scan in scans])
else:
preproc.setdefault('swabold', []).append(session)
preproc.setdefault('abold',
[]).append(strip_prefix_filename(session, 2))
preproc.setdefault('bold',
[]).append(strip_prefix_filename(session, 3))
return preproc
def get_intra_preproc(mat_file, work_dir, n_scans, memory=Memory(None)):
mat = memory.cache(load_matfile)(mat_file)['SPM']
preproc = {}
get_motion_file = False
if len(n_scans) > 1:
preproc['motion'] = []
for session in mat.Sess:
preproc['motion'].append(session.C.C.tolist())
if session.C.C.size == 0:
get_motion_file = True
else:
preproc['motion'] = [mat.Sess.C.C.tolist()]
if mat.Sess.C.C.size == 0:
get_motion_file = True
swabold = check_paths(mat.xY.P)
if len(nb.load(makeup_path(work_dir, swabold[0])).shape) == 4:
swabold = np.unique(swabold)
else:
swabold = np.split(swabold, np.cumsum(n_scans)[:-1])
if get_motion_file:
preproc['motion'] = []
for session in swabold:
session_dir = find_data_dir(work_dir, check_path(session[0]))
if get_motion_file:
motion_file = glob.glob(os.path.join(session_dir, 'rp_*.txt'))[0]
motion = np.fromfile(motion_file, sep=' ')
motion = motion.reshape(motion.shape[0] / 6, 6)
preproc['motion'].append(motion)
if isinstance(session, (list, np.ndarray)):
scans = [os.path.join(session_dir, os.path.split(scan)[1].strip())
for scan in session]
preproc.setdefault('swabold', []).append(scans)
preproc.setdefault('abold', []).append(
[strip_prefix_filename(scan, 2) for scan in scans])
preproc.setdefault('bold', []).append(
[strip_prefix_filename(scan, 3) for scan in scans])
else:
preproc.setdefault('swabold', []).append(session)
preproc.setdefault('abold', []).append(
strip_prefix_filename(session, 2))
preproc.setdefault('bold', []).append(
strip_prefix_filename(session, 3))
return preproc
def parse_spm8_preproc(work_dir, step):
doc = {}
if hasattr(step, 'spatial') and hasattr(step.spatial, 'preproc'):
doc['anatomy'] = makeup_path(work_dir,
check_path(step.spatial.preproc.data))
doc['wmanatomy'] = prefix_filename(doc['anatomy'], 'wm')
if hasattr(step, 'temporal'):
doc['n_slices'] = int(step.temporal.st.nslices)
doc['ref_slice'] = int(step.temporal.st.refslice)
doc['slice_order'] = step.temporal.st.so.tolist()
doc['ta'] = float(step.temporal.st.ta)
doc['tr'] = float(step.temporal.st.tr)
doc['bold'] = []
doc['swabold'] = []
if len(step.temporal.st.scans[0].shape) == 0:
bold = [step.temporal.st.scans]
else:
bold = step.temporal.st.scans
for session in bold:
data_dir = find_data_dir(work_dir, str(session[0]))
doc['bold'].append(
check_paths([
os.path.join(data_dir,
os.path.split(str(x))[1]) for x in session
]))
doc['swabold'].append(
check_paths([
prefix_filename(
os.path.join(data_dir,
os.path.split(str(x))[1]), 'swa')
for x in session
]))
doc['n_scans'] = [len(s) for s in doc['bold']]
return doc