def fit(self, mask_file='', mode='+', zscore=2):
""" Process/filter/threshold the output to make it ready for plot.
If no mask_file is set, will pick the output from GIFT.
Parameters
----------
mask_file: str, optional
Path to the brain mask file to be used for thresholding.
mode: str, optional
Choices: '+' for positive threshold,
'+-' for positive and negative threshold and
'-' for negative threshold.
zscore: int or float, optional
Value of the Z-score thresholding.
"""
if not mask_file:
mask_file = fetch_one_file(self.ica_dir,
self._mask_fname,
pat_type='re.match')
super(MIALABICAResultsPlotter, self).fit(mask_file=mask_file,
mode=mode,
zscore=zscore)
def _check_output(self):
# read the groups file
subj_files = self._get_subject_files()
n_timepoints = len(subj_files)
# load the loadings file
loads = self._load_loadings()
# check shapes
if n_timepoints != loads.shape[0]:
raise AttributeError(
'Shape mismatch between list of subjects {} and loadings data shape {}.'
.format(n_timepoints, loads.shape))
# load components image file to check shape match
# this doesn't make much sense to be here (because it does not use the components image
# but it checks if the ICA output is consistent).
compsf = fetch_one_file(self.ica_dir, self._comps_fname)
comps_img = niimg.load_img(compsf)
n_ics = loads.shape[1]
if comps_img.get_data().shape[-1] != n_ics:
raise AttributeError(
'Shape mismatch between loadings matrix {} and components data shape {}.'
.format(loads.shape,
comps_img.get_data().shape))
def _fetch_components_file(self):
if self._comps_fname is None:
raise NotImplementedError(
'This is a generic class to support the output from different applications,'
'please use a derived class from ICAResultsPlotter.')
return fetch_one_file(self.ica_dir, self._comps_fname)
def _load_timecourses(self):
""" Return the timecourses image file and checks if the shape is correct."""
# load the timecourses file
tcsf = fetch_one_file(self.ica_dir,
self._tcs_fname,
pat_type='re.match')
tcs = niimg.load_img(tcsf).get_data()
return tcs
def _get_subject_files(self):
""" Load the .mat file with subjects lists, mainly to get the order in
which subjects are introduced in the other matrices.
"""
from itertools import chain
subjsf = fetch_one_file(self.ica_dir, self._subjects_fname)
mat_file = sio.loadmat(subjsf)['files']
return [
f.strip() for f in list(
chain.from_iterable(
chain.from_iterable(chain.from_iterable(mat_file))))
]
def _fetch_components_file(self):
return fetch_one_file(self.ica_dir, self._comps_fname)
def _fetch_components_file(self):
return fetch_one_file(self.ica_dir,
self._comps_fname,
pat_type='re.match')
def load_mask(self):
""" Return the mask image. """
mask_file = fetch_one_file(self.ica_dir,
self._mask_fname,
pat_type='re.match')
return niimg.load_img(mask_file)
def _load_loadings(self):
loadf = fetch_one_file(self.ica_dir, self._tcs_fname)
loads = niimg.load_img(loadf).get_data()
return loads