def transform_imgs(self, imgs_list, confounds=None, copy=True, n_jobs=1,
shelve=False):
"""Prepare multi subject data in parallel
Parameters
----------
imgs_list: list of Niimg-like objects
See http://nilearn.github.io/manipulating_visualizing/manipulating_images.html#niimg.
List of imgs file to prepare. One item per subject.
confounds: list of confounds, optional
List of confounds (2D arrays or filenames pointing to CSV
files). Must be of same length than imgs_list.
copy: boolean, optional
If True, guarantees that output array has no memory in common with
input array.
n_jobs: integer, optional
The number of cpus to use to do the computation. -1 means
'all cpus'.
Returns
-------
region_signals: list of 2D numpy.ndarray
List of signal for each element per subject.
shape: list of (number of scans, number of elements)
"""
if not hasattr(self, 'mask_img_'):
raise ValueError('It seems that %s has not been fitted. '
'You must call fit() before calling transform().'
% self.__class__.__name__)
target_fov = None
if self.target_affine is None:
# Force resampling on first image
target_fov = 'first'
niimg_iter = _iter_check_niimg(imgs_list, ensure_ndim=None,
atleast_4d=False,
target_fov=target_fov,
memory=self.memory,
memory_level=self.memory_level,
verbose=self.verbose)
if confounds is None:
confounds = itertools.repeat(None, len(imgs_list))
# Ignore the mask-computing params: they are not useful and will
# just invalidate the cache for no good reason
# target_shape and target_affine are conveyed implicitly in mask_img
params = get_params(self.__class__, self,
ignore=['mask_img', 'mask_args', 'mask_strategy',
'copy'])
data = Parallel(n_jobs=n_jobs)(
delayed(shelved_filter_and_mask)(self, imgs, params,
confounds=cfs,
copy=copy,
shelve=shelve,
squeeze=True)
for imgs, cfs in izip(niimg_iter, confounds))
return data
def transform_imgs(self,
imgs_list,
confounds=None,
copy=True,
n_jobs=1,
shelve=False):
"""Prepare multi subject data in parallel
Parameters
----------
imgs_list: list of Niimg-like objects
See http://nilearn.github.io/manipulating_visualizing/manipulating_images.html#niimg.
List of imgs file to prepare. One item per subject.
confounds: list of confounds, optional
List of confounds (2D arrays or filenames pointing to CSV
files). Must be of same length than imgs_list.
copy: boolean, optional
If True, guarantees that output array has no memory in common with
input array.
n_jobs: integer, optional
The number of cpus to use to do the computation. -1 means
'all cpus'.
Returns
-------
region_signals: list of 2D numpy.ndarray
List of signal for each element per subject.
shape: list of (number of scans, number of elements)
"""
if not hasattr(self, 'mask_img_'):
raise ValueError(
'It seems that %s has not been fitted. '
'You must call fit() before calling transform().' %
self.__class__.__name__)
target_fov = None
if self.target_affine is None:
# Force resampling on first image
target_fov = 'first'
niimg_iter = _iter_check_niimg(imgs_list,
ensure_ndim=None,
atleast_4d=False,
target_fov=target_fov,
memory=self.memory,
memory_level=self.memory_level,
verbose=self.verbose)
if confounds is None:
confounds = itertools.repeat(None, len(imgs_list))
# Ignore the mask-computing params: they are not useful and will
# just invalidate the cache for no good reason
# target_shape and target_affine are conveyed implicitly in mask_img
params = get_params(
self.__class__,
self,
ignore=['mask_img', 'mask_args', 'mask_strategy', 'copy'])
data = Parallel(n_jobs=n_jobs)(
delayed(shelved_filter_and_mask)(self,
imgs,
params,
confounds=cfs,
copy=copy,
shelve=shelve,
squeeze=True)
for imgs, cfs in izip(niimg_iter, confounds))
return data
# First we plot DMN without region extraction, interested in only index=[3]
img = image.index_img(components_img, 3)
coords = plotting.find_xyz_cut_coords(img)
display = plotting.plot_stat_map(img,
cut_coords=((0, -52, 29)),
colorbar=False,
title='ICA map: DMN mode')
# Now, we plot DMN after region extraction to show that connected regions are
# nicely separated. Each brain extracted region is indicated with separate color
# For this, we take the indices of the all regions extracted related to original
# ICA map 3.
regions_indices_of_map3 = np.where(np.array(regions_index) == 3)
display = plotting.plot_anat(cut_coords=((0, -52, 29)),
title='Extracted regions in DMN mode')
# Now add as an overlay by looping over all the regions for right
# temporoparietal function, posterior cingulate cortex, medial prefrontal
# cortex, left temporoparietal junction
color_list = [[0., 1., 0.29, 1.], [0., 1., 0.54, 1.], [0., 1., 0.78, 1.],
[0., 0.96, 1., 1.], [0., 0.73, 1., 1.], [0., 0.47, 1., 1.],
[0., 0.22, 1., 1.], [0.01, 0., 1., 1.], [0.26, 0., 1., 1.]]
for each_index_of_map3, color in izip(regions_indices_of_map3[0], color_list):
display.add_overlay(image.index_img(regions_extracted_img,
each_index_of_map3),
cmap=plotting.cm.alpha_cmap(color))
plotting.show()
coords = plotting.find_xyz_cut_coords(img)
display = plotting.plot_stat_map(img, cut_coords=((0, -52, 29)), colorbar=False, title="ICA map: DMN mode")
# Now, we plot DMN after region extraction to show that connected regions are
# nicely separated. Each brain extracted region is indicated with separate color
# For this, we take the indices of the all regions extracted related to original
# ICA map 3.
regions_indices_of_map3 = np.where(np.array(regions_index) == 3)
display = plotting.plot_anat(cut_coords=((0, -52, 29)), title="Extracted regions in DMN mode")
# Now add as an overlay by looping over all the regions for right
# temporoparietal function, posterior cingulate cortex, medial prefrontal
# cortex, left temporoparietal junction
color_list = [
[0.0, 1.0, 0.29, 1.0],
[0.0, 1.0, 0.54, 1.0],
[0.0, 1.0, 0.78, 1.0],
[0.0, 0.96, 1.0, 1.0],
[0.0, 0.73, 1.0, 1.0],
[0.0, 0.47, 1.0, 1.0],
[0.0, 0.22, 1.0, 1.0],
[0.01, 0.0, 1.0, 1.0],
[0.26, 0.0, 1.0, 1.0],
]
for each_index_of_map3, color in izip(regions_indices_of_map3[0], color_list):
display.add_overlay(image.index_img(regions_extracted_img, each_index_of_map3), cmap=plotting.cm.alpha_cmap(color))
plotting.show()
