def load_fmri_vol_data(mmvt):
fmri_vol_fnames = glob.glob(
op.join(mu.get_user_fol(), 'fmri',
'{}.*'.format(mmvt.coloring.get_fmri_vol_fname())))
fmri_vol = None
if len(fmri_vol_fnames) > 0:
fmri_vol_fname = fmri_vol_fnames[0]
if mu.file_type(fmri_vol_fname) in ('mgz', 'nii', 'nii.gz'):
fmri_vol = nib.load(fmri_vol_fname).get_data()
elif mu.file_type(fmri_vol_fname) == 'npy':
fmri_vol = np.load(fmri_vol_fname)
return fmri_vol
def _load_labels_data(labels_data_fname):
labels_data_type = mu.file_type(labels_data_fname)
if labels_data_type == 'npz':
d = mu.load_npz_to_bag(labels_data_fname)
elif labels_data_type == 'mat':
d = mu.load_mat_to_bag(labels_data_fname)
d.names = mu.matlab_cell_str_to_list(d.names)
d.atlas = d.atlas[0] if not isinstance(d.atlas, str) else d.atlas
#d.cmap = d.cmap[0] if not isinstance(d.cmap, str) else d.cmap
else:
print('Currently we support only mat and npz files')
return False
labels, data = d.names, d.data
if 'atlas' not in d:
atlas = mu.check_atlas_by_labels_names(labels)
if atlas == '':
print('The labeling file must contains an atlas field!')
return False
else:
atlas = str(d.atlas)
LabelsPanel.labels_data_atlas = atlas
labels = [l.replace('.label', '') for l in labels]
cb_title = str(d.get('title', ''))
labels_min = d.get('data_min', np.min(data))
labels_max = d.get('data_max', np.max(data))
cmap = str(d.get('cmap', None))
return d, labels, data, atlas, cb_title, labels_max, labels_min, cmap
def get_slices_names(ind=None):
slices_name_type = set([mu.file_type(img.name) for img in bpy.data.images if img.name != 'Render Result'])
if len(slices_name_type) > 1:
print('get_slices_names: different images types! {}'.format(slices_name_type))
return []
slice_file_type = list(slices_name_type)[0]
return ['{}{}.{}'.format(pres, '_{}'.format(ind) if ind is not None else '',
slice_file_type) for pres in get_perspectives()]
def clean_nii_temp_files(fmri_file_template, user_fol=''):
if user_fol == '':
user_fol = mu.get_user_fol()
file_type = mu.file_type(fmri_file_template)
file_temp = op.join(
user_fol, 'fmri', '{}'.format(
fmri_file_template.replace('{hemi}', '?h')[:-len(file_type)]))
mu.delete_files('{}{}'.format(file_temp, file_type))
mu.delete_files('{}{}'.format(file_temp, 'mgz'))
def _load_labels_data(labels_data_fname):
labels_data_type = mu.file_type(labels_data_fname)
if labels_data_type == 'npz':
d = mu.load_npz_to_bag(labels_data_fname)
elif labels_data_type == 'mat':
d = mu.load_mat_to_bag(labels_data_fname)
d.names = mu.matlab_cell_str_to_list(d.names)
d.atlas = d.atlas[0] if not isinstance(d.atlas, str) else d.atlas
#d.cmap = d.cmap[0] if not isinstance(d.cmap, str) else d.cmap
else:
print('Currently we support only mat and npz files')
return False
labels, data = d.names, d.data
if len(labels) == 0:
return False
if isinstance(labels[0], mne.Label):
labels = [l.name for l in labels]
if data.ndim == 2:
if data.shape[0] == len(labels):
data = data[:, bpy.context.scene.frame_current]
elif data.shape[1] == len(labels):
data = data[bpy.context.scene.frame_current]
else:
data = data.mean(
axis=1) if data.shape[0] == len(labels) else data.mean(axis=0)
if 'atlas' not in d:
atlas = mu.check_atlas_by_labels_names(labels)
if atlas == '':
print('The labeling file must contains an atlas field!')
return False
else:
atlas = str(d.atlas)
LabelsPanel.atlas_in_annot_files = atlas in _addon(
).where_am_i.get_annot_files()
LabelsPanel.labels_data_atlas = atlas
labels = [l.replace('.label', '') for l in labels]
cb_title = str(d.get('title', ''))
labels_min = d.get('data_min', np.min(data))
labels_max = d.get('data_max', np.max(data))
cmap = str(d.get('cmap', None))
return d, labels, data, atlas, cb_title, labels_max, labels_min, cmap
def init(mmvt,
modality,
modality_data=None,
colormap=None,
subject='',
mmvt_dir=''):
if subject == '':
subject = mu.get_user()
if mmvt_dir == '':
mmvt_dir = mu.file_fol()
if modality_data is None:
if modality == 'mri':
fname = op.join(mmvt_dir, subject, 'freeview', 'mri_data.npz')
elif modality == 't2':
fname = op.join(mmvt_dir, subject, 'freeview', 't2_data.npz')
elif modality == 'ct':
fname = op.join(mmvt_dir, subject, 'ct',
'ct_data.npz'.format(modality))
if op.isfile(fname):
modality_data = mu.Bag(np.load(fname))
else:
print('To see the slices the following command is being called:'.
format(modality))
print(
'python -m src.preproc.anatomy -s {} -f save_images_data_and_header'
.format(mu.get_user()))
mu.run_mmvt_func(
'src.preproc.anatomy',
'save_subject_orig_trans,save_images_data_and_header')
# cmd = '{} -m src.preproc.anatomy -s {} -f save_subject_orig_trans,save_images_data_and_header --ignore_missing 1'.format(
# bpy.context.scene.python_cmd, mu.get_user())
# mu.run_command_in_new_thread(cmd, False, cwd=mu.get_mmvt_code_root())
return None
if colormap is None:
if op.isfile(op.join(mmvt_dir, 'color_maps', 'gray.npy')):
colormap_fname = op.join(mmvt_dir, 'color_maps', 'gray.npy')
else:
colormap_fname = glob.glob(op.join(mmvt_dir, 'color_maps',
'*.npy'))[0]
print("Can't find The gray colormap!")
colormap = np.load(colormap_fname)
affine = np.array(modality_data.affine, float)
data = modality_data.data
clim = modality_data.precentiles
if IN_BLENDER:
bpy.context.scene.slices_x_min, bpy.context.scene.slices_x_max = clim
colors_ratio = modality_data.colors_ratio
codes = axcodes2ornt(aff2axcodes(affine))
order = np.argsort([c[0] for c in codes])
print(modality, order)
flips = np.array([c[1] < 0 for c in codes])[order]
flips[0] = not flips[0]
sizes = [data.shape[order] for order in order]
scalers = voxel_sizes(affine)
r = [
scalers[order[2]] / scalers[order[1]],
scalers[order[2]] / scalers[order[0]],
scalers[order[1]] / scalers[order[0]]
]
extras = [0] * 3
pial_vol_mask_fname = op.join(mmvt_dir, subject, 'freeview',
'pial_vol_mask.npy')
pial_vol_mask = np.load(pial_vol_mask_fname) if op.isfile(
pial_vol_mask_fname) else None
dural_vol_mask_fname = op.join(mmvt_dir, subject, 'freeview',
'dural_vol_mask.npy')
dural_vol_mask = np.load(dural_vol_mask_fname) if op.isfile(
dural_vol_mask_fname) else None
fmri_vol_fnames = glob.glob(
op.join(mu.get_user_fol(), 'fmri',
'{}.*'.format(mmvt.coloring.get_fmri_vol_fname())))
fmri_vol = None
if len(fmri_vol_fnames) > 0:
fmri_vol_fname = fmri_vol_fnames[0]
if mu.file_type(fmri_vol_fname) in ('mgz', 'nii', 'nii.gz'):
fmri_vol = nib.load(fmri_vol_fname).get_data()
elif mu.file_type(fmri_vol_fname) == 'npy':
fmri_vol = np.load(fmri_vol_fname)
self = mu.Bag(
dict(data=data,
affine=affine,
order=order,
sizes=sizes,
flips=flips,
clim=clim,
r=r,
colors_ratio=colors_ratio,
colormap=colormap,
coordinates=[],
modality=modality,
extras=extras,
pial_vol_mask=pial_vol_mask,
dural_vol_mask=dural_vol_mask,
fmri_vol=fmri_vol))
return self
