def check_registration():
map_configs = ''
maps = {}
particularly_bad_subjects = [
'mgh_1002', 'mgh_1004', 'mgh_1008', 'mgh_1009', 'mgh_1012', 'mgh_1013',
'mgh_1015', 'mgh_1017', 'mgh_1021', 'mgh_1022', 'mgh_1032'
]
for subject in os.listdir(output_pjoin()):
# for subject in particularly_bad_subjects:
point_map = mdt.load_nifti(
output_pjoin(subject,
'warped_BinghamNODDI_r1_w_in0.w')).get_data()
std_map = mdt.load_nifti(
output_pjoin(subject,
'warped_BinghamNODDI_r1_w_in0.w.std')).get_data()
maps[subject + '.std'] = std_map
maps[subject] = point_map
map_configs += '''
{0}:
scale: {{use_max: true, use_min: true, vmax: 0.8, vmin: 0.0}}
{0}.std:
scale: {{use_max: true, use_min: true, vmax: 0.1, vmin: 0.0}}
'''.format(subject)
config = '''
colorbar_settings:
location: right
nmr_ticks: 4
power_limits: [-3, 4]
round_precision: 3
visible: false
grid_layout:
- Rectangular
- cols: null
rows: 4
spacings: {bottom: 0.03, hspace: 0.15, left: 0.1, right: 0.86, top: 0.97, wspace: 0.4}
slice_index: 90
zoom:
p0: {x: 16, y: 14}
p1: {x: 161, y: 200}
colormap_masked_color: 'k'
'''
if map_configs:
config += '''
map_plot_options:
''' + map_configs + '''
'''
config += '''
maps_to_show: [''' + ', '.join(sorted(maps)) + ''']
'''
mdt.view_maps(maps, config=config)
def correct_mgh_image_position(input_fname, output_fname=None):
"""The HCP MGH data is ill-positioned for the registration algorithm, this function corrects that."""
header = mdt.load_nifti(input_fname).get_header()
data = mdt.load_nifti(input_fname).get_data()
if output_fname is None:
output_fname = input_fname
mdt.write_nifti(data[:, ::-1], output_fname, header)
def _get_subject_maps(model_name, map_name):
data_name = '{}_{}'.format(model_name, map_name)
map_list = []
for subject in os.listdir(registration_pjoin()):
if subject in subjects_to_filter:
continue
data = mdt.load_nifti(
registration_pjoin(subject, 'warped_' + data_name)).get_data()
map_list.append(data)
return map_list
def __init__(self, channels, x0=None, cl_device_ind=None, **kwargs):
"""Reconstruct the input using the STARC method.
Args:
channels (list): the list of input nifti files, one for each channel element. Every nifti file
should be a 4d matrix with on the 4th dimension all the time series. The length of this list
should equal the number of input channels.
x0 (ndarray or str): optional, the set of weights to use as a starting point for the fitting routine.
cl_device_ind (int or list of int): the list of indices into :func:`mct.utils.get_cl_devices` that you want
to use for the OpenCL based optimization.
"""
super().__init__(channels, **kwargs)
cl_environments = None
if cl_device_ind is not None:
if not isinstance(cl_device_ind, (tuple, list)):
cl_device_ind = [cl_device_ind]
cl_environments = [get_cl_devices()[ind] for ind in cl_device_ind]
self.cl_runtime_info = CLRuntimeInfo(cl_environments=cl_environments)
self._x0 = x0
if isinstance(self._x0, str):
self._x0 = mdt.load_nifti(x0).get_data()
def check_registration():
map_configs = ''
maps = {}
for subject in os.listdir(output_pjoin()):
fa_map = mdt.load_nifti(output_pjoin(subject, 'warped_Tensor_Tensor.FA')).get_data()
maps[subject] = fa_map
map_configs += '''
{}:
scale: {{use_max: true, use_min: true, vmax: 0.5, vmin: 0.0}}
'''.format(subject)
config = '''
colorbar_settings:
location: right
nmr_ticks: 4
power_limits: [-3, 4]
round_precision: 3
visible: false
grid_layout:
- Rectangular
- cols: null
rows: 4
spacings: {bottom: 0.03, hspace: 0.15, left: 0.1, right: 0.86, top: 0.97, wspace: 0.4}
slice_index: 90
zoom:
p0: {x: 16, y: 14}
p1: {x: 161, y: 200}
colormap_masked_color: 'k'
'''
config += '''
map_plot_options:
''' + map_configs + '''
maps_to_show: [''' + ', '.join(sorted(maps)) + ''']
'''
mdt.view_maps(maps, config=config)
def run(self, args, extra_args):
file_names = []
images = []
for file in args.input_files:
globbed = glob.glob(file)
if globbed:
for fname in globbed:
file_names.append(fname)
images.append(
mdt.load_nifti(os.path.realpath(fname)).get_data())
else:
file_names.append(file)
images.append(
mdt.load_nifti(os.path.realpath(file)).get_data())
if args.verbose:
print('')
if args.input_4d:
images = self._images_3d_to_4d(images)
context_dict = {'input': images, 'i': images, 'np': np, 'mdt': mdt}
alpha_chars = list('abcdefghjklmnopqrstuvwxyz')
for ind, image in enumerate(images):
context_dict.update({alpha_chars[ind]: image})
if args.verbose:
print('Input {ind} ({alpha}):'.format(ind=ind,
alpha=alpha_chars[ind]))
print(' name: {}'.format(
split_image_path(file_names[ind])[1]))
print(' shape: {}'.format(str(image.shape)))
if args.verbose:
print('')
print("Evaluating: '{expr}'".format(expr=args.expr))
if args.as_expression:
output = eval(args.expr, context_dict)
else:
expr = textwrap.dedent('''
def mdt_image_math():
{}
output = mdt_image_math()
''').format(args.expr)
exec(expr, context_dict)
output = context_dict['output']
if args.verbose:
print('')
if isinstance(output, np.ndarray):
print('Output shape: {shape}'.format(shape=str(output.shape)))
else:
print('Output is single value')
print('Output: ')
print('')
print(output)
else:
if not args.write_output:
print(output)
if args.verbose:
print('')
if args.write_output:
if isinstance(output, Sequence):
if args.output_file:
output_file = os.path.realpath(args.output_file)
dirname, basename, ext = split_image_path(output_file)
for ind, element in enumerate(output):
mdt.write_nifti(
element, dirname + basename + '_' + str(ind) + ext,
mdt.load_nifti(file_names[0]).header)
else:
for ind, element in enumerate(output):
output_file = os.path.realpath(file_names[ind])
mdt.write_nifti(element, output_file,
mdt.load_nifti(file_names[ind]).header)
else:
if args.output_file:
output_file = os.path.realpath(args.output_file)
else:
output_file = os.path.realpath(file_names[0])
mdt.write_nifti(output, output_file,
mdt.load_nifti(file_names[0]).header)
__maintainer__ = 'Robbert Harms'
__email__ = '*****@*****.**'
__licence__ = 'LGPL v3'
output_pjoin = mdt.make_path_joiner(
'/home/robbert/phd-data/papers/uncertainty_paper/registration/')
mask = mdt.load_brain_mask(
'/usr/share/data/fsl-mni152-templates/FMRIB58_FA_1mm.nii.gz')
mask = binary_erosion(mask, iterations=1)
maps = {}
subjects_to_load = ['mgh_1005', 'mgh_1016', 'mgh_1017']
for subject in subjects_to_load:
point_map = mdt.load_nifti(
output_pjoin(subject, 'warped_BinghamNODDI_r1_w_in0.w')).get_data()
std_map = mdt.load_nifti(
output_pjoin(subject,
'warped_BinghamNODDI_r1_w_in0.w.std')).get_data()
maps[subject + '.std'] = std_map
maps[subject] = point_map
mdt.apply_mask(maps, mask)
# height : 1000px
mdt.view_maps(maps,
config='''
colorbar_settings:
location: right
def run(self, args):
write_output = args.output_file is not None
if write_output:
output_file = os.path.realpath(args.output_file)
if os.path.isfile(output_file):
os.remove(output_file)
file_names = []
for file in args.input_files:
file_names.extend(glob.glob(file))
if args.verbose:
print('')
images = [mdt.load_nifti(dwi_image).get_data() for dwi_image in file_names]
if args.input_4d:
images = self._images_3d_to_4d(images)
context_dict = {'input': images, 'i': images, 'np': np, 'mdt': mdt}
alpha_chars = list('abcdefghjklmnopqrstuvwxyz')
for ind, image in enumerate(images):
context_dict.update({alpha_chars[ind]: image})
if args.verbose:
print('Input {ind} ({alpha}):'.format(ind=ind, alpha=alpha_chars[ind]))
print(' name: {}'.format(split_image_path(file_names[ind])[1]))
print(' shape: {}'.format(str(image.shape)))
if args.verbose:
print('')
print("Evaluating: '{expr}'".format(expr=args.expr))
if args.as_expression:
output = eval(args.expr, context_dict)
else:
expr = textwrap.dedent('''
def mdt_image_math():
{}
output = mdt_image_math()
''').format(args.expr)
exec(expr, context_dict)
output = context_dict['output']
if args.verbose:
print('')
if isinstance(output, np.ndarray):
print('Output shape: {shape}'.format(shape=str(output.shape)))
else:
print('Output is single value')
print('Output: ')
print('')
print(output)
else:
if not write_output:
print(output)
if args.verbose:
print('')
if write_output:
mdt.write_image(output_file, output, mdt.load_nifti(file_names[0]).get_header())