def atlas_statistics(file_list, atlas_list):
"""
For each atlas name provided it ƒcalculates for the input image the mean for each region in the atlas and saves it to a TSV file.
:param in_image: A Nifti image
:param in_atlas_list: List of names of atlas to be applied
:return: List of paths to tsv files
"""
from os import getcwd
from os.path import abspath, join
from nipype.utils.filemanip import split_filename
from clinica.utils.atlas import AtlasAbstract
from clinica.utils.statistics import statistics_on_atlas
from clinica.utils.stream import cprint
orig_dir, base, ext = split_filename(file_list)
atlas_classes = AtlasAbstract.__subclasses__()
atlas_statistics_list = []
for atlas in atlas_list:
for atlas_class in atlas_classes:
if atlas_class.get_name_atlas() == atlas:
#out_atlas_statistics = abspath(join(getcwd(), base + '_space-' + atlas + '_map-graymatter_statistics.tsv'))
out_atlas_statistics = abspath(
join('./' + base + '_space-' + atlas +
'_map-graymatter_statistics.tsv'))
cprint(out_atlas_statistics)
statistics_on_atlas(file_list, atlas_class(),
out_atlas_statistics)
atlas_statistics_list.append(out_atlas_statistics)
break
return atlas_statistics_list
def weights_to_nifti(weights, atlas, output_filename):
"""
Args:
atlas:
weights:
output_filename:
Returns:
"""
from clinica.utils.atlas import AtlasAbstract
atlas_path = None
atlas_classes = AtlasAbstract.__subclasses__()
for atlas_class in atlas_classes:
if atlas_class.get_name_atlas() == atlas:
atlas_path = atlas_class.get_atlas_labels()
if not atlas_path:
raise ValueError("Atlas path not found for atlas name " + atlas)
atlas_image = nib.load(atlas_path)
atlas_data = atlas_image.get_fdata(dtype="float32")
labels = list(set(atlas_data.ravel()))
output_image_weights = np.array(atlas_data, dtype="f")
for i, n in enumerate(labels):
index = np.array(np.where(atlas_data == n))
output_image_weights[index[0, :], index[1, :],
index[2, :]] = weights[i]
affine = atlas_image.get_affine()
header = atlas_image.get_header()
output_image = nib.Nifti1Image(output_image_weights, affine, header)
nib.save(output_image, output_filename)
def atlas_statistics(in_image, atlas_list):
"""
For each atlas name provided it calculates for the input image the mean for each region in the atlas and saves it to a TSV file.
Args:
in_image: A Nifti image
atlas_list: List of names of atlas to be applied
Returns:
List of paths to TSV files
"""
from os.path import abspath, join
from nipype.utils.filemanip import split_filename
from clinica.utils.atlas import AtlasAbstract
from clinica.utils.statistics import statistics_on_atlas
from clinica.utils.filemanip import get_subject_id
from clinica.utils.ux import print_end_image
subject_id = get_subject_id(in_image)
orig_dir, base, ext = split_filename(in_image)
atlas_classes = AtlasAbstract.__subclasses__()
atlas_statistics_list = []
for atlas in atlas_list:
for atlas_class in atlas_classes:
if atlas_class.get_name_atlas() == atlas:
out_atlas_statistics = abspath(
join('./' + base + '_space-' + atlas +
'_map-graymatter_statistics.tsv'))
statistics_on_atlas(in_image, atlas_class(),
out_atlas_statistics)
atlas_statistics_list.append(out_atlas_statistics)
print_end_image(subject_id)
return atlas_statistics_list
def atlas_statistics(in_image, in_atlas_list):
"""Generate regional measure from atlas_list in TSV files.
For each atlas name provided it calculates for the input image the mean
for each region in the atlas and saves it to a TSV file.
Args:
in_image: A Nifti image
in_atlas_list: List of names of atlas to be applied
Returns:
List of paths to TSV files
"""
from os import getcwd
from os.path import abspath, join
from nipype.utils.filemanip import split_filename
from clinica.utils.atlas import AtlasAbstract
from clinica.utils.statistics import statistics_on_atlas
orig_dir, base, ext = split_filename(in_image)
atlas_classes = AtlasAbstract.__subclasses__()
atlas_statistics_list = []
for atlas in in_atlas_list:
for atlas_class in atlas_classes:
if atlas_class.get_name_atlas() == atlas:
out_atlas_statistics = abspath(
join(getcwd(),
base + "_space-" + atlas + "_statistics.tsv"))
statistics_on_atlas(in_image, atlas_class(),
out_atlas_statistics)
atlas_statistics_list.append(out_atlas_statistics)
break
return atlas_statistics_list
