def combine(self):
super().combine()
statistic_maps = {}
for name in self._sample_storage:
samples_path = os.path.join(self._output_dir,
name + '.samples.npy')
samples = open_memmap(samples_path, mode='r')
statistic_maps[name] = np.mean(samples, axis=1)
statistic_maps[name + '.std'] = np.std(samples, axis=1)
write_all_as_nifti(restore_volumes(statistic_maps, self._mask),
os.path.join(self._output_dir, 'univariate_normal'),
nifti_header=self._nifti_header,
gzip=self._write_volumes_gzipped)
write_all_as_nifti({'UsedMask': self._mask},
self._output_dir,
nifti_header=self._nifti_header,
gzip=self._write_volumes_gzipped)
if not self._keep_samples:
for ind, name in enumerate(self._model.get_free_param_names()):
os.remove(os.path.join(self._output_dir,
name + '.samples.npy'))
else:
return load_samples(self._output_dir)
def _combine_volumes(self, output_dir, tmp_storage_dir, nifti_header, maps_subdir=''):
"""Combine volumes found in subdirectories to a final volume.
Args:
output_dir (str): the location for the output files
tmp_storage_dir (str): the directory with the temporary results
maps_subdir (str): the subdirectory for both the output directory as the tmp storage directory.
If this is set we will load the results from a subdirectory (with this name) from the tmp_storage_dir
and write the results to a subdirectory (with this name) in the output dir.
Returns:
dict: the dictionary with the ROIs for every volume, by parameter name
"""
full_output_dir = os.path.join(output_dir, maps_subdir)
if not os.path.exists(full_output_dir):
os.makedirs(full_output_dir)
for fname in os.listdir(full_output_dir):
if fname.endswith('.nii.gz'):
os.remove(os.path.join(full_output_dir, fname))
map_names = list(map(lambda p: os.path.splitext(os.path.basename(p))[0],
glob.glob(os.path.join(tmp_storage_dir, maps_subdir, '*.npy'))))
chunks_dir = os.path.join(tmp_storage_dir, maps_subdir)
for map_name in map_names:
data = np.load(os.path.join(chunks_dir, map_name + '.npy'), mmap_mode='r')
write_all_as_nifti({map_name: data}, full_output_dir, nifti_header=nifti_header,
gzip=self._write_volumes_gzipped)
def _combine_volumes_write_out(info_pair):
"""Write out the given information to a nifti volume.
Needs to be used by ModelProcessor._combine_volumes
"""
map_name, info_list = info_pair
chunks_dir, output_dir, nifti_header, write_gzipped = info_list
data = np.load(os.path.join(chunks_dir, map_name + '.npy'), mmap_mode='r')
write_all_as_nifti({map_name: data}, output_dir, nifti_header=nifti_header, gzip=write_gzipped)
def write_volume_maps(maps, directory, header=None, overwrite_volumes=True, gzip=True):
"""Write a dictionary with maps to the given directory using the given header.
Args:
maps (dict): The maps with as keys the map names and as values 3d or 4d maps
directory (str): The dir to write to
header: The Nibabel Image Header
overwrite_volumes (boolean): If we want to overwrite the volumes if they are present.
gzip (boolean): if we want to write the results gzipped
"""
from mdt.lib.nifti import write_all_as_nifti
write_all_as_nifti(maps, directory, nifti_header=header, overwrite_volumes=overwrite_volumes, gzip=gzip)