Source code for nighres.laminar.profile_sampling

import os
import sys
import numpy as np
import nibabel as nb
import nighresjava
from import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, _check_available_memory

[docs]def profile_sampling(profile_surface_image, intensity_image, save_data=False, overwrite=False, output_dir=None, file_name=None): '''Sampling data on multiple intracortical layers Parameters ----------- profile_surface_image: niimg 4D image containing levelset representations of different intracortical surfaces on which data should be sampled intensity_image: niimg Image from which data should be sampled save_data: bool Save output data to file (default is False) overwrite: bool Overwrite existing results (default is False) output_dir: str, optional Path to desired output directory, will be created if it doesn't exist file_name: str, optional Desired base name for output files with file extension (suffixes will be added) Returns ----------- dict Dictionary collecting outputs under the following keys (suffix of output files in brackets) * result (niimg): 4D profile image , where the 4th dimension represents the profile for each voxel (_lps-data) Notes ---------- Original Java module by Pierre-Louis Bazin and Juliane Dinse ''' print('\nProfile sampling') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, intensity_image) profile_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=intensity_image, suffix='lps-data')) if overwrite is False \ and os.path.isfile(profile_file) : print("skip computation (use existing results)") output = {'result': profile_file} return output # start VM if not already running try: mem = _check_available_memory() nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max']) except ValueError: pass # initate class sampler = nighresjava.LaminarProfileSampling() # load the data surface_img = load_volume(profile_surface_image) surface_data = surface_img.get_data() hdr = surface_img.header aff = surface_img.affine resolution = [x.item() for x in hdr.get_zooms()] dimensions = surface_data.shape intensity_data = load_volume(intensity_image).get_data() # pass inputs sampler.setIntensityImage(nighresjava.JArray('float')( (intensity_data.flatten('F')).astype(float))) sampler.setProfileSurfaceImage(nighresjava.JArray('float')( (surface_data.flatten('F')).astype(float))) sampler.setResolutions(resolution[0], resolution[1], resolution[2]) sampler.setDimensions(dimensions[0], dimensions[1], dimensions[2], dimensions[3]) # execute class try: sampler.execute() except: # if the Java module fails, reraise the error it throws print("\n The underlying Java code did not execute cleanly: ") print(sys.exc_info()[0]) raise return # collecting outputs profile_data = np.reshape(np.array( sampler.getProfileMappedIntensityImage(), dtype=np.float32), dimensions, 'F') hdr['cal_max'] = np.nanmax(profile_data) profiles = nb.Nifti1Image(profile_data, aff, hdr) if save_data: save_volume(profile_file, profiles) return {'result': profile_file} else: return {'result': profiles}