Source code for nighres.laminar.profile_averaging

import os
import sys
import numpy
import nibabel
import nighresjava
from ..io import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, _check_available_memory


[docs]def profile_averaging(profile_surface_image, intensity_image, roi_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 roi_image: niimg Label image of the region to sample 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) * weights (niimg): weight image, representing the weighting of profiles in the estimation (_lpa-weight) * best ([float]): the estimated best profile * median ([float]): the estimated median profile * iqr ([float]): the estimated IQR profile Notes ---------- Original Java module by Pierre-Louis Bazin ''' print('\nProfile averaging') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, intensity_image) weight_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=intensity_image, suffix='lpa-weight')) sample_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=intensity_image, suffix='lpa-best',ext='txt')) median_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=intensity_image, suffix='lpa-med',ext='txt')) iqr_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=intensity_image, suffix='lpa-iqr',ext='txt')) if overwrite is False \ and os.path.isfile(weight_file) and os.path.isfile(sample_file) \ and os.path.isfile(median_file) and os.path.isfile(iqr_file) : print("skip computation (use existing results)") output = {'weights': weight_file, 'best': numpy.loadtxt(sample_file), 'median': numpy.loadtxt(median_file), 'iqr': numpy.loadtxt(iqr_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.LaminarProfileAveraging() # 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() roi_data = load_volume(roi_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.setRoiMask(nighresjava.JArray('int')( (roi_data.flatten('F')).astype(int).tolist())) 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 weight_data = numpy.reshape(numpy.array( sampler.getProfileWeights(), dtype=numpy.float32), (dimensions[0],dimensions[1],dimensions[2]), 'F') sample = numpy.array(sampler.getSampleProfile(), dtype=numpy.float32) median = numpy.array(sampler.getMedianProfile(), dtype=numpy.float32) iqr = numpy.array(sampler.getIqrProfile(), dtype=numpy.float32) hdr['cal_max'] = numpy.nanmax(weight_data) weights = nibabel.Nifti1Image(weight_data, aff, hdr) if save_data: save_volume(weight_file, weights) numpy.savetxt(sample_file, sample) numpy.savetxt(median_file, median) numpy.savetxt(iqr_file, iqr) return {'weights': weight_file} else: return {'weights': weights}