import os
import sys
import numpy as np
import nibabel as nb
import nighresjava
from ..io 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}