Source code for AFQ.utils.models

import numpy as np
import nibabel as nib
import dipy.core.gradients as dpg

[docs]def prepare_data(data_files, bval_files, bvec_files, mask=None, b0_threshold=50): """ Parameters ---------- data_files : str or list Files containing DWI data. If this is a str, that's the full path to a single file. If it's a list, each entry is a full path. bval_files : str or list Equivalent to `data_files`. bvec_files : str or list Equivalent to `data_files`. mask : str or Nifti1Image object Returns ------- img : Nifti1Image data : ndarray gtab : GradientTable mask : ndarray """ types = [type(f) for f in [data_files, bval_files, bvec_files]] if len(set(types)) > 1: e_s = "Please provide consistent inputs to `prepare_data`. All file" e_s += " inputs should be either lists of full paths, or a string" e_s += " with one full path." raise ValueError(e_s) if isinstance(data_files, str): data_files = [data_files] bval_files = [bval_files] bvec_files = [bvec_files] # Load the mask if it is a string if isinstance(mask, str): mask = nib.load(mask).get_fdata().astype(bool) img_array = [nib.load(dfile) for dfile in data_files] data_array = [img.get_fdata() for img in img_array] data = np.concatenate(data_array, -1) bvals_array = [np.loadtxt(bval_file) for bval_file in bval_files] bvals = np.concatenate(bvals_array) bvecs_array = [np.loadtxt(bvec_file) for bvec_file in bvec_files] bvecs = np.concatenate(bvecs_array, -1) gtab = dpg.gradient_table(bvals, bvecs, b0_threshold=b0_threshold) return img_array[-1], data, gtab, mask