old_contents: import numpy as np
  import nibabel as nib
  import dipy.core.gradients as dpg
  
  
  def prepare_data(data_files, bval_files, bvec_files, mask=None,
                   b0_threshold=0):
      """
      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, data, gtab, mask
  
new_contents: import numpy as np
  import nibabel as nib
  import dipy.core.gradients as dpg
  
  
  def prepare_data(data_files, bval_files, bvec_files, mask=None,
                   b0_threshold=0):
      """
      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
  
subject: Return the last Nifti1Image in models.py