code: def test_FiberFit():
      data_file, bval_file, bvec_file = dpd.get_fnames("small_64D")
      data = load_nifti_data(data_file)
      bvals, bvecs = read_bvals_bvecs(bval_file, bvec_file)
      gtab = grad.gradient_table(bvals, bvecs)
      FM = life.FiberModel(gtab)
      evals = [0.0015, 0.0005, 0.0005]
  
      streamline = [
          [[1, 2, 3], [4, 5, 3], [5, 6, 3], [6, 7, 3]],
          [[1, 2, 3], [4, 5, 3], [5, 6, 3]],
      ]
  
      fiber_matrix, vox_coords = FM.setup(streamline, np.eye(4), evals)
  
      w = np.array([0.5, 0.5])
      sig = opt.spdot(fiber_matrix, w) + 1.0  # Add some isotropic stuff
      S0 = data[..., gtab.b0s_mask]
      this_data = np.zeros((10, 10, 10, 64))
      this_data[
          vox_coords[:, 0], vox_coords[:, 1], vox_coords[:, 2]
      ] = (
          sig.reshape((4, 64))
          * S0[vox_coords[:, 0], vox_coords[:, 1], vox_coords[:, 2]]
      )
  
      # Grab some realistic S0 values:
      this_data = np.concatenate(
          [data[..., gtab.b0s_mask], this_data], -1
      )
  
      fit = FM.fit(this_data, streamline, np.eye(4))
      npt.assert_almost_equal(fit.predict()[1], fit.data[1], decimal=-1)
  
      # Predict with an input GradientTable
      npt.assert_almost_equal(
          fit.predict(gtab)[1], fit.data[1], decimal=-1
      )
  
      npt.assert_almost_equal(
          this_data[
              vox_coords[:, 0], vox_coords[:, 1], vox_coords[:, 2]
          ],
          fit.data,
      )
  
file_path: dipy/dipy/tracking/tests/test_life.py
imports: import os.path
  import dipy.core.gradients
  import dipy.core.optimize
  import dipy.data
  from dipy.io.image import load_nifti_data
  from dipy.io.gradients import read_bvals_bvecs
  from dipy.io.stateful_tractogram import Space, StatefulTractogram
  import dipy.tracking.life
  import nibabel
  import numpy
  import numpy.testing
  import scipy.linalg
label_desc: Use image node_id 6 for calling a global function with example usage: test_FiberFit() without return types