code: def test_enhancement_kernel():
      """Test if the kernel values are correct by comparison against the values
      originally calculated by implementation in Mathematica, and at the same time
      checks the symmetry of the kernel."""
  
      D33 = 1.0
      D44 = 0.04
      t = 1
      k = EnhancementKernel(
          D33, D44, t, orientations=0, force_recompute=True
      )
  
      y = np.array([0.0, 0.0, 0.0])
      v = np.array([0.0, 0.0, 1.0])
      orientationlist = [
          [0.0, 0.0, 1.0],
          [-0.0527864, 0.688191, 0.723607],
          [-0.67082, -0.16246, 0.723607],
          [-0.0527864, -0.688191, 0.723607],
          [0.638197, -0.262866, 0.723607],
          [0.831052, 0.238856, 0.502295],
          [0.262866, -0.809017, -0.525731],
          [0.812731, 0.295242, -0.502295],
          [-0.029644, 0.864188, -0.502295],
          [-0.831052, 0.238856, -0.502295],
          [-0.638197, -0.262866, -0.723607],
          [-0.436009, 0.864188, -0.251148],
          [-0.687157, -0.681718, 0.251148],
          [0.67082, -0.688191, 0.276393],
          [0.67082, 0.688191, 0.276393],
          [0.947214, 0.16246, -0.276393],
          [-0.861803, -0.425325, -0.276393],
      ]
      positionlist = [
          [-0.108096, 0.0412229, 0.339119],
          [0.220647, -0.422053, 0.427524],
          [-0.337432, -0.0644619, -0.340777],
          [0.172579, -0.217602, -0.292446],
          [-0.271575, -0.125249, -0.350906],
          [-0.483807, 0.326651, 0.191993],
          [-0.480936, -0.0718426, 0.33202],
          [0.497193, -0.00585659, -0.251344],
          [0.237737, 0.013634, -0.471988],
          [0.367569, -0.163581, 0.0723955],
          [0.47859, -0.143252, 0.318579],
          [-0.21474, -0.264929, -0.46786],
          [-0.0684234, 0.0342464, 0.0942475],
          [0.344272, 0.423119, -0.303866],
          [0.0430714, 0.216233, -0.308475],
          [0.386085, 0.127333, 0.0503609],
          [0.334723, 0.071415, 0.403906],
      ]
      kernelvalues = [
          0.10701063104295713,
          0.0030052117308328923,
          0.003125410084676201,
          0.0031765819772012613,
          0.003127254657020615,
          0.0001295130396491743,
          6.882352014430076e-14,
          1.3821277371353332e-13,
          1.3951939946082493e-13,
          1.381612071786285e-13,
          5.0861109163441125e-17,
          1.0722120295517027e-10,
          2.425145934791457e-6,
          3.557919265806602e-6,
          3.6669510385105265e-6,
          5.97473789679846e-11,
          6.155412262223178e-11,
      ]
  
      for p in range(len(orientationlist)):
          r = np.array(orientationlist[p])
          x = np.array(positionlist[p])
          npt.assert_almost_equal(
              k.evaluate_kernel(x, y, r, v), kernelvalues[p]
          )
  
file_path: dipy/dipy/denoise/tests/test_kernel.py
imports: import warnings
  from dipy.denoise.enhancement_kernel import EnhancementKernel
  from dipy.denoise.shift_twist_convolution import convolve, convolve_sf
  from dipy.reconst.shm import sh_to_sf, sf_to_sh, descoteaux07_legacy_msg
  from dipy.core.sphere import Sphere
  import numpy
  import numpy.testing
label_desc: Use image node_id 1 for calling a global function with example usage: test_enhancement_kernel() without return types