def test_errormap(tmpdir):
# Single-Spectual
# Make two fake 2*2*2 voxel volumes
# John von Neumann's birthday
volume1 = np.array([[[2.0, 8.0], [1.0, 2.0]], [[1.0, 9.0], [0.0, 3.0]]])
# Alan Turing's birthday
volume2 = np.array([[[0.0, 7.0], [2.0, 3.0]], [[1.0, 9.0], [1.0, 2.0]]])
mask = np.array([[[1, 0], [0, 1]], [[1, 0], [0, 1]]])
img1 = nb.Nifti1Image(volume1, np.eye(4))
img2 = nb.Nifti1Image(volume2, np.eye(4))
maskimg = nb.Nifti1Image(mask, np.eye(4))
nb.save(img1, tmpdir.join("von.nii.gz").strpath)
nb.save(img2, tmpdir.join("alan.nii.gz").strpath)
nb.save(maskimg, tmpdir.join("mask.nii.gz").strpath)
# Default metric
errmap = ErrorMap()
errmap.inputs.in_tst = tmpdir.join("von.nii.gz").strpath
errmap.inputs.in_ref = tmpdir.join("alan.nii.gz").strpath
errmap.out_map = tmpdir.join("out_map.nii.gz").strpath
result = errmap.run()
assert result.outputs.distance == 1.125
# Square metric
errmap.inputs.metric = "sqeuclidean"
result = errmap.run()
assert result.outputs.distance == 1.125
# Linear metric
errmap.inputs.metric = "euclidean"
result = errmap.run()
assert result.outputs.distance == 0.875
# Masked
errmap.inputs.mask = tmpdir.join("mask.nii.gz").strpath
result = errmap.run()
assert result.outputs.distance == 1.0
# Multi-Spectual
# Raymond Vahan Damadian's birthday
volume3 = np.array([[[1.0, 6.0], [0.0, 3.0]], [[1.0, 9.0], [3.0, 6.0]]])
msvolume1 = np.zeros(shape=(2, 2, 2, 2))
msvolume1[:, :, :, 0] = volume1
msvolume1[:, :, :, 1] = volume3
msimg1 = nb.Nifti1Image(msvolume1, np.eye(4))
msvolume2 = np.zeros(shape=(2, 2, 2, 2))
msvolume2[:, :, :, 0] = volume3
msvolume2[:, :, :, 1] = volume1
msimg2 = nb.Nifti1Image(msvolume2, np.eye(4))
nb.save(msimg1, tmpdir.join("von-ray.nii.gz").strpath)
nb.save(msimg2, tmpdir.join("alan-ray.nii.gz").strpath)
errmap.inputs.in_tst = tmpdir.join("von-ray.nii.gz").strpath
errmap.inputs.in_ref = tmpdir.join("alan-ray.nii.gz").strpath
errmap.inputs.metric = "sqeuclidean"
result = errmap.run()
assert result.outputs.distance == 5.5
errmap.inputs.metric = "euclidean"
result = errmap.run()
assert result.outputs.distance == np.float32(1.25 * (2**0.5))
def test_errormap():
tempdir = mkdtemp()
# Single-Spectual
# Make two fake 2*2*2 voxel volumes
volume1 = np.array([[[2.0, 8.0], [1.0, 2.0]],
[[1.0, 9.0], [0.0,
3.0]]]) # John von Neumann's birthday
volume2 = np.array([[[0.0, 7.0], [2.0, 3.0]],
[[1.0, 9.0], [1.0, 2.0]]]) # Alan Turing's birthday
mask = np.array([[[1, 0], [0, 1]], [[1, 0], [0, 1]]])
img1 = nib.Nifti1Image(volume1, np.eye(4))
img2 = nib.Nifti1Image(volume2, np.eye(4))
maskimg = nib.Nifti1Image(mask, np.eye(4))
nib.save(img1, os.path.join(tempdir, 'von.nii.gz'))
nib.save(img2, os.path.join(tempdir, 'alan.nii.gz'))
nib.save(maskimg, os.path.join(tempdir, 'mask.nii.gz'))
# Default metric
errmap = ErrorMap()
errmap.inputs.in_tst = os.path.join(tempdir, 'von.nii.gz')
errmap.inputs.in_ref = os.path.join(tempdir, 'alan.nii.gz')
errmap.out_map = os.path.join(tempdir, 'out_map.nii.gz')
result = errmap.run()
yield assert_equal, result.outputs.distance, 1.125
# Square metric
errmap.inputs.metric = 'sqeuclidean'
result = errmap.run()
yield assert_equal, result.outputs.distance, 1.125
# Linear metric
errmap.inputs.metric = 'euclidean'
result = errmap.run()
yield assert_equal, result.outputs.distance, 0.875
# Masked
errmap.inputs.mask = os.path.join(tempdir, 'mask.nii.gz')
result = errmap.run()
yield assert_equal, result.outputs.distance, 1.0
## Multi-Spectual
volume3 = np.array([[[1.0, 6.0], [0.0, 3.0]],
[[1.0, 9.0],
[3.0, 6.0]]]) # Raymond Vahan Damadian's birthday
msvolume1 = np.zeros(shape=(2, 2, 2, 2))
msvolume1[:, :, :, 0] = volume1
msvolume1[:, :, :, 1] = volume3
msimg1 = nib.Nifti1Image(msvolume1, np.eye(4))
msvolume2 = np.zeros(shape=(2, 2, 2, 2))
msvolume2[:, :, :, 0] = volume3
msvolume2[:, :, :, 1] = volume1
msimg2 = nib.Nifti1Image(msvolume2, np.eye(4))
nib.save(msimg1, os.path.join(tempdir, 'von-ray.nii.gz'))
nib.save(msimg2, os.path.join(tempdir, 'alan-ray.nii.gz'))
errmap.inputs.in_tst = os.path.join(tempdir, 'von-ray.nii.gz')
errmap.inputs.in_ref = os.path.join(tempdir, 'alan-ray.nii.gz')
errmap.inputs.metric = 'sqeuclidean'
result = errmap.run()
yield assert_equal, result.outputs.distance, 5.5
errmap.inputs.metric = 'euclidean'
result = errmap.run()
yield assert_equal, result.outputs.distance, np.float32(1.25 * (2**0.5))
