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test_gpr.py
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
#
# Copyright The NiPreps Developers <[email protected]>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# We support and encourage derived works from this project, please read
# about our expectations at
#
# https://www.nipreps.org/community/licensing/
#
from collections import namedtuple
import numpy as np
import pytest
from dipy.io import read_bvals_bvecs
from eddymotion.model import gpr
GradientTablePatch = namedtuple("gtab", ["bvals", "bvecs"])
THETAS = np.linspace(0, np.pi / 2, num=50)
EXPECTED_EXPONENTIAL = [
1.0,
0.93789795,
0.87965256,
0.82502433,
0.77378862,
0.72573476,
0.68066514,
0.63839443,
0.59874883,
0.5615653,
0.52669094,
0.49398235,
0.46330503,
0.43453284,
0.40754745,
0.38223792,
0.35850016,
0.33623656,
0.31535558,
0.29577135,
0.27740334,
0.26017603,
0.24401856,
0.22886451,
0.21465155,
0.20132125,
0.18881879,
0.17709275,
0.16609493,
0.15578009,
0.14610583,
0.13703236,
0.12852236,
0.12054086,
0.11305503,
0.10603408,
0.09944914,
0.09327315,
0.08748069,
0.08204796,
0.07695262,
0.0721737,
0.06769156,
0.06348778,
0.05954506,
0.05584719,
0.05237896,
0.04912612,
0.04607529,
0.04321392,
]
EXPECTED_SPHERICAL = [
1.00000000e00,
9.60914866e-01,
9.21882843e-01,
8.82957040e-01,
8.44190567e-01,
8.05636535e-01,
7.67348053e-01,
7.29378232e-01,
6.91780182e-01,
6.54607013e-01,
6.17911835e-01,
5.81747758e-01,
5.46167893e-01,
5.11225349e-01,
4.76973237e-01,
4.43464666e-01,
4.10752747e-01,
3.78890590e-01,
3.47931306e-01,
3.17928003e-01,
2.88933793e-01,
2.61001786e-01,
2.34185091e-01,
2.08536818e-01,
1.84110079e-01,
1.60957982e-01,
1.39133639e-01,
1.18690159e-01,
9.96806519e-02,
8.21582285e-02,
6.61759987e-02,
5.17870726e-02,
3.90445603e-02,
2.80015720e-02,
1.87112177e-02,
1.12266077e-02,
5.60085192e-03,
1.88706063e-03,
1.38343910e-04,
0.00000000e00,
0.00000000e00,
0.00000000e00,
0.00000000e00,
0.00000000e00,
0.00000000e00,
0.00000000e00,
0.00000000e00,
0.00000000e00,
0.00000000e00,
0.00000000e00,
]
# No need to use normalized vectors: compute_pairwise_angles takes care of it.
# The [-1, 0, 1].T vector serves as a case where e.g. the angle between vector
# [1, 0, 0] and the former is 135 unless the closest polarity flag is set to
# True, in which case it yields 45
@pytest.mark.parametrize(
("bvecs1", "bvecs2", "closest_polarity", "expected"),
[
(
np.array(
[
[1, 0, 0, 1, 1, 0, -1],
[0, 1, 0, 1, 0, 1, 0],
[0, 0, 1, 0, 1, 1, 1],
]
),
None,
True,
np.array(
[
[0.0, np.pi / 2, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 2, np.pi / 4],
[np.pi / 2, 0.0, np.pi / 2, np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 2],
[np.pi / 2, np.pi / 2, 0.0, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 4],
[np.pi / 4, np.pi / 4, np.pi / 2, 0.0, np.pi / 3, np.pi / 3, np.pi / 3],
[np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 3, 0.0, np.pi / 3, np.pi / 2],
[np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 3, np.pi / 3, 0.0, np.pi / 3],
[np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 3, np.pi / 2, np.pi / 3, 0.0],
]
),
),
(
np.array(
[
[1, 0, 0, 1, 1, 0, -1],
[0, 1, 0, 1, 0, 1, 0],
[0, 0, 1, 0, 1, 1, 1],
]
),
None,
False,
np.array(
[
[0.0, np.pi / 2, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 2, 3 * np.pi / 4],
[np.pi / 2, 0.0, np.pi / 2, np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 2],
[np.pi / 2, np.pi / 2, 0.0, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 4],
[np.pi / 4, np.pi / 4, np.pi / 2, 0.0, np.pi / 3, np.pi / 3, 2 * np.pi / 3],
[np.pi / 4, np.pi / 2, np.pi / 4, np.pi / 3, 0.0, np.pi / 3, np.pi / 2],
[np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 3, np.pi / 3, 0.0, np.pi / 3],
[
3 * np.pi / 4,
np.pi / 2,
np.pi / 4,
2 * np.pi / 3,
np.pi / 2,
np.pi / 3,
0.0,
],
]
),
),
(
np.array(
[
[1, 0, 0, 1, 1, 0, -1],
[0, 1, 0, 1, 0, 1, 0],
[0, 0, 1, 0, 1, 1, 1],
]
),
np.array(
[
[1, -1],
[0, 0],
[0, 1],
]
),
True,
np.array(
[
[0.0, np.pi / 4],
[np.pi / 2, np.pi / 2],
[np.pi / 2, np.pi / 4],
[np.pi / 4, np.pi / 3],
[np.pi / 4, np.pi / 2],
[np.pi / 2, np.pi / 3],
[np.pi / 4, 0.0],
]
),
),
(
np.array(
[
[1, 0, 0, 1, 1, 0, -1],
[0, 1, 0, 1, 0, 1, 0],
[0, 0, 1, 0, 1, 1, 1],
]
),
np.array(
[
[1, -1],
[0, 0],
[0, 1],
]
),
False,
np.array(
[
[0.0, 3 * np.pi / 4],
[np.pi / 2, np.pi / 2],
[np.pi / 2, np.pi / 4],
[np.pi / 4, 2 * np.pi / 3],
[np.pi / 4, np.pi / 2],
[np.pi / 2, np.pi / 3],
[3 * np.pi / 4, 0.0],
]
),
),
],
)
def test_compute_pairwise_angles(bvecs1, bvecs2, closest_polarity, expected):
# DIPY requires the vectors to be normalized
_bvecs1 = (bvecs1 / np.linalg.norm(bvecs1, axis=0)).T
_bvecs2 = None
if bvecs2 is not None:
_bvecs2 = (bvecs2 / np.linalg.norm(bvecs2, axis=0)).T
obtained = gpr.compute_pairwise_angles(_bvecs1, _bvecs2, closest_polarity)
if _bvecs2 is not None:
assert (_bvecs1.shape[0], _bvecs2.shape[0]) == obtained.shape
assert obtained.shape == expected.shape
np.testing.assert_array_almost_equal(obtained, expected, decimal=2)
@pytest.mark.parametrize("covariance", ["Spherical", "Exponential", "SquaredExponential"])
def test_kernel_single_shell(repodata, covariance):
"""Check kernel construction."""
bvals, bvecs = read_bvals_bvecs(
str(repodata / "ds000114_singleshell.bval"),
str(repodata / "ds000114_singleshell.bvec"),
)
bvecs = bvecs[bvals > 10]
KernelType = getattr(gpr, f"{covariance}Kriging")
kernel = KernelType()
K = kernel(bvecs)
assert K.shape == (bvecs.shape[0],) * 2
assert np.allclose(np.diagonal(K), kernel.diag(bvecs))
K_predict = kernel(bvecs, [bvecs[10, ...]])
assert K_predict.shape == (K.shape[0], 1)
K_predict = kernel(bvecs, bvecs[10:14, ...])
assert K_predict.shape == (K.shape[0], 4)
# ToDo
@pytest.mark.parametrize(
("bvals1", "bvals2", "expected"),
[
(
np.array(
[
[1000, 1000, 1000, 1000],
]
),
None,
np.array(
[
[0, 0, 0, 0],
]
),
),
(
np.array(
[
[1000, 1000, 1000, 1000],
[2000, 2000, 2000],
]
),
None,
np.array(
[
[1000, 1000, 1000, 1000],
]
),
),
],
)
def test_compute_shell_distance(bvals1, bvals2, expected):
obtained = gpr.compute_shell_distance(bvals1, bvals2)
if bvals2 is not None:
assert (bvals1.shape[0], bvals2.shape[0]) == obtained.shape
assert obtained.shape == expected.shape
np.testing.assert_array_almost_equal(obtained, expected, decimal=2)
@pytest.mark.parametrize("covariance", ["SquaredExponential"])
def test_kernel_multi_shell(repodata, covariance):
"""Check kernel construction."""
bvals, bvecs = read_bvals_bvecs(
str(repodata / "ds000114_multishell.bval"),
str(repodata / "ds000114_multishell.bvec"),
)
bvals = bvals[bvals > 10]
KernelType = getattr(gpr, f"{covariance}Kriging")
kernel = KernelType()
K = kernel(bvals)
assert K.shape == (bvals.shape[0],) * 2