Merge pull request #213 from jhlegarreta/AddSingleShellCovModels

ENH: Add single-shell covariance models

src/eddymotion/model/dmri_covariance.py

@@ -0,0 +1,83 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright 2024 The NiPreps Developers <nipreps@gmail.com>
+#
+# 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/
+#
+import numpy as np
+
+
+def _ensure_positive_scale(a):
+    if a <= 0:
+        raise ValueError(f"a must be strictly positive. Provided: {a}")
+
+
+def compute_exponential_covariance(theta, a):
+    r"""Compute the exponential covariance matrix following eq. 9 in [Andersson15]_.
+
+    .. math::
+
+        C(\theta) = \exp(- \frac{\theta}{a})
+
+    Parameters
+    ----------
+    theta : :obj:`~numpy.ndarray`
+        Pairwise angles across diffusion gradient encoding directions.
+    a : :obj:`float`
+        Positive scale parameter that here determines the "distance" at which θ
+        the covariance goes to zero.
+
+    Returns
+    -------
+    :obj:`~numpy.ndarray`
+        Exponential covariance function values.
+    """
+
+    _ensure_positive_scale(a)
+
+    return np.exp(-theta / a)
+
+
+def compute_spherical_covariance(theta, a):
+    r"""Compute the spherical covariance matrix following eq. 10 in [Andersson15]_.
+
+    .. math::
+
+        C(\theta) = \begin{cases}
+            1 - \frac{3 \theta}{2 a} + \frac{\theta^3}{2 a^3} & \textnormal{if} \; \theta \leq a \\
+            0 & \textnormal{if} \; \theta > a
+        \end{cases}
+
+    Parameters
+    ----------
+    theta : :obj:`~numpy.ndarray`
+        Pairwise angles across diffusion gradient encoding directions.
+    a : :obj:`float`
+        Positive scale parameter that here determines the "distance" at which θ
+        the covariance goes to zero.
+
+    Returns
+    -------
+    :obj:`~numpy.ndarray`
+        Spherical covariance matrix.
+    """
+
+    _ensure_positive_scale(a)
+
+    return np.where(theta <= a, 1 - 3 * (theta / a) ** 2 + 2 * (theta / a) ** 3, 0)

src/eddymotion/model/tests/test_dmri_covariance.py

@@ -0,0 +1,65 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright 2024 The NiPreps Developers <nipreps@gmail.com>
+#
+# 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/
+#
+import numpy as np
+import pytest
+
+from eddymotion.model.dmri_covariance import (
+    compute_exponential_covariance,
+    compute_spherical_covariance,
+)
+
+
+@pytest.mark.parametrize(
+    ("theta", "a", "expected"),
+    [
+        (
+            np.asarray(
+                [0.0, np.pi / 2, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 2, np.pi / 4],
+            ),
+            1.0,
+            np.asarray(
+                [1.0, 0.20787958, 0.20787958, 0.45593813, 0.45593813, 0.20787958, 0.45593813]
+            ),
+        )
+    ],
+)
+def test_compute_exponential_covariance(theta, a, expected):
+    obtained = compute_exponential_covariance(theta, a)
+    assert np.allclose(obtained, expected)
+
+
+@pytest.mark.parametrize(
+    ("theta", "a", "expected"),
+    [
+        (
+            np.asarray(
+                [0.0, np.pi / 2, np.pi / 2, np.pi / 4, np.pi / 4, np.pi / 2, np.pi / 4],
+            ),
+            1.0,
+            np.asarray([1.0, 0.0, 0.0, 0.11839532, 0.11839532, 0.0, 0.11839532]),
+        )
+    ],
+)
+def test_compute_spherical_covariance(theta, a, expected):
+    obtained = compute_spherical_covariance(theta, a)
+    assert np.allclose(obtained, expected)