Add: test for lovo_split

test/test_splitting.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 2021 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/
+#
+"""Unit test testing the lovo_split function."""
+import pytest
+import numpy as np
+from eddymotion.data.dmri import DWI
+from eddymotion.data.splitting import lovo_split
+
+
+def test_lovo_split(datadir):
+    """
+    Test the lovo_split function.
+
+    Parameters:
+    - datadir: The directory containing the test data.
+
+    Returns:
+    None
+    """
+    data = DWI.from_filename(datadir / "dwi.h5")
+
+    # Set zeros in dataobj and gradients of the dwi object
+    data.dataobj[:] = 0
+    data.gradients[:] = 0
+
+    # Select a random index
+    index = np.random.randint(len(data))
+
+    # Set 1 in dataobj and gradients of the dwi object at this specific index
+    data.dataobj[..., index] = 1
+    data.gradients[..., index] = 1
+
+    # Apply the lovo_split function at the specified index
+    (train_data, train_gradients), \
+        (test_data, test_gradients) = lovo_split(data, index)
+
+    # Check if the test data contains only 1s
+    # and the train data contains only 0s after the split
+    assert np.all(test_data == 1)
+    assert np.all(train_data == 0)
+    assert np.all(test_gradients == 1)
+    assert np.all(train_gradients == 0)
+