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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
import pytest
from numpy.testing import assert_array_equal
from mne.stats import combine_adjacency
pytest.importorskip("sklearn")
@pytest.mark.parametrize(
"shape",
[
(1,),
(2,),
(1, 1),
(1, 2),
(2, 1),
(3, 4),
(1, 1, 1),
(1, 1, 2),
(3, 4, 5),
],
)
def test_adjacency_equiv(shape):
"""Test adjacency equivalence for lattice adjacency."""
from sklearn.feature_extraction import grid_to_graph
# sklearn requires at least two dimensions
sk_shape = shape if len(shape) > 1 else (shape + (1,))
conn_sk = grid_to_graph(*sk_shape).toarray()
conn = combine_adjacency(*shape)
want_shape = (np.prod(shape),) * 2
assert conn.shape == conn_sk.shape == want_shape
assert (conn.data == 1.0).all()
conn = conn.toarray()
# we end up with some duplicates that can turn into 2's and 3's,
# eventually we might want to keep these as 1's but it's easy enough
# with a .astype(bool) (also matches sklearn output) so let's leave it
# for now
assert np.isin(conn, [0, 1, 2, 3]).all()
assert conn.shape == conn_sk.shape
assert_array_equal(conn, conn_sk)
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