add tests for umv/ndg reshape routines

tests/test_ndg.py

@@ -174,7 +174,7 @@ def test_nd_2pt_array(shape: tuple):
     (3, 4, 5, 6),
     (3, 2, 2, 6, 2),
     (3, 4, 5, 6, 7),
-], ids=['1d_o2', '1d_o4', '2d_o2', '2d_o4', '3d_o2', '3d_o4', '4d_o2', '4d_o4', '5d_o2', '5d_o4'])
+])
 def test_nd_array(shape: tuple):
     xdata = [np.arange(s) for s in shape]
     ydata = np.arange(0, np.prod(shape)).reshape(shape)

tests/test_reshape.py

@@ -0,0 +1,71 @@
+# -*- coding: utf-8 -*-
+
+import pytest
+import numpy as np
+
+from csaps._reshape import (  # noqa
+    umv_coeffs_to_flatten,
+    umv_coeffs_to_canonical,
+    ndg_coeffs_to_flatten,
+    ndg_coeffs_to_canonical,
+)
+
+
+@pytest.mark.parametrize('shape_canonical, shape_flatten, pieces', [
+    ((2, 1), (1, 2), 1),
+    ((3, 6), (1, 18), 6),
+    ((4, 3), (1, 12), 3),
+    ((4, 30), (1, 120), 30),
+    ((4, 5, 2), (2, 20), 5),
+    ((4, 6, 3), (3, 24), 6),
+    ((4, 120, 53), (53, 480), 120),
+])
+def test_umv_coeffs_reshape(shape_canonical: tuple, shape_flatten: tuple, pieces: int):
+    np.random.seed(1234)
+    arr_canonical_expected = np.random.randint(0, 99, size=shape_canonical)
+
+    arr_flatten = umv_coeffs_to_flatten(arr_canonical_expected)
+    assert arr_flatten.shape == shape_flatten
+
+    arr_canonical_actual = umv_coeffs_to_canonical(arr_flatten, pieces)
+    np.testing.assert_array_equal(arr_canonical_actual, arr_canonical_expected)
+
+
+@pytest.mark.parametrize('shape_canonical, shape_flatten, pieces', [
+    # 1-d 2-ordered
+    ((2, 3), (2, 3), (3,)),
+    ((2, 4), (2, 4), (4,)),
+    ((2, 5), (2, 5), (5,)),
+
+    # 1-d 3-ordered
+    ((3, 3), (3, 3), (3,)),
+    ((3, 4), (3, 4), (4,)),
+    ((3, 5), (3, 5), (5,)),
+
+    # 1-d 4-ordered
+    ((4, 3), (4, 3), (3,)),
+    ((4, 4), (4, 4), (4,)),
+    ((4, 5), (4, 5), (5,)),
+
+    # 2-d {2,4}-ordered
+    ((2, 4, 3, 4), (6, 16), (3, 4)),
+    ((4, 2, 3, 3), (12, 6), (3, 3)),
+    ((4, 2, 4, 3), (16, 6), (4, 3)),
+    ((2, 4, 4, 4), (8, 16), (4, 4)),
+
+    # 2-d {4,4}-ordered
+    ((4, 4, 3, 3), (12, 12), (3, 3)),
+
+    # 3-d {4,4,4}-ordered
+    ((4, 4, 4, 3, 3, 3), (12, 12, 12), (3, 3, 3)),
+    ((4, 4, 4, 3, 5, 7), (12, 20, 28), (3, 5, 7)),
+])
+def test_ndg_coeffs_reshape(shape_canonical: tuple, shape_flatten: tuple, pieces: tuple):
+    np.random.seed(1234)
+    arr_canonical_expected = np.random.randint(0, 99, size=shape_canonical)
+
+    arr_flatten = ndg_coeffs_to_flatten(arr_canonical_expected)
+    assert arr_flatten.shape == shape_flatten
+
+    arr_canonical_actual = ndg_coeffs_to_canonical(arr_flatten, pieces)
+    np.testing.assert_array_equal(arr_canonical_actual, arr_canonical_expected)