diff options
Diffstat (limited to 'src/silx/math/test/test_marchingcubes.py')
| -rw-r--r-- | src/silx/math/test/test_marchingcubes.py | 63 |
1 files changed, 31 insertions, 32 deletions
diff --git a/src/silx/math/test/test_marchingcubes.py b/src/silx/math/test/test_marchingcubes.py index 7c60414..7ac171e 100644 --- a/src/silx/math/test/test_marchingcubes.py +++ b/src/silx/math/test/test_marchingcubes.py @@ -26,8 +26,6 @@ __authors__ = ["T. Vincent"] __license__ = "MIT" __date__ = "17/01/2018" -import unittest - import numpy from silx.utils.testutils import ParametricTestCase @@ -38,8 +36,7 @@ from silx.math import marchingcubes class TestMarchingCubes(ParametricTestCase): """Tests of marching cubes""" - def assertAllClose(self, array1, array2, msg=None, - rtol=1e-05, atol=1e-08): + def assertAllClose(self, array1, array2, msg=None, rtol=1e-05, atol=1e-08): """Assert that the 2 numpy.ndarrays are almost equal. :param str msg: Message to provide when assert fails @@ -55,9 +52,9 @@ class TestMarchingCubes(ParametricTestCase): # No isosurface cube_zero = numpy.zeros((2, 2, 2), dtype=numpy.float32) - result = marchingcubes.MarchingCubes(cube_zero, 1.) + result = marchingcubes.MarchingCubes(cube_zero, 1.0) self.assertEqual(result.shape, cube_zero.shape) - self.assertEqual(result.isolevel, 1.) + self.assertEqual(result.isolevel, 1.0) self.assertEqual(result.invert_normals, True) vertices, normals, indices = result @@ -84,43 +81,45 @@ class TestMarchingCubes(ParametricTestCase): # isosurface perpendicular to dim 0 (Z) cube = numpy.array( - (((0., 0.), (0., 0.)), - ((1., 1.), (1., 1.))), dtype=numpy.float32) + (((0.0, 0.0), (0.0, 0.0)), ((1.0, 1.0), (1.0, 1.0))), dtype=numpy.float32 + ) level = 0.5 vertices, normals, indices = marchingcubes.MarchingCubes( - cube, level, invert_normals=False) + cube, level, invert_normals=False + ) self.assertAllClose(vertices[:, 0], level) - self.assertAllClose(normals, (1., 0., 0.)) + self.assertAllClose(normals, (1.0, 0.0, 0.0)) self.assertEqual(len(indices), 2) # isosurface perpendicular to dim 1 (Y) cube = numpy.array( - (((0., 0.), (1., 1.)), - ((0., 0.), (1., 1.))), dtype=numpy.float32) + (((0.0, 0.0), (1.0, 1.0)), ((0.0, 0.0), (1.0, 1.0))), dtype=numpy.float32 + ) level = 0.2 vertices, normals, indices = marchingcubes.MarchingCubes(cube, level) self.assertAllClose(vertices[:, 1], level) - self.assertAllClose(normals, (0., -1., 0.)) + self.assertAllClose(normals, (0.0, -1.0, 0.0)) self.assertEqual(len(indices), 2) # isosurface perpendicular to dim 2 (X) cube = numpy.array( - (((0., 1.), (0., 1.)), - ((0., 1.), (0., 1.))), dtype=numpy.float32) + (((0.0, 1.0), (0.0, 1.0)), ((0.0, 1.0), (0.0, 1.0))), dtype=numpy.float32 + ) level = 0.9 vertices, normals, indices = marchingcubes.MarchingCubes( - cube, level, invert_normals=False) + cube, level, invert_normals=False + ) self.assertAllClose(vertices[:, 2], level) - self.assertAllClose(normals, (0., 0., 1.)) + self.assertAllClose(normals, (0.0, 0.0, 1.0)) self.assertEqual(len(indices), 2) # isosurface normal in dim1, dim 0 (Y, Z) plane cube = numpy.array( - (((0., 0.), (0., 0.)), - ((0., 0.), (1., 1.))), dtype=numpy.float32) + (((0.0, 0.0), (0.0, 0.0)), ((0.0, 0.0), (1.0, 1.0))), dtype=numpy.float32 + ) level = 0.5 vertices, normals, indices = marchingcubes.MarchingCubes(cube, level) - self.assertAllClose(normals[:, 2], 0.) + self.assertAllClose(normals[:, 2], 0.0) self.assertEqual(len(indices), 2) def test_sampling(self): @@ -146,26 +145,26 @@ class TestMarchingCubes(ParametricTestCase): with self.subTest(sampling=sampling): sampling = numpy.array(sampling) - data = 1e6 * numpy.ones( - sampling * size, dtype=numpy.float32) + data = 1e6 * numpy.ones(sampling * size, dtype=numpy.float32) # Copy ref chessboard in data according to sampling - data[::sampling[0], ::sampling[1], ::sampling[2]] = chessboard + data[:: sampling[0], :: sampling[1], :: sampling[2]] = chessboard - result = marchingcubes.MarchingCubes(data, isolevel, - sampling=sampling) + result = marchingcubes.MarchingCubes(data, isolevel, sampling=sampling) # Compare vertices normalized with shape self.assertAllClose( ref_result.get_vertices() / ref_result.shape, result.get_vertices() / result.shape, - atol=0., rtol=0.) + atol=0.0, + rtol=0.0, + ) # Compare normals # This comparison only works for normals aligned with axes # otherwise non uniform sampling would make different normals - self.assertAllClose(ref_result.get_normals(), - result.get_normals(), - atol=0., rtol=0.) + self.assertAllClose( + ref_result.get_normals(), result.get_normals(), atol=0.0, rtol=0.0 + ) - self.assertAllClose(ref_result.get_indices(), - result.get_indices(), - atol=0., rtol=0.) + self.assertAllClose( + ref_result.get_indices(), result.get_indices(), atol=0.0, rtol=0.0 + ) |