331 lines
16 KiB
Python
331 lines
16 KiB
Python
# pylint: disable=missing-docstring
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import numpy as np
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from scipy._lib._array_api import (
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assert_almost_equal, xp_assert_close, xp_assert_equal
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)
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import pytest
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from pytest import raises
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import scipy.signal._spline_filters as bsp
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from scipy import signal
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class TestBSplines:
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"""Test behaviors of B-splines. Some of the values tested against were
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returned as of SciPy 1.1.0 and are included for regression testing
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purposes. Others (at integer points) are compared to theoretical
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expressions (cf. Unser, Aldroubi, Eden, IEEE TSP 1993, Table 1)."""
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def test_spline_filter(self):
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rng = np.random.RandomState(12457)
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# Test the type-error branch
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raises(TypeError, bsp.spline_filter, np.asarray([0]), 0)
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# Test the real branch
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data_array_real = rng.rand(12, 12)
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# make the magnitude exceed 1, and make some negative
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data_array_real = 10*(1-2*data_array_real)
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result_array_real = np.asarray(
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[[-.463312621, 8.33391222, .697290949, 5.28390836,
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5.92066474, 6.59452137, 9.84406950, -8.78324188,
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7.20675750, -8.17222994, -4.38633345, 9.89917069],
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[2.67755154, 6.24192170, -3.15730578, 9.87658581,
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-9.96930425, 3.17194115, -4.50919947, 5.75423446,
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9.65979824, -8.29066885, .971416087, -2.38331897],
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[-7.08868346, 4.89887705, -1.37062289, 7.70705838,
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2.51526461, 3.65885497, 5.16786604, -8.77715342e-03,
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4.10533325, 9.04761993, -.577960351, 9.86382519],
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[-4.71444301, -1.68038985, 2.84695116, 1.14315938,
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-3.17127091, 1.91830461, 7.13779687, -5.35737482,
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-9.66586425, -9.87717456, 9.93160672, 4.71948144],
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[9.49551194, -1.92958436, 6.25427993, -9.05582911,
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3.97562282, 7.68232426, -1.04514824, -5.86021443,
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-8.43007451, 5.47528997, 2.06330736, -8.65968112],
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[-8.91720100, 8.87065356, 3.76879937, 2.56222894,
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-.828387146, 8.72288903, 6.42474741, -6.84576083,
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9.94724115, 6.90665380, -6.61084494, -9.44907391],
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[9.25196790, -.774032030, 7.05371046, -2.73505725,
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2.53953305, -1.82889155, 2.95454824, -1.66362046,
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5.72478916, -3.10287679, 1.54017123, -7.87759020],
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[-3.98464539, -2.44316992, -1.12708657, 1.01725672,
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-8.89294671, -5.42145629, -6.16370321, 2.91775492,
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9.64132208, .702499998, -2.02622392, 1.56308431],
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[-2.22050773, 7.89951554, 5.98970713, -7.35861835,
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5.45459283, -7.76427957, 3.67280490, -4.05521315,
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4.51967507, -3.22738749, -3.65080177, 3.05630155],
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[-6.21240584, -.296796126, -8.34800163, 9.21564563,
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-3.61958784, -4.77120006, -3.99454057, 1.05021988e-03,
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-6.95982829, 6.04380797, 8.43181250, -2.71653339],
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[1.19638037, 6.99718842e-02, 6.72020394, -2.13963198,
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3.75309875, -5.70076744, 5.92143551, -7.22150575,
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-3.77114594, -1.11903194, -5.39151466, 3.06620093],
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[9.86326886, 1.05134482, -7.75950607, -3.64429655,
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7.81848957, -9.02270373, 3.73399754, -4.71962549,
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-7.71144306, 3.78263161, 6.46034818, -4.43444731]])
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xp_assert_close(bsp.spline_filter(data_array_real, 0),
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result_array_real)
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def test_spline_filter_complex(self):
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rng = np.random.RandomState(12457)
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data_array_complex = rng.rand(7, 7) + rng.rand(7, 7)*1j
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# make the magnitude exceed 1, and make some negative
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data_array_complex = 10*(1+1j-2*data_array_complex)
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result_array_complex = np.asarray(
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[[-4.61489230e-01-1.92994022j, 8.33332443+6.25519943j,
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6.96300745e-01-9.05576038j, 5.28294849+3.97541356j,
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5.92165565+7.68240595j, 6.59493160-1.04542804j,
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9.84503460-5.85946894j],
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[-8.78262329-8.4295969j, 7.20675516+5.47528982j,
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-8.17223072+2.06330729j, -4.38633347-8.65968037j,
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9.89916801-8.91720295j, 2.67755103+8.8706522j,
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6.24192142+3.76879835j],
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[-3.15627527+2.56303072j, 9.87658501-0.82838702j,
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-9.96930313+8.72288895j, 3.17193985+6.42474651j,
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-4.50919819-6.84576082j, 5.75423431+9.94723988j,
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9.65979767+6.90665293j],
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[-8.28993416-6.61064005j, 9.71416473e-01-9.44907284j,
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-2.38331890+9.25196648j, -7.08868170-0.77403212j,
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4.89887714+7.05371094j, -1.37062311-2.73505688j,
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7.70705748+2.5395329j],
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[2.51528406-1.82964492j, 3.65885472+2.95454836j,
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5.16786575-1.66362023j, -8.77737999e-03+5.72478867j,
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4.10533333-3.10287571j, 9.04761887+1.54017115j,
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-5.77960968e-01-7.87758923j],
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[9.86398506-3.98528528j, -4.71444130-2.44316983j,
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-1.68038976-1.12708664j, 2.84695053+1.01725709j,
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1.14315915-8.89294529j, -3.17127085-5.42145538j,
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1.91830420-6.16370344j],
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[7.13875294+2.91851187j, -5.35737514+9.64132309j,
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-9.66586399+0.70250005j, -9.87717438-2.0262239j,
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9.93160629+1.5630846j, 4.71948051-2.22050714j,
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9.49550819+7.8995142j]])
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# FIXME: for complex types, the computations are done in
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# single precision (reason unclear). When this is changed,
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# this test needs updating.
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xp_assert_close(bsp.spline_filter(data_array_complex, 0),
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result_array_complex, rtol=1e-6)
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def test_gauss_spline(self):
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np.random.seed(12459)
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assert_almost_equal(bsp.gauss_spline(0, 0), 1.381976597885342)
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xp_assert_close(bsp.gauss_spline(np.asarray([1.]), 1),
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np.asarray([0.04865217]), atol=1e-9
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)
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def test_gauss_spline_list(self):
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# regression test for gh-12152 (accept array_like)
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knots = [-1.0, 0.0, -1.0]
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assert_almost_equal(bsp.gauss_spline(knots, 3),
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np.asarray([0.15418033, 0.6909883, 0.15418033])
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)
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def test_cspline1d(self):
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np.random.seed(12462)
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xp_assert_equal(bsp.cspline1d(np.asarray([0])), [0.])
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c1d = np.asarray([1.21037185, 1.86293902, 2.98834059, 4.11660378,
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4.78893826])
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# test lamda != 0
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xp_assert_close(bsp.cspline1d(np.asarray([1., 2, 3, 4, 5]), 1), c1d)
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c1d0 = np.asarray([0.78683946, 2.05333735, 2.99981113, 3.94741812,
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5.21051638])
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xp_assert_close(bsp.cspline1d(np.asarray([1., 2, 3, 4, 5])), c1d0)
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def test_qspline1d(self):
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np.random.seed(12463)
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xp_assert_equal(bsp.qspline1d(np.asarray([0])), [0.])
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# test lamda != 0
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raises(ValueError, bsp.qspline1d, np.asarray([1., 2, 3, 4, 5]), 1.)
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raises(ValueError, bsp.qspline1d, np.asarray([1., 2, 3, 4, 5]), -1.)
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q1d0 = np.asarray([0.85350007, 2.02441743, 2.99999534, 3.97561055,
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5.14634135])
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xp_assert_close(bsp.qspline1d(np.asarray([1., 2, 3, 4, 5])), q1d0)
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def test_cspline1d_eval(self):
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np.random.seed(12464)
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xp_assert_close(bsp.cspline1d_eval(np.asarray([0., 0]), [0.]),
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np.asarray([0.])
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)
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xp_assert_equal(bsp.cspline1d_eval(np.asarray([1., 0, 1]), []),
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np.asarray([])
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)
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x = [-3, -2, -1, 0, 1, 2, 3, 4, 5, 6]
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dx = x[1] - x[0]
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newx = [-6., -5.5, -5., -4.5, -4., -3.5, -3., -2.5, -2., -1.5, -1.,
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-0.5, 0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6.,
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6.5, 7., 7.5, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12.,
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12.5]
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y = np.asarray([4.216, 6.864, 3.514, 6.203, 6.759, 7.433, 7.874, 5.879,
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1.396, 4.094])
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cj = bsp.cspline1d(y)
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newy = np.asarray([6.203, 4.41570658, 3.514, 5.16924703, 6.864, 6.04643068,
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4.21600281, 6.04643068, 6.864, 5.16924703, 3.514,
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4.41570658, 6.203, 6.80717667, 6.759, 6.98971173, 7.433,
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7.79560142, 7.874, 7.41525761, 5.879, 3.18686814, 1.396,
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2.24889482, 4.094, 2.24889482, 1.396, 3.18686814, 5.879,
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7.41525761, 7.874, 7.79560142, 7.433, 6.98971173, 6.759,
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6.80717667, 6.203, 4.41570658])
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xp_assert_close(bsp.cspline1d_eval(cj, newx, dx=dx, x0=x[0]), newy)
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def test_qspline1d_eval(self):
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np.random.seed(12465)
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xp_assert_close(bsp.qspline1d_eval(np.asarray([0., 0]), [0.]),
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np.asarray([0.])
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)
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xp_assert_equal(bsp.qspline1d_eval(np.asarray([1., 0, 1]), []),
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np.asarray([])
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)
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x = [-3, -2, -1, 0, 1, 2, 3, 4, 5, 6]
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dx = x[1]-x[0]
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newx = [-6., -5.5, -5., -4.5, -4., -3.5, -3., -2.5, -2., -1.5, -1.,
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-0.5, 0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6.,
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6.5, 7., 7.5, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12.,
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12.5]
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y = np.asarray([4.216, 6.864, 3.514, 6.203, 6.759, 7.433, 7.874, 5.879,
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1.396, 4.094])
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cj = bsp.qspline1d(y)
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newy = np.asarray([6.203, 4.49418159, 3.514, 5.18390821, 6.864, 5.91436915,
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4.21600002, 5.91436915, 6.864, 5.18390821, 3.514,
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4.49418159, 6.203, 6.71900226, 6.759, 7.03980488, 7.433,
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7.81016848, 7.874, 7.32718426, 5.879, 3.23872593, 1.396,
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2.34046013, 4.094, 2.34046013, 1.396, 3.23872593, 5.879,
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7.32718426, 7.874, 7.81016848, 7.433, 7.03980488, 6.759,
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6.71900226, 6.203, 4.49418159])
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xp_assert_close(bsp.qspline1d_eval(cj, newx, dx=dx, x0=x[0]), newy)
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# i/o dtypes with scipy 1.9.1, likely fixed by backwards compat
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sepfir_dtype_map = {np.uint8: np.float32, int: np.float64,
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np.float32: np.float32, float: float,
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np.complex64: np.complex64, complex: complex}
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class TestSepfir2d:
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def test_sepfir2d_invalid_filter(self):
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filt = np.array([1.0, 2.0, 4.0, 2.0, 1.0])
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image = np.random.rand(7, 9)
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# No error for odd lengths
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signal.sepfir2d(image, filt, filt[2:])
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# Row or column filter must be odd
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with pytest.raises(ValueError, match="odd length"):
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signal.sepfir2d(image, filt, filt[1:])
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with pytest.raises(ValueError, match="odd length"):
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signal.sepfir2d(image, filt[1:], filt)
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# Filters must be 1-dimensional
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with pytest.raises(ValueError, match="object too deep"):
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signal.sepfir2d(image, filt.reshape(1, -1), filt)
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with pytest.raises(ValueError, match="object too deep"):
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signal.sepfir2d(image, filt, filt.reshape(1, -1))
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def test_sepfir2d_invalid_image(self):
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filt = np.array([1.0, 2.0, 4.0, 2.0, 1.0])
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image = np.random.rand(8, 8)
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# Image must be 2 dimensional
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with pytest.raises(ValueError, match="object too deep"):
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signal.sepfir2d(image.reshape(4, 4, 4), filt, filt)
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with pytest.raises(ValueError, match="object of too small depth"):
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signal.sepfir2d(image[0], filt, filt)
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@pytest.mark.parametrize('dtyp',
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[np.uint8, int, np.float32, float, np.complex64, complex]
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)
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def test_simple(self, dtyp):
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# test values on a paper-and-pencil example
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a = np.array([[1, 2, 3, 3, 2, 1],
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[1, 2, 3, 3, 2, 1],
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[1, 2, 3, 3, 2, 1],
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[1, 2, 3, 3, 2, 1]], dtype=dtyp)
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h1 = [0.5, 1, 0.5]
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h2 = [1]
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result = signal.sepfir2d(a, h1, h2)
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dt = sepfir_dtype_map[dtyp]
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expected = np.asarray([[2.5, 4. , 5.5, 5.5, 4. , 2.5],
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[2.5, 4. , 5.5, 5.5, 4. , 2.5],
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[2.5, 4. , 5.5, 5.5, 4. , 2.5],
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[2.5, 4. , 5.5, 5.5, 4. , 2.5]], dtype=dt)
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xp_assert_close(result, expected, atol=1e-16)
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result = signal.sepfir2d(a, h2, h1)
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expected = np.asarray([[2., 4., 6., 6., 4., 2.],
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[2., 4., 6., 6., 4., 2.],
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[2., 4., 6., 6., 4., 2.],
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[2., 4., 6., 6., 4., 2.]], dtype=dt)
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xp_assert_close(result, expected, atol=1e-16)
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@pytest.mark.parametrize('dtyp',
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[np.uint8, int, np.float32, float, np.complex64, complex]
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)
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def test_strided(self, dtyp):
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a = np.array([[1, 2, 3, 3, 2, 1, 1, 2, 3],
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[1, 2, 3, 3, 2, 1, 1, 2, 3],
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[1, 2, 3, 3, 2, 1, 1, 2, 3],
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[1, 2, 3, 3, 2, 1, 1, 2, 3]])
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h1, h2 = [0.5, 1, 0.5], [1]
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result_strided = signal.sepfir2d(a[:, ::2], h1, h2)
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result_contig = signal.sepfir2d(a[:, ::2].copy(), h1, h2)
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xp_assert_close(result_strided, result_contig, atol=1e-15)
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assert result_strided.dtype == result_contig.dtype
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@pytest.mark.xfail(reason="XXX: filt.size > image.shape: flaky")
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def test_sepfir2d_strided_2(self):
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# XXX: this test is flaky: fails on some reruns, with
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# result[0, 1] and result[1, 1] being ~1e+224.
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np.random.seed(1234)
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filt = np.array([1.0, 2.0, 4.0, 2.0, 1.0, 3.0, 2.0])
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image = np.random.rand(4, 4)
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expected = np.asarray([[36.018162, 30.239061, 38.71187 , 43.878183],
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[38.180999, 35.824583, 43.525247, 43.874945],
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[43.269533, 40.834018, 46.757772, 44.276423],
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[49.120928, 39.681844, 43.596067, 45.085854]])
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xp_assert_close(signal.sepfir2d(image, filt, filt[::3]), expected)
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@pytest.mark.xfail(reason="XXX: flaky. pointers OOB on some platforms")
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@pytest.mark.parametrize('dtyp',
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[np.uint8, int, np.float32, float, np.complex64, complex]
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)
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def test_sepfir2d_strided_3(self, dtyp):
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# NB: 'image' and 'filt' dtypes match here. Otherwise we can run into
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# unsafe casting errors for many combinations. Historically, dtype handling
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# in `sepfir2d` is a tad baroque; fixing it is an enhancement.
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filt = np.array([1, 2, 4, 2, 1, 3, 2], dtype=dtyp)
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image = np.asarray([[0, 3, 0, 1, 2],
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[2, 2, 3, 3, 3],
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[0, 1, 3, 0, 3],
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[2, 3, 0, 1, 3],
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[3, 3, 2, 1, 2]], dtype=dtyp)
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expected = [[123., 101., 91., 136., 127.],
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[133., 125., 126., 152., 160.],
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[136., 137., 150., 162., 177.],
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[133., 124., 132., 148., 147.],
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[173., 158., 152., 164., 141.]]
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expected = np.asarray(expected)
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result = signal.sepfir2d(image, filt, filt[::3])
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xp_assert_close(result, expected, atol=1e-15)
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assert result.dtype == sepfir_dtype_map[dtyp]
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expected = [[22., 35., 41., 31., 47.],
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[27., 39., 48., 47., 55.],
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[33., 42., 49., 53., 59.],
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[39., 44., 41., 36., 48.],
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[67., 62., 47., 34., 46.]]
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expected = np.asarray(expected)
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result = signal.sepfir2d(image, filt[::3], filt[::3])
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xp_assert_close(result, expected, atol=1e-15)
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assert result.dtype == sepfir_dtype_map[dtyp]
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def test_cspline2d():
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np.random.seed(181819142)
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image = np.random.rand(71, 73)
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signal.cspline2d(image, 8.0)
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|
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def test_qspline2d():
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np.random.seed(181819143)
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image = np.random.rand(71, 73)
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signal.qspline2d(image)
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