366 lines
17 KiB
Python
366 lines
17 KiB
Python
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# pylint: disable=missing-docstring
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import math
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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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from scipy import signal
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skip_xp_backends = pytest.mark.skip_xp_backends
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xfail_xp_backends = pytest.mark.xfail_xp_backends
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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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@skip_xp_backends(cpu_only=True, exceptions=["cupy"])
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def test_spline_filter(self, xp):
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rng = np.random.RandomState(12457)
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# Test the type-error branch
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raises(TypeError, signal.spline_filter, xp.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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data_array_real = xp.asarray(data_array_real)
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result_array_real = xp.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]], dtype=xp.float64)
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xp_assert_close(signal.spline_filter(data_array_real, 0),
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result_array_real)
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@skip_xp_backends(cpu_only=True, exceptions=["cupy"])
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def test_spline_filter_complex(self, xp):
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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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data_array_complex = xp.asarray(data_array_complex)
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result_array_complex = xp.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]], dtype=xp.complex128)
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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(signal.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, xp):
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assert math.isclose(signal.gauss_spline(0, 0), 1.381976597885342)
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xp_assert_close(signal.gauss_spline(xp.asarray([1.]), 1),
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xp.asarray([0.04865217]), atol=1e-9
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)
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@skip_xp_backends(np_only=True, reason="deliberate: array-likes are accepted")
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def test_gauss_spline_list(self, xp):
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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(signal.gauss_spline(knots, 3),
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np.asarray([0.15418033, 0.6909883, 0.15418033])
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)
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@skip_xp_backends(cpu_only=True)
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def test_cspline1d(self, xp):
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xp_assert_equal(signal.cspline1d(xp.asarray([0])),
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xp.asarray([0.], dtype=xp.float64))
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c1d = xp.asarray([1.21037185, 1.86293902, 2.98834059, 4.11660378,
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4.78893826], dtype=xp.float64)
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# test lamda != 0
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xp_assert_close(signal.cspline1d(xp.asarray([1., 2, 3, 4, 5]), 1), c1d)
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c1d0 = xp.asarray([0.78683946, 2.05333735, 2.99981113, 3.94741812,
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5.21051638], dtype=xp.float64)
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xp_assert_close(signal.cspline1d(xp.asarray([1., 2, 3, 4, 5])), c1d0)
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@skip_xp_backends(cpu_only=True)
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def test_qspline1d(self, xp):
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xp_assert_equal(signal.qspline1d(xp.asarray([0])),
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xp.asarray([0.], dtype=xp.float64))
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# test lamda != 0
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raises(ValueError, signal.qspline1d, xp.asarray([1., 2, 3, 4, 5]), 1.)
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raises(ValueError, signal.qspline1d, xp.asarray([1., 2, 3, 4, 5]), -1.)
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q1d0 = xp.asarray([0.85350007, 2.02441743, 2.99999534, 3.97561055,
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5.14634135], dtype=xp.float64)
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xp_assert_close(
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signal.qspline1d(xp.asarray([1., 2, 3, 4, 5], dtype=xp.float64)), q1d0
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)
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@skip_xp_backends(cpu_only=True)
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def test_cspline1d_eval(self, xp):
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r = signal.cspline1d_eval(xp.asarray([0., 0], dtype=xp.float64),
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xp.asarray([0.], dtype=xp.float64))
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xp_assert_close(r, xp.asarray([0.], dtype=xp.float64))
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r = signal.cspline1d_eval(xp.asarray([1., 0, 1], dtype=xp.float64),
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xp.asarray([], dtype=xp.float64))
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xp_assert_equal(r, xp.asarray([], dtype=xp.float64))
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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 = xp.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 = signal.cspline1d(y)
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newy = xp.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], dtype=xp.float64)
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xp_assert_close(
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signal.cspline1d_eval(cj, xp.asarray(newx), dx=dx, x0=x[0]), newy
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)
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with pytest.raises(ValueError,
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match="Spline coefficients 'cj' must not be empty."):
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signal.cspline1d_eval(xp.asarray([], dtype=xp.float64),
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xp.asarray([0.0], dtype=xp.float64))
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@skip_xp_backends(cpu_only=True)
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def test_qspline1d_eval(self, xp):
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xp_assert_close(signal.qspline1d_eval(xp.asarray([0., 0]), xp.asarray([0.])),
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xp.asarray([0.])
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)
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xp_assert_equal(signal.qspline1d_eval(xp.asarray([1., 0, 1]), xp.asarray([])),
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xp.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 = xp.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 = signal.qspline1d(y)
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newy = xp.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], dtype=xp.float64)
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r = signal.qspline1d_eval(
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cj, xp.asarray(newx, dtype=xp.float64), dx=dx, x0=x[0]
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)
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xp_assert_close(r, newy)
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with pytest.raises(ValueError,
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match="Spline coefficients 'cj' must not be empty."):
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signal.qspline1d_eval(xp.asarray([], dtype=xp.float64),
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xp.asarray([0.0], dtype=xp.float64))
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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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@skip_xp_backends(np_only=True)
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class TestSepfir2d:
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def test_sepfir2d_invalid_filter(self, xp):
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filt = xp.asarray([1.0, 2.0, 4.0, 2.0, 1.0])
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image = np.random.rand(7, 9)
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image = xp.asarray(image)
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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, xp.reshape(filt, (1, -1)), filt)
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with pytest.raises(ValueError, match="object too deep"):
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signal.sepfir2d(image, filt, xp.reshape(filt, (1, -1)))
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def test_sepfir2d_invalid_image(self, xp):
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filt = xp.asarray([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 = xp.asarray(image)
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|
|
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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(xp.reshape(image, (4, 4, 4)), filt, filt)
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|
|
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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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|
|
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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, xp):
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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],
|
||
|
|
[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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|
|
|
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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.],
|
||
|
|
[2., 4., 6., 6., 4., 2.]], dtype=dt)
|
||
|
|
xp_assert_close(result, expected, atol=1e-16)
|
||
|
|
|
||
|
|
@skip_xp_backends(np_only=True, reason="TODO: convert this test")
|
||
|
|
@pytest.mark.parametrize('dtyp',
|
||
|
|
[np.uint8, int, np.float32, float, np.complex64, complex]
|
||
|
|
)
|
||
|
|
def test_strided(self, dtyp, xp):
|
||
|
|
a = np.array([[1, 2, 3, 3, 2, 1, 1, 2, 3],
|
||
|
|
[1, 2, 3, 3, 2, 1, 1, 2, 3],
|
||
|
|
[1, 2, 3, 3, 2, 1, 1, 2, 3],
|
||
|
|
[1, 2, 3, 3, 2, 1, 1, 2, 3]])
|
||
|
|
h1, h2 = [0.5, 1, 0.5], [1]
|
||
|
|
result_strided = signal.sepfir2d(a[:, ::2], h1, h2)
|
||
|
|
result_contig = signal.sepfir2d(a[:, ::2].copy(), h1, h2)
|
||
|
|
xp_assert_close(result_strided, result_contig, atol=1e-15)
|
||
|
|
assert result_strided.dtype == result_contig.dtype
|
||
|
|
|
||
|
|
@skip_xp_backends(np_only=True, reason="TODO: convert this test")
|
||
|
|
@pytest.mark.xfail(reason="XXX: filt.size > image.shape: flaky")
|
||
|
|
def test_sepfir2d_strided_2(self, xp):
|
||
|
|
# XXX: this test is flaky: fails on some reruns, with
|
||
|
|
# result[0, 1] and result[1, 1] being ~1e+224.
|
||
|
|
filt = np.array([1.0, 2.0, 4.0, 2.0, 1.0, 3.0, 2.0])
|
||
|
|
image = np.random.rand(4, 4)
|
||
|
|
|
||
|
|
expected = np.asarray([[36.018162, 30.239061, 38.71187 , 43.878183],
|
||
|
|
[38.180999, 35.824583, 43.525247, 43.874945],
|
||
|
|
[43.269533, 40.834018, 46.757772, 44.276423],
|
||
|
|
[49.120928, 39.681844, 43.596067, 45.085854]])
|
||
|
|
xp_assert_close(signal.sepfir2d(image, filt, filt[::3]), expected)
|
||
|
|
|
||
|
|
@skip_xp_backends(np_only=True, reason="TODO: convert this test")
|
||
|
|
@pytest.mark.xfail(reason="XXX: flaky. pointers OOB on some platforms")
|
||
|
|
@pytest.mark.parametrize('dtyp',
|
||
|
|
[np.uint8, int, np.float32, float, np.complex64, complex]
|
||
|
|
)
|
||
|
|
def test_sepfir2d_strided_3(self, dtyp, xp):
|
||
|
|
# NB: 'image' and 'filt' dtypes match here. Otherwise we can run into
|
||
|
|
# unsafe casting errors for many combinations. Historically, dtype handling
|
||
|
|
# in `sepfir2d` is a tad baroque; fixing it is an enhancement.
|
||
|
|
filt = np.array([1, 2, 4, 2, 1, 3, 2], dtype=dtyp)
|
||
|
|
image = np.asarray([[0, 3, 0, 1, 2],
|
||
|
|
[2, 2, 3, 3, 3],
|
||
|
|
[0, 1, 3, 0, 3],
|
||
|
|
[2, 3, 0, 1, 3],
|
||
|
|
[3, 3, 2, 1, 2]], dtype=dtyp)
|
||
|
|
|
||
|
|
expected = [[123., 101., 91., 136., 127.],
|
||
|
|
[133., 125., 126., 152., 160.],
|
||
|
|
[136., 137., 150., 162., 177.],
|
||
|
|
[133., 124., 132., 148., 147.],
|
||
|
|
[173., 158., 152., 164., 141.]]
|
||
|
|
expected = np.asarray(expected)
|
||
|
|
result = signal.sepfir2d(image, filt, filt[::3])
|
||
|
|
xp_assert_close(result, expected, atol=1e-15)
|
||
|
|
assert result.dtype == sepfir_dtype_map[dtyp]
|
||
|
|
|
||
|
|
expected = [[22., 35., 41., 31., 47.],
|
||
|
|
[27., 39., 48., 47., 55.],
|
||
|
|
[33., 42., 49., 53., 59.],
|
||
|
|
[39., 44., 41., 36., 48.],
|
||
|
|
[67., 62., 47., 34., 46.]]
|
||
|
|
expected = np.asarray(expected)
|
||
|
|
result = signal.sepfir2d(image, filt[::3], filt[::3])
|
||
|
|
xp_assert_close(result, expected, atol=1e-15)
|
||
|
|
assert result.dtype == sepfir_dtype_map[dtyp]
|
||
|
|
|
||
|
|
|
||
|
|
def test_cspline2d(xp):
|
||
|
|
rng = np.random.RandomState(181819142)
|
||
|
|
image = rng.rand(71, 73)
|
||
|
|
signal.cspline2d(image, 8.0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_qspline2d(xp):
|
||
|
|
rng = np.random.RandomState(181819143)
|
||
|
|
image = rng.rand(71, 73)
|
||
|
|
signal.qspline2d(image)
|