Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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274 lines
10 KiB
274 lines
10 KiB
4 years ago
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# Code adapted from "upfirdn" python library with permission:
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#
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# Copyright (c) 2009, Motorola, Inc
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#
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# All Rights Reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met:
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#
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# * Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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#
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# * Neither the name of Motorola nor the names of its contributors may be
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# used to endorse or promote products derived from this software without
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# specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
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# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import numpy as np
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from itertools import product
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from numpy.testing import assert_equal, assert_allclose
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from pytest import raises as assert_raises
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import pytest
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from scipy.signal import upfirdn, firwin
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from scipy.signal._upfirdn import _output_len, _upfirdn_modes
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from scipy.signal._upfirdn_apply import _pad_test
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def upfirdn_naive(x, h, up=1, down=1):
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"""Naive upfirdn processing in Python.
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Note: arg order (x, h) differs to facilitate apply_along_axis use.
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"""
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h = np.asarray(h)
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out = np.zeros(len(x) * up, x.dtype)
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out[::up] = x
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out = np.convolve(h, out)[::down][:_output_len(len(h), len(x), up, down)]
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return out
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class UpFIRDnCase(object):
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"""Test _UpFIRDn object"""
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def __init__(self, up, down, h, x_dtype):
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self.up = up
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self.down = down
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self.h = np.atleast_1d(h)
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self.x_dtype = x_dtype
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self.rng = np.random.RandomState(17)
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def __call__(self):
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# tiny signal
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self.scrub(np.ones(1, self.x_dtype))
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# ones
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self.scrub(np.ones(10, self.x_dtype)) # ones
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# randn
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x = self.rng.randn(10).astype(self.x_dtype)
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if self.x_dtype in (np.complex64, np.complex128):
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x += 1j * self.rng.randn(10)
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self.scrub(x)
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# ramp
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self.scrub(np.arange(10).astype(self.x_dtype))
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# 3D, random
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size = (2, 3, 5)
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x = self.rng.randn(*size).astype(self.x_dtype)
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if self.x_dtype in (np.complex64, np.complex128):
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x += 1j * self.rng.randn(*size)
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for axis in range(len(size)):
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self.scrub(x, axis=axis)
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x = x[:, ::2, 1::3].T
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for axis in range(len(size)):
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self.scrub(x, axis=axis)
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def scrub(self, x, axis=-1):
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yr = np.apply_along_axis(upfirdn_naive, axis, x,
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self.h, self.up, self.down)
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want_len = _output_len(len(self.h), x.shape[axis], self.up, self.down)
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assert yr.shape[axis] == want_len
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y = upfirdn(self.h, x, self.up, self.down, axis=axis)
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assert y.shape[axis] == want_len
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assert y.shape == yr.shape
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dtypes = (self.h.dtype, x.dtype)
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if all(d == np.complex64 for d in dtypes):
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assert_equal(y.dtype, np.complex64)
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elif np.complex64 in dtypes and np.float32 in dtypes:
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assert_equal(y.dtype, np.complex64)
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elif all(d == np.float32 for d in dtypes):
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assert_equal(y.dtype, np.float32)
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elif np.complex128 in dtypes or np.complex64 in dtypes:
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assert_equal(y.dtype, np.complex128)
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else:
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assert_equal(y.dtype, np.float64)
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assert_allclose(yr, y)
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_UPFIRDN_TYPES = (int, np.float32, np.complex64, float, complex)
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class TestUpfirdn(object):
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def test_valid_input(self):
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assert_raises(ValueError, upfirdn, [1], [1], 1, 0) # up or down < 1
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assert_raises(ValueError, upfirdn, [], [1], 1, 1) # h.ndim != 1
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assert_raises(ValueError, upfirdn, [[1]], [1], 1, 1)
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@pytest.mark.parametrize('len_h', [1, 2, 3, 4, 5])
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@pytest.mark.parametrize('len_x', [1, 2, 3, 4, 5])
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def test_singleton(self, len_h, len_x):
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# gh-9844: lengths producing expected outputs
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h = np.zeros(len_h)
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h[len_h // 2] = 1. # make h a delta
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x = np.ones(len_x)
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y = upfirdn(h, x, 1, 1)
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want = np.pad(x, (len_h // 2, (len_h - 1) // 2), 'constant')
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assert_allclose(y, want)
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def test_shift_x(self):
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# gh-9844: shifted x can change values?
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y = upfirdn([1, 1], [1.], 1, 1)
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assert_allclose(y, [1, 1]) # was [0, 1] in the issue
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y = upfirdn([1, 1], [0., 1.], 1, 1)
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assert_allclose(y, [0, 1, 1])
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# A bunch of lengths/factors chosen because they exposed differences
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# between the "old way" and new way of computing length, and then
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# got `expected` from MATLAB
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@pytest.mark.parametrize('len_h, len_x, up, down, expected', [
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(2, 2, 5, 2, [1, 0, 0, 0]),
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(2, 3, 6, 3, [1, 0, 1, 0, 1]),
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(2, 4, 4, 3, [1, 0, 0, 0, 1]),
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(3, 2, 6, 2, [1, 0, 0, 1, 0]),
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(4, 11, 3, 5, [1, 0, 0, 1, 0, 0, 1]),
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])
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def test_length_factors(self, len_h, len_x, up, down, expected):
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# gh-9844: weird factors
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h = np.zeros(len_h)
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h[0] = 1.
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x = np.ones(len_x)
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y = upfirdn(h, x, up, down)
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assert_allclose(y, expected)
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@pytest.mark.parametrize('down, want_len', [ # lengths from MATLAB
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(2, 5015),
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(11, 912),
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(79, 127),
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])
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def test_vs_convolve(self, down, want_len):
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# Check that up=1.0 gives same answer as convolve + slicing
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random_state = np.random.RandomState(17)
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try_types = (int, np.float32, np.complex64, float, complex)
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size = 10000
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for dtype in try_types:
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x = random_state.randn(size).astype(dtype)
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if dtype in (np.complex64, np.complex128):
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x += 1j * random_state.randn(size)
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h = firwin(31, 1. / down, window='hamming')
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yl = upfirdn_naive(x, h, 1, down)
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y = upfirdn(h, x, up=1, down=down)
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assert y.shape == (want_len,)
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assert yl.shape[0] == y.shape[0]
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assert_allclose(yl, y, atol=1e-7, rtol=1e-7)
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@pytest.mark.parametrize('x_dtype', _UPFIRDN_TYPES)
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@pytest.mark.parametrize('h', (1., 1j))
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@pytest.mark.parametrize('up, down', [(1, 1), (2, 2), (3, 2), (2, 3)])
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def test_vs_naive_delta(self, x_dtype, h, up, down):
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UpFIRDnCase(up, down, h, x_dtype)()
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@pytest.mark.parametrize('x_dtype', _UPFIRDN_TYPES)
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@pytest.mark.parametrize('h_dtype', _UPFIRDN_TYPES)
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@pytest.mark.parametrize('p_max, q_max',
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list(product((10, 100), (10, 100))))
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def test_vs_naive(self, x_dtype, h_dtype, p_max, q_max):
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tests = self._random_factors(p_max, q_max, h_dtype, x_dtype)
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for test in tests:
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test()
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def _random_factors(self, p_max, q_max, h_dtype, x_dtype):
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n_rep = 3
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longest_h = 25
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random_state = np.random.RandomState(17)
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tests = []
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for _ in range(n_rep):
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# Randomize the up/down factors somewhat
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p_add = q_max if p_max > q_max else 1
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q_add = p_max if q_max > p_max else 1
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p = random_state.randint(p_max) + p_add
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q = random_state.randint(q_max) + q_add
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# Generate random FIR coefficients
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len_h = random_state.randint(longest_h) + 1
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h = np.atleast_1d(random_state.randint(len_h))
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h = h.astype(h_dtype)
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if h_dtype == complex:
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h += 1j * random_state.randint(len_h)
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tests.append(UpFIRDnCase(p, q, h, x_dtype))
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return tests
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@pytest.mark.parametrize('mode', _upfirdn_modes)
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def test_extensions(self, mode):
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"""Test vs. manually computed results for modes not in numpy's pad."""
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x = np.array([1, 2, 3, 1], dtype=float)
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npre, npost = 6, 6
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y = _pad_test(x, npre=npre, npost=npost, mode=mode)
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if mode == 'antisymmetric':
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y_expected = np.asarray(
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[3, 1, -1, -3, -2, -1, 1, 2, 3, 1, -1, -3, -2, -1, 1, 2])
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elif mode == 'antireflect':
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y_expected = np.asarray(
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[1, 2, 3, 1, -1, 0, 1, 2, 3, 1, -1, 0, 1, 2, 3, 1])
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elif mode == 'smooth':
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y_expected = np.asarray(
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[-5, -4, -3, -2, -1, 0, 1, 2, 3, 1, -1, -3, -5, -7, -9, -11])
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elif mode == "line":
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lin_slope = (x[-1] - x[0]) / (len(x) - 1)
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left = x[0] + np.arange(-npre, 0, 1) * lin_slope
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right = x[-1] + np.arange(1, npost + 1) * lin_slope
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y_expected = np.concatenate((left, x, right))
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else:
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y_expected = np.pad(x, (npre, npost), mode=mode)
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assert_allclose(y, y_expected)
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@pytest.mark.parametrize(
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'size, h_len, mode, dtype',
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product(
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[8],
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[4, 5, 26], # include cases with h_len > 2*size
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_upfirdn_modes,
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[np.float32, np.float64, np.complex64, np.complex128],
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)
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)
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def test_modes(self, size, h_len, mode, dtype):
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random_state = np.random.RandomState(5)
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x = random_state.randn(size).astype(dtype)
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if dtype in (np.complex64, np.complex128):
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x += 1j * random_state.randn(size)
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h = np.arange(1, 1 + h_len, dtype=x.real.dtype)
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y = upfirdn(h, x, up=1, down=1, mode=mode)
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# expected result: pad the input, filter with zero padding, then crop
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npad = h_len - 1
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if mode in ['antisymmetric', 'antireflect', 'smooth', 'line']:
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# use _pad_test test function for modes not supported by np.pad.
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xpad = _pad_test(x, npre=npad, npost=npad, mode=mode)
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else:
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xpad = np.pad(x, npad, mode=mode)
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ypad = upfirdn(h, xpad, up=1, down=1, mode='constant')
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y_expected = ypad[npad:-npad]
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atol = rtol = np.finfo(dtype).eps * 1e2
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assert_allclose(y, y_expected, atol=atol, rtol=rtol)
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