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176 lines
6.5 KiB
Python
176 lines
6.5 KiB
Python
6 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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from scipy.signal import upfirdn, firwin, lfilter
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from scipy.signal._upfirdn import _output_len
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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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y = upfirdn(self.h, x, self.up, self.down, axis=axis)
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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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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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def test_vs_lfilter(self):
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# Check that up=1.0 gives same answer as lfilter + 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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down_factors = [2, 11, 79]
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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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for down in down_factors:
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h = firwin(31, 1. / down, window='hamming')
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yl = lfilter(h, 1.0, x)[::down]
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y = upfirdn(h, x, up=1, down=down)
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assert_allclose(yl, y[:yl.size], atol=1e-7, rtol=1e-7)
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def test_vs_naive(self):
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tests = []
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try_types = (int, np.float32, np.complex64, float, complex)
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# Simple combinations of factors
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for x_dtype, h in product(try_types, (1., 1j)):
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tests.append(UpFIRDnCase(1, 1, h, x_dtype))
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tests.append(UpFIRDnCase(2, 2, h, x_dtype))
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tests.append(UpFIRDnCase(3, 2, h, x_dtype))
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tests.append(UpFIRDnCase(2, 3, h, x_dtype))
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# mixture of big, small, and both directions (net up and net down)
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# use all combinations of data and filter dtypes
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factors = (100, 10) # up/down factors
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cases = product(factors, factors, try_types, try_types)
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for case in cases:
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tests += self._random_factors(*case)
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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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