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spam-classifier / venv /lib /python3.11 /site-packages /scipy /signal /tests /test_signaltools.py
Sam Chaudry
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 import sys
from concurrent.futures import ThreadPoolExecutor, as_completed
from decimal import Decimal
from itertools import product
from math import gcd
import pytest
from pytest import raises as assert_raises
from numpy.testing import (
assert_equal,
assert_almost_equal, assert_array_equal, assert_array_almost_equal,
assert_allclose, assert_, assert_array_less,
suppress_warnings)
from numpy import array, arange
import numpy as np
from scipy import fft as sp_fft
from scipy.ndimage import correlate1d
from scipy.optimize import fmin, linear_sum_assignment
from scipy import signal
from scipy.signal import (
correlate, correlate2d, correlation_lags, convolve, convolve2d,
fftconvolve, oaconvolve, choose_conv_method, envelope,
hilbert, hilbert2, lfilter, lfilter_zi, filtfilt, butter, zpk2tf, zpk2sos,
invres, invresz, vectorstrength, lfiltic, tf2sos, sosfilt, sosfiltfilt,
sosfilt_zi, tf2zpk, BadCoefficients, detrend, unique_roots, residue,
residuez)
from scipy.signal.windows import hann
from scipy.signal._signaltools import (_filtfilt_gust, _compute_factors,
_group_poles)
from scipy.signal._upfirdn import _upfirdn_modes
from scipy._lib import _testutils
from scipy._lib._array_api import xp_assert_close
from scipy._lib._util import ComplexWarning, np_long, np_ulong
class _TestConvolve:
def test_basic(self):
a = [3, 4, 5, 6, 5, 4]
b = [1, 2, 3]
c = convolve(a, b)
assert_array_equal(c, array([3, 10, 22, 28, 32, 32, 23, 12]))
def test_same(self):
a = [3, 4, 5]
b = [1, 2, 3, 4]
c = convolve(a, b, mode="same")
assert_array_equal(c, array([10, 22, 34]))
def test_same_eq(self):
a = [3, 4, 5]
b = [1, 2, 3]
c = convolve(a, b, mode="same")
assert_array_equal(c, array([10, 22, 22]))
def test_complex(self):
x = array([1 + 1j, 2 + 1j, 3 + 1j])
y = array([1 + 1j, 2 + 1j])
z = convolve(x, y)
assert_array_equal(z, array([2j, 2 + 6j, 5 + 8j, 5 + 5j]))
def test_zero_rank(self):
a = 1289
b = 4567
c = convolve(a, b)
assert_equal(c, a * b)
def test_broadcastable(self):
a = np.arange(27).reshape(3, 3, 3)
b = np.arange(3)
for i in range(3):
b_shape = [1]*3
b_shape[i] = 3
x = convolve(a, b.reshape(b_shape), method='direct')
y = convolve(a, b.reshape(b_shape), method='fft')
assert_allclose(x, y)
def test_single_element(self):
a = array([4967])
b = array([3920])
c = convolve(a, b)
assert_equal(c, a * b)
def test_2d_arrays(self):
a = [[1, 2, 3], [3, 4, 5]]
b = [[2, 3, 4], [4, 5, 6]]
c = convolve(a, b)
d = array([[2, 7, 16, 17, 12],
[10, 30, 62, 58, 38],
[12, 31, 58, 49, 30]])
assert_array_equal(c, d)
def test_input_swapping(self):
small = arange(8).reshape(2, 2, 2)
big = 1j * arange(27).reshape(3, 3, 3)
big += arange(27)[::-1].reshape(3, 3, 3)
out_array = array(
[[[0 + 0j, 26 + 0j, 25 + 1j, 24 + 2j],
[52 + 0j, 151 + 5j, 145 + 11j, 93 + 11j],
[46 + 6j, 133 + 23j, 127 + 29j, 81 + 23j],
[40 + 12j, 98 + 32j, 93 + 37j, 54 + 24j]],
[[104 + 0j, 247 + 13j, 237 + 23j, 135 + 21j],
[282 + 30j, 632 + 96j, 604 + 124j, 330 + 86j],
[246 + 66j, 548 + 180j, 520 + 208j, 282 + 134j],
[142 + 66j, 307 + 161j, 289 + 179j, 153 + 107j]],
[[68 + 36j, 157 + 103j, 147 + 113j, 81 + 75j],
[174 + 138j, 380 + 348j, 352 + 376j, 186 + 230j],
[138 + 174j, 296 + 432j, 268 + 460j, 138 + 278j],
[70 + 138j, 145 + 323j, 127 + 341j, 63 + 197j]],
[[32 + 72j, 68 + 166j, 59 + 175j, 30 + 100j],
[68 + 192j, 139 + 433j, 117 + 455j, 57 + 255j],
[38 + 222j, 73 + 499j, 51 + 521j, 21 + 291j],
[12 + 144j, 20 + 318j, 7 + 331j, 0 + 182j]]])
assert_array_equal(convolve(small, big, 'full'), out_array)
assert_array_equal(convolve(big, small, 'full'), out_array)
assert_array_equal(convolve(small, big, 'same'),
out_array[1:3, 1:3, 1:3])
assert_array_equal(convolve(big, small, 'same'),
out_array[0:3, 0:3, 0:3])
assert_array_equal(convolve(small, big, 'valid'),
out_array[1:3, 1:3, 1:3])
assert_array_equal(convolve(big, small, 'valid'),
out_array[1:3, 1:3, 1:3])
def test_invalid_params(self):
a = [3, 4, 5]
b = [1, 2, 3]
assert_raises(ValueError, convolve, a, b, mode='spam')
assert_raises(ValueError, convolve, a, b, mode='eggs', method='fft')
assert_raises(ValueError, convolve, a, b, mode='ham', method='direct')
assert_raises(ValueError, convolve, a, b, mode='full', method='bacon')
assert_raises(ValueError, convolve, a, b, mode='same', method='bacon')
class TestConvolve(_TestConvolve):
def test_valid_mode2(self):
# See gh-5897
a = [1, 2, 3, 6, 5, 3]
b = [2, 3, 4, 5, 3, 4, 2, 2, 1]
expected = [70, 78, 73, 65]
out = convolve(a, b, 'valid')
assert_array_equal(out, expected)
out = convolve(b, a, 'valid')
assert_array_equal(out, expected)
a = [1 + 5j, 2 - 1j, 3 + 0j]
b = [2 - 3j, 1 + 0j]
expected = [2 - 3j, 8 - 10j]
out = convolve(a, b, 'valid')
assert_array_equal(out, expected)
out = convolve(b, a, 'valid')
assert_array_equal(out, expected)
def test_same_mode(self):
a = [1, 2, 3, 3, 1, 2]
b = [1, 4, 3, 4, 5, 6, 7, 4, 3, 2, 1, 1, 3]
c = convolve(a, b, 'same')
d = array([57, 61, 63, 57, 45, 36])
assert_array_equal(c, d)
def test_invalid_shapes(self):
# By "invalid," we mean that no one
# array has dimensions that are all at
# least as large as the corresponding
# dimensions of the other array. This
# setup should throw a ValueError.
a = np.arange(1, 7).reshape((2, 3))
b = np.arange(-6, 0).reshape((3, 2))
assert_raises(ValueError, convolve, *(a, b), **{'mode': 'valid'})
assert_raises(ValueError, convolve, *(b, a), **{'mode': 'valid'})
def test_convolve_method(self, n=100):
# this types data structure was manually encoded instead of
# using custom filters on the soon-to-be-removed np.sctypes
types = {'uint16', 'uint64', 'int64', 'int32',
'complex128', 'float64', 'float16',
'complex64', 'float32', 'int16',
'uint8', 'uint32', 'int8', 'bool'}
args = [(t1, t2, mode) for t1 in types for t2 in types
for mode in ['valid', 'full', 'same']]
# These are random arrays, which means test is much stronger than
# convolving testing by convolving two np.ones arrays
rng = np.random.RandomState(42)
array_types = {'i': rng.choice([0, 1], size=n),
'f': rng.randn(n)}
array_types['b'] = array_types['u'] = array_types['i']
array_types['c'] = array_types['f'] + 0.5j*array_types['f']
for t1, t2, mode in args:
x1 = array_types[np.dtype(t1).kind].astype(t1)
x2 = array_types[np.dtype(t2).kind].astype(t2)
results = {key: convolve(x1, x2, method=key, mode=mode)
for key in ['fft', 'direct']}
assert_equal(results['fft'].dtype, results['direct'].dtype)
if 'bool' in t1 and 'bool' in t2:
assert_equal(choose_conv_method(x1, x2), 'direct')
continue
# Found by experiment. Found approx smallest value for (rtol, atol)
# threshold to have tests pass.
if any([t in {'complex64', 'float32'} for t in [t1, t2]]):
kwargs = {'rtol': 1.0e-4, 'atol': 1e-6}
elif 'float16' in [t1, t2]:
# atol is default for np.allclose
kwargs = {'rtol': 1e-3, 'atol': 1e-3}
else:
# defaults for np.allclose (different from assert_allclose)
kwargs = {'rtol': 1e-5, 'atol': 1e-8}
assert_allclose(results['fft'], results['direct'], **kwargs)
def test_convolve_method_large_input(self):
# This is really a test that convolving two large integers goes to the
# direct method even if they're in the fft method.
for n in [10, 20, 50, 51, 52, 53, 54, 60, 62]:
z = np.array([2**n], dtype=np.int64)
fft = convolve(z, z, method='fft')
direct = convolve(z, z, method='direct')
# this is the case when integer precision gets to us
# issue #6076 has more detail, hopefully more tests after resolved
if n < 50:
assert_equal(fft, direct)
assert_equal(fft, 2**(2*n))
assert_equal(direct, 2**(2*n))
def test_mismatched_dims(self):
# Input arrays should have the same number of dimensions
assert_raises(ValueError, convolve, [1], 2, method='direct')
assert_raises(ValueError, convolve, 1, [2], method='direct')
assert_raises(ValueError, convolve, [1], 2, method='fft')
assert_raises(ValueError, convolve, 1, [2], method='fft')
assert_raises(ValueError, convolve, [1], [[2]])
assert_raises(ValueError, convolve, [3], 2)
@pytest.mark.thread_unsafe
def test_dtype_deprecation(self):
# gh-21211
a = np.asarray([1, 2, 3, 6, 5, 3], dtype=object)
b = np.asarray([2, 3, 4, 5, 3, 4, 2, 2, 1], dtype=object)
with pytest.deprecated_call(match="dtype=object is not supported"):
convolve(a, b)
class _TestConvolve2d:
def test_2d_arrays(self):
a = [[1, 2, 3], [3, 4, 5]]
b = [[2, 3, 4], [4, 5, 6]]
d = array([[2, 7, 16, 17, 12],
[10, 30, 62, 58, 38],
[12, 31, 58, 49, 30]])
e = convolve2d(a, b)
assert_array_equal(e, d)
def test_valid_mode(self):
e = [[2, 3, 4, 5, 6, 7, 8], [4, 5, 6, 7, 8, 9, 10]]
f = [[1, 2, 3], [3, 4, 5]]
h = array([[62, 80, 98, 116, 134]])
g = convolve2d(e, f, 'valid')
assert_array_equal(g, h)
# See gh-5897
g = convolve2d(f, e, 'valid')
assert_array_equal(g, h)
def test_valid_mode_complx(self):
e = [[2, 3, 4, 5, 6, 7, 8], [4, 5, 6, 7, 8, 9, 10]]
f = np.array([[1, 2, 3], [3, 4, 5]], dtype=complex) + 1j
h = array([[62.+24.j, 80.+30.j, 98.+36.j, 116.+42.j, 134.+48.j]])
g = convolve2d(e, f, 'valid')
assert_array_almost_equal(g, h)
# See gh-5897
g = convolve2d(f, e, 'valid')
assert_array_equal(g, h)
def test_fillvalue(self):
a = [[1, 2, 3], [3, 4, 5]]
b = [[2, 3, 4], [4, 5, 6]]
fillval = 1
c = convolve2d(a, b, 'full', 'fill', fillval)
d = array([[24, 26, 31, 34, 32],
[28, 40, 62, 64, 52],
[32, 46, 67, 62, 48]])
assert_array_equal(c, d)
def test_fillvalue_errors(self):
msg = "could not cast `fillvalue` directly to the output "
with np.testing.suppress_warnings() as sup:
sup.filter(ComplexWarning, "Casting complex values")
with assert_raises(ValueError, match=msg):
convolve2d([[1]], [[1, 2]], fillvalue=1j)
msg = "`fillvalue` must be scalar or an array with "
with assert_raises(ValueError, match=msg):
convolve2d([[1]], [[1, 2]], fillvalue=[1, 2])
def test_fillvalue_empty(self):
# Check that fillvalue being empty raises an error:
assert_raises(ValueError, convolve2d, [[1]], [[1, 2]],
fillvalue=[])
def test_wrap_boundary(self):
a = [[1, 2, 3], [3, 4, 5]]
b = [[2, 3, 4], [4, 5, 6]]
c = convolve2d(a, b, 'full', 'wrap')
d = array([[80, 80, 74, 80, 80],
[68, 68, 62, 68, 68],
[80, 80, 74, 80, 80]])
assert_array_equal(c, d)
def test_sym_boundary(self):
a = [[1, 2, 3], [3, 4, 5]]
b = [[2, 3, 4], [4, 5, 6]]
c = convolve2d(a, b, 'full', 'symm')
d = array([[34, 30, 44, 62, 66],
[52, 48, 62, 80, 84],
[82, 78, 92, 110, 114]])
assert_array_equal(c, d)
@pytest.mark.parametrize('func', [convolve2d, correlate2d])
@pytest.mark.parametrize('boundary, expected',
[('symm', [[37.0, 42.0, 44.0, 45.0]]),
('wrap', [[43.0, 44.0, 42.0, 39.0]])])
def test_same_with_boundary(self, func, boundary, expected):
# Test boundary='symm' and boundary='wrap' with a "long" kernel.
# The size of the kernel requires that the values in the "image"
# be extended more than once to handle the requested boundary method.
# This is a regression test for gh-8684 and gh-8814.
image = np.array([[2.0, -1.0, 3.0, 4.0]])
kernel = np.ones((1, 21))
result = func(image, kernel, mode='same', boundary=boundary)
# The expected results were calculated "by hand". Because the
# kernel is all ones, the same result is expected for convolve2d
# and correlate2d.
assert_array_equal(result, expected)
def test_boundary_extension_same(self):
# Regression test for gh-12686.
# Use ndimage.convolve with appropriate arguments to create the
# expected result.
import scipy.ndimage as ndi
a = np.arange(1, 10*3+1, dtype=float).reshape(10, 3)
b = np.arange(1, 10*10+1, dtype=float).reshape(10, 10)
c = convolve2d(a, b, mode='same', boundary='wrap')
assert_array_equal(c, ndi.convolve(a, b, mode='wrap', origin=(-1, -1)))
def test_boundary_extension_full(self):
# Regression test for gh-12686.
# Use ndimage.convolve with appropriate arguments to create the
# expected result.
import scipy.ndimage as ndi
a = np.arange(1, 3*3+1, dtype=float).reshape(3, 3)
b = np.arange(1, 6*6+1, dtype=float).reshape(6, 6)
c = convolve2d(a, b, mode='full', boundary='wrap')
apad = np.pad(a, ((3, 3), (3, 3)), 'wrap')
assert_array_equal(c, ndi.convolve(apad, b, mode='wrap')[:-1, :-1])
def test_invalid_shapes(self):
# By "invalid," we mean that no one
# array has dimensions that are all at
# least as large as the corresponding
# dimensions of the other array. This
# setup should throw a ValueError.
a = np.arange(1, 7).reshape((2, 3))
b = np.arange(-6, 0).reshape((3, 2))
assert_raises(ValueError, convolve2d, *(a, b), **{'mode': 'valid'})
assert_raises(ValueError, convolve2d, *(b, a), **{'mode': 'valid'})
class TestConvolve2d(_TestConvolve2d):
def test_same_mode(self):
e = [[1, 2, 3], [3, 4, 5]]
f = [[2, 3, 4, 5, 6, 7, 8], [4, 5, 6, 7, 8, 9, 10]]
g = convolve2d(e, f, 'same')
h = array([[22, 28, 34],
[80, 98, 116]])
assert_array_equal(g, h)
def test_valid_mode2(self):
# See gh-5897
e = [[1, 2, 3], [3, 4, 5]]
f = [[2, 3, 4, 5, 6, 7, 8], [4, 5, 6, 7, 8, 9, 10]]
expected = [[62, 80, 98, 116, 134]]
out = convolve2d(e, f, 'valid')
assert_array_equal(out, expected)
out = convolve2d(f, e, 'valid')
assert_array_equal(out, expected)
e = [[1 + 1j, 2 - 3j], [3 + 1j, 4 + 0j]]
f = [[2 - 1j, 3 + 2j, 4 + 0j], [4 - 0j, 5 + 1j, 6 - 3j]]
expected = [[27 - 1j, 46. + 2j]]
out = convolve2d(e, f, 'valid')
assert_array_equal(out, expected)
# See gh-5897
out = convolve2d(f, e, 'valid')
assert_array_equal(out, expected)
def test_consistency_convolve_funcs(self):
# Compare np.convolve, signal.convolve, signal.convolve2d
a = np.arange(5)
b = np.array([3.2, 1.4, 3])
for mode in ['full', 'valid', 'same']:
assert_almost_equal(np.convolve(a, b, mode=mode),
signal.convolve(a, b, mode=mode))
assert_almost_equal(np.squeeze(
signal.convolve2d([a], [b], mode=mode)),
signal.convolve(a, b, mode=mode))
def test_invalid_dims(self):
assert_raises(ValueError, convolve2d, 3, 4)
assert_raises(ValueError, convolve2d, [3], [4])
assert_raises(ValueError, convolve2d, [[[3]]], [[[4]]])
@pytest.mark.slow
@pytest.mark.xfail_on_32bit("Can't create large array for test")
def test_large_array(self):
# Test indexing doesn't overflow an int (gh-10761)
n = 2**31 // (1000 * np.int64().itemsize)
_testutils.check_free_memory(2 * n * 1001 * np.int64().itemsize / 1e6)
# Create a chequered pattern of 1s and 0s
a = np.zeros(1001 * n, dtype=np.int64)
a[::2] = 1
a = np.lib.stride_tricks.as_strided(a, shape=(n, 1000), strides=(8008, 8))
count = signal.convolve2d(a, [[1, 1]])
fails = np.where(count > 1)
assert fails[0].size == 0
class TestFFTConvolve:
@pytest.mark.parametrize('axes', ['', None, 0, [0], -1, [-1]])
def test_real(self, axes):
a = array([1, 2, 3])
expected = array([1, 4, 10, 12, 9.])
if axes == '':
out = fftconvolve(a, a)
else:
out = fftconvolve(a, a, axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', [1, [1], -1, [-1]])
def test_real_axes(self, axes):
a = array([1, 2, 3])
expected = array([1, 4, 10, 12, 9.])
a = np.tile(a, [2, 1])
expected = np.tile(expected, [2, 1])
out = fftconvolve(a, a, axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', ['', None, 0, [0], -1, [-1]])
def test_complex(self, axes):
a = array([1 + 1j, 2 + 2j, 3 + 3j])
expected = array([0 + 2j, 0 + 8j, 0 + 20j, 0 + 24j, 0 + 18j])
if axes == '':
out = fftconvolve(a, a)
else:
out = fftconvolve(a, a, axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', [1, [1], -1, [-1]])
def test_complex_axes(self, axes):
a = array([1 + 1j, 2 + 2j, 3 + 3j])
expected = array([0 + 2j, 0 + 8j, 0 + 20j, 0 + 24j, 0 + 18j])
a = np.tile(a, [2, 1])
expected = np.tile(expected, [2, 1])
out = fftconvolve(a, a, axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', ['',
None,
[0, 1],
[1, 0],
[0, -1],
[-1, 0],
[-2, 1],
[1, -2],
[-2, -1],
[-1, -2]])
def test_2d_real_same(self, axes):
a = array([[1, 2, 3],
[4, 5, 6]])
expected = array([[1, 4, 10, 12, 9],
[8, 26, 56, 54, 36],
[16, 40, 73, 60, 36]])
if axes == '':
out = fftconvolve(a, a)
else:
out = fftconvolve(a, a, axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', [[1, 2],
[2, 1],
[1, -1],
[-1, 1],
[-2, 2],
[2, -2],
[-2, -1],
[-1, -2]])
def test_2d_real_same_axes(self, axes):
a = array([[1, 2, 3],
[4, 5, 6]])
expected = array([[1, 4, 10, 12, 9],
[8, 26, 56, 54, 36],
[16, 40, 73, 60, 36]])
a = np.tile(a, [2, 1, 1])
expected = np.tile(expected, [2, 1, 1])
out = fftconvolve(a, a, axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', ['',
None,
[0, 1],
[1, 0],
[0, -1],
[-1, 0],
[-2, 1],
[1, -2],
[-2, -1],
[-1, -2]])
def test_2d_complex_same(self, axes):
a = array([[1 + 2j, 3 + 4j, 5 + 6j],
[2 + 1j, 4 + 3j, 6 + 5j]])
expected = array([
[-3 + 4j, -10 + 20j, -21 + 56j, -18 + 76j, -11 + 60j],
[10j, 44j, 118j, 156j, 122j],
[3 + 4j, 10 + 20j, 21 + 56j, 18 + 76j, 11 + 60j]
])
if axes == '':
out = fftconvolve(a, a)
else:
out = fftconvolve(a, a, axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', [[1, 2],
[2, 1],
[1, -1],
[-1, 1],
[-2, 2],
[2, -2],
[-2, -1],
[-1, -2]])
def test_2d_complex_same_axes(self, axes):
a = array([[1 + 2j, 3 + 4j, 5 + 6j],
[2 + 1j, 4 + 3j, 6 + 5j]])
expected = array([
[-3 + 4j, -10 + 20j, -21 + 56j, -18 + 76j, -11 + 60j],
[10j, 44j, 118j, 156j, 122j],
[3 + 4j, 10 + 20j, 21 + 56j, 18 + 76j, 11 + 60j]
])
a = np.tile(a, [2, 1, 1])
expected = np.tile(expected, [2, 1, 1])
out = fftconvolve(a, a, axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', ['', None, 0, [0], -1, [-1]])
def test_real_same_mode(self, axes):
a = array([1, 2, 3])
b = array([3, 3, 5, 6, 8, 7, 9, 0, 1])
expected_1 = array([35., 41., 47.])
expected_2 = array([9., 20., 25., 35., 41., 47., 39., 28., 2.])
if axes == '':
out = fftconvolve(a, b, 'same')
else:
out = fftconvolve(a, b, 'same', axes=axes)
assert_array_almost_equal(out, expected_1)
if axes == '':
out = fftconvolve(b, a, 'same')
else:
out = fftconvolve(b, a, 'same', axes=axes)
assert_array_almost_equal(out, expected_2)
@pytest.mark.parametrize('axes', [1, -1, [1], [-1]])
def test_real_same_mode_axes(self, axes):
a = array([1, 2, 3])
b = array([3, 3, 5, 6, 8, 7, 9, 0, 1])
expected_1 = array([35., 41., 47.])
expected_2 = array([9., 20., 25., 35., 41., 47., 39., 28., 2.])
a = np.tile(a, [2, 1])
b = np.tile(b, [2, 1])
expected_1 = np.tile(expected_1, [2, 1])
expected_2 = np.tile(expected_2, [2, 1])
out = fftconvolve(a, b, 'same', axes=axes)
assert_array_almost_equal(out, expected_1)
out = fftconvolve(b, a, 'same', axes=axes)
assert_array_almost_equal(out, expected_2)
@pytest.mark.parametrize('axes', ['', None, 0, [0], -1, [-1]])
def test_valid_mode_real(self, axes):
# See gh-5897
a = array([3, 2, 1])
b = array([3, 3, 5, 6, 8, 7, 9, 0, 1])
expected = array([24., 31., 41., 43., 49., 25., 12.])
if axes == '':
out = fftconvolve(a, b, 'valid')
else:
out = fftconvolve(a, b, 'valid', axes=axes)
assert_array_almost_equal(out, expected)
if axes == '':
out = fftconvolve(b, a, 'valid')
else:
out = fftconvolve(b, a, 'valid', axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', [1, [1]])
def test_valid_mode_real_axes(self, axes):
# See gh-5897
a = array([3, 2, 1])
b = array([3, 3, 5, 6, 8, 7, 9, 0, 1])
expected = array([24., 31., 41., 43., 49., 25., 12.])
a = np.tile(a, [2, 1])
b = np.tile(b, [2, 1])
expected = np.tile(expected, [2, 1])
out = fftconvolve(a, b, 'valid', axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', ['', None, 0, [0], -1, [-1]])
def test_valid_mode_complex(self, axes):
a = array([3 - 1j, 2 + 7j, 1 + 0j])
b = array([3 + 2j, 3 - 3j, 5 + 0j, 6 - 1j, 8 + 0j])
expected = array([45. + 12.j, 30. + 23.j, 48 + 32.j])
if axes == '':
out = fftconvolve(a, b, 'valid')
else:
out = fftconvolve(a, b, 'valid', axes=axes)
assert_array_almost_equal(out, expected)
if axes == '':
out = fftconvolve(b, a, 'valid')
else:
out = fftconvolve(b, a, 'valid', axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', [1, [1], -1, [-1]])
def test_valid_mode_complex_axes(self, axes):
a = array([3 - 1j, 2 + 7j, 1 + 0j])
b = array([3 + 2j, 3 - 3j, 5 + 0j, 6 - 1j, 8 + 0j])
expected = array([45. + 12.j, 30. + 23.j, 48 + 32.j])
a = np.tile(a, [2, 1])
b = np.tile(b, [2, 1])
expected = np.tile(expected, [2, 1])
out = fftconvolve(a, b, 'valid', axes=axes)
assert_array_almost_equal(out, expected)
out = fftconvolve(b, a, 'valid', axes=axes)
assert_array_almost_equal(out, expected)
def test_valid_mode_ignore_nonaxes(self):
# See gh-5897
a = array([3, 2, 1])
b = array([3, 3, 5, 6, 8, 7, 9, 0, 1])
expected = array([24., 31., 41., 43., 49., 25., 12.])
a = np.tile(a, [2, 1])
b = np.tile(b, [1, 1])
expected = np.tile(expected, [2, 1])
out = fftconvolve(a, b, 'valid', axes=1)
assert_array_almost_equal(out, expected)
def test_empty(self):
# Regression test for #1745: crashes with 0-length input.
assert_(fftconvolve([], []).size == 0)
assert_(fftconvolve([5, 6], []).size == 0)
assert_(fftconvolve([], [7]).size == 0)
def test_zero_rank(self):
a = array(4967)
b = array(3920)
out = fftconvolve(a, b)
assert_equal(out, a * b)
def test_single_element(self):
a = array([4967])
b = array([3920])
out = fftconvolve(a, b)
assert_equal(out, a * b)
@pytest.mark.parametrize('axes', ['', None, 0, [0], -1, [-1]])
def test_random_data(self, axes):
np.random.seed(1234)
a = np.random.rand(1233) + 1j * np.random.rand(1233)
b = np.random.rand(1321) + 1j * np.random.rand(1321)
expected = np.convolve(a, b, 'full')
if axes == '':
out = fftconvolve(a, b, 'full')
else:
out = fftconvolve(a, b, 'full', axes=axes)
assert_(np.allclose(out, expected, rtol=1e-10))
@pytest.mark.parametrize('axes', [1, [1], -1, [-1]])
def test_random_data_axes(self, axes):
np.random.seed(1234)
a = np.random.rand(1233) + 1j * np.random.rand(1233)
b = np.random.rand(1321) + 1j * np.random.rand(1321)
expected = np.convolve(a, b, 'full')
a = np.tile(a, [2, 1])
b = np.tile(b, [2, 1])
expected = np.tile(expected, [2, 1])
out = fftconvolve(a, b, 'full', axes=axes)
assert_(np.allclose(out, expected, rtol=1e-10))
@pytest.mark.parametrize('axes', [[1, 4],
[4, 1],
[1, -1],
[-1, 1],
[-4, 4],
[4, -4],
[-4, -1],
[-1, -4]])
def test_random_data_multidim_axes(self, axes):
a_shape, b_shape = (123, 22), (132, 11)
np.random.seed(1234)
a = np.random.rand(*a_shape) + 1j * np.random.rand(*a_shape)
b = np.random.rand(*b_shape) + 1j * np.random.rand(*b_shape)
expected = convolve2d(a, b, 'full')
a = a[:, :, None, None, None]
b = b[:, :, None, None, None]
expected = expected[:, :, None, None, None]
a = np.moveaxis(a.swapaxes(0, 2), 1, 4)
b = np.moveaxis(b.swapaxes(0, 2), 1, 4)
expected = np.moveaxis(expected.swapaxes(0, 2), 1, 4)
# use 1 for dimension 2 in a and 3 in b to test broadcasting
a = np.tile(a, [2, 1, 3, 1, 1])
b = np.tile(b, [2, 1, 1, 4, 1])
expected = np.tile(expected, [2, 1, 3, 4, 1])
out = fftconvolve(a, b, 'full', axes=axes)
assert_allclose(out, expected, rtol=1e-10, atol=1e-10)
@pytest.mark.slow
@pytest.mark.parametrize(
'n',
list(range(1, 100)) +
list(range(1000, 1500)) +
np.random.RandomState(1234).randint(1001, 10000, 5).tolist())
def test_many_sizes(self, n):
a = np.random.rand(n) + 1j * np.random.rand(n)
b = np.random.rand(n) + 1j * np.random.rand(n)
expected = np.convolve(a, b, 'full')
out = fftconvolve(a, b, 'full')
assert_allclose(out, expected, atol=1e-10)
out = fftconvolve(a, b, 'full', axes=[0])
assert_allclose(out, expected, atol=1e-10)
@pytest.mark.thread_unsafe
def test_fft_nan(self):
n = 1000
rng = np.random.default_rng(43876432987)
sig_nan = rng.standard_normal(n)
for val in [np.nan, np.inf]:
sig_nan[100] = val
coeffs = signal.firwin(200, 0.2)
msg = "Use of fft convolution.*|invalid value encountered.*"
with pytest.warns(RuntimeWarning, match=msg):
signal.convolve(sig_nan, coeffs, mode='same', method='fft')
def fftconvolve_err(*args, **kwargs):
raise RuntimeError('Fell back to fftconvolve')
def gen_oa_shapes(sizes):
return [(a, b) for a, b in product(sizes, repeat=2)
if abs(a - b) > 3]
def gen_oa_shapes_2d(sizes):
shapes0 = gen_oa_shapes(sizes)
shapes1 = gen_oa_shapes(sizes)
shapes = [ishapes0+ishapes1 for ishapes0, ishapes1 in
zip(shapes0, shapes1)]
modes = ['full', 'valid', 'same']
return [ishapes+(imode,) for ishapes, imode in product(shapes, modes)
if imode != 'valid' or
(ishapes[0] > ishapes[1] and ishapes[2] > ishapes[3]) or
(ishapes[0] < ishapes[1] and ishapes[2] < ishapes[3])]
def gen_oa_shapes_eq(sizes):
return [(a, b) for a, b in product(sizes, repeat=2)
if a >= b]
class TestOAConvolve:
@pytest.mark.slow()
@pytest.mark.parametrize('shape_a_0, shape_b_0',
gen_oa_shapes_eq(list(range(100)) +
list(range(100, 1000, 23)))
)
def test_real_manylens(self, shape_a_0, shape_b_0):
a = np.random.rand(shape_a_0)
b = np.random.rand(shape_b_0)
expected = fftconvolve(a, b)
out = oaconvolve(a, b)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('shape_a_0, shape_b_0',
gen_oa_shapes([50, 47, 6, 4, 1]))
@pytest.mark.parametrize('is_complex', [True, False])
@pytest.mark.parametrize('mode', ['full', 'valid', 'same'])
def test_1d_noaxes(self, shape_a_0, shape_b_0,
is_complex, mode, monkeypatch):
a = np.random.rand(shape_a_0)
b = np.random.rand(shape_b_0)
if is_complex:
a = a + 1j*np.random.rand(shape_a_0)
b = b + 1j*np.random.rand(shape_b_0)
expected = fftconvolve(a, b, mode=mode)
monkeypatch.setattr(signal._signaltools, 'fftconvolve',
fftconvolve_err)
out = oaconvolve(a, b, mode=mode)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', [0, 1])
@pytest.mark.parametrize('shape_a_0, shape_b_0',
gen_oa_shapes([50, 47, 6, 4]))
@pytest.mark.parametrize('shape_a_extra', [1, 3])
@pytest.mark.parametrize('shape_b_extra', [1, 3])
@pytest.mark.parametrize('is_complex', [True, False])
@pytest.mark.parametrize('mode', ['full', 'valid', 'same'])
def test_1d_axes(self, axes, shape_a_0, shape_b_0,
shape_a_extra, shape_b_extra,
is_complex, mode, monkeypatch):
ax_a = [shape_a_extra]*2
ax_b = [shape_b_extra]*2
ax_a[axes] = shape_a_0
ax_b[axes] = shape_b_0
a = np.random.rand(*ax_a)
b = np.random.rand(*ax_b)
if is_complex:
a = a + 1j*np.random.rand(*ax_a)
b = b + 1j*np.random.rand(*ax_b)
expected = fftconvolve(a, b, mode=mode, axes=axes)
monkeypatch.setattr(signal._signaltools, 'fftconvolve',
fftconvolve_err)
out = oaconvolve(a, b, mode=mode, axes=axes)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('shape_a_0, shape_b_0, '
'shape_a_1, shape_b_1, mode',
gen_oa_shapes_2d([50, 47, 6, 4]))
@pytest.mark.parametrize('is_complex', [True, False])
def test_2d_noaxes(self, shape_a_0, shape_b_0,
shape_a_1, shape_b_1, mode,
is_complex, monkeypatch):
a = np.random.rand(shape_a_0, shape_a_1)
b = np.random.rand(shape_b_0, shape_b_1)
if is_complex:
a = a + 1j*np.random.rand(shape_a_0, shape_a_1)
b = b + 1j*np.random.rand(shape_b_0, shape_b_1)
expected = fftconvolve(a, b, mode=mode)
monkeypatch.setattr(signal._signaltools, 'fftconvolve',
fftconvolve_err)
out = oaconvolve(a, b, mode=mode)
assert_array_almost_equal(out, expected)
@pytest.mark.parametrize('axes', [[0, 1], [0, 2], [1, 2]])
@pytest.mark.parametrize('shape_a_0, shape_b_0, '
'shape_a_1, shape_b_1, mode',
gen_oa_shapes_2d([50, 47, 6, 4]))
@pytest.mark.parametrize('shape_a_extra', [1, 3])
@pytest.mark.parametrize('shape_b_extra', [1, 3])
@pytest.mark.parametrize('is_complex', [True, False])
def test_2d_axes(self, axes, shape_a_0, shape_b_0,
shape_a_1, shape_b_1, mode,
shape_a_extra, shape_b_extra,
is_complex, monkeypatch):
ax_a = [shape_a_extra]*3
ax_b = [shape_b_extra]*3
ax_a[axes[0]] = shape_a_0
ax_b[axes[0]] = shape_b_0
ax_a[axes[1]] = shape_a_1
ax_b[axes[1]] = shape_b_1
a = np.random.rand(*ax_a)
b = np.random.rand(*ax_b)
if is_complex:
a = a + 1j*np.random.rand(*ax_a)
b = b + 1j*np.random.rand(*ax_b)
expected = fftconvolve(a, b, mode=mode, axes=axes)
monkeypatch.setattr(signal._signaltools, 'fftconvolve',
fftconvolve_err)
out = oaconvolve(a, b, mode=mode, axes=axes)
assert_array_almost_equal(out, expected)
def test_empty(self):
# Regression test for #1745: crashes with 0-length input.
assert_(oaconvolve([], []).size == 0)
assert_(oaconvolve([5, 6], []).size == 0)
assert_(oaconvolve([], [7]).size == 0)
def test_zero_rank(self):
a = array(4967)
b = array(3920)
out = oaconvolve(a, b)
assert_equal(out, a * b)
def test_single_element(self):
a = array([4967])
b = array([3920])
out = oaconvolve(a, b)
assert_equal(out, a * b)
class TestAllFreqConvolves:
@pytest.mark.parametrize('convapproach',
[fftconvolve, oaconvolve])
def test_invalid_shapes(self, convapproach):
a = np.arange(1, 7).reshape((2, 3))
b = np.arange(-6, 0).reshape((3, 2))
with assert_raises(ValueError,
match="For 'valid' mode, one must be at least "
"as large as the other in every dimension"):
convapproach(a, b, mode='valid')
@pytest.mark.parametrize('convapproach',
[fftconvolve, oaconvolve])
def test_invalid_shapes_axes(self, convapproach):
a = np.zeros([5, 6, 2, 1])
b = np.zeros([5, 6, 3, 1])
with assert_raises(ValueError,
match=r"incompatible shapes for in1 and in2:"
r" \(5L?, 6L?, 2L?, 1L?\) and"
r" \(5L?, 6L?, 3L?, 1L?\)"):
convapproach(a, b, axes=[0, 1])
@pytest.mark.parametrize('a,b',
[([1], 2),
(1, [2]),
([3], [[2]])])
@pytest.mark.parametrize('convapproach',
[fftconvolve, oaconvolve])
def test_mismatched_dims(self, a, b, convapproach):
with assert_raises(ValueError,
match="in1 and in2 should have the same"
" dimensionality"):
convapproach(a, b)
@pytest.mark.parametrize('convapproach',
[fftconvolve, oaconvolve])
def test_invalid_flags(self, convapproach):
with assert_raises(ValueError,
match="acceptable mode flags are 'valid',"
" 'same', or 'full'"):
convapproach([1], [2], mode='chips')
with assert_raises(ValueError,
match="when provided, axes cannot be empty"):
convapproach([1], [2], axes=[])
with assert_raises(ValueError, match="axes must be a scalar or "
"iterable of integers"):
convapproach([1], [2], axes=[[1, 2], [3, 4]])
with assert_raises(ValueError, match="axes must be a scalar or "
"iterable of integers"):
convapproach([1], [2], axes=[1., 2., 3., 4.])
with assert_raises(ValueError,
match="axes exceeds dimensionality of input"):
convapproach([1], [2], axes=[1])
with assert_raises(ValueError,
match="axes exceeds dimensionality of input"):
convapproach([1], [2], axes=[-2])
with assert_raises(ValueError,
match="all axes must be unique"):
convapproach([1], [2], axes=[0, 0])
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
@pytest.mark.parametrize('dtype', [np.longdouble, np.clongdouble])
def test_longdtype_input(self, dtype):
x = np.random.random((27, 27)).astype(dtype)
y = np.random.random((4, 4)).astype(dtype)
if np.iscomplexobj(dtype()):
x += .1j
y -= .1j
res = fftconvolve(x, y)
assert_allclose(res, convolve(x, y, method='direct'))
assert res.dtype == dtype
class TestMedFilt:
IN = [[50, 50, 50, 50, 50, 92, 18, 27, 65, 46],
[50, 50, 50, 50, 50, 0, 72, 77, 68, 66],
[50, 50, 50, 50, 50, 46, 47, 19, 64, 77],
[50, 50, 50, 50, 50, 42, 15, 29, 95, 35],
[50, 50, 50, 50, 50, 46, 34, 9, 21, 66],
[70, 97, 28, 68, 78, 77, 61, 58, 71, 42],
[64, 53, 44, 29, 68, 32, 19, 68, 24, 84],
[3, 33, 53, 67, 1, 78, 74, 55, 12, 83],
[7, 11, 46, 70, 60, 47, 24, 43, 61, 26],
[32, 61, 88, 7, 39, 4, 92, 64, 45, 61]]
OUT = [[0, 50, 50, 50, 42, 15, 15, 18, 27, 0],
[0, 50, 50, 50, 50, 42, 19, 21, 29, 0],
[50, 50, 50, 50, 50, 47, 34, 34, 46, 35],
[50, 50, 50, 50, 50, 50, 42, 47, 64, 42],
[50, 50, 50, 50, 50, 50, 46, 55, 64, 35],
[33, 50, 50, 50, 50, 47, 46, 43, 55, 26],
[32, 50, 50, 50, 50, 47, 46, 45, 55, 26],
[7, 46, 50, 50, 47, 46, 46, 43, 45, 21],
[0, 32, 33, 39, 32, 32, 43, 43, 43, 0],
[0, 7, 11, 7, 4, 4, 19, 19, 24, 0]]
KERNEL_SIZE = [7,3]
def test_basic(self):
d = signal.medfilt(self.IN, self.KERNEL_SIZE)
e = signal.medfilt2d(np.array(self.IN, float), self.KERNEL_SIZE)
assert_array_equal(d, self.OUT)
assert_array_equal(d, e)
@pytest.mark.parametrize('dtype', [np.ubyte, np.byte, np.ushort, np.short,
np_ulong, np_long, np.ulonglong, np.ulonglong,
np.float32, np.float64])
def test_types(self, dtype):
# volume input and output types match
in_typed = np.array(self.IN, dtype=dtype)
assert_equal(signal.medfilt(in_typed).dtype, dtype)
assert_equal(signal.medfilt2d(in_typed).dtype, dtype)
@pytest.mark.parametrize('dtype', [np.bool_, np.complex64, np.complex128,
np.clongdouble, np.float16, np.object_,
"float96", "float128"])
def test_invalid_dtypes(self, dtype):
# We can only test this on platforms that support a native type of float96 or
# float128; comparing to np.longdouble allows us to filter out non-native types
if (dtype in ["float96", "float128"]
and np.finfo(np.longdouble).dtype != dtype):
pytest.skip(f"Platform does not support {dtype}")
in_typed = np.array(self.IN, dtype=dtype)
with pytest.raises(ValueError, match="not supported"):
signal.medfilt(in_typed)
with pytest.raises(ValueError, match="not supported"):
signal.medfilt2d(in_typed)
def test_none(self):
# gh-1651, trac #1124. Ensure this does not segfault.
msg = "dtype=object is not supported by medfilt"
with assert_raises(ValueError, match=msg):
signal.medfilt(None)
def test_odd_strides(self):
# Avoid a regression with possible contiguous
# numpy arrays that have odd strides. The stride value below gets
# us into wrong memory if used (but it does not need to be used)
dummy = np.arange(10, dtype=np.float64)
a = dummy[5:6]
a.strides = 16
assert_(signal.medfilt(a, 1) == 5.)
@pytest.mark.parametrize("dtype", [np.ubyte, np.float32, np.float64])
def test_medfilt2d_parallel(self, dtype):
in_typed = np.array(self.IN, dtype=dtype)
expected = np.array(self.OUT, dtype=dtype)
# This is used to simplify the indexing calculations.
assert in_typed.shape == expected.shape
# We'll do the calculation in four chunks. M1 and N1 are the dimensions
# of the first output chunk. We have to extend the input by half the
# kernel size to be able to calculate the full output chunk.
M1 = expected.shape[0] // 2
N1 = expected.shape[1] // 2
offM = self.KERNEL_SIZE[0] // 2 + 1
offN = self.KERNEL_SIZE[1] // 2 + 1
def apply(chunk):
# in = slice of in_typed to use.
# sel = slice of output to crop it to the correct region.
# out = slice of output array to store in.
M, N = chunk
if M == 0:
Min = slice(0, M1 + offM)
Msel = slice(0, -offM)
Mout = slice(0, M1)
else:
Min = slice(M1 - offM, None)
Msel = slice(offM, None)
Mout = slice(M1, None)
if N == 0:
Nin = slice(0, N1 + offN)
Nsel = slice(0, -offN)
Nout = slice(0, N1)
else:
Nin = slice(N1 - offN, None)
Nsel = slice(offN, None)
Nout = slice(N1, None)
# Do the calculation, but do not write to the output in the threads.
chunk_data = in_typed[Min, Nin]
med = signal.medfilt2d(chunk_data, self.KERNEL_SIZE)
return med[Msel, Nsel], Mout, Nout
# Give each chunk to a different thread.
output = np.zeros_like(expected)
with ThreadPoolExecutor(max_workers=4) as pool:
chunks = {(0, 0), (0, 1), (1, 0), (1, 1)}
futures = {pool.submit(apply, chunk) for chunk in chunks}
# Store each result in the output as it arrives.
for future in as_completed(futures):
data, Mslice, Nslice = future.result()
output[Mslice, Nslice] = data
assert_array_equal(output, expected)
class TestWiener:
def test_basic(self):
g = array([[5, 6, 4, 3],
[3, 5, 6, 2],
[2, 3, 5, 6],
[1, 6, 9, 7]], 'd')
h = array([[2.16374269, 3.2222222222, 2.8888888889, 1.6666666667],
[2.666666667, 4.33333333333, 4.44444444444, 2.8888888888],
[2.222222222, 4.4444444444, 5.4444444444, 4.801066874837],
[1.33333333333, 3.92735042735, 6.0712560386, 5.0404040404]])
assert_array_almost_equal(signal.wiener(g), h, decimal=6)
assert_array_almost_equal(signal.wiener(g, mysize=3), h, decimal=6)
padtype_options = ["mean", "median", "minimum", "maximum", "line"]
padtype_options += _upfirdn_modes
class TestResample:
def test_basic(self):
# Some basic tests
# Regression test for issue #3603.
# window.shape must equal to sig.shape[0]
sig = np.arange(128)
num = 256
win = signal.get_window(('kaiser', 8.0), 160)
assert_raises(ValueError, signal.resample, sig, num, window=win)
# Other degenerate conditions
assert_raises(ValueError, signal.resample_poly, sig, 'yo', 1)
assert_raises(ValueError, signal.resample_poly, sig, 1, 0)
assert_raises(ValueError, signal.resample_poly, sig, 2, 1, padtype='')
assert_raises(ValueError, signal.resample_poly, sig, 2, 1,
padtype='mean', cval=10)
# test for issue #6505 - should not modify window.shape when axis ≠ 0
sig2 = np.tile(np.arange(160), (2, 1))
signal.resample(sig2, num, axis=-1, window=win)
assert_(win.shape == (160,))
@pytest.mark.parametrize('window', (None, 'hamming'))
@pytest.mark.parametrize('N', (20, 19))
@pytest.mark.parametrize('num', (100, 101, 10, 11))
def test_rfft(self, N, num, window):
# Make sure the speed up using rfft gives the same result as the normal
# way using fft
x = np.linspace(0, 10, N, endpoint=False)
y = np.cos(-x**2/6.0)
assert_allclose(signal.resample(y, num, window=window),
signal.resample(y + 0j, num, window=window).real)
y = np.array([np.cos(-x**2/6.0), np.sin(-x**2/6.0)])
y_complex = y + 0j
assert_allclose(
signal.resample(y, num, axis=1, window=window),
signal.resample(y_complex, num, axis=1, window=window).real,
atol=1e-9)
def test_input_domain(self):
# Test if both input domain modes produce the same results.
tsig = np.arange(256) + 0j
fsig = sp_fft.fft(tsig)
num = 256
assert_allclose(
signal.resample(fsig, num, domain='freq'),
signal.resample(tsig, num, domain='time'),
atol=1e-9)
@pytest.mark.parametrize('nx', (1, 2, 3, 5, 8))
@pytest.mark.parametrize('ny', (1, 2, 3, 5, 8))
@pytest.mark.parametrize('dtype', ('float', 'complex'))
def test_dc(self, nx, ny, dtype):
x = np.array([1] * nx, dtype)
y = signal.resample(x, ny)
assert_allclose(y, [1] * ny)
@pytest.mark.thread_unsafe # due to Cython fused types, see cython#6506
@pytest.mark.parametrize('padtype', padtype_options)
def test_mutable_window(self, padtype):
# Test that a mutable window is not modified
impulse = np.zeros(3)
window = np.random.RandomState(0).randn(2)
window_orig = window.copy()
signal.resample_poly(impulse, 5, 1, window=window, padtype=padtype)
assert_array_equal(window, window_orig)
@pytest.mark.parametrize('padtype', padtype_options)
def test_output_float32(self, padtype):
# Test that float32 inputs yield a float32 output
x = np.arange(10, dtype=np.float32)
h = np.array([1, 1, 1], dtype=np.float32)
y = signal.resample_poly(x, 1, 2, window=h, padtype=padtype)
assert y.dtype == np.float32
@pytest.mark.parametrize('padtype', padtype_options)
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
def test_output_match_dtype(self, padtype, dtype):
# Test that the dtype of x is preserved per issue #14733
x = np.arange(10, dtype=dtype)
y = signal.resample_poly(x, 1, 2, padtype=padtype)
assert y.dtype == x.dtype
@pytest.mark.parametrize(
"method, ext, padtype",
[("fft", False, None)]
+ list(
product(
["polyphase"], [False, True], padtype_options,
)
),
)
def test_resample_methods(self, method, ext, padtype):
# Test resampling of sinusoids and random noise (1-sec)
rate = 100
rates_to = [49, 50, 51, 99, 100, 101, 199, 200, 201]
# Sinusoids, windowed to avoid edge artifacts
t = np.arange(rate) / float(rate)
freqs = np.array((1., 10., 40.))[:, np.newaxis]
x = np.sin(2 * np.pi * freqs * t) * hann(rate)
for rate_to in rates_to:
t_to = np.arange(rate_to) / float(rate_to)
y_tos = np.sin(2 * np.pi * freqs * t_to) * hann(rate_to)
if method == 'fft':
y_resamps = signal.resample(x, rate_to, axis=-1)
else:
if ext and rate_to != rate:
# Match default window design
g = gcd(rate_to, rate)
up = rate_to // g
down = rate // g
max_rate = max(up, down)
f_c = 1. / max_rate
half_len = 10 * max_rate
window = signal.firwin(2 * half_len + 1, f_c,
window=('kaiser', 5.0))
polyargs = {'window': window, 'padtype': padtype}
else:
polyargs = {'padtype': padtype}
y_resamps = signal.resample_poly(x, rate_to, rate, axis=-1,
**polyargs)
for y_to, y_resamp, freq in zip(y_tos, y_resamps, freqs):
if freq >= 0.5 * rate_to:
y_to.fill(0.) # mostly low-passed away
if padtype in ['minimum', 'maximum']:
assert_allclose(y_resamp, y_to, atol=3e-1)
else:
assert_allclose(y_resamp, y_to, atol=1e-3)
else:
assert_array_equal(y_to.shape, y_resamp.shape)
corr = np.corrcoef(y_to, y_resamp)[0, 1]
assert_(corr > 0.99, msg=(corr, rate, rate_to))
# Random data
rng = np.random.RandomState(0)
x = hann(rate) * np.cumsum(rng.randn(rate)) # low-pass, wind
for rate_to in rates_to:
# random data
t_to = np.arange(rate_to) / float(rate_to)
y_to = np.interp(t_to, t, x)
if method == 'fft':
y_resamp = signal.resample(x, rate_to)
else:
y_resamp = signal.resample_poly(x, rate_to, rate,
padtype=padtype)
assert_array_equal(y_to.shape, y_resamp.shape)
corr = np.corrcoef(y_to, y_resamp)[0, 1]
assert_(corr > 0.99, msg=corr)
# More tests of fft method (Master 0.18.1 fails these)
if method == 'fft':
x1 = np.array([1.+0.j, 0.+0.j])
y1_test = signal.resample(x1, 4)
# upsampling a complex array
y1_true = np.array([1.+0.j, 0.5+0.j, 0.+0.j, 0.5+0.j])
assert_allclose(y1_test, y1_true, atol=1e-12)
x2 = np.array([1., 0.5, 0., 0.5])
y2_test = signal.resample(x2, 2) # downsampling a real array
y2_true = np.array([1., 0.])
assert_allclose(y2_test, y2_true, atol=1e-12)
def test_poly_vs_filtfilt(self):
# Check that up=1.0 gives same answer as filtfilt + slicing
random_state = np.random.RandomState(17)
try_types = (int, np.float32, np.complex64, float, complex)
size = 10000
down_factors = [2, 11, 79]
for dtype in try_types:
x = random_state.randn(size).astype(dtype)
if dtype in (np.complex64, np.complex128):
x += 1j * random_state.randn(size)
# resample_poly assumes zeros outside of signl, whereas filtfilt
# can only constant-pad. Make them equivalent:
x[0] = 0
x[-1] = 0
for down in down_factors:
h = signal.firwin(31, 1. / down, window='hamming')
yf = filtfilt(h, 1.0, x, padtype='constant')[::down]
# Need to pass convolved version of filter to resample_poly,
# since filtfilt does forward and backward, but resample_poly
# only goes forward
hc = convolve(h, h[::-1])
y = signal.resample_poly(x, 1, down, window=hc)
assert_allclose(yf, y, atol=1e-7, rtol=1e-7)
def test_correlate1d(self):
for down in [2, 4]:
for nx in range(1, 40, down):
for nweights in (32, 33):
x = np.random.random((nx,))
weights = np.random.random((nweights,))
y_g = correlate1d(x, weights[::-1], mode='constant')
y_s = signal.resample_poly(
x, up=1, down=down, window=weights)
assert_allclose(y_g[::down], y_s)
@pytest.mark.parametrize('dtype', [np.int32, np.float32])
def test_gh_15620(self, dtype):
data = np.array([0, 1, 2, 3, 2, 1, 0], dtype=dtype)
actual = signal.resample_poly(data,
up=2,
down=1,
padtype='smooth')
assert np.count_nonzero(actual) > 0
class TestCSpline1DEval:
def test_basic(self):
y = array([1, 2, 3, 4, 3, 2, 1, 2, 3.0])
x = arange(len(y))
dx = x[1] - x[0]
cj = signal.cspline1d(y)
x2 = arange(len(y) * 10.0) / 10.0
y2 = signal.cspline1d_eval(cj, x2, dx=dx, x0=x[0])
# make sure interpolated values are on knot points
assert_array_almost_equal(y2[::10], y, decimal=5)
def test_complex(self):
# create some smoothly varying complex signal to interpolate
x = np.arange(2)
y = np.zeros(x.shape, dtype=np.complex64)
T = 10.0
f = 1.0 / T
y = np.exp(2.0J * np.pi * f * x)
# get the cspline transform
cy = signal.cspline1d(y)
# determine new test x value and interpolate
xnew = np.array([0.5])
ynew = signal.cspline1d_eval(cy, xnew)
assert_equal(ynew.dtype, y.dtype)
class TestOrderFilt:
def test_basic(self):
assert_array_equal(signal.order_filter([1, 2, 3], [1, 0, 1], 1),
[2, 3, 2])
class _TestLinearFilter:
def generate(self, shape):
x = np.linspace(0, np.prod(shape) - 1, np.prod(shape)).reshape(shape)
return self.convert_dtype(x)
def convert_dtype(self, arr):
if self.dtype == np.dtype('O'):
arr = np.asarray(arr)
out = np.empty(arr.shape, self.dtype)
iter = np.nditer([arr, out], ['refs_ok','zerosize_ok'],
[['readonly'],['writeonly']])
for x, y in iter:
y[...] = self.type(x[()])
return out
else:
return np.asarray(arr, dtype=self.dtype)
def test_rank_1_IIR(self):
x = self.generate((6,))
b = self.convert_dtype([1, -1])
a = self.convert_dtype([0.5, -0.5])
y_r = self.convert_dtype([0, 2, 4, 6, 8, 10.])
assert_array_almost_equal(lfilter(b, a, x), y_r)
def test_rank_1_FIR(self):
x = self.generate((6,))
b = self.convert_dtype([1, 1])
a = self.convert_dtype([1])
y_r = self.convert_dtype([0, 1, 3, 5, 7, 9.])
assert_array_almost_equal(lfilter(b, a, x), y_r)
def test_rank_1_IIR_init_cond(self):
x = self.generate((6,))
b = self.convert_dtype([1, 0, -1])
a = self.convert_dtype([0.5, -0.5])
zi = self.convert_dtype([1, 2])
y_r = self.convert_dtype([1, 5, 9, 13, 17, 21])
zf_r = self.convert_dtype([13, -10])
y, zf = lfilter(b, a, x, zi=zi)
assert_array_almost_equal(y, y_r)
assert_array_almost_equal(zf, zf_r)
def test_rank_1_FIR_init_cond(self):
x = self.generate((6,))
b = self.convert_dtype([1, 1, 1])
a = self.convert_dtype([1])
zi = self.convert_dtype([1, 1])
y_r = self.convert_dtype([1, 2, 3, 6, 9, 12.])
zf_r = self.convert_dtype([9, 5])
y, zf = lfilter(b, a, x, zi=zi)
assert_array_almost_equal(y, y_r)
assert_array_almost_equal(zf, zf_r)
def test_rank_2_IIR_axis_0(self):
x = self.generate((4, 3))
b = self.convert_dtype([1, -1])
a = self.convert_dtype([0.5, 0.5])
y_r2_a0 = self.convert_dtype([[0, 2, 4], [6, 4, 2], [0, 2, 4],
[6, 4, 2]])
y = lfilter(b, a, x, axis=0)
assert_array_almost_equal(y_r2_a0, y)
def test_rank_2_IIR_axis_1(self):
x = self.generate((4, 3))
b = self.convert_dtype([1, -1])
a = self.convert_dtype([0.5, 0.5])
y_r2_a1 = self.convert_dtype([[0, 2, 0], [6, -4, 6], [12, -10, 12],
[18, -16, 18]])
y = lfilter(b, a, x, axis=1)
assert_array_almost_equal(y_r2_a1, y)
def test_rank_2_IIR_axis_0_init_cond(self):
x = self.generate((4, 3))
b = self.convert_dtype([1, -1])
a = self.convert_dtype([0.5, 0.5])
zi = self.convert_dtype(np.ones((4,1)))
y_r2_a0_1 = self.convert_dtype([[1, 1, 1], [7, -5, 7], [13, -11, 13],
[19, -17, 19]])
zf_r = self.convert_dtype([-5, -17, -29, -41])[:, np.newaxis]
y, zf = lfilter(b, a, x, axis=1, zi=zi)
assert_array_almost_equal(y_r2_a0_1, y)
assert_array_almost_equal(zf, zf_r)
def test_rank_2_IIR_axis_1_init_cond(self):
x = self.generate((4,3))
b = self.convert_dtype([1, -1])
a = self.convert_dtype([0.5, 0.5])
zi = self.convert_dtype(np.ones((1,3)))
y_r2_a0_0 = self.convert_dtype([[1, 3, 5], [5, 3, 1],
[1, 3, 5], [5, 3, 1]])
zf_r = self.convert_dtype([[-23, -23, -23]])
y, zf = lfilter(b, a, x, axis=0, zi=zi)
assert_array_almost_equal(y_r2_a0_0, y)
assert_array_almost_equal(zf, zf_r)
def test_rank_3_IIR(self):
x = self.generate((4, 3, 2))
b = self.convert_dtype([1, -1])
a = self.convert_dtype([0.5, 0.5])
for axis in range(x.ndim):
y = lfilter(b, a, x, axis)
y_r = np.apply_along_axis(lambda w: lfilter(b, a, w), axis, x)
assert_array_almost_equal(y, y_r)
def test_rank_3_IIR_init_cond(self):
x = self.generate((4, 3, 2))
b = self.convert_dtype([1, -1])
a = self.convert_dtype([0.5, 0.5])
for axis in range(x.ndim):
zi_shape = list(x.shape)
zi_shape[axis] = 1
zi = self.convert_dtype(np.ones(zi_shape))
zi1 = self.convert_dtype([1])
y, zf = lfilter(b, a, x, axis, zi)
def lf0(w):
return lfilter(b, a, w, zi=zi1)[0]
def lf1(w):
return lfilter(b, a, w, zi=zi1)[1]
y_r = np.apply_along_axis(lf0, axis, x)
zf_r = np.apply_along_axis(lf1, axis, x)
assert_array_almost_equal(y, y_r)
assert_array_almost_equal(zf, zf_r)
def test_rank_3_FIR(self):
x = self.generate((4, 3, 2))
b = self.convert_dtype([1, 0, -1])
a = self.convert_dtype([1])
for axis in range(x.ndim):
y = lfilter(b, a, x, axis)
y_r = np.apply_along_axis(lambda w: lfilter(b, a, w), axis, x)
assert_array_almost_equal(y, y_r)
def test_rank_3_FIR_init_cond(self):
x = self.generate((4, 3, 2))
b = self.convert_dtype([1, 0, -1])
a = self.convert_dtype([1])
for axis in range(x.ndim):
zi_shape = list(x.shape)
zi_shape[axis] = 2
zi = self.convert_dtype(np.ones(zi_shape))
zi1 = self.convert_dtype([1, 1])
y, zf = lfilter(b, a, x, axis, zi)
def lf0(w):
return lfilter(b, a, w, zi=zi1)[0]
def lf1(w):
return lfilter(b, a, w, zi=zi1)[1]
y_r = np.apply_along_axis(lf0, axis, x)
zf_r = np.apply_along_axis(lf1, axis, x)
assert_array_almost_equal(y, y_r)
assert_array_almost_equal(zf, zf_r)
def test_zi_pseudobroadcast(self):
x = self.generate((4, 5, 20))
b,a = signal.butter(8, 0.2, output='ba')
b = self.convert_dtype(b)
a = self.convert_dtype(a)
zi_size = b.shape[0] - 1
# lfilter requires x.ndim == zi.ndim exactly. However, zi can have
# length 1 dimensions.
zi_full = self.convert_dtype(np.ones((4, 5, zi_size)))
zi_sing = self.convert_dtype(np.ones((1, 1, zi_size)))
y_full, zf_full = lfilter(b, a, x, zi=zi_full)
y_sing, zf_sing = lfilter(b, a, x, zi=zi_sing)
assert_array_almost_equal(y_sing, y_full)
assert_array_almost_equal(zf_full, zf_sing)
# lfilter does not prepend ones
assert_raises(ValueError, lfilter, b, a, x, -1, np.ones(zi_size))
def test_scalar_a(self):
# a can be a scalar.
x = self.generate(6)
b = self.convert_dtype([1, 0, -1])
a = self.convert_dtype([1])
y_r = self.convert_dtype([0, 1, 2, 2, 2, 2])
y = lfilter(b, a[0], x)
assert_array_almost_equal(y, y_r)
def test_zi_some_singleton_dims(self):
# lfilter doesn't really broadcast (no prepending of 1's). But does
# do singleton expansion if x and zi have the same ndim. This was
# broken only if a subset of the axes were singletons (gh-4681).
x = self.convert_dtype(np.zeros((3,2,5), 'l'))
b = self.convert_dtype(np.ones(5, 'l'))
a = self.convert_dtype(np.array([1,0,0]))
zi = np.ones((3,1,4), 'l')
zi[1,:,:] *= 2
zi[2,:,:] *= 3
zi = self.convert_dtype(zi)
zf_expected = self.convert_dtype(np.zeros((3,2,4), 'l'))
y_expected = np.zeros((3,2,5), 'l')
y_expected[:,:,:4] = [[[1]], [[2]], [[3]]]
y_expected = self.convert_dtype(y_expected)
# IIR
y_iir, zf_iir = lfilter(b, a, x, -1, zi)
assert_array_almost_equal(y_iir, y_expected)
assert_array_almost_equal(zf_iir, zf_expected)
# FIR
y_fir, zf_fir = lfilter(b, a[0], x, -1, zi)
assert_array_almost_equal(y_fir, y_expected)
assert_array_almost_equal(zf_fir, zf_expected)
def base_bad_size_zi(self, b, a, x, axis, zi):
b = self.convert_dtype(b)
a = self.convert_dtype(a)
x = self.convert_dtype(x)
zi = self.convert_dtype(zi)
assert_raises(ValueError, lfilter, b, a, x, axis, zi)
def test_bad_size_zi(self):
# rank 1
x1 = np.arange(6)
self.base_bad_size_zi([1], [1], x1, -1, [1])
self.base_bad_size_zi([1, 1], [1], x1, -1, [0, 1])
self.base_bad_size_zi([1, 1], [1], x1, -1, [[0]])
self.base_bad_size_zi([1, 1], [1], x1, -1, [0, 1, 2])
self.base_bad_size_zi([1, 1, 1], [1], x1, -1, [[0]])
self.base_bad_size_zi([1, 1, 1], [1], x1, -1, [0, 1, 2])
self.base_bad_size_zi([1], [1, 1], x1, -1, [0, 1])
self.base_bad_size_zi([1], [1, 1], x1, -1, [[0]])
self.base_bad_size_zi([1], [1, 1], x1, -1, [0, 1, 2])
self.base_bad_size_zi([1, 1, 1], [1, 1], x1, -1, [0])
self.base_bad_size_zi([1, 1, 1], [1, 1], x1, -1, [[0], [1]])
self.base_bad_size_zi([1, 1, 1], [1, 1], x1, -1, [0, 1, 2])
self.base_bad_size_zi([1, 1, 1], [1, 1], x1, -1, [0, 1, 2, 3])
self.base_bad_size_zi([1, 1], [1, 1, 1], x1, -1, [0])
self.base_bad_size_zi([1, 1], [1, 1, 1], x1, -1, [[0], [1]])
self.base_bad_size_zi([1, 1], [1, 1, 1], x1, -1, [0, 1, 2])
self.base_bad_size_zi([1, 1], [1, 1, 1], x1, -1, [0, 1, 2, 3])
# rank 2
x2 = np.arange(12).reshape((4,3))
# for axis=0 zi.shape should == (max(len(a),len(b))-1, 3)
self.base_bad_size_zi([1], [1], x2, 0, [0])
# for each of these there are 5 cases tested (in this order):
# 1. not deep enough, right # elements
# 2. too deep, right # elements
# 3. right depth, right # elements, transposed
# 4. right depth, too few elements
# 5. right depth, too many elements
self.base_bad_size_zi([1, 1], [1], x2, 0, [0,1,2])
self.base_bad_size_zi([1, 1], [1], x2, 0, [[[0,1,2]]])
self.base_bad_size_zi([1, 1], [1], x2, 0, [[0], [1], [2]])
self.base_bad_size_zi([1, 1], [1], x2, 0, [[0,1]])
self.base_bad_size_zi([1, 1], [1], x2, 0, [[0,1,2,3]])
self.base_bad_size_zi([1, 1, 1], [1], x2, 0, [0,1,2,3,4,5])
self.base_bad_size_zi([1, 1, 1], [1], x2, 0, [[[0,1,2],[3,4,5]]])
self.base_bad_size_zi([1, 1, 1], [1], x2, 0, [[0,1],[2,3],[4,5]])
self.base_bad_size_zi([1, 1, 1], [1], x2, 0, [[0,1],[2,3]])
self.base_bad_size_zi([1, 1, 1], [1], x2, 0, [[0,1,2,3],[4,5,6,7]])
self.base_bad_size_zi([1], [1, 1], x2, 0, [0,1,2])
self.base_bad_size_zi([1], [1, 1], x2, 0, [[[0,1,2]]])
self.base_bad_size_zi([1], [1, 1], x2, 0, [[0], [1], [2]])
self.base_bad_size_zi([1], [1, 1], x2, 0, [[0,1]])
self.base_bad_size_zi([1], [1, 1], x2, 0, [[0,1,2,3]])
self.base_bad_size_zi([1], [1, 1, 1], x2, 0, [0,1,2,3,4,5])
self.base_bad_size_zi([1], [1, 1, 1], x2, 0, [[[0,1,2],[3,4,5]]])
self.base_bad_size_zi([1], [1, 1, 1], x2, 0, [[0,1],[2,3],[4,5]])
self.base_bad_size_zi([1], [1, 1, 1], x2, 0, [[0,1],[2,3]])
self.base_bad_size_zi([1], [1, 1, 1], x2, 0, [[0,1,2,3],[4,5,6,7]])
self.base_bad_size_zi([1, 1, 1], [1, 1], x2, 0, [0,1,2,3,4,5])
self.base_bad_size_zi([1, 1, 1], [1, 1], x2, 0, [[[0,1,2],[3,4,5]]])
self.base_bad_size_zi([1, 1, 1], [1, 1], x2, 0, [[0,1],[2,3],[4,5]])
self.base_bad_size_zi([1, 1, 1], [1, 1], x2, 0, [[0,1],[2,3]])
self.base_bad_size_zi([1, 1, 1], [1, 1], x2, 0, [[0,1,2,3],[4,5,6,7]])
# for axis=1 zi.shape should == (4, max(len(a),len(b))-1)
self.base_bad_size_zi([1], [1], x2, 1, [0])
self.base_bad_size_zi([1, 1], [1], x2, 1, [0,1,2,3])
self.base_bad_size_zi([1, 1], [1], x2, 1, [[[0],[1],[2],[3]]])
self.base_bad_size_zi([1, 1], [1], x2, 1, [[0, 1, 2, 3]])
self.base_bad_size_zi([1, 1], [1], x2, 1, [[0],[1],[2]])
self.base_bad_size_zi([1, 1], [1], x2, 1, [[0],[1],[2],[3],[4]])
self.base_bad_size_zi([1, 1, 1], [1], x2, 1, [0,1,2,3,4,5,6,7])
self.base_bad_size_zi([1, 1, 1], [1], x2, 1, [[[0,1],[2,3],[4,5],[6,7]]])
self.base_bad_size_zi([1, 1, 1], [1], x2, 1, [[0,1,2,3],[4,5,6,7]])
self.base_bad_size_zi([1, 1, 1], [1], x2, 1, [[0,1],[2,3],[4,5]])
self.base_bad_size_zi([1, 1, 1], [1], x2, 1, [[0,1],[2,3],[4,5],[6,7],[8,9]])
self.base_bad_size_zi([1], [1, 1], x2, 1, [0,1,2,3])
self.base_bad_size_zi([1], [1, 1], x2, 1, [[[0],[1],[2],[3]]])
self.base_bad_size_zi([1], [1, 1], x2, 1, [[0, 1, 2, 3]])
self.base_bad_size_zi([1], [1, 1], x2, 1, [[0],[1],[2]])
self.base_bad_size_zi([1], [1, 1], x2, 1, [[0],[1],[2],[3],[4]])
self.base_bad_size_zi([1], [1, 1, 1], x2, 1, [0,1,2,3,4,5,6,7])
self.base_bad_size_zi([1], [1, 1, 1], x2, 1, [[[0,1],[2,3],[4,5],[6,7]]])
self.base_bad_size_zi([1], [1, 1, 1], x2, 1, [[0,1,2,3],[4,5,6,7]])
self.base_bad_size_zi([1], [1, 1, 1], x2, 1, [[0,1],[2,3],[4,5]])
self.base_bad_size_zi([1], [1, 1, 1], x2, 1, [[0,1],[2,3],[4,5],[6,7],[8,9]])
self.base_bad_size_zi([1, 1, 1], [1, 1], x2, 1, [0,1,2,3,4,5,6,7])
self.base_bad_size_zi([1, 1, 1], [1, 1], x2, 1, [[[0,1],[2,3],[4,5],[6,7]]])
self.base_bad_size_zi([1, 1, 1], [1, 1], x2, 1, [[0,1,2,3],[4,5,6,7]])
self.base_bad_size_zi([1, 1, 1], [1, 1], x2, 1, [[0,1],[2,3],[4,5]])
self.base_bad_size_zi([1, 1, 1], [1, 1], x2, 1, [[0,1],[2,3],[4,5],[6,7],[8,9]])
def test_empty_zi(self):
# Regression test for #880: empty array for zi crashes.
x = self.generate((5,))
a = self.convert_dtype([1])
b = self.convert_dtype([1])
zi = self.convert_dtype([])
y, zf = lfilter(b, a, x, zi=zi)
assert_array_almost_equal(y, x)
assert_equal(zf.dtype, self.dtype)
assert_equal(zf.size, 0)
def test_lfiltic_bad_zi(self):
# Regression test for #3699: bad initial conditions
a = self.convert_dtype([1])
b = self.convert_dtype([1])
# "y" sets the datatype of zi, so it truncates if int
zi = lfiltic(b, a, [1., 0])
zi_1 = lfiltic(b, a, [1, 0])
zi_2 = lfiltic(b, a, [True, False])
assert_array_equal(zi, zi_1)
assert_array_equal(zi, zi_2)
def test_short_x_FIR(self):
# regression test for #5116
# x shorter than b, with non None zi fails
a = self.convert_dtype([1])
b = self.convert_dtype([1, 0, -1])
zi = self.convert_dtype([2, 7])
x = self.convert_dtype([72])
ye = self.convert_dtype([74])
zfe = self.convert_dtype([7, -72])
y, zf = lfilter(b, a, x, zi=zi)
assert_array_almost_equal(y, ye)
assert_array_almost_equal(zf, zfe)
def test_short_x_IIR(self):
# regression test for #5116
# x shorter than b, with non None zi fails
a = self.convert_dtype([1, 1])
b = self.convert_dtype([1, 0, -1])
zi = self.convert_dtype([2, 7])
x = self.convert_dtype([72])
ye = self.convert_dtype([74])
zfe = self.convert_dtype([-67, -72])
y, zf = lfilter(b, a, x, zi=zi)
assert_array_almost_equal(y, ye)
assert_array_almost_equal(zf, zfe)
def test_do_not_modify_a_b_IIR(self):
x = self.generate((6,))
b = self.convert_dtype([1, -1])
b0 = b.copy()
a = self.convert_dtype([0.5, -0.5])
a0 = a.copy()
y_r = self.convert_dtype([0, 2, 4, 6, 8, 10.])
y_f = lfilter(b, a, x)
assert_array_almost_equal(y_f, y_r)
assert_equal(b, b0)
assert_equal(a, a0)
def test_do_not_modify_a_b_FIR(self):
x = self.generate((6,))
b = self.convert_dtype([1, 0, 1])
b0 = b.copy()
a = self.convert_dtype([2])
a0 = a.copy()
y_r = self.convert_dtype([0, 0.5, 1, 2, 3, 4.])
y_f = lfilter(b, a, x)
assert_array_almost_equal(y_f, y_r)
assert_equal(b, b0)
assert_equal(a, a0)
@pytest.mark.parametrize("a", [1.0, [1.0], np.array(1.0)])
@pytest.mark.parametrize("b", [1.0, [1.0], np.array(1.0)])
def test_scalar_input(self, a, b):
data = np.random.randn(10)
assert_allclose(
lfilter(np.array([1.0]), np.array([1.0]), data),
lfilter(b, a, data))
@pytest.mark.thread_unsafe
def test_dtype_deprecation(self):
# gh-21211
a = np.asarray([1, 2, 3, 6, 5, 3], dtype=object)
b = np.asarray([2, 3, 4, 5, 3, 4, 2, 2, 1], dtype=object)
with pytest.deprecated_call(match="dtype=object is not supported"):
lfilter(a, b, [1, 2, 3, 4])
class TestLinearFilterFloat32(_TestLinearFilter):
dtype = np.dtype('f')
class TestLinearFilterFloat64(_TestLinearFilter):
dtype = np.dtype('d')
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
class TestLinearFilterFloatExtended(_TestLinearFilter):
dtype = np.dtype('g')
class TestLinearFilterComplex64(_TestLinearFilter):
dtype = np.dtype('F')
class TestLinearFilterComplex128(_TestLinearFilter):
dtype = np.dtype('D')
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
class TestLinearFilterComplexExtended(_TestLinearFilter):
dtype = np.dtype('G')
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
class TestLinearFilterDecimal(_TestLinearFilter):
dtype = np.dtype('O')
def type(self, x):
return Decimal(str(x))
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
class TestLinearFilterObject(_TestLinearFilter):
dtype = np.dtype('O')
type = float
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
def test_lfilter_bad_object():
# lfilter: object arrays with non-numeric objects raise TypeError.
# Regression test for ticket #1452.
if hasattr(sys, 'abiflags') and 'd' in sys.abiflags:
pytest.skip('test is flaky when run with python3-dbg')
assert_raises(TypeError, lfilter, [1.0], [1.0], [1.0, None, 2.0])
assert_raises(TypeError, lfilter, [1.0], [None], [1.0, 2.0, 3.0])
assert_raises(TypeError, lfilter, [None], [1.0], [1.0, 2.0, 3.0])
_pmf = pytest.mark.filterwarnings('ignore::DeprecationWarning')
def test_lfilter_notimplemented_input():
# Should not crash, gh-7991
assert_raises(NotImplementedError, lfilter, [2,3], [4,5], [1,2,3,4,5])
@pytest.mark.parametrize('dt', [np.ubyte, np.byte, np.ushort, np.short,
np_ulong, np_long, np.ulonglong, np.ulonglong,
np.float32, np.float64,
pytest.param(np.longdouble, marks=_pmf),
pytest.param(Decimal, marks=_pmf)]
)
class TestCorrelateReal:
def _setup_rank1(self, dt):
a = np.linspace(0, 3, 4).astype(dt)
b = np.linspace(1, 2, 2).astype(dt)
y_r = np.array([0, 2, 5, 8, 3]).astype(dt)
return a, b, y_r
def equal_tolerance(self, res_dt):
# default value of keyword
decimal = 6
try:
dt_info = np.finfo(res_dt)
if hasattr(dt_info, 'resolution'):
decimal = int(-0.5*np.log10(dt_info.resolution))
except Exception:
pass
return decimal
def equal_tolerance_fft(self, res_dt):
# FFT implementations convert longdouble arguments down to
# double so don't expect better precision, see gh-9520
if res_dt == np.longdouble:
return self.equal_tolerance(np.float64)
else:
return self.equal_tolerance(res_dt)
def test_method(self, dt):
if dt == Decimal:
method = choose_conv_method([Decimal(4)], [Decimal(3)])
assert_equal(method, 'direct')
else:
a, b, y_r = self._setup_rank3(dt)
y_fft = correlate(a, b, method='fft')
y_direct = correlate(a, b, method='direct')
assert_array_almost_equal(y_r,
y_fft,
decimal=self.equal_tolerance_fft(y_fft.dtype),)
assert_array_almost_equal(y_r,
y_direct,
decimal=self.equal_tolerance(y_direct.dtype),)
assert_equal(y_fft.dtype, dt)
assert_equal(y_direct.dtype, dt)
def test_rank1_valid(self, dt):
a, b, y_r = self._setup_rank1(dt)
y = correlate(a, b, 'valid')
assert_array_almost_equal(y, y_r[1:4])
assert_equal(y.dtype, dt)
# See gh-5897
y = correlate(b, a, 'valid')
assert_array_almost_equal(y, y_r[1:4][::-1])
assert_equal(y.dtype, dt)
def test_rank1_same(self, dt):
a, b, y_r = self._setup_rank1(dt)
y = correlate(a, b, 'same')
assert_array_almost_equal(y, y_r[:-1])
assert_equal(y.dtype, dt)
def test_rank1_full(self, dt):
a, b, y_r = self._setup_rank1(dt)
y = correlate(a, b, 'full')
assert_array_almost_equal(y, y_r)
assert_equal(y.dtype, dt)
def _setup_rank3(self, dt):
a = np.linspace(0, 39, 40).reshape((2, 4, 5), order='F').astype(
dt)
b = np.linspace(0, 23, 24).reshape((2, 3, 4), order='F').astype(
dt)
y_r = array([[[0., 184., 504., 912., 1360., 888., 472., 160.],
[46., 432., 1062., 1840., 2672., 1698., 864., 266.],
[134., 736., 1662., 2768., 3920., 2418., 1168., 314.],
[260., 952., 1932., 3056., 4208., 2580., 1240., 332.],
[202., 664., 1290., 1984., 2688., 1590., 712., 150.],
[114., 344., 642., 960., 1280., 726., 296., 38.]],
[[23., 400., 1035., 1832., 2696., 1737., 904., 293.],
[134., 920., 2166., 3680., 5280., 3306., 1640., 474.],
[325., 1544., 3369., 5512., 7720., 4683., 2192., 535.],
[571., 1964., 3891., 6064., 8272., 4989., 2324., 565.],
[434., 1360., 2586., 3920., 5264., 3054., 1312., 230.],
[241., 700., 1281., 1888., 2496., 1383., 532., 39.]],
[[22., 214., 528., 916., 1332., 846., 430., 132.],
[86., 484., 1098., 1832., 2600., 1602., 772., 206.],
[188., 802., 1698., 2732., 3788., 2256., 1018., 218.],
[308., 1006., 1950., 2996., 4052., 2400., 1078., 230.],
[230., 692., 1290., 1928., 2568., 1458., 596., 78.],
[126., 354., 636., 924., 1212., 654., 234., 0.]]],
dtype=np.float64).astype(dt)
return a, b, y_r
def test_rank3_valid(self, dt):
a, b, y_r = self._setup_rank3(dt)
y = correlate(a, b, "valid")
assert_array_almost_equal(y, y_r[1:2, 2:4, 3:5])
assert_equal(y.dtype, dt)
# See gh-5897
y = correlate(b, a, "valid")
assert_array_almost_equal(y, y_r[1:2, 2:4, 3:5][::-1, ::-1, ::-1])
assert_equal(y.dtype, dt)
def test_rank3_same(self, dt):
a, b, y_r = self._setup_rank3(dt)
y = correlate(a, b, "same")
assert_array_almost_equal(y, y_r[0:-1, 1:-1, 1:-2])
assert_equal(y.dtype, dt)
def test_rank3_all(self, dt):
a, b, y_r = self._setup_rank3(dt)
y = correlate(a, b)
assert_array_almost_equal(y, y_r)
assert_equal(y.dtype, dt)
class TestCorrelate:
# Tests that don't depend on dtype
def test_invalid_shapes(self):
# By "invalid," we mean that no one
# array has dimensions that are all at
# least as large as the corresponding
# dimensions of the other array. This
# setup should throw a ValueError.
a = np.arange(1, 7).reshape((2, 3))
b = np.arange(-6, 0).reshape((3, 2))
assert_raises(ValueError, correlate, *(a, b), **{'mode': 'valid'})
assert_raises(ValueError, correlate, *(b, a), **{'mode': 'valid'})
def test_invalid_params(self):
a = [3, 4, 5]
b = [1, 2, 3]
assert_raises(ValueError, correlate, a, b, mode='spam')
assert_raises(ValueError, correlate, a, b, mode='eggs', method='fft')
assert_raises(ValueError, correlate, a, b, mode='ham', method='direct')
assert_raises(ValueError, correlate, a, b, mode='full', method='bacon')
assert_raises(ValueError, correlate, a, b, mode='same', method='bacon')
def test_mismatched_dims(self):
# Input arrays should have the same number of dimensions
assert_raises(ValueError, correlate, [1], 2, method='direct')
assert_raises(ValueError, correlate, 1, [2], method='direct')
assert_raises(ValueError, correlate, [1], 2, method='fft')
assert_raises(ValueError, correlate, 1, [2], method='fft')
assert_raises(ValueError, correlate, [1], [[2]])
assert_raises(ValueError, correlate, [3], 2)
def test_numpy_fastpath(self):
a = [1, 2, 3]
b = [4, 5]
assert_allclose(correlate(a, b, mode='same'), [5, 14, 23])
a = [1, 2, 3]
b = [4, 5, 6]
assert_allclose(correlate(a, b, mode='same'), [17, 32, 23])
assert_allclose(correlate(a, b, mode='full'), [6, 17, 32, 23, 12])
assert_allclose(correlate(a, b, mode='valid'), [32])
@pytest.mark.thread_unsafe
def test_dtype_deprecation(self):
# gh-21211
a = np.asarray([1, 2, 3, 6, 5, 3], dtype=object)
b = np.asarray([2, 3, 4, 5, 3, 4, 2, 2, 1], dtype=object)
with pytest.deprecated_call(match="dtype=object is not supported"):
correlate(a, b)
@pytest.mark.parametrize("mode", ["valid", "same", "full"])
@pytest.mark.parametrize("behind", [True, False])
@pytest.mark.parametrize("input_size", [100, 101, 1000, 1001, 10000, 10001])
def test_correlation_lags(mode, behind, input_size):
# generate random data
rng = np.random.RandomState(0)
in1 = rng.standard_normal(input_size)
offset = int(input_size/10)
# generate offset version of array to correlate with
if behind:
# y is behind x
in2 = np.concatenate([rng.standard_normal(offset), in1])
expected = -offset
else:
# y is ahead of x
in2 = in1[offset:]
expected = offset
# cross correlate, returning lag information
correlation = correlate(in1, in2, mode=mode)
lags = correlation_lags(in1.size, in2.size, mode=mode)
# identify the peak
lag_index = np.argmax(correlation)
# Check as expected
assert_equal(lags[lag_index], expected)
# Correlation and lags shape should match
assert_equal(lags.shape, correlation.shape)
def test_correlation_lags_invalid_mode():
with pytest.raises(ValueError, match="Mode asdfgh is invalid"):
correlation_lags(100, 100, mode="asdfgh")
@pytest.mark.parametrize('dt', [np.csingle, np.cdouble,
pytest.param(np.clongdouble, marks=_pmf)])
class TestCorrelateComplex:
# The decimal precision to be used for comparing results.
# This value will be passed as the 'decimal' keyword argument of
# assert_array_almost_equal().
# Since correlate may chose to use FFT method which converts
# longdoubles to doubles internally don't expect better precision
# for longdouble than for double (see gh-9520).
def decimal(self, dt):
if dt == np.clongdouble:
dt = np.cdouble
return int(2 * np.finfo(dt).precision / 3)
def _setup_rank1(self, dt, mode):
np.random.seed(9)
a = np.random.randn(10).astype(dt)
a += 1j * np.random.randn(10).astype(dt)
b = np.random.randn(8).astype(dt)
b += 1j * np.random.randn(8).astype(dt)
y_r = (correlate(a.real, b.real, mode=mode) +
correlate(a.imag, b.imag, mode=mode)).astype(dt)
y_r += 1j * (-correlate(a.real, b.imag, mode=mode) +
correlate(a.imag, b.real, mode=mode))
return a, b, y_r
def test_rank1_valid(self, dt):
a, b, y_r = self._setup_rank1(dt, 'valid')
y = correlate(a, b, 'valid')
assert_array_almost_equal(y, y_r, decimal=self.decimal(dt))
assert_equal(y.dtype, dt)
# See gh-5897
y = correlate(b, a, 'valid')
assert_array_almost_equal(y, y_r[::-1].conj(), decimal=self.decimal(dt))
assert_equal(y.dtype, dt)
def test_rank1_same(self, dt):
a, b, y_r = self._setup_rank1(dt, 'same')
y = correlate(a, b, 'same')
assert_array_almost_equal(y, y_r, decimal=self.decimal(dt))
assert_equal(y.dtype, dt)
def test_rank1_full(self, dt):
a, b, y_r = self._setup_rank1(dt, 'full')
y = correlate(a, b, 'full')
assert_array_almost_equal(y, y_r, decimal=self.decimal(dt))
assert_equal(y.dtype, dt)
def test_swap_full(self, dt):
d = np.array([0.+0.j, 1.+1.j, 2.+2.j], dtype=dt)
k = np.array([1.+3.j, 2.+4.j, 3.+5.j, 4.+6.j], dtype=dt)
y = correlate(d, k)
assert_equal(y, [0.+0.j, 10.-2.j, 28.-6.j, 22.-6.j, 16.-6.j, 8.-4.j])
def test_swap_same(self, dt):
d = [0.+0.j, 1.+1.j, 2.+2.j]
k = [1.+3.j, 2.+4.j, 3.+5.j, 4.+6.j]
y = correlate(d, k, mode="same")
assert_equal(y, [10.-2.j, 28.-6.j, 22.-6.j])
def test_rank3(self, dt):
a = np.random.randn(10, 8, 6).astype(dt)
a += 1j * np.random.randn(10, 8, 6).astype(dt)
b = np.random.randn(8, 6, 4).astype(dt)
b += 1j * np.random.randn(8, 6, 4).astype(dt)
y_r = (correlate(a.real, b.real)
+ correlate(a.imag, b.imag)).astype(dt)
y_r += 1j * (-correlate(a.real, b.imag) + correlate(a.imag, b.real))
y = correlate(a, b, 'full')
assert_array_almost_equal(y, y_r, decimal=self.decimal(dt) - 1)
assert_equal(y.dtype, dt)
def test_rank0(self, dt):
a = np.array(np.random.randn()).astype(dt)
a += 1j * np.array(np.random.randn()).astype(dt)
b = np.array(np.random.randn()).astype(dt)
b += 1j * np.array(np.random.randn()).astype(dt)
y_r = (correlate(a.real, b.real)
+ correlate(a.imag, b.imag)).astype(dt)
y_r += 1j * np.array(-correlate(a.real, b.imag) +
correlate(a.imag, b.real))
y = correlate(a, b, 'full')
assert_array_almost_equal(y, y_r, decimal=self.decimal(dt) - 1)
assert_equal(y.dtype, dt)
assert_equal(correlate([1], [2j]), correlate(1, 2j))
assert_equal(correlate([2j], [3j]), correlate(2j, 3j))
assert_equal(correlate([3j], [4]), correlate(3j, 4))
class TestCorrelate2d:
def test_consistency_correlate_funcs(self):
# Compare np.correlate, signal.correlate, signal.correlate2d
a = np.arange(5)
b = np.array([3.2, 1.4, 3])
for mode in ['full', 'valid', 'same']:
assert_almost_equal(np.correlate(a, b, mode=mode),
signal.correlate(a, b, mode=mode))
assert_almost_equal(np.squeeze(signal.correlate2d([a], [b],
mode=mode)),
signal.correlate(a, b, mode=mode))
# See gh-5897
if mode == 'valid':
assert_almost_equal(np.correlate(b, a, mode=mode),
signal.correlate(b, a, mode=mode))
assert_almost_equal(np.squeeze(signal.correlate2d([b], [a],
mode=mode)),
signal.correlate(b, a, mode=mode))
def test_invalid_shapes(self):
# By "invalid," we mean that no one
# array has dimensions that are all at
# least as large as the corresponding
# dimensions of the other array. This
# setup should throw a ValueError.
a = np.arange(1, 7).reshape((2, 3))
b = np.arange(-6, 0).reshape((3, 2))
assert_raises(ValueError, signal.correlate2d, *(a, b), **{'mode': 'valid'})
assert_raises(ValueError, signal.correlate2d, *(b, a), **{'mode': 'valid'})
def test_complex_input(self):
assert_equal(signal.correlate2d([[1]], [[2j]]), -2j)
assert_equal(signal.correlate2d([[2j]], [[3j]]), 6)
assert_equal(signal.correlate2d([[3j]], [[4]]), 12j)
class TestLFilterZI:
def test_basic(self):
a = np.array([1.0, -1.0, 0.5])
b = np.array([1.0, 0.0, 2.0])
zi_expected = np.array([5.0, -1.0])
zi = lfilter_zi(b, a)
assert_array_almost_equal(zi, zi_expected)
def test_scale_invariance(self):
# Regression test. There was a bug in which b was not correctly
# rescaled when a[0] was nonzero.
b = np.array([2, 8, 5])
a = np.array([1, 1, 8])
zi1 = lfilter_zi(b, a)
zi2 = lfilter_zi(2*b, 2*a)
assert_allclose(zi2, zi1, rtol=1e-12)
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
def test_types(self, dtype):
b = np.zeros((8), dtype=dtype)
a = np.array([1], dtype=dtype)
assert_equal(np.real(signal.lfilter_zi(b, a)).dtype, dtype)
class TestFiltFilt:
filtfilt_kind = 'tf'
def filtfilt(self, zpk, x, axis=-1, padtype='odd', padlen=None,
method='pad', irlen=None):
if self.filtfilt_kind == 'tf':
b, a = zpk2tf(*zpk)
return filtfilt(b, a, x, axis, padtype, padlen, method, irlen)
elif self.filtfilt_kind == 'sos':
sos = zpk2sos(*zpk)
return sosfiltfilt(sos, x, axis, padtype, padlen)
def test_basic(self):
zpk = tf2zpk([1, 2, 3], [1, 2, 3])
out = self.filtfilt(zpk, np.arange(12))
assert_allclose(out, arange(12), atol=5.28e-11)
def test_sine(self):
rate = 2000
t = np.linspace(0, 1.0, rate + 1)
# A signal with low frequency and a high frequency.
xlow = np.sin(5 * 2 * np.pi * t)
xhigh = np.sin(250 * 2 * np.pi * t)
x = xlow + xhigh
zpk = butter(8, 0.125, output='zpk')
# r is the magnitude of the largest pole.
r = np.abs(zpk[1]).max()
eps = 1e-5
# n estimates the number of steps for the
# transient to decay by a factor of eps.
n = int(np.ceil(np.log(eps) / np.log(r)))
# High order lowpass filter...
y = self.filtfilt(zpk, x, padlen=n)
# Result should be just xlow.
err = np.abs(y - xlow).max()
assert_(err < 1e-4)
# A 2D case.
x2d = np.vstack([xlow, xlow + xhigh])
y2d = self.filtfilt(zpk, x2d, padlen=n, axis=1)
assert_equal(y2d.shape, x2d.shape)
err = np.abs(y2d - xlow).max()
assert_(err < 1e-4)
# Use the previous result to check the use of the axis keyword.
# (Regression test for ticket #1620)
y2dt = self.filtfilt(zpk, x2d.T, padlen=n, axis=0)
assert_equal(y2d, y2dt.T)
def test_axis(self):
# Test the 'axis' keyword on a 3D array.
x = np.arange(10.0 * 11.0 * 12.0).reshape(10, 11, 12)
zpk = butter(3, 0.125, output='zpk')
y0 = self.filtfilt(zpk, x, padlen=0, axis=0)
y1 = self.filtfilt(zpk, np.swapaxes(x, 0, 1), padlen=0, axis=1)
assert_array_equal(y0, np.swapaxes(y1, 0, 1))
y2 = self.filtfilt(zpk, np.swapaxes(x, 0, 2), padlen=0, axis=2)
assert_array_equal(y0, np.swapaxes(y2, 0, 2))
def test_acoeff(self):
if self.filtfilt_kind != 'tf':
return # only necessary for TF
# test for 'a' coefficient as single number
out = signal.filtfilt([.5, .5], 1, np.arange(10))
assert_allclose(out, np.arange(10), rtol=1e-14, atol=1e-14)
def test_gust_simple(self):
if self.filtfilt_kind != 'tf':
pytest.skip('gust only implemented for TF systems')
# The input array has length 2. The exact solution for this case
# was computed "by hand".
x = np.array([1.0, 2.0])
b = np.array([0.5])
a = np.array([1.0, -0.5])
y, z1, z2 = _filtfilt_gust(b, a, x)
assert_allclose([z1[0], z2[0]],
[0.3*x[0] + 0.2*x[1], 0.2*x[0] + 0.3*x[1]])
assert_allclose(y, [z1[0] + 0.25*z2[0] + 0.25*x[0] + 0.125*x[1],
0.25*z1[0] + z2[0] + 0.125*x[0] + 0.25*x[1]])
def test_gust_scalars(self):
if self.filtfilt_kind != 'tf':
pytest.skip('gust only implemented for TF systems')
# The filter coefficients are both scalars, so the filter simply
# multiplies its input by b/a. When it is used in filtfilt, the
# factor is (b/a)**2.
x = np.arange(12)
b = 3.0
a = 2.0
y = filtfilt(b, a, x, method="gust")
expected = (b/a)**2 * x
assert_allclose(y, expected)
class TestSOSFiltFilt(TestFiltFilt):
filtfilt_kind = 'sos'
def test_equivalence(self):
"""Test equivalence between sosfiltfilt and filtfilt"""
x = np.random.RandomState(0).randn(1000)
for order in range(1, 6):
zpk = signal.butter(order, 0.35, output='zpk')
b, a = zpk2tf(*zpk)
sos = zpk2sos(*zpk)
y = filtfilt(b, a, x)
y_sos = sosfiltfilt(sos, x)
assert_allclose(y, y_sos, atol=1e-12, err_msg=f'order={order}')
def filtfilt_gust_opt(b, a, x):
"""
An alternative implementation of filtfilt with Gustafsson edges.
This function computes the same result as
`scipy.signal._signaltools._filtfilt_gust`, but only 1-d arrays
are accepted. The problem is solved using `fmin` from `scipy.optimize`.
`_filtfilt_gust` is significantly faster than this implementation.
"""
def filtfilt_gust_opt_func(ics, b, a, x):
"""Objective function used in filtfilt_gust_opt."""
m = max(len(a), len(b)) - 1
z0f = ics[:m]
z0b = ics[m:]
y_f = lfilter(b, a, x, zi=z0f)[0]
y_fb = lfilter(b, a, y_f[::-1], zi=z0b)[0][::-1]
y_b = lfilter(b, a, x[::-1], zi=z0b)[0][::-1]
y_bf = lfilter(b, a, y_b, zi=z0f)[0]
value = np.sum((y_fb - y_bf)**2)
return value
m = max(len(a), len(b)) - 1
zi = lfilter_zi(b, a)
ics = np.concatenate((x[:m].mean()*zi, x[-m:].mean()*zi))
result = fmin(filtfilt_gust_opt_func, ics, args=(b, a, x),
xtol=1e-10, ftol=1e-12,
maxfun=10000, maxiter=10000,
full_output=True, disp=False)
opt, fopt, niter, funcalls, warnflag = result
if warnflag > 0:
raise RuntimeError("minimization failed in filtfilt_gust_opt: "
"warnflag=%d" % warnflag)
z0f = opt[:m]
z0b = opt[m:]
# Apply the forward-backward filter using the computed initial
# conditions.
y_b = lfilter(b, a, x[::-1], zi=z0b)[0][::-1]
y = lfilter(b, a, y_b, zi=z0f)[0]
return y, z0f, z0b
def check_filtfilt_gust(b, a, shape, axis, irlen=None):
# Generate x, the data to be filtered.
np.random.seed(123)
x = np.random.randn(*shape)
# Apply filtfilt to x. This is the main calculation to be checked.
y = filtfilt(b, a, x, axis=axis, method="gust", irlen=irlen)
# Also call the private function so we can test the ICs.
yg, zg1, zg2 = _filtfilt_gust(b, a, x, axis=axis, irlen=irlen)
# filtfilt_gust_opt is an independent implementation that gives the
# expected result, but it only handles 1-D arrays, so use some looping
# and reshaping shenanigans to create the expected output arrays.
xx = np.swapaxes(x, axis, -1)
out_shape = xx.shape[:-1]
yo = np.empty_like(xx)
m = max(len(a), len(b)) - 1
zo1 = np.empty(out_shape + (m,))
zo2 = np.empty(out_shape + (m,))
for indx in product(*[range(d) for d in out_shape]):
yo[indx], zo1[indx], zo2[indx] = filtfilt_gust_opt(b, a, xx[indx])
yo = np.swapaxes(yo, -1, axis)
zo1 = np.swapaxes(zo1, -1, axis)
zo2 = np.swapaxes(zo2, -1, axis)
assert_allclose(y, yo, rtol=1e-8, atol=1e-9)
assert_allclose(yg, yo, rtol=1e-8, atol=1e-9)
assert_allclose(zg1, zo1, rtol=1e-8, atol=1e-9)
assert_allclose(zg2, zo2, rtol=1e-8, atol=1e-9)
@pytest.mark.fail_slow(10)
def test_choose_conv_method():
for mode in ['valid', 'same', 'full']:
for ndim in [1, 2]:
n, k, true_method = 8, 6, 'direct'
x = np.random.randn(*((n,) * ndim))
h = np.random.randn(*((k,) * ndim))
method = choose_conv_method(x, h, mode=mode)
assert_equal(method, true_method)
method_try, times = choose_conv_method(x, h, mode=mode, measure=True)
assert_(method_try in {'fft', 'direct'})
assert_(isinstance(times, dict))
assert_('fft' in times.keys() and 'direct' in times.keys())
x = np.array([2**51], dtype=np.int64)
h = x.copy()
assert_equal(choose_conv_method(x, h, mode=mode), 'direct')
@pytest.mark.thread_unsafe
def test_choose_conv_dtype_deprecation():
# gh-21211
a = np.asarray([1, 2, 3, 6, 5, 3], dtype=object)
b = np.asarray([2, 3, 4, 5, 3, 4, 2, 2, 1], dtype=object)
with pytest.deprecated_call(match="dtype=object is not supported"):
choose_conv_method(a, b)
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
def test_choose_conv_method_2():
for mode in ['valid', 'same', 'full']:
x = [Decimal(3), Decimal(2)]
h = [Decimal(1), Decimal(4)]
assert_equal(choose_conv_method(x, h, mode=mode), 'direct')
n = 10
for not_fft_conv_supp in ["complex256", "complex192"]:
if hasattr(np, not_fft_conv_supp):
x = np.ones(n, dtype=not_fft_conv_supp)
h = x.copy()
assert_equal(choose_conv_method(x, h, mode=mode), 'direct')
@pytest.mark.fail_slow(10)
def test_filtfilt_gust():
# Design a filter.
z, p, k = signal.ellip(3, 0.01, 120, 0.0875, output='zpk')
# Find the approximate impulse response length of the filter.
eps = 1e-10
r = np.max(np.abs(p))
approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
np.random.seed(123)
b, a = zpk2tf(z, p, k)
for irlen in [None, approx_impulse_len]:
signal_len = 5 * approx_impulse_len
# 1-d test case
check_filtfilt_gust(b, a, (signal_len,), 0, irlen)
# 3-d test case; test each axis.
for axis in range(3):
shape = [2, 2, 2]
shape[axis] = signal_len
check_filtfilt_gust(b, a, shape, axis, irlen)
# Test case with length less than 2*approx_impulse_len.
# In this case, `filtfilt_gust` should behave the same as if
# `irlen=None` was given.
length = 2*approx_impulse_len - 50
check_filtfilt_gust(b, a, (length,), 0, approx_impulse_len)
class TestDecimate:
def test_bad_args(self):
x = np.arange(12)
assert_raises(TypeError, signal.decimate, x, q=0.5, n=1)
assert_raises(TypeError, signal.decimate, x, q=2, n=0.5)
def test_basic_IIR(self):
x = np.arange(12)
y = signal.decimate(x, 2, n=1, ftype='iir', zero_phase=False).round()
assert_array_equal(y, x[::2])
def test_basic_FIR(self):
x = np.arange(12)
y = signal.decimate(x, 2, n=1, ftype='fir', zero_phase=False).round()
assert_array_equal(y, x[::2])
def test_shape(self):
# Regression test for ticket #1480.
z = np.zeros((30, 30))
d0 = signal.decimate(z, 2, axis=0, zero_phase=False)
assert_equal(d0.shape, (15, 30))
d1 = signal.decimate(z, 2, axis=1, zero_phase=False)
assert_equal(d1.shape, (30, 15))
def test_phaseshift_FIR(self):
with suppress_warnings() as sup:
sup.filter(BadCoefficients, "Badly conditioned filter")
self._test_phaseshift(method='fir', zero_phase=False)
def test_zero_phase_FIR(self):
with suppress_warnings() as sup:
sup.filter(BadCoefficients, "Badly conditioned filter")
self._test_phaseshift(method='fir', zero_phase=True)
def test_phaseshift_IIR(self):
self._test_phaseshift(method='iir', zero_phase=False)
def test_zero_phase_IIR(self):
self._test_phaseshift(method='iir', zero_phase=True)
def _test_phaseshift(self, method, zero_phase):
rate = 120
rates_to = [15, 20, 30, 40] # q = 8, 6, 4, 3
t_tot = 100 # Need to let antialiasing filters settle
t = np.arange(rate*t_tot+1) / float(rate)
# Sinusoids at 0.8*nyquist, windowed to avoid edge artifacts
freqs = np.array(rates_to) * 0.8 / 2
d = (np.exp(1j * 2 * np.pi * freqs[:, np.newaxis] * t)
* signal.windows.tukey(t.size, 0.1))
for rate_to in rates_to:
q = rate // rate_to
t_to = np.arange(rate_to*t_tot+1) / float(rate_to)
d_tos = (np.exp(1j * 2 * np.pi * freqs[:, np.newaxis] * t_to)
* signal.windows.tukey(t_to.size, 0.1))
# Set up downsampling filters, match v0.17 defaults
if method == 'fir':
n = 30
system = signal.dlti(signal.firwin(n + 1, 1. / q,
window='hamming'), 1.)
elif method == 'iir':
n = 8
wc = 0.8*np.pi/q
system = signal.dlti(*signal.cheby1(n, 0.05, wc/np.pi))
# Calculate expected phase response, as unit complex vector
if zero_phase is False:
_, h_resps = signal.freqz(system.num, system.den,
freqs/rate*2*np.pi)
h_resps /= np.abs(h_resps)
else:
h_resps = np.ones_like(freqs)
y_resamps = signal.decimate(d.real, q, n, ftype=system,
zero_phase=zero_phase)
# Get phase from complex inner product, like CSD
h_resamps = np.sum(d_tos.conj() * y_resamps, axis=-1)
h_resamps /= np.abs(h_resamps)
subnyq = freqs < 0.5*rate_to
# Complex vectors should be aligned, only compare below nyquist
assert_allclose(np.angle(h_resps.conj()*h_resamps)[subnyq], 0,
atol=1e-3, rtol=1e-3)
def test_auto_n(self):
# Test that our value of n is a reasonable choice (depends on
# the downsampling factor)
sfreq = 100.
n = 1000
t = np.arange(n) / sfreq
# will alias for decimations (>= 15)
x = np.sqrt(2. / n) * np.sin(2 * np.pi * (sfreq / 30.) * t)
assert_allclose(np.linalg.norm(x), 1., rtol=1e-3)
x_out = signal.decimate(x, 30, ftype='fir')
assert_array_less(np.linalg.norm(x_out), 0.01)
def test_long_float32(self):
# regression: gh-15072. With 32-bit float and either lfilter
# or filtfilt, this is numerically unstable
x = signal.decimate(np.ones(10_000, dtype=np.float32), 10)
assert not any(np.isnan(x))
def test_float16_upcast(self):
# float16 must be upcast to float64
x = signal.decimate(np.ones(100, dtype=np.float16), 10)
assert x.dtype.type == np.float64
def test_complex_iir_dlti(self):
# regression: gh-17845
# centre frequency for filter [Hz]
fcentre = 50
# filter passband width [Hz]
fwidth = 5
# sample rate [Hz]
fs = 1e3
z, p, k = signal.butter(2, 2*np.pi*fwidth/2, output='zpk', fs=fs)
z = z.astype(complex) * np.exp(2j * np.pi * fcentre/fs)
p = p.astype(complex) * np.exp(2j * np.pi * fcentre/fs)
system = signal.dlti(z, p, k)
t = np.arange(200) / fs
# input
u = (np.exp(2j * np.pi * fcentre * t)
+ 0.5 * np.exp(-2j * np.pi * fcentre * t))
ynzp = signal.decimate(u, 2, ftype=system, zero_phase=False)
ynzpref = signal.lfilter(*signal.zpk2tf(z, p, k),
u)[::2]
assert_equal(ynzp, ynzpref)
yzp = signal.decimate(u, 2, ftype=system, zero_phase=True)
yzpref = signal.filtfilt(*signal.zpk2tf(z, p, k),
u)[::2]
assert_allclose(yzp, yzpref, rtol=1e-10, atol=1e-13)
def test_complex_fir_dlti(self):
# centre frequency for filter [Hz]
fcentre = 50
# filter passband width [Hz]
fwidth = 5
# sample rate [Hz]
fs = 1e3
numtaps = 20
# FIR filter about 0Hz
bbase = signal.firwin(numtaps, fwidth/2, fs=fs)
# rotate these to desired frequency
zbase = np.roots(bbase)
zrot = zbase * np.exp(2j * np.pi * fcentre/fs)
# FIR filter about 50Hz, maintaining passband gain of 0dB
bz = bbase[0] * np.poly(zrot)
system = signal.dlti(bz, 1)
t = np.arange(200) / fs
# input
u = (np.exp(2j * np.pi * fcentre * t)
+ 0.5 * np.exp(-2j * np.pi * fcentre * t))
ynzp = signal.decimate(u, 2, ftype=system, zero_phase=False)
ynzpref = signal.upfirdn(bz, u, up=1, down=2)[:100]
assert_equal(ynzp, ynzpref)
yzp = signal.decimate(u, 2, ftype=system, zero_phase=True)
yzpref = signal.resample_poly(u, 1, 2, window=bz)
assert_equal(yzp, yzpref)
class TestHilbert:
def test_bad_args(self):
x = np.array([1.0 + 0.0j])
assert_raises(ValueError, hilbert, x)
x = np.arange(8.0)
assert_raises(ValueError, hilbert, x, N=0)
def test_hilbert_theoretical(self):
# test cases by Ariel Rokem
decimal = 14
pi = np.pi
t = np.arange(0, 2 * pi, pi / 256)
a0 = np.sin(t)
a1 = np.cos(t)
a2 = np.sin(2 * t)
a3 = np.cos(2 * t)
a = np.vstack([a0, a1, a2, a3])
h = hilbert(a)
h_abs = np.abs(h)
h_angle = np.angle(h)
h_real = np.real(h)
# The real part should be equal to the original signals:
assert_almost_equal(h_real, a, decimal)
# The absolute value should be one everywhere, for this input:
assert_almost_equal(h_abs, np.ones(a.shape), decimal)
# For the 'slow' sine - the phase should go from -pi/2 to pi/2 in
# the first 256 bins:
assert_almost_equal(h_angle[0, :256],
np.arange(-pi / 2, pi / 2, pi / 256),
decimal)
# For the 'slow' cosine - the phase should go from 0 to pi in the
# same interval:
assert_almost_equal(
h_angle[1, :256], np.arange(0, pi, pi / 256), decimal)
# The 'fast' sine should make this phase transition in half the time:
assert_almost_equal(h_angle[2, :128],
np.arange(-pi / 2, pi / 2, pi / 128),
decimal)
# Ditto for the 'fast' cosine:
assert_almost_equal(
h_angle[3, :128], np.arange(0, pi, pi / 128), decimal)
# The imaginary part of hilbert(cos(t)) = sin(t) Wikipedia
assert_almost_equal(h[1].imag, a0, decimal)
def test_hilbert_axisN(self):
# tests for axis and N arguments
a = np.arange(18).reshape(3, 6)
# test axis
aa = hilbert(a, axis=-1)
assert_equal(hilbert(a.T, axis=0), aa.T)
# test 1d
assert_almost_equal(hilbert(a[0]), aa[0], 14)
# test N
aan = hilbert(a, N=20, axis=-1)
assert_equal(aan.shape, [3, 20])
assert_equal(hilbert(a.T, N=20, axis=0).shape, [20, 3])
# the next test is just a regression test,
# no idea whether numbers make sense
a0hilb = np.array([0.000000000000000e+00 - 1.72015830311905j,
1.000000000000000e+00 - 2.047794505137069j,
1.999999999999999e+00 - 2.244055555687583j,
3.000000000000000e+00 - 1.262750302935009j,
4.000000000000000e+00 - 1.066489252384493j,
5.000000000000000e+00 + 2.918022706971047j,
8.881784197001253e-17 + 3.845658908989067j,
-9.444121133484362e-17 + 0.985044202202061j,
-1.776356839400251e-16 + 1.332257797702019j,
-3.996802888650564e-16 + 0.501905089898885j,
1.332267629550188e-16 + 0.668696078880782j,
-1.192678053963799e-16 + 0.235487067862679j,
-1.776356839400251e-16 + 0.286439612812121j,
3.108624468950438e-16 + 0.031676888064907j,
1.332267629550188e-16 - 0.019275656884536j,
-2.360035624836702e-16 - 0.1652588660287j,
0.000000000000000e+00 - 0.332049855010597j,
3.552713678800501e-16 - 0.403810179797771j,
8.881784197001253e-17 - 0.751023775297729j,
9.444121133484362e-17 - 0.79252210110103j])
assert_almost_equal(aan[0], a0hilb, 14, 'N regression')
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
def test_hilbert_types(self, dtype):
in_typed = np.zeros(8, dtype=dtype)
assert_equal(np.real(signal.hilbert(in_typed)).dtype, dtype)
class TestHilbert2:
def test_bad_args(self):
# x must be real.
x = np.array([[1.0 + 0.0j]])
assert_raises(ValueError, hilbert2, x)
# x must be rank 2.
x = np.arange(24).reshape(2, 3, 4)
assert_raises(ValueError, hilbert2, x)
# Bad value for N.
x = np.arange(16).reshape(4, 4)
assert_raises(ValueError, hilbert2, x, N=0)
assert_raises(ValueError, hilbert2, x, N=(2, 0))
assert_raises(ValueError, hilbert2, x, N=(2,))
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
def test_hilbert2_types(self, dtype):
in_typed = np.zeros((2, 32), dtype=dtype)
assert_equal(np.real(signal.hilbert2(in_typed)).dtype, dtype)
class TestEnvelope:
"""Unit tests for function `._signaltools.envelope()`. """
@staticmethod
def assert_close(actual, desired, msg):
"""Little helper to compare to arrays with proper tolerances"""
xp_assert_close(actual, desired, atol=1e-12, rtol=1e-12, err_msg=msg)
def test_envelope_invalid_parameters(self):
"""For `envelope()` Raise all exceptions that are used to verify function
parameters. """
with pytest.raises(ValueError,
match=r"Invalid parameter axis=2 for z.shape=.*"):
envelope(np.ones(3), axis=2)
with pytest.raises(ValueError,
match=r"z.shape\[axis\] not > 0 for z.shape=.*"):
envelope(np.ones((3, 0)), axis=1)
for bp_in in [(0, 1, 2), (0, 2.), (None, 2.)]:
ts = ', '.join(map(str, bp_in))
with pytest.raises(ValueError,
match=rf"bp_in=\({ts}\) isn't a 2-tuple of.*"):
# noinspection PyTypeChecker
envelope(np.ones(4), bp_in=bp_in)
with pytest.raises(ValueError,
match="n_out=10.0 is not a positive integer or.*"):
# noinspection PyTypeChecker
envelope(np.ones(4), n_out=10.)
for bp_in in [(-1, 3), (1, 1), (0, 10)]:
with pytest.raises(ValueError,
match=r"`-n//2 <= bp_in\[0\] < bp_in\[1\] <=.*"):
envelope(np.ones(4), bp_in=bp_in)
with pytest.raises(ValueError, match="residual='undefined' not in .*"):
# noinspection PyTypeChecker
envelope(np.ones(4), residual='undefined')
def test_envelope_verify_parameters(self):
"""Ensure that the various parametrizations produce compatible results. """
Z, Zr_a = [4, 2, 2, 3, 0], [4, 0, 0, 6, 0, 0, 0, 0]
z = sp_fft.irfft(Z)
n = len(z)
# the reference envelope:
ze2_0, zr_0 = envelope(z, (1, 3), residual='all', squared=True)
self.assert_close(sp_fft.rfft(ze2_0), np.array([4, 2, 0, 0, 0]).astype(complex),
msg="Envelope calculation error")
self.assert_close(sp_fft.rfft(zr_0), np.array([4, 0, 0, 3, 0]).astype(complex),
msg="Residual calculation error")
ze_1, zr_1 = envelope(z, (1, 3), residual='all', squared=False)
self.assert_close(ze_1**2, ze2_0,
msg="Unsquared versus Squared envelope calculation error")
self.assert_close(zr_1, zr_0,
msg="Unsquared versus Squared residual calculation error")
ze2_2, zr_2 = envelope(z, (1, 3), residual='all', squared=True, n_out=3*n)
self.assert_close(ze2_2[::3], ze2_0,
msg="3x up-sampled envelope calculation error")
self.assert_close(zr_2[::3], zr_0,
msg="3x up-sampled residual calculation error")
ze2_3, zr_3 = envelope(z, (1, 3), residual='lowpass', squared=True)
self.assert_close(ze2_3, ze2_0,
msg="`residual='lowpass'` envelope calculation error")
self.assert_close(sp_fft.rfft(zr_3), np.array([4, 0, 0, 0, 0]).astype(complex),
msg="`residual='lowpass'` residual calculation error")
ze2_4 = envelope(z, (1, 3), residual=None, squared=True)
self.assert_close(ze2_4, ze2_0,
msg="`residual=None` envelope calculation error")
# compare complex analytic signal to real version
Z_a = np.copy(Z)
Z_a[1:] *= 2
z_a = sp_fft.ifft(Z_a, n=n) # analytic signal of Z
self.assert_close(z_a.real, z,
msg="Reference analytic signal error")
ze2_a, zr_a = envelope(z_a, (1, 3), residual='all', squared=True)
self.assert_close(ze2_a, ze2_0.astype(complex), # dtypes must match
msg="Complex envelope calculation error")
self.assert_close(sp_fft.fft(zr_a), np.array(Zr_a).astype(complex),
msg="Complex residual calculation error")
@pytest.mark.parametrize(
" Z, bp_in, Ze2_desired, Zr_desired",
[([1, 0, 2, 2, 0], (1, None), [4, 2, 0, 0, 0], [1, 0, 0, 0, 0]),
([4, 0, 2, 0, 0], (0, None), [4, 0, 2, 0, 0], [0, 0, 0, 0, 0]),
([4, 0, 0, 2, 0], (None, None), [4, 0, 0, 2, 0], [0, 0, 0, 0, 0]),
([0, 0, 2, 2, 0], (1, 3), [2, 0, 0, 0, 0], [0, 0, 0, 2, 0]),
([4, 0, 2, 2, 0], (-3, 3), [4, 0, 2, 0, 0], [0, 0, 0, 2, 0]),
([4, 0, 3, 4, 0], (None, 1), [2, 0, 0, 0, 0], [0, 0, 3, 4, 0]),
([4, 0, 3, 4, 0], (None, 0), [0, 0, 0, 0, 0], [4, 0, 3, 4, 0])])
def test_envelope_real_signals(self, Z, bp_in, Ze2_desired, Zr_desired):
"""Test envelope calculation with real-valued test signals.
The comparisons are performed in the Fourier space, since it makes evaluating
the bandpass filter behavior straightforward. Note that also the squared
envelope can be easily calculated by hand, if one recalls that coefficients of
a complex-valued Fourier series representing the signal can be directly
determined by an FFT and that the absolute square of a Fourier series is again
a Fourier series.
"""
z = sp_fft.irfft(Z)
ze2, zr = envelope(z, bp_in, residual='all', squared=True)
ze2_lp, zr_lp = envelope(z, bp_in, residual='lowpass', squared=True)
Ze2, Zr, Ze2_lp, Zr_lp = (sp_fft.rfft(z_) for z_ in (ze2, zr, ze2_lp, zr_lp))
Ze2_desired = np.array(Ze2_desired).astype(complex)
Zr_desired = np.array(Zr_desired).astype(complex)
self.assert_close(Ze2, Ze2_desired,
msg="Envelope calculation error (residual='all')")
self.assert_close(Zr, Zr_desired,
msg="Residual calculation error (residual='all')")
if bp_in[1] is not None:
Zr_desired[bp_in[1]:] = 0
self.assert_close(Ze2_lp, Ze2_desired,
msg="Envelope calculation error (residual='lowpass')")
self.assert_close(Zr_lp, Zr_desired,
msg="Residual calculation error (residual='lowpass')")
@pytest.mark.parametrize(
" Z, bp_in, Ze2_desired, Zr_desired",
[([0, 5, 0, 5, 0], (None, None), [5, 0, 10, 0, 5], [0, 0, 0, 0, 0]),
([1, 5, 0, 5, 2], (-1, 2), [5, 0, 10, 0, 5], [1, 0, 0, 0, 2]),
([1, 2, 6, 0, 6, 3], (-1, 2), [0, 6, 0, 12, 0, 6], [1, 2, 0, 0, 0, 3])
])
def test_envelope_complex_signals(self, Z, bp_in, Ze2_desired, Zr_desired):
"""Test envelope calculation with complex-valued test signals.
We only need to test for the complex envelope here, since the ``Nones``s in the
bandpass filter were already tested in the previous test.
"""
z = sp_fft.ifft(sp_fft.ifftshift(Z))
ze2, zr = envelope(z, bp_in, residual='all', squared=True)
Ze2, Zr = (sp_fft.fftshift(sp_fft.fft(z_)) for z_ in (ze2, zr))
self.assert_close(Ze2, np.array(Ze2_desired).astype(complex),
msg="Envelope calculation error")
self.assert_close(Zr, np.array(Zr_desired).astype(complex),
msg="Residual calculation error")
def test_envelope_verify_axis_parameter(self):
"""Test for multi-channel envelope calculations. """
z = sp_fft.irfft([[1, 0, 2, 2, 0], [7, 0, 4, 4, 0]])
Ze2_desired = np.array([[4, 2, 0, 0, 0], [16, 8, 0, 0, 0]],
dtype=complex)
Zr_desired = np.array([[1, 0, 0, 0, 0], [7, 0, 0, 0, 0]], dtype=complex)
ze2, zr = envelope(z, squared=True, axis=1)
ye2T, yrT = envelope(z.T, squared=True, axis=0)
Ze2, Ye2, Zr, Yr = (sp_fft.rfft(z_) for z_ in (ze2, ye2T.T, zr, yrT.T))
self.assert_close(Ze2, Ze2_desired, msg="2d envelope calculation error")
self.assert_close(Zr, Zr_desired, msg="2d residual calculation error")
self.assert_close(Ye2, Ze2_desired, msg="Transposed 2d envelope calc. error")
self.assert_close(Yr, Zr_desired, msg="Transposed 2d residual calc. error")
def test_envelope_verify_axis_parameter_complex(self):
"""Test for multi-channel envelope calculations with complex values. """
z = sp_fft.ifft(sp_fft.ifftshift([[1, 5, 0, 5, 2], [1, 10, 0, 10, 2]], axes=1))
Ze2_des = np.array([[5, 0, 10, 0, 5], [20, 0, 40, 0, 20],],
dtype=complex)
Zr_des = np.array([[1, 0, 0, 0, 2], [1, 0, 0, 0, 2]], dtype=complex)
kw = dict(bp_in=(-1, 2), residual='all', squared=True)
ze2, zr = envelope(z, axis=1, **kw)
ye2T, yrT = envelope(z.T, axis=0, **kw)
Ze2, Ye2, Zr, Yr = (sp_fft.fftshift(sp_fft.fft(z_), axes=1)
for z_ in (ze2, ye2T.T, zr, yrT.T))
self.assert_close(Ze2, Ze2_des, msg="2d envelope calculation error")
self.assert_close(Zr, Zr_des, msg="2d residual calculation error")
self.assert_close(Ye2, Ze2_des, msg="Transposed 2d envelope calc. error")
self.assert_close(Yr, Zr_des, msg="Transposed 2d residual calc. error")
@pytest.mark.parametrize('X', [[4, 0, 0, 1, 2], [4, 0, 0, 2, 1, 2]])
def test_compare_envelope_hilbert(self, X):
"""Compare output of `envelope()` and `hilbert()`. """
x = sp_fft.irfft(X)
e_hil = np.abs(hilbert(x))
e_env = envelope(x, (None, None), residual=None)
self.assert_close(e_hil, e_env, msg="Hilbert-Envelope comparison error")
class TestPartialFractionExpansion:
@staticmethod
def assert_rp_almost_equal(r, p, r_true, p_true, decimal=7):
r_true = np.asarray(r_true)
p_true = np.asarray(p_true)
distance = np.hypot(abs(p[:, None] - p_true),
abs(r[:, None] - r_true))
rows, cols = linear_sum_assignment(distance)
assert_almost_equal(p[rows], p_true[cols], decimal=decimal)
assert_almost_equal(r[rows], r_true[cols], decimal=decimal)
def test_compute_factors(self):
factors, poly = _compute_factors([1, 2, 3], [3, 2, 1])
assert_equal(len(factors), 3)
assert_almost_equal(factors[0], np.poly([2, 2, 3]))
assert_almost_equal(factors[1], np.poly([1, 1, 1, 3]))
assert_almost_equal(factors[2], np.poly([1, 1, 1, 2, 2]))
assert_almost_equal(poly, np.poly([1, 1, 1, 2, 2, 3]))
factors, poly = _compute_factors([1, 2, 3], [3, 2, 1],
include_powers=True)
assert_equal(len(factors), 6)
assert_almost_equal(factors[0], np.poly([1, 1, 2, 2, 3]))
assert_almost_equal(factors[1], np.poly([1, 2, 2, 3]))
assert_almost_equal(factors[2], np.poly([2, 2, 3]))
assert_almost_equal(factors[3], np.poly([1, 1, 1, 2, 3]))
assert_almost_equal(factors[4], np.poly([1, 1, 1, 3]))
assert_almost_equal(factors[5], np.poly([1, 1, 1, 2, 2]))
assert_almost_equal(poly, np.poly([1, 1, 1, 2, 2, 3]))
def test_group_poles(self):
unique, multiplicity = _group_poles(
[1.0, 1.001, 1.003, 2.0, 2.003, 3.0], 0.1, 'min')
assert_equal(unique, [1.0, 2.0, 3.0])
assert_equal(multiplicity, [3, 2, 1])
def test_residue_general(self):
# Test are taken from issue #4464, note that poles in scipy are
# in increasing by absolute value order, opposite to MATLAB.
r, p, k = residue([5, 3, -2, 7], [-4, 0, 8, 3])
assert_almost_equal(r, [1.3320, -0.6653, -1.4167], decimal=4)
assert_almost_equal(p, [-0.4093, -1.1644, 1.5737], decimal=4)
assert_almost_equal(k, [-1.2500], decimal=4)
r, p, k = residue([-4, 8], [1, 6, 8])
assert_almost_equal(r, [8, -12])
assert_almost_equal(p, [-2, -4])
assert_equal(k.size, 0)
r, p, k = residue([4, 1], [1, -1, -2])
assert_almost_equal(r, [1, 3])
assert_almost_equal(p, [-1, 2])
assert_equal(k.size, 0)
r, p, k = residue([4, 3], [2, -3.4, 1.98, -0.406])
self.assert_rp_almost_equal(
r, p, [-18.125 - 13.125j, -18.125 + 13.125j, 36.25],
[0.5 - 0.2j, 0.5 + 0.2j, 0.7])
assert_equal(k.size, 0)
r, p, k = residue([2, 1], [1, 5, 8, 4])
self.assert_rp_almost_equal(r, p, [-1, 1, 3], [-1, -2, -2])
assert_equal(k.size, 0)
r, p, k = residue([3, -1.1, 0.88, -2.396, 1.348],
[1, -0.7, -0.14, 0.048])
assert_almost_equal(r, [-3, 4, 1])
assert_almost_equal(p, [0.2, -0.3, 0.8])
assert_almost_equal(k, [3, 1])
r, p, k = residue([1], [1, 2, -3])
assert_almost_equal(r, [0.25, -0.25])
assert_almost_equal(p, [1, -3])
assert_equal(k.size, 0)
r, p, k = residue([1, 0, -5], [1, 0, 0, 0, -1])
self.assert_rp_almost_equal(r, p,
[1, 1.5j, -1.5j, -1], [-1, -1j, 1j, 1])
assert_equal(k.size, 0)
r, p, k = residue([3, 8, 6], [1, 3, 3, 1])
self.assert_rp_almost_equal(r, p, [1, 2, 3], [-1, -1, -1])
assert_equal(k.size, 0)
r, p, k = residue([3, -1], [1, -3, 2])
assert_almost_equal(r, [-2, 5])
assert_almost_equal(p, [1, 2])
assert_equal(k.size, 0)
r, p, k = residue([2, 3, -1], [1, -3, 2])
assert_almost_equal(r, [-4, 13])
assert_almost_equal(p, [1, 2])
assert_almost_equal(k, [2])
r, p, k = residue([7, 2, 3, -1], [1, -3, 2])
assert_almost_equal(r, [-11, 69])
assert_almost_equal(p, [1, 2])
assert_almost_equal(k, [7, 23])
r, p, k = residue([2, 3, -1], [1, -3, 4, -2])
self.assert_rp_almost_equal(r, p, [4, -1 + 3.5j, -1 - 3.5j],
[1, 1 - 1j, 1 + 1j])
assert_almost_equal(k.size, 0)
def test_residue_leading_zeros(self):
# Leading zeros in numerator or denominator must not affect the answer.
r0, p0, k0 = residue([5, 3, -2, 7], [-4, 0, 8, 3])
r1, p1, k1 = residue([0, 5, 3, -2, 7], [-4, 0, 8, 3])
r2, p2, k2 = residue([5, 3, -2, 7], [0, -4, 0, 8, 3])
r3, p3, k3 = residue([0, 0, 5, 3, -2, 7], [0, 0, 0, -4, 0, 8, 3])
assert_almost_equal(r0, r1)
assert_almost_equal(r0, r2)
assert_almost_equal(r0, r3)
assert_almost_equal(p0, p1)
assert_almost_equal(p0, p2)
assert_almost_equal(p0, p3)
assert_almost_equal(k0, k1)
assert_almost_equal(k0, k2)
assert_almost_equal(k0, k3)
def test_resiude_degenerate(self):
# Several tests for zero numerator and denominator.
r, p, k = residue([0, 0], [1, 6, 8])
assert_almost_equal(r, [0, 0])
assert_almost_equal(p, [-2, -4])
assert_equal(k.size, 0)
r, p, k = residue(0, 1)
assert_equal(r.size, 0)
assert_equal(p.size, 0)
assert_equal(k.size, 0)
with pytest.raises(ValueError, match="Denominator `a` is zero."):
residue(1, 0)
def test_residuez_general(self):
r, p, k = residuez([1, 6, 6, 2], [1, -(2 + 1j), (1 + 2j), -1j])
self.assert_rp_almost_equal(r, p, [-2+2.5j, 7.5+7.5j, -4.5-12j],
[1j, 1, 1])
assert_almost_equal(k, [2j])
r, p, k = residuez([1, 2, 1], [1, -1, 0.3561])
self.assert_rp_almost_equal(r, p,
[-0.9041 - 5.9928j, -0.9041 + 5.9928j],
[0.5 + 0.3257j, 0.5 - 0.3257j],
decimal=4)
assert_almost_equal(k, [2.8082], decimal=4)
r, p, k = residuez([1, -1], [1, -5, 6])
assert_almost_equal(r, [-1, 2])
assert_almost_equal(p, [2, 3])
assert_equal(k.size, 0)
r, p, k = residuez([2, 3, 4], [1, 3, 3, 1])
self.assert_rp_almost_equal(r, p, [4, -5, 3], [-1, -1, -1])
assert_equal(k.size, 0)
r, p, k = residuez([1, -10, -4, 4], [2, -2, -4])
assert_almost_equal(r, [0.5, -1.5])
assert_almost_equal(p, [-1, 2])
assert_almost_equal(k, [1.5, -1])
r, p, k = residuez([18], [18, 3, -4, -1])
self.assert_rp_almost_equal(r, p,
[0.36, 0.24, 0.4], [0.5, -1/3, -1/3])
assert_equal(k.size, 0)
r, p, k = residuez([2, 3], np.polymul([1, -1/2], [1, 1/4]))
assert_almost_equal(r, [-10/3, 16/3])
assert_almost_equal(p, [-0.25, 0.5])
assert_equal(k.size, 0)
r, p, k = residuez([1, -2, 1], [1, -1])
assert_almost_equal(r, [0])
assert_almost_equal(p, [1])
assert_almost_equal(k, [1, -1])
r, p, k = residuez(1, [1, -1j])
assert_almost_equal(r, [1])
assert_almost_equal(p, [1j])
assert_equal(k.size, 0)
r, p, k = residuez(1, [1, -1, 0.25])
assert_almost_equal(r, [0, 1])
assert_almost_equal(p, [0.5, 0.5])
assert_equal(k.size, 0)
r, p, k = residuez(1, [1, -0.75, .125])
assert_almost_equal(r, [-1, 2])
assert_almost_equal(p, [0.25, 0.5])
assert_equal(k.size, 0)
r, p, k = residuez([1, 6, 2], [1, -2, 1])
assert_almost_equal(r, [-10, 9])
assert_almost_equal(p, [1, 1])
assert_almost_equal(k, [2])
r, p, k = residuez([6, 2], [1, -2, 1])
assert_almost_equal(r, [-2, 8])
assert_almost_equal(p, [1, 1])
assert_equal(k.size, 0)
r, p, k = residuez([1, 6, 6, 2], [1, -2, 1])
assert_almost_equal(r, [-24, 15])
assert_almost_equal(p, [1, 1])
assert_almost_equal(k, [10, 2])
r, p, k = residuez([1, 0, 1], [1, 0, 0, 0, 0, -1])
self.assert_rp_almost_equal(r, p,
[0.2618 + 0.1902j, 0.2618 - 0.1902j,
0.4, 0.0382 - 0.1176j, 0.0382 + 0.1176j],
[-0.8090 + 0.5878j, -0.8090 - 0.5878j,
1.0, 0.3090 + 0.9511j, 0.3090 - 0.9511j],
decimal=4)
assert_equal(k.size, 0)
def test_residuez_trailing_zeros(self):
# Trailing zeros in numerator or denominator must not affect the
# answer.
r0, p0, k0 = residuez([5, 3, -2, 7], [-4, 0, 8, 3])
r1, p1, k1 = residuez([5, 3, -2, 7, 0], [-4, 0, 8, 3])
r2, p2, k2 = residuez([5, 3, -2, 7], [-4, 0, 8, 3, 0])
r3, p3, k3 = residuez([5, 3, -2, 7, 0, 0], [-4, 0, 8, 3, 0, 0, 0])
assert_almost_equal(r0, r1)
assert_almost_equal(r0, r2)
assert_almost_equal(r0, r3)
assert_almost_equal(p0, p1)
assert_almost_equal(p0, p2)
assert_almost_equal(p0, p3)
assert_almost_equal(k0, k1)
assert_almost_equal(k0, k2)
assert_almost_equal(k0, k3)
def test_residuez_degenerate(self):
r, p, k = residuez([0, 0], [1, 6, 8])
assert_almost_equal(r, [0, 0])
assert_almost_equal(p, [-2, -4])
assert_equal(k.size, 0)
r, p, k = residuez(0, 1)
assert_equal(r.size, 0)
assert_equal(p.size, 0)
assert_equal(k.size, 0)
with pytest.raises(ValueError, match="Denominator `a` is zero."):
residuez(1, 0)
with pytest.raises(ValueError,
match="First coefficient of determinant `a` must "
"be non-zero."):
residuez(1, [0, 1, 2, 3])
def test_inverse_unique_roots_different_rtypes(self):
# This test was inspired by GitHub issue 2496.
r = [3 / 10, -1 / 6, -2 / 15]
p = [0, -2, -5]
k = []
b_expected = [0, 1, 3]
a_expected = [1, 7, 10, 0]
# With the default tolerance, the rtype does not matter
# for this example.
for rtype in ('avg', 'mean', 'min', 'minimum', 'max', 'maximum'):
b, a = invres(r, p, k, rtype=rtype)
assert_allclose(b, b_expected)
assert_allclose(a, a_expected)
b, a = invresz(r, p, k, rtype=rtype)
assert_allclose(b, b_expected)
assert_allclose(a, a_expected)
def test_inverse_repeated_roots_different_rtypes(self):
r = [3 / 20, -7 / 36, -1 / 6, 2 / 45]
p = [0, -2, -2, -5]
k = []
b_expected = [0, 0, 1, 3]
b_expected_z = [-1/6, -2/3, 11/6, 3]
a_expected = [1, 9, 24, 20, 0]
for rtype in ('avg', 'mean', 'min', 'minimum', 'max', 'maximum'):
b, a = invres(r, p, k, rtype=rtype)
assert_allclose(b, b_expected, atol=1e-14)
assert_allclose(a, a_expected)
b, a = invresz(r, p, k, rtype=rtype)
assert_allclose(b, b_expected_z, atol=1e-14)
assert_allclose(a, a_expected)
def test_inverse_bad_rtype(self):
r = [3 / 20, -7 / 36, -1 / 6, 2 / 45]
p = [0, -2, -2, -5]
k = []
with pytest.raises(ValueError, match="`rtype` must be one of"):
invres(r, p, k, rtype='median')
with pytest.raises(ValueError, match="`rtype` must be one of"):
invresz(r, p, k, rtype='median')
def test_invresz_one_coefficient_bug(self):
# Regression test for issue in gh-4646.
r = [1]
p = [2]
k = [0]
b, a = invresz(r, p, k)
assert_allclose(b, [1.0])
assert_allclose(a, [1.0, -2.0])
def test_invres(self):
b, a = invres([1], [1], [])
assert_almost_equal(b, [1])
assert_almost_equal(a, [1, -1])
b, a = invres([1 - 1j, 2, 0.5 - 3j], [1, 0.5j, 1 + 1j], [])
assert_almost_equal(b, [3.5 - 4j, -8.5 + 0.25j, 3.5 + 3.25j])
assert_almost_equal(a, [1, -2 - 1.5j, 0.5 + 2j, 0.5 - 0.5j])
b, a = invres([0.5, 1], [1 - 1j, 2 + 2j], [1, 2, 3])
assert_almost_equal(b, [1, -1 - 1j, 1 - 2j, 0.5 - 3j, 10])
assert_almost_equal(a, [1, -3 - 1j, 4])
b, a = invres([-1, 2, 1j, 3 - 1j, 4, -2],
[-1, 2 - 1j, 2 - 1j, 3, 3, 3], [])
assert_almost_equal(b, [4 - 1j, -28 + 16j, 40 - 62j, 100 + 24j,
-292 + 219j, 192 - 268j])
assert_almost_equal(a, [1, -12 + 2j, 53 - 20j, -96 + 68j, 27 - 72j,
108 - 54j, -81 + 108j])
b, a = invres([-1, 1j], [1, 1], [1, 2])
assert_almost_equal(b, [1, 0, -4, 3 + 1j])
assert_almost_equal(a, [1, -2, 1])
def test_invresz(self):
b, a = invresz([1], [1], [])
assert_almost_equal(b, [1])
assert_almost_equal(a, [1, -1])
b, a = invresz([1 - 1j, 2, 0.5 - 3j], [1, 0.5j, 1 + 1j], [])
assert_almost_equal(b, [3.5 - 4j, -8.5 + 0.25j, 3.5 + 3.25j])
assert_almost_equal(a, [1, -2 - 1.5j, 0.5 + 2j, 0.5 - 0.5j])
b, a = invresz([0.5, 1], [1 - 1j, 2 + 2j], [1, 2, 3])
assert_almost_equal(b, [2.5, -3 - 1j, 1 - 2j, -1 - 3j, 12])
assert_almost_equal(a, [1, -3 - 1j, 4])
b, a = invresz([-1, 2, 1j, 3 - 1j, 4, -2],
[-1, 2 - 1j, 2 - 1j, 3, 3, 3], [])
assert_almost_equal(b, [6, -50 + 11j, 100 - 72j, 80 + 58j,
-354 + 228j, 234 - 297j])
assert_almost_equal(a, [1, -12 + 2j, 53 - 20j, -96 + 68j, 27 - 72j,
108 - 54j, -81 + 108j])
b, a = invresz([-1, 1j], [1, 1], [1, 2])
assert_almost_equal(b, [1j, 1, -3, 2])
assert_almost_equal(a, [1, -2, 1])
def test_inverse_scalar_arguments(self):
b, a = invres(1, 1, 1)
assert_almost_equal(b, [1, 0])
assert_almost_equal(a, [1, -1])
b, a = invresz(1, 1, 1)
assert_almost_equal(b, [2, -1])
assert_almost_equal(a, [1, -1])
class TestVectorstrength:
def test_single_1dperiod(self):
events = np.array([.5])
period = 5.
targ_strength = 1.
targ_phase = .1
strength, phase = vectorstrength(events, period)
assert_equal(strength.ndim, 0)
assert_equal(phase.ndim, 0)
assert_almost_equal(strength, targ_strength)
assert_almost_equal(phase, 2 * np.pi * targ_phase)
def test_single_2dperiod(self):
events = np.array([.5])
period = [1, 2, 5.]
targ_strength = [1.] * 3
targ_phase = np.array([.5, .25, .1])
strength, phase = vectorstrength(events, period)
assert_equal(strength.ndim, 1)
assert_equal(phase.ndim, 1)
assert_array_almost_equal(strength, targ_strength)
assert_almost_equal(phase, 2 * np.pi * targ_phase)
def test_equal_1dperiod(self):
events = np.array([.25, .25, .25, .25, .25, .25])
period = 2
targ_strength = 1.
targ_phase = .125
strength, phase = vectorstrength(events, period)
assert_equal(strength.ndim, 0)
assert_equal(phase.ndim, 0)
assert_almost_equal(strength, targ_strength)
assert_almost_equal(phase, 2 * np.pi * targ_phase)
def test_equal_2dperiod(self):
events = np.array([.25, .25, .25, .25, .25, .25])
period = [1, 2, ]
targ_strength = [1.] * 2
targ_phase = np.array([.25, .125])
strength, phase = vectorstrength(events, period)
assert_equal(strength.ndim, 1)
assert_equal(phase.ndim, 1)
assert_almost_equal(strength, targ_strength)
assert_almost_equal(phase, 2 * np.pi * targ_phase)
def test_spaced_1dperiod(self):
events = np.array([.1, 1.1, 2.1, 4.1, 10.1])
period = 1
targ_strength = 1.
targ_phase = .1
strength, phase = vectorstrength(events, period)
assert_equal(strength.ndim, 0)
assert_equal(phase.ndim, 0)
assert_almost_equal(strength, targ_strength)
assert_almost_equal(phase, 2 * np.pi * targ_phase)
def test_spaced_2dperiod(self):
events = np.array([.1, 1.1, 2.1, 4.1, 10.1])
period = [1, .5]
targ_strength = [1.] * 2
targ_phase = np.array([.1, .2])
strength, phase = vectorstrength(events, period)
assert_equal(strength.ndim, 1)
assert_equal(phase.ndim, 1)
assert_almost_equal(strength, targ_strength)
assert_almost_equal(phase, 2 * np.pi * targ_phase)
def test_partial_1dperiod(self):
events = np.array([.25, .5, .75])
period = 1
targ_strength = 1. / 3.
targ_phase = .5
strength, phase = vectorstrength(events, period)
assert_equal(strength.ndim, 0)
assert_equal(phase.ndim, 0)
assert_almost_equal(strength, targ_strength)
assert_almost_equal(phase, 2 * np.pi * targ_phase)
def test_partial_2dperiod(self):
events = np.array([.25, .5, .75])
period = [1., 1., 1., 1.]
targ_strength = [1. / 3.] * 4
targ_phase = np.array([.5, .5, .5, .5])
strength, phase = vectorstrength(events, period)
assert_equal(strength.ndim, 1)
assert_equal(phase.ndim, 1)
assert_almost_equal(strength, targ_strength)
assert_almost_equal(phase, 2 * np.pi * targ_phase)
def test_opposite_1dperiod(self):
events = np.array([0, .25, .5, .75])
period = 1.
targ_strength = 0
strength, phase = vectorstrength(events, period)
assert_equal(strength.ndim, 0)
assert_equal(phase.ndim, 0)
assert_almost_equal(strength, targ_strength)
def test_opposite_2dperiod(self):
events = np.array([0, .25, .5, .75])
period = [1.] * 10
targ_strength = [0.] * 10
strength, phase = vectorstrength(events, period)
assert_equal(strength.ndim, 1)
assert_equal(phase.ndim, 1)
assert_almost_equal(strength, targ_strength)
def test_2d_events_ValueError(self):
events = np.array([[1, 2]])
period = 1.
assert_raises(ValueError, vectorstrength, events, period)
def test_2d_period_ValueError(self):
events = 1.
period = np.array([[1]])
assert_raises(ValueError, vectorstrength, events, period)
def test_zero_period_ValueError(self):
events = 1.
period = 0
assert_raises(ValueError, vectorstrength, events, period)
def test_negative_period_ValueError(self):
events = 1.
period = -1
assert_raises(ValueError, vectorstrength, events, period)
def assert_allclose_cast(actual, desired, rtol=1e-7, atol=0):
"""Wrap assert_allclose while casting object arrays."""
if actual.dtype.kind == 'O':
dtype = np.array(actual.flat[0]).dtype
actual, desired = actual.astype(dtype), desired.astype(dtype)
assert_allclose(actual, desired, rtol, atol)
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
@pytest.mark.parametrize('func', (sosfilt, lfilter))
def test_nonnumeric_dtypes(func):
x = [Decimal(1), Decimal(2), Decimal(3)]
b = [Decimal(1), Decimal(2), Decimal(3)]
a = [Decimal(1), Decimal(2), Decimal(3)]
x = np.array(x)
assert x.dtype.kind == 'O'
desired = lfilter(np.array(b, float), np.array(a, float), x.astype(float))
if func is sosfilt:
actual = sosfilt([b + a], x)
else:
actual = lfilter(b, a, x)
assert all(isinstance(x, Decimal) for x in actual)
assert_allclose(actual.astype(float), desired.astype(float))
# Degenerate cases
if func is lfilter:
args = [1., 1.]
else:
args = [tf2sos(1., 1.)]
with pytest.raises(ValueError, match='must be at least 1-D'):
func(*args, x=1.)
@pytest.mark.parametrize('dt', 'fdFD')
class TestSOSFilt:
# The test_rank* tests are pulled from _TestLinearFilter
def test_rank1(self, dt):
x = np.linspace(0, 5, 6).astype(dt)
b = np.array([1, -1]).astype(dt)
a = np.array([0.5, -0.5]).astype(dt)
# Test simple IIR
y_r = np.array([0, 2, 4, 6, 8, 10.]).astype(dt)
sos = tf2sos(b, a)
assert_array_almost_equal(sosfilt(tf2sos(b, a), x), y_r)
# Test simple FIR
b = np.array([1, 1]).astype(dt)
# NOTE: This was changed (rel. to TestLinear...) to add a pole @zero:
a = np.array([1, 0]).astype(dt)
y_r = np.array([0, 1, 3, 5, 7, 9.]).astype(dt)
assert_array_almost_equal(sosfilt(tf2sos(b, a), x), y_r)
b = [1, 1, 0]
a = [1, 0, 0]
x = np.ones(8)
sos = np.concatenate((b, a))
sos.shape = (1, 6)
y = sosfilt(sos, x)
assert_allclose(y, [1, 2, 2, 2, 2, 2, 2, 2])
def test_rank2(self, dt):
shape = (4, 3)
x = np.linspace(0, np.prod(shape) - 1, np.prod(shape)).reshape(shape)
x = x.astype(dt)
b = np.array([1, -1]).astype(dt)
a = np.array([0.5, 0.5]).astype(dt)
y_r2_a0 = np.array([[0, 2, 4], [6, 4, 2], [0, 2, 4], [6, 4, 2]],
dtype=dt)
y_r2_a1 = np.array([[0, 2, 0], [6, -4, 6], [12, -10, 12],
[18, -16, 18]], dtype=dt)
y = sosfilt(tf2sos(b, a), x, axis=0)
assert_array_almost_equal(y_r2_a0, y)
y = sosfilt(tf2sos(b, a), x, axis=1)
assert_array_almost_equal(y_r2_a1, y)
def test_rank3(self, dt):
shape = (4, 3, 2)
x = np.linspace(0, np.prod(shape) - 1, np.prod(shape)).reshape(shape)
b = np.array([1, -1]).astype(dt)
a = np.array([0.5, 0.5]).astype(dt)
# Test last axis
y = sosfilt(tf2sos(b, a), x)
for i in range(x.shape[0]):
for j in range(x.shape[1]):
assert_array_almost_equal(y[i, j], lfilter(b, a, x[i, j]))
def test_initial_conditions(self, dt):
b1, a1 = signal.butter(2, 0.25, 'low')
b2, a2 = signal.butter(2, 0.75, 'low')
b3, a3 = signal.butter(2, 0.75, 'low')
b = np.convolve(np.convolve(b1, b2), b3)
a = np.convolve(np.convolve(a1, a2), a3)
sos = np.array((np.r_[b1, a1], np.r_[b2, a2], np.r_[b3, a3]))
x = np.random.rand(50).astype(dt)
# Stopping filtering and continuing
y_true, zi = lfilter(b, a, x[:20], zi=np.zeros(6))
y_true = np.r_[y_true, lfilter(b, a, x[20:], zi=zi)[0]]
assert_allclose_cast(y_true, lfilter(b, a, x))
y_sos, zi = sosfilt(sos, x[:20], zi=np.zeros((3, 2)))
y_sos = np.r_[y_sos, sosfilt(sos, x[20:], zi=zi)[0]]
assert_allclose_cast(y_true, y_sos)
# Use a step function
zi = sosfilt_zi(sos)
x = np.ones(8, dt)
y, zf = sosfilt(sos, x, zi=zi)
assert_allclose_cast(y, np.ones(8))
assert_allclose_cast(zf, zi)
# Initial condition shape matching
x.shape = (1, 1) + x.shape # 3D
assert_raises(ValueError, sosfilt, sos, x, zi=zi)
zi_nd = zi.copy()
zi_nd.shape = (zi.shape[0], 1, 1, zi.shape[-1])
assert_raises(ValueError, sosfilt, sos, x,
zi=zi_nd[:, :, :, [0, 1, 1]])
y, zf = sosfilt(sos, x, zi=zi_nd)
assert_allclose_cast(y[0, 0], np.ones(8))
assert_allclose_cast(zf[:, 0, 0, :], zi)
def test_initial_conditions_3d_axis1(self, dt):
# Test the use of zi when sosfilt is applied to axis 1 of a 3-d input.
# Input array is x.
x = np.random.RandomState(159).randint(0, 5, size=(2, 15, 3))
x = x.astype(dt)
# Design a filter in ZPK format and convert to SOS
zpk = signal.butter(6, 0.35, output='zpk')
sos = zpk2sos(*zpk)
nsections = sos.shape[0]
# Filter along this axis.
axis = 1
# Initial conditions, all zeros.
shp = list(x.shape)
shp[axis] = 2
shp = [nsections] + shp
z0 = np.zeros(shp)
# Apply the filter to x.
yf, zf = sosfilt(sos, x, axis=axis, zi=z0)
# Apply the filter to x in two stages.
y1, z1 = sosfilt(sos, x[:, :5, :], axis=axis, zi=z0)
y2, z2 = sosfilt(sos, x[:, 5:, :], axis=axis, zi=z1)
# y should equal yf, and z2 should equal zf.
y = np.concatenate((y1, y2), axis=axis)
assert_allclose_cast(y, yf, rtol=1e-10, atol=1e-13)
assert_allclose_cast(z2, zf, rtol=1e-10, atol=1e-13)
# let's try the "step" initial condition
zi = sosfilt_zi(sos)
zi.shape = [nsections, 1, 2, 1]
zi = zi * x[:, 0:1, :]
y = sosfilt(sos, x, axis=axis, zi=zi)[0]
# check it against the TF form
b, a = zpk2tf(*zpk)
zi = lfilter_zi(b, a)
zi.shape = [1, zi.size, 1]
zi = zi * x[:, 0:1, :]
y_tf = lfilter(b, a, x, axis=axis, zi=zi)[0]
assert_allclose_cast(y, y_tf, rtol=1e-10, atol=1e-13)
def test_bad_zi_shape(self, dt):
# The shape of zi is checked before using any values in the
# arguments, so np.empty is fine for creating the arguments.
x = np.empty((3, 15, 3), dt)
sos = np.zeros((4, 6))
zi = np.empty((4, 3, 3, 2)) # Correct shape is (4, 3, 2, 3)
with pytest.raises(ValueError, match='should be all ones'):
sosfilt(sos, x, zi=zi, axis=1)
sos[:, 3] = 1.
with pytest.raises(ValueError, match='Invalid zi shape'):
sosfilt(sos, x, zi=zi, axis=1)
def test_sosfilt_zi(self, dt):
sos = signal.butter(6, 0.2, output='sos')
zi = sosfilt_zi(sos)
y, zf = sosfilt(sos, np.ones(40, dt), zi=zi)
assert_allclose_cast(zf, zi, rtol=1e-13)
# Expected steady state value of the step response of this filter:
ss = np.prod(sos[:, :3].sum(axis=-1) / sos[:, 3:].sum(axis=-1))
assert_allclose_cast(y, ss, rtol=1e-13)
# zi as array-like
_, zf = sosfilt(sos, np.ones(40, dt), zi=zi.tolist())
assert_allclose_cast(zf, zi, rtol=1e-13)
@pytest.mark.thread_unsafe
def test_dtype_deprecation(self, dt):
# gh-21211
sos = np.asarray([1, 2, 3, 1, 5, 3], dtype=object).reshape(1, 6)
x = np.asarray([2, 3, 4, 5, 3, 4, 2, 2, 1], dtype=object)
with pytest.deprecated_call(match="dtype=object is not supported"):
sosfilt(sos, x)
class TestDeconvolve:
def test_basic(self):
# From docstring example
original = [0, 1, 0, 0, 1, 1, 0, 0]
impulse_response = [2, 1]
recorded = [0, 2, 1, 0, 2, 3, 1, 0, 0]
recovered, remainder = signal.deconvolve(recorded, impulse_response)
assert_allclose(recovered, original)
def test_n_dimensional_signal(self):
recorded = [[0, 0], [0, 0]]
impulse_response = [0, 0]
with pytest.raises(ValueError, match="signal must be 1-D."):
quotient, remainder = signal.deconvolve(recorded, impulse_response)
def test_n_dimensional_divisor(self):
recorded = [0, 0]
impulse_response = [[0, 0], [0, 0]]
with pytest.raises(ValueError, match="divisor must be 1-D."):
quotient, remainder = signal.deconvolve(recorded, impulse_response)
class TestDetrend:
def test_basic(self):
detrended = detrend(array([1, 2, 3]))
detrended_exact = array([0, 0, 0])
assert_array_almost_equal(detrended, detrended_exact)
def test_copy(self):
x = array([1, 1.2, 1.5, 1.6, 2.4])
copy_array = detrend(x, overwrite_data=False)
inplace = detrend(x, overwrite_data=True)
assert_array_almost_equal(copy_array, inplace)
@pytest.mark.parametrize('kind', ['linear', 'constant'])
@pytest.mark.parametrize('axis', [0, 1, 2])
def test_axis(self, axis, kind):
data = np.arange(5*6*7).reshape(5, 6, 7)
detrended = detrend(data, type=kind, axis=axis)
assert detrended.shape == data.shape
def test_bp(self):
data = [0, 1, 2] + [5, 0, -5, -10]
detrended = detrend(data, type='linear', bp=3)
assert_allclose(detrended, 0, atol=1e-14)
# repeat with ndim > 1 and axis
data = np.asarray(data)[None, :, None]
detrended = detrend(data, type="linear", bp=3, axis=1)
assert_allclose(detrended, 0, atol=1e-14)
# breakpoint index > shape[axis]: raises
with assert_raises(ValueError):
detrend(data, type="linear", bp=3)
@pytest.mark.parametrize('bp', [np.array([0, 2]), [0, 2]])
def test_detrend_array_bp(self, bp):
# regression test for https://github.com/scipy/scipy/issues/18675
rng = np.random.RandomState(12345)
x = rng.rand(10)
# bp = np.array([0, 2])
res = detrend(x, bp=bp)
res_scipy_191 = np.array([-4.44089210e-16, -2.22044605e-16,
-1.11128506e-01, -1.69470553e-01, 1.14710683e-01, 6.35468419e-02,
3.53533144e-01, -3.67877935e-02, -2.00417675e-02, -1.94362049e-01])
assert_allclose(res, res_scipy_191, atol=1e-14)
class TestUniqueRoots:
def test_real_no_repeat(self):
p = [-1.0, -0.5, 0.3, 1.2, 10.0]
unique, multiplicity = unique_roots(p)
assert_almost_equal(unique, p, decimal=15)
assert_equal(multiplicity, np.ones(len(p)))
def test_real_repeat(self):
p = [-1.0, -0.95, -0.89, -0.8, 0.5, 1.0, 1.05]
unique, multiplicity = unique_roots(p, tol=1e-1, rtype='min')
assert_almost_equal(unique, [-1.0, -0.89, 0.5, 1.0], decimal=15)
assert_equal(multiplicity, [2, 2, 1, 2])
unique, multiplicity = unique_roots(p, tol=1e-1, rtype='max')
assert_almost_equal(unique, [-0.95, -0.8, 0.5, 1.05], decimal=15)
assert_equal(multiplicity, [2, 2, 1, 2])
unique, multiplicity = unique_roots(p, tol=1e-1, rtype='avg')
assert_almost_equal(unique, [-0.975, -0.845, 0.5, 1.025], decimal=15)
assert_equal(multiplicity, [2, 2, 1, 2])
def test_complex_no_repeat(self):
p = [-1.0, 1.0j, 0.5 + 0.5j, -1.0 - 1.0j, 3.0 + 2.0j]
unique, multiplicity = unique_roots(p)
assert_almost_equal(unique, p, decimal=15)
assert_equal(multiplicity, np.ones(len(p)))
def test_complex_repeat(self):
p = [-1.0, -1.0 + 0.05j, -0.95 + 0.15j, -0.90 + 0.15j, 0.0,
0.5 + 0.5j, 0.45 + 0.55j]
unique, multiplicity = unique_roots(p, tol=1e-1, rtype='min')
assert_almost_equal(unique, [-1.0, -0.95 + 0.15j, 0.0, 0.45 + 0.55j],
decimal=15)
assert_equal(multiplicity, [2, 2, 1, 2])
unique, multiplicity = unique_roots(p, tol=1e-1, rtype='max')
assert_almost_equal(unique,
[-1.0 + 0.05j, -0.90 + 0.15j, 0.0, 0.5 + 0.5j],
decimal=15)
assert_equal(multiplicity, [2, 2, 1, 2])
unique, multiplicity = unique_roots(p, tol=1e-1, rtype='avg')
assert_almost_equal(
unique, [-1.0 + 0.025j, -0.925 + 0.15j, 0.0, 0.475 + 0.525j],
decimal=15)
assert_equal(multiplicity, [2, 2, 1, 2])
def test_gh_4915(self):
p = np.roots(np.convolve(np.ones(5), np.ones(5)))
true_roots = [-(-1)**(1/5), (-1)**(4/5), -(-1)**(3/5), (-1)**(2/5)]
unique, multiplicity = unique_roots(p)
unique = np.sort(unique)
assert_almost_equal(np.sort(unique), true_roots, decimal=7)
assert_equal(multiplicity, [2, 2, 2, 2])
def test_complex_roots_extra(self):
unique, multiplicity = unique_roots([1.0, 1.0j, 1.0])
assert_almost_equal(unique, [1.0, 1.0j], decimal=15)
assert_equal(multiplicity, [2, 1])
unique, multiplicity = unique_roots([1, 1 + 2e-9, 1e-9 + 1j], tol=0.1)
assert_almost_equal(unique, [1.0, 1e-9 + 1.0j], decimal=15)
assert_equal(multiplicity, [2, 1])
def test_single_unique_root(self):
p = np.random.rand(100) + 1j * np.random.rand(100)
unique, multiplicity = unique_roots(p, 2)
assert_almost_equal(unique, [np.min(p)], decimal=15)
assert_equal(multiplicity, [100])