peacock-data-public-datasets-idc-cronscript
/
venv
/lib
/python3.10
/site-packages
/scipy
/fft
/tests
/test_helper.py
"""Includes test functions for fftpack.helper module | |
Copied from fftpack.helper by Pearu Peterson, October 2005 | |
Modified for Array API, 2023 | |
""" | |
from scipy.fft._helper import next_fast_len, _init_nd_shape_and_axes | |
from numpy.testing import assert_equal | |
from pytest import raises as assert_raises | |
import pytest | |
import numpy as np | |
import sys | |
from scipy.conftest import array_api_compatible | |
from scipy._lib._array_api import xp_assert_close, SCIPY_DEVICE | |
from scipy import fft | |
pytestmark = [array_api_compatible, pytest.mark.usefixtures("skip_if_array_api")] | |
skip_if_array_api = pytest.mark.skip_if_array_api | |
_5_smooth_numbers = [ | |
2, 3, 4, 5, 6, 8, 9, 10, | |
2 * 3 * 5, | |
2**3 * 3**5, | |
2**3 * 3**3 * 5**2, | |
] | |
def test_next_fast_len(): | |
for n in _5_smooth_numbers: | |
assert_equal(next_fast_len(n), n) | |
def _assert_n_smooth(x, n): | |
x_orig = x | |
if n < 2: | |
assert False | |
while True: | |
q, r = divmod(x, 2) | |
if r != 0: | |
break | |
x = q | |
for d in range(3, n+1, 2): | |
while True: | |
q, r = divmod(x, d) | |
if r != 0: | |
break | |
x = q | |
assert x == 1, \ | |
f'x={x_orig} is not {n}-smooth, remainder={x}' | |
class TestNextFastLen: | |
def test_next_fast_len(self): | |
np.random.seed(1234) | |
def nums(): | |
yield from range(1, 1000) | |
yield 2**5 * 3**5 * 4**5 + 1 | |
for n in nums(): | |
m = next_fast_len(n) | |
_assert_n_smooth(m, 11) | |
assert m == next_fast_len(n, False) | |
m = next_fast_len(n, True) | |
_assert_n_smooth(m, 5) | |
def test_np_integers(self): | |
ITYPES = [np.int16, np.int32, np.int64, np.uint16, np.uint32, np.uint64] | |
for ityp in ITYPES: | |
x = ityp(12345) | |
testN = next_fast_len(x) | |
assert_equal(testN, next_fast_len(int(x))) | |
def testnext_fast_len_small(self): | |
hams = { | |
1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 8, 8: 8, 14: 15, 15: 15, | |
16: 16, 17: 18, 1021: 1024, 1536: 1536, 51200000: 51200000 | |
} | |
for x, y in hams.items(): | |
assert_equal(next_fast_len(x, True), y) | |
def testnext_fast_len_big(self): | |
hams = { | |
510183360: 510183360, 510183360 + 1: 512000000, | |
511000000: 512000000, | |
854296875: 854296875, 854296875 + 1: 859963392, | |
196608000000: 196608000000, 196608000000 + 1: 196830000000, | |
8789062500000: 8789062500000, 8789062500000 + 1: 8796093022208, | |
206391214080000: 206391214080000, | |
206391214080000 + 1: 206624260800000, | |
470184984576000: 470184984576000, | |
470184984576000 + 1: 470715894135000, | |
7222041363087360: 7222041363087360, | |
7222041363087360 + 1: 7230196133913600, | |
# power of 5 5**23 | |
11920928955078125: 11920928955078125, | |
11920928955078125 - 1: 11920928955078125, | |
# power of 3 3**34 | |
16677181699666569: 16677181699666569, | |
16677181699666569 - 1: 16677181699666569, | |
# power of 2 2**54 | |
18014398509481984: 18014398509481984, | |
18014398509481984 - 1: 18014398509481984, | |
# above this, int(ceil(n)) == int(ceil(n+1)) | |
19200000000000000: 19200000000000000, | |
19200000000000000 + 1: 19221679687500000, | |
288230376151711744: 288230376151711744, | |
288230376151711744 + 1: 288325195312500000, | |
288325195312500000 - 1: 288325195312500000, | |
288325195312500000: 288325195312500000, | |
288325195312500000 + 1: 288555831593533440, | |
} | |
for x, y in hams.items(): | |
assert_equal(next_fast_len(x, True), y) | |
def test_keyword_args(self): | |
assert next_fast_len(11, real=True) == 12 | |
assert next_fast_len(target=7, real=False) == 7 | |
class Test_init_nd_shape_and_axes: | |
def test_py_0d_defaults(self, xp): | |
x = xp.asarray(4) | |
shape = None | |
axes = None | |
shape_expected = () | |
axes_expected = [] | |
shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes) | |
assert shape_res == shape_expected | |
assert axes_res == axes_expected | |
def test_xp_0d_defaults(self, xp): | |
x = xp.asarray(7.) | |
shape = None | |
axes = None | |
shape_expected = () | |
axes_expected = [] | |
shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes) | |
assert shape_res == shape_expected | |
assert axes_res == axes_expected | |
def test_py_1d_defaults(self, xp): | |
x = xp.asarray([1, 2, 3]) | |
shape = None | |
axes = None | |
shape_expected = (3,) | |
axes_expected = [0] | |
shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes) | |
assert shape_res == shape_expected | |
assert axes_res == axes_expected | |
def test_xp_1d_defaults(self, xp): | |
x = xp.arange(0, 1, .1) | |
shape = None | |
axes = None | |
shape_expected = (10,) | |
axes_expected = [0] | |
shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes) | |
assert shape_res == shape_expected | |
assert axes_res == axes_expected | |
def test_py_2d_defaults(self, xp): | |
x = xp.asarray([[1, 2, 3, 4], | |
[5, 6, 7, 8]]) | |
shape = None | |
axes = None | |
shape_expected = (2, 4) | |
axes_expected = [0, 1] | |
shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes) | |
assert shape_res == shape_expected | |
assert axes_res == axes_expected | |
def test_xp_2d_defaults(self, xp): | |
x = xp.arange(0, 1, .1) | |
x = xp.reshape(x, (5, 2)) | |
shape = None | |
axes = None | |
shape_expected = (5, 2) | |
axes_expected = [0, 1] | |
shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes) | |
assert shape_res == shape_expected | |
assert axes_res == axes_expected | |
def test_xp_5d_defaults(self, xp): | |
x = xp.zeros([6, 2, 5, 3, 4]) | |
shape = None | |
axes = None | |
shape_expected = (6, 2, 5, 3, 4) | |
axes_expected = [0, 1, 2, 3, 4] | |
shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes) | |
assert shape_res == shape_expected | |
assert axes_res == axes_expected | |
def test_xp_5d_set_shape(self, xp): | |
x = xp.zeros([6, 2, 5, 3, 4]) | |
shape = [10, -1, -1, 1, 4] | |
axes = None | |
shape_expected = (10, 2, 5, 1, 4) | |
axes_expected = [0, 1, 2, 3, 4] | |
shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes) | |
assert shape_res == shape_expected | |
assert axes_res == axes_expected | |
def test_xp_5d_set_axes(self, xp): | |
x = xp.zeros([6, 2, 5, 3, 4]) | |
shape = None | |
axes = [4, 1, 2] | |
shape_expected = (4, 2, 5) | |
axes_expected = [4, 1, 2] | |
shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes) | |
assert shape_res == shape_expected | |
assert axes_res == axes_expected | |
def test_xp_5d_set_shape_axes(self, xp): | |
x = xp.zeros([6, 2, 5, 3, 4]) | |
shape = [10, -1, 2] | |
axes = [1, 0, 3] | |
shape_expected = (10, 6, 2) | |
axes_expected = [1, 0, 3] | |
shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes) | |
assert shape_res == shape_expected | |
assert axes_res == axes_expected | |
def test_shape_axes_subset(self, xp): | |
x = xp.zeros((2, 3, 4, 5)) | |
shape, axes = _init_nd_shape_and_axes(x, shape=(5, 5, 5), axes=None) | |
assert shape == (5, 5, 5) | |
assert axes == [1, 2, 3] | |
def test_errors(self, xp): | |
x = xp.zeros(1) | |
with assert_raises(ValueError, match="axes must be a scalar or " | |
"iterable of integers"): | |
_init_nd_shape_and_axes(x, shape=None, axes=[[1, 2], [3, 4]]) | |
with assert_raises(ValueError, match="axes must be a scalar or " | |
"iterable of integers"): | |
_init_nd_shape_and_axes(x, shape=None, axes=[1., 2., 3., 4.]) | |
with assert_raises(ValueError, | |
match="axes exceeds dimensionality of input"): | |
_init_nd_shape_and_axes(x, shape=None, axes=[1]) | |
with assert_raises(ValueError, | |
match="axes exceeds dimensionality of input"): | |
_init_nd_shape_and_axes(x, shape=None, axes=[-2]) | |
with assert_raises(ValueError, | |
match="all axes must be unique"): | |
_init_nd_shape_and_axes(x, shape=None, axes=[0, 0]) | |
with assert_raises(ValueError, match="shape must be a scalar or " | |
"iterable of integers"): | |
_init_nd_shape_and_axes(x, shape=[[1, 2], [3, 4]], axes=None) | |
with assert_raises(ValueError, match="shape must be a scalar or " | |
"iterable of integers"): | |
_init_nd_shape_and_axes(x, shape=[1., 2., 3., 4.], axes=None) | |
with assert_raises(ValueError, | |
match="when given, axes and shape arguments" | |
" have to be of the same length"): | |
_init_nd_shape_and_axes(xp.zeros([1, 1, 1, 1]), | |
shape=[1, 2, 3], axes=[1]) | |
with assert_raises(ValueError, | |
match="invalid number of data points" | |
r" \(\[0\]\) specified"): | |
_init_nd_shape_and_axes(x, shape=[0], axes=None) | |
with assert_raises(ValueError, | |
match="invalid number of data points" | |
r" \(\[-2\]\) specified"): | |
_init_nd_shape_and_axes(x, shape=-2, axes=None) | |
class TestFFTShift: | |
def test_definition(self, xp): | |
x = xp.asarray([0., 1, 2, 3, 4, -4, -3, -2, -1]) | |
y = xp.asarray([-4., -3, -2, -1, 0, 1, 2, 3, 4]) | |
xp_assert_close(fft.fftshift(x), y) | |
xp_assert_close(fft.ifftshift(y), x) | |
x = xp.asarray([0., 1, 2, 3, 4, -5, -4, -3, -2, -1]) | |
y = xp.asarray([-5., -4, -3, -2, -1, 0, 1, 2, 3, 4]) | |
xp_assert_close(fft.fftshift(x), y) | |
xp_assert_close(fft.ifftshift(y), x) | |
def test_inverse(self, xp): | |
for n in [1, 4, 9, 100, 211]: | |
x = xp.asarray(np.random.random((n,))) | |
xp_assert_close(fft.ifftshift(fft.fftshift(x)), x) | |
def test_axes_keyword(self, xp): | |
freqs = xp.asarray([[0., 1, 2], [3, 4, -4], [-3, -2, -1]]) | |
shifted = xp.asarray([[-1., -3, -2], [2, 0, 1], [-4, 3, 4]]) | |
xp_assert_close(fft.fftshift(freqs, axes=(0, 1)), shifted) | |
xp_assert_close(fft.fftshift(freqs, axes=0), fft.fftshift(freqs, axes=(0,))) | |
xp_assert_close(fft.ifftshift(shifted, axes=(0, 1)), freqs) | |
xp_assert_close(fft.ifftshift(shifted, axes=0), | |
fft.ifftshift(shifted, axes=(0,))) | |
xp_assert_close(fft.fftshift(freqs), shifted) | |
xp_assert_close(fft.ifftshift(shifted), freqs) | |
def test_uneven_dims(self, xp): | |
""" Test 2D input, which has uneven dimension sizes """ | |
freqs = xp.asarray([ | |
[0, 1], | |
[2, 3], | |
[4, 5] | |
], dtype=xp.float64) | |
# shift in dimension 0 | |
shift_dim0 = xp.asarray([ | |
[4, 5], | |
[0, 1], | |
[2, 3] | |
], dtype=xp.float64) | |
xp_assert_close(fft.fftshift(freqs, axes=0), shift_dim0) | |
xp_assert_close(fft.ifftshift(shift_dim0, axes=0), freqs) | |
xp_assert_close(fft.fftshift(freqs, axes=(0,)), shift_dim0) | |
xp_assert_close(fft.ifftshift(shift_dim0, axes=[0]), freqs) | |
# shift in dimension 1 | |
shift_dim1 = xp.asarray([ | |
[1, 0], | |
[3, 2], | |
[5, 4] | |
], dtype=xp.float64) | |
xp_assert_close(fft.fftshift(freqs, axes=1), shift_dim1) | |
xp_assert_close(fft.ifftshift(shift_dim1, axes=1), freqs) | |
# shift in both dimensions | |
shift_dim_both = xp.asarray([ | |
[5, 4], | |
[1, 0], | |
[3, 2] | |
], dtype=xp.float64) | |
xp_assert_close(fft.fftshift(freqs, axes=(0, 1)), shift_dim_both) | |
xp_assert_close(fft.ifftshift(shift_dim_both, axes=(0, 1)), freqs) | |
xp_assert_close(fft.fftshift(freqs, axes=[0, 1]), shift_dim_both) | |
xp_assert_close(fft.ifftshift(shift_dim_both, axes=[0, 1]), freqs) | |
# axes=None (default) shift in all dimensions | |
xp_assert_close(fft.fftshift(freqs, axes=None), shift_dim_both) | |
xp_assert_close(fft.ifftshift(shift_dim_both, axes=None), freqs) | |
xp_assert_close(fft.fftshift(freqs), shift_dim_both) | |
xp_assert_close(fft.ifftshift(shift_dim_both), freqs) | |
class TestFFTFreq: | |
def test_definition(self, xp): | |
device = SCIPY_DEVICE | |
try: | |
x = xp.asarray([0, 1, 2, 3, 4, -4, -3, -2, -1], | |
dtype=xp.float64, device=device) | |
x2 = xp.asarray([0, 1, 2, 3, 4, -5, -4, -3, -2, -1], | |
dtype=xp.float64, device=device) | |
except TypeError: | |
x = xp.asarray([0, 1, 2, 3, 4, -4, -3, -2, -1], dtype=xp.float64) | |
x2 = xp.asarray([0, 1, 2, 3, 4, -5, -4, -3, -2, -1], | |
dtype=xp.float64) | |
y = xp.asarray(9 * fft.fftfreq(9, xp=xp), dtype=xp.float64) | |
xp_assert_close(y, x) | |
y = xp.asarray(9 * xp.pi * fft.fftfreq(9, xp.pi, xp=xp), dtype=xp.float64) | |
xp_assert_close(y, x) | |
y = xp.asarray(10 * fft.fftfreq(10, xp=xp), dtype=xp.float64) | |
xp_assert_close(y, x2) | |
y = xp.asarray(10 * xp.pi * fft.fftfreq(10, xp.pi, xp=xp), dtype=xp.float64) | |
xp_assert_close(y, x2) | |
class TestRFFTFreq: | |
def test_definition(self, xp): | |
device = SCIPY_DEVICE | |
try: | |
x = xp.asarray([0, 1, 2, 3, 4], dtype=xp.float64, device=device) | |
x2 = xp.asarray([0, 1, 2, 3, 4, 5], dtype=xp.float64, device=device) | |
except TypeError: | |
# work around the `device` keyword not being implemented in numpy yet | |
x = xp.asarray([0, 1, 2, 3, 4], dtype=xp.float64) | |
x2 = xp.asarray([0, 1, 2, 3, 4, 5], dtype=xp.float64) | |
y = xp.asarray(9 * fft.rfftfreq(9, xp=xp), dtype=xp.float64) | |
xp_assert_close(y, x) | |
y = xp.asarray(9 * xp.pi * fft.rfftfreq(9, xp.pi, xp=xp), dtype=xp.float64) | |
xp_assert_close(y, x) | |
y = xp.asarray(10 * fft.rfftfreq(10, xp=xp), dtype=xp.float64) | |
xp_assert_close(y, x2) | |
y = xp.asarray(10 * xp.pi * fft.rfftfreq(10, xp.pi, xp=xp), dtype=xp.float64) | |
xp_assert_close(y, x2) | |