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from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.random import random
from numpy.testing import TestCase, run_module_suite, assert_array_almost_equal
from numpy.testing import assert_array_equal
import threading
import sys
if sys.version_info[0] >= 3:
import queue
else:
import Queue as queue
def fft1(x):
L = len(x)
phase = -2j*np.pi*(np.arange(L)/float(L))
phase = np.arange(L).reshape(-1, 1) * phase
return np.sum(x*np.exp(phase), axis=1)
class TestFFTShift(TestCase):
def test_fft_n(self):
self.assertRaises(ValueError, np.fft.fft, [1, 2, 3], 0)
class TestFFT1D(TestCase):
def test_fft(self):
x = random(30) + 1j*random(30)
assert_array_almost_equal(fft1(x), np.fft.fft(x))
assert_array_almost_equal(fft1(x) / np.sqrt(30),
np.fft.fft(x, norm="ortho"))
def test_ifft(self):
x = random(30) + 1j*random(30)
assert_array_almost_equal(x, np.fft.ifft(np.fft.fft(x)))
assert_array_almost_equal(
x, np.fft.ifft(np.fft.fft(x, norm="ortho"), norm="ortho"))
def test_fft2(self):
x = random((30, 20)) + 1j*random((30, 20))
assert_array_almost_equal(np.fft.fft(np.fft.fft(x, axis=1), axis=0),
np.fft.fft2(x))
assert_array_almost_equal(np.fft.fft2(x) / np.sqrt(30 * 20),
np.fft.fft2(x, norm="ortho"))
def test_ifft2(self):
x = random((30, 20)) + 1j*random((30, 20))
assert_array_almost_equal(np.fft.ifft(np.fft.ifft(x, axis=1), axis=0),
np.fft.ifft2(x))
assert_array_almost_equal(np.fft.ifft2(x) * np.sqrt(30 * 20),
np.fft.ifft2(x, norm="ortho"))
def test_fftn(self):
x = random((30, 20, 10)) + 1j*random((30, 20, 10))
assert_array_almost_equal(
np.fft.fft(np.fft.fft(np.fft.fft(x, axis=2), axis=1), axis=0),
np.fft.fftn(x))
assert_array_almost_equal(np.fft.fftn(x) / np.sqrt(30 * 20 * 10),
np.fft.fftn(x, norm="ortho"))
def test_ifftn(self):
x = random((30, 20, 10)) + 1j*random((30, 20, 10))
assert_array_almost_equal(
np.fft.ifft(np.fft.ifft(np.fft.ifft(x, axis=2), axis=1), axis=0),
np.fft.ifftn(x))
assert_array_almost_equal(np.fft.ifftn(x) * np.sqrt(30 * 20 * 10),
np.fft.ifftn(x, norm="ortho"))
def test_rfft(self):
x = random(30)
for n in [x.size, 2*x.size]:
for norm in [None, 'ortho']:
assert_array_almost_equal(
np.fft.fft(x, n=n, norm=norm)[:(n//2 + 1)],
np.fft.rfft(x, n=n, norm=norm))
assert_array_almost_equal(np.fft.rfft(x, n=n) / np.sqrt(n),
np.fft.rfft(x, n=n, norm="ortho"))
def test_irfft(self):
x = random(30)
assert_array_almost_equal(x, np.fft.irfft(np.fft.rfft(x)))
assert_array_almost_equal(
x, np.fft.irfft(np.fft.rfft(x, norm="ortho"), norm="ortho"))
def test_rfft2(self):
x = random((30, 20))
assert_array_almost_equal(np.fft.fft2(x)[:, :11], np.fft.rfft2(x))
assert_array_almost_equal(np.fft.rfft2(x) / np.sqrt(30 * 20),
np.fft.rfft2(x, norm="ortho"))
def test_irfft2(self):
x = random((30, 20))
assert_array_almost_equal(x, np.fft.irfft2(np.fft.rfft2(x)))
assert_array_almost_equal(
x, np.fft.irfft2(np.fft.rfft2(x, norm="ortho"), norm="ortho"))
def test_rfftn(self):
x = random((30, 20, 10))
assert_array_almost_equal(np.fft.fftn(x)[:, :, :6], np.fft.rfftn(x))
assert_array_almost_equal(np.fft.rfftn(x) / np.sqrt(30 * 20 * 10),
np.fft.rfftn(x, norm="ortho"))
def test_irfftn(self):
x = random((30, 20, 10))
assert_array_almost_equal(x, np.fft.irfftn(np.fft.rfftn(x)))
assert_array_almost_equal(
x, np.fft.irfftn(np.fft.rfftn(x, norm="ortho"), norm="ortho"))
def test_hfft(self):
x = random(14) + 1j*random(14)
x_herm = np.concatenate((random(1), x, random(1)))
x = np.concatenate((x_herm, x[::-1].conj()))
assert_array_almost_equal(np.fft.fft(x), np.fft.hfft(x_herm))
assert_array_almost_equal(np.fft.hfft(x_herm) / np.sqrt(30),
np.fft.hfft(x_herm, norm="ortho"))
def test_ihttf(self):
x = random(14) + 1j*random(14)
x_herm = np.concatenate((random(1), x, random(1)))
x = np.concatenate((x_herm, x[::-1].conj()))
assert_array_almost_equal(x_herm, np.fft.ihfft(np.fft.hfft(x_herm)))
assert_array_almost_equal(
x_herm, np.fft.ihfft(np.fft.hfft(x_herm, norm="ortho"),
norm="ortho"))
def test_all_1d_norm_preserving(self):
# verify that round-trip transforms are norm-preserving
x = random(30)
x_norm = np.linalg.norm(x)
n = x.size * 2
func_pairs = [(np.fft.fft, np.fft.ifft),
(np.fft.rfft, np.fft.irfft),
# hfft: order so the first function takes x.size samples
# (necessary for comparison to x_norm above)
(np.fft.ihfft, np.fft.hfft),
]
for forw, back in func_pairs:
for n in [x.size, 2*x.size]:
for norm in [None, 'ortho']:
tmp = forw(x, n=n, norm=norm)
tmp = back(tmp, n=n, norm=norm)
assert_array_almost_equal(x_norm,
np.linalg.norm(tmp))
class TestFFTThreadSafe(TestCase):
threads = 16
input_shape = (800, 200)
def _test_mtsame(self, func, *args):
def worker(args, q):
q.put(func(*args))
q = queue.Queue()
expected = func(*args)
# Spin off a bunch of threads to call the same function simultaneously
t = [threading.Thread(target=worker, args=(args, q))
for i in range(self.threads)]
[x.start() for x in t]
[x.join() for x in t]
# Make sure all threads returned the correct value
for i in range(self.threads):
assert_array_equal(q.get(timeout=5), expected,
'Function returned wrong value in multithreaded context')
def test_fft(self):
a = np.ones(self.input_shape) * 1+0j
self._test_mtsame(np.fft.fft, a)
def test_ifft(self):
a = np.ones(self.input_shape) * 1+0j
self._test_mtsame(np.fft.ifft, a)
def test_rfft(self):
a = np.ones(self.input_shape)
self._test_mtsame(np.fft.rfft, a)
def test_irfft(self):
a = np.ones(self.input_shape) * 1+0j
self._test_mtsame(np.fft.irfft, a)
if __name__ == "__main__":
run_module_suite()
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