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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 (
- assert_array_almost_equal, assert_array_equal, assert_raises,
- )
- 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(object):
- def test_fft_n(self):
- assert_raises(ValueError, np.fft.fft, [1, 2, 3], 0)
- class TestFFT1D(object):
- 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(object):
- 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)
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