874 lines
30 KiB
Python
874 lines
30 KiB
Python
# Created by Pearu Peterson, September 2002
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from numpy.testing import (assert_, assert_equal, assert_array_almost_equal,
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assert_array_almost_equal_nulp, assert_array_less)
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import pytest
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from pytest import raises as assert_raises
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from scipy.fftpack import ifft, fft, fftn, ifftn, rfft, irfft, fft2
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from numpy import (arange, array, asarray, zeros, dot, exp, pi,
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swapaxes, double, cdouble)
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import numpy as np
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import numpy.fft
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from numpy.random import rand
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# "large" composite numbers supported by FFTPACK
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LARGE_COMPOSITE_SIZES = [
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2**13,
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2**5 * 3**5,
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2**3 * 3**3 * 5**2,
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]
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SMALL_COMPOSITE_SIZES = [
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2,
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2*3*5,
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2*2*3*3,
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]
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# prime
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LARGE_PRIME_SIZES = [
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2011
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]
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SMALL_PRIME_SIZES = [
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29
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]
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def _assert_close_in_norm(x, y, rtol, size, rdt):
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# helper function for testing
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err_msg = f"size: {size} rdt: {rdt}"
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assert_array_less(np.linalg.norm(x - y), rtol*np.linalg.norm(x), err_msg)
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def random(size):
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return rand(*size)
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def direct_dft(x):
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x = asarray(x)
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n = len(x)
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y = zeros(n, dtype=cdouble)
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w = -arange(n)*(2j*pi/n)
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for i in range(n):
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y[i] = dot(exp(i*w), x)
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return y
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def direct_idft(x):
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x = asarray(x)
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n = len(x)
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y = zeros(n, dtype=cdouble)
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w = arange(n)*(2j*pi/n)
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for i in range(n):
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y[i] = dot(exp(i*w), x)/n
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return y
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def direct_dftn(x):
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x = asarray(x)
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for axis in range(len(x.shape)):
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x = fft(x, axis=axis)
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return x
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def direct_idftn(x):
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x = asarray(x)
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for axis in range(len(x.shape)):
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x = ifft(x, axis=axis)
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return x
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def direct_rdft(x):
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x = asarray(x)
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n = len(x)
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w = -arange(n)*(2j*pi/n)
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r = zeros(n, dtype=double)
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for i in range(n//2+1):
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y = dot(exp(i*w), x)
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if i:
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r[2*i-1] = y.real
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if 2*i < n:
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r[2*i] = y.imag
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else:
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r[0] = y.real
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return r
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def direct_irdft(x):
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x = asarray(x)
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n = len(x)
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x1 = zeros(n, dtype=cdouble)
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for i in range(n//2+1):
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if i:
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if 2*i < n:
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x1[i] = x[2*i-1] + 1j*x[2*i]
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x1[n-i] = x[2*i-1] - 1j*x[2*i]
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else:
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x1[i] = x[2*i-1]
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else:
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x1[0] = x[0]
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return direct_idft(x1).real
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class _TestFFTBase:
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def setup_method(self):
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self.cdt = None
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self.rdt = None
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np.random.seed(1234)
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def test_definition(self):
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x = np.array([1,2,3,4+1j,1,2,3,4+2j], dtype=self.cdt)
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y = fft(x)
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assert_equal(y.dtype, self.cdt)
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y1 = direct_dft(x)
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assert_array_almost_equal(y,y1)
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x = np.array([1,2,3,4+0j,5], dtype=self.cdt)
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assert_array_almost_equal(fft(x),direct_dft(x))
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def test_n_argument_real(self):
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x1 = np.array([1,2,3,4], dtype=self.rdt)
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x2 = np.array([1,2,3,4], dtype=self.rdt)
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y = fft([x1,x2],n=4)
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assert_equal(y.dtype, self.cdt)
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assert_equal(y.shape,(2,4))
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assert_array_almost_equal(y[0],direct_dft(x1))
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assert_array_almost_equal(y[1],direct_dft(x2))
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def _test_n_argument_complex(self):
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x1 = np.array([1,2,3,4+1j], dtype=self.cdt)
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x2 = np.array([1,2,3,4+1j], dtype=self.cdt)
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y = fft([x1,x2],n=4)
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assert_equal(y.dtype, self.cdt)
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assert_equal(y.shape,(2,4))
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assert_array_almost_equal(y[0],direct_dft(x1))
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assert_array_almost_equal(y[1],direct_dft(x2))
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def test_invalid_sizes(self):
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assert_raises(ValueError, fft, [])
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assert_raises(ValueError, fft, [[1,1],[2,2]], -5)
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class TestDoubleFFT(_TestFFTBase):
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def setup_method(self):
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self.cdt = np.complex128
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self.rdt = np.float64
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class TestSingleFFT(_TestFFTBase):
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def setup_method(self):
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self.cdt = np.complex64
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self.rdt = np.float32
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reason = ("single-precision FFT implementation is partially disabled, "
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"until accuracy issues with large prime powers are resolved")
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@pytest.mark.xfail(run=False, reason=reason)
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def test_notice(self):
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pass
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class TestFloat16FFT:
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def test_1_argument_real(self):
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x1 = np.array([1, 2, 3, 4], dtype=np.float16)
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y = fft(x1, n=4)
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assert_equal(y.dtype, np.complex64)
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assert_equal(y.shape, (4, ))
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assert_array_almost_equal(y, direct_dft(x1.astype(np.float32)))
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def test_n_argument_real(self):
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x1 = np.array([1, 2, 3, 4], dtype=np.float16)
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x2 = np.array([1, 2, 3, 4], dtype=np.float16)
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y = fft([x1, x2], n=4)
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assert_equal(y.dtype, np.complex64)
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assert_equal(y.shape, (2, 4))
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assert_array_almost_equal(y[0], direct_dft(x1.astype(np.float32)))
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assert_array_almost_equal(y[1], direct_dft(x2.astype(np.float32)))
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class _TestIFFTBase:
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def setup_method(self):
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np.random.seed(1234)
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def test_definition(self):
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x = np.array([1,2,3,4+1j,1,2,3,4+2j], self.cdt)
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y = ifft(x)
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y1 = direct_idft(x)
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assert_equal(y.dtype, self.cdt)
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assert_array_almost_equal(y,y1)
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x = np.array([1,2,3,4+0j,5], self.cdt)
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assert_array_almost_equal(ifft(x),direct_idft(x))
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def test_definition_real(self):
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x = np.array([1,2,3,4,1,2,3,4], self.rdt)
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y = ifft(x)
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assert_equal(y.dtype, self.cdt)
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y1 = direct_idft(x)
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assert_array_almost_equal(y,y1)
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x = np.array([1,2,3,4,5], dtype=self.rdt)
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assert_equal(y.dtype, self.cdt)
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assert_array_almost_equal(ifft(x),direct_idft(x))
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def test_random_complex(self):
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for size in [1,51,111,100,200,64,128,256,1024]:
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x = random([size]).astype(self.cdt)
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x = random([size]).astype(self.cdt) + 1j*x
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y1 = ifft(fft(x))
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y2 = fft(ifft(x))
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assert_equal(y1.dtype, self.cdt)
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assert_equal(y2.dtype, self.cdt)
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assert_array_almost_equal(y1, x)
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assert_array_almost_equal(y2, x)
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def test_random_real(self):
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for size in [1,51,111,100,200,64,128,256,1024]:
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x = random([size]).astype(self.rdt)
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y1 = ifft(fft(x))
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y2 = fft(ifft(x))
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assert_equal(y1.dtype, self.cdt)
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assert_equal(y2.dtype, self.cdt)
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assert_array_almost_equal(y1, x)
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assert_array_almost_equal(y2, x)
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def test_size_accuracy(self):
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# Sanity check for the accuracy for prime and non-prime sized inputs
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if self.rdt == np.float32:
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rtol = 1e-5
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elif self.rdt == np.float64:
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rtol = 1e-10
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for size in LARGE_COMPOSITE_SIZES + LARGE_PRIME_SIZES:
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np.random.seed(1234)
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x = np.random.rand(size).astype(self.rdt)
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y = ifft(fft(x))
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_assert_close_in_norm(x, y, rtol, size, self.rdt)
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y = fft(ifft(x))
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_assert_close_in_norm(x, y, rtol, size, self.rdt)
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x = (x + 1j*np.random.rand(size)).astype(self.cdt)
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y = ifft(fft(x))
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_assert_close_in_norm(x, y, rtol, size, self.rdt)
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y = fft(ifft(x))
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_assert_close_in_norm(x, y, rtol, size, self.rdt)
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def test_invalid_sizes(self):
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assert_raises(ValueError, ifft, [])
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assert_raises(ValueError, ifft, [[1,1],[2,2]], -5)
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class TestDoubleIFFT(_TestIFFTBase):
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def setup_method(self):
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self.cdt = np.complex128
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self.rdt = np.float64
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class TestSingleIFFT(_TestIFFTBase):
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def setup_method(self):
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self.cdt = np.complex64
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self.rdt = np.float32
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class _TestRFFTBase:
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def setup_method(self):
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np.random.seed(1234)
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def test_definition(self):
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for t in [[1, 2, 3, 4, 1, 2, 3, 4], [1, 2, 3, 4, 1, 2, 3, 4, 5]]:
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x = np.array(t, dtype=self.rdt)
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y = rfft(x)
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y1 = direct_rdft(x)
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assert_array_almost_equal(y,y1)
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assert_equal(y.dtype, self.rdt)
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def test_invalid_sizes(self):
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assert_raises(ValueError, rfft, [])
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assert_raises(ValueError, rfft, [[1,1],[2,2]], -5)
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# See gh-5790
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class MockSeries:
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def __init__(self, data):
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self.data = np.asarray(data)
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def __getattr__(self, item):
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try:
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return getattr(self.data, item)
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except AttributeError as e:
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raise AttributeError("'MockSeries' object "
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f"has no attribute '{item}'") from e
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def test_non_ndarray_with_dtype(self):
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x = np.array([1., 2., 3., 4., 5.])
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xs = _TestRFFTBase.MockSeries(x)
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expected = [1, 2, 3, 4, 5]
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rfft(xs)
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# Data should not have been overwritten
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assert_equal(x, expected)
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assert_equal(xs.data, expected)
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def test_complex_input(self):
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assert_raises(TypeError, rfft, np.arange(4, dtype=np.complex64))
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class TestRFFTDouble(_TestRFFTBase):
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def setup_method(self):
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self.cdt = np.complex128
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self.rdt = np.float64
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class TestRFFTSingle(_TestRFFTBase):
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def setup_method(self):
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self.cdt = np.complex64
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self.rdt = np.float32
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class _TestIRFFTBase:
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def setup_method(self):
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np.random.seed(1234)
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def test_definition(self):
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x1 = [1,2,3,4,1,2,3,4]
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x1_1 = [1,2+3j,4+1j,2+3j,4,2-3j,4-1j,2-3j]
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x2 = [1,2,3,4,1,2,3,4,5]
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x2_1 = [1,2+3j,4+1j,2+3j,4+5j,4-5j,2-3j,4-1j,2-3j]
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def _test(x, xr):
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y = irfft(np.array(x, dtype=self.rdt))
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y1 = direct_irdft(x)
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assert_equal(y.dtype, self.rdt)
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assert_array_almost_equal(y,y1, decimal=self.ndec)
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assert_array_almost_equal(y,ifft(xr), decimal=self.ndec)
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_test(x1, x1_1)
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_test(x2, x2_1)
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def test_random_real(self):
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for size in [1,51,111,100,200,64,128,256,1024]:
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x = random([size]).astype(self.rdt)
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y1 = irfft(rfft(x))
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y2 = rfft(irfft(x))
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assert_equal(y1.dtype, self.rdt)
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assert_equal(y2.dtype, self.rdt)
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assert_array_almost_equal(y1, x, decimal=self.ndec,
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err_msg="size=%d" % size)
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assert_array_almost_equal(y2, x, decimal=self.ndec,
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err_msg="size=%d" % size)
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def test_size_accuracy(self):
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# Sanity check for the accuracy for prime and non-prime sized inputs
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if self.rdt == np.float32:
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rtol = 1e-5
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elif self.rdt == np.float64:
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rtol = 1e-10
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for size in LARGE_COMPOSITE_SIZES + LARGE_PRIME_SIZES:
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np.random.seed(1234)
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x = np.random.rand(size).astype(self.rdt)
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y = irfft(rfft(x))
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_assert_close_in_norm(x, y, rtol, size, self.rdt)
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y = rfft(irfft(x))
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_assert_close_in_norm(x, y, rtol, size, self.rdt)
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def test_invalid_sizes(self):
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assert_raises(ValueError, irfft, [])
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assert_raises(ValueError, irfft, [[1,1],[2,2]], -5)
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def test_complex_input(self):
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assert_raises(TypeError, irfft, np.arange(4, dtype=np.complex64))
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# self.ndec is bogus; we should have a assert_array_approx_equal for number of
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# significant digits
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class TestIRFFTDouble(_TestIRFFTBase):
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def setup_method(self):
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self.cdt = np.complex128
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self.rdt = np.float64
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self.ndec = 14
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class TestIRFFTSingle(_TestIRFFTBase):
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def setup_method(self):
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self.cdt = np.complex64
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self.rdt = np.float32
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self.ndec = 5
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class Testfft2:
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def setup_method(self):
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np.random.seed(1234)
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def test_regression_244(self):
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"""FFT returns wrong result with axes parameter."""
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# fftn (and hence fft2) used to break when both axes and shape were
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# used
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x = numpy.ones((4, 4, 2))
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y = fft2(x, shape=(8, 8), axes=(-3, -2))
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y_r = numpy.fft.fftn(x, s=(8, 8), axes=(-3, -2))
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assert_array_almost_equal(y, y_r)
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def test_invalid_sizes(self):
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assert_raises(ValueError, fft2, [[]])
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assert_raises(ValueError, fft2, [[1, 1], [2, 2]], (4, -3))
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class TestFftnSingle:
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def setup_method(self):
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np.random.seed(1234)
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def test_definition(self):
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x = [[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9]]
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y = fftn(np.array(x, np.float32))
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assert_(y.dtype == np.complex64,
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msg="double precision output with single precision")
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y_r = np.array(fftn(x), np.complex64)
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assert_array_almost_equal_nulp(y, y_r)
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@pytest.mark.parametrize('size', SMALL_COMPOSITE_SIZES + SMALL_PRIME_SIZES)
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def test_size_accuracy_small(self, size):
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x = np.random.rand(size, size) + 1j*np.random.rand(size, size)
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y1 = fftn(x.real.astype(np.float32))
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y2 = fftn(x.real.astype(np.float64)).astype(np.complex64)
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assert_equal(y1.dtype, np.complex64)
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assert_array_almost_equal_nulp(y1, y2, 2000)
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@pytest.mark.parametrize('size', LARGE_COMPOSITE_SIZES + LARGE_PRIME_SIZES)
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def test_size_accuracy_large(self, size):
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x = np.random.rand(size, 3) + 1j*np.random.rand(size, 3)
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y1 = fftn(x.real.astype(np.float32))
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y2 = fftn(x.real.astype(np.float64)).astype(np.complex64)
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assert_equal(y1.dtype, np.complex64)
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assert_array_almost_equal_nulp(y1, y2, 2000)
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def test_definition_float16(self):
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x = [[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9]]
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y = fftn(np.array(x, np.float16))
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assert_equal(y.dtype, np.complex64)
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y_r = np.array(fftn(x), np.complex64)
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assert_array_almost_equal_nulp(y, y_r)
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@pytest.mark.parametrize('size', SMALL_COMPOSITE_SIZES + SMALL_PRIME_SIZES)
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def test_float16_input_small(self, size):
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x = np.random.rand(size, size) + 1j*np.random.rand(size, size)
|
|
y1 = fftn(x.real.astype(np.float16))
|
|
y2 = fftn(x.real.astype(np.float64)).astype(np.complex64)
|
|
|
|
assert_equal(y1.dtype, np.complex64)
|
|
assert_array_almost_equal_nulp(y1, y2, 5e5)
|
|
|
|
@pytest.mark.parametrize('size', LARGE_COMPOSITE_SIZES + LARGE_PRIME_SIZES)
|
|
def test_float16_input_large(self, size):
|
|
x = np.random.rand(size, 3) + 1j*np.random.rand(size, 3)
|
|
y1 = fftn(x.real.astype(np.float16))
|
|
y2 = fftn(x.real.astype(np.float64)).astype(np.complex64)
|
|
|
|
assert_equal(y1.dtype, np.complex64)
|
|
assert_array_almost_equal_nulp(y1, y2, 2e6)
|
|
|
|
|
|
class TestFftn:
|
|
def setup_method(self):
|
|
np.random.seed(1234)
|
|
|
|
def test_definition(self):
|
|
x = [[1, 2, 3],
|
|
[4, 5, 6],
|
|
[7, 8, 9]]
|
|
y = fftn(x)
|
|
assert_array_almost_equal(y, direct_dftn(x))
|
|
|
|
x = random((20, 26))
|
|
assert_array_almost_equal(fftn(x), direct_dftn(x))
|
|
|
|
x = random((5, 4, 3, 20))
|
|
assert_array_almost_equal(fftn(x), direct_dftn(x))
|
|
|
|
def test_axes_argument(self):
|
|
# plane == ji_plane, x== kji_space
|
|
plane1 = [[1, 2, 3],
|
|
[4, 5, 6],
|
|
[7, 8, 9]]
|
|
plane2 = [[10, 11, 12],
|
|
[13, 14, 15],
|
|
[16, 17, 18]]
|
|
plane3 = [[19, 20, 21],
|
|
[22, 23, 24],
|
|
[25, 26, 27]]
|
|
ki_plane1 = [[1, 2, 3],
|
|
[10, 11, 12],
|
|
[19, 20, 21]]
|
|
ki_plane2 = [[4, 5, 6],
|
|
[13, 14, 15],
|
|
[22, 23, 24]]
|
|
ki_plane3 = [[7, 8, 9],
|
|
[16, 17, 18],
|
|
[25, 26, 27]]
|
|
jk_plane1 = [[1, 10, 19],
|
|
[4, 13, 22],
|
|
[7, 16, 25]]
|
|
jk_plane2 = [[2, 11, 20],
|
|
[5, 14, 23],
|
|
[8, 17, 26]]
|
|
jk_plane3 = [[3, 12, 21],
|
|
[6, 15, 24],
|
|
[9, 18, 27]]
|
|
kj_plane1 = [[1, 4, 7],
|
|
[10, 13, 16], [19, 22, 25]]
|
|
kj_plane2 = [[2, 5, 8],
|
|
[11, 14, 17], [20, 23, 26]]
|
|
kj_plane3 = [[3, 6, 9],
|
|
[12, 15, 18], [21, 24, 27]]
|
|
ij_plane1 = [[1, 4, 7],
|
|
[2, 5, 8],
|
|
[3, 6, 9]]
|
|
ij_plane2 = [[10, 13, 16],
|
|
[11, 14, 17],
|
|
[12, 15, 18]]
|
|
ij_plane3 = [[19, 22, 25],
|
|
[20, 23, 26],
|
|
[21, 24, 27]]
|
|
ik_plane1 = [[1, 10, 19],
|
|
[2, 11, 20],
|
|
[3, 12, 21]]
|
|
ik_plane2 = [[4, 13, 22],
|
|
[5, 14, 23],
|
|
[6, 15, 24]]
|
|
ik_plane3 = [[7, 16, 25],
|
|
[8, 17, 26],
|
|
[9, 18, 27]]
|
|
ijk_space = [jk_plane1, jk_plane2, jk_plane3]
|
|
ikj_space = [kj_plane1, kj_plane2, kj_plane3]
|
|
jik_space = [ik_plane1, ik_plane2, ik_plane3]
|
|
jki_space = [ki_plane1, ki_plane2, ki_plane3]
|
|
kij_space = [ij_plane1, ij_plane2, ij_plane3]
|
|
x = array([plane1, plane2, plane3])
|
|
|
|
assert_array_almost_equal(fftn(x),
|
|
fftn(x, axes=(-3, -2, -1))) # kji_space
|
|
assert_array_almost_equal(fftn(x), fftn(x, axes=(0, 1, 2)))
|
|
assert_array_almost_equal(fftn(x, axes=(0, 2)), fftn(x, axes=(0, -1)))
|
|
y = fftn(x, axes=(2, 1, 0)) # ijk_space
|
|
assert_array_almost_equal(swapaxes(y, -1, -3), fftn(ijk_space))
|
|
y = fftn(x, axes=(2, 0, 1)) # ikj_space
|
|
assert_array_almost_equal(swapaxes(swapaxes(y, -1, -3), -1, -2),
|
|
fftn(ikj_space))
|
|
y = fftn(x, axes=(1, 2, 0)) # jik_space
|
|
assert_array_almost_equal(swapaxes(swapaxes(y, -1, -3), -3, -2),
|
|
fftn(jik_space))
|
|
y = fftn(x, axes=(1, 0, 2)) # jki_space
|
|
assert_array_almost_equal(swapaxes(y, -2, -3), fftn(jki_space))
|
|
y = fftn(x, axes=(0, 2, 1)) # kij_space
|
|
assert_array_almost_equal(swapaxes(y, -2, -1), fftn(kij_space))
|
|
|
|
y = fftn(x, axes=(-2, -1)) # ji_plane
|
|
assert_array_almost_equal(fftn(plane1), y[0])
|
|
assert_array_almost_equal(fftn(plane2), y[1])
|
|
assert_array_almost_equal(fftn(plane3), y[2])
|
|
|
|
y = fftn(x, axes=(1, 2)) # ji_plane
|
|
assert_array_almost_equal(fftn(plane1), y[0])
|
|
assert_array_almost_equal(fftn(plane2), y[1])
|
|
assert_array_almost_equal(fftn(plane3), y[2])
|
|
|
|
y = fftn(x, axes=(-3, -2)) # kj_plane
|
|
assert_array_almost_equal(fftn(x[:, :, 0]), y[:, :, 0])
|
|
assert_array_almost_equal(fftn(x[:, :, 1]), y[:, :, 1])
|
|
assert_array_almost_equal(fftn(x[:, :, 2]), y[:, :, 2])
|
|
|
|
y = fftn(x, axes=(-3, -1)) # ki_plane
|
|
assert_array_almost_equal(fftn(x[:, 0, :]), y[:, 0, :])
|
|
assert_array_almost_equal(fftn(x[:, 1, :]), y[:, 1, :])
|
|
assert_array_almost_equal(fftn(x[:, 2, :]), y[:, 2, :])
|
|
|
|
y = fftn(x, axes=(-1, -2)) # ij_plane
|
|
assert_array_almost_equal(fftn(ij_plane1), swapaxes(y[0], -2, -1))
|
|
assert_array_almost_equal(fftn(ij_plane2), swapaxes(y[1], -2, -1))
|
|
assert_array_almost_equal(fftn(ij_plane3), swapaxes(y[2], -2, -1))
|
|
|
|
y = fftn(x, axes=(-1, -3)) # ik_plane
|
|
assert_array_almost_equal(fftn(ik_plane1),
|
|
swapaxes(y[:, 0, :], -1, -2))
|
|
assert_array_almost_equal(fftn(ik_plane2),
|
|
swapaxes(y[:, 1, :], -1, -2))
|
|
assert_array_almost_equal(fftn(ik_plane3),
|
|
swapaxes(y[:, 2, :], -1, -2))
|
|
|
|
y = fftn(x, axes=(-2, -3)) # jk_plane
|
|
assert_array_almost_equal(fftn(jk_plane1),
|
|
swapaxes(y[:, :, 0], -1, -2))
|
|
assert_array_almost_equal(fftn(jk_plane2),
|
|
swapaxes(y[:, :, 1], -1, -2))
|
|
assert_array_almost_equal(fftn(jk_plane3),
|
|
swapaxes(y[:, :, 2], -1, -2))
|
|
|
|
y = fftn(x, axes=(-1,)) # i_line
|
|
for i in range(3):
|
|
for j in range(3):
|
|
assert_array_almost_equal(fft(x[i, j, :]), y[i, j, :])
|
|
y = fftn(x, axes=(-2,)) # j_line
|
|
for i in range(3):
|
|
for j in range(3):
|
|
assert_array_almost_equal(fft(x[i, :, j]), y[i, :, j])
|
|
y = fftn(x, axes=(0,)) # k_line
|
|
for i in range(3):
|
|
for j in range(3):
|
|
assert_array_almost_equal(fft(x[:, i, j]), y[:, i, j])
|
|
|
|
y = fftn(x, axes=()) # point
|
|
assert_array_almost_equal(y, x)
|
|
|
|
def test_shape_argument(self):
|
|
small_x = [[1, 2, 3],
|
|
[4, 5, 6]]
|
|
large_x1 = [[1, 2, 3, 0],
|
|
[4, 5, 6, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 0, 0, 0]]
|
|
|
|
y = fftn(small_x, shape=(4, 4))
|
|
assert_array_almost_equal(y, fftn(large_x1))
|
|
|
|
y = fftn(small_x, shape=(3, 4))
|
|
assert_array_almost_equal(y, fftn(large_x1[:-1]))
|
|
|
|
def test_shape_axes_argument(self):
|
|
small_x = [[1, 2, 3],
|
|
[4, 5, 6],
|
|
[7, 8, 9]]
|
|
large_x1 = array([[1, 2, 3, 0],
|
|
[4, 5, 6, 0],
|
|
[7, 8, 9, 0],
|
|
[0, 0, 0, 0]])
|
|
y = fftn(small_x, shape=(4, 4), axes=(-2, -1))
|
|
assert_array_almost_equal(y, fftn(large_x1))
|
|
y = fftn(small_x, shape=(4, 4), axes=(-1, -2))
|
|
|
|
assert_array_almost_equal(y, swapaxes(
|
|
fftn(swapaxes(large_x1, -1, -2)), -1, -2))
|
|
|
|
def test_shape_axes_argument2(self):
|
|
# Change shape of the last axis
|
|
x = numpy.random.random((10, 5, 3, 7))
|
|
y = fftn(x, axes=(-1,), shape=(8,))
|
|
assert_array_almost_equal(y, fft(x, axis=-1, n=8))
|
|
|
|
# Change shape of an arbitrary axis which is not the last one
|
|
x = numpy.random.random((10, 5, 3, 7))
|
|
y = fftn(x, axes=(-2,), shape=(8,))
|
|
assert_array_almost_equal(y, fft(x, axis=-2, n=8))
|
|
|
|
# Change shape of axes: cf #244, where shape and axes were mixed up
|
|
x = numpy.random.random((4, 4, 2))
|
|
y = fftn(x, axes=(-3, -2), shape=(8, 8))
|
|
assert_array_almost_equal(y,
|
|
numpy.fft.fftn(x, axes=(-3, -2), s=(8, 8)))
|
|
|
|
def test_shape_argument_more(self):
|
|
x = zeros((4, 4, 2))
|
|
with assert_raises(ValueError,
|
|
match="when given, axes and shape arguments"
|
|
" have to be of the same length"):
|
|
fftn(x, shape=(8, 8, 2, 1))
|
|
|
|
def test_invalid_sizes(self):
|
|
with assert_raises(ValueError,
|
|
match="invalid number of data points"
|
|
r" \(\[1, 0\]\) specified"):
|
|
fftn([[]])
|
|
|
|
with assert_raises(ValueError,
|
|
match="invalid number of data points"
|
|
r" \(\[4, -3\]\) specified"):
|
|
fftn([[1, 1], [2, 2]], (4, -3))
|
|
|
|
|
|
class TestIfftn:
|
|
dtype = None
|
|
cdtype = None
|
|
|
|
def setup_method(self):
|
|
np.random.seed(1234)
|
|
|
|
@pytest.mark.parametrize('dtype,cdtype,maxnlp',
|
|
[(np.float64, np.complex128, 2000),
|
|
(np.float32, np.complex64, 3500)])
|
|
def test_definition(self, dtype, cdtype, maxnlp):
|
|
x = np.array([[1, 2, 3],
|
|
[4, 5, 6],
|
|
[7, 8, 9]], dtype=dtype)
|
|
y = ifftn(x)
|
|
assert_equal(y.dtype, cdtype)
|
|
assert_array_almost_equal_nulp(y, direct_idftn(x), maxnlp)
|
|
|
|
x = random((20, 26))
|
|
assert_array_almost_equal_nulp(ifftn(x), direct_idftn(x), maxnlp)
|
|
|
|
x = random((5, 4, 3, 20))
|
|
assert_array_almost_equal_nulp(ifftn(x), direct_idftn(x), maxnlp)
|
|
|
|
@pytest.mark.parametrize('maxnlp', [2000, 3500])
|
|
@pytest.mark.parametrize('size', [1, 2, 51, 32, 64, 92])
|
|
def test_random_complex(self, maxnlp, size):
|
|
x = random([size, size]) + 1j*random([size, size])
|
|
assert_array_almost_equal_nulp(ifftn(fftn(x)), x, maxnlp)
|
|
assert_array_almost_equal_nulp(fftn(ifftn(x)), x, maxnlp)
|
|
|
|
def test_invalid_sizes(self):
|
|
with assert_raises(ValueError,
|
|
match="invalid number of data points"
|
|
r" \(\[1, 0\]\) specified"):
|
|
ifftn([[]])
|
|
|
|
with assert_raises(ValueError,
|
|
match="invalid number of data points"
|
|
r" \(\[4, -3\]\) specified"):
|
|
ifftn([[1, 1], [2, 2]], (4, -3))
|
|
|
|
|
|
class FakeArray:
|
|
def __init__(self, data):
|
|
self._data = data
|
|
self.__array_interface__ = data.__array_interface__
|
|
|
|
|
|
class FakeArray2:
|
|
def __init__(self, data):
|
|
self._data = data
|
|
|
|
def __array__(self, dtype=None, copy=None):
|
|
return self._data
|
|
|
|
|
|
class TestOverwrite:
|
|
"""Check input overwrite behavior of the FFT functions."""
|
|
|
|
real_dtypes = (np.float32, np.float64)
|
|
dtypes = real_dtypes + (np.complex64, np.complex128)
|
|
fftsizes = [8, 16, 32]
|
|
|
|
def _check(self, x, routine, fftsize, axis, overwrite_x):
|
|
x2 = x.copy()
|
|
for fake in [lambda x: x, FakeArray, FakeArray2]:
|
|
routine(fake(x2), fftsize, axis, overwrite_x=overwrite_x)
|
|
|
|
sig = "{}({}{!r}, {!r}, axis={!r}, overwrite_x={!r})".format(
|
|
routine.__name__, x.dtype, x.shape, fftsize, axis, overwrite_x)
|
|
if not overwrite_x:
|
|
assert_equal(x2, x, err_msg="spurious overwrite in %s" % sig)
|
|
|
|
def _check_1d(self, routine, dtype, shape, axis, overwritable_dtypes,
|
|
fftsize, overwrite_x):
|
|
np.random.seed(1234)
|
|
if np.issubdtype(dtype, np.complexfloating):
|
|
data = np.random.randn(*shape) + 1j*np.random.randn(*shape)
|
|
else:
|
|
data = np.random.randn(*shape)
|
|
data = data.astype(dtype)
|
|
|
|
self._check(data, routine, fftsize, axis,
|
|
overwrite_x=overwrite_x)
|
|
|
|
@pytest.mark.parametrize('dtype', dtypes)
|
|
@pytest.mark.parametrize('fftsize', fftsizes)
|
|
@pytest.mark.parametrize('overwrite_x', [True, False])
|
|
@pytest.mark.parametrize('shape,axes', [((16,), -1),
|
|
((16, 2), 0),
|
|
((2, 16), 1)])
|
|
def test_fft_ifft(self, dtype, fftsize, overwrite_x, shape, axes):
|
|
overwritable = (np.complex128, np.complex64)
|
|
self._check_1d(fft, dtype, shape, axes, overwritable,
|
|
fftsize, overwrite_x)
|
|
self._check_1d(ifft, dtype, shape, axes, overwritable,
|
|
fftsize, overwrite_x)
|
|
|
|
@pytest.mark.parametrize('dtype', real_dtypes)
|
|
@pytest.mark.parametrize('fftsize', fftsizes)
|
|
@pytest.mark.parametrize('overwrite_x', [True, False])
|
|
@pytest.mark.parametrize('shape,axes', [((16,), -1),
|
|
((16, 2), 0),
|
|
((2, 16), 1)])
|
|
def test_rfft_irfft(self, dtype, fftsize, overwrite_x, shape, axes):
|
|
overwritable = self.real_dtypes
|
|
self._check_1d(irfft, dtype, shape, axes, overwritable,
|
|
fftsize, overwrite_x)
|
|
self._check_1d(rfft, dtype, shape, axes, overwritable,
|
|
fftsize, overwrite_x)
|
|
|
|
def _check_nd_one(self, routine, dtype, shape, axes, overwritable_dtypes,
|
|
overwrite_x):
|
|
np.random.seed(1234)
|
|
if np.issubdtype(dtype, np.complexfloating):
|
|
data = np.random.randn(*shape) + 1j*np.random.randn(*shape)
|
|
else:
|
|
data = np.random.randn(*shape)
|
|
data = data.astype(dtype)
|
|
|
|
def fftshape_iter(shp):
|
|
if len(shp) <= 0:
|
|
yield ()
|
|
else:
|
|
for j in (shp[0]//2, shp[0], shp[0]*2):
|
|
for rest in fftshape_iter(shp[1:]):
|
|
yield (j,) + rest
|
|
|
|
if axes is None:
|
|
part_shape = shape
|
|
else:
|
|
part_shape = tuple(np.take(shape, axes))
|
|
|
|
for fftshape in fftshape_iter(part_shape):
|
|
self._check(data, routine, fftshape, axes,
|
|
overwrite_x=overwrite_x)
|
|
if data.ndim > 1:
|
|
self._check(data.T, routine, fftshape, axes,
|
|
overwrite_x=overwrite_x)
|
|
|
|
@pytest.mark.parametrize('dtype', dtypes)
|
|
@pytest.mark.parametrize('overwrite_x', [True, False])
|
|
@pytest.mark.parametrize('shape,axes', [((16,), None),
|
|
((16,), (0,)),
|
|
((16, 2), (0,)),
|
|
((2, 16), (1,)),
|
|
((8, 16), None),
|
|
((8, 16), (0, 1)),
|
|
((8, 16, 2), (0, 1)),
|
|
((8, 16, 2), (1, 2)),
|
|
((8, 16, 2), (0,)),
|
|
((8, 16, 2), (1,)),
|
|
((8, 16, 2), (2,)),
|
|
((8, 16, 2), None),
|
|
((8, 16, 2), (0, 1, 2))])
|
|
def test_fftn_ifftn(self, dtype, overwrite_x, shape, axes):
|
|
overwritable = (np.complex128, np.complex64)
|
|
self._check_nd_one(fftn, dtype, shape, axes, overwritable,
|
|
overwrite_x)
|
|
self._check_nd_one(ifftn, dtype, shape, axes, overwritable,
|
|
overwrite_x)
|
|
|
|
|
|
@pytest.mark.parametrize('func', [fftn, ifftn, fft2])
|
|
def test_shape_axes_ndarray(func):
|
|
# Test fftn and ifftn work with NumPy arrays for shape and axes arguments
|
|
# Regression test for gh-13342
|
|
a = np.random.rand(10, 10)
|
|
|
|
expect = func(a, shape=(5, 5))
|
|
actual = func(a, shape=np.array([5, 5]))
|
|
assert_equal(expect, actual)
|
|
|
|
expect = func(a, axes=(-1,))
|
|
actual = func(a, axes=np.array([-1,]))
|
|
assert_equal(expect, actual)
|
|
|
|
expect = func(a, shape=(4, 7), axes=(1, 0))
|
|
actual = func(a, shape=np.array([4, 7]), axes=np.array([1, 0]))
|
|
assert_equal(expect, actual)
|