我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用numpy.fft.irfftn()。
def run_test_c2r_impl(self, shape, axes, fftshift=False): ishape = list(shape) oshape = list(shape) ishape[axes[-1]] = shape[axes[-1]] // 2 + 1 oshape[axes[-1]] = (ishape[axes[-1]] - 1) * 2 ishape[-1] *= 2 # For complex known_data = np.random.normal(size=ishape).astype(np.float32).view(np.complex64) idata = bf.ndarray(known_data, space='cuda') odata = bf.ndarray(shape=oshape, dtype='f32', space='cuda') fft = Fft() fft.init(idata, odata, axes=axes, apply_fftshift=fftshift) fft.execute(idata, odata) # Note: Numpy applies normalization while CUFFT does not norm = reduce(lambda a, b: a * b, [shape[d] for d in axes]) if fftshift: known_data = np.fft.ifftshift(known_data, axes=axes) known_result = gold_irfftn(known_data, axes=axes) * norm compare(odata.copy('system'), known_result)
def test_not_last_axis_success(self): ar, ai = np.random.random((2, 16, 8, 32)) a = ar + 1j*ai axes = (-2,) # Should not raise error fft.irfftn(a, axes=axes)
def compute(a, b): """ Compute an optimal displacement between two ndarrays. Finds the displacement between two ndimensional arrays. Arrays must be of the same size. Algorithm uses a cross correlation, computed efficiently through an n-dimensional fft. Parameters ---------- a : ndarray The first array b : ndarray The second array """ from numpy.fft import rfftn, irfftn from numpy import unravel_index, argmax # compute real-valued cross-correlation in fourier domain s = a.shape f = rfftn(a) f *= rfftn(b).conjugate() c = abs(irfftn(f, s)) # find location of maximum inds = unravel_index(argmax(c), s) # fix displacements that are greater than half the total size pairs = zip(inds, a.shape) # cast to basic python int for serialization adjusted = [int(d - n) if d > n // 2 else int(d) for (d, n) in pairs] return Displacement(adjusted)
