# jax.numpy.fft.rfftn#

jax.numpy.fft.rfftn(a, s=None, axes=None, norm=None)[source]#

Compute the N-dimensional discrete Fourier Transform for real input.

LAX-backend implementation of `numpy.fft.rfftn()`.

Original docstring below.

This function computes the N-dimensional discrete Fourier Transform over any number of axes in an M-dimensional real array by means of the Fast Fourier Transform (FFT). By default, all axes are transformed, with the real transform performed over the last axis, while the remaining transforms are complex.

Parameters:
• a (array_like) â€“ Input array, taken to be real.

• s (sequence of ints, optional) â€“ Shape (length along each transformed axis) to use from the input. (`s[0]` refers to axis 0, `s[1]` to axis 1, etc.). The final element of s corresponds to n for `rfft(x, n)`, while for the remaining axes, it corresponds to n for `fft(x, n)`. Along any axis, if the given shape is smaller than that of the input, the input is cropped. If it is larger, the input is padded with zeros. if s is not given, the shape of the input along the axes specified by axes is used.

• axes (sequence of ints, optional) â€“ Axes over which to compute the FFT. If not given, the last `len(s)` axes are used, or all axes if s is also not specified.

• norm ({"backward", "ortho", "forward"}, optional) â€“

Returns:

out â€“ The truncated or zero-padded input, transformed along the axes indicated by axes, or by a combination of s and a, as explained in the parameters section above. The length of the last axis transformed will be `s[-1]//2+1`, while the remaining transformed axes will have lengths according to s, or unchanged from the input.

Return type:

complex ndarray