rfftn(a, s=None, axes=None, norm=None)¶
Compute the N-dimensional discrete Fourier Transform for real input.
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.
Input array, taken to be real.
Shape (length along each transformed axis) to use from the input.
s refers to axis 0,
s 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
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 over which to compute the FFT. If not given, the last
axes are used, or all axes if s is also not specified.
New in version 1.10.0.
Normalization mode (see
numpy.fft). Default is None.
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
while the remaining transformed axes will have lengths according to
s, or unchanged from the input.
If s and axes have different length.
If an element of axes is larger than than the number of axes of a.
The transform for real input is performed over the last transformation
axis, as by
rfft, then the transform over the remaining axes is
performed as by
fftn. The order of the output is as for
rfft for the
final transformation axis, and as for
fftn for the remaining
fft for details, definitions and conventions used.
>>> a = np.ones((2, 2, 2)) >>> np.fft.rfftn(a) array([[[8.+0.j, 0.+0.j], # may vary [0.+0.j, 0.+0.j]], [[0.+0.j, 0.+0.j], [0.+0.j, 0.+0.j]]])
>>> np.fft.rfftn(a, axes=(2, 0)) array([[[4.+0.j, 0.+0.j], # may vary [4.+0.j, 0.+0.j]], [[0.+0.j, 0.+0.j], [0.+0.j, 0.+0.j]]])