# jax.numpy.fft.ifftn#

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

Compute the N-dimensional inverse discrete Fourier Transform.

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

Original docstring below.

This function computes the inverse of the N-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, `ifftn(fftn(a)) == a` to within numerical accuracy. For a description of the definitions and conventions used, see numpy.fft.

The input, analogously to ifft, should be ordered in the same way as is returned by fftn, i.e. it should have the term for zero frequency in all axes in the low-order corner, the positive frequency terms in the first half of all axes, the term for the Nyquist frequency in the middle of all axes and the negative frequency terms in the second half of all axes, in order of decreasingly negative frequency.

Parameters:
• a (array_like) â€“ Input array, can be complex.

• s (sequence of ints, optional) â€“ Shape (length of each transformed axis) of the output (`s[0]` refers to axis 0, `s[1]` to axis 1, etc.). This corresponds to `n` for `ifft(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. See notes for issue on ifft zero padding.

• axes (sequence of ints, optional) â€“ Axes over which to compute the IFFT. If not given, the last `len(s)` axes are used, or all axes if s is also not specified. Repeated indices in axes means that the inverse transform over that axis is performed multiple times.

• 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 or a, as explained in the parameters section above.

Return type:

complex ndarray