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Module « numpy.fft »

Fonction ihfft - module numpy.fft

Signature de la fonction ihfft

def ihfft(a, n=None, axis=-1, norm=None, out=None) 

Description

help(numpy.fft.ihfft)

Compute the inverse FFT of a signal that has Hermitian symmetry.

Parameters
----------
a : array_like
    Input array.
n : int, optional
    Length of the inverse FFT, the number of points along
    transformation axis in the input to use.  If `n` is smaller than
    the length of the input, the input is cropped.  If it is larger,
    the input is padded with zeros. If `n` is not given, the length of
    the input along the axis specified by `axis` is used.
axis : int, optional
    Axis over which to compute the inverse FFT. If not given, the last
    axis is used.
norm : {"backward", "ortho", "forward"}, optional
    Normalization mode (see `numpy.fft`). Default is "backward".
    Indicates which direction of the forward/backward pair of transforms
    is scaled and with what normalization factor.

    .. versionadded:: 1.20.0

        The "backward", "forward" values were added.

out : complex ndarray, optional
    If provided, the result will be placed in this array. It should be
    of the appropriate shape and dtype.

    .. versionadded:: 2.0.0

Returns
-------
out : complex ndarray
    The truncated or zero-padded input, transformed along the axis
    indicated by `axis`, or the last one if `axis` is not specified.
    The length of the transformed axis is ``n//2 + 1``.

See also
--------
hfft, irfft

Notes
-----
`hfft`/`ihfft` are a pair analogous to `rfft`/`irfft`, but for the
opposite case: here the signal has Hermitian symmetry in the time
domain and is real in the frequency domain. So here it's `hfft` for
which you must supply the length of the result if it is to be odd:

* even: ``ihfft(hfft(a, 2*len(a) - 2)) == a``, within roundoff error,
* odd: ``ihfft(hfft(a, 2*len(a) - 1)) == a``, within roundoff error.

Examples
--------
>>> import numpy as np
>>> spectrum = np.array([ 15, -4, 0, -1, 0, -4])
>>> np.fft.ifft(spectrum)
array([1.+0.j,  2.+0.j,  3.+0.j,  4.+0.j,  3.+0.j,  2.+0.j]) # may vary
>>> np.fft.ihfft(spectrum)
array([ 1.-0.j,  2.-0.j,  3.-0.j,  4.-0.j]) # may vary

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