Vous êtes un professionnel et vous avez besoin d'une formation ? Deep Learning avec Python
et Keras et Tensorflow Voir le programme détaillé

Module « numpy.fft »

Fonction ifft2 - module numpy.fft

Signature de la fonction ifft2

def ifft2(a, s=None, axes=(-2, -1), norm=None, out=None) 

Description

help(numpy.fft.ifft2)

Compute the 2-dimensional inverse discrete Fourier Transform.

This function computes the inverse of the 2-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, ``ifft2(fft2(a)) == a``
to within numerical accuracy.  By default, the inverse transform is
computed over the last two axes of the input array.

The input, analogously to `ifft`, should be ordered in the same way as is
returned by `fft2`, i.e. it should have the term for zero frequency
in the low-order corner of the two axes, the positive frequency terms in
the first half of these axes, the term for the Nyquist frequency in the
middle of the axes and the negative frequency terms in the second half of
both axes, in order of decreasingly negative frequency.

Parameters
----------
a : array_like
    Input array, can be complex.
s : sequence of ints, optional
    Shape (length of each 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 each 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.

    .. versionchanged:: 2.0

        If it is ``-1``, the whole input is used (no padding/trimming).

    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.

    .. deprecated:: 2.0

        If `s` is not ``None``, `axes` must not be ``None`` either.

    .. deprecated:: 2.0

        `s` must contain only ``int`` s, not ``None`` values. ``None``
        values currently mean that the default value for ``n`` is used
        in the corresponding 1-D transform, but this behaviour is
        deprecated.

axes : sequence of ints, optional
    Axes over which to compute the FFT.  If not given, the last two
    axes are used.  A repeated index in `axes` means the transform over
    that axis is performed multiple times.  A one-element sequence means
    that a one-dimensional FFT is performed. Default: ``(-2, -1)``.

    .. deprecated:: 2.0

        If `s` is specified, the corresponding `axes` to be transformed
        must not be ``None``.

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 for all axes (and hence is
    incompatible with passing in all but the trivial ``s``).

    .. versionadded:: 2.0.0

Returns
-------
out : complex ndarray
    The truncated or zero-padded input, transformed along the axes
    indicated by `axes`, or the last two axes if `axes` is not given.

Raises
------
ValueError
    If `s` and `axes` have different length, or `axes` not given and
    ``len(s) != 2``.
IndexError
    If an element of `axes` is larger than than the number of axes of `a`.

See Also
--------
numpy.fft : Overall view of discrete Fourier transforms, with definitions
     and conventions used.
fft2 : The forward 2-dimensional FFT, of which `ifft2` is the inverse.
ifftn : The inverse of the *n*-dimensional FFT.
fft : The one-dimensional FFT.
ifft : The one-dimensional inverse FFT.

Notes
-----
`ifft2` is just `ifftn` with a different default for `axes`.

See `ifftn` for details and a plotting example, and `numpy.fft` for
definition and conventions used.

Zero-padding, analogously with `ifft`, is performed by appending zeros to
the input along the specified dimension.  Although this is the common
approach, it might lead to surprising results.  If another form of zero
padding is desired, it must be performed before `ifft2` is called.

Examples
--------
>>> import numpy as np
>>> a = 4 * np.eye(4)
>>> np.fft.ifft2(a)
array([[1.+0.j,  0.+0.j,  0.+0.j,  0.+0.j], # may vary
       [0.+0.j,  0.+0.j,  0.+0.j,  1.+0.j],
       [0.+0.j,  0.+0.j,  1.+0.j,  0.+0.j],
       [0.+0.j,  1.+0.j,  0.+0.j,  0.+0.j]])

Vous êtes un professionnel et vous avez besoin d'une formation ? Calcul scientifique
avec Python Voir le programme détaillé