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# numpy.fft.irfft2

fft.irfft2(a, s=None, axes=(-2, -1), norm=None, out=None)

  Computes the inverse of `rfft2`.

  Parameters:
    a array_like

      The input array

    s sequence of ints, optional

      Shape of the real output to the inverse FFT.

      Changed in version 2.0: If it is `-1`, the whole input is used (no padding/trimming).

      Deprecated since version 2.0: If s is not `None`, axes must not be `None` either.

      Deprecated since version 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

      The axes over which to compute the inverse fft. Default: `(-2, -1)`, the last two axes.

      Deprecated since version 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.

      New in version 1.20.0: The “backward”, “forward” values were added.

    out ndarray, optional

      If provided, the result will be placed in this array. It should be of the appropriate shape and dtype for the last transformation.

      New in version 2.0.0.

  Returns:
    out ndarray

      The result of the inverse real 2-D FFT.

  See also

  `rfft2`

    The forward two-dimensional FFT of real input, of which `irfft2` is the inverse.

  `rfft`

    The one-dimensional FFT for real input.

  `irfft`

    The inverse of the one-dimensional FFT of real input.

  `irfftn`

    Compute the inverse of the N-dimensional FFT of real input.

  Notes

  This is really `irfftn` with different defaults. For more details see `irfftn`.

  Examples

      >>> import numpy as np
      >>> a = np.mgrid[:5, :5][0]
      >>> A = np.fft.rfft2(a)
      >>> np.fft.irfft2(A, s=a.shape)
      array([[0., 0., 0., 0., 0.],
             [1., 1., 1., 1., 1.],
             [2., 2., 2., 2., 2.],
             [3., 3., 3., 3., 3.],
             [4., 4., 4., 4., 4.]])