(x_gpu, y_gpu, plan, scale=False)
Inverse Fast Fourier Transform.

    Compute the inverse FFT of some data in device memory using the
    specified plan.

    Parameters
    ----------
    x_gpu : pycuda.gpuarray.GPUArray
        Input array.
    y_gpu : pycuda.gpuarray.GPUArray
        Inverse FFT of input array.
    plan : Plan
        FFT plan.
    scale : bool, optional
        If True, scale the computed inverse FFT by the number of
        elements in the output array.

    Examples
    --------
    >>> import pycuda.autoinit
    >>> import pycuda.gpuarray as gpuarray
    >>> import numpy as np
    >>> from skcuda.fft import fft, Plan
    >>> N = 128
    >>> x = np.asarray(np.random.rand(N), np.float32)
    >>> xf = np.asarray(np.fft.fft(x), np.complex64)
    >>> xf_gpu = gpuarray.to_gpu(xf[0:N/2+1])
    >>> x_gpu = gpuarray.empty(N, np.float32)
    >>> plan = Plan(N, np.complex64, np.float32)
    >>> ifft(xf_gpu, x_gpu, plan, True)
    >>> np.allclose(x, x_gpu.get(), atol=1e-6)
    True

    Notes
    -----
    For complex to real transformations, this function assumes the
    input contains N/2+1 non-redundant FFT coefficents of a signal of
    length N.
Inverse Fast Fourier Transform.
Inverse Fast Fourier Transform .
def ifft(x_gpu, y_gpu, plan, scale=False):
    """
    Inverse Fast Fourier Transform.

    Compute the inverse FFT of some data in device memory using the
    specified plan.

    Parameters
    ----------
    x_gpu : pycuda.gpuarray.GPUArray
        Input array.
    y_gpu : pycuda.gpuarray.GPUArray
        Inverse FFT of input array.
    plan : Plan
        FFT plan.
    scale : bool, optional
        If True, scale the computed inverse FFT by the number of
        elements in the output array.

    Examples
    --------
    >>> import pycuda.autoinit
    >>> import pycuda.gpuarray as gpuarray
    >>> import numpy as np
    >>> from skcuda.fft import fft, Plan
    >>> N = 128
    >>> x = np.asarray(np.random.rand(N), np.float32)
    >>> xf = np.asarray(np.fft.fft(x), np.complex64)
    >>> xf_gpu = gpuarray.to_gpu(xf[0:N/2+1])
    >>> x_gpu = gpuarray.empty(N, np.float32)
    >>> plan = Plan(N, np.complex64, np.float32)
    >>> ifft(xf_gpu, x_gpu, plan, True)
    >>> np.allclose(x, x_gpu.get(), atol=1e-6)
    True

    Notes
    -----
    For complex to real transformations, this function assumes the
    input contains N/2+1 non-redundant FFT coefficents of a signal of
    length N.
    """

    if scale == True:
        _fft(x_gpu, y_gpu, plan, cufft.CUFFT_INVERSE, y_gpu.size/plan.batch)
    else:
        _fft(x_gpu, y_gpu, plan, cufft.CUFFT_INVERSE)
def ifft ( x_gpu , y_gpu , plan , scale = False ) : if scale == True : _fft ( x_gpu , y_gpu , plan , cufft . CUFFT_INVERSE , y_gpu . size / plan . batch ) else : _fft ( x_gpu , y_gpu , plan , cufft . CUFFT_INVERSE )