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

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

    Parameters
    ----------
    x_gpu : pycuda.gpuarray.GPUArray
        Input array.
    y_gpu : pycuda.gpuarray.GPUArray
        FFT of input array.
    plan : Plan
        FFT plan.
    scale : bool, optional
        If True, scale the computed FFT by the number of elements in
        the input 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.fft.fft(x)
    >>> x_gpu = gpuarray.to_gpu(x)
    >>> xf_gpu = gpuarray.empty(N/2+1, np.complex64)
    >>> plan = Plan(x.shape, np.float32, np.complex64)
    >>> fft(x_gpu, xf_gpu, plan)
    >>> np.allclose(xf[0:N/2+1], xf_gpu.get(), atol=1e-6)
    True

    Returns
    -------
    y_gpu : pycuda.gpuarray.GPUArray
        Computed FFT.

    Notes
    -----
    For real to complex transformations, this function computes
    N/2+1 non-redundant coefficients of a length-N input signal.
Fast Fourier Transform.
Fast Fourier Transform .
def fft(x_gpu, y_gpu, plan, scale=False):
    """
    Fast Fourier Transform.

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

    Parameters
    ----------
    x_gpu : pycuda.gpuarray.GPUArray
        Input array.
    y_gpu : pycuda.gpuarray.GPUArray
        FFT of input array.
    plan : Plan
        FFT plan.
    scale : bool, optional
        If True, scale the computed FFT by the number of elements in
        the input 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.fft.fft(x)
    >>> x_gpu = gpuarray.to_gpu(x)
    >>> xf_gpu = gpuarray.empty(N/2+1, np.complex64)
    >>> plan = Plan(x.shape, np.float32, np.complex64)
    >>> fft(x_gpu, xf_gpu, plan)
    >>> np.allclose(xf[0:N/2+1], xf_gpu.get(), atol=1e-6)
    True

    Returns
    -------
    y_gpu : pycuda.gpuarray.GPUArray
        Computed FFT.

    Notes
    -----
    For real to complex transformations, this function computes
    N/2+1 non-redundant coefficients of a length-N input signal.
    """

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