def convolve_fft ( array , kernel ) : array = np . asarray ( array , dtype = np . complex ) kernel = np . asarray ( kernel , dtype = np . complex ) if array . ndim != kernel . ndim : raise ValueError ( "Image and kernel must have same number of " "dimensions" ) array_shape = array . shape kernel_shape = kernel . shape new_shape = np . array ( array_shape ) + np . array ( kernel_shape ) array_slices = [ ] kernel_slices = [ ] for ( new_dimsize , array_dimsize , kernel_dimsize ) in zip ( new_shape , array_shape , kernel_shape ) : center = new_dimsize - ( new_dimsize + 1 ) // 2 array_slices += [ slice ( center - array_dimsize // 2 , center + ( array_dimsize + 1 ) // 2 ) ] kernel_slices += [ slice ( center - kernel_dimsize // 2 , center + ( kernel_dimsize + 1 ) // 2 ) ] array_slices = tuple ( array_slices ) kernel_slices = tuple ( kernel_slices ) if not np . all ( new_shape == array_shape ) : big_array = np . zeros ( new_shape , dtype = np . complex ) big_array [ array_slices ] = array else : big_array = array if not np . all ( new_shape == kernel_shape ) : big_kernel = np . zeros ( new_shape , dtype = np . complex ) big_kernel [ kernel_slices ] = kernel else : big_kernel = kernel array_fft = np . fft . fftn ( big_array ) kernel_fft = np . fft . fftn ( np . fft . ifftshift ( big_kernel ) ) rifft = np . fft . ifftn ( array_fft * kernel_fft ) return rifft [ array_slices ] . real
python convolution kernel to fuse boundary
Convolve an array with a kernel using FFT . Implemntation based on the convolve_fft function from astropy .
def convolve_fft(array, kernel):
    """
    Convolve an array with a kernel using FFT.
    Implemntation based on the convolve_fft function from astropy.

    https://github.com/astropy/astropy/blob/master/astropy/convolution/convolve.py
    """

    array = np.asarray(array, dtype=np.complex)
    kernel = np.asarray(kernel, dtype=np.complex)

    if array.ndim != kernel.ndim:
        raise ValueError("Image and kernel must have same number of "
                         "dimensions")

    array_shape = array.shape
    kernel_shape = kernel.shape
    new_shape = np.array(array_shape) + np.array(kernel_shape)

    array_slices = []
    kernel_slices = []
    for (new_dimsize, array_dimsize, kernel_dimsize) in zip(
            new_shape, array_shape, kernel_shape):
        center = new_dimsize - (new_dimsize + 1) // 2
        array_slices += [slice(center - array_dimsize // 2,
                         center + (array_dimsize + 1) // 2)]
        kernel_slices += [slice(center - kernel_dimsize // 2,
                          center + (kernel_dimsize + 1) // 2)]

    array_slices = tuple(array_slices)
    kernel_slices = tuple(kernel_slices)

    if not np.all(new_shape == array_shape):
        big_array = np.zeros(new_shape, dtype=np.complex)
        big_array[array_slices] = array
    else:
        big_array = array

    if not np.all(new_shape == kernel_shape):
        big_kernel = np.zeros(new_shape, dtype=np.complex)
        big_kernel[kernel_slices] = kernel
    else:
        big_kernel = kernel

    array_fft = np.fft.fftn(big_array)
    kernel_fft = np.fft.fftn(np.fft.ifftshift(big_kernel))

    rifft = np.fft.ifftn(array_fft * kernel_fft)

    return rifft[array_slices].real