convolve

fft_psd_tools.convolve(array, kernel, boundary='fill', fill_value=0, crop=True, return_fft=False, fftshift=True, fft_pad=True, psf_pad=False, interpolate_nan=False, quiet=False, ignore_edge_zeros=False, min_wt=0.0, normalize_kernel=False, use_numpy_fft=True, nthreads=1, complextype=<type 'numpy.complex128'>, use_rfft=False)

Convolve an ndarray with an nd-kernel. Returns a convolved image with shape = array.shape. Assumes image & kernel are centered.

Parameters:

array: `numpy.ndarray`

Array to be convolved with kernel

kernel: `numpy.ndarray`

Will be normalized if normalize_kernel is set. Assumed to be centered (i.e., shifts may result if your kernel is asymmetric)

Returns:

default: array convolved with kernel

if return_fft: fft(array) * fft(kernel)

• if fftshift: Determines whether the fft will be shifted before returning

if not(`crop`) : Returns the image, but with the fft-padded size

instead of the input size

Examples

>>> convolvend([1,0,3],[1,1,1])
array([ 1.,  4.,  3.])

>>> convolvend([1,np.nan,3],[1,1,1],quiet=True)
array([ 1.,  4.,  3.])

>>> convolvend([1,0,3],[0,1,0])
array([ 1.,  0.,  3.])

>>> convolvend([1,2,3],[1])
array([ 1.,  2.,  3.])

>>> convolvend([1,np.nan,3],[0,1,0], interpolate_nan=True)
array([ 1.,  0.,  3.])

>>> convolvend([1,np.nan,3],[0,1,0], interpolate_nan=True, min_wt=1e-8)
array([  1.,  nan,   3.])

>>> convolvend([1,np.nan,3],[1,1,1], interpolate_nan=True)
array([ 1.,  4.,  3.])

>>> convolvend([1,np.nan,3],[1,1,1], interpolate_nan=True, normalize_kernel=True, ignore_edge_zeros=True)
array([ 1.,  2.,  3.])