convolvend

image_registration.fft_tools.convolve_nd.convolvend(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)

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

Also note that the astropy.convolution convolver is a more up-to-date version of this one.

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)

boundary: str, optional

A flag indicating how to handle boundaries:

• ‘fill’set values outside the array boundary to fill_value

  (default)

• ‘wrap’ : periodic boundary

interpolate_nan: bool

attempts to re-weight assuming NAN values are meant to be ignored, not treated as zero. If this is off, all NaN values will be treated as zero.

ignore_edge_zeros: bool

Ignore the zero-pad-created zeros. This will effectively decrease the kernel area on the edges but will not re-normalize the kernel. This parameter may result in ‘edge-brightening’ effects if you’re using a normalized kernel

min_wt: float

If ignoring NANs/zeros, force all grid points with a weight less than this value to NAN (the weight of a grid point with no ignored neighbors is 1.0). If min_wt == 0.0, then all zero-weight points will be set to zero instead of NAN (which they would be otherwise, because 1/0 = nan). See the examples below

normalize_kernel: function or boolean

if specified, function to divide kernel by to normalize it. e.g., normalize_kernel=np.sum means that kernel will be modified to be: kernel = kernel / np.sum(kernel). If True, defaults to normalize_kernel = np.sum

fft_pad: bool

Default on. Zero-pad image to the nearest 2^n

psf_pad: bool

Default off. Zero-pad image to be at least the sum of the image sizes (in order to avoid edge-wrapping when smoothing)

crop: bool

Default on. Return an image of the size of the largest input image. If the images are asymmetric in opposite directions, will return the largest image in both directions. For example, if an input image has shape [100,3] but a kernel with shape [6,6] is used, the output will be [100,6].

return_fft: bool

Return the fft(image)*fft(kernel) instead of the convolution (which is ifft(fft(image)*fft(kernel))). Useful for making PSDs.

fftshift: bool

If return_fft on, will shift & crop image to appropriate dimensions

nthreads: int

if fftw3 is installed, can specify the number of threads to allow FFTs to use. Probably only helpful for large arrays

use_numpy_fft: bool

Force the code to use the numpy FFTs instead of FFTW even if FFTW is installed

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.])