smooth

image_registration.fft_tools.smooth(image, kernelwidth=3, kerneltype='gaussian', trapslope=None, silent=True, psf_pad=True, interp_nan=False, nwidths='max', min_nwidths=6, return_kernel=False, normalize_kernel=<function sum>, downsample=False, downsample_factor=None, **kwargs)

Returns a smoothed image using a gaussian, boxcar, or tophat kernel

Parameters:

kernelwidth

width of kernel in pixels (see definitions below)

kerneltypegaussian, boxcar, or tophat

• a gaussian, uses a gaussian with sigma = kernelwidth (in pixels) out to [nwidths]-sigma

• a boxcar is a kernelwidth x kernelwidth square

• a tophat is a flat circle with radius = kernelwidth

psf_pad[True]

will pad the input image to be the image size + PSF. Slows things down but removes edge-wrapping effects (see convolve) This option should be set to false if the edges of your image are symmetric.

interp_nan[False]

Will replace NaN points in an image with the smoothed average of its neighbors (you can still simply ignore NaN values by setting ignore_nan=True but leaving interp_nan=False)

silent[True]

turn it off to get verbose statements about kernel types

return_kernel[False]

If set to true, will return the kernel as the second return value

nwidths[‘max’]

number of kernel widths wide to make the kernel. Set to ‘max’ to match the image shape, otherwise use any integer

min_nwidths[6]

minimum number of gaussian widths to make the kernel (the kernel will be larger than the image if the image size is < min_widths*kernelsize)

normalize_kernel

Should the kernel preserve the map sum (i.e. kernel.sum() = 1) or the kernel peak (i.e. kernel.max() = 1) ? Must be a function that can operate on a numpy array

downsample

downsample after smoothing?

downsample_factor

if None, default to kernelwidth

Notes

Note that the kernel is forced to be even sized on each axis to assure no offset when smoothing.