|AHELP for CIAO 4.9||
Smooth the pixel values in an IMAGE crate (useful for add_image)
smooth_image_crate(crate, type, arguments...)
This routines smoothes the pixel values stored in an image crate. It is useful if you want to display the Crate using add_crate or make_figure, so that the WCS information is retained. If you want to smooth an image and save it to disk it is suggested that the aconvolve tool be used rather than this one.
The smoothing options are given below:
|gauss||sigma, nsigma=5||Smooth with a gaussian; sigma is in pixels and nsigma gives the half-width of the box over which the gaussian should be calculated.|
|boxcar||radius||Smooth with a box-car kernel of the given radius (in pixels). This is a square kernel with all pixels set to the same value.|
|tophat||radius||Smooth with a top-hat kernel of the given radius (in pixels). This is a circlular kernel where all pixels within the radius are set to the same value and those outside are set to 0.|
|image||kernel, norm=True, origin=None||Smooth with the given image (a 2D numpy array). The kernel is normalized by default (each pixel in the kernel is divided by the total signal in the kernel); this can be turned off by setting norm to False. The origin of the kernel is taken to be the image center; to change this set the origin parameter to the location of the pixel using (y,x), with x and y starting at 0 for the first pixel in the kernel. See "ahelp ciao_smooth" for more information on these parameters.|
|file||filename, norm=True, origin=None||Smooth with the image in the given file. The norm and origin parameters have the same meaning as the image option above.|
|none||Leaves the pixel values unchanged.|
This routine modifies the data in the crate; it does not return a modified copy. There is no way to undo the changes made by this routine other than to use read_file() to re-read in the original data.
The routine can be loaded into a ChIPS, Sherpa or a Python script by saying:
from crates_contrib.utils import *
The write_file() command can be used to write the smoothed data out to a new file. As mentioned above, the aconvolve tool is more appropriate if you wish to use the smoothed image with other CIAO tools.
chips> from crates_contrib.utils import * chips> cr = read_file("img.fits") chips> smooth_image_crate(cr, "gauss", 5) chips> add_image(cr) chips> set_plot_title(r"gaussian smoothing (\sigma=5)")
The gaussian-smoothed version of the data in img.fits is displayed using the ChIPS add_image() command.
chips> cr = read_file("img.fits") chips> smooth_image_crate(cr, "tophat", 3) chips> add_image(cr)
Here the image is smoothed with a 3-pixel radius top-hat kernel.
chips> cr = read_file("img.fits") chips> smooth_image_crate(cr, "file", "psf.fits")
Here the image is smoothed by the contents of the image file "psf.fits".
chips> cr = read_file("img.fits") chips> k = np.asarray([0,1,0,1,2,1,0,1,0]).reshape(3,3) chips> smooth_image_crate(cr, "image", k)
The image is smoothed by a user-supplied kernel, in this case:
chips> print(k) [[0 1 0] [1 2 1] [0 1 0]]
chips> cr = read_file("img.fits") chips> smooth_image_crate(cr, "gauss", 3) chips> scale_image_crate(cr, "arcsinh") chips> make_figure(cr, "image")
Here we smooth an image and then apply an arcsinh transform before displaying it in ChIPS.
In this example we create a routine called getfile which reads in an image from a file, smooths it with a 3-pixel radius top-hat function, applies square-root scaling to it, and then returns the crate.
chips> def getfile(fname): cr = read_file(fname) smooth_image_crate(cr, "tophat", 3) scale_image_crate(cr, "sqrt") return cr
This routine can then be used to create a three-color image using the following set of commands:
chips> r = getfile("evt2.fits[energy=500:1500][bin sky=::4]") chips> g = getfile("evt2.fits[energy=1500:3500][bin sky=::4]") chips> b = getfile("evt2.fits[energy=3500:700][bin sky=::4]") chips> add_image(r, g, b)
See the bug pages on the CIAO website for an up-to-date listing of known bugs.
Refer to the CIAO bug pages for an up-to-date listing of known issues.