Gaussian Gradient Filter

Construct Gaussian Gradient Filtered images of Chandra data (this is important because we use the fact that each pixel is 0.492”)

Please fill out input file and run python GGF_main.py data.i

This will create GGF images (and fits) for the given sigma values.

Documentation:

Construct gaussian gradient filtered images of Chandra data

GGF.GGF_main.combine_ggf(img_dir, infiles, radius_bins, weight_bins, fits_file)

Combine GGF plots. For each radial region, we choose a weight for the image.

  1. Create mask for each image based of weight_bins and radius_bins

  2. Add weighted images together for each bin

  3. Reconstruct complete weighted image by recombining weighted, binned image

The image files all need to be the same size and of the same region!

Parameters

  • img_dir (str) – Full path to image files

  • infiles (str) – List of input files – GGF

  • radius_bins (str) – List of radii used for binning

  • weight_bins (str) – List of bin weights corresponding to each GGF image

  • fits_file (str) – Input Fits Image File for header info

Returns

Reconstructed weighted-GGF image and fits file

GGF.GGF_main.ggf1(infile, outfile, sigma)

Creates a log-scaled, smoothed, gaussian gradient filtered image (in that order) from a fits file

Parameters

  • infile (str) – fits image file to read in

  • outfile (str) – fits file to create

  • sigma (int) – sigma value for gaussian used in filtering

Returns

Both fits file and png image

GGF.GGF_main.make_radial_mask(img_array, radii, center_pixel)

Create mask for image based off weight bins, center pixel, and radial values

Parameters

  • img_array (array) – numpy array from reading in image

  • radii (float,float) – (R_in,R_out) tuple of inner and outer radius WITHIN mask

  • center_pixel (float,float) – (X,Y) tuple of central pixel from which the radial bins expand

Returns

radial mask for image as np array of booleans