I've looked all over the place and am not finding a solution to this issue. I feel like it should be fairly straightforward, but we'll see.
I have a .FITS format data cube and I need to collapse it into a 2D FITS image. The data cube has two spacial dimensions and one spectral/velocity dimension.
Just looking for a simple python routine to load in the cube and flatten all these layers (i.e. integrate them along the spectral/velocity axis). Thanks for any help.
OK, this seems to work:
import pyfits
import numpy as np
hdulist = pyfits.open(filename)
header = hdulist[0].header
data = hdulist[0].data
data = np.nan_to_num(data)
new_data = data[0]
for i in range(1,84): #this depends on number of layers or pages
new_data += data[i]
hdu = pyfits.PrimaryHDU(new_data)
hdu.writeto(new_filename)
One problem with this routine is that WCS coordinates (which are attached to the original data cube) are lost during this conversion.