fits

I have three fits images in the form of 2D numpy arrays. I want to median combine them, that is, generate an output array in which each pixel is the median of the same pixel in the three input arrays. This can be done easily on IRAF using imcombine. Is there a way to do this on Python without looping through the entire array and taking the median of each pixel? The easiest way to do this is: Stack the 2d arrays to form a 3d array Compute the median using numpy.median passing axis=0 to compute along the dimension of stacking. You're essentially computing an element-wise median. Here's a simple

I'm currently working with some fits tables and I'm having trouble with outputting in Astropy.io.fits. Essentially, I am slicing out a bunch of rows that have data for objects I'm not interested in, but when I save the new table all of those rows have magically reappeared. For example: import astropy.io.fits as fits import numpy as np hdu = fits.open('some_fits_file.fits')[1].data sample_slice = [True True True False False True] hdu_sliced = hdu[sample_slice] Now my naive mind expects that "hdu" has 6 rows and hdu_sliced has 4 rows, which is what you would get if you used np.size(). So if I

I am tying to get the physical sky coordinates of a given pixel from within a python script. I would like to use astropy's WCS, but I'll do anything from within python. I have tried these two snippets of code. from astropy.io import fits from astropy.wcs import WCS def astropymethod1(img): # from http://astropy.readthedocs.org/en/latest/wcs/ w = WCS(img) lon, lat = w.all_pix2world( 100., 100., 1) print lon, lat def astropymethod2(img): # from http://astropy.readthedocs.org/en/latest/wcs/ hdu = fits.open(img) w = WCS(hdu[0].header) lon, lat = w.wcs_pix2world(100., 100., 1) print lon, lat The