I'm using Healpy (the HEALPix tools developed in Python) to read and write full-sky CMB maps.
I am confused as to how the coordinates of pixels on a sky map translates into entries of a numpy ndarray. How does one HEALPix pixel in a FITS file translate into an ndarray entry?
For example, let's say I have a generated CMB map, Nside=64, lmax=64, using the default RING scheme. The total number of pixels is given by Npix=12*Nside**2. So, for my example, that is 49152 total number of pixels.
The FITS file I have is in the format 1 column by 48 rows. (The 1 column is all temperature values.) So, I transform this file into a n-dim numpy ndarray. The shape of this array gives (48,1024).
Question 1: Is this 2-d array a projection of all the map pixels on the sky into "matrix" form? I think it is. Using my example, 48*1024 = 49152, the total number of pixels.
Question 2: Where does the 1024 standard come from? What does this mean in terms of a HEALPix map and its coordinates? How does one 2-dim ndarray entry (pixel) relate to another (by angle, by HEALPix position, etc.)?
For another example, map Nside=1024. When I convert this into a 2-dimensional ndarray, I get the shape (12288, 1024).
EDIT: Why does this convention of 1024-element arrays exist? How does this correspond to the pixel coordinates on a map?
the best source of information about the HEALPIX pixelization is the Healpix primer:
http://healpix.jpl.nasa.gov/html/intro.htm
A map is a 1-dimensional vector, each index is a pixel in the sky.
you can turn pixel number into coordinates in the sky using hp.pix2ang.
Shaping it in rows of 1024 is just a trick to make read/write to disk faster.