Getting startedΒΆ
The cleanest way to use the functionality in healpix is to make use of the
high-level HEALPix
class. The
HEALPix
class should be initialized with the nside
parameter which controls the resolution of the pixellization - it is the number
of pixels on the side of each of the 12 top-level HEALPix pixels:
>>> from astropy_healpix import HEALPix
>>> hp = HEALPix(nside=16)
As described in the references above, HEALPix pixel indices can follow two
different ordering conventions - the nested convention and the ring
convention. By default, the HEALPix
class assumes the ring
ordering convention, but it is possible to explicitly specify the convention to
use using the order
argument, for example:
>>> hp = HEALPix(nside=16, order='ring')
or:
>>> hp = HEALPix(nside=16, order='nested')
Once this class has been set up, you can access various properties and methods related to the HEALPix pixellization. For example, you can calculate the number of pixels as well as the pixel area or resolution:
>>> hp.npix
3072
>>> hp.pixel_area
<Quantity 0.0040906154343617095 sr>
>>> hp.pixel_resolution
<Quantity 219.87113035631398 arcmin>
As you can see, when appropriate the properties and the methods on the
HEALPix
class return Astropy high-level classes such as
Quantity
, Longitude
, and
so on.
For example, the healpix_to_lonlat()
method can be used
to convert HEALPix indices to Longitude
and
Latitude
objects:
>>> lon, lat = hp.healpix_to_lonlat([1, 442, 2200])
>>> lon
<Longitude [ 0.83448555, 1.63624617, 0.4712389 ] rad>
>>> lat
<Latitude [ 0.08343009, 0.94842784,-0.78529135] rad>
The HEALPix
class includes methods that take or return
SkyCoord
objects (we will take a look at this in
the Celestial coordinates section).
In the subsequent sections of the documentation, we will take a closer look at converting between coordinate systems, as well as more advanced features such as interpolation and cone searches.