Healpix doc

app/routes/docs.notices.healpix.mdx

@@ -0,0 +1,65 @@
+---
+handle:
+  breadcrumb: HEALPix Maps
+---
+
+import { Highlight } from '~/components/Highlight'
+import { Tab, Tabs } from '~/components/tabs'
+
+# HEALPix Sky Maps
+
+HEALPix stands for <b>H</b>ierarchical, <b>E</b>qual <b>A</b>rea, and iso-<b>L</b>atitude <b>Pix</b>elisation is a scheme for indexing positions on the unit sphere.
+For localization of events, the multi-messenger community uses the standard (HEALPix)[https://healpix.sourceforge.io] format with the file extension `.fits.gz`, as well as multi-resolution HEALPix format with the file extension `.multiorder.fits`. The preferred format is the multi-resolution HEALPix format, that is only format explicitly listed in the GCN.
+
+## Multi-Order Sky Maps
+
+GCN strongly encourages the use of multi-order sky maps. These are a more complex form of healpix sky maps that utilize a variable resolution, with higher probability regions having higher resolution and lower probability regions being encoded with a lower resolution. This is signicantly more efficient than single-resolution healpix sky maps with respect to storage footprint and read speed. However, interpreting these multi-order sky maps can be more complex.
+
+## Working with HEALPix Maps
+
+HEALPix data is distrubuted in standard format with the file extension ```.fits.gz```. These files are FITS image files and can be utilized with many common FITS tools. These files usually stored as HEALPix projection,
+
+### Reading Sky Maps
+
+Sky maps can be parsed using python; to start, import a handful of packages (note: while this documentation covers the use of ```astropy-healpix```, there are several packages that are ab)
+```
+import astropy_healpix as ah
+import numpy as np
+
+from astropy import units as u
+from astropy.table import QTable
+```
+
+A given sky map can then be read in as
+```
+skymap = QTable.read('skymap.multiofits)
+```
+
+### Most Probable Sky Location
+
+The index of the highest probability point can be found by doing the following
+```
+hp_index = np.argmax(skymap['PROBDENSITY'])
+uniq = skymap[hq_index]['UNIQ']
+
+level, ipix = ah.uniq_to_level_ipix(uniq)
+nside = ah.level_to_nside(level)
+
+ra, dec = ah.healpix_to_lonlat(ipix, nside, order='nested')
+```
+
+### Probability Density at a Known Position
+
+Similarly,
+
+### Find the 90% Probability Region
+
+## Further documentation
+
+Additional information can be found the (LIGO website)[https://emfollow.docs.ligo.org/userguide/tutorial/multiorder_sky maps.html#what-problem-do-multi-resolution-sky-maps-solve]
+
+## Libraries for reference
+
+(mhealpy)[https://mhealpy.readthedocs.io/en/latest/]
+
+(astropy-healpix)[https://astropy-healpix.readthedocs.io/en/latest/]

app/routes/docs.tsx

@@ -177,6 +177,9 @@ export default function () {
                     <NavLink key="schema" to="notices/schema">
                       Unified Schema
                     </NavLink>,
+                    <NavLink key="healpix" to="notices/healpix">
+                      HEALPix
+                    </NavLink>,
                     <NavLink key="archive" to="notices/archive">
                       Archive
                     </NavLink>,