ligo-skymap-plot now uses multi-resolution HEALPix natively

Enhancements to the command-line tool ligo-skymap-plot:

• Read in and reproject sky maps in multi-resolution format. This significantly decreases memory consumption when plotting high-resolution sky maps.

• The algorithm that calculates credible areas printed in the plot legend now matches the algorithm in ligo.skymap.postprocess.crossmatch: it does linear interpolation of area when the credible level falls between pixels.

ligo/skymap/plot/tests/test_reproject_from_healpix_moc.py

+from astropy.io.fits import Header
+from astropy.table import Table
+from astropy_healpix import HEALPix
+import numpy as np
+from reproject import reproject_from_healpix
+
+from ...moc import nest2uniq
+from ..reproject_from_healpix_moc import reproject_from_healpix_moc
+
+
+def test_reproject_from_healpix_moc():
+    header = Header({
+        'NAXIS': 2,
+        'NAXIS1': 180,
+        'NAXIS2': 180,
+        'CRPIX1': 90.5,
+        'CRPIX2': 90.5,
+        'CRVAL1': 180,
+        'CRVAL2': 0,
+        'CDELT1': 1,
+        'CDELT2': 1,
+        'CTYPE1': 'RA---CAR',
+        'CTYPE2': 'DEC--CAR',
+        'RADESYS': 'ICRS',
+    })
+    hpx = HEALPix(nside=32)
+    map_flat = np.arange(hpx.npix)
+
+    map_moc = Table({
+        'UNIQ': nest2uniq(hpx.level, hpx.ring_to_nested(map_flat)),
+        'IMG': map_flat,
+    })
+
+    expected = reproject_from_healpix(
+        (map_flat, 'ICRS'), header, order='nearest-neighbor', nested=False)
+    result = reproject_from_healpix_moc(
+        (map_moc, 'ICRS'), header, 'nearest-neighbor')
+
+    np.testing.assert_array_equal(result, expected)