def from_cone(ra, dec, error, dateobs):
localization_name = "%.5f_%.5f_%.5f" % (ra, dec, error)
center = SkyCoord(ra * u.deg, dec * u.deg)
radius = error * u.deg
# Determine resolution such that there are at least
# 16 pixels across the error radius.
hpx = HEALPix(pixel_resolution_to_nside(radius / 16, round='up'),
'nested', frame=ICRS())
# Find all pixels in the 4-sigma error circle.
ipix = hpx.cone_search_skycoord(center, 4 * radius)
# Convert to multi-resolution pixel indices and sort.
uniq = moc.nest2uniq(nside_to_level(hpx.nside), ipix)
i = np.argsort(uniq)
ipix = ipix[i]
uniq = uniq[i]
# Evaluate Gaussian.
distance = hpx.healpix_to_skycoord(ipix).separation(center)
probdensity = np.exp(-0.5 * np.square(distance / radius).to_value(
u.dimensionless_unscaled))
probdensity /= probdensity.sum() * hpx.pixel_area.to_value(u.steradian)
models.db.session.merge(
models.Localization(
localization_name=localization_name,
dateobs=dateobs,
uniq=uniq.tolist(),
probdensity=probdensity.tolist()))
models.db.session.commit()
return localization_name
def from_cone(ra, dec, error):
localization_name = "%.5f_%.5f_%.5f" % (ra, dec, error)
center = SkyCoord(ra * u.deg, dec * u.deg)
radius = error * u.deg
# Determine resolution such that there are at least
# 16 pixels across the error radius.
hpx = HEALPix(pixel_resolution_to_nside(radius / 16, round='up'),
'nested',
frame=ICRS())
# Find all pixels in the 4-sigma error circle.
ipix = hpx.cone_search_skycoord(center, 4 * radius)
# Convert to multi-resolution pixel indices and sort.
uniq = ligo.skymap.moc.nest2uniq(nside_to_level(hpx.nside),
ipix.astype(np.int64))
i = np.argsort(uniq)
ipix = ipix[i]
uniq = uniq[i]
# Evaluate Gaussian.
distance = hpx.healpix_to_skycoord(ipix).separation(center)
probdensity = np.exp(
-0.5 * np.square(distance / radius).to_value(u.dimensionless_unscaled))
probdensity /= probdensity.sum() * hpx.pixel_area.to_value(u.steradian)
skymap = table.Table(
[np.asarray(uniq), np.asarray(probdensity)],
names=['UNIQ', 'PROBDENSITY'])
return skymap, uniq
