def test_healpix_nuniq():
hpix_file = os.path.join(resource_filename('astropath', 'tests'), 'files',
'FRB201123_hpix_uniform.fits.gz')
hpix = Table.read(hpix_file)
header = fits.open(hpix_file)[1].header
nside = 2**header['MOCORDER']
localiz = dict(type='healpix',
healpix_data=hpix,
healpix_nside=nside,
healpix_ordering='NUNIQ',
healpix_coord='C')
assert localization.vet_localization(localiz)
# L_wx
cent_ra = 263.6671241047224
cent_dec = -50.76756723228885
# Calculate L_wx
box_hwidth = 60.
step_size = 1.
ngrid = int(np.round(2 * box_hwidth / step_size))
x = np.linspace(-box_hwidth, box_hwidth, ngrid)
xcoord, ycoord = np.meshgrid(x, x)
ra = cent_ra + xcoord / 3600. / np.cos(cent_dec * units.deg).value
dec = cent_dec + ycoord / 3600.
L_wx = localization.calc_LWx(ra, dec, localiz)
def localization_from_hpfile(hpix_file:str) -> dict:
""" Generate a localization dict from
a healpix file
Args:
hpix_file (str):
Returns:
dict: localization dict
- type (str)
- healpix_data (np.ndarray) : probability density values
- healpix_nside (int) : NSIDE
- healpix_ordering (str) : healpix ordering
- healpix_coord (str) : ??
"""
# Read
hpix = Table.read(hpix_file)
header = fits.open(hpix_file)[1].header
nside = 2**header['MOCORDER']
localiz = dict(type='healpix',
healpix_data=hpix,
healpix_nside=nside,
healpix_ordering='NUNIQ',
healpix_coord='C')
# Vet
assert localization.vet_localization(localiz)
return localiz
def test_wcs():
# Load up the localization
lfile = os.path.join(resource_filename('astropath', 'tests'), 'files',
'mask_frb201123_localization.fits.gz')
hdul = fits.open(lfile)
data = hdul[0].data
header = hdul[0].header
wcs = WCS(header)
# Normalize
data /= np.sum(data)
localiz = dict(type='wcs', wcs_data=data, wcs_WCS=wcs)
assert localization.vet_localization(localiz)
# Approx center
in_region = np.where(data > 0.)
coord = wcs.pixel_to_world(in_region[1], in_region[0])
cent_ra = np.mean(coord.ra.deg)
cent_dec = np.mean(coord.dec.deg)
# Calculate L_wx
box_hwidth = 60.
step_size = 1.
ngrid = int(np.round(2 * box_hwidth / step_size))
x = np.linspace(-box_hwidth, box_hwidth, ngrid)
xcoord, ycoord = np.meshgrid(x, x)
ra = cent_ra + xcoord / 3600. / np.cos(cent_dec * units.deg).value
dec = cent_dec + ycoord / 3600.
L_wx = localization.calc_LWx(ra, dec, localiz)
def init_localization(self, ltype: str, **kwargs):
"""Ingets the localization information
Args:
ltype (str): Localization type [eelipse, wcs, healpix]
kwargs: Other parameters definig the localization
Depends on the ltype. See localization.py
"""
self.localiz = dict(type=ltype)
self.localiz.update(kwargs)
# Vet
assert localization.vet_localization(
self.localiz), 'Bad candidate prior input'
logging.info("Localization is ready!")
def test_error_ellipse():
# This follows the FRB example Notebook
# Set up localization
frb_coord = SkyCoord('21h44m25.255s -40d54m00.10s', frame='icrs')
eellipse = dict(a=0.1, b=0.1, theta=0.)
localiz = dict(type='eellipse', center_coord=frb_coord, eellipse=eellipse)
assert localization.vet_localization(localiz)
# Candidates
cand_file = os.path.join(resource_filename('astropath', 'data'),
'frb_example', 'frb180924_candidates.csv')
candidates = pandas.read_csv(cand_file, index_col=0)
c_coords = SkyCoord(ra=candidates.ra, dec=candidates.dec, unit='deg')
# Priors
theta_prior = dict(PDF='exp', max=6., scale=1.)
cand_prior = dict(P_O_method='inverse', P_U=0., name='Adopted')
assert priors.vet_theta_prior(theta_prior)
assert priors.vet_cand_prior(cand_prior, candidates)
# Raw priors
raw_prior_Oi = priors.raw_prior_Oi(cand_prior['P_O_method'],
candidates.half_light.values,
mag=candidates.VLT_FORS2_g.values)
candidates['P_O_raw'] = raw_prior_Oi
# Normalize
candidates['P_O'] = priors.renorm_priors(candidates.P_O_raw.values,
cand_prior['P_U'])
# P(x|O)
p_xOi = bayesian.px_Oi_fixedgrid(
30., # box radius for grid, in arcsec
localiz,
c_coords,
candidates.half_light.values,
theta_prior,
step_size=0.02)
candidates['p_xO'] = p_xOi
# p(x)
p_x = np.sum(candidates.P_O * candidates.p_xO)
# Posteriors
P_Oix = candidates.P_O * p_xOi / p_x
candidates['P_Ox'] = P_Oix
# Test
assert np.isclose(candidates['P_Ox'].max(), 0.98951951218604)