def __init__(self, datar, randomr, nofz, universe_params=None):
#
# make sure catalogs have the desired format
#
# cosmology
if universe_params is None:
universe_params = universeparams
cosmo = Cosmology(Om0=universe_params['Om0'],
H0=universe_params['H0'],
flat=universe_params['flat'])
data = catit(datar)
randoms = catit(randomr)
data = nb.ArrayCatalog(data)
randoms = nb.ArrayCatalog(randoms)
data['Position'] = SkyToCartesian(data['RA'],
data['DEC'],
data['Z'],
cosmo=cosmo)
randoms['Position'] = SkyToCartesian(randoms['RA'],
randoms['DEC'],
randoms['Z'],
cosmo=cosmo)
#
self.data = data
self.randoms = randoms
self.nofz = nofz
def finalize_data(s, cosmo, P0_FKP=None):
"""
Finalize the creation of a CatalogSource from a data file by
adding 'Position', 'Weight', and 'FKPWeight'.
Parameters
----------
s : CatalogSource
the catalog source object
cosmo : Cosmology
the cosmology parameters
P0_FKP : float, optional
the P0 value to use to for FKPWeights
"""
from nbodykit.transform import SkyToCartesian
# add the Position column
s['Position'] = SkyToCartesian(s['RA'],
s['DEC'],
s['Z'],
cosmo,
degrees=True)
# add systematic weights
if 'WEIGHT_NOZ' in s and 'WEIGHT_FOCAL' not in s:
s['Weight'] = s['WEIGHT_SYSTOT'] * (s['WEIGHT_NOZ'] + s['WEIGHT_CP'] -
1.)
elif 'WEIGHT_FOCAL' in s:
s['Weight'] = s['WEIGHT_SYSTOT'] * s['WEIGHT_CP'] * s['WEIGHT_FOCAL']
# FKP WEIGHT
if P0_FKP is not None:
s['FKPWeight'] = 1. / (1 + s['NZ'] * P0_FKP)
valid = (randoms['Z'] >= ZMIN) & (randoms['Z'] <= ZMAX)
if 'COMP_BOSS' in randoms.columns:
valid &= randoms['COMP_BOSS'] > compmin
if 'sector_SSR' in randoms.columns:
valid &= randoms['sector_SSR'] > compmin
valid &= randoms['RA'] > 140.
randoms = randoms[valid]
# the fiducial BOSS DR12 cosmology
cosmo = Cosmology(h=0.676).match(Omega0_m=0.31)
# add Cartesian position column
data['Position'] = SkyToCartesian(data['RA'], data['DEC'], data['Z'], cosmo=cosmo)
randoms['Position'] = SkyToCartesian(randoms['RA'], randoms['DEC'], randoms['Z'], cosmo=cosmo)
# apply the Completeness weights to both data and randoms
if sys_tot:
if rank ==0:print('including sys_tot')
data['WEIGHT'] = data['WEIGHT_SYSTOT'] * data['WEIGHT_NOZ'] * data['WEIGHT_CP']
else:
if rank ==0:print('excluding sys_tot')
data['WEIGHT'] = data['WEIGHT_NOZ'] * data['WEIGHT_CP'] # data['WEIGHT_SYSTOT']
randoms['WEIGHT'] = randoms['WEIGHT_SYSTOT'] * randoms['WEIGHT_NOZ'] * randoms['WEIGHT_CP']
# combine the data and randoms into a single catalog
fkp = nb.FKPCatalog(data, randoms, nbar='NZ')
mesh = fkp.to_mesh(Nmesh=nmesh, fkp_weight='WEIGHT_FKP', comp_weight='WEIGHT', window='tsc')
print(cat)
cat.write('../dat/lrg_cat.fits', format='fits', overwrite=True)
indices = np.random.choice(len(cat), 500, replace=False)
cat = cat[indices]
cat['Dec'] = cat['DEC']
del cat['DEC']
cat['COLOR'] = 'FUCHSIA'
# cat['ALPHA'] = 0.2
print(cat)
# cat.write('../dat/lrg_cat_viewer.fits', format='fits', overwrite=True)
#
# np.savetxt('../dat/lrg_cat_viewer.txt', cat['RA', 'Dec'])
pos = SkyToCartesian(cat['RA'], cat['Dec'], cat['REDSHIFT'], Planck15)
pos = np.array(pos)
pos[:, 0] -= pos[:, 0].mean()
pos[:, 1] -= pos[:, 1].mean()
pos[:, 2] -= pos[:, 2].mean()
np.savetxt('../dat/lrg_cat_pos.txt', pos, delimiter=',')
def main(ns):
data = BigFileCatalog(ns.cat, dataset=ns.data)
rand = BigFileCatalog(ns.cat, dataset=ns.rand)
dndz_data = RedshiftHistogram(data, fsky=1.0, cosmo=Planck15, redshift='Z')
ssdata = data.persist(['RA', 'DEC', 'Z'])
ssrand = rand.persist(['RA', 'DEC', 'Z'])
dndz_data = RedshiftHistogram(ssdata, fsky=1, cosmo=Planck15, redshift='Z')
dndz_rand = RedshiftHistogram(ssrand, fsky=1, cosmo=Planck15, redshift='Z')
hmap = healpix.histogrammap(ssdata['RA'].compute(),
ssdata['DEC'].compute(),
nside=128,
perarea=True)
data_hmap = data.comm.allreduce(hmap.astype('f8'))
hmap = healpix.histogrammap(ssrand['RA'].compute(),
ssrand['DEC'].compute(),
nside=128,
perarea=True)
rand_hmap = data.comm.allreduce(hmap.astype('f8'))
ssdata['NZ'] = dndz_data.interpolate(ssdata['Z'])
ssrand['NZ'] = dndz_data.interpolate(ssrand['Z'])
ssdata['Position'] = SkyToCartesian(ssdata['RA'], ssdata['DEC'],
ssdata['Z'], Planck15)
ssrand['Position'] = SkyToCartesian(ssrand['RA'], ssrand['DEC'],
ssrand['Z'], Planck15)
cat = FKPCatalog(
data=ssdata,
randoms=ssrand,
)
fkp = ConvolvedFFTPower(cat, poles=[0, 2, 4], Nmesh=512 * 4)
basename, ext = ns.output.rsplit('.', 1)
fkp.save(basename + '-fkp' + '.' + ext)
edges = numpy.arange(4, 140, 4)
everydata = max(int(ssdata.size / 40000 + 0.5), 1)
everyrand = max(int(ssrand.size / 40000 + 0.5), 1)
xir = SurveyData2PCF(mode='2d',
data1=ssdata[::everydata],
randoms1=ssrand[::everyrand],
cosmo=Planck15,
redshift='Z',
edges=edges,
Nmu=10,
show_progress=True)
xir.save(basename + '-xir' + '.' + ext)
if data.comm.rank == 0:
data_hmap.tofile(basename + 'data-hmap.f8')
rand_hmap.tofile(basename + 'rand-hmap.f8')
from matplotlib.figure import Figure
from matplotlib.gridspec import GridSpec
from matplotlib.backends.backend_agg import FigureCanvasAgg
fig = Figure(figsize=(6, 12), dpi=200)
gs = GridSpec(5, 2)
for i, l in enumerate([0, 2, 4]):
ax = fig.add_subplot(gs[i, 0])
ax.plot(fkp.poles['k'],
fkp.poles[f'power_{l}'] - fkp.attrs['shotnoise'],
label=f'l={l}')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('k [h/Mpc]')
ax.set_ylabel('P(k)')
ax.grid()
ax.legend()
poles = xir.corr.to_poles([0, 2, 4])
for i, l in enumerate([0, 2, 4]):
ax = fig.add_subplot(gs[i, 1])
ax.plot(poles['r'],
poles['r']**2 * poles[f'corr_{l}'],
label=f'l = {l}')
ax.set_xlabel('r [Mpc/h]')
ax.set_ylabel('r^2 xi(r)')
ax.grid()
ax.legend()
ax.set_title('Downsample D%d R%d' % (everydata, everyrand))
ax = fig.add_subplot(gs[3, 0])
ax.plot(dndz_data.bin_centers, dndz_data.nbar, label='n(z) data')
ax.plot(dndz_rand.bin_centers,
dndz_rand.nbar * (data.csize / rand.csize),
label='n(z) * D / R random')
ax.grid()
ax.legend()
ax.set_xlabel('z')
ax.set_ylabel('n(z)')
ax = fig.add_subplot(gs[3, 1])
ax.hist(data_hmap, histtype='step', label='n(A) data', log=True)
ax.hist(rand_hmap * data.csize / rand.csize,
histtype='step',
label='n(A) * D / R random',
log=True)
ax.set_xlabel("N per sqdeg")
ax.set_ylabel('counts')
ax.legend()
ax.grid()
ax = fig.add_subplot(gs[4, 0], projection="ast.mollweide")
ax.mapshow(data_hmap)
ax.set_title("data")
ax = fig.add_subplot(gs[4, 1], projection="ast.mollweide")
ax.mapshow(rand_hmap)
ax.set_title("randoms")
canvas = FigureCanvasAgg(fig)
fig.tight_layout()
fig.savefig(basename + '.png', bbox_inches='tight')
def sky_to_xyz(self, cosmo):
self['Position'] = SkyToCartesian(self['RA'], self['DEC'], self['Z'], cosmo=cosmo)