def plotWpRp(ipos, ppos, outbase, zmax=40., h=0.7, Lb=400, rmin=0.1, rmax=2, rstep=0.1, ax = None ):
"""
Make a comparison plot between the input projected correlation function
and the predicted projected correlation function
"""
Lb = Lb / h
nrbins = ( rmax - rmin ) / rstep
rbins = np.logspace( rmin, rmax, nrbins )
rcen = ( rbins[:-1] + rbins[1:] ) / 2
iwprp, icov = projected_correlation( ipos, rbins, zmax, Lb, jackknife_nside=3 )
pwprp, pcov = projected_correlation( ppos, rbins, zmax, Lb, jackknife_nside=3 )
iwpe = np.sqrt( np.diagonal( icov ) )
pwpe = np.sqrt( np.diagonal( pcov ) )
if ax == None:
f, ax = plt.subplots(1)
ax.set_yscale( 'log', nonposy = 'clip' )
ax.set_xscale( 'log', nonposx = 'clip' )
ax.set_ylabel( r'$w_{p}(r_{p})$', fontsize=20 )
ax.set_xlabel( r'$r_{p} [Mpc\cdot h^{-1}]$', fontsize=20 )
ax.errorbar( rcen, iwprp, yerr = iwpe, label='Original Halos' )
ax.errorbar( rcen, pwprp, yerr = pwpe, label='Added Halos' )
plt.legend()
#plt.tight_layout()
plt.savefig( outbase+'_wprp.png' )
wdtype = np.dtype( [ ('r', float), ('iwprp', float), ('pwprp', float),
('iwprpe', float), ('pwprpe', float ) ] )
wprp = np.ndarray( len( rcen ), dtype = wdtype )
wprp[ 'r' ] = rcen
wprp[ 'iwprp' ] = iwprp
wprp[ 'pwprp' ] = pwprp
wprp[ 'iwprpe' ] = iwpe
wprp[ 'pwprpe' ] = pwpe
fitsio.write(outbase+'_wprp.fit', wprp)
return f, ax
def calculate_xi(cat):
"""
Given a catalog of galaxies, compute the correlation function using
approriate helper functions from CorrelationFunction.py
"""
rbins = np.logspace(np.log10(rpmin), np.log10(rpmax), Nrp+1)
pos = np.zeros((len(cat), 3), order='C')
pos[:, 0] = cat['x']/h
pos[:, 1] = cat['y']/h
pos[:, 2] = cat['z']/h + cat['vz']/h/100.0
xi, cov = projected_correlation(pos, rbins, zmax, L, jackknife_nside=3)
return xi, cov
def calculate_xi(gals, box_size, projected=True, jack_nside=3, rpmin=0.1,
rpmax=20, Nrp=25):
"""
Given a catalog of galaxies, compute the correlation function using
approriate helper functions from CorrelationFunction.py
"""
rbins = np.logspace(np.log10(rpmin), np.log10(rpmax), Nrp+1)
pos = np.zeros((len(gals), 3), order='C')
if projected:
coords = ['x', 'y', 'zp']
else:
coords = ['x', 'y', 'z']
for i, coord in enumerate(coords):
pos[:, i] = gals[coord]/h
return projected_correlation(pos, rbins, zmax, box_size/h,
jackknife_nside=jack_nside)
