def projected_correlation_function(table_l, table_r, rp_bins, pi_max):
n_l = len(table_l)
n_r = len(table_r)
n = multiprocessing.cpu_count()
c = constants.c / 1000.0
ll = DDrppi_mocks(True, 2, n, pi_max, rp_bins, table_l['ra'],
table_l['dec'], table_l['z'] * c)['npairs']
rr = DDrppi_mocks(True, 2, n, pi_max, rp_bins, table_r['ra'],
table_r['dec'],
table_r['z'].astype(np.float64) * c)['npairs']
lr = DDrppi_mocks(False,
2,
n,
pi_max,
rp_bins,
table_l['ra'],
table_l['dec'],
table_l['z'] * c,
RA2=table_r['ra'],
DEC2=table_r['dec'],
CZ2=table_r['z'].astype(np.float64) * c)['npairs']
wp = convert_rp_pi_counts_to_wp(n_l, n_l, n_r, n_r, ll, lr, lr, rr,
len(rp_bins) - 1, pi_max)
return wp
def reference_survey_tpcf(data1, randoms1, edges, pimax, data2=None, randoms2=None):
"""Reference projected 2PCF using Corrfunc"""
from Corrfunc.utils import convert_3d_counts_to_cf, convert_rp_pi_counts_to_wp
from Corrfunc.mocks import DDrppi_mocks
if data2 is None:
data2 = data1
if randoms2 is None:
randoms2 = randoms1
# updack the columns to pass to Corrfunc
ra_d1, dec_d1, r_d1 = data1.T
ra_r1, dec_r1, r_r1 = randoms1.T
ra_d2, dec_d2, r_d2 = data2.T
ra_r2, dec_r2, r_r2 = randoms2.T
# the Corrfunc keywords
kws = {}
kws['cosmology'] = 1
kws['nthreads'] = 1
kws['pimax'] = pimax
kws['binfile'] = edges
kws['is_comoving_dist'] = True
# do the pair counting
D1D2 = DDrppi_mocks(0, RA1=ra_d1, DEC1=dec_d1, CZ1=r_d1, RA2=ra_d2, DEC2=dec_d2, CZ2=r_d2, **kws)
D1R2 = DDrppi_mocks(0, RA1=ra_d1, DEC1=dec_d1, CZ1=r_d1, RA2=ra_r2, DEC2=dec_r2, CZ2=r_r2, **kws)
D2R1 = DDrppi_mocks(0, RA1=ra_d2, DEC1=dec_d2, CZ1=r_d2, RA2=ra_r1, DEC2=dec_r1, CZ2=r_r1, **kws)
R1R2 = DDrppi_mocks(0, RA1=ra_r1, DEC1=dec_r1, CZ1=r_r1, RA2=ra_r2, DEC2=dec_r2, CZ2=r_r2, **kws)
# combine using Landy-Szalay
ND1 = len(ra_d1); ND2 = len(ra_d2)
NR1 = len(ra_r1); NR2 = len(ra_r2)
CF = convert_3d_counts_to_cf(ND1, ND2, NR1, NR2, D1D2, D1R2, D2R1, R1R2).reshape((-1,pimax))
wp = convert_rp_pi_counts_to_wp(ND1, ND2, NR1, NR2, D1D2, D1R2, D2R1, R1R2, len(edges)-1, pimax)
D1D2 = D1D2.reshape((-1,pimax))
D1R2 = D1R2.reshape((-1,pimax))
D2R1 = D2R1.reshape((-1,pimax))
R1R2 = R1R2.reshape((-1,pimax))
return D1D2, D1R2, D2R1, R1R2, CF, wp
def get_wp(RA, DEC, CZ):
N = len(RA)
weights = np.ones_like(RA)
# Random points
rand_num = eco_nobuff.size * 5
rand_RA = np.round(random.uniform(eco_nobuff.radeg.min(),eco_nobuff.radeg.max(),\
rand_num), 5)
rand_DEC = np.round(random.uniform(eco_nobuff.dedeg.min(),eco_nobuff.dedeg.max(),\
rand_num), 7)
rand_CZ = np.round(random.uniform(eco_nobuff.grpcz.min(),eco_nobuff.grpcz.max(),\
rand_num), 1)
rand_N = len(rand_RA)
rand_weights = np.ones_like(rand_RA)
nbins = 10
bins = np.logspace(np.log10(0.1), np.log10(20.0), nbins + 1)
cosmology = 2 # Planck
nthreads = 2
pimax = 25.0
# Auto pair counts in DD
autocorr = 1
DD_counts = DDrppi_mocks(autocorr, cosmology, nthreads, pimax, bins, RA, \
DEC, CZ, weights1=weights, weight_type='pair_product')
# Auto pair counts in RR
RR_counts = DDrppi_mocks(autocorr, cosmology, nthreads, pimax, bins, rand_RA, \
rand_DEC, rand_CZ, weights1=rand_weights, weight_type='pair_product')
# Cross pair counts in DR
autocorr = 0
DR_counts = DDrppi_mocks(autocorr, cosmology, nthreads, pimax, bins, RA, DEC, \
CZ, RA2=rand_RA, DEC2=rand_DEC, CZ2=rand_CZ, weights1=weights, \
weights2=rand_weights, weight_type='pair_product')
wp = convert_rp_pi_counts_to_wp(N, N, rand_N, rand_N, DD_counts, DR_counts, \
DR_counts, RR_counts, nbins, pimax)
return bins, wp
print('DD')
DD_counts = DDrppi_mocks(autocorr, cosmology, nthreads, pimax, bins, RA, DEC,
CZ)
# Cross pair counts in DR
autocorr = 0
print('DR')
DR_counts = DDrppi_mocks(autocorr,
cosmology,
nthreads,
pimax,
bins,
RA,
DEC,
CZ,
RA2=rand_RA,
DEC2=rand_DEC,
CZ2=rand_CZ)
# Auto pairs counts in RR
autocorr = 1
print('RR')
RR_counts = DDrppi_mocks(autocorr, cosmology, nthreads, pimax, bins, rand_RA,
rand_DEC, rand_CZ)
# All the pair counts are done, get the angular correlation function
wp = convert_rp_pi_counts_to_wp(N, N, rand_N, rand_N, DD_counts, DR_counts,
DR_counts, RR_counts, nbins, pimax)
print wp
def counts(ra_data,
dec_data,
z_data,
ra_rand,
dec_rand,
z_rand,
rpbins,
pimax,
weights_data=None,
weights_rand=None,
pibinwidth=1,
comoving=False):
assert (len(ra_data) == len(dec_data) and len(ra_data) == len(z_data))
assert (len(ra_rand) == len(dec_rand) and len(ra_rand) == len(z_rand))
ndata = len(ra_data)
nrand = len(ra_rand)
nbins = len(rpbins) - 1
pibins = np.arange(0, pimax + pibinwidth, pibinwidth)
if comoving:
zdf = pd.DataFrame(z_data)
z_data = zdf.apply(get_comoving_dist)[0].values
rzdf = pd.DataFrame(z_rand)
z_rand = rzdf.apply(get_comoving_dist)[0].values
dd_res_corrfunc = DDrppi_mocks(1,
2,
0,
pimax,
rpbins,
ra_data,
dec_data,
z_data,
is_comoving_dist=comoving)
dr_res_corrfunc = DDrppi_mocks(0,
2,
0,
pimax,
rpbins,
ra_data,
dec_data,
z_data,
RA2=ra_rand,
DEC2=dec_rand,
CZ2=z_rand,
is_comoving_dist=comoving)
rr_res_corrfunc = DDrppi_mocks(1,
2,
0,
pimax,
rpbins,
ra_rand,
dec_rand,
z_rand,
is_comoving_dist=comoving)
wp = convert_rp_pi_counts_to_wp(ndata, ndata, nrand, nrand,
dd_res_corrfunc, dr_res_corrfunc,
dr_res_corrfunc, rr_res_corrfunc, nbins,
pimax)
dd_rp_pi_corrfunc = np.zeros((len(pibins) - 1, len(rpbins) - 1))
dr_rp_pi_corrfunc = np.zeros((len(pibins) - 1, len(rpbins) - 1))
rr_rp_pi_corrfunc = np.zeros((len(pibins) - 1, len(rpbins) - 1))
for m in range(len(pibins) - 1):
for n in range(len(rpbins) - 1):
idx = (len(pibins) - 1) * n + m
dd_rp_pi_corrfunc[m][n] = dd_res_corrfunc[idx][4]
dr_rp_pi_corrfunc[m][n] = dr_res_corrfunc[idx][4]
rr_rp_pi_corrfunc[m][n] = rr_res_corrfunc[idx][4]
estimator_corrfunc = calc_ls(dd_rp_pi_corrfunc, dr_rp_pi_corrfunc,
rr_rp_pi_corrfunc, ndata, nrand)
wprp_corrfunc = 2 * pibinwidth * np.sum(estimator_corrfunc, axis=0)
est_ls, wprp_nopi = calc_wprp_nopi(dd_rp_pi_corrfunc, dr_rp_pi_corrfunc,
rr_rp_pi_corrfunc, ndata, nrand)
return wp, wprp_corrfunc, wprp_nopi