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star_correlation.py
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146 lines (111 loc) · 7.29 KB
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import numpy as np
import fitsio as fio
import os
import treecorr
band = 'H'
if os.path.exists('/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/StarCat_'+band+'_sample.fits'):
d = fio.read('/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/StarCat_'+band+'_sample.fits')
else:
d_type = [('ra', 'f8'), ('dec', 'f8'), ('bind_x', 'f8'), ('bind_y', 'f8'), ('x_out', 'f8'), ('y_out', 'f8'), ('xI_out', 'f8'), ('yI_out', 'f8'), ('dx', 'f8'), ('dy', 'f8'), ('amp_hsm_galsim', 'f8'), ('dx_hsm_galsim', 'f8'), ('dy_hsm_galsim', 'f8'), ('sig_hsm_galsim', 'f8'), ('g1_hsm_galsim', 'f8'), ('g2_hsm_galsim', 'f8'), ('amp_hsm_crout', 'f8'), ('dx_hsm_crout', 'f8'), ('dy_hsm_crout', 'f8'), ('sig_hsm_crout', 'f8'), ('g1_hsm_crout', 'f8'), ('g2_hsm_crout', 'f8'), ('mean_fid', 'f8'), ('nepoch', 'f8'), ('g1_noise_white', 'f8'), ('g2_noise_white', 'f8'), ('s2n_white', 'f8'), ('g1_noise_1f', 'f8'), ('g2_noise_1f', 'f8'), ('s2n_1f', 'f8')]
d = np.loadtxt('/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/StarCat_'+band+'.txt', dtype=d_type)
fio.write('/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/StarCat_'+band+'_sample.fits', d)
print('number of objects', len(d))
def _find_psi(ra, dec, ra_ctr, dec_ctr):
ra = np.radians(ra)
dec = np.radians(dec)
ra_ctr = np.radians(ra_ctr)
dec_ctr = np.radians(dec_ctr)
zeta = np.arctan(np.cos(dec)*np.sin(ra-ra_ctr) / (-np.sin(dec)*np.cos(dec_ctr) + np.sin(dec_ctr)*np.cos(dec)*np.cos(ra-ra_ctr)))
eta = np.arctan(np.cos(dec_ctr)*np.sin(ra_ctr-ra) / (-np.sin(dec_ctr)*np.cos(dec) + np.sin(dec)*np.cos(dec_ctr)*np.cos(ra-ra_ctr)))
psi = eta - zeta
psi_r = psi - np.pi*np.round(psi/np.pi)
return psi_r
def _compute_treecorr_angle(g1, g2, psi):
e1 = np.cos(2*psi)*g1 + np.sin(2*psi)*g2
e2 = -np.sin(2*psi)*g1 + np.cos(2*psi)*g2
return e1, e2
def _compute_GG_corr(ra, dec, g1, g2, out_f, xy=False):
bin_config = dict(
sep_units = 'arcmin',
bin_slop = 0.1,
min_sep = 0.5,
max_sep = 80,
nbins = 15,
var_method = 'jackknife'
)
# shear-shear
f_pc = '/hpc/group/cosmology/masaya/imcom_phase1/patch_center.txt'
gg = treecorr.GGCorrelation(bin_config)
if xy:
cat = treecorr.Catalog(x=xy[0], y=xy[1], x_units='arcsec', y_units='arcsec', g1=g1, g2=g2, patch_centers=f_pc)
else:
cat = treecorr.Catalog(ra=ra, dec=dec, ra_units='deg', dec_units='deg', g1=g1, g2=g2, patch_centers=f_pc)
gg.process(cat)
gg.write(out_f)
def _compute_NG_corr(ra, dec, g1, g2, out_f):
bin_config = dict(
sep_units = 'arcmin',
bin_slop = 0.1,
min_sep = 0.5,
max_sep = 80,
nbins = 15,
var_method = 'jackknife'
)
# count-shear
f_pc = '/hpc/group/cosmology/masaya/imcom_phase1/patch_center.txt'
ng = treecorr.NGCorrelation(bin_config)
cat1 = treecorr.Catalog(ra=ra, dec=dec, ra_units='deg', dec_units='deg', patch_centers=f_pc)
cat2 = treecorr.Catalog(ra=ra, dec=dec, ra_units='deg', dec_units='deg', g1=g1, g2=g2, patch_centers=f_pc)
ng.process(cat1, cat2)
ng.write(out_f)
def _compute_NK_corr(ra, dec, kappa, out_f):
bin_config = dict(
sep_units = 'arcmin',
bin_slop = 0.1,
min_sep = 0.5,
max_sep = 80,
nbins = 15,
var_method = 'jackknife'
)
# count-kappa
f_pc = '/hpc/group/cosmology/masaya/imcom_phase1/patch_center.txt'
nk = treecorr.NKCorrelation(bin_config)
cat1 = treecorr.Catalog(ra=ra, dec=dec, ra_units='deg', dec_units='deg', patch_centers=f_pc)
cat2 = treecorr.Catalog(ra=ra, dec=dec, ra_units='deg', dec_units='deg', k=kappa, patch_centers=f_pc)
nk.process(cat1, cat2)
nk.write(out_f)
sph_correction = False
if sph_correction:
ra_ctr = 53.000000
dec_ctr = -40.000000
psi = _find_psi(d['ra'], d['dec'], ra_ctr, dec_ctr)
g1, g2 = _compute_treecorr_angle(d['g1_hsm_galsim'], d['g2_hsm_galsim'], psi)
# 2pcf
_compute_GG_corr(d['ra'], d['dec'], d['g1_hsm_galsim'], d['g2_hsm_galsim'], '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_shear-shear_galsim_sample.fits')
_compute_NG_corr(d['ra'], d['dec'], d['g1_hsm_galsim'], d['g2_hsm_galsim'], '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_sky-shear_galsim_sample.fits')
for fid_cut in [40, 45, 50]:
print('fidelity cut: ', fid_cut)
d = d[d['mean_fid'] > fid_cut]
_compute_GG_corr(d['ra'], d['dec'], d['g1_hsm_galsim'], d['g2_hsm_galsim'], '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_shear-shear_galsim_sample_fid'+str(fid_cut)+'.fits') #, xy=[d['x_out'], d['y_out']])
_compute_NG_corr(d['ra'], d['dec'], d['g1_hsm_galsim'], d['g2_hsm_galsim'], '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_sky-shear_galsim_sample_fid'+str(fid_cut)+'.fits')
_compute_NK_corr(d['ra'], d['dec'], d['sig_hsm_galsim'], '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_sky-sigma_galsim_sample_fid'+str(fid_cut)+'.fits')
# noise bias (white noise)
mean_noise_g1 = np.mean(d['g1_noise_white']/d['s2n_white']**2)
mean_noise_g2 = np.mean(d['g2_noise_white']/d['s2n_white']**2)
err_noise_g1 = np.std(d['g1_noise_white']/d['s2n_white']**2)/np.sqrt(len(d['g1_noise_white']))
err_noise_g2 = np.std(d['g2_noise_white']/d['s2n_white']**2)/np.sqrt(len(d['g2_noise_white']))
print('noise bias g1: ', '{:.7f}'.format(mean_noise_g1), '+/-', '{:.7f}'.format(err_noise_g1))
print('noise bias g2: ', '{:.7f}'.format(mean_noise_g2), '+/-', '{:.7f}'.format(err_noise_g2))
_compute_GG_corr(d['ra'], d['dec'], d['g1_noise_white']/d['s2n_white']**2, d['g2_noise_white']/d['s2n_white']**2, '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_noise-noise_galsim_sample_whitenoise.fits')
_compute_NK_corr(d['ra'], d['dec'], d['g1_noise_white']/d['s2n_white']**2, '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_sky-noise_galsim_sample_g1_whitenoise.fits')
_compute_NK_corr(d['ra'], d['dec'], d['g2_noise_white']/d['s2n_white']**2, '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_sky-noise_galsim_sample_g2_whitenoise.fits')
# noise bias (1/f noise)
mean_noise_g1 = np.mean(d['g1_noise_1f']/d['s2n_1f']**2)
mean_noise_g2 = np.mean(d['g2_noise_1f']/d['s2n_1f']**2)
err_noise_g1 = np.std(d['g1_noise_1f']/d['s2n_1f']**2)/np.sqrt(len(d['g1_noise_1f']))
err_noise_g2 = np.std(d['g2_noise_1f']/d['s2n_1f']**2)/np.sqrt(len(d['g2_noise_1f']))
print('noise bias g1: ', '{:.7f}'.format(mean_noise_g1), '+/-', '{:.7f}'.format(err_noise_g1))
print('noise bias g2: ', '{:.7f}'.format(mean_noise_g2), '+/-', '{:.7f}'.format(err_noise_g2))
_compute_GG_corr(d['ra'], d['dec'], d['g1_noise_1f']/d['s2n_1f']**2, d['g2_noise_1f']/d['s2n_1f']**2, '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_noise-noise_galsim_sample_1fnoise.fits')
_compute_NK_corr(d['ra'], d['dec'], d['g1_noise_1f']/d['s2n_1f']**2, '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_sky-noise_galsim_sample_g1_1fnoise.fits')
_compute_NK_corr(d['ra'], d['dec'], d['g2_noise_1f']/d['s2n_1f']**2, '/hpc/group/cosmology/phy-lsst/my137/imcom/out/summary_statistics/star_'+band+'_sky-noise_galsim_sample_g2_1fnoise.fits')