def cont_sys_main():
from systematics import callingEliGoldMask
from systematics_module import contCorrection
resultpath = '/n/des/lee.5922/Dropbox/repositories/CMASS/code/result_cat/'
#result_gold_stripe82 = fitsio.read(resultpath+'result_gold_2_stripe82.fits')
#dmass_gold_stripe82, dmass_gold_stripe82_ellipse = resampleWithPth(result_gold_stripe82)
#clean_cmass_data_des = fitsio.read(resultpath+'clean_cmass_data_des_gold2.fits')
result_y1a1 = fitsio.read('/n/des/lee.5922/data/y1a1_coadd/dmass_y1a1.fits')
dmass_y1a1, _ = resampleWithPth(result_y1a1)
dmass_y1a1_st82 = dmass_y1a1[dmass_y1a1['DEC']> -3.0 ]
#clean_cmass_data_des = fitsio.read(resultpath+'clean_cmass_data_des.fits')
from xd import addphotoz
gold_photo_z = io.getDESY1A1catalogs(keyword = 'Y1A1_GOLD_STRIPE82_0', gold=True)
dmass_y1a1_st82 = addphotoz(des = dmass_y1a1_st82, im3shape=gold_photo_z)
# calling Eli mask
GoldMask = callingEliGoldMask()
GoldMask_st82 = GoldMask[ GoldMask['DEC'] > -3.0]
cmass = getSGCCMASSphotoObjcat()
SGCmask = (cmass['RA'] < 100 ) | (cmass['RA'] > 300)
cmass = cmass[SGCmask]
""" cmass SGC """
cmass_st82 = Cuts.keepGoodRegion(cmass)
cmass_rand = fitsio.read('/n/des/lee.5922/data/cmass_cat/'+'random0_DR12v5_CMASS_South.fits.gz')
from systematics import hpRaDecToHEALPixel
origin_HealInds = hpRaDecToHEALPixel( cmass_rand['RA'],cmass_rand['DEC'], nside= nside, nest= False)
CC = contCorrection.CorrectContaminant()
CC.obs_data = dmass_y1a1.copy()
CC.f_c, CC.cont_data, CC.true_data = CC.get_cont_data(obs = dmass_y1a1_st82, true = cmass_st82)
CC.w_obs_data = np.loadtxt('data_txt/acf_comparison_y1a1.txt')
#CC.w_true_data = np.loadtxt('data_txt/acf_comparison_cmass_SGC.txt')
"""
CC.w_cont_data = np.loadtxt('data_txt/acf_comparison_weightedcont.txt')
CC.w_cross_data = np.loadtxt('data_txt/acf_cross_weightedcont.txt')
"""
CC.L_obs_data = np.loadtxt('data_txt/Ashley_lensing.txt')
CC.L_true_data = np.loadtxt('data_txt/Ashley_true_lensing.txt')
CC.L_cont_data = np.loadtxt('data_txt/lensing_weightedcont_prop.txt')
mask_cmass = MatchHPArea(cat = cmass, origin_cat = cmass_rand, nside = 1024)
#CC.w_obs_data = CC.angular_correlation( data=CC.obs_data, rand=GoldMask, weight = [None, GoldMask['FRAC']], suffix = '')
CC.w_true_data = CC.angular_correlation( data=mask_cmass, rand = cmass_rand, weight = None, suffix = '_cmass_SGC')
CC.w_cont_data = CC.angular_correlation( data=CC.cont_data, rand=GoldMask_st82, weight = [ None, GoldMask_st82['FRAC']], suffix = '_coadd_st82_cont')
CC.w_cross_data = CC.cross_angular_correlation(data = CC.true_data, data2 = CC.cont_data, rand = GoldMask_st82, rand2= GoldMask_st82, weight = [None, GoldMask_st82['FRAC'], None, GoldMask_st82['FRAC']], suffix = '_coadd_st82_cross')
doVisualization_Angcorr( labels = ['coadd_st82_cont', 'y1a1', 'cmass_NGC'] )
rmin, rmax = 0.02, 10.
CC.eps82, CC.eps = CC.ang_stat_err(rmin = 0.02, rmax = 10.0)
CC.eps_len82, CC.eps_len = CC.len_stat_err(rmin = 0.02, rmax = None)
# bias
r_ang, ang_bias_sys = CC.Angsys_from_bias()
r_len, len_bias_sys = CC.Lensys_from_bias()
m_fL, i_fL = CC.MaxFc_from_AngSys_L(rmin = rmin, rmax = rmax)
m_fR, i_fR = CC.MaxFc_from_AngSys_R(rmin = rmin, rmax = rmax)
m_fL_len, i_fL_len = CC.MaxFc_from_LensSys_L(rmin = rmin, rmax = None)
m_fR_len, i_fR_len = CC.MaxFc_from_LensSys_R(rmin = rmin, rmax = None)
"""
def main():
# masking
# galaxy density for all
# plot (check)
# galaxy density for one (plot) -> get weight
# apply weight and calculate galaxy density for the next property (plot)
# repeat
# calling catalog
from systematics import GalaxyDensity_Systematics,loadSystematicMaps, chisquare_dof, MatchHPArea, SysMapBadRegionMask, callingEliGoldMask
#balrog_y1a1 = fitsio.read('result_cat/result_balrog_EMHUFF.fits')
#balrog_SSPT = balrog_y1a1[balrog_y1a1['DEC'] < -3]
#rand_clean = Cuts.keepGoodRegion(balrog_SSPT)
# dmass
result_y1a1 = fitsio.read('/n/des/lee.5922/data/y1a1_coadd/dmass_y1a1.fits')
dmass_y1a1, _ = resampleWithPth(result_y1a1)
resultpath = '/n/des/lee.5922/Dropbox/repositories/CMASS/code/result_cat/'
result_gold_stripe82 = fitsio.read(resultpath+'result_gold_stripe82.fits')
dmass_gold_stripe82, _ = resampleWithPth(result_gold_stripe82)
resultpath = '/n/des/lee.5922/Dropbox/repositories/CMASS/code/result_cat/'
result_gold_stripe82 = fitsio.read(resultpath+'result_gold_2_stripe82.fits')
dmass_gold_stripe82, _ = resampleWithPth(result_gold_stripe82)
clean_cmass_data_des = fitsio.read(resultpath+'clean_cmass_data_des_gold2.fits')
# calling Eli mask
GoldMask = callingEliGoldMask()
GoldMask_st82 = GoldMask[ GoldMask['DEC'] > -3.0 ]
angular_correlation(data = dmass_gold_stripe82, rand = GoldMask_st82, weight = [None, GoldMask_st82['FRAC']], suffix = '_gold_stripe82')
# ----------------------------------------------------------------
kind = 'STRIPE82'
nside = 4096
njack = 10
property = ['AIRMASS', 'SKYBRITE', 'FWHM', 'SKYSIGMA', 'NSTARS']
filter = ['g', 'r', 'i', 'z']
# Calling maps
MaskDic = {}
for i,p in enumerate(property):
if p == 'NSTARS':
filename = 'y1a1_gold_1.0.2_stars_nside1024.fits'
sysMap = loadSystematicMaps( filename = filename, property = p, filter = 'g', nside = 1024 , kind = kind )
if kind is 'STRIPE82': sysMap = sysMap[sysMap['DEC'] > -3]
mapname = 'sys_'+p+'_'+'g'
MaskDic[mapname] = sysMap
else :
nside = 4096
filter = ['g', 'r', 'i', 'z']
filename = None
for j,f in enumerate(filter):
sysMap = loadSystematicMaps( filename = filename, property = p, filter = f, nside = nside , kind = kind)
#goodIndmask = np.in1d( sysMap['PIXEL'], GoldMask['PIXEL'])
#sysMap = sysMap[goodIndmask]
#sysMap = Cuts.SpatialCuts(sysMap, ra =ra, ra2=ra2, dec=dec, dec2=dec2 )
mapname = 'sys_'+p+'_'+f
MaskDic[mapname] = sysMap
# basic cutoff mask
cutvalue = {'AIRMASS':[1.4, 1.35, 1.35, 1.35],
'SKYBRITE':[150, 380, 1150, 2500],
'FWHM':[6.5, 5.5, 5.0, 4.5],
'SKYSIGMA':[6.5, 9.5, 18, 26],
'NSTARS':[5, 5, 5, 5 ]}
cutvalue1 = {'AIRMASS':[1.45, 1.45, 1.45, 1.45],
'SKYBRITE':[3000, 3000, 3000, 3000],
'FWHM':[6.5, 5.0, 4.5, 4.0],
'SKYSIGMA':[40, 40, 40, 40],
'NSTARS':[501, 501, 501, 501 ]}
for i,p in enumerate(property):
correctMask = np.ones(dmass_gold_stripe82.size, dtype=bool)
if p is 'NSTARS':
nside = 1024
filter = ['g']
else :
nside = 4096
filter = ['g', 'r', 'i', 'z']
for j,f in enumerate(filter):
mapname = 'sys_'+p+'_'+f
maskedsysMap, mask = SysMapBadRegionMask(dmass_gold_stripe82, MaskDic[mapname], nside = nside, cond = '<=', val = cutvalue[p][j])
correctMask = correctMask * mask
MaskDic[mapname+'_masked'] = maskedsysMap
MaskDic[p+'_mask'] = correctMask
#angular_correlation(data = dmass_SSPT[correctMask], rand = rand_clean, weight = [None, None], suffix = '_'+kind+'_'+p+'_masked')
print p+'_mask', np.sum(correctMask)
TotalMask = MaskDic['AIRMASS_mask'] * MaskDic['FWHM_mask'] # * MaskDic['SKYSIGMA_mask'] * MaskDic['SKYBRITE_mask']# * MaskDic['NSTARS_mask']
clean_dmass = dmass_gold_stripe82[TotalMask]
#angular_correlation(data = clean_dmass, rand = GoldMask, weight = [None, None], suffix = '_'+kind+'_masked')
# pixel fraction weights for each gal
from systematics import hpRaDecToHEALPixel
import pandas as pd
catHpInd = hpRaDecToHEALPixel(clean_dmass['RA'], clean_dmass['DEC'], nside=4096, nest = False)
catHpInd = pd.DataFrame(catHpInd)
catHpInd.columns = ['PIXEL']
GoldMask_pd = pd.DataFrame(GoldMask)
frac_weight = pd.merge( catHpInd, GoldMask_pd, how = 'left', on = 'PIXEL')
frac_weight = frac_weight.to_records()
property = ['AIRMASS', 'SKYBRITE', 'FWHM', 'SKYSIGMA', 'NSTARS']
# calculating galaxy density and weights iterately
from systematics import ReciprocalWeights, jksampling
from systematics_module.corr import angular_correlation
re_weights = np.ones(clean_dmass.size)
#re_weights = frac_weight['FRAC']
#re_weights = GoldMask['FRAC']
rand_clean = None
#correctMask = np.ones(clean_dmass.size, dtype=bool)
re_weights_dic = {}
for p in property:
if p is 'NSTARS':
nside = 1024
filter = ['g']
else :
nside = 4096
filter = ['g', 'r', 'i', 'z']
for j,f in enumerate(filter):
mapname = 'sys_'+p+'_'+f+'_masked'
bins, Bdensity, Berr, Bf_area = GalaxyDensity_Systematics(clean_dmass, MaskDic[mapname], nside = nside, raTag = 'RA', decTag='DEC', property = p, filter = f, weight = re_weights)
#bins = bins/np.sum(sysMap['SIGNAL']) *len(sysMap)
#B_jkerr = jksampling(clean_dmass, MaskDic[mapname], property = p, nside = nside, njack = 30, raTag = 'RA', decTag = 'DEC' )
filename = 'data_txt/systematic_'+p+'_'+f+'_'+kind+'_masked.txt'
#DAT = np.column_stack(( bins-(bins[1]-bins[0])*0.1, Bdensity, Berr, Bf_area, Bdensity, Berr, Bf_area ))
DAT = np.column_stack(( bins, Bdensity, Berr, Bf_area, Bdensity, Berr, Bf_area ))
np.savetxt(filename, DAT, delimiter = ' ', header = 'bins, Cdensity, Cerr, Cfarea, Bdensity, Berr, Bfarea, B_jkerr')
print "saving data to ", filename
#doVisualization_ngal(property = p, nside = nside, kind = kind, suffix='_masked')
"""
cutvalue[p][j] = 1.3
maskedsysMap, mask = SysMapBadRegionMask(dmass_SSPT, MaskDic[mapname], nside = nside, cond = '<=', val = cutvalue[p][j])
correctMask = correctMask * mask
MaskDic[mapname+'_masked'] = maskedsysMap
clean_dmass = dmass_SSPT[correctMask]
"""
re_weights = re_weights * ReciprocalWeights( catalog = clean_dmass, sysMap = MaskDic[mapname], property = p, filter = f, nside = nside, kind = kind )
re_weights_dic['re_weight_'+p] = re_weights
# check
bins, Bdensity, Berr, Bf_area = GalaxyDensity_Systematics(clean_dmass, MaskDic[mapname], nside = nside, raTag = 'RA', decTag='DEC', property = p, filter = f, weight = re_weights)
filename = 'data_txt/systematic_'+p+'_'+f+'_'+kind+'_corrected.txt'
DAT = np.column_stack(( bins, Bdensity, Berr, Bf_area, Bdensity, Berr, Bf_area ))
np.savetxt(filename, DAT, delimiter = ' ', header = 'bins, Cdensity, Cerr, Cfarea, Bdensity, Berr, Bfarea')
print "saving data to ", filename
#doVisualization_ngal(property = p, nside = nside, kind = kind, suffix='_corrected')
#doVisualization_ngal(property = p, nside = nside, kind = kind, suffix='_masked')
#doVisualization_ngal(property = p, nside = nside, kind = kind, suffix='_corrected')
#angular_correlation(data = clean_dmass, rand = rand_clean, weight = [re_weights, None], suffix = '_'+kind+'_w_'+p)
angular_correlation(data = dmass_y1a1, rand = GoldMask, weight = [None, None], suffix = '_'+kind+'_noweight')
angular_correlation(data = clean_dmass, rand = GoldMask, weight = [None, None], suffix = '_'+kind+'_masked')
for p in property:
doVisualization_ngal(property = p, nside = 4096, kind = kind, suffix='_masked')
#doVisualization_ngal(property = p, nside = nside, kind = kind, suffix='_corrected')
rw = re_weights_dic['re_weight_'+p]
angular_correlation(data = clean_dmass, rand = GoldMask, weight = [rw, None], suffix = '_'+kind+'_w_'+p)
# apply weights to correlation function
property = [ 'AIRMASS', 'SKYBRITE', 'FWHM', 'SKYSIGMA', 'NSTARS']
kind = 'Y1A1'
# corr com
# corr comparison
labels = ['y1a1']
linestyle = ['-']+['--' for i in labels[:-1]]
fmt = ['.']+['o' for i in labels[:-2]]+['.']
color = ['red'] + [None for i in labels[:-1]]
corr_txt = [np.loadtxt('data_txt/acf_comparison_'+s+'.txt') for s in labels]
corr_txt2 = np.loadtxt('data_txt/acf_comparison_cmass_SGC.txt')
thetaS, wS, Sjkerr = corr_txt2[:,0], corr_txt2[:,1], corr_txt2[:,2]
fig, (ax, ax2) = plt.subplots(2,1, figsize = (10,15))
ax.errorbar( thetaS, wS, yerr = Sjkerr, label = 'CMASS SGC', color='black', alpha = 0.5)
ax2.errorbar( thetaS, wS-wS, yerr = 10 * Sjkerr, label = 'CMASS SGC', color='black', alpha = 0.9)
for i in range(len(labels)):
thetaD, wD, Djkerr = corr_txt[i][:,0], corr_txt[i][:,1], corr_txt[i][:,2]
if labels[i] == 'wnstar+mask': markersize = 12
else : markersize = 5
ax.errorbar( thetaD*(0.95+0.02*i), wD, yerr = Djkerr, fmt = fmt[i], linestyle = linestyle[i] ,label = labels[i], color = color[i], markersize = markersize)
ax2.errorbar( thetaD*(0.95+0.05*i), 10 * (wD - wS), yerr = 10 *Djkerr, fmt = fmt[i], linestyle = linestyle[i], label = labels[i],color = color[i], markersize=markersize)
ax.set_xlim(1e-2, 50)
#ax.set_ylim(-0.02 , 0.5)
ax.set_ylim(0.0001 , 5.0)
ax.set_xlabel(r'$\theta(deg)$')
ax.set_ylabel(r'${w(\theta)}$')
ax.set_xscale('log')
ax.set_yscale('log')
ax.legend(loc = 'best')
ax.set_title(' angular correlation ')
ax2.axhline(y=0.0, color = 'black')
ax2.set_xlim(1e-1, 10)
ax2.set_ylim(-.2, .5)
ax2.set_xscale('log')
ax2.set_xlabel(r'$\theta(deg)$')
ax2.set_ylabel(r'$10 \times$ $($ ${w}$ - ${w_{\rm{true}}}$ $)$')
ax2.legend(loc='best')
figname = 'figure/acf_comparison.png'
fig.savefig(figname)
print 'writing plot to ', figname