print " %s" % c i = 0 for f in filt_list: print "\n Filt set:",f file = folder_name + file_name + f + '_' + sample + flag + '.bpz' zt, zp, od = np.loadtxt(file,usecols= (zt_col, zp_col, od_col), unpack = True) dz = (zp - zt) / (1 + zt) dz_all = np.copy(dz) _, X = np.loadtxt(file, usecols= (0, col_list[c]), unpack = True) X_all = np.copy(X) od = tls.od_renorm(od) od = tls.od2eff(od) mask = (od > 0.5) dz = dz[mask] X = X[mask] print 'Completness=', 100*float(len(X))/float(len(X_all)) print len(dz), len(dz_all) dz_bin = pztls.binsplit(dz, X, binning[c]) dz_bin_all = pztls.binsplit(dz_all, X_all, binning[c]) k = 0 for e in estim_list: print " %s" % e
N_mpix = len(mpix2hpix)
print "N_mpix =", N_mpix
#Generating jacknife map conversion..............
jk_hmap = hp.ud_grade(mask_hmap, nside_jk)
jkhpix2jkmpix = idmaskmap(jk_hmap)
jkmpix2jkhpix = np.nonzero(jk_hmap)[0]
N_jkmpix = len(jkmpix2jkhpix)
print "N_jkpix =", len(jk_hmap)
print "N_jkmpix =", N_jkmpix
cat['p']['val']['mpix'] = hpix2mpix[cat['p']['val']['hpix']]
for i in cat:
#cat[i]['val']['od'] = odrescal(cat[i]['val']['od'], od_range)
cat[i]['val']['od'] = tls.od_renorm(cat[i]['val']['od'], od_min = od_range[0])
od_cut = tls.od_cut(cat['s']['val']['od'], eff_cut)
if(cat_list['s'] == True):
#Generating Nz....................................
#if not os.path.exists('./Nz'):
# os.makedirs('./Nz')
mycolors = tls.spectral(n_od, 0.2,1.)
for bin in zbin:
c = 0
for od in od_cut:
mask = cut(bin, od, cat['s']['val']['zp'], cat['s']['val']['od'])
#Template rescaling.........
#cat_all['type'] -= 1
#cat_all['type'] *= 10
print "Catalog information:\n"
for i in col_list:
print "\n" + i + " min =", cat_all[i].min()
print i + " max =", cat_all[i].max()
zt_all, zp_all, od_all = np.loadtxt(file_path,usecols= (zt_col, zp_col, od_col), unpack = True)
if (error == 'True'): od_all = pztls.err2od(od_all)
od_all = tls.od_renorm(od_all)
od_all = tls.od2eff(od_all)
od_cut = eff_cut
#od_cut = tls.od_cut(od_all, eff_cut)
print "\nCatalog analysis:"
n_od = len(od_cut)
#mycolors = tls.spectral(n_od, 0.0, 1.0)
cmap = plt.cm.get_cmap(name='jet')
i_od = 0
for o in od_cut:
print "\nodds>",o
cat = {}