ab_file_example = ab_path+sed_models[0][:-3]+filter_x1+'.AB'
z_ab = U.get_data(ab_file_example,0)
nz = len(z_ab)
# Creating matrix where to save the data.
ab_filter_x1 = N.zeros((nz,n_models),float)
ab_filter_x2 = N.zeros((nz,n_models),float)
ab_filter_y1 = N.zeros((nz,n_models),float)
ab_filter_y2 = N.zeros((nz,n_models),float)
for ii in range(n_models):
# Reading AB files for each template and filter.
ab_filter_x1_model = ab_path+sed_models[ii][:-3]+filter_x1+'.AB'
z_x1_model, mag_x1_model = U.get_data(ab_filter_x1_model,(0,1))
ab_filter_x1[:,ii] = B.flux2mag(mag_x1_model)
ab_filter_x2_model = ab_path+sed_models[ii][:-3]+filter_x2+'.AB'
z_x2_model, mag_x2_model = U.get_data(ab_filter_x2_model,(0,1))
ab_filter_x2[:,ii] = B.flux2mag(mag_x2_model)
ab_filter_y1_model = ab_path+sed_models[ii][:-3]+filter_y1+'.AB'
z_y1_model, mag_y1_model = U.get_data(ab_filter_y1_model,(0,1))
ab_filter_y1[:,ii] = B.flux2mag(mag_y1_model)
ab_filter_y2_model = ab_path+sed_models[ii][:-3]+filter_y2+'.AB'
z_y2_model, mag_y2_model = U.get_data(ab_filter_y2_model,(0,1))
ab_filter_y2[:,ii] = B.flux2mag(mag_y2_model)
for zz in range(res_z):
pos_z = N.argmin(abs(z_range[zz]-z_ab))
ug_t1 = [1.6,1.7,1.7,1.7,1.7,1.7,1.7,1.8,1.80,1.9]
gz_t2 = [1.4,1.5,1.5,1.5,1.6,1.6,1.7,1.8,1.9,1.9]
ug_t2 = [1.8,1.8,1.9,1.9,2.0,2.0,2.0,2.0,2.0,2.1]
"""
# New values
gz_t1 = [1.17, 1.24, 1.28, 1.28, 1.30, 1.35, 1.50, 1.52, 1.56, 1.65, 1.70]
ug_t1 = [1.85, 1.85, 1.85, 1.85, 1.85, 1.85, 1.85, 1.85, 1.85, 1.85, 1.85]
gz_t2 = [1.25, 1.25, 1.25, 1.45, 1.50, 1.60, 1.70, 1.70, 1.75, 1.80, 2.00]
ug_t2 = [2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25]
for ii in range(n_models):
# Reading AB files for each template and filter.
ab_filter_x1_model = ab_path + sed_models[ii][:-3] + filter_x1 + '.AB'
z_x1_model, mag_x1_model = U.get_data(ab_filter_x1_model, (0, 1))
ab_filter_x1 = B.flux2mag(mag_x1_model)
ab_filter_x2_model = ab_path + sed_models[ii][:-3] + filter_x2 + '.AB'
z_x2_model, mag_x2_model = U.get_data(ab_filter_x2_model, (0, 1))
ab_filter_x2 = B.flux2mag(mag_x2_model)
ab_filter_y1_model = ab_path + sed_models[ii][:-3] + filter_y1 + '.AB'
z_y1_model, mag_y1_model = U.get_data(ab_filter_y1_model, (0, 1))
ab_filter_y1 = B.flux2mag(mag_y1_model)
ab_filter_y2_model = ab_path + sed_models[ii][:-3] + filter_y2 + '.AB'
z_y2_model, mag_y2_model = U.get_data(ab_filter_y2_model, (0, 1))
ab_filter_y2 = B.flux2mag(mag_y2_model)
for zz in range(nz):
pos_z = N.argmin(abs(z_x1_model - z_bins[zz]))
nf = len(filters)
ft, fob, efob = P.getinfo4_mosaic_uncert_obsvsmod(columns, fluxcomp)
mo = U.get_data(fluxcomp, 1)
g = N.greater_equal(mo, 14) * N.less_equal(mo, 20)
base = N.arange(14., 21., 1.)
basem_histo = N.arange(-1, 1, 0.01)
scatts = N.zeros(nf)
means = N.zeros(nf)
for ii in range(nf):
nameout = fluxcomp[:-15] + '%s.magzpdist.png' % (filters[ii][:-4])
nameout2 = fluxcomp[:-15] + 'magzpdist.txt'
plt.figure(112, figsize=(8, 10.), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
mt = B.flux2mag(ft[ii])
mob = B.flux2mag(fob[ii])
emob = N.where(
abs(fob[ii][:]) > 0,
B.e_frac2mag(efob[ii][:]) / fob[ii][:], -99)
uno = plt.axes([0.15, .675, 0.75, 0.265])
dm = mt[g] - mob[g]
mmo = mo[g]
emmob = emob[g]
mtt = mt[g]
mobb = mob[g]
g2 = N.less(abs(dm), 0.3)
dm2, mo2, emo2, mt2, mob2 = U.multicompress(g2,
(dm, mmo, emmob, mtt, mobb))
a1, a2, a3 = plt.hist(dm[g2], basem_histo, facecolor='grey', alpha=0.2)
plt.xlim(-0.49, 0.49)
# filtro2 = 'zSDSS'
sed_ab_filter_1 = root + '%s.SPLUS_%s.AB.txt' % (sed, filtro1)
sed_ab_filter_2 = root + '%s.SPLUS_%s.AB.txt' % (sed, filtro2)
z_AB_1, f_AB_1 = U.get_data(sed_ab_filter_1, (0, 1))
z_AB_2, f_AB_2 = U.get_data(sed_ab_filter_2, (0, 1))
good_z_range = N.less_equal(z_AB_1, zmax)
good_z_range *= N.greater(f_AB_1, 0) * N.greater(f_AB_2, 0)
f_AB_redu_1 = f_AB_1[good_z_range]
z_AB_redu_1 = z_AB_1[good_z_range]
f_AB_redu_2 = f_AB_2[good_z_range]
z_AB_redu_2 = z_AB_2[good_z_range]
mag_AB_redu_1 = B.flux2mag(abs(f_AB_redu_1))
mag_AB_redu_2 = B.flux2mag(abs(f_AB_redu_2))
outfilename = root + 'tracks_sed%s_0.0z%.1f_%s%s.txt' % (sed, zmax, filtro1,
filtro2)
U.put_data(outfilename, (z_AB_redu_2, mag_AB_redu_1 - mag_AB_redu_2),
'z %s-%s' % (filtro1, filtro2))
"""
z,gr_ell = U.get_data('/Users/albertomolino/doctorado/articulos/SPLUS/StarGalaxy/AB/tracks_sedEll3_A_0_0.0z0.5_gSDSSrSDSS.txt',(0,1));z,rz_ell = U.get_data('/Users/albertomolino/doctorado/articulos/SPLUS/StarGalaxy/AB/tracks_sedEll3_A_0_0.0z0.5_rSDSSzSDSS.txt',(0,1));z,gr_Sa = U.get_data('/Users/albertomolino/doctorado/articulos/SPLUS/StarGalaxy/AB/tracks_sedSa_A_1_0.0z0.5_gSDSSrSDSS.txt',(0,1)); z,rz_Sa = U.get_data('/Users/albertomolino/doctorado/articulos/SPLUS/StarGalaxy/AB/tracks_sedEll3_A_0_0.0z0.5_rSDSSzSDSS.txt',(0,1)); plt.plot(gr_Sa[::10],rz_Sa[::10],'-bo');plt.plot(gr_ell[::10],rz_ell[::10],'-ro')
z,gr_ell = U.get_data('/Users/albertomolino/doctorado/articulos/SPLUS/StarGalaxy/AB/tracks_sedEll3_A_0_0.0z2.0_gSDSSrSDSS.txt',(0,1));z,rz_ell = U.get_data('/Users/albertomolino/doctorado/articulos/SPLUS/StarGalaxy/AB/tracks_sedEll3_A_0_0.0z2.0_rSDSSzSDSS.txt',(0,1));z,gr_Sa = U.get_data('/Users/albertomolino/doctorado/articulos/SPLUS/StarGalaxy/AB/tracks_sedSa_A_1_0.0z2.0_gSDSSrSDSS.txt',(0,1)); z,rz_Sa = U.get_data('/Users/albertomolino/doctorado/articulos/SPLUS/StarGalaxy/AB/tracks_sedSa_A_1_0.0z2.0_rSDSSzSDSS.txt',(0,1)); plt.plot(gr_Sa[::10],rz_Sa[::10],'-bo');plt.plot(gr_ell[::10],rz_ell[::10],'-ro')
plt.xlabel('$g-r$',size=25,labelpad=1)
plt.ylabel('$r-z$',size=25,labelpad=1)
plt.grid()
#emob = B.e_frac2mag(efob1[ii][:])/float(fob1[ii][:])
plt.figure(1, figsize=(24, 12), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
bin_labels = [0, 6]
base_x = N.linspace(-3.0, 3.0, 300)
for ss in range(12):
plt.subplot(2, 6, ss + 1)
if ss == 0:
g_1 = U.greater_equal(mag1, mmin) * U.less_equal(mag1, mmax + 3)
else:
g_1 = U.greater_equal(mag1, mmin) * U.less_equal(mag1, mmax)
dm2_1 = B.flux2mag(fob1[ss][g_1]) - B.flux2mag(ft1[ss][g_1])
emob = B.e_frac2mag(efob1[ss][g_1]) / fob1[ss][g_1]
sense_values_1 = N.less(abs(dm2_1), 5.)
sense_values_1 *= N.less(abs(emob), 1.)
dm2_1, emob2 = U.multicompress(sense_values_1, (dm2_1, emob))
#dm2_1 = N.compress(sense_values_1,dm2_1)
valor = base_x * 0.
for ii in range(len(emob2)):
temporal = A.gaussian(base_x, emob2[ss], 0.00, 1)
valor += temporal * temporal
valor = N.sqrt(valor)
if ss in [0, 1, 2, 3, 4]:
basem = basem_low
#basem2 = basem2_low
def SEDSEx_noise_comparison222(columns,fluxcomp,m_min,m_max,delta_m):
"""
fluxcomp = 'master.flux_comparison'
columns = 'master.columns'
SEDSEx_noise_comparison(columns,fluxcomp,14,21,0.3)
"""
basem = N.arange(m_min,m_max+delta_m,delta_m)
ft,fob,efobs = A.get_fluxes(columns,fluxcomp)
filters = B.get_filter_list(columns)
nf = len(filters)
for ss in range(nf):
nband = ss
mt = B.flux2mag(ft[:][nband])
mo = B.flux2mag(fob[:][nband])
emo = B.e_frac2mag(efobs[:][nband])/(fob[:][nband])
g = N.less(abs(mo),28)
mor,mtr,emor = U.multicompress(g,(mo,mt,emo))
dm = mtr-mor
g2 = N.less(abs(dm),1.)
mor,mtr,emor = U.multicompress(g2,(mor,mtr,emor))
linea_sdm = U.bin_stats(mor,dm,basem,'std_mad') #SED
linea_sem = U.bin_stats(mor,emor,basem,'std_mad') #SEx
linea_sem = N.where(linea_sem<1.0e-4,0.0,linea_sem)
linea_dm = U.bin_stats(mor,dm,basem,'mean_robust') #SED
linea_em = U.bin_stats(mor,emor,basem,'mean_robust') #SEx
linea2 = linea_dm+linea_sdm #SED
linea1 = linea_sem+linea_em #SEx
"""
plt.figure(1, figsize = (11,9.5),dpi=80, facecolor='w', edgecolor='k')
plt.clf()
plt.subplot(211)
plt.semilogy(basem,abs(linea_sdm),'r-',basem,linea_sem,'k-',lw=12)
plt.legend(['SED','SEx'],loc='lower left',fontsize=30)
plt.title('dispersions. Filter %s'%(filters[ss]),size=30)
plt.grid()
plt.xlim(m_min-delta_m,m_max+delta_m)
plt.ylim(0.0001,1.)
plt.xticks(fontsize=22)
plt.yticks(fontsize=22)
# plt.xlabel('magnitude',size=22)
plt.ylabel('$\delta m$',size=22)
plt.subplot(212)
plt.semilogy(basem,abs(linea_dm),'r-',basem,linea_em,'k-',lw=12)
#plt.legend(['SED','SEx'],loc='lower left',fontsize=30)
plt.title('mean',size=30)
plt.grid()
plt.xlim(m_min-delta_m,m_max+delta_m)
plt.ylim(0.001,1.)
plt.xticks(fontsize=22)
plt.yticks(fontsize=22)
plt.xlabel('magnitude',size=22)
plt.ylabel('$\delta m$',size=22)
plt.savefig(final_root+'SEDdm1_Filter_%s.png'%(filters[ss]))
"""
plt.figure(1, figsize = (11,9.5),dpi=80, facecolor='w', edgecolor='k')
plt.clf()
plt.semilogy(basem,abs(linea_sdm),'r-',basem,linea_sem,'k-',lw=12)
plt.semilogy(basem,abs(linea_dm),'r--',basem,linea_em,'k--',lw=12)
plt.title('Filter %s'%(filters[ss]),size=30)
plt.xlim(m_min-delta_m,m_max+delta_m)
plt.ylim(0.0001,1.)
plt.xticks(fontsize=22)
plt.yticks(fontsize=22)
plt.ylabel('$\delta m$',size=22)
plt.legend(['$\sigma_{SED}$','$\sigma_{SEx}$','$\mu_{SED}$','$\mu_{SEx}$'],loc='lower left',fontsize=30)
plt.grid()
plt.xlabel('magnitude',size=22)
plt.savefig(final_root+'SEDdm1_Filter_%s.png'%(filters[ss]))
plt.figure(2, figsize = (11,9.5),dpi=80, facecolor='w', edgecolor='k')
plt.clf()
plt.semilogy(basem,abs(linea2),'r-',basem,linea1,'k-',lw=12)
plt.legend(['SED','SEx'],loc='lower left',fontsize=30)
plt.title('mean+disp. Filter %s'%(filters[ss]),size=30)
plt.grid()
plt.xlim(m_min-delta_m,m_max+delta_m)
plt.ylim(0.01,1.)
plt.xticks(fontsize=22)
plt.yticks(fontsize=22)
plt.xlabel('magnitude',size=22)
plt.ylabel('$\delta m$',size=22)
plt.savefig(final_root+'SEDdm2_Filter_%s.png'%(filters[ss]))