def select_jplus_galaxies(fwhm, mr):
seeing = get_seeing_from_data(fwhm, mr)
fw2 = fwhm[mr < 19]
galaxies = N.greater(fwhm, seeing + (U.std_mad(fw2) * 3.))
return galaxies
def select_jplus_stars(fwhm, mr):
seeing = get_seeing_from_data(fwhm, mr)
fw2 = fwhm[mr < 19]
stars = N.less(fwhm, seeing + (U.std_mad(fw2) * 3.))
return stars
def get_sigma_z(ddz,vector,base):
n_ele = len(base)-1
values = N.zeros(n_ele)
base2 = base[:-1]+((base[1]-base[0])/2.)
for ii in range(n_ele):
good = N.greater_equal(vector,base[ii])
good *= N.less_equal(vector,base[ii+1])
values[ii] = U.std_mad(ddz[good])
return values,base2
def get_NMAD_vs_seeing(bpzfile, mmax):
bpzs = U.get_str(bpzfile, 0)
nb = len(bpzs)
valor = N.zeros(nb)
for ii in range(nb):
zb, zs, mo = U.get_data(bpzs[ii], (1, 9, 10))
dz = (zb - zs) / (1. + zs)
good = N.less(mo, mmax)
valor[ii] = U.std_mad(dz[good])
return valor
def get_seeing_from_data_pro(fwhm, mr):
"""
:param fwhm:
:param mr:
:return:
"""
dm = 0.1
base = N.arange(0., 7, dm)
stars = N.greater_equal(mr, 14) * N.less_equal(mr, 18)
b1, b2 = N.histogram(fwhm[stars], base)
pos = N.argmax(b1)
seeing = b2[pos] + (dm / 2.)
redu = N.less_equal(
fwhm,
U.mean_robust(fwhm[stars]) + U.std_mad(fwhm[stars]) * 2.)
redu *= N.greater_equal(
fwhm,
U.mean_robust(fwhm[stars]) - U.std_mad(fwhm[stars]) * 2.)
redu *= N.greater_equal(mr, 14) * N.less_equal(mr, 18)
return seeing, redu
zb, zs, mo, ods = U.get_data(master_spz_auto, (1, 9, 10, 5))
dz = (zb - zs) / (1. + zs)
plt.figure(1, figsize=(19, 12), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
plt.subplot(131)
for ii in range(n_m):
print ' '
for jj in range(n_z):
good = N.greater_equal(mo, base_m[ii])
good *= N.less_equal(mo, base_m[ii + 1])
good *= N.greater_equal(zs, base_z[jj])
good *= N.less_equal(zs, base_z[jj + 1])
good *= N.greater_equal(ods, 0.)
if len(dz[good]) > min_ng:
valor_auto[ii, jj] = U.std_mad(dz[good])
else:
valor_auto[ii, jj] = -1.
for ii in range(n_m):
for jj in range(n_z):
if valor_auto[ii, jj] > 0:
plt.scatter(base_z2[jj],
base_m2[ii],
s=square_size,
c=valor_auto[ii, jj],
marker=u's',
cmap=cm.PuOr,
alpha=0.95,
vmin=0.0,
vmax=0.050)
alphas = [0.2, 0.4, 0.6, 0.8, 1.0]
#AUTO apertures
zb, zs, mo, ods = U.get_data(master_bpz_auto, (1, 9, 10, 5))
dz = (zb - zs) / (1. + zs)
for ii in range(n_m):
print ' '
for jj in range(n_z):
good = N.greater_equal(mo, base_m[ii])
good *= N.less_equal(mo, base_m[ii + 1])
good *= N.greater_equal(zs, base_z[jj])
good *= N.less_equal(zs, base_z[jj + 1])
good *= N.greater_equal(ods, 0.)
if len(dz[good]) > 100:
valor_auto[ii, jj] = U.std_mad(dz[good])
else:
valor_auto[ii, jj] = -1.
# Plot
plt.figure(1, figsize=(9, 8), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
for ii in range(n_m):
for jj in range(n_z):
if valor_auto[ii, jj] > 0:
plt.scatter(base_z2[jj],
base_m2[ii],
s=4000,
c=valor_auto[ii, jj],
marker=u's',
cmap=cm.PuOr,
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.ylabel('$m^{sp}_{z}$ - $m^{sd}_{z}$', size=25, labelpad=-1)
plt.xlabel('$m^{z,sd}$', size=23, labelpad=-10)
dos = plt.axes([.8, .12, .15, .87])
a1, a2, a3 = plt.hist(delta_z[::res] - (U.mean_robust(delta_z[z_sp < 19])),
base_color,
orientation='horizontal',
color='grey',
alpha=0.5,
normed=True)
plt.grid()
plt.ylim(y_min, y_max)
plt.xticks([])
plt.yticks([])
plt.savefig('/Users/albertomolino/Desktop/dm_z.png', dpi=100)
print 'std_mad_U', U.std_mad(delta_u[u_sp < 19])
print 'std_mad_G', U.std_mad(delta_g[g_sp < 19])
print 'std_mad_R', U.std_mad(delta_r[r_sp < 19])
print 'std_mad_I', U.std_mad(delta_i[i_sp < 19])
print 'std_mad_Z', U.std_mad(delta_z[z_sp < 19])
"""
std_mad_U 0.059304
std_mad_G 0.0474432
std_mad_R 0.0266868
std_mad_I 0.0474432
std_mad_Z 0.0311346
"""
delta_z = 0.01
base_z = N.arange(z_min, z_max + delta_z, delta_z)
base_z2 = base_z[:-1] + ((base_z[1] - base_z[0]) / 2.)
#All bands
ruta = '/Users/albertomolino/Postdoc/T80S_Pipeline/Commisioning/S82/Dec2017/splus_cats_NGSL/'
b0 = ruta + 'COSMOSeB11new_recal/master.STRIPE82_Photometry.m21_COSMOSeB11new_recal_redu.bpz'
zb0, zs0, m0, chi0, od0, tb0 = U.get_data(b0, (1, 9, 10, 8, 5, 4))
good0 = N.greater_equal(od0, 0.1) * N.less(chi0, 10)
zb0, zs0, m0, tb0 = U.multicompress(good0, (zb0, zs0, m0, tb0))
dz0 = (zb0 - zs0) / (1. + zs0)
valor0 = N.zeros(len(base_m) - 1)
for ii in range(len(valor0)):
good = N.greater_equal(m0, base_m[ii])
good *= N.less_equal(m0, base_m[ii + 1])
valor0[ii] = U.std_mad(dz0[good])
#valor0[2]=0.0039
valor0[1] = 0.0052
#5-bands
b1 = ruta + 'using_5sdss_bands/master.STRIPE82_Photometry.m21_COSMOSeB11new_recal_5bands.bpz'
zb1, zs1, m1, chi1, od1, tb1 = U.get_data(b1, (1, 9, 10, 8, 5, 4))
good1 = N.greater_equal(od1, 0.1) * N.less(chi1, 10)
zb1, zs1, m1, tb1 = U.multicompress(good1, (zb1, zs1, m1, tb1))
dz1 = (zb1 - zs1) / (1. + zs1)
valor1 = N.zeros(len(base_m) - 1)
for ii in range(len(valor1)):
good = N.greater_equal(m1, base_m[ii])
good *= N.less_equal(m1, base_m[ii + 1])
valor1[ii] = U.std_mad(dz1[good])
valor1[0] = 0.018
final_zpe_1[ii, :], final_zpo_1[ii, :] = U.get_data(
cats_names1[ii], (3, 4), 12)
for ii in range(n_cats_2):
final_zpe_2[ii, :], final_zpo_2[ii, :] = U.get_data(
cats_names2[ii], (3, 4), 12)
av_zp_off_1 = U.sum(final_zpo_1, axis=0) / (1. * n_cats_1)
av_zp_off_2 = U.sum(final_zpo_2, axis=0) / (1. * n_cats_2)
#av_zp_err_1 = U.sum(final_zpe_1,axis=0)/(1.*n_cats_1)
#av_zp_err_2 = U.sum(final_zpe_2,axis=0)/(1.*n_cats_2)
av_zp_err_1 = N.zeros(12)
av_zp_err_2 = N.zeros(12)
for ii in range(12):
av_zp_err_1[ii] = U.std_mad(final_zpo_1[ii, :])
av_zp_err_2[ii] = U.std_mad(final_zpo_2[ii, :])
plt.figure(2, figsize=(14, 8), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
aaa = av_zp_off_1 - av_zp_off_1[7]
bbb = av_zp_off_2 - av_zp_off_2[7]
plt.errorbar(base_filtros, (av_zp_off_1 - N.mean(av_zp_off_1)) * 100.,
av_zp_err_1 * 100.,
fmt="-s",
color='blue',
alpha=0.5,
ms=10,
lw=7)
plt.errorbar(base_filtros, (av_zp_off_2 - N.mean(av_zp_off_2)) * 100.,
av_zp_err_2 * 100.,
