def replace_photo_uncert(catalog, columns):
"""
"""
data = C.loaddata(catalog) # Loading the whole catalog content.
head = C.loadheader(catalog) # Loading the original header.
mm = A.get_magnitudes(catalog, columns)
em = A.get_errmagnitudes(catalog, columns)
filters = B.get_filter_list(columns)
nl = len(mm[:,
0]) # nl is the number of detections inside every single band.
nf = len(mm[0, :]) # nf is the number of bands inside the catalog.
errmag = U.zeros((nl, nf),
float) # Where the new photo errors will be saved.
for jj in range(nf):
maglim = B.get_limitingmagnitude(mm[:, jj], em[:, jj], 1., 0.25)
print 'Limiting Magnitude for filter %s: %.3f' % (filters[jj], maglim)
for ii in range(nl):
if mm[ii, jj] == -99.:
errmag[ii, jj] = 0.00
elif mm[ii, jj] == 99.:
errmag[ii, jj] = maglim
else:
errmag[ii, jj] = em[ii, jj]
# New values of mags error overwrites now the original data.
vars, evars, posref, zpe, zpo = get_usefulcolumns(columns)
data[:, evars] = errmag[:, U.arange(nf)]
finalcatalog = catalog[:-3] + 'upp.cat'
C.savedata(data, finalcatalog, dir="",
header=head) # Saving & creating a new catalog.
def replace_kerttu_errmags(catalog, columns, finalcatalog):
"""
import alhambra_kerttu_fixerrmags as AFM
catalog = '/Users/albertomolino/doctorado/articulos/ALHAMBRA/kerttu/test_photoz/kerttu.cat'
columns = '/Users/albertomolino/doctorado/articulos/ALHAMBRA/kerttu/test_photoz/kerttu.columns'
finalcatalog = '/Users/albertomolino/doctorado/articulos/ALHAMBRA/kerttu/test_photoz/kerttu3.cat'
AFM.replace_kerttu_errmag(catalog,columns,finalcatalog)
------
"""
data = C.loaddata(catalog) # Loading the whole catalog content.
head = C.loadheader(catalog) # Loading the original header.
mm = A.get_magnitudes(catalog, columns)
em = A.get_errmagnitudes(catalog, columns)
filters = B.get_filter_list(columns)
nl = len(mm[:,
0]) # nl is the number of detections inside every single band.
nf = len(mm[0, :]) # nf is the number of bands inside the catalog.
errmag = U.zeros((nl, nf),
float) # Where the new photo errors will be saved.
for jj in range(nf):
for ii in range(nl):
if mm[ii, jj] == -99.: errmag[ii, jj] = 0.00
else: errmag[ii, jj] = em[ii, jj]
# New values of mags error overwrites now the original data.
vars, evars, posref, zpe, zpo = A.get_usefulcolumns(columns)
data[:, evars] = errmag[:, U.arange(nf)]
C.savedata(data, finalcatalog, dir="",
header=head) # Saving & creating a new catalog.
import numpy as N
import useful as U
import bpz_tools as B
import phz_plots as P
import matplotlib.pyplot as plt
sedlist = 'GOSMOSeB11.list'
root = '/Users/albertomolino/doctorado/articulos/SPLUS/paper0/membership/'
columns = root + 'splus_auto_cali.columns'
fluxcomp = root + 'members_regular.flux_comparison'
bpz = root + 'members_regular.bpz'
zb, zs, chi2 = U.get_data(bpz, (1, 9, 8))
dz = (zb - zs) / (1. + zs)
hdf5file = root + 'members_regular.hdf5'
filters = B.get_filter_list(columns)
ids = U.get_data(fluxcomp, 0)
pos = N.where(ids == 4539)[0][0]
z, x1, x2, x3 = P.getPDZ(hdf5file, pos)
base_y = N.arange(0., 1., 0.1)
#plt.figure(1, figsize = (6,6),dpi=75, facecolor='w', edgecolor='k')
plt.clf()
P.plotsedfittingbyID(ids[pos], columns, fluxcomp, sedlist, filters)
plt.xlabel('Wavelength $[\AA]$', size=18, labelpad=-2)
plt.ylabel('Magnitude', size=20, labelpad=2)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
#plt.ylim(23.,17)
plt.xlim(3200, 9300)
siete = plt.axes([0.5, .215, 0.375, 0.375])
y = x3 / x3.sum()
def check_variable_candidate(alhambraid):
"""
It replaces failed magnitudes in the ALHAMBRA catalogues (artificial absorptions,
non-observed sources assigned as non-detected with upper limits) by m=-99,em=99
It might decrease the amount of low Odds at bright magnitudes.
----
import alhambra_photools as A
A.replacing_fakeabsorptions(2,1,2)
A.check_sample(image,catalog,posID,posX,posY,posMAG)
A.alhambra_id_finder(ra1,dec1)
idd = int(id[pos])
A.alhambra_colorstamp_byID(id)
f,p,c,ids = A.alhambra_id_finder(37.4992,1.2482)
"""
field = int(str(alhambraid)[3])
pointing = int(str(alhambraid)[4])
ccd = int(str(alhambraid)[5])
numero = str(alhambraid)[-5:]
print numero
for ii in range(2):
if numero[0] == '0': numero = numero[1:]
print numero
root2cats = '/Volumes/amb22/catalogos/reduction_v4f/f0%i/' % (field)
catalog = root2cats + 'originals/f0%ip0%i_colorproext_%i_ISO.cat' % (
field, pointing, ccd)
cols1 = root2cats + 'f0%ip0%i_%i_tot_ISO_eB10.columns' % (field, pointing,
ccd)
cols2 = root2cats + 'f0%ip0%i_colorproext_%i_ISO_phz_eB10.columns' % (
field, pointing, ccd)
if os.path.exists(cols1): columns = cols1
else: columns = cols2
filters = B.get_filter_list(columns)
print filters
data = C.loaddata(catalog) # Loading the whole catalog content.
head = C.loadheader(catalog) # Loading the original header.
m = A.get_magnitudes(catalog, columns)
# em = get_errmagnitudes(catalog,columns)
root2 = '/Volumes/amb22/catalogos/reduction_v4e/'
fluxc1 = root2 + 'f0%i/f0%ip0%i_colorproext_%i_ISO_phz_eB11.flux_comparison' % (
field, field, pointing, ccd)
fluxc2 = root2 + 'f0%i/f0%ip0%i_colorproext_%i_ISO.flux_comparison' % (
field, field, pointing, ccd)
if os.path.exists(fluxc1): fluxc = fluxc1
else: fluxc = fluxc2
ido, ftt, foo, efoo, zb, tb, mm = P.get_usefulfluxcomparison(
columns, fluxc)
pos = A.get_position(ido, int(numero))
print ido[pos], mm[pos]
plt.figure(1, figsize=(10, 7), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
# P.plot1sedfitting(foo[:,jj],efoo[:,jj],ftt[:,jj],zb[jj],tb[jj],root_bpz_sed+'eB11.list',filters)
plt.plot(U.arange(20) + 1, foo[0:20, pos], 'k-', alpha=0.4, lw=6)
plt.plot(U.arange(20) + 1, foo[0:20, pos], 'ko', alpha=0.4, ms=12)
plt.errorbar(U.arange(20) + 1,
foo[0:20, pos], (efoo[0:20, pos] / 1.),
fmt="ko",
alpha=0.4,
ms=10)
minf = (foo[0:20, pos].min()) * 1.1
maxf = (foo[0:20, pos].max()) * 1.1
maxef = (efoo[0:20, pos].max()) * 1.1
# plt.ylim(minf-maxef,maxf+maxef)
plt.xlim(0, 21)
plt.xlabel('Filter', size=25)
plt.ylabel('Flux', size=25)
plt.legend(['Magnitude: %.2f' % (m[pos][-1])],
loc='upper right',
numpoints=1,
fontsize=20)
plt.title(alhambraid, size=25)
plt.grid()
plt.show()
namefig = '/Users/albertomolino/doctorado/photo/variability/analysis/vocheck.ID%s.png' % (
alhambraid)
plt.savefig(namefig, dpi=125)
outcat = '/Users/albertomolino/doctorado/photo/variability/analysis/vocheck.ID%s.cat' % (
alhambraid)
A.select_rows_bylist_pro(catalog, ido[pos], outcat)
print ' '
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]))