def select_galaxies_JPLUS_catalog(catalog):
"""
import sys
sys.path.append('/Users/albertomolino/doctorado/photo/programas/')
import useful as U
import numpy as N
import jplus_calib_tools
from jplus_calib_tools import select_jplus_galaxies,num_tiles
catalog = '/Users/albertomolino/jplus_data_download/SV02_March07/SV02_March07.clean.cat'
"""
tile_num, obj_id, ra, dec, fw = U.get_data(catalog, (0, 1, 2, 3, 4))
u_m, u_em, g_m, g_em, r_m, r_em = U.get_data(catalog, (5, 6, 7, 8, 9, 10))
i_m, i_em, z_m, z_em, f395_m, f395_em = U.get_data(
catalog, (11, 12, 13, 14, 15, 16))
f410_m, f410_em, f430_m, f430_em, f515_m, f515_em = U.get_data(
catalog, (17, 18, 19, 20, 21, 22))
f660_m, f660_em, f861_m, f861_em = U.get_data(catalog, (23, 24, 25, 26))
header_cat = '# 1.tile_id 2.object_id 3.ra 4.dec 5.fwhm 6.uJAVA 7.uJAVA_err 8.gSDSS \
9.gSDSS_err 10.rSDSS 11.rSDSS_err 12.iSDSS 13.iSDSS_err 14.zSDSS 15.zSDSS_err \
16.J0395 17.J0395_err 18.J0410 19.J0410_err 20.J0430 21.J0430_err \
22.J0515 23.J0515_err 24.J0660 25.J0660_err 26.J0861 27.J0861_err '
only_tiles = num_tiles(tile_num) # tiles w/o duplications
n_tiles = len(only_tiles) # number of Tiles
for ii in range(n_tiles):
ref_tile = only_tiles[ii]
g1 = N.less(abs(tile_num - ref_tile), 1)
tile_cat = catalog[:-3] + '%i.gal.cat' % (ref_tile)
tile_num_r, obj_id_r, ra_r, dec_r, fw_r = U.multicompress(
g1, (tile_num, obj_id, ra, dec, fw))
u_m_r, u_em_r, g_m_r, g_em_r, r_m_r, r_em_r = U.multicompress(
g1, (u_m, u_em, g_m, g_em, r_m, r_em))
i_m_r, i_em_r, z_m_r, z_em_r, f395_m_r, f395_em_r = U.multicompress(
g1, (i_m, i_em, z_m, z_em, f395_m, f395_em))
f410_m_r, f410_em_r, f430_m_r, f430_em_r, f515_m_r, f515_em_r = U.multicompress(
g1, (f410_m, f410_em, f430_m, f430_em, f515_m, f515_em))
f660_m_r, f660_em_r, f861_m_r, f861_em_r = U.multicompress(
g1, (f660_m, f660_em, f861_m, f861_em))
gs = select_jplus_galaxies(fw_r, r_m_r)
# out_cat = maincat[:-3]+'gal.cat'
U.put_data(
tile_cat,
(tile_num_r[gs], obj_id_r[gs], ra_r[gs], dec_r[gs], fw_r[gs],
u_m_r[gs], u_em_r[gs], g_m_r[gs], g_em_r[gs], r_m_r[gs],
r_em_r[gs], i_m_r[gs], i_em_r[gs], z_m_r[gs], z_em_r[gs],
f395_m_r[gs], f395_em_r[gs], f410_m_r[gs], f410_em_r[gs],
f430_m_r[gs], f430_em_r[gs], f515_m_r[gs], f515_em_r[gs],
f660_m_r[gs], f660_em_r[gs], f861_m_r[gs], f861_em_r[gs]),
header_cat)
def get_SExt_assoc_files(pepe):
"""
It creates the associated catalogues with
the detections to be included in the analysis.
"""
for ii in range(7):
for jj in range(4):
for kk in range(4):
cat = root + '/f0%i/alhambra.F0%iP0%iC0%i.ColorProBPZ.cat' % (
ii + 2, ii + 2, jj + 1, kk + 1)
if os.path.exists(cat):
ids = U.get_str(cat, 0)
x, y, ar, ra, dec, mm = U.get_data(cat,
(6, 7, 8, 4, 5, 65))
nameout = root + '/f0%i/alhambra.F0%iP0%iC0%i.ColorProBPZ.coo' % (
ii + 2, ii + 2, jj + 1, kk + 1)
good = U.less_equal(abs(mm), 23.0)
ids = U.compress(good, (ids))
x, y, ar, ra, dec, mm = U.multicompress(
good, (x, y, ar, ra, dec, mm))
ne = len(x)
fileout = open(nameout, 'w')
fileout.write('# X Y AREA ID RA DEC F814W \n')
print 'Analyzing ', cat
for ss in range(ne):
linea = '%.3f %.3f %i %s %f %f %.2f \n' % (
x[ss], y[ss], ar[ss], ids[ss], ra[ss], dec[ss],
mm[ss])
fileout.write(linea)
fileout.close()
def get_PDZerrDistribution(hdf5file, bpzfile, columns):
"""
It returns the error distribution based on PDZs.
---
hdf5file = '/Users/albertomolino/doctorado/photo/catalogos/specz/spzPDZs/alhambra.spz.hdf5'
bpzfile = '/Users/albertomolino/doctorado/photo/catalogos/specz/spzPDZs/alhambra.spz.bpz'
columns = '/Users/albertomolino/doctorado/photo/catalogos/specz/spzPDZs/alhambra.spz.columns'
"""
ids, zb, zs, mo = U.get_data(bpzfile, (0, 1, 11, 12))
# ids,zb,zs,mo = U.get_data(bpzfile,(0,1,9,10))
good = N.greater(abs(mo), 17.) * N.less(abs(mo), 25.)
# good = N.greater(abs(mo),22.)*N.less(abs(mo),23.)
ids, zb, zs, mo = U.multicompress(good, (ids, zb, zs, mo))
ng = len(ids)
#Readin the PDZs...
p = h5py.File(hdf5file, mode='r')
pdzo = p.get('FullProbability')
pdz = pdzo[good, :, :]
zz = p.get('redshift')[:]
dz = zz[2] - zz[1]
basez2 = N.arange(-0.1, 0.1, dz)
basez2b = basez2[:-1] + ((basez2[1] - basez2[0]) / 2.)
nz = len(basez2)
delta_z_pdzs = N.zeros(nz - 1)
# Computing the z error distr. function
# based on peak values.
delta_z_peaks = (zb - zs) / (1. + zs)
a1, a2 = N.histogram(delta_z_peaks, basez2)
for ii in range(ng):
pdz_mot = U.sum(pdz[ii, :, :], axis=1)
delta_z_pdzs += U.match_resol(zz - zb[ii], pdz_mot, basez2b)
plt.figure(12, figsize=(8.5, 10.), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
plt.subplot(211)
plt.plot(basez2b, a1 / float(sum(a1)), 'b-', lw=12, alpha=0.6)
plt.plot(basez2b,
delta_z_pdzs / float(sum(delta_z_pdzs)),
'r-',
lw=5,
alpha=0.9)
plt.grid()
plt.xlim(-0.1, 0.1)
plt.ylabel('P(z)', size=20, labelpad=+1)
plt.legend(['peaks', 'pdfs'], loc='upper left', fontsize=20)
plt.subplot(212)
resi = 2
plt.plot(
basez2b[::resi],
abs((a1[::resi] / float(sum(a1))) -
(delta_z_pdzs[::resi] / float(sum(delta_z_pdzs)))), 'k-')
plt.grid()
plt.xlim(-0.1, 0.1)
plt.xlabel('$\delta_{z}$', size=30)
def numberdensity_2(C1,C2):
"""
As the other ones but fixing the ranges manually.
USAGE: matrix,axis2,axis1 = CC_numberdensity_contour(V-B, R-I)
"""
axis1 = N.arange(0.0,0.5,0.0025) #SPLUS FWHM mosaic
axis2 = N.arange(-0.3,0.3,0.0025) #SPLUS FWHM mosaic
matrix = N.zeros((len(axis1),len(axis2)),float)
for ii in range(len(axis1)-1):
for jj in range(len(axis2)-1):
good = N.greater_equal(C1,axis1[ii]) * N.less_equal(C1,axis1[ii+1])
good *= N.greater_equal(C2,axis2[jj]) * N.less_equal(C2,axis2[jj+1])
value1,value2 = U.multicompress(good,(C1,C2))
number = len(value1)
matrix[ii,jj] = number
return matrix,axis2,axis1
def runZPcal_catalogue(reference, frame, final):
"""
----
filter_ref_cat,alig_frame_cat,alig_cal_frame_cat
"""
plots = 1
data2 = C.loaddata(frame) # Loading the whole catalog2 content.
head2 = C.loadheader(frame) # Loading the original header2.
pos_mags = 12 # ([12,20,21,22])
mag_r = U.get_data(reference, 12)
mag_f = U.get_data(frame, 12)
# good_sample = U.greater_equal(mag_r,16.) * U.less_equal(mag_r,21.5)
good_sample = U.greater_equal(mag_r, 16.) * U.less_equal(mag_r, 19.)
mag_r2, mag_f2 = U.multicompress(good_sample, (mag_r, mag_f))
offset = U.mean_robust(mag_f2 - mag_r2)
if plots:
plt.figure(11, figsize=(12, 9), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
plt.plot(mag_r, (mag_f - mag_r - offset), 'ko', ms=10, alpha=0.1)
plt.xlim(16, 25)
plt.ylim(-5, 5.)
plt.xlabel('AB', size=25)
plt.ylabel('Mf-Mr', size=25)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.legend(['Offset: %.4f' % (offset)], loc='upper right', numpoints=1)
plt.title(A.getfilename(frame), size=15)
plt.grid()
figurename = final[:-3] + 'png'
print 'figurename: ', figurename
plt.savefig(figurename, dpi=100)
plt.close()
# Here it saves the offset in an ASCII file
fileout = open(final[:-3] + 'txt', 'w')
linea = '%s %.5f \n' % (final, offset)
fileout.write(linea)
fileout.close()
# The offset is only applied to m!=99. magnitudes.
new_mags = U.where(abs(mag_f) < 99, mag_f - offset, mag_f)
data2[:, pos_mags] = new_mags
C.savedata(data2, final, dir="", header=head2)
print ' '
# Root to data mainroot = '/Users/albertomolino/Postdoc/T80S_Pipeline/Commisioning/S82/Dec2017/' root2cats = mainroot + 'splus_cats_NGSL/' #bpz_cat = root2cats + 'COSMOSeB11new_recal/master.STRIPE82_Photometry.m21_COSMOSeB11new_recal_redu.bpz' #bpz_cat = root2cats + 'PriorSM/masterBPZ_PriorSM.bpz' bpz_cat = root2cats + 'COSMOSeB11new_recal/PriorSM/masterBPZ_PriorSM.bpz' # Reading data zb,mo,od,zs,chi2,tb = U.get_data(bpz_cat,(1,10,5,9,8,4)) # Doing some cleaning good = N.less(chi2,30) * N.greater_equal(mo,14) good *= N.greater_equal(od,0.01) good *= N.less_equal(zs,0.5) zb,mo,od,zs,chi2,tb = U.multicompress(good,(zb,mo,od,zs,chi2,tb)) # Defining three samples. mag_1 = 17 mag_2 = 19 mag_3 = 21 gm1 = N.less_equal(mo,mag_1) #* N.greater(tb,4.5) gm2 = N.less_equal(mo,mag_2) gm3 = N.less_equal(mo,mag_3) # Defining basis for histograms base_histo = N.arange(-0.2,0.2,0.01) base_histo_2 = [-0.1,-0.05,0.0,0.05,0.1] base_histo_3 = [-0.2,-0.1,0.0,0.1,0.2] base_line = N.arange(0.,1.,0.1) base_3 = N.arange(0.,0.6,0.1)
sys.path.append('/Users/albertomolino/doctorado/photo/programas/')
import numpy as N
import useful as U
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
root = '/Users/albertomolino/Postdoc/T80S_Pipeline/Commisioning/S82/Dec2017/'
root += 'data_quality/photometry/SDSSSPLUS/'
u_cat = root + 'master_SPLUS_STRIPE82_to_SDSS_Ivezick_stars_Uband.cat'
griz_cat = root + 'master_SPLUS_STRIPE82_to_SDSS_Ivezick_stars_griz.cat'
s2nu, u_sp, u_sd = U.get_data(u_cat, (2, 3, 4))
delta_u = (u_sp - u_sd)
good_u = N.greater_equal(s2nu, 5.) * N.less_equal(abs(delta_u), 2.)
good_u *= N.less_equal(u_sd, 22.)
u_sp, u_sd, delta_u = U.multicompress(good_u, (u_sp, u_sd, delta_u))
s2n, g_sp, r_sp, i_sp, z_sp = U.get_data(griz_cat, (2, 3, 4, 5, 6)) # SPLUS
g_sd, r_sd, i_sd, z_sd = U.get_data(griz_cat, (7, 8, 9, 10)) # SDSS
delta_g = (g_sp - g_sd)
delta_r = (r_sp - r_sd)
delta_i = (i_sp - i_sd)
delta_z = (z_sp - z_sd)
good_griz = N.greater_equal(s2n, 5.) * N.less_equal(abs(delta_g), 2.)
good_griz *= N.less_equal(abs(delta_r), 2.) * N.less_equal(abs(delta_i), 2.)
good_griz *= N.less_equal(abs(delta_z), 2.)
g_sp, r_sp, i_sp, z_sp = U.multicompress(good_griz, (g_sp, r_sp, i_sp, z_sp))
g_sd, r_sd, i_sd, z_sd = U.multicompress(good_griz, (g_sd, r_sd, i_sd, z_sd))
delta_g, delta_r = U.multicompress(good_griz, (delta_g, delta_r))
delta_i, delta_z = U.multicompress(good_griz, (delta_i, delta_z))
def figura33(lista):
"""
I'm using the stellar classification from the version_e.
----
import alhambra_completeness as alhc
lista = '/Volumes/amb22/catalogos/reduction_v4d/globalcats/lista.list'
alhc.figura33(lista)
"""
blue = 0
red = 1
cats = U.get_str(lista, 0)
cats2 = U.get_str(lista, 1)
nc = len(cats)
dx = 0.2
dy = 0.4
nxbins = 4
nybins = 2
ods = 0.05
mmin = 16.0
mmax = 23.75
zbmin = 0.0001
zbmax = 1.4
Mmin = -24
Mmax = -17
if red:
Tbmin = 1 # 7.
Tbmax = 5 # 11.
resolmag = 0.2 # 0.2
resolz = 0.05
if blue:
Tbmin = 7.
Tbmax = 11.
resolmag = 0.2
resolz = 0.05
resol = 0.025
areas = ([0.45, 0.47, 0.23, 0.24, 0.47, 0.47, 0.46, 2.79])
plt.figure(111, figsize=(21.5, 11.5), dpi=70, facecolor='w', edgecolor='k')
ss = 0
for jj in range(nybins):
for ii in range(nxbins):
# Reading data from catalogs.
mo, zb, tb, odds, m814 = U.get_data(cats[ss], (81, 72, 75, 76, 62))
sf = U.get_data(cats2[ss], 71)
# mo,zb,tb,sf,odds,m814 = U.get_data(cats[ss],(81,72,75,71,76,62))
g = U.greater_equal(abs(m814), mmin) * U.less_equal(
abs(m814), mmax)
# g* = U.greater_equal(odds,ods)
g *= U.greater_equal(tb, Tbmin) * U.less_equal(tb, Tbmax)
g *= U.less_equal(sf, 0.8)
yy = -0.014 * m814 + 0.38
g *= U.greater(odds, yy)
g *= U.less_equal(mo, Mmax + resol) * U.greater(mo, Mmin - resol)
g *= U.greater(zb, zbmin) * U.less_equal(zb, zbmax)
mo, zb, tb, odds = U.multicompress(g, (mo, zb, tb, odds))
print 'dimension', len(mo)
# Plotting density.
# cuadrado = plt.axes([.1+(ii*dx),.1+((nybins-jj-1)*dy),dx,dy])
if ii == nxbins - 1:
cuadrado = plt.axes([
.1 + (ii * dx), .1 + ((nybins - jj - 1) * dy),
dx + (dx * 0.2), dy
])
else:
cuadrado = plt.axes(
[.1 + (ii * dx), .1 + ((nybins - jj - 1) * dy), dx, dy])
matrix, axis2, axis1 = rs.CC_numberdensity_contour_zvolume(
zb, mo, resolz, resolmag, 1)
if blue:
plt.contourf(axis2,
axis1,
U.log10(matrix / areas[ss]),
250,
vmin=-11.,
vmax=-7.) # blue galaxies
if red:
plt.contourf(axis2,
axis1,
U.log10(matrix / areas[ss]),
250,
vmin=-12.,
vmax=-7.65) # red galaxies
if ii == nxbins - 1:
aa = plt.colorbar(pad=0., format='%.1f')
aa.set_label('Log. Density [N/Mpc$^{3}$/deg$^{2}$]', size=18)
if jj != nybins - 1: plt.setp(cuadrado, xticks=[])
if ii != 0: plt.setp(cuadrado, yticks=[])
if jj == nybins - 1:
plt.xlabel('M$_{B}$', size=27)
plt.xticks(fontsize=17)
if ii == 0:
plt.ylabel('redshift', size=28)
plt.yticks(fontsize=17)
# plotting axis manually
base1 = U.arange(Mmin, Mmax + 1., 1.)
base2 = U.arange(0, zbmax + (2. * resol), resol)
dim1 = len(base1)
dim2 = len(base2)
for rr in range(dim1):
plt.plot(base2 * 0. + base1[rr],
base2,
'k--',
linewidth=1.,
alpha=0.25)
for rr in range(dim2):
plt.plot(base1,
base1 * 0. + base2[rr],
'k--',
linewidth=1.,
alpha=0.25)
# plt.grid()
plt.ylim(zbmin + 0.0001, zbmax - 0.001)
plt.xlim(Mmin + 0.0001, Mmax - 0.0001)
if ss == 7: labelleg = 'Global'
else: labelleg = 'A%i' % (ss + 2)
xypos = (Mmax - 1.6, zbmax - 0.18)
if ss == 7: xypos = (Mmax - 3.5, zbmax - 0.18)
plt.annotate(labelleg, xy=xypos, fontsize=40, color='black')
ss += 1
plt.savefig('completeness.alhambra.png', dpi=200)
def get_PDZerrDistribution_byMagnitudes(hdf5file, bpzfile, columns):
"""
It returns the error distribution based on PDZs.
---
import alhambrahdf5 as AH
#hdf5file = '/Users/albertomolino/doctorado/photo/catalogos/reduction_v5/GOLD/alhambragold.hdf5'
#bpzfile = '/Users/albertomolino/doctorado/photo/catalogos/reduction_v5/GOLD/alhambragold.bpz'
#columns = '/Users/albertomolino/doctorado/photo/catalogos/reduction_v5/GOLD/alhambragold.columns'
hdf5file = '/Users/albertomolino/doctorado/photo/catalogos/specz/spzPDZs/alhambra.spz.hdf5'
bpzfile = '/Users/albertomolino/doctorado/photo/catalogos/specz/spzPDZs/alhambra.spz.bpz'
columns = '/Users/albertomolino/doctorado/photo/catalogos/specz/spzPDZs/alhambra.spz.columns'
basez2b,delta_z_peaks,delta_z_pdzs = AH.get_PDZerrDistribution_byMagnitudes(hdf5file,bpzfile,columns)
"""
basem = N.arange(18, 26, 2)
# basem = N.arange(18,25,2)
nm = len(basem)
ids, zb, zs, mo = U.get_data(bpzfile, (0, 1, 11, 12))
# ids,zb,zs,mo = U.get_data(bpzfile,(0,1,9,10))
#Readin the PDZs...
p = h5py.File(hdf5file, mode='r')
pdzo = p.get('FullProbability')
zz = p.get('redshift')[:]
dz = zz[2] - zz[1]
basez2 = N.arange(-0.1, 0.1, dz)
basez2b = basez2[:-1] + ((basez2[1] - basez2[0]) / 2.)
nz = len(basez2)
# Defining the final outputs.
delta_z_pdzs = N.zeros((nm - 1, nz - 1), float)
delta_z_peaks = N.zeros((nm - 1, nz - 1), float)
for ii in range(nm - 1):
good = N.greater_equal(mo, basem[ii]) * N.less_equal(mo, basem[ii + 1])
idr, zbr, zsr, mor = U.multicompress(good, (ids, zb, zs, mo))
ng = len(idr)
pdz = pdzo[good, :, :]
# Computing the z error distr. function
# based on peak values.
temporal_delta_z_peaks = (zbr - zsr) / (1. + zsr)
a1, a2 = N.histogram(temporal_delta_z_peaks, basez2)
delta_z_peaks[ii, :] = a1[:]
for jj in range(ng):
pdz_mot = U.sum(pdz[jj, :, :], axis=1)
delta_z_pdzs[ii, :] += U.match_resol(zz - zbr[jj], pdz_mot,
basez2b)
# plt.figure(12, figsize = (8.5,10.),dpi=80, facecolor='w', edgecolor='k')
# plt.clf()
# plt.subplot(211)
# plt.plot(basez2b,a1/float(sum(a1)),'b-',lw=12,alpha=0.6)
# plt.plot(basez2b,delta_z_pdzs/float(sum(delta_z_pdzs)),'r-',lw=5,alpha=0.9)
# plt.grid()
# plt.xlim(-0.1,0.1)
# plt.ylabel('P(z)',size=20,labelpad=+1)
# plt.legend(['peaks','pdfs'],loc='upper left',fontsize=20)
# plt.subplot(212)
# resi = 2
# plt.plot(basez2b[::resi],abs((a1[::resi]/float(sum(a1)))-(delta_z_pdzs[::resi]/float(sum(delta_z_pdzs)))),'k-')
# plt.grid()
# plt.xlim(-0.1,0.1)
# plt.xlabel('$\delta_{z}$',size=30)
return basez2b, delta_z_peaks, delta_z_pdzs
def flagging_dobledetections(cat1,cat2):
"""
This serves to append an extra column (each to both inputted catalogs)
indicating either a detection was repeated and with the lowest S/N
of the two.
Sources flagged as 1 are those detections to be excluded when combining
both catalogs into a single one.
--------
import alhambra_overlap as alhov
cat1 = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p02_colorproext_1_ISO.cat'
cat2 = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p01_colorproext_1_ISO.cat'
alhov.flagging_dobledetections(cat1,cat2)
"""
id1,ra1,dec1,x1,y1,s2n1 = U.get_data(cat1,(0,1,2,3,4,14))
id2,ra2,dec2,x2,y2,s2n2 = U.get_data(cat2,(0,1,2,3,4,14))
ne1 = len(id1)
ne2 = len(id2)
g1 = U.greater_equal(ra1,min(ra2))
g2 = U.less_equal(ra2,max(ra1))
id1r,ra1r,dec1r,x1r,y1r,s2n1r = U.multicompress(g1,(id1,ra1,dec1,x1,y1,s2n1))
id2r,ra2r,dec2r,x2r,y2r,s2n2r = U.multicompress(g2,(id2,ra2,dec2,x2,y2,s2n2))
flag1 = U.zeros(ne1)
flag2 = U.zeros(ne2)
dim1 = len(id1r)
dim2 = len(id2r)
print 'dim1,dim2',dim1,dim2
if dim1>0 and dim2>0:
print 'Matching samples....'
pepe = matching_vects_ddet(id1r,ra1r,dec1r,id2r,ra2r,dec2r,0.000312) # We use now X,Y instead RA,Dec
# Purging null elements
matchidcol = pepe[:,0].astype(int)
good_det1 = U.greater(matchidcol,0) # Excluding 0's (non matched detections)
matchidcol = U.compress(good_det1,(matchidcol))
matchidsp = pepe[:,1].astype(int)
good_det2 = U.greater(matchidsp,0) # Excluding 0's (non matched detections)
matchidsp = U.compress(good_det2,(matchidsp))
if len(matchidcol) == len(matchidsp) and len(matchidcol) >0 :
newdim = len(matchidsp)
print 'Dimension of matching',newdim
idr1 = U.zeros(newdim)
idr2 = U.zeros(newdim)
s2nr1 = U.zeros(newdim)
s2nr2 = U.zeros(newdim)
for ii in range(newdim):
idr1index = ap.id2pos(id1r,matchidcol[ii])
idr2index = ap.id2pos(id2r,matchidsp[ii])
idr1[ii] = id1r[idr1index]
s2nr1[ii] = s2n1r[idr1index]
idr2[ii] = id2r[idr2index]
s2nr2[ii] = s2n2r[idr2index]
# Select/Purge detections according to its S/N
marcador1 = U.zeros(newdim)
marcador2 = U.zeros(newdim)
for ss in range(newdim):
cociente = s2nr1[ss]/s2nr2[ss]
if cociente >= 1.: marcador1[ss] = 1.
else: marcador2[ss] = 1.
cond1 = U.less(marcador1,1)
cond2 = U.less(marcador2,1)
idr1b = U.compress(cond1,idr1)
dim1rr = len(idr1b)
idr2b = U.compress(cond2,idr2)
dim2rr = len(idr2b)
# Two new IDs (finalid1 & finalid2) are generated with
# the final elements to be included in the output catalog.
for hh1 in range(ne1):
if id1[hh1] in idr1b:
flag1[hh1] = 1
for hh2 in range(ne2):
if id2[hh2] in idr2b:
flag2[hh2] = 1
# A new smaller catalog will be created containing specz info as an extra column.
outcat1 = ap.decapfile(cat1)+'.doubledetect.cat'
outcat2 = ap.decapfile(cat2)+'.doubledetect.cat'
print 'outcat1',outcat1
print 'outcat2',outcat2
ap.appendcol(cat1,flag1,'Flag2Detected',outcat1)
ap.appendcol(cat2,flag2,'Flag2Detected',outcat2)
# Renaming files
ap.renamefile(cat1,cat1+'.old.cat')
if not os.path.exists(cat1): ap.renamefile(outcat1,cat1)
ap.renamefile(cat2,cat2+'.old.cat')
if not os.path.exists(cat2): ap.renamefile(outcat2,cat2)
else:
print 'No common sources in betwen the catalogs'
# A new smaller catalog will be created containing specz info as an extra column.
outcat1 = ap.decapfile(cat1)+'.doubledetect.cat'
outcat2 = ap.decapfile(cat2)+'.doubledetect.cat'
print 'outcat1',outcat1
print 'outcat2',outcat2
ap.appendcol(cat1,flag1*0,'Flag2Detected',outcat1)
ap.appendcol(cat2,flag2*0,'Flag2Detected',outcat2)
# Renaming files
ap.renamefile(cat1,cat1+'.old.cat')
if not os.path.exists(cat1): ap.renamefile(outcat1,cat1)
ap.renamefile(cat2,cat2+'.old.cat')
if not os.path.exists(cat2): ap.renamefile(outcat2,cat2)
final_pdf_blue = pb
final_pdf_global = pg
n_gals = ng
else:
final_pdf_red += pr
final_pdf_blue += pb
final_pdf_global += pg
n_gals += ng
if odds_cut:
gm = N.less_equal(mo, mag_bins[ii])
gm *= N.greater(odd, 0.89)
else:
gm = N.less_equal(mo, mag_bins[ii])
idr, zbr = U.multicompress(gm, (ids, zb))
#print 'len(idr)',len(idr)
for ff in range(len(idr)):
if ii < 1:
zbs_1.append(zbr[ff])
elif ii == 1:
zbs_2.append(zbr[ff])
else:
zbs_3.append(zbr[ff])
kk += 1
#print 'kk,ng',kk,n_gals
#final_pdf_red *= n_gals
#final_pdf_blue *= n_gals
#final_pdf_global *= n_gals
# Saving P(z|R<Ri)
out_file.write('# 14 F0861_auto \n')
out_file.write('# 15 z_auto \n')
out_file.write('# ID RA Dec U_auto F0378_auto F0395_auto '
'F0410_auto F0430_auto G_auto F0515_auto '
'R_auto F0660_auto I_auto F0861_auto z_auto'\n)
for ii in range(n_cats):
print 'reading catalogue %i '%(ii+1)
ids,ra,dec,u,f378,f395,f410,f430 = U.get_data(cats[ii],(0,1,2,15,24,33,42,51))
g,f515,r,f660,i,f861,z,ps = U.get_data(cats[ii],(60,69,78,87,96,105,114,132))
#Selecting good stars
good = N.greater_equal(ps,0.9)
good *= N.less_equal(abs(u),30) * N.less_equal(abs(g),30)
good *= N.less_equal(abs(r),30) * N.less_equal(abs(i),30)
good *= N.less_equal(abs(z),30) * N.less_equal(abs(f378),30)
good *= N.less_equal(abs(f395),30) * N.less_equal(abs(f410),30)
good *= N.less_equal(abs(f430),30) * N.less_equal(abs(f515),30)
good *= N.less_equal(abs(f660),30) * N.less_equal(abs(f861),30)
# Compressing.
ids,ra,dec,u,f378,f395,f410,f430 = U.multicompress(good,(ids,ra,dec,u,f378,f395,f410,f430))
g,f515,r,f660,i,f861,z = U.multicompress(good,(g,f515,r,f660,i,f861,z))
# Writing data into new catalogue.
n_stars = len(ids)
for ss in range(n_stars):
linea = '%i %.4f %.4f %.2f '%(ids[ss],ra[ss],dec[ss],u[ss])
linea += '%.2f %.2f %.2f %.2f '%(f378[ss],f395[ss],f410[ss],f430[ss])
linea += '%.2f %.2f %.2f %.2f '%(g[ss],f515[ss],r[ss],f660[ss])
linea += '%.2f %.2f %.2f '%(i[ss],f861[ss],z[ss])
out_file.write(linea+'\n')
out_file.close()
base_z2 = base_z[:-1] + ((base_z[1] - base_z[0]) / 2.)
base_m2 = base_m[:-1] + ((base_m[1] - base_m[0]) / 2.)
n_z = len(base_z)
n_m = len(base_m)
valor_auto = N.zeros((n_m, n_z), float)
alphas = [0.2, 0.4, 0.6, 0.8, 1.0]
#AUTO apertures
zb, zs, mo = U.get_data(master_bpz_auto, (1, 9, 10))
dz = (zb - zs) / (1. + zs)
satur = N.greater(mo, 14)
for ii in range(n_m):
good_1 = N.less_equal(mo, base_m[ii])
zs_r, dz_r = U.multicompress(good_1, (zs, dz))
for jj in range(n_z):
#good = N.greater_equal(zs_r,base_z[jj])
good = N.less_equal(zs_r, base_z[jj])
linea = 'm<%.2f,' % (base_m[ii])
linea += 'z<%.2f' % (base_z[jj])
linea += ': %i' % (len(zs_r[good]))
if len(zs_r[good]) > 100:
valor_auto[ii, jj] = U.std_mad(dz_r[good])
else:
valor_auto[ii, jj] = -1.
linea += ', dz/1+z: %.3f' % (valor_auto[ii, jj])
print linea
#pausa = raw_input('paused')
# Plot
def overlaygalaxies2(image, cat, posx, posy, cond, shape, legend, save,
outfile):
"""
Similar to version 1 but now the info display has nothing to do
with the ID from the catalogue. Just an extra variable.
This serves to create a color image with just stars.
============
import alhambra_webpage as alhw
import useful as U
image = '/Volumes/amb22/imagenes/f02/color_images/f02p01_OPTICAL_1.png'
cat = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p01_colorproext_1_ISO.cat'
posid = 0
posx = 3
posy = 4
sf = U.get_data(cat,71)
cond = U.greater_equal(sf,0.7)
shape = 'circle'
save = 'yes'
outfile = '/Volumes/amb22/catalogos/reduction_v4e/stars/f02/f02p01_stars_1.png'
alhw.overlaygalaxies2(image,cat,posx,posy,cond,shape,legend,save,outfile)
"""
colorfile = image
im = Image.open(colorfile)
imsize = nx, ny = im.size
stamp = im.crop((0, 0, nx, ny))
draw = ImageDraw.Draw(stamp)
x, y = U.get_data(cat, (posx, posy))
x, y, legend = U.multicompress(cond, (x, y, legend))
for ii in range(len(x)):
xx = x[ii]
yy = y[ii]
shapesize = 40 # aa * 1.05
colores = (255, 255, 1)
if shape != 'None':
if shape == 'circle':
draw.ellipse((xx - shapesize, ny - yy - shapesize,
xx + shapesize, ny - yy + shapesize),
fill=None)
elif shape == 'crosshair':
draw.line((xx + shapesize, yy, xx + shapesize - 10, yy),
fill=None,
width=3)
draw.line((xx - shapesize, yy, xx - shapesize + 10, yy),
fill=None,
width=3)
draw.line((xx, yy + shapesize, xx, yy + shapesize - 10),
fill=None,
width=3)
draw.line((xx, yy - shapesize, xx, yy - shapesize + 10),
fill=None,
width=3)
elif shape == 'rectangle':
draw.rectangle((dx - shapesize, dy - shapesize, dx + shapesize,
dy + shapesize),
fill=colores) # fill=None)
else:
print 'Shape not found!. It will not be overlaid...'
label = '%.2f' % (legend[ii])
draw.text((xx - (shapesize / 2.), ny - yy - (shapesize / 2.) - 15),
label,
fill=(255, 255, 1))
if save == 'yes':
if outfile != 'None':
stamp.save(outfile)
else:
stamp.save('imcutoff.png')
def globalimage_zb(image, cat, posx, posy, posarea, poszb, shape, save,
outfile):
"""
============
from alhambra_webpage import *
image = '/Users/albertomolino/Desktop/emss2137/emss2137.png'
cat = '/Volumes/amb2/SUBARU/emss2137/catalogs/MS2137_Subaru.bpz.2.cat'
posx = 3
posy = 4
posarea = 5
poszb = 17
shape = 'circle'
save = 'yes'
outfile = '/Users/albertomolino/Desktop/emss2137/emss2137_22.png'
globalimage_zb(image,cat,posx,posy,posarea,poszb,shape,save,outfile)
-----------------
import alhambra_photools
from alhambra_photools import *
import alhambra_webpage
from alhambra_webpage import *
image = '/Users/albertomolino/Desktop/macs1206/macs1206.color.png'
cat = '/Users/albertomolino/Desktop/UDF/Molino12/catalogs/ColorPro/macs1206_UDFconf_NIR_July2012_ISO_RS.cat'
posx = 3
posy = 4
shape = 'circle'
save = 'yes'
posarea = 5
poszb = 0
outfile = '/Users/albertomolino/Desktop/macs1206/macs1206.color.RS.purged.png'
globalimage_zb(image,cat,posx,posy,posarea,poszb,shape,save,outfile)
--------
import alhambra_photools
from alhambra_photools import *
import alhambra_webpage
from alhambra_webpage import *
image = '/Users/albertomolino/Desktop/rxj2248/HST/rxj2248_acs.png'
cat = '/Users/albertomolino/Desktop/rxj2248/HST/catalogs/rxj2248_IR_RedSeq.cat'
posx = 3
posy = 4
shape = 'circle'
save = 'yes'
posarea = 5
poszb = 0
outfile = '/Users/albertomolino/Desktop/rxj2248/HST/rxj2248_acs.RS.png'
globalimage_zb(image,cat,posx,posy,posarea,poszb,shape,save,outfile)
--------
"""
colorfile = image
im = Image.open(colorfile)
imsize = nx, ny = im.size
stamp = im.crop((0, 0, nx, ny))
draw = ImageDraw.Draw(stamp)
try:
x, y, area, zb = U.get_data(cat, (posx, posy, posarea, poszb))
sf = U.get_data(cat, 74)
# good = greater(zb,0.28) * less(zb,0.34)
good = U.greater(zb, 0.001) * U.less(sf, 0.7)
x, y, area, zb = U.multicompress(good, (x, y, area, zb))
except:
print 'Impossible to read the data from catalog. Check it out!!'
for ii in range(len(x)):
xx = x[ii]
yy = y[ii]
aa = area[ii]
zzb = zb[ii]
zbval = ' %.2f ' % (zzb)
# print 'x,y',xx,yy
# print 'zbval',zbval
# print 'ii',ii
# dx = dy = 1.5 * aa
shapesize = 20 # aa * 1.05
# print 'shapesize',shapesize
# dxo = dyo = 0
colores = (255, 255, 1)
# if zzb < 0.1 : colores = (255,1,1) # red
# elif zzb >= 0.1 and zzb < 0.3 : colores = (255,255,1) # yellow
# elif zzb >= 0.3 and zzb < 1. : colores = (1,255,1) # Green
# elif zzb >= 1. and zzb < 3. : colores = (1,255,255) # Blue
# else: colores = (255,1,255) # Purple
if shape != 'None':
if shape == 'circle':
draw.ellipse((xx - shapesize, ny - yy - shapesize,
xx + shapesize, ny - yy + shapesize),
fill=None)
elif shape == 'crosshair':
draw.line((xx + shapesize, yy, xx + shapesize - 10, yy),
fill=None,
width=3)
draw.line((xx - shapesize, yy, xx - shapesize + 10, yy),
fill=None,
width=3)
draw.line((xx, yy + shapesize, xx, yy + shapesize - 10),
fill=None,
width=3)
draw.line((xx, yy - shapesize, xx, yy - shapesize + 10),
fill=None,
width=3)
elif shape == 'rectangle':
draw.rectangle((dx - shapesize, dy - shapesize, dx + shapesize,
dy + shapesize),
fill=colores) # fill=None)
else:
print 'Shape not found!. It will not be overlaid...'
draw.text((xx - (shapesize / 2.), ny - yy - (shapesize / 2.)),
zbval,
fill=(255, 255, 1))
# draw.text((xx-(1.5*shapesize),ny-yy-(2.*shapesize)),zbval,fill=(255,255,255))
# # draw.text((xx-(1.5*shapesize),ny-yy-(2.*shapesize)),zbval,fill=colores)
if save == 'yes':
if outfile != 'None':
stamp.save(outfile)
else:
stamp.save('imcutoff.png')
def overlaygalaxies(image,
cat,
posid,
posx,
posy,
cond,
shape,
save,
outfile,
extra=None):
"""
This serves to create a color image with just stars.
============
import alhambra_webpage as alhw
import useful as U
image = '/Volumes/amb22/imagenes/f02/color_images/f02p01_OPTICAL_1.png'
cat = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p01_colorproext_1_ISO.cat'
posid = 0
posx = 3
posy = 4
sf = U.get_data(cat,71)
cond = U.greater_equal(sf,0.7)
shape = 'circle'
save = 'yes'
outfile = '/Volumes/amb22/catalogos/reduction_v4e/stars/f02/f02p01_stars_1.png'
alhw.overlaygalaxies(image,cat,posid,posx,posy,cond,shape,save,outfile)
"""
colorfile = image
im = Image.open(colorfile)
imsize = nx, ny = im.size
stamp = im.crop((0, 0, nx, ny))
draw = ImageDraw.Draw(stamp)
ids, x, y = U.get_data(cat, (posid, posx, posy))
ids, x, y = U.multicompress(cond, (ids, x, y))
for ii in range(len(x)):
iid = ids[ii]
xx = x[ii]
yy = y[ii]
shapesize = 10 # aa * 1.05
colores = (255, 255, 1)
if shape != 'None':
if shape == 'circle':
draw.ellipse((xx - shapesize, ny - yy - shapesize,
xx + shapesize, ny - yy + shapesize),
fill=None)
elif shape == 'crosshair':
draw.line((xx + shapesize, yy, xx + shapesize - 10, yy),
fill=None,
width=3)
draw.line((xx - shapesize, yy, xx - shapesize + 10, yy),
fill=None,
width=3)
draw.line((xx, yy + shapesize, xx, yy + shapesize - 10),
fill=None,
width=3)
draw.line((xx, yy - shapesize, xx, yy - shapesize + 10),
fill=None,
width=3)
elif shape == 'rectangle':
draw.rectangle((dx - shapesize, dy - shapesize, dx + shapesize,
dy + shapesize),
fill=colores) # fill=None)
else:
print 'Shape not found!. It will not be overlaid...'
if extra == None:
label = 'ID:814%i' % (iid)
else:
uno = int(int(extra) + iid)
label = 'ID:%i' % (uno)
draw.text((xx - (shapesize / 2.), ny - yy - (shapesize / 2.) - 15),
label,
fill=(255, 255, 1))
if save == 'yes':
if outfile != 'None':
stamp.save(outfile)
else:
stamp.save('imcutoff.png')
# Creating the file where to save the data.
file_out_name = final_path + 'psf.Rband_centeref.cat'
file_out = open(file_out_name, 'w')
file_out.write('# x y fwhm \n')
# Starting the game...
for sss in range(n_cats):
field = os.path.basename(cats[sss])[9:-15]
master_cat = root_to_cats + 'STRIPE82-%s_Photometry.cat' % (field) ##
print 'reading catalog %i out of %i ' % (sss + 1, n_cats)
x, y, fwhm, mr = U.get_data(master_cat, (3, 4, 8, 84))
fwhm_master, stars = sct.get_seeing_from_data_pro(fwhm, mr)
# Reading FWHM from Individual R images.
ind_catalog = root_to_ind + 'sex_STRIPE82-%s_R_swp.cat' % (field) ##
fwhm_indiv = U.get_data(ind_catalog, 6)
x_stars, y_stars, fw_stars = U.multicompress(stars, (x, y, fwhm_indiv))
center_pos_x = N.less(abs(x_stars - N.mean(x_stars)), 1000.)
center_pos_y = N.less(abs(y_stars - N.mean(y_stars)), 1000.)
fw_stars_center = fw_stars[center_pos_x * center_pos_y]
for ii in range(len(x_stars)):
linea = '%i %i ' % (x_stars[ii], y_stars[ii])
mean_value_center = U.mean_robust(fw_stars_center)
#valor = (fw_stars[ii]*fw_stars[ii])-(mean_value_center*mean_value_center)
valor = (fw_stars[ii] - mean_value_center)
linea += '%.2f \n' % (valor)
file_out.write(linea)
file_out.close()
if os.path.exists(file_out_name):
xx, yy, ff = U.get_data(file_out_name, (0, 1, 2))
n_models = len(sed_models)
# Reading the S-PLUS/S82 data (spec-z cat)
#bpz_cat = root_to_cats+'bpz/'+'master.STRIPE82_Photometry.m21.bpz'
if splus:
photo_cat = root_to_cats + 'cat/' + 'master.STRIPE82_Photometry.m21.cat'
u, du, g, dg, r, dr, i, z, dz, zs = U.get_data(
photo_cat, (15, 16, 60, 61, 78, 79, 96, 114, 115, 125))
else:
photo_cat = '/Users/albertomolino/Postdoc/T80S_Pipeline/targets/SDSS_S82/'
photo_cat += 'catalogues/stripe82_spz_extcorr.cat'
u, g, r, i, z, zs = U.get_data(photo_cat, (4, 6, 8, 10, 12, 3))
clean_data = N.less_equal(abs(u - g), 5.) * N.less_equal(abs(g - z), 5.)
#clean_data *= N.less_equal(du,0.1) * N.less_equal(dg,0.07) * N.less_equal(dz,0.07)
u, g, r, i, z, zs = U.multicompress(clean_data, (u, g, r, i, z, zs))
# Defining colours
color_x = g - z
color_y = u - g
# Estimating dimensionality
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)
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)
plt.grid()
plt.title(filters[ii][:-4], size=20)
# plt.xlabel('mt-mob',size=20)
plt.ylabel('Counts', size=20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
dos = plt.axes([0.15, .1, 0.75, 0.57])
plt.plot(dm2[::10], mo2[::10], 'bo', ms=2, alpha=0.1)
plt.errorbar(dm2[::10],
mo2[::10], [emo2[::10], emo2[::10]],
fmt="bo",
ms=2,
def get_PDZerrDistribution_byTemplates(hdf5file, bpzfile, m_max):
"""
It returns the error distribution based on PDZs.
---
import splus_s82_hdf5_tools as to
root = '/Users/albertomolino/Postdoc/T80S_Pipeline/Commisioning/'
root += 'S82/Dec2017/splus_cats_NGSL/'
hdf5list = root+'hdf5.list'
bpzlist = root+'bpz/master.STRIPE82_Photometry.m21.bpz.list'
hdf5_files = U.get_str(hdf5list,0)
n_hdf5 = len(hdf5_files)
bpz_files = U.get_str(bpzlist,0)
n_bpz = len(bpz_files)
for ii in range(n_bpz):
name = os.path.basename(hdf5_files[ii])
print name
try: z,dp,df = to.get_PDZerrDistribution_byTemplates(hdf5_files[ii],bpz_files[ii],19)
except: print 'Impossible to run on ',name
"""
plots = 1
# starting plots if necessary
if plots:
plt.figure(12,
figsize=(8.5, 10.),
dpi=80,
facecolor='w',
edgecolor='k')
try:
ids, zb, zs, mo, tb, odd = U.get_data(bpzfile, (0, 1, 11, 12, 4, 5))
except:
ids, zb, zs, mo, tb, odd = U.get_data(bpzfile, (0, 1, 9, 10, 4, 5))
good = N.less_equal(mo, m_max)
ids, zb, zs, mo, tb, odd = U.multicompress(good,
(ids, zb, zs, mo, tb, odd))
ng = len(ids)
#Readin the PDZs...
p = h5py.File(hdf5file, mode='r')
#pdzo = p.get('FullProbability')
pdz = p.get('Likelihood')
pdz = pdz[good, :, :]
zz = p.get('redshift')[:]
dz = (zz[2] - zz[1]) * 100.
basez2 = N.arange(-0.2, 0.2, dz)
basez2b = basez2[:-1] + ((basez2[1] - basez2[0]) / 2.)
nz = len(basez2)
res = 1
# Computing the z error distr. function
# based on peak values.
delta_z_peaks = (zb - zs) / (1. + zs)
a1, a2 = N.histogram(delta_z_peaks, basez2)
delta_z_pdzs = N.zeros(nz - 1)
for ii in range(ng):
pdz_mot = U.sum(pdz[ii, :, :], axis=1)
pdz_mot_peak = pdz_mot / float(max(pdz_mot))
# To get rid of long tails in PDFs with low probabilities.
pdz_mot_peak = N.where(pdz_mot_peak < 1.0e-4, 0., pdz_mot_peak)
pdz_mot_norm = pdz_mot_peak / float(sum(pdz_mot_peak))
pdz_mot_norm = N.where(pdz_mot_norm < 0., 0., pdz_mot_norm)
#pdz_mot_norm = pdz_mot/float(sum(pdz_mot))
pdz_mot_norm_resample = U.match_resol(zz - zs[ii], pdz_mot_norm,
basez2b)
pdz_mot_norm_resample = N.where(pdz_mot_norm_resample < 0., 0.,
pdz_mot_norm_resample)
delta_z_pdzs += pdz_mot_norm_resample[:]
"""
if plots:
plt.clf()
plt.subplot(121)
peak_zb_pos = N.argmax(pdz_mot_norm[::res])
print zz[peak_zb_pos]
plt.plot(zz[::res]-zs[ii],pdz_mot_norm[::res],'-',lw=5,alpha=0.6)
#plt.plot(zz[::res]-zz[peak_zb_pos],pdz_mot_norm[::res],'-',lw=5,alpha=0.6)
plt.grid()
plt.xlim(-0.2,0.2)
#plt.ylim(0.001,0.1)
plt.xlabel('$\delta_{z}$',size=30)
plt.ylabel('P(z)',size=20,labelpad=+1)
plt.legend(['R=%.2f''\n''T=%.1f''\n''O=%.1f'%(mo[ii],tb[ii],odd[ii])],loc='upper right')
plt.title('zb = %.2f, zs = %.2f, dz/1+z = %.2f'%(zb[ii],zs[ii],delta_z_peaks[ii]),size=20)
plt.subplot(122)
plt.plot(basez2b,delta_z_pdzs,'k-',lw=5)
plt.grid()
plt.xlim(-0.2,0.2)
#plt.ylim(0.001,0.1)
plt.xlabel('$\delta_{z}$',size=30)
plt.ylabel('P(z)',size=20,labelpad=+1)
pausa = raw_input('press a bottom to continue')
"""
# New variables to handle data easily.
# It scales the normalized PDFs by the ng!
norm_dz_peaks = a1 / float(sum(a1))
norm_dz_pdfs = delta_z_pdzs / float(sum(delta_z_pdzs))
if plots:
plt.figure(11,
figsize=(8.5, 10.),
dpi=80,
facecolor='w',
edgecolor='k')
plt.clf()
#plt.subplot(212)
plt.plot(basez2b, norm_dz_peaks, 'b-', lw=8, alpha=0.6)
plt.plot(basez2b, norm_dz_pdfs, 'r-', lw=5, alpha=0.9)
plt.grid()
plt.xlim(-0.2, 0.2)
plt.ylabel('P(z)', size=20, labelpad=+1)
plt.legend(['peaks', 'pdfs'], loc='upper left', fontsize=20)
plt.xlabel('$\delta_{z}$', size=30)
plot_filename = hdf5file[:-4] + 'deltaz.mmax%.2fAB.png' % (m_max)
plt.savefig(plot_filename, dpi=80)
# Saving data into a file.
output_filename = hdf5file[:-4] + 'deltaz.mmax%.2fAB.mat' % (m_max)
U.put_data(output_filename, (basez2b, norm_dz_peaks, norm_dz_pdfs),
'z dz_peak dz_PDFs')
return basez2b, norm_dz_peaks, norm_dz_pdfs
def purging_dobledetections(cat1,cat2):
"""
import alhambra_overlap
from alhambra_overlap import *
cat1 = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p02_colorproext_1_ISO.cat'
cat2 = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p01_colorproext_1_ISO.cat'
purging_dobledetections(cat1,cat2)
"""
id1,ra1,dec1,x1,y1,s2n1 = U.get_data(cat1,(0,1,2,3,4,14))
id2,ra2,dec2,x2,y2,s2n2 = U.get_data(cat2,(0,1,2,3,4,14))
ne1 = len(id1)
ne2 = len(id2)
g1 = U.greater_equal(ra1,min(ra2))
g2 = U.less_equal(ra2,max(ra1))
id1r,ra1r,dec1r,x1r,y1r,s2n1r = U.multicompress(g1,(id1,ra1,dec1,x1,y1,s2n1))
id2r,ra2r,dec2r,x2r,y2r,s2n2r = U.multicompress(g2,(id2,ra2,dec2,x2,y2,s2n2))
dim1 = len(id1r)
dim2 = len(id2r)
print 'dim1,dim2',dim1,dim2
if dim1>0 and dim2>0:
print 'Matching samples....'
pepe = matching_vects_ddet(id1r,ra1r,dec1r,id2r,ra2r,dec2r,0.000312) # We use now X,Y instead RA,Dec
# Purging null elements
matchidcol = pepe[:,0].astype(int)
good_det1 = U.greater(matchidcol,0) # Excluding 0's (non matched detections)
matchidcol = U.compress(good_det1,(matchidcol))
matchidsp = pepe[:,1].astype(int)
good_det2 = U.greater(matchidsp,0) # Excluding 0's (non matched detections)
matchidsp = U.compress(good_det2,(matchidsp))
if len(matchidcol) == len(matchidsp) and len(matchidcol) >0 :
newdim = len(matchidsp)
print 'Dimension of matching',newdim
idr1 = U.zeros(newdim)
idr2 = U.zeros(newdim)
s2nr1 = U.zeros(newdim)
s2nr2 = U.zeros(newdim)
for ii in range(newdim):
idr1index = ap.id2pos(id1r,matchidcol[ii])
idr2index = ap.id2pos(id2r,matchidsp[ii])
idr1[ii] = id1r[idr1index]
s2nr1[ii] = s2n1r[idr1index]
idr2[ii] = id2r[idr2index]
s2nr2[ii] = s2n2r[idr2index]
# Select/Purge detections according to its S/N
marcador1 = U.zeros(newdim)
marcador2 = U.zeros(newdim)
for ss in range(newdim):
cociente = s2nr1[ss]/s2nr2[ss]
if cociente >= 1.: marcador1[ss] = 1.
else: marcador2[ss] = 1.
cond1 = U.less(marcador1,1)
cond2 = U.less(marcador2,1)
idr1b = U.compress(cond1,idr1)
dim1rr = len(idr1b)
idr2b = U.compress(cond2,idr2)
dim2rr = len(idr2b)
print ''
print 'Number of detections to be removed from cat1: ', dim1rr
print 'Number of detections to be removed from cat2: ', dim2rr
print ''
# Two new IDs (finalid1 & finalid2) are generated with
# the final elements to be included in the output catalog.
finalid1 = U.zeros((ne1-dim1rr))
finalid2 = U.zeros((ne2-dim2rr))
kk1 = 0
for hh1 in range(ne1):
if id1[hh1] not in idr1b:
finalid1[kk1] = id1[hh1]
kk1 += 1
print 'kk1',kk1
kk2 = 0
for hh2 in range(ne2):
if id2[hh2] not in idr2b:
if kk2 <= (ne2-dim2rr-1):
finalid2[kk2] = id2[hh2]
kk2+=1
print 'kk2',kk2
# A new smaller catalog will be created containing specz info as an extra column.
outcat1 = ap.decapfile(cat1)+'.wo2detect.cat'
outcat2 = ap.decapfile(cat2)+'.wo2detect.cat'
print 'outcat1',outcat1
print 'outcat2',outcat2
ap.select_rows_bylist(cat1,finalid1,outcat1)
ap.select_rows_bylist(cat2,finalid2,outcat2)
else:
print 'No common sources in betwen the catalogs'
coordinates = root_to_cats + 'coo/'
if not os.path.exists(coordinates):
cmd = '/bin/mkdir %s' % (coordinates)
os.system(cmd)
# Creating (if necessary) new files containing IDs,RA,DEC from each S-PLUS/S82 tile.
for ii in range(n_cats):
field = os.path.basename(cats[ii])[9:-15]
new_radec_filename = coordinates + 'STRIPE82_%s_radec.coo' % (field)
if not os.path.exists(new_radec_filename):
new_file = open(new_radec_filename, 'w')
print 'Reading file %i out of %i' % (ii + 1, n_cats)
ids, ra, dec, x, y = U.get_data(cats[ii], (0, 1, 2, 3, 4))
good_xy = N.greater_equal(x, 2500) * N.less_equal(x, 7000)
good_xy *= N.greater_equal(y, 2500) * N.less_equal(y, 7000)
ids, ra, dec, x, y = U.multicompress(good_xy, (ids, ra, dec, x, y))
n_gals = len(ids)
for ss in range(n_gals):
#linea = '%s %i %.7f %.7f \n'%(field,ids[ss],ra[ss],dec[ss])
linea = '%s %i %.7f %.7f %.1f %.1f \n' % (
field, ids[ss], ra[ss], dec[ss], x[ss], y[ss])
new_file.write(linea)
new_file.close()
# It performs a match between S-PLUS coo and SDSS coo to
for ggg in range(n_cats):
radec_match_cat = coordinates + os.path.basename(cats[ggg][:-3] +
'radec.match2sdss.cat')
if not os.path.exists(radec_match_cat):
print 'Cross-matching file %i out of %i' % (ggg + 1, n_cats)
field = os.path.basename(cats[ggg])[9:-15]
cats_names = U.get_str(root2cats + lista_cats, 0)
n_cats = len(cats_names)
out_filename = final_root + 'countsxamplif'
if not os.path.exists(out_filename):
# Variables where to store the information
counts = N.zeros((2, 8), float) # 2x8 amplifiers.
for ii in range(n_cats):
catalog = cats_names[ii]
print 'Reading file: ', catalog.split('/')[-1]
x, y, mr = U.get_data(catalog, (3, 4, 78))
good_sample = N.greater_equal(mr, 14)
good_sample *= N.less_equal(mr, 20)
x, y, mr = U.multicompress(good_sample, (x, y, mr))
#Defining edges
min_x = min(x)
max_x = max(x)
min_y = min(y)
max_y = max(y)
dx = (max_x - min_x) / 8.
dy = (max_y - min_y) / 2.
#print 'dx: ',dx
#print 'dy: ',dy
# Starting loop
for jj in range(2):
for hh in range(8):
def match_spz_sample(cluster): # TO CHECK
finalcat1 = catalog2[:-3]+'CLASH.redu.cat'
finalcat2 = catalog2[:-3]+'nada.cat'
# if not os.path.exists(finalcat1):
if not os.path.exists(finalcat2):
# print 'Final catalog does not exist yet.'
if os.path.exists(catalog1) and os.path.exists(catalog2):
# It matches up detections to its Spectroscopic Sample.
# Reading specz catalog
print 'Reading info1 before matching...'
speczsample = catalog1
idsp,xsp,ysp = U.get_data(speczsample,(0,3,4))
goodsp = U.greater_equal(xsp,1500) * U.less_equal(xsp,3500)
goodsp *= U.greater_equal(ysp,1500) * U.less_equal(ysp,3500)
idsp,xsp,ysp = U.multicompress(goodsp,(idsp,xsp,ysp))
print 'New dimension for specz catalogue: ',len(xsp)
# rasp,decsp,xsp,ysp,zsp = get_data(speczsample,(0,1,2,3,4))
# xsp,ysp,zsp = get_data(speczsample,(1,2,7))
####### idsp = U.arange(len(xsp))+1
# idsp = arange(len(rasp))+1
# Reading ColorPro catalog
print 'Reading info2 before matching...'
idcol,xcol,ycol = U.get_data(catalog2,(0,3,4))
print 'Dimension for input catalogue before compressing: ',len(idcol)
gsp = U.greater_equal(xcol,1500) * U.less_equal(xcol,3500)
gsp *= U.greater_equal(ycol,1500) * U.less_equal(ycol,3500)
idcol,xcol,ycol = U.multicompress(gsp,(idcol,xcol,ycol))
print 'Dimension for input catalogue after compressing: ',len(idcol)
# Using "matching_vects" to match up samples...
print 'Matching samples....'
pepe = CT.matching_vects(idcol,xcol,ycol,idsp,xsp,ysp,1.1) # We use now X,Y instead RA,Dec
# Compressing matches for ColorPro...
print 'Compressing matches...'
matchidcol = pepe[:,0].astype(int)
gdet_col = U.greater(matchidcol,0) # Excluding 0's (non matched detections)
matchidcol = U.compress(gdet_col,(matchidcol))
# Compressing matches for Spectroscopic...
matchidsp = pepe[:,1].astype(int)
gdet_spz = U.greater(matchidsp,0) # Excluding 0's (non matched detections)
matchidsp = U.compress(gdet_spz,(matchidsp))
print 'len(idcol)',len(idcol)
print 'len(idsp)',len(idsp)
if len(matchidcol) == len(matchidsp):
print 'Creating idredu & zsredu '
print 'Dimension of matchidsp ',len(matchidsp)
idredu = U.zeros(len(matchidsp))
idspredu = U.zeros(len(matchidsp))
for ii in range(len(matchidsp)):
colindex = A.id2pos(idcol,matchidcol[ii]) # Position for Index idcol
spzindex = A.id2pos(idsp,matchidsp[ii]) # Position for Index idsp
idredu[ii] = idcol[colindex] # ID for ColorPro
idspredu[ii] = idsp[spzindex] # Specz for Specz
# A new smaller catalog will be created containing specz info as an extra column.
print 'Selecting by rows... '
finalcat1 = catalog2[:-3]+'UDF.redu.cat'
finalcat2 = catalog2[:-3]+'CLASH.redu.cat'
U.put_data(catalog2[:-3]+'idsfrommatch.txt',(idredu,idspredu))
A.select_rows_bylist_sorted(catalog1,idspredu,finalcat1)
A.select_rows_bylist_sorted(catalog2,idredu,finalcat2)
ra_p, dec_p = U.get_data(cats_names[ggg], (1, 2))
x_p, y_p = U.get_data(cats_names[ggg], (3, 4))
ra_s, dec_s, z_s, r_s = U.get_data(sdss_s82_spz_cat, (0, 1, 2, 3))
# Let's remove edges.
xmin = np.min(x_p) + 300
xmax = np.max(x_p) - 300
ymin = np.min(y_p) + 300
ymax = np.max(y_p) - 300
good_photo = np.greater_equal(x_p, xmin)
good_photo *= np.less_equal(x_p, xmax)
good_photo *= np.greater_equal(x_p, ymin)
good_photo *= np.less_equal(x_p, ymax)
# Compress the photometric sample.
ra_p, dec_p = U.multicompress(good_photo, (ra_p, dec_p))
# Common area
area_ra = np.greater_equal(ra_s, min(ra_p))
area_ra *= np.less_equal(ra_s, max(ra_p))
area_dec = np.greater_equal(dec_s, min(dec_p))
area_dec *= np.less_equal(dec_s, max(dec_p))
good_area = area_ra * area_dec
#Compressing
ra_s, dec_s, z_s, r_s = U.multicompress(good_area,
(ra_s, dec_s, z_s, r_s))
# Saving
redu_spec = cats_names[ggg][:-3] + 'spez.areacommon.cat'
U.put_data(redu_spec, (ra_s, dec_s, z_s, r_s), '# ra dec zs mr')
# Here we find the missing galaxies
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
elif ss in [5, 6]:
basem = basem_med
else:
basem = basem_high
for ss in range(n_total):
linea = '%i %i %.2f %i %.2f %.2f \n' % (
x_global[ss], y_global[ss], s2n_global[ss], Flag_global[ss],
fw_global[ss], mr_global[ss])
file_out.write(linea)
file_out.close()
#########
#psfcat = final_path+'psfnew.cat'
psfcat = final_path + 'psfnew_dividedbyseeing.cat'
x, y, s2n, Flag, fw, mr = U.get_data(psfcat, (0, 1, 2, 3, 4, 5))
good = N.greater(s2n, 100) * N.greater(mr, 13.5) * N.less(mr, 18.5) * N.less(
Flag, 1)
#good *= N.greater(fw,0.92)*N.less(fw,1.05)
x, y, s2n, Flag, fw, mr = U.multicompress(good, (x, y, s2n, Flag, fw, mr))
rad = N.sqrt((x - N.mean(x)) * (x - N.mean(x)) + (y - N.mean(y)) *
(y - N.mean(y)))
rad2 = rad * 0.55 / 60.
plt.figure(1)
mat, v1, v2 = R.CC_numberdensity_contour_rangefixed(fw, rad2, 0.1, 0, 1, 0)
base = N.arange(0.8, 1.2, 0.1)
plt.figure(2, figsize=(17, 10), dpi=70, facecolor='w', edgecolor='k')
plt.clf()
dos = plt.axes([0.35, 0.215, 0.2, 0.7])
a1, a2, a3 = plt.hist(fw2,
base,
facecolor='blue',
alpha=0.15,
m_max = 19.
delta_m = 0.2
base_m = N.arange(m_min, m_max + delta_m, delta_m)
base_m2 = base_m[:-1] + ((base_m[1] - base_m[0]) / 2.)
z_min = 0.005
z_max = 0.4
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)
filter_y2 = 'g_SDSS' #Models sed_models = U.get_str(sed_path+sed_lib,0) n_models = len(sed_models) # SDSS/S82 Contours sdss_s82_path = '/Users/albertomolino/Postdoc/T80S_Pipeline/targets/SDSS_S82/' sdss_s82_cat = sdss_s82_path+'catalogues/stripe82_spz_extcorr.cat' u_mag,g_mag,r_mag,i_mag,z_mag,z_spec = U.get_data(sdss_s82_cat,(4,6,8,10,12,3)) good_sample = N.less(abs(u_mag-g_mag),5.) good_sample*= N.less(abs(g_mag-z_mag),5.) good_sample*= N.greater_equal(z_spec,z_min) good_sample*= N.less_equal(z_spec,z_max) u_mag,g_mag,r_mag = U.multicompress(good_sample,(u_mag,g_mag,r_mag)) i_mag,z_mag,z_spec = U.multicompress(good_sample,(i_mag,z_mag,z_spec)) #Reduce sample size! sdss_res = 3 u_mag,g_mag,r_mag = u_mag[::sdss_res],g_mag[::sdss_res],r_mag[::sdss_res] i_mag,z_mag,z_spec = i_mag[::sdss_res],z_mag[::sdss_res],z_spec[::sdss_res] #Reading contours path_contour_map = sdss_s82_path+'catalogues/contours/' x_cmap,y_cmap = U.get_data(path_contour_map+'S82cont2.dat',(0,1)) z_cmap = U.get_2Darray(path_contour_map+'S82cont.dat') # Estimating dimensionality ab_file_example = ab_path+sed_models[0][:-3]+filter_x1+'.AB'
'green',
'red',
'purple',
]
zb_all, odds_all, mo_all, chi2_all, spty_all = U.get_data(
master_bpz_auto, (1, 5, 9, 8, 4))
zb_spz, zs, odds_spz, mo_spz, chi2_spz, spty_spz = U.get_data(
master_spz_auto, (1, 9, 5, 10, 8, 4))
good_sptype_all = N.greater_equal(spty_all, t_min) * N.less_equal(
spty_all, t_max)
good_sptype_spz = N.greater_equal(spty_spz, t_min) * N.less_equal(
spty_spz, t_max)
zb_all, odds_all, mo_all, chi2_all = U.multicompress(
good_sptype_all, (zb_all, odds_all, mo_all, chi2_all))
zb_spz, zs, odds_spz, mo_spz, chi2_spz = U.multicompress(
good_sptype_spz, (zb_spz, zs, odds_spz, mo_spz, chi2_spz))
valor_auto_m_spz = N.zeros((len(base_o), (len(base_m) - 1)), float)
valor_auto_m_all = N.zeros((len(base_o), (len(base_m) - 1)), float)
valor_auto_z_spz = N.zeros((len(base_o), (len(base_z) - 1)), float)
#valor_auto_z_all = N.zeros((len(base_o),(len(base_z)-1)),float)
plt.figure(1, figsize=(9, 8), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
plt.subplot(121)
# As a function of AB
for jj in range(len(base_o)):
for ii in range(len(base_m) - 1):
#all
