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 compute_Wittman_CIs(hdf5file, zs):
"""
It calculates the (HPD) CI intervals
and F(C) for galaxies with zs redshift values.
"""
verbose = 0
p = h5py.File(hdf5file, mode='r')
pdz = p.get('FullProbability')
zz = p.get('redshift')[:]
ngal = N.shape(pdz)[0]
print 'N.shape(pdz)', N.shape(pdz)
if ngal <> len(zs):
print 'Dimension missmatch!'
sys.exit()
ci_values = N.zeros(ngal)
for ii in range(ngal):
pdz_gal = U.sum(pdz[ii, :, :], axis=1)
# dz = 0.001
# x=N.arange(-3.*sigma_g,3.*sigma_g+dz/100.,dz)
# sigma_g=0.02
# gaus = N.exp(-(x/sigma_g)**2)
# pepe = N.convolve(pdz_gal,gaus,1)
pdz_gal /= pdz_gal.sum() #Norm the distr.
if verbose:
print 'PDF-size', N.shape(pdz_gal)
print 'z-length', len(zz)
dz = abs(zz - zs[ii])
pos_z = N.where(dz == min(dz))[0][0]
pdz_th_value = pdz_gal[pos_z]
if verbose:
print 'pdz_th_value', pdz_th_value
good_z_ranges = N.greater_equal(pdz_gal, pdz_th_value)
pdz_r = N.compress(good_z_ranges, pdz_gal)
ci_values[ii] = pdz_r.sum()
#print pdz_r.sum()
#if ii<10:
# plt.clf()
# plt.plot(zz,pdz_gal,'k-')
# plt.plot(zz[good_z_ranges],pdz_gal[good_z_ranges],'-r.')
# plt.plot(zz[pos_z],pdz_gal[pos_z],'go',ms=10)
#pausa = raw_input('paused')
return ci_values
def comparing_populations_HDF5(hdf5file):
p = h5py.File(hdf5file, mode='r')
pdz = p.get('FullProbability')
# pdz = p.get('Likelihood')
zz = p.get('redshift')[:]
tt = p.get('type')[:]
nz = len(zz)
ng = N.shape(pdz)[0]
probs = U.zeros((nz, 2), float)
for ii in range(ng):
pepe1 = U.sum(pdzr[ii, :, 0:35], axis=1)
pepe2 = U.sum(pdzr[ii, :, 36:], axis=1)
pepe3 = (U.sum(pepe1) + U.sum(pepe2)) * 1.
# Normalized PDZs
pdz1 = pepe1 / pepe3
pdz2 = pepe2 / pepe3
probs[:, 0] += pdz1
probs[:, 1] += pdz2
plt.plot(zz[::30],
probs[::30, 0] * 275.,
'r-',
zz[::30],
probs[::30, 1] * 275,
'b-',
lw=5)
plt.xlim(0., 1.5)
for ii in range(ng):
pepe1 = U.sum(paca[ii, :, 0:35], axis=1)
pepe2 = U.sum(paca[ii, :, 36:], axis=1)
pepe3 = (U.sum(pepe1) + U.sum(pepe2)) * 1.
# Normalized PDZs
pdz1 = pepe1 / pepe3
pdz2 = pepe2 / pepe3
probs[:, 0] += pdz1
probs[:, 1] += pdz2
pepe[ss, :, :] / (pepe[ss, :, :].max()), 0)
except:
print 'Impossible to read: ', os.path.basename(hdf5_files[ii])
#for ss in range(nm):
# full_table[ss,:,:] = final_mat[ss,:,:]
fp_file.close()
#if plots:
plt.figure(22, figsize=(13, 6), dpi=70, facecolor='w', edgecolor='k')
plt.clf()
for ss in range(4):
plt.subplot(1, 4, ss + 1)
if ss < 1:
yo = U.sum(final_mat[0:3, 50:-100, :], axis=0)
else:
yo = final_mat[3 + ss, 50:-100, :]
yo2 = N.where(yo < minval, 0., yo)
print yo2.min()
print yo2.max()
print ''
plt.contour(zz[50:-100],
tt,
N.log10(yo2).T,
800,
linewidths=2,
vmin=-1.0,
vmax=1.)
if ss < 1:
plt.title('R$\leq$%s' % (m[ss + 3]), size=20)
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 alhambra_get2Dmatrix_HDF5_likelihood(inputfile,
cond,
finalname=None,
normed=1):
"""
Given a probs class, plot z versus T density plot.
import alhambrahdf5
from alhambrahdf5 import *
inputfile='/Volumes/CLASH/ALHAMBRA/f02p02_colorproext_1_ISO_phz_eB10.hdf5'
bpz = '/Volumes/CLASH/ALHAMBRA/f02p02_colorproext_1_ISO_phz_eB11.Prior1peak.bpz'
ids,mo = U.get_data(bpz,(0,11))
good = U.greater_equal(mo,18.) * U.less_equal(mo,23.)
finalname='/Users/amb/Desktop/testmat/f02p02c01.18m25.norm.mat'
mat = alhambra_get2Dmatrix_HDF5_likelihood(inputfile,good,finalname,1)
-----------
"""
p = h5py.File(inputfile, mode='r')
pdz = p.get('/Probs_z_T/Full_Probability')
z = p.get('/Probs_z_T/redshift')
tt = p.get('/Probs_z_T/type')
ll = p.get('/Probs_z_T/Likelihood')
# Example: pdz.shape (9651, 7000, 81)
no = pdz.shape[0]
nz = pdz.shape[1]
nt = pdz.shape[2]
kk = 0
for ii in range(no):
print '%i out of %i' % (ii + 1, no - 1)
if cond[ii] == True:
if kk < 1:
if normed == 1:
pepe = U.sum(ll[ii, :, :], axis=1)
xx = pepe / U.sum(pepe)
elif normed == 2:
for ss in range(81):
a = ll[ii, :, ss]
if a.sum() > 1.0e-30:
b = a / sum(a)
else:
xx = lik[ii, :, :]
else:
if normed == 1:
pepe = U.sum(ll[ii, :, :], axis=1)
xx += pepe / U.sum(pepe)
elif normed == 2:
for ss in range(81):
a = ll[ii, :, ss]
if a.sum() > 1.0e-30:
b += a / sum(a)
else:
xx += ll[ii, :, :]
kk += 1
if normed == 2: xx = b / b.sum()
if finalname == None: outname = inputfile + '.mat'
else: outname = finalname
# U.put_2Darray(outname,xx)
return xx
idpos = 0
hdf5file = '/Users/albertomolino/Desktop/CLASH/SN_Colfax/colfaxHost.hdf5'
p = h5py.File(hdf5file, mode='r')
pdf = p.get('Likelihood')
z = p.get('redshift')
zz = z[:]
t = p.get('type')
tt = t[:]
deltazz = [zz[1] - zz[0]]
deltatt = [tt[1] - tt[0]]
# deltazz2 = deltazz[0]/2.
# deltatt2 = deltatt[0]/2.
basez = U.arange(zz.min(), zz.max() + deltazz, deltazz)
baset = U.arange(tt.min(), tt.max() + deltatt, deltatt)
matris = pdf[idpos, :, :]
temps = U.sum(pdf[idpos, :, :], axis=0)
reds = U.sum(pdf[idpos, :, :], axis=1)
plt.figure(15, figsize=(12., 9.5), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
plt.ion()
plt.show()
nullfmt = plt.NullFormatter() # no labels
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
plt.figure(15, figsize=(8.5, 7.5), dpi=80, facecolor='w', edgecolor='k')
axScatter = plt.axes(rect_scatter)
def test_use(self):
self.assertLess(useful.sum(1,3),5)
std_value[ii, jj] = float(pepa[2])
out_value[ii, jj] = float(pepa[3])
num_sourc[ii, jj] = float(pepa[4])
except:
med_value[ii, jj] = 0.0
std_value[ii, jj] = 0.0
out_value[ii, jj] = 0.0
num_sourc[ii, jj] = 0.0
print 'Model med std out num '
for jj in range(n_models):
good = N.greater(std_value[:, jj], 0.0001)
linea = '%i, %.3f, %.3f, ' % (jj + 1, U.mean_robust(
med_value[good, jj]), U.mean_robust(std_value[good, jj]))
linea += '%.3f %i ' % (U.mean_robust(
out_value[good, jj]), U.sum(num_sourc[good, jj]))
print linea
base_sz = N.arange(0.0005, 0.095, 0.015)
base_sz_2 = N.arange(-0.095, 0.095, 0.015)
plt.clf()
for ss in range(n_models):
plt.subplot(3, 4, ss + 1)
good = N.greater(std_value[:, ss], 0.0001)
a1, a2, a3 = plt.hist(std_value[good, ss], base_sz, alpha=0.5, normed=1)
b1, b2, b3 = plt.hist(med_value[good, ss],
base_sz_2,
facecolor='red',
alpha=0.5,
normed=1)
plt.legend([
def global_PDZ(hdf5file, m_max):
"""
It returns the global P(z)
"""
p = h5py.File(hdf5file, mode='r')
# pdz = p.get('FullProbability')
pdz = p.get('Likelihood')
z = p.get('redshift')
mmm = p.get('m_0')[:]
good_sample = N.less_equal(abs(mmm), m_max)
pdz = pdz[good_sample, :, :]
ngal = N.shape(pdz)[0]
globalpdz_red = N.zeros(len(z))
globalpdz_blue = N.zeros(len(z))
globalpdz3_all = N.zeros(len(z))
for ii in range(ngal):
try:
pepe1 = U.sum(pdz[ii, :, 0:75], axis=1)
except:
pepe1 = z * 0.
try:
pepe2 = U.sum(pdz[ii, :, 75:], axis=1)
except:
pepe2 = z * 0.
try:
pepe66 = U.sum(pdz[ii, :, :], axis=1)
except:
pepe66 = z * 0.
try:
pepe3 = (U.sum(pepe1) + U.sum(pepe2)) * 1.
except:
pepe3 = z * 0.
if ii == 0:
try:
globalpdz_red = (pepe1 / pepe3)
except:
globalpdz_red = z * 0.
try:
globalpdz_blue = (pepe2 / pepe3)
except:
globalpdz_blue = z * 0.
# try: globalpdz3_all = pepe66
try:
globalpdz3_all = (pepe66 / pepe66.sum())
except:
globalpdz_all = z * 0.
else:
try:
globalpdz_red += (pepe1 / pepe3)
except:
globalpdz_red += z * 0.
try:
globalpdz_blue += (pepe2 / pepe3)
except:
globalpdz_blue += z * 0.
try:
globalpdz3_all += (pepe66 / pepe66.sum())
# try: globalpdz3_all += pepe66
except:
globalpdz_all += z * 0.
return z, globalpdz_red / (ngal * 1.), globalpdz_blue / (
ngal * 1.), globalpdz3_all / (ngal * 1.)
def getPDF_by_mag_and_weights(hdf5file, m_max, weights):
"""
It returns the global P(z)
---
m_mag: maximum magnitude to be considered.
weights: vector with P(det|gal).
"""
p = h5py.File(hdf5file, mode='r')
pdz = p.get('FullProbability')
# pdz = p.get('Likelihood')
z = p.get('redshift')
mmm = p.get('m_0')[:]
good_sample = N.less_equal(mmm, m_max)
pdz = pdz[good_sample, :, :]
ngal = N.shape(pdz)[0]
weights_redu = weights[good_sample]
globalpdz_red = N.zeros(len(z))
globalpdz_blue = N.zeros(len(z))
globalpdz3_all = N.zeros(len(z))
for ii in range(ngal):
try:
pepe1 = U.sum(pdz[ii, :, 0:75], axis=1)
except:
pepe1 = z * 0.
try:
pepe2 = U.sum(pdz[ii, :, 75:], axis=1)
except:
pepe2 = z * 0.
try:
pepe66 = U.sum(pdz[ii, :, :], axis=1)
except:
pepe66 = z * 0.
try:
pepe3 = (U.sum(pepe1) + U.sum(pepe2)) * 1.
except:
pepe3 = z * 0.
if ii == 0:
try:
globalpdz_red = (pepe1 / pepe3) * weights_redu[ii]
except:
globalpdz_red = z * 0.
try:
globalpdz_blue = (pepe2 / pepe3) * weights_redu[ii]
except:
globalpdz_blue = z * 0.
# try: globalpdz3_all = pepe66
try:
globalpdz3_all = (pepe66 / pepe66.sum()) * weights_redu[ii]
except:
globalpdz_all = z * 0.
else:
try:
globalpdz_red += (pepe1 / pepe3) * weights_redu[ii]
except:
globalpdz_red += z * 0.
try:
globalpdz_blue += (pepe2 / pepe3) * weights_redu[ii]
except:
globalpdz_blue += z * 0.
try:
globalpdz3_all += (pepe66 / pepe66.sum()) * weights_redu[ii]
# try: globalpdz3_all += pepe66
except:
globalpdz3_all += z * 0.
return z, globalpdz_red, globalpdz_blue, globalpdz3_all, ngal
def getPDF_by_mag_templates_and_weights(hdf5file, m_max, weights):
"""
It returns the global P(z) for each template individually.
---
m_mag: maximum magnitude to be considered.
weights: vector with P(det|gal).
"""
p = h5py.File(hdf5file, mode='r')
# pdz = p.get('FullProbability')
pdz = p.get('Likelihood')
z = p.get('redshift')
mmm = p.get('m_0')[:]
#good_m_sample = N.less_equal(mmm,m_max)
#pdz = pdz[good_m_sample,:,:]
ngal = N.shape(pdz)[0]
#weights_redu = weights[good_m_sample]
weights_redu = weights * 1.
tts = p.get('type')[:]
tbs = N.unique(tts.astype(int)) # indiv integers.
ntb = len(tbs)
global_pdfs = N.zeros((len(z), ntb), 'float')
ngal = 20
for ii in range(ngal):
# I need to separate the PDF per each type.
print 'Analyzing galaxy %i' % (ii + 1)
for uu in range(ntb):
if uu < 1:
good_T_sample = N.less_equal(tts, tbs[uu + 1] - 0.5)
elif ii == ntb - 1:
good_T_sample = N.greater_equal(tts, tbs[uu] - 0.5)
else:
good_T_sample = N.greater_equal(tts, tbs[uu] - 0.5)
good_T_sample *= N.less_equal(tts, tbs[uu] + 0.5)
# Global PDF_ii
global_pdf_galaxy_ii = U.sum(pdz[ii, :, :], axis=1)
# Now PDF_ii for each template (uu: good_T_sample).
if ii < 1:
global_pdfs[:, uu] = U.sum(pdz[ii, :, good_T_sample], axis=1)
if global_pdfs[:, uu].sum() > 0.:
global_pdfs[:,
uu] /= (1. * global_pdf_galaxy_ii) # Normalize
global_pdfs[:, uu] *= weights_redu[
ii] # Probability of being galaxy.
else:
global_pdfs[:, uu] = N.zeros(len(z))
else:
global_pdfs[:, uu] += U.sum(pdz[ii, :, good_T_sample], axis=1)
if global_pdfs[:, uu].sum() > 0.:
global_pdfs[:,
uu] /= (1. * global_pdf_galaxy_ii) # Normalize
global_pdfs[:, uu] *= weights_redu[
ii] # Probability of being galaxy.
else:
global_pdfs[:, uu] += N.zeros(len(z))
return z, global_pdfs
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
filters = [
'u', 'J0378', 'J0395', 'J0410', 'J0430', 'g', 'J0515', 'r', 'J0660', 'i',
'J0861', 'z'
]
base_filtros = N.arange(12) + 1
final_zpo = N.zeros((n_cats, 12), float)
final_zpe = N.zeros((n_cats, 12), float)
for ii in range(n_cats):
cali_columns = cats_names[ii][:-4] + '.spz.z05.%s_cali.columns' % (
aperture)
final_zpe[ii, :], final_zpo[ii, :] = U.get_data(cali_columns, (3, 4), 12)
pepe = U.sum(final_zpe, axis=0) / (1. * n_cats)
pepo = U.sum(final_zpo, axis=0) / (1. * n_cats)
plt.figure(1, figsize=(14, 8), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
plt.errorbar(base_filtros,
pepo - pepo[7],
pepe,
fmt="-rs",
alpha=0.5,
ms=10,
lw=7)
for ii in range(n_cats):
plt.plot(base_filtros,
final_zpo[ii, :] - final_zpo[ii, 7],
'k.',