def test_age():
"""Test integrated age against analytical age."""
z = numpy.arange(0, 10.0, 0.05)
cosmo = {}
cosmo['omega_M_0'] = numpy.array([[0.99],[0.01],[0.3]])
cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']
cosmo['h'] = 0.7
cd.set_omega_k_0(cosmo)
linestyle = ['-', ':', '--']
gyr = 1e9 * cc.yr_s
tl = cd.lookback_time(z, **cosmo)
age = cd.age(z, **cosmo)
age_ana = cd.age_flat(z, **cosmo)
pylab.figure(figsize=(6,6))
for i in range(len(linestyle)):
pylab.plot(z, (tl/gyr)[i], ls=linestyle[i], color='0.5')
pylab.plot(z, (age/gyr)[i], ls=linestyle[i], color='r')
pylab.plot(z, (age_ana/gyr)[i], ls=linestyle[i], color='k')
pylab.xlabel("redshift z")
pylab.ylabel(r"age $t_L/$Gyr")
pylab.figure(figsize=(6,6))
for i in range(len(linestyle)):
pylab.plot(z, ((age - age_ana)/age_ana)[i], ls=linestyle[i],
color='k')
# Make sure errors are small:
ntest.assert_array_less((numpy.abs((age - age_ana)/age_ana)[i]),
3e-13)
pylab.xlabel("redshift z")
pylab.ylabel(r"age: (integral - analytical)/analytical")
def params_z(self, z):
"""Return interp/extrapolated Schechter function parameters."""
if self.extrap_var == 'z':
return {'MStar':self._MStarfunc(z),
'phiStar':self._phiStarfunc(z),
'alpha':self._alphafunc(z)}
elif self.extrap_var == 't':
z = numpy.atleast_1d(z)
t = cd.age(z, **self.cosmo)[0]
return self.params_t(t)
def params_z(self, z):
"""Return interp/extrapolated Schechter function parameters."""
if self.extrap_var == 'z':
return {
'MStar': self._MStarfunc(z),
'phiStar': self._phiStarfunc(z),
'alpha': self._alphafunc(z)
}
elif self.extrap_var == 't':
z = numpy.atleast_1d(z)
t = cd.age(z, **self.cosmo)[0]
return self.params_t(t)
def age_t(z, **cosmo):
z = np.atleast_1d(z)
if cosmo == {}:
cosmo_in = {
'omega_M_0': 0.3,
'omega_lambda_0': 0.7,
'omega_k_0': 0.0,
'h': 0.70
}
else:
cosmo_in = cosmo
results = cd.age(z, **cosmo_in) / cc.Gyr_s
return results
def get_lookback_time(self, z):
"""Calculates the lookback time to redshift z.
Uses :func:`cosmolopy.distance.age` and
:func:`cosmolopy.distance.lookback_time`,
which are based on equation 30 of David Hogg's `Distance
measures in cosmology. <https://arxiv.org/abs/astro-ph/9905116>`_
Args:
z (float): Redshift for which to calculate the lookback time
Returns:
(float): Lookback time in years
"""
sec = cd.age(z, **self.cosmo)
return sec / cc.yr_s
def calculate_age_stars(ro_in=None,
dset_in=None,
converted=False,
time_proper=True):
"""
Parameters
----------
converted: bool
if the epoch field is in code unit or converted to some physical unit
Return
------
starsFormedatUniverseAge:
age of the universe when the star particle was created in Myr
"""
if converted:
raise ValueError(
"Epoch field should be in code unit for this function to work properly.."
)
if (time_proper):
# switch depends on ramses run setup
import cosmolopy.distance as cd
import cosmolopy.constants as cc
cosmo = {
'omega_M_0': ro_in.info["omega_m"],
'omega_lambda_0': ro_in.info["omega_l"],
'h': ro_in.info["H0"] / 100.
}
cosmo = cd.set_omega_k_0(cosmo)
t_z0 = cd.age(0., **cosmo) / (cc.Gyr_s / 1.e+3) # Myr
ram2myr = ro_in.info["unit_time"].express(
C.Myr) / ro_in.info["aexp"]**2
starsFormedatUniverseAge = t_z0 + dset_in["epoch"][:] * ram2myr
else:
Myr_unit_time = ro_in.info["unit_time"].express(C.Myr)
starsFormedatUniverseAge = (ro_in.info["time"] -
dset_in["epoch"][:]) * Myr_unit_time
return starsFormedatUniverseAge
def test_t_0():
"""Check the age of the universe we get using WMAP cosmologies.
We only find agreement to 3 sig figs, not the 4 specified in the
WMAP paper.
The results of test_age.py show that we're doing the integral
correctly, so I think the problem is that we're not taking into
account some of the higher-order effects included in the WMAP
numbers.
"""
dz = 0.1
z = numpy.arange(80., 0. - 1.5 * dz, -1. * dz)
flat = True
cosmos = [
cparam.WMAP7_BAO_H0_mean(flat=True),
cparam.WMAP7_ML(flat=True),
cparam.WMAP5_BAO_SN_mean(flat=True),
cparam.WMAP5_ML(flat=True),
cparam.WMAP5_mean(flat=True)
]
for cosmo in cosmos:
age = cd.age(0.0, **cosmo)
age_flat = cd.age_flat(0.0, **cosmo)
gyr = 1e9 * cc.yr_s
age /= gyr
age_flat /= gyr
print "integrated age: ",
print tu.fractional_diff_string(age, cosmo['t_0'], 4)
ntest.assert_approx_equal(age,
cosmo['t_0'],
significant=3,
err_msg="Integrated age doesn't match WMAP")
print "analytical age: ",
print tu.fractional_diff_string(age_flat, cosmo['t_0'], 4)
ntest.assert_approx_equal(age_flat,
cosmo['t_0'],
significant=3,
err_msg="Analytical age doesn't match WMAP")
def test_figure6():
"""Plot Hogg fig. 6: The dimensionless lookback time t_L/t_H and age t/t_H.
The three curves are for the three world models,
- Einstein-de Sitter (omega_M, omega_lambda) = (1, 0) [solid]
: Low-density (0.05, 0) [dotted]
-- High lambda, (0.2, 0.8) [dashed]
Hubble distance DH = c / H0
z from 0--5
t/th from 0--1.2
"""
z = numpy.arange(0, 5.05, 0.05)
cosmo = {}
cosmo['omega_M_0'] = numpy.array([[1.0], [0.05], [0.2]])
cosmo['omega_lambda_0'] = numpy.array([[0.0], [0.0], [0.8]])
cosmo['h'] = 0.5
cd.set_omega_k_0(cosmo)
linestyle = ['-', ':', '--']
th = 1 / cd.hubble_z(0, **cosmo)
tl = cd.lookback_time(z, **cosmo)
age = cd.age(z, **cosmo)
pylab.figure(figsize=(6, 6))
for i in range(len(linestyle)):
pylab.plot(z, (tl / th)[i], ls=linestyle[i])
pylab.plot(z, (age / th)[i], ls=linestyle[i])
pylab.xlim(0, 5)
pylab.ylim(0, 1.2)
pylab.xlabel("redshift z")
pylab.ylabel(r"lookback timne $t_L/t_H$")
pylab.title("compare to " + inspect.stack()[0][3].replace('test_', '') +
" (astro-ph/9905116v4)")
def test_figure6():
"""Plot Hogg fig. 6: The dimensionless lookback time t_L/t_H and age t/t_H.
The three curves are for the three world models,
- Einstein-de Sitter (omega_M, omega_lambda) = (1, 0) [solid]
: Low-density (0.05, 0) [dotted]
-- High lambda, (0.2, 0.8) [dashed]
Hubble distance DH = c / H0
z from 0--5
t/th from 0--1.2
"""
z = numpy.arange(0, 5.05, 0.05)
cosmo = {}
cosmo['omega_M_0'] = numpy.array([[1.0],[0.05],[0.2]])
cosmo['omega_lambda_0'] = numpy.array([[0.0],[0.0],[0.8]])
cosmo['h'] = 0.5
cd.set_omega_k_0(cosmo)
linestyle = ['-', ':', '--']
th = 1/ cd.hubble_z(0, **cosmo)
tl = cd.lookback_time(z, **cosmo)
age = cd.age(z, **cosmo)
pylab.figure(figsize=(6,6))
for i in range(len(linestyle)):
pylab.plot(z, (tl/th)[i], ls=linestyle[i])
pylab.plot(z, (age/th)[i], ls=linestyle[i])
pylab.xlim(0,5)
pylab.ylim(0,1.2)
pylab.xlabel("redshift z")
pylab.ylabel(r"lookback timne $t_L/t_H$")
pylab.title("compare to " + inspect.stack()[0][3].replace('test_', '') +
" (astro-ph/9905116v4)")
def test_t_0():
"""Check the age of the universe we get using WMAP cosmologies.
We only find agreement to 3 sig figs, not the 4 specified in the
WMAP paper.
The results of test_age.py show that we're doing the integral
correctly, so I think the problem is that we're not taking into
account some of the higher-order effects included in the WMAP
numbers.
"""
dz = 0.1
z = numpy.arange(80., 0. - 1.5 * dz, -1. * dz)
flat = True
cosmos = [cparam.WMAP7_BAO_H0_mean(flat=True),
cparam.WMAP7_ML(flat=True),
cparam.WMAP5_BAO_SN_mean(flat=True),
cparam.WMAP5_ML(flat=True),
cparam.WMAP5_mean(flat=True)]
for cosmo in cosmos:
age = cd.age(0.0, **cosmo)
age_flat = cd.age_flat(0.0, **cosmo)
gyr = 1e9 * cc.yr_s
age /= gyr
age_flat /= gyr
print "integrated age: ",
print tu.fractional_diff_string(age, cosmo['t_0'], 4)
ntest.assert_approx_equal(age, cosmo['t_0'], significant=3,
err_msg="Integrated age doesn't match WMAP")
print "analytical age: ",
print tu.fractional_diff_string(age_flat, cosmo['t_0'], 4)
ntest.assert_approx_equal(age_flat, cosmo['t_0'], significant=3,
err_msg="Analytical age doesn't match WMAP")
def test_age():
"""Test integrated age against analytical age."""
z = numpy.arange(0, 10.0, 0.05)
cosmo = {}
cosmo['omega_M_0'] = numpy.array([[0.99], [0.01], [0.3]])
cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']
cosmo['h'] = 0.7
cd.set_omega_k_0(cosmo)
linestyle = ['-', ':', '--']
gyr = 1e9 * cc.yr_s
tl = cd.lookback_time(z, **cosmo)
age = cd.age(z, **cosmo)
age_ana = cd.age_flat(z, **cosmo)
pylab.figure(figsize=(6, 6))
for i in range(len(linestyle)):
pylab.plot(z, (tl / gyr)[i], ls=linestyle[i], color='0.5')
pylab.plot(z, (age / gyr)[i], ls=linestyle[i], color='r')
pylab.plot(z, (age_ana / gyr)[i], ls=linestyle[i], color='k')
pylab.xlabel("redshift z")
pylab.ylabel(r"age $t_L/$Gyr")
pylab.figure(figsize=(6, 6))
for i in range(len(linestyle)):
pylab.plot(z, ((age - age_ana) / age_ana)[i],
ls=linestyle[i],
color='k')
# Make sure errors are small:
ntest.assert_array_less((numpy.abs((age - age_ana) / age_ana)[i]),
3e-13)
pylab.xlabel("redshift z")
pylab.ylabel(r"age: (integral - analytical)/analytical")
def get_evolv(ID0, PA, Z=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], f_comp = 0, fil_path = './FILT/', inputs=None, dust_model=0, DIR_TMP='./templates/', delt_sfh = 0.01):
#
# delt_sfh (float): delta t of input SFH in Gyr.
#
# Returns: SED as function of age, based on SF and Z histories;
#
print('This function may take a while.')
flim = 0.01
lsfrl = -1 # log SFR low limit
mmax = 1000
Txmax = 4 # Max x value
lmmin = 10.3
nage = np.arange(0,len(age),1)
fnc = Func(Z, nage, dust_model=dust_model) # Set up the number of Age/ZZ
bfnc = Basic(Z)
age = np.asarray(age)
################
# RF colors.
import os.path
home = os.path.expanduser('~')
c = 3.e18 # A/s
chimax = 1.
mag0 = 25.0
d = 10**(73.6/2.5) * 1e-18 # From [ergs/s/cm2/A] to [ergs/s/cm2/Hz]
###########################
# Open result file
###########################
file = 'summary_' + ID0 + '_PA' + PA + '.fits'
hdul = fits.open(file) # open a FITS file
zbes = hdul[0].header['z']
chinu= hdul[1].data['chi']
uv= hdul[1].data['uv']
vj= hdul[1].data['vj']
RA = 0
DEC = 0
rek = 0
erekl= 0
ereku= 0
mu = 1.0
nn = 0
qq = 0
enn = 0
eqq = 0
try:
RA = hdul[0].header['RA']
DEC = hdul[0].header['DEC']
except:
RA = 0
DEC = 0
try:
SN = hdul[0].header['SN']
except:
###########################
# Get SN of Spectra
###########################
file = 'templates/spec_obs_' + ID0 + '_PA' + PA + '.cat'
fds = np.loadtxt(file, comments='#')
nrs = fds[:,0]
lams = fds[:,1]
fsp = fds[:,2]
esp = fds[:,3]
consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
if len((fsp/esp)[consp]>10):
SN = np.median((fsp/esp)[consp])
else:
SN = 1
Asum = 0
A50 = np.arange(len(age), dtype='float32')
for aa in range(len(A50)):
A50[aa] = hdul[1].data['A'+str(aa)][1]
Asum += A50[aa]
####################
# For cosmology
####################
DL = cd.luminosity_distance(zbes, **cosmo) * Mpc_cm # Luminositydistance in cm
Cons = (4.*np.pi*DL**2/(1.+zbes))
Tuni = cd.age(zbes, use_flat=True, **cosmo)
Tuni0 = (Tuni/cc.Gyr_s - age[:])
delT = np.zeros(len(age),dtype='float32')
delTl = np.zeros(len(age),dtype='float32')
delTu = np.zeros(len(age),dtype='float32')
for aa in range(len(age)):
if aa == 0:
delTl[aa] = age[aa]
delTu[aa] = (age[aa+1]-age[aa])/2.
delT[aa] = delTu[aa] + delTl[aa]
elif Tuni/cc.Gyr_s < age[aa]:
delTl[aa] = (age[aa]-age[aa-1])/2.
delTu[aa] = delTl[aa] #10.
delT[aa] = delTu[aa] + delTl[aa]
elif aa == len(age)-1:
delTl[aa] = (age[aa]-age[aa-1])/2.
delTu[aa] = Tuni/cc.Gyr_s - age[aa]
delT[aa] = delTu[aa] + delTl[aa]
else:
delTl[aa] = (age[aa]-age[aa-1])/2.
delTu[aa] = (age[aa+1]-age[aa])/2.
delT[aa] = delTu[aa] + delTl[aa]
delT[:] *= 1e9 # Gyr to yr
delTl[:] *= 1e9 # Gyr to yr
delTu[:] *= 1e9 # Gyr to yr
##############################
# Load Pickle
##############################
samplepath = './'
pfile = 'chain_' + ID0 + '_PA' + PA + '_corner.cpkl'
niter = 0
data = loadcpkl(os.path.join(samplepath+'/'+pfile))
try:
ndim = data['ndim'] # By default, use ndim and burnin values contained in the cpkl file, if present.
burnin = data['burnin']
nmc = data['niter']
nwalk = data['nwalkers']
Nburn = burnin #* nwalk/10/2 # I think this takes 3/4 of samples
#if nmc>1000:
# Nburn = 500
samples = data['chain'][:]
except:
print(' = > NO keys of ndim and burnin found in cpkl, use input keyword values')
return -1
######################
# Mass-to-Light ratio.
######################
AM = np.zeros((len(age), mmax), dtype='float32') # Mass in each bin.
AC = np.zeros((len(age), mmax), dtype='float32') # Cumulative mass in each bin.
AL = np.zeros((len(age), mmax), dtype='float32') # Cumulative light in each bin.
ZM = np.zeros((len(age), mmax), dtype='float32') # Z.
ZC = np.zeros((len(age), mmax), dtype='float32') # Cumulative Z.
ZL = np.zeros((len(age), mmax), dtype='float32') # Light weighted cumulative Z.
TC = np.zeros((len(age), mmax), dtype='float32') # Mass weighted T.
TL = np.zeros((len(age), mmax), dtype='float32') # Light weighted T.
ZMM= np.zeros((len(age), mmax), dtype='float32') # Mass weighted Z.
ZML= np.zeros((len(age), mmax), dtype='float32') # Light weighted Z.
SF = np.zeros((len(age), mmax), dtype='float32') # SFR
Av = np.zeros(mmax, dtype='float32') # SFR
# ##############################
# Add simulated scatter in quad
# if files are available.
# ##############################
if inputs:
f_zev = int(inputs['ZEVOL'])
else:
f_zev = 1
eZ_mean = 0
try:
meanfile = './sim_SFH_mean.cat'
dfile = np.loadtxt(meanfile, comments='#')
eA = dfile[:,2]
eZ = dfile[:,4]
eAv= np.mean(dfile[:,6])
if f_zev == 0:
eZ_mean = np.mean(eZ[:])
eZ[:] = age * 0 #+ eZ_mean
else:
try:
f_zev = int(prihdr['ZEVOL'])
if f_zev == 0:
eZ_mean = np.mean(eZ[:])
eZ = age * 0
except:
pass
except:
print('No simulation file (%s).\nError may be underestimated.' % meanfile)
eA = age * 0
eZ = age * 0
eAv= 0
mm = 0
#mmax = 10
#print('mmax is set to 10')
for mm in range(mmax):
mtmp = np.random.randint(len(samples))# + Nburn
AAtmp = np.zeros(len(age), dtype='float32')
ZZtmp = np.zeros(len(age), dtype='float32')
mslist= np.zeros(len(age), dtype='float32')
Av_tmp = samples['Av'][mtmp]
f0 = fits.open(DIR_TMP + 'ms_' + ID0 + '_PA' + PA + '.fits')
sedpar = f0[1]
f1 = fits.open(DIR_TMP + 'ms.fits')
mloss = f1[1].data
Avrand = np.random.uniform(-eAv, eAv)
if Av_tmp + Avrand<0:
Av[mm] = 0
else:
Av[mm] = Av_tmp + Avrand
for aa in range(len(age)):
AAtmp[aa] = samples['A'+str(aa)][mtmp]/mu
try:
ZZtmp[aa] = samples['Z'+str(aa)][mtmp]
except:
ZZtmp[aa] = samples['Z0'][mtmp]
nZtmp = bfnc.Z2NZ(ZZtmp[aa])
mslist[aa] = sedpar.data['ML_'+str(nZtmp)][aa]
ml = mloss['ms_'+str(nZtmp)][aa]
Arand = np.random.uniform(-eA[aa],eA[aa])
Zrand = np.random.uniform(-eZ[aa],eZ[aa])
AM[aa, mm] = AAtmp[aa] * mslist[aa] * 10**Arand
AL[aa, mm] = AM[aa, mm] / mslist[aa]
SF[aa, mm] = AAtmp[aa] * mslist[aa] / delT[aa] / ml * 10**Arand
ZM[aa, mm] = ZZtmp[aa] + Zrand
ZMM[aa, mm]= (10 ** ZZtmp[aa]) * AAtmp[aa] * mslist[aa] * 10**Zrand
ZML[aa, mm]= ZMM[aa, mm] / mslist[aa]
for aa in range(len(age)):
AC[aa, mm] = np.sum(AM[aa:, mm])
ZC[aa, mm] = np.log10(np.sum(ZMM[aa:, mm])/AC[aa, mm])
ZL[aa, mm] = np.log10(np.sum(ZML[aa:, mm])/np.sum(AL[aa:, mm]))
if f_zev == 0: # To avoid random fluctuation in A.
ZC[aa, mm] = ZM[aa, mm]
ACs = 0
ALs = 0
for bb in range(aa, len(age), 1):
tmpAA = 10**np.random.uniform(-eA[bb],eA[bb])
tmpTT = np.random.uniform(-delT[bb]/1e9,delT[bb]/1e9)
TC[aa, mm] += (age[bb]+tmpTT) * AAtmp[bb] * mslist[bb] * tmpAA
TL[aa, mm] += (age[bb]+tmpTT) * AAtmp[bb] * tmpAA
ACs += AAtmp[bb] * mslist[bb] * tmpAA
ALs += AAtmp[bb] * tmpAA
TC[aa, mm] /= ACs
TL[aa, mm] /= ALs
Avtmp = np.percentile(Av[:],[16,50,84])
#############
# Plot
#############
AMp = np.zeros((len(age),3), dtype='float32')
ACp = np.zeros((len(age),3), dtype='float32')
ZMp = np.zeros((len(age),3), dtype='float32')
ZCp = np.zeros((len(age),3), dtype='float32')
SFp = np.zeros((len(age),3), dtype='float32')
for aa in range(len(age)):
AMp[aa,:] = np.percentile(AM[aa,:], [16,50,84])
ACp[aa,:] = np.percentile(AC[aa,:], [16,50,84])
ZMp[aa,:] = np.percentile(ZM[aa,:], [16,50,84])
ZCp[aa,:] = np.percentile(ZC[aa,:], [16,50,84])
SFp[aa,:] = np.percentile(SF[aa,:], [16,50,84])
###################
msize = np.zeros(len(age), dtype='float32')
for aa in range(len(age)):
if A50[aa]/Asum>flim: # if >1%
msize[aa] = 150 * A50[aa]/Asum
conA = (msize>=0)
# Make template;
tbegin = np.min(Tuni/cc.Gyr_s-age)
tuniv_hr = np.arange(tbegin,Tuni/cc.Gyr_s,delt_sfh) # in Gyr
sfh_hr_in= np.interp(tuniv_hr,(Tuni/cc.Gyr_s-age)[::-1],SFp[:,1][::-1])
zh_hr_in = np.interp(tuniv_hr,(Tuni/cc.Gyr_s-age)[::-1],ZCp[:,1][::-1])
# FSPS
con_sfh = (tuniv_hr>0)
import fsps
nimf = int(inputs['NIMF'])
try:
fneb = int(inputs['ADD_NEBULAE'])
except:
fneb = 0
if fneb == 1:
print('Metallicity is set to logZ/Zsun=%.2f'%(np.max(zh_hr_in)))
sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, logzsol=np.max(zh_hr_in), sfh=3, dust_type=2, dust2=0.0, add_neb_emission=True)
sp.set_tabular_sfh(tuniv_hr[con_sfh], sfh_hr_in[con_sfh])
else:
sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=3, imf_type=nimf, sfh=3, dust_type=2, dust2=0.0, add_neb_emission=False)
sp.set_tabular_sfh(tuniv_hr[con_sfh], sfh_hr_in[con_sfh], Z=10**zh_hr_in[con_sfh])
col01 = []
t_get = tuniv_hr[con_sfh]
#con_tget = ((Tuni/cc.Gyr_s-age)>0)
#t_get = (Tuni/cc.Gyr_s-age)[con_tget][::-1]
for ss in range(len(t_get)):
wave0, flux0 = sp.get_spectrum(tage=t_get[ss], peraa=True) # if peraa=True, in unit of L/AA
if ss == 0:
spec_mul_nu_conv = np.zeros((len(t_get),len(wave0)),dtype='float32')
#ax2.plot(wave0, flux0, linestyle='-', color='b')
#plt.show()
print('Template %d is done.'%(ss))
wavetmp = wave0*(1.+zbes)
spec_mul_nu = flamtonu(wavetmp, flux0) # Conversion from Flambda to Fnu.
Lsun = 3.839 * 1e33 #erg s-1
stmp_common = 1e10 # 1 tmp is in 1e10Lsun
spec_mul_nu_conv[ss,:] = spec_mul_nu[:]
spec_mul_nu_conv[ss,:] *= Lsun/(4.*np.pi*DL**2/(1.+zbes))
Ls = 10**sp.log_lbol
spec_mul_nu_conv[ss,:] *= (1./Ls)*stmp_common # in unit of erg/s/Hz/cm2/ms[ss].
consave = (wavetmp/(1.+zbes)<20000) # AA
if ss == 0:
nd_ap = np.arange(0,len(wave0),1)
col1 = fits.Column(name='wavelength', format='E', unit='AA', disp='obs', array=wavetmp[consave])
col2 = fits.Column(name='colnum', format='K', unit='', array=nd_ap[consave])
col00 = [col1, col2]
col3 = fits.Column(name='age', format='E', unit='Gyr', array=t_get)
col4 = fits.Column(name='sfh', format='E', unit='Msun/yr', array=sfh_hr_in[con_sfh])
col5 = fits.Column(name='zh', format='E', unit='Zsun', array=zh_hr_in[con_sfh])
col01 = [col3,col4,col5]
colspec_all = fits.Column(name='fspec_'+str(ss), format='E', unit='Fnu', disp='%s'%(t_get[ss]), array=spec_mul_nu_conv[ss,:][consave])
col00.append(colspec_all)
coldefs_spec = fits.ColDefs(col00)
hdu = fits.BinTableHDU.from_columns(coldefs_spec)
hdu.writeto(DIR_TMP + 'obsspec_' + ID0 + '_PA' + PA + '.fits', overwrite=True)
coldefs_spec = fits.ColDefs(col01)
hdu = fits.BinTableHDU.from_columns(coldefs_spec)
hdu.writeto(DIR_TMP + 'obshist_' + ID0 + '_PA' + PA + '.fits', overwrite=True)
def plot_sfh(ID0, PA, Z=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], f_comp = 0, fil_path = './FILT/', inputs=None, dust_model=0, DIR_TMP='./templates/',f_SFMS=False):
#
#
#
flim = 0.01
lsfrl = -1 # log SFR low limit
mmax = 1000
Txmax = 4 # Max x value
lmmin = 9.5 #10.3
nage = np.arange(0,len(age),1)
fnc = Func(Z, nage, dust_model=dust_model) # Set up the number of Age/ZZ
bfnc = Basic(Z)
age = np.asarray(age)
################
# RF colors.
import os.path
home = os.path.expanduser('~')
c = 3.e18 # A/s
chimax = 1.
mag0 = 25.0
d = 10**(73.6/2.5) * 1e-18 # From [ergs/s/cm2/A] to [ergs/s/cm2/Hz]
#d = 10**(-73.6/2.5) # From [ergs/s/cm2/Hz] to [ergs/s/cm2/A]
#############
# Plot.
#############
fig = plt.figure(figsize=(8,2.8))
fig.subplots_adjust(top=0.88, bottom=0.18, left=0.07, right=0.99, hspace=0.15, wspace=0.3)
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
#ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(133)
ax1t = ax1.twiny()
ax2t = ax2.twiny()
#ax3t = ax3.twiny()
ax4t = ax4.twiny()
##################
# Fitting Results
##################
#DIR_TMP = './templates/'
SNlim = 3 # avobe which SN line is shown.
###########################
# Open result file
###########################
file = 'summary_' + ID0 + '_PA' + PA + '.fits'
hdul = fits.open(file) # open a FITS file
zbes = hdul[0].header['z']
chinu= hdul[1].data['chi']
uv= hdul[1].data['uv']
vj= hdul[1].data['vj']
RA = 0
DEC = 0
rek = 0
erekl= 0
ereku= 0
mu = 1.0
nn = 0
qq = 0
enn = 0
eqq = 0
try:
RA = hdul[0].header['RA']
DEC = hdul[0].header['DEC']
except:
RA = 0
DEC = 0
try:
SN = hdul[0].header['SN']
except:
###########################
# Get SN of Spectra
###########################
file = 'templates/spec_obs_' + ID0 + '_PA' + PA + '.cat'
fds = np.loadtxt(file, comments='#')
nrs = fds[:,0]
lams = fds[:,1]
fsp = fds[:,2]
esp = fds[:,3]
consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
if len((fsp/esp)[consp]>10):
SN = np.median((fsp/esp)[consp])
else:
SN = 1
Asum = 0
A50 = np.arange(len(age), dtype='float32')
for aa in range(len(A50)):
A50[aa] = hdul[1].data['A'+str(aa)][1]
Asum += A50[aa]
####################
# For cosmology
####################
DL = cd.luminosity_distance(zbes, **cosmo) * Mpc_cm # Luminositydistance in cm
Cons = (4.*np.pi*DL**2/(1.+zbes))
Tuni = cd.age(zbes, use_flat=True, **cosmo)
Tuni0 = (Tuni/cc.Gyr_s - age[:])
delT = np.zeros(len(age),dtype='float32')
delTl = np.zeros(len(age),dtype='float32')
delTu = np.zeros(len(age),dtype='float32')
for aa in range(len(age)):
if aa == 0:
delTl[aa] = age[aa]
delTu[aa] = (age[aa+1]-age[aa])/2.
delT[aa] = delTu[aa] + delTl[aa]
elif Tuni/cc.Gyr_s < age[aa]:
delTl[aa] = (age[aa]-age[aa-1])/2.
delTu[aa] = delTl[aa] #10.
delT[aa] = delTu[aa] + delTl[aa]
elif aa == len(age)-1:
delTl[aa] = (age[aa]-age[aa-1])/2.
delTu[aa] = Tuni/cc.Gyr_s - age[aa]
delT[aa] = delTu[aa] + delTl[aa]
else:
delTl[aa] = (age[aa]-age[aa-1])/2.
delTu[aa] = (age[aa+1]-age[aa])/2.
delT[aa] = delTu[aa] + delTl[aa]
delT[:] *= 1e9 # Gyr to yr
delTl[:] *= 1e9 # Gyr to yr
delTu[:] *= 1e9 # Gyr to yr
#print(age, delT, delTu, delTl)
##############################
# Load Pickle
##############################
samplepath = './'
pfile = 'chain_' + ID0 + '_PA' + PA + '_corner.cpkl'
niter = 0
data = loadcpkl(os.path.join(samplepath+'/'+pfile))
try:
#if 1>0:
ndim = data['ndim'] # By default, use ndim and burnin values contained in the cpkl file, if present.
burnin = data['burnin']
nmc = data['niter']
nwalk = data['nwalkers']
Nburn = burnin #* nwalk/10/2 # I think this takes 3/4 of samples
#if nmc>1000:
# Nburn = 500
samples = data['chain'][:]
except:
print(' = > NO keys of ndim and burnin found in cpkl, use input keyword values')
return -1
######################
# Mass-to-Light ratio.
######################
#ms = np.zeros(len(age), dtype='float32')
# Wht do you want from MCMC sampler?
AM = np.zeros((len(age), mmax), dtype='float32') # Mass in each bin.
AC = np.zeros((len(age), mmax), dtype='float32') # Cumulative mass in each bin.
AL = np.zeros((len(age), mmax), dtype='float32') # Cumulative light in each bin.
ZM = np.zeros((len(age), mmax), dtype='float32') # Z.
ZC = np.zeros((len(age), mmax), dtype='float32') # Cumulative Z.
ZL = np.zeros((len(age), mmax), dtype='float32') # Light weighted cumulative Z.
TC = np.zeros((len(age), mmax), dtype='float32') # Mass weighted T.
TL = np.zeros((len(age), mmax), dtype='float32') # Light weighted T.
ZMM= np.zeros((len(age), mmax), dtype='float32') # Mass weighted Z.
ZML= np.zeros((len(age), mmax), dtype='float32') # Light weighted Z.
SF = np.zeros((len(age), mmax), dtype='float32') # SFR
Av = np.zeros(mmax, dtype='float32') # SFR
# ##############################
# Add simulated scatter in quad
# if files are available.
# ##############################
if inputs:
f_zev = int(inputs['ZEVOL'])
else:
f_zev = 1
eZ_mean = 0
try:
#meanfile = '/Users/tmorishita/Documents/Astronomy/sim_tran/sim_SFH_mean.cat'
meanfile = './sim_SFH_mean.cat'
dfile = np.loadtxt(meanfile, comments='#')
eA = dfile[:,2]
eZ = dfile[:,4]
eAv= np.mean(dfile[:,6])
if f_zev == 0:
eZ_mean = np.mean(eZ[:])
eZ[:] = age * 0 #+ eZ_mean
else:
try:
f_zev = int(prihdr['ZEVOL'])
if f_zev == 0:
eZ_mean = np.mean(eZ[:])
eZ = age * 0
except:
pass
except:
print('No simulation file (%s).\nError may be underestimated.' % meanfile)
eA = age * 0
eZ = age * 0
eAv= 0
mm = 0
#while mm<mmax:
for mm in range(mmax):
mtmp = np.random.randint(len(samples))# + Nburn
AAtmp = np.zeros(len(age), dtype='float32')
ZZtmp = np.zeros(len(age), dtype='float32')
mslist= np.zeros(len(age), dtype='float32')
Av_tmp = samples['Av'][mtmp]
f0 = fits.open(DIR_TMP + 'ms_' + ID0 + '_PA' + PA + '.fits')
sedpar = f0[1]
f1 = fits.open(DIR_TMP + 'ms.fits')
mloss = f1[1].data
Avrand = np.random.uniform(-eAv, eAv)
if Av_tmp + Avrand<0:
Av[mm] = 0
else:
Av[mm] = Av_tmp + Avrand
for aa in range(len(age)):
AAtmp[aa] = samples['A'+str(aa)][mtmp]/mu
try:
ZZtmp[aa] = samples['Z'+str(aa)][mtmp]
except:
ZZtmp[aa] = samples['Z0'][mtmp]
nZtmp = bfnc.Z2NZ(ZZtmp[aa])
mslist[aa] = sedpar.data['ML_'+str(nZtmp)][aa]
ml = mloss['ms_'+str(nZtmp)][aa]
Arand = np.random.uniform(-eA[aa],eA[aa])
Zrand = np.random.uniform(-eZ[aa],eZ[aa])
AM[aa, mm] = AAtmp[aa] * mslist[aa] * 10**Arand
AL[aa, mm] = AM[aa, mm] / mslist[aa]
SF[aa, mm] = AAtmp[aa] * mslist[aa] / delT[aa] / ml * 10**Arand
ZM[aa, mm] = ZZtmp[aa] + Zrand
ZMM[aa, mm]= (10 ** ZZtmp[aa]) * AAtmp[aa] * mslist[aa] * 10**Zrand
ZML[aa, mm]= ZMM[aa, mm] / mslist[aa]
for aa in range(len(age)):
AC[aa, mm] = np.sum(AM[aa:, mm])
ZC[aa, mm] = np.log10(np.sum(ZMM[aa:, mm])/AC[aa, mm])
ZL[aa, mm] = np.log10(np.sum(ZML[aa:, mm])/np.sum(AL[aa:, mm]))
if f_zev == 0: # To avoid random fluctuation in A.
ZC[aa, mm] = ZM[aa, mm]
ACs = 0
ALs = 0
for bb in range(aa, len(age), 1):
tmpAA = 10**np.random.uniform(-eA[bb],eA[bb])
tmpTT = np.random.uniform(-delT[bb]/1e9,delT[bb]/1e9)
TC[aa, mm] += (age[bb]+tmpTT) * AAtmp[bb] * mslist[bb] * tmpAA
TL[aa, mm] += (age[bb]+tmpTT) * AAtmp[bb] * tmpAA
ACs += AAtmp[bb] * mslist[bb] * tmpAA
ALs += AAtmp[bb] * tmpAA
TC[aa, mm] /= ACs
TL[aa, mm] /= ALs
#Avtmp = np.percentile(samples['Av'][:],[16,50,84])
Avtmp = np.percentile(Av[:],[16,50,84])
#############
# Plot
#############
AMp = np.zeros((len(age),3), dtype='float32')
ACp = np.zeros((len(age),3), dtype='float32')
ZMp = np.zeros((len(age),3), dtype='float32')
ZCp = np.zeros((len(age),3), dtype='float32')
SFp = np.zeros((len(age),3), dtype='float32')
for aa in range(len(age)):
AMp[aa,:] = np.percentile(AM[aa,:], [16,50,84])
ACp[aa,:] = np.percentile(AC[aa,:], [16,50,84])
ZMp[aa,:] = np.percentile(ZM[aa,:], [16,50,84])
ZCp[aa,:] = np.percentile(ZC[aa,:], [16,50,84])
SFp[aa,:] = np.percentile(SF[aa,:], [16,50,84])
###################
msize = np.zeros(len(age), dtype='float32')
for aa in range(len(age)):
if A50[aa]/Asum>flim: # if >1%
msize[aa] = 150 * A50[aa]/Asum
conA = (msize>=0)
ax1.fill_between(age[conA], np.log10(SFp[:,0])[conA], np.log10(SFp[:,2])[conA], linestyle='-', color='k', alpha=0.3)
#ax1.fill_between(age, np.log10(SFp[:,0]), np.log10(SFp[:,2]), linestyle='-', color='k', alpha=0.3)
ax1.scatter(age[conA], np.log10(SFp[:,1])[conA], marker='.', c='k', s=msize[conA])
ax1.errorbar(age[conA], np.log10(SFp[:,1])[conA], xerr=[delTl[:][conA]/1e9,delTu[:][conA]/1e9], yerr=[np.log10(SFp[:,1])[conA]-np.log10(SFp[:,0])[conA], np.log10(SFp[:,2])[conA]-np.log10(SFp[:,1])[conA]], linestyle='-', color='k', lw=0.5, marker='')
# Get SFMS;
IMF = int(inputs['NIMF'])
SFMS_16 = get_SFMS(zbes,age,ACp[:,0],IMF=IMF)
SFMS_50 = get_SFMS(zbes,age,ACp[:,1],IMF=IMF)
SFMS_84 = get_SFMS(zbes,age,ACp[:,2],IMF=IMF)
f_rejuv,t_quench,t_rejuv = check_rejuv(age,np.log10(SFp[:,:]),np.log10(ACp[:,:]),np.log10(SFMS_50))
#print(f_rejuv,t_quench,t_rejuv)
# Plot MS?
if f_SFMS:
#ax1.fill_between(age[conA], np.log10(SFMS_16)[conA], np.log10(SFMS_84)[conA], linestyle='-', color='b', alpha=0.3)
ax1.fill_between(age[conA], np.log10(SFMS_50)[conA]-0.2, np.log10(SFMS_50)[conA]+0.2, linestyle='-', color='b', alpha=0.3)
ax1.plot(age[conA], np.log10(SFMS_50)[conA], linestyle='--', color='b', alpha=0.5)
#
# Fit with delayed exponential??
#
minsfr = 1e-10
if f_comp == 1:
#
# Plot exp model?
#
DIR_exp = '/Users/tmorishita/Documents/Astronomy/sedfitter_exp/'
file = DIR_exp + 'summary_' + ID0 + '_PA' + PA + '.fits'
hdul = fits.open(file) # open a FITS file
t0 = 10**float(hdul[1].data['age'][1])
tau = 10**float(hdul[1].data['tau'][1])
A = float(hdul[1].data['A'][1])
Zexp= hdul[1].data['Z']
Mexp= float(hdul[1].data['ms'][1])
deltt0 = 0.01 # in Gyr
tt0 = np.arange(0.01,10,deltt0)
sfr = SFH_dec(np.max(age)-t0, tau, A, tt=tt0)
mtmp = np.sum(sfr * deltt0 * 1e9)
C_exp = Mexp/mtmp
ax1.plot(np.max(age) - tt0, np.log10(sfr*C_exp), marker='', linestyle='--', color='r', lw=1.5, alpha=0.7, zorder=-4, label='')
mctmp = tt0 * 0
for ii in range(len(tt0)):
mctmp[ii] = np.sum(sfr[:ii] * deltt0 * 1e9)
ax2.plot(np.max(age) - tt0, np.log10(mctmp*C_exp), marker='', linestyle='--', color='r', lw=1.5, alpha=0.7, zorder=-4)
ax4.errorbar(t0, Zexp[1], yerr=[[Zexp[1]-Zexp[0]], [Zexp[2]-Zexp[1]]], marker='s', color='r', alpha=0.7, zorder=-4, capsize=0)
sfr_exp = sfr
mc_exp = mctmp*C_exp
'''
#
# Plot delay model?
#
DIR_exp = '/Users/tmorishita//Documents/Astronomy/sedfitter_del/'
file = DIR_exp + 'summary_' + ID0 + '_PA' + PA + '.fits'
hdul = fits.open(file) # open a FITS file
t0 = 10**float(hdul[1].data['age'][0])
tau = 10**float(hdul[1].data['tau'][0])
A = float(hdul[1].data['A'][0])
Mdel= float(hdul[1].data['ms'][1])
tt0 = np.arange(0.01,10,0.01)
sfr = SFH_del(np.max(age)-t0, tau, A, tt=tt0)
mtmp = np.sum(sfr * deltt0 * 1e9)
C_del = Mdel/mtmp
'''
#
# Mass in each bin
#
ax2label = ''
ax2.fill_between(age[conA], np.log10(ACp[:,0])[conA], np.log10(ACp[:,2])[conA], linestyle='-', color='k', alpha=0.3)
ax2.errorbar(age[conA], np.log10(ACp[:,1])[conA], xerr=[delTl[:][conA]/1e9,delTu[:][conA]/1e9], yerr=[np.log10(ACp[:,1])[conA]-np.log10(ACp[:,0])[conA],np.log10(ACp[:,2])[conA]-np.log10(ACp[:,1])[conA]], linestyle='-', color='k', lw=0.5, label=ax2label)
ax2.scatter(age[conA], np.log10(ACp[:,1])[conA], marker='.', c='k', s=msize)
y2min = np.max([lmmin,np.min(np.log10(ACp[:,0])[conA])])
y2max = np.max(np.log10(ACp[:,2])[conA])+0.05
if f_comp == 1:
y2max = np.max([y2max, np.log10(np.max(mc_exp))+0.05])
y2min = np.min([y2min, np.log10(np.max(mc_exp))-0.05])
if (y2max-y2min)<0.2:
y2min -= 0.2
#
# Total Metal
#
ax4.fill_between(age[conA], ZCp[:,0][conA], ZCp[:,2][conA], linestyle='-', color='k', alpha=0.3)
ax4.scatter(age[conA], ZCp[:,1][conA], marker='.', c='k', s=msize[conA])
ax4.errorbar(age[conA], ZCp[:,1][conA], yerr=[ZCp[:,1][conA]-ZCp[:,0][conA],ZCp[:,2][conA]-ZCp[:,1][conA]], linestyle='-', color='k', lw=0.5)
fw_sfr = open('SFH_' + ID0 + '_PA' + PA + '.txt', 'w')
fw_sfr.write('# time_l time_u SFR SFR16 SFR84\n')
fw_sfr.write('# (Gyr) (Gyr) (M/yr) (M/yr) (M/yr)\n')
fw_met = open('ZH_' + ID0 + '_PA' + PA + '.txt', 'w')
fw_met.write('# time_l time_u logZ logZ16 logZ84\n')
fw_met.write('# (Gyr) (Gyr) (logZsun) (logZsun) (logZsun)\n')
for ii in range(len(age)-1,0-1,-1):
t0 = Tuni/cc.Gyr_s - age[ii]
fw_sfr.write('%.2f %.2f %.2f %.2f %.2f\n'%(t0-delTl[ii]/1e9, t0+delTl[ii]/1e9, SFp[ii,1], SFp[ii,0], SFp[ii,2]))
fw_met.write('%.2f %.2f %.2f %.2f %.2f\n'%(t0-delTl[ii]/1e9, t0+delTl[ii]/1e9, ZCp[ii,1], ZCp[ii,0], ZCp[ii,2]))
fw_sfr.close()
fw_met.close()
#########################
# Title
#########################
#ax1.set_title('Each $t$-bin', fontsize=12)
#ax2.set_title('Net system', fontsize=12)
#ax3.set_title('Each $t$-bin', fontsize=12)
#ax4.set_title('Net system', fontsize=12)
#############
# Axis
#############
#ax1.set_xlabel('$t$ (Gyr)', fontsize=12)
ax1.set_ylabel('$\log \dot{M}_*/M_\odot$yr$^{-1}$', fontsize=12)
#ax1.set_ylabel('$\log M_*/M_\odot$', fontsize=12)
lsfru = 2.8
if np.max(np.log10(SFp[:,2]))>2.8:
lsfru = np.max(np.log10(SFp[:,2]))+0.1
if f_comp == 1:
lsfru = np.max([lsfru, np.log10(np.max(sfr_exp*C_exp))])
ax1.set_xlim(0.008, Txmax)
ax1.set_ylim(lsfrl, lsfru)
ax1.set_xscale('log')
#ax2.set_xlabel('$t$ (Gyr)', fontsize=12)
ax2.set_ylabel('$\log M_*/M_\odot$', fontsize=12)
ax2.set_xlim(0.008, Txmax)
ax2.set_ylim(y2min, y2max)
ax2.set_xscale('log')
ax2.text(0.01, y2min + 0.07*(y2max-y2min), 'ID: %s\n$z_\mathrm{obs.}:%.2f$\n$\log M_\mathrm{*}/M_\odot:%.2f$\n$\log Z_\mathrm{*}/Z_\odot:%.2f$\n$\log T_\mathrm{*}$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$'%(ID0, zbes, np.log10(ACp[0,1]), ZCp[0,1], np.log10(np.percentile(TC[0,:],50)), Avtmp[1]), fontsize=9)
#
# Brief Summary
#
# Writing SED param in a fits file;
# Header
prihdr = fits.Header()
prihdr['ID'] = ID0
prihdr['PA'] = PA
prihdr['z'] = zbes
prihdr['RA'] = RA
prihdr['DEC'] = DEC
prihdr['Re_kpc'] = rek
prihdr['Ser_n'] = nn
prihdr['Axis_q'] = qq
prihdr['e_Re'] = (erekl+ereku)/2.
prihdr['e_n'] = enn
prihdr['e_q'] = eqq
# Add rejuv properties;
prihdr['f_rejuv']= f_rejuv
prihdr['t_quen'] = t_quench
prihdr['t_rejuv']= t_rejuv
prihdu = fits.PrimaryHDU(header=prihdr)
col01 = []
# Redshift
zmc = hdul[1].data['zmc']
col50 = fits.Column(name='zmc', format='E', unit='', array=zmc[:])
col01.append(col50)
# Stellar mass
ACP = [np.log10(ACp[0,0]), np.log10(ACp[0,1]), np.log10(ACp[0,2])]
col50 = fits.Column(name='Mstel', format='E', unit='logMsun', array=ACP[:])
col01.append(col50)
# Metallicity_MW
ZCP = [ZCp[0,0], ZCp[0,1], ZCp[0,2]]
col50 = fits.Column(name='Z_MW', format='E', unit='logZsun', array=ZCP[:])
col01.append(col50)
# Age_mw
para = [np.log10(np.percentile(TC[0,:],16)), np.log10(np.percentile(TC[0,:],50)), np.log10(np.percentile(TC[0,:],84))]
col50 = fits.Column(name='T_MW', format='E', unit='logGyr', array=para[:])
col01.append(col50)
# Metallicity_LW
ZCP = [ZL[0,0], ZL[0,1], ZL[0,2]]
col50 = fits.Column(name='Z_LW', format='E', unit='logZsun', array=ZCP[:])
col01.append(col50)
# Age_lw
para = [np.log10(np.percentile(TL[0,:],16)), np.log10(np.percentile(TL[0,:],50)), np.log10(np.percentile(TL[0,:],84))]
col50 = fits.Column(name='T_LW', format='E', unit='logGyr', array=para[:])
col01.append(col50)
# Dust
para = [Avtmp[0], Avtmp[1], Avtmp[2]]
col50 = fits.Column(name='AV', format='E', unit='mag', array=para[:])
col01.append(col50)
# U-V
para = [uv[0], uv[1], uv[2]]
col50 = fits.Column(name='U-V', format='E', unit='mag', array=para[:])
col01.append(col50)
# V-J
para = [vj[0], vj[1], vj[2]]
col50 = fits.Column(name='V-J', format='E', unit='mag', array=para[:])
col01.append(col50)
colms = fits.ColDefs(col01)
dathdu = fits.BinTableHDU.from_columns(colms)
hdu = fits.HDUList([prihdu, dathdu])
hdu.writeto('SFH_' + ID0 + '_PA' + PA + '_param.fits', overwrite=True)
#
# SFH
#
zzall = np.arange(1.,12,0.01)
Tall = cd.age(zzall, use_flat=True, **cosmo)/cc.Gyr_s
fw = open('SFH_' + ID0 + '_PA' + PA + '_sfh.cat', 'w')
fw.write('%s'%(ID0))
for mm in range(len(age)):
mmtmp = np.argmin(np.abs(Tall - Tuni0[mm]))
zztmp = zzall[mmtmp]
fw.write(' %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f'%(zztmp, Tuni0[mm], np.log10(ACp[mm,1]), (np.log10(ACp[mm,1])-np.log10(ACp[mm,0])), (np.log10(ACp[mm,2])-np.log10(ACp[mm,1])), ZCp[mm,1], ZCp[mm,1]-ZCp[mm,0], ZCp[mm,2]-ZCp[mm,1], SFp[mm,1], SFp[mm,1]-SFp[mm,0], SFp[mm,2]-SFp[mm,1]))
fw.write('\n')
fw.close()
#print('%s & $%.5e$ & $%.5e$ & $%.3f$ & $%.2f_{-%.2f}^{+%.2f}$ & $%.2f_{-%.2f}^{+%.2f}$ & $%.2f_{-%.2f}^{+%.2f}$ & $%.2f_{-%.2f}^{+%.2f}$ & $%.2f_{-%.2f}^{+%.2f}$ & $%.2f_{-%.2f}^{+%.2f}$ & $%.2f_{-%.2f}^{+%.2f}$ & $%.1f$ & $%.1f$ & $%.2f$\\\\'%(ID0, RA, DEC, zbes, np.log10(ACp[0,1]), (np.log10(ACp[0,1])-np.log10(ACp[0,0])), (np.log10(ACp[0,2])-np.log10(ACp[0,1])), ACp[7,1]/ACp[0,1], ACp[7,1]/ACp[0,1]-ACp[7,0]/ACp[0,1], ACp[7,2]/ACp[0,1]-ACp[7,1]/ACp[0,1], ZCp[0,1], ZCp[0,1]-ZCp[0,0], ZCp[0,2]-ZCp[0,1], np.percentile(TC[0,:],50), np.percentile(TC[0,:],50)-np.percentile(TC[0,:],16), np.percentile(TC[0,:],84)-np.percentile(TC[0,:],50), Avtmp[1], Avtmp[1]-Avtmp[0], Avtmp[2]-Avtmp[1], uv[1], uv[1]-uv[0], uv[2]-uv[1], vj[1], vj[1]-vj[0], vj[2]-vj[1], chinu[1], SN, rek))
dely2 = 0.1
while (y2max-y2min)/dely2>7:
dely2 *= 2.
y2ticks = np.arange(y2min, y2max, dely2)
ax2.set_yticks(y2ticks)
ax2.set_yticklabels(np.arange(y2min, y2max, 0.1), minor=False)
ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
#ax2.yaxis.set_major_locator(plt.MaxNLocator(4))
#ax3.set_xlabel('$t$ (Gyr)', fontsize=12)
#ax3.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
y3min, y3max = np.min(Z), np.max(Z)
#ax3.set_xlim(0.008, Txmax)
#ax3.set_ylim(y3min, y3max)
#ax3.set_xscale('log')
#ax3.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
fwt = open('T_' + ID0 + '_PA' + PA + '_sfh.cat', 'w')
fwt.write('%s %.3f %.3f %.3f'%(ID0, np.percentile(TC[0,:],50), np.percentile(TC[0,:],16), np.percentile(TC[0,:],84)))
fwt.close()
# For redshift
if zbes<4:
if zbes<2:
zred = [zbes, 2, 3, 6]
#zredl = ['$z_\mathrm{obs.}$', 2, 3, 6]
zredl = ['$z_\mathrm{obs.}$', 2, 3, 6]
elif zbes<2.5:
zred = [zbes, 2.5, 3, 6]
zredl = ['$z_\mathrm{obs.}$', 2.5, 3, 6]
elif zbes<3.:
zred = [zbes, 3, 6]
zredl = ['$z_\mathrm{obs.}$', 3, 6]
else:
zred = [zbes, 6]
zredl = ['$z_\mathrm{obs.}$', 6]
else:
zred = [zbes, 6, 7, 9]
zredl = ['$z_\mathrm{obs.}$', 6, 7, 9]
Tzz = np.zeros(len(zred), dtype='float32')
for zz in range(len(zred)):
Tzz[zz] = (Tuni - cd.age(zred[zz], use_flat=True, **cosmo))/cc.Gyr_s
if Tzz[zz] < 0.01:
Tzz[zz] = 0.01
#print(zred, Tzz)
#ax3t.set_xscale('log')
#ax3t.set_xlim(0.008, Txmax)
ax1.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
ax2.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
ax4.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
ax4.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
ax1t.set_xscale('log')
ax1t.set_xlim(0.008, Txmax)
ax2t.set_xscale('log')
ax2t.set_xlim(0.008, Txmax)
ax4t.set_xscale('log')
ax4t.set_xlim(0.008, Txmax)
ax4.set_xlim(0.008, Txmax)
ax4.set_ylim(y3min-0.05, y3max)
ax4.set_xscale('log')
ax4.set_yticks([-0.8, -0.4, 0., 0.4])
ax4.set_yticklabels(['-0.8', '-0.4', '0', '0.4'])
#ax4.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
#ax3.yaxis.labelpad = -2
ax4.yaxis.labelpad = -2
ax1t.set_xticklabels(zredl[:])
ax1t.set_xticks(Tzz[:])
ax1t.tick_params(axis='x', labelcolor='k')
ax1t.xaxis.set_ticks_position('none')
#ax1t.plot(Tzz, Tzz*0+y3max+(y3max-y3min)*.00, marker='|', color='k', ms=3, linestyle='None')
ax2t.set_xticklabels(zredl[:])
ax2t.set_xticks(Tzz[:])
ax2t.tick_params(axis='x', labelcolor='k')
ax2t.xaxis.set_ticks_position('none')
#ax2t.plot(Tzz, Tzz*0+y3max+(y3max-y3min)*.00, marker='|', color='k', ms=3, linestyle='None')
ax4t.set_xticklabels(zredl[:])
ax4t.set_xticks(Tzz[:])
ax4t.tick_params(axis='x', labelcolor='k')
ax4t.xaxis.set_ticks_position('none')
ax4t.plot(Tzz, Tzz*0+y3max+(y3max-y3min)*.00, marker='|', color='k', ms=3, linestyle='None')
ax1.plot(Tzz, Tzz*0+lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
ax2.plot(Tzz, Tzz*0+y2max+(y2max-y2min)*.00, marker='|', color='k', ms=3, linestyle='None')
####################
## Save
####################
#plt.show()
#ax1.legend(loc=2, fontsize=8)
#ax2.legend(loc=3, fontsize=8)
plt.savefig('SFH_' + ID0 + '_PA' + PA + '_pcl.png')
def plot_evolv(ID0, PA, Z=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], f_comp = 0, fil_path = './FILT/', inputs=None, dust_model=0, DIR_TMP='./templates/', delt_sfh = 0.01, nmc=300):
#
# delt_sfh (float): delta t of input SFH in Gyr.
#
# Returns: SED as function of age, based on SF and Z histories;
#
################
flim = 0.01
lsfrl = -1 # log SFR low limit
mmax = 1000
Txmax = 4 # Max x value
lmmin = 10.3
nage = np.arange(0,len(age),1)
fnc = Func(Z, nage, dust_model=dust_model) # Set up the number of Age/ZZ
bfnc = Basic(Z)
age = np.asarray(age)
################
# RF colors.
import os.path
home = os.path.expanduser('~')
c = 3.e18 # A/s
chimax = 1.
mag0 = 25.0
d = 10**(73.6/2.5) * 1e-18 # From [ergs/s/cm2/A] to [ergs/s/cm2/Hz]
#############
# Plot.
#############
fig = plt.figure(figsize=(5,2.6))
fig.subplots_adjust(top=0.96, bottom=0.16, left=0.12, right=0.99, hspace=0.15, wspace=0.15)
#ax1 = fig.add_subplot(131)
#ax2 = fig.add_subplot(132)
#ax3 = fig.add_subplot(133)
ax2 = fig.add_subplot(121)
ax3 = fig.add_subplot(122)
###########################
# Open result file
###########################
file = 'summary_' + ID0 + '_PA' + PA + '.fits'
hdul = fits.open(file) # open a FITS file
zbes = hdul[0].header['z']
chinu= hdul[1].data['chi']
uv= hdul[1].data['uv']
vj= hdul[1].data['vj']
try:
RA = hdul[0].header['RA']
DEC = hdul[0].header['DEC']
except:
RA = 0
DEC = 0
try:
SN = hdul[0].header['SN']
except:
###########################
# Get SN of Spectra
###########################
file = 'templates/spec_obs_' + ID0 + '_PA' + PA + '.cat'
fds = np.loadtxt(file, comments='#')
nrs = fds[:,0]
lams = fds[:,1]
fsp = fds[:,2]
esp = fds[:,3]
consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
if len((fsp/esp)[consp]>10):
SN = np.median((fsp/esp)[consp])
else:
SN = 1
Asum = 0
A50 = np.arange(len(age), dtype='float32')
for aa in range(len(A50)):
A50[aa] = hdul[1].data['A'+str(aa)][1]
Asum += A50[aa]
# Cosmo;
DL = cd.luminosity_distance(zbes, **cosmo) * Mpc_cm # Luminositydistance in cm
Cons = (4.*np.pi*DL**2/(1.+zbes))
Tuni = cd.age(zbes, use_flat=True, **cosmo)
Tuni0 = (Tuni/cc.Gyr_s - age[:])
# Open summary;
file = 'summary_' + ID0 + '_PA' + PA + '.fits'
fd = fits.open(file)[1].data
#print(fits.open(file)[1].header)
Avtmp = fd['Av0']
uvtmp = fd['uv']
vjtmp = fd['vj']
#ax2.plot(vj[1],uv[1],color='gray',marker='s',ms=3)
# SFH
file = DIR_TMP + 'obshist_' + ID0 + '_PA' + PA + '.fits'
fd = fits.open(file)[1].data
age = fd['age']
sfh = fd['sfh']
zh = fd['zh']
# Open FSPS temp;
file = DIR_TMP + 'obsspec_' + ID0 + '_PA' + PA + '.fits'
fd = fits.open(file)[1].data
wave = fd['wavelength']
nr = fd['colnum']
uvall= age * 0 - 99
vjall= age * 0 - 99
delp = -10
flag = False
flag2= False
#for ii in range(1,len(age),10):
for ii in range(len(age)-1,-1,delp):
flux = fd['fspec_%d'%(ii)]
flux_att = madau_igm_abs(wave/(1.+zbes), flux, zbes)
flux_d, xxd, nrd = dust_calz(wave/(1.+zbes), flux_att, 0.0, nr)
#ax1.plot(xxd,flux_d,linestyle='-',lw=0.3+0.1*ii/len(age))
band0 = ['u','v','j']
lmconv,fconv = filconv(band0, xxd, flux_d, fil_path) # flux in fnu
uv = -2.5*np.log10(fconv[0]/fconv[1])
vj = -2.5*np.log10(fconv[1]/fconv[2])
uvall[ii] = uv
vjall[ii] = vj
#ax2.plot(vjall[ii],uvall[ii],marker='s',ms=5,linestyle='-',zorder=5)
if flag and not flag2:
flux_d, xxd, nrd = dust_calz(wave/(1.+zbes), flux_att, Avtmp[1], nr)
lmconv,fconv = filconv(band0, xxd, flux_d, fil_path) # flux in fnu
uv_av = -2.5*np.log10(fconv[0]/fconv[1])
vj_av = -2.5*np.log10(fconv[1]/fconv[2])
delvj, deluv = vj_av-vj, uv_av-uv
flag2 = True
flag = True
#import matplotlib.colormaps as cm
conuvj = (vjall>-90)&(uvall>-90)
ax2.plot(vjall[conuvj],uvall[conuvj],marker='s',markeredgecolor='k',color='none',ms=5,zorder=4)
ax2.scatter(vjall[conuvj],uvall[conuvj],marker='s',c=age[conuvj],cmap='jet',s=8,zorder=5)
ax2.plot(vjall[conuvj],uvall[conuvj],marker='',color='k',ms=3,linestyle='-',zorder=3)
ax3.plot(age,np.log10(sfh),color='b', linewidth=1, linestyle='-',label=r'$\log$SFR$/M_\odot$yr$^{-1}$')
ax3.plot(age,zh,color='r', linewidth=1, linestyle='-',label=r'$\log Z_*/Z_\odot$')
'''
ax1.set_xlim(1000,40000)
ax1.set_xscale('log')
ax1.set_ylim(1e-2,1e2)
ax1.set_yscale('log')
ax1.set_xlabel('Wavelength')
'''
ax2.set_xlim(-0.3,2.3)
ax2.set_ylim(-0.3,2.3)
ax2.set_xlabel('$V-J\,$/ mag')
ax2.set_ylabel('$U-V\,$/ mag')
ax3.set_xlabel('age / Gyr')
#ax3.set_ylabel('$\log$SFR$/M_\odot$yr$^{-1}$ or $\log Z_*/Z_\odot$')
##
prog_path = '/Users/tmorishita/GitHub/gsf/gsf/example/misc/'
data_uvj = np.loadtxt(prog_path+'f2.cat',comments='#')
x=data_uvj[:,0]
y=data_uvj[:,1]
ax2.plot(x,y,color="gray",lw=1,ls="-")
data_uvj = np.loadtxt(prog_path+'g2.cat',comments='#')
x=data_uvj[:,0]
y=data_uvj[:,1]
ax2.plot(x,y,color="gray",lw=1,ls="-")
data_uvj = np.loadtxt(prog_path+'h2.cat',comments='#')
x=data_uvj[:,0]
y=data_uvj[:,1]
ax2.plot(x,y,color="gray",lw=1,ls="-")
try:
av=np.array([1.2,0.,delvj,deluv])
X,Y,U,V=zip(av)
ax2.quiver(X,Y,U,V,angles='xy',scale_units='xy',scale=1,linewidth=1,color="k")
except:
pass
ax2.text(-0.1,2.1,'Quiescent',fontsize=11,color='orangered')
ax2.text(1.3,-0.2,'Starforming',fontsize=11,color='royalblue')
ax3.legend(loc=3)
#plt.show()
plt.savefig('hist_' + ID0 + '_PA' + PA + '.pdf')
def plotLFevo(
hist=None,
params=B2008,
extrap_var='t',
#maglim = -21.07 - 2.5 * numpy.log10(0.2)):
maglim=-21. - 2.5 * numpy.log10(0.2),
z_max=20.0,
skipIon=True):
"""Plot evolution of luminosity function params and total luminsity.
Schechter function at each redshift is integrated up to maglim to
find total luminsity.
"""
for (k, v) in params.iteritems():
params[k] = numpy.asarray(v)
if hist is None:
cosmo = cp.WMAP7_BAO_H0_mean(flat=True)
hist = LFHistory(params, extrap_var=extrap_var, **cosmo)
else:
params = hist.params
extrap_var = hist.extrap_var
cosmo = hist.cosmo
z = params['z']
t = cd.age(z, **cosmo)[0] / cc.yr_s
MStar = params['MStar']
phiStar = params['phiStar']
alpha = params['alpha']
if maglim is None:
ltot = schechterTotLM(MStar=MStar, phiStar=phiStar, alpha=alpha)
else:
ltot = schechterCumuLM(magnitudeAB=maglim,
MStar=MStar,
phiStar=phiStar,
alpha=alpha)
print(hist._MStarfunc.extrap_string())
zPlot = numpy.arange(z.min() - 0.1, z_max, 0.1)
tPlot = cd.age(zPlot, **cosmo)[0] / cc.yr_s
newparams = hist.params_z(zPlot)
MPlot = newparams['MStar']
phiPlot = newparams['phiStar']
alphaPlot = newparams['alpha']
if maglim is None:
ltotPlot = hist.schechterTotLM(zPlot)
else:
ltotPlot = hist.schechterCumuLM(zPlot, magnitudeAB=maglim)
# From Table 6 of 2008ApJ...686..230B
lB2008 = 10.**numpy.array([26.18, 25.85, 25.72, 25.32, 25.14])
iPhot = hist.iPhotonRateDensity_z(zPlot, maglim=maglim)
# iPhotFunc = lambda t1: cc.yr_s * hist.iPhotonRateDensity_t(t1,
# maglim=maglim)
if not skipIon:
xH = hist.ionization(zPlot, maglim)
import pylab
pylab.figure(1)
pylab.gcf().set_label('LFion_vs_z')
if skipIon:
pylab.subplot(111)
else:
pylab.subplot(211)
pylab.plot(zPlot, iPhot)
if not skipIon:
pylab.subplot(212)
pylab.plot(zPlot, xH)
pylab.ylim(0.0, 1.5)
pylab.figure(2)
pylab.gcf().set_label('LFparams_vs_z')
pylab.subplot(311)
pylab.plot(z, MStar, '-')
pylab.plot(z, MStar, 'o')
pylab.plot(zPlot, MPlot, ':')
pylab.subplot(312)
pylab.plot(z, phiStar, '-')
pylab.plot(z, phiStar, 'o')
pylab.plot(zPlot, phiPlot, ':')
pylab.subplot(313)
pylab.plot(z, alpha, '-')
pylab.plot(z, alpha, 'o')
pylab.plot(zPlot, alphaPlot, ':')
pylab.figure(3)
pylab.gcf().set_label('LFparams_vs_t')
pylab.subplot(311)
pylab.plot(t, MStar, '-')
pylab.plot(t, MStar, 'o')
pylab.plot(tPlot, MPlot, ':')
pylab.subplot(312)
pylab.plot(t, phiStar, '-')
pylab.plot(t, phiStar, '.')
pylab.plot(tPlot, phiPlot, ':')
pylab.subplot(313)
pylab.plot(t, alpha, '-')
pylab.plot(t, alpha, 'o')
pylab.plot(tPlot, alphaPlot, ':')
pylab.figure(4)
pylab.gcf().set_label('LFlum_vs_z')
pylab.subplot(121)
pylab.plot(z, ltot, 'o')
pylab.plot(z, lB2008, 'x')
pylab.plot(zPlot, ltotPlot)
pylab.subplot(122)
pylab.plot(t, ltot, 'o')
pylab.plot(t, lB2008, 'x')
pylab.plot(tPlot, ltotPlot)
def main(self,
ID0,
PA0,
zgal,
flag_m,
zprev,
Cz0,
Cz1,
mcmcplot=True,
fzvis=1,
specplot=1,
fneld=0,
ntemp=5,
sigz=1.0,
ezmin=0.01,
f_move=False,
f_disp=False
): # flag_m related to redshift error in redshift check func.
#
# sigz (float): confidence interval for redshift fit.
# ezmin (float): minimum error in redshift.
#
print('########################')
print('### Fitting Function ###')
print('########################')
start = timeit.default_timer()
inputs = self.inputs
DIR_TMP = inputs['DIR_TEMP']
if os.path.exists(DIR_TMP) == False:
os.mkdir(DIR_TMP)
# For error parameter
ferr = 0
#
# Age
#
age = inputs['AGE']
age = [float(x.strip()) for x in age.split(',')]
nage = np.arange(0, len(age), 1)
#
# Metallicity
#
Zmax, Zmin = float(inputs['ZMAX']), float(inputs['ZMIN'])
delZ = float(inputs['DELZ'])
Zall = np.arange(Zmin, Zmax, delZ) # in logZsun
# For minimizer.
delZtmp = delZ
#delZtmp = 0.4 # to increase speed.
# For z prior.
delzz = 0.001
zlimu = 6.
snlim = 1
zliml = zgal - 0.5
agemax = cd.age(zgal, use_flat=True, **cosmo) / cc.Gyr_s
# N of param:
try:
ndim = int(inputs['NDIM'])
print('No of params are : %d' % (ndim))
except:
if int(inputs['ZEVOL']) == 1:
ndim = int(len(nage) * 2 + 1)
print('Metallicity evolution is on.')
if int(inputs['ZMC']) == 1:
ndim += 1
print('No of params are : %d' % (ndim))
else:
ndim = int(len(nage) + 1 + 1)
print('Metallicity evolution is off.')
if int(inputs['ZMC']) == 1:
ndim += 1
print('No of params are : %d' % (ndim))
pass
#
# Line
#
LW0 = inputs['LINE']
LW0 = [float(x.strip()) for x in LW0.split(',')]
#
# Params for MCMC
#
nmc = int(inputs['NMC'])
nwalk = int(inputs['NWALK'])
nmc_cz = int(inputs['NMCZ'])
nwalk_cz = int(inputs['NWALKZ'])
f_Zevol = int(inputs['ZEVOL'])
#f_zvis = int(inputs['ZVIS'])
try:
fzmc = int(inputs['ZMC'])
except:
fzmc = 0
#
# If FIR data;
#
try:
DT0 = float(inputs['TDUST_LOW'])
DT1 = float(inputs['TDUST_HIG'])
dDT = float(inputs['TDUST_DEL'])
Temp = np.arange(DT0, DT1, dDT)
f_dust = True
print('FIR fit is on.')
except:
f_dust = False
pass
#
# Tau for MCMC parameter; not as fitting parameters.
#
tau0 = inputs['TAU0']
tau0 = [float(x.strip()) for x in tau0.split(',')]
#
# Dust model specification;
#
try:
dust_model = int(inputs['DUST_MODEL'])
except:
dust_model = 0
from .function_class import Func
from .basic_func import Basic
fnc = Func(Zall, nage, dust_model=dust_model,
DIR_TMP=DIR_TMP) # Set up the number of Age/ZZ
bfnc = Basic(Zall)
# Open ascii file and stock to array.
#lib = open_spec(ID0, PA0)
lib = fnc.open_spec_fits(ID0, PA0, fall=0, tau0=tau0)
lib_all = fnc.open_spec_fits(ID0, PA0, fall=1, tau0=tau0)
if f_dust:
lib_dust = fnc.open_spec_dust_fits(ID0,
PA0,
Temp,
fall=0,
tau0=tau0)
lib_dust_all = fnc.open_spec_dust_fits(ID0,
PA0,
Temp,
fall=1,
tau0=tau0)
#################
# Observed Data
#################
##############
# Spectrum
##############
dat = np.loadtxt(DIR_TMP + 'spec_obs_' + ID0 + '_PA' + PA0 + '.cat',
comments='#')
NR = dat[:, 0]
x = dat[:, 1]
fy00 = dat[:, 2]
ey00 = dat[:, 3]
con0 = (NR < 1000)
fy0 = fy00[con0] * Cz0
ey0 = ey00[con0] * Cz0
con1 = (NR >= 1000) & (NR < 10000)
fy1 = fy00[con1] * Cz1
ey1 = ey00[con1] * Cz1
# BB data in spec_obs are not in use.
#con2 = (NR>=10000) # BB
#fy2 = fy00[con2]
#ey2 = ey00[con2]
##############
# Broadband
##############
dat = np.loadtxt(DIR_TMP + 'bb_obs_' + ID0 + '_PA' + PA0 + '.cat',
comments='#')
NRbb = dat[:, 0]
xbb = dat[:, 1]
fybb = dat[:, 2]
eybb = dat[:, 3]
exbb = dat[:, 4]
fy2 = fybb
ey2 = eybb
fy01 = np.append(fy0, fy1)
fy = np.append(fy01, fy2)
ey01 = np.append(ey0, ey1)
ey = np.append(ey01, ey2)
wht = 1. / np.square(ey)
wht2 = check_line_man(fy, x, wht, fy, zprev, LW0)
sn = fy / ey
#####################
# Function fo MCMC
#####################
def residual(
pars,
fy,
wht2,
f_fir,
out=False): # x, y, wht are taken from out of the definition.
#
# Returns: residual of model and data.
# out: model as second output. For lnprob func.
# f_fir: syntax. If dust component is on or off.
vals = pars.valuesdict()
model, x1 = fnc.tmp04(ID0, PA0, vals, zprev, lib, tau0=tau0)
if f_fir:
model_dust, x1_dust = fnc.tmp04_dust(ID0,
PA0,
vals,
zprev,
lib_dust,
tau0=tau0)
n_optir = len(model)
# Add dust flux to opt/IR grid.
model[:] += model_dust[:n_optir]
#print(model_dust)
# then append only FIR flux grid.
model = np.append(model, model_dust[n_optir:])
x1 = np.append(x1, x1_dust[n_optir:])
#plt.plot(x1,model,'r.')
#plt.show()
if ferr == 1:
f = vals['f']
else:
f = 0 # temporary... (if f is param, then take from vals dictionary.)
con_res = (model > 0) & (wht2 > 0) #& (fy>0)
sig = np.sqrt(1. / wht2 + f**2 * model**2)
'''
contmp = x1>1e6 & (wht2>0)
try:
print(x1[contmp],model[contmp],fy[contmp],np.log10(vals['MDUST']))
except:
pass
'''
if not out:
if fy is None:
print('Data is none')
return model[con_res]
else:
return (model - fy)[con_res] / sig[
con_res] # i.e. residual/sigma. Because is_weighted = True.
if out:
if fy is None:
print('Data is none')
return model[con_res], model
else:
return (model - fy)[con_res] / sig[
con_res], model # i.e. residual/sigma. Because is_weighted = True.
def lnprob(pars, fy, wht2, f_fir):
#
# Returns: posterior.
#
vals = pars.valuesdict()
if ferr == 1:
f = vals['f']
else:
f = 0 # temporary... (if f is param, then take from vals dictionary.)
resid, model = residual(pars, fy, wht2, f_fir, out=True)
con_res = (model > 0) & (wht2 > 0)
sig = np.sqrt(1. / wht2 + f**2 * model**2)
lnlike = -0.5 * np.sum(resid**2 +
np.log(2 * 3.14 * sig[con_res]**2))
#print(np.log(2 * 3.14 * 1) * len(sig[con_res]), np.sum(np.log(2 * 3.14 * sig[con_res]**2)))
#Av = vals['Av']
#if Av<0:
# return -np.inf
#else:
# respr = 0 #np.log(1)
respr = 0 # Flat prior...
return lnlike + respr
###############################
# Add parameters
###############################
fit_params = Parameters()
for aa in range(len(age)):
if age[aa] == 99 or age[aa] > agemax:
fit_params.add('A' + str(aa), value=0, min=0, max=1e-10)
else:
fit_params.add('A' + str(aa), value=1, min=0, max=1e3)
#####################
# Dust attenuation
#####################
try:
Avmin = float(inputs['AVMIN'])
Avmax = float(inputs['AVMAX'])
fit_params.add('Av', value=Avmin, min=Avmin, max=Avmax)
except:
fit_params.add('Av', value=0.2, min=0, max=4.0)
#####################
# Metallicity
#####################
if int(inputs['ZEVOL']) == 1:
for aa in range(len(age)):
if age[aa] == 99 or age[aa] > agemax:
fit_params.add('Z' + str(aa), value=0, min=0, max=1e-10)
else:
fit_params.add('Z' + str(aa),
value=0,
min=np.min(Zall),
max=np.max(Zall))
elif inputs['ZFIX']:
#print('Z is fixed')
ZFIX = float(inputs['ZFIX'])
aa = 0
fit_params.add('Z' + str(aa), value=0, min=ZFIX, max=ZFIX + 0.01)
else:
aa = 0
fit_params.add('Z' + str(aa),
value=0,
min=np.min(Zall),
max=np.max(Zall))
####################################
# Initial Metallicity Determination
####################################
chidef = 1e5
Zbest = 0
fwz = open('Z_' + ID0 + '_PA' + PA0 + '.cat', 'w')
fwz.write('# ID Zini chi/nu AA Av Zbest\n')
fwz.write('# FNELD = %d\n' % fneld)
nZtmp = int((Zmax - Zmin) / delZtmp)
ZZtmp = np.arange(Zmin, Zmax, delZtmp)
# How to get initial parameters?
# Nelder;
if fneld == 1:
fit_name = 'nelder'
for zz in range(len(ZZtmp)):
ZZ = ZZtmp[zz]
if int(inputs['ZEVOL']) == 1:
for aa in range(len(age)):
fit_params['Z' + str(aa)].value = ZZ
else:
aa = 0
fit_params['Z' + str(aa)].value = ZZ
out_tmp = minimize(
residual,
fit_params,
args=(fy, wht2, False),
method=fit_name) # nelder is the most efficient.
keys = fit_report(out_tmp).split('\n')
csq = 99999
rcsq = 99999
for key in keys:
if key[4:7] == 'chi':
skey = key.split(' ')
csq = float(skey[14])
if key[4:7] == 'red':
skey = key.split(' ')
rcsq = float(skey[7])
fitc = [csq, rcsq] # Chi2, Reduced-chi2
fwz.write('%s %.2f %.5f' % (ID0, ZZ, fitc[1]))
AA_tmp = np.zeros(len(age), dtype='float32')
ZZ_tmp = np.zeros(len(age), dtype='float32')
for aa in range(len(age)):
AA_tmp[aa] = out_tmp.params['A' + str(aa)].value
fwz.write(' %.5f' % (AA_tmp[aa]))
Av_tmp = out_tmp.params['Av'].value
fwz.write(' %.5f' % (Av_tmp))
if int(inputs['ZEVOL']) == 1:
for aa in range(len(age)):
ZZ_tmp[aa] = out_tmp.params['Z' + str(aa)].value
fwz.write(' %.5f' % (ZZ_tmp[aa]))
else:
aa = 0
ZZ_tmp[aa] = out_tmp.params['Z' + str(aa)].value
fwz.write(' %.5f' % (ZZ_tmp[aa]))
fwz.write('\n')
if fitc[1] < chidef:
chidef = fitc[1]
out = out_tmp
# Or
# Powell;
else:
fit_name = 'powell'
for zz in range(0, nZtmp, 2):
ZZ = zz * delZtmp + np.min(Zall)
if int(inputs['ZEVOL']) == 1:
for aa in range(len(age)):
fit_params['Z' + str(aa)].value = ZZ
else:
aa = 0
fit_params['Z' + str(aa)].value = ZZ
out_tmp = minimize(
residual,
fit_params,
args=(fy, wht2, False),
method=fit_name) # powel is the more accurate.
keys = fit_report(out_tmp).split('\n')
csq = 99999
rcsq = 99999
for key in keys:
if key[4:7] == 'chi':
skey = key.split(' ')
csq = float(skey[14])
if key[4:7] == 'red':
skey = key.split(' ')
rcsq = float(skey[7])
fitc = [csq, rcsq] # Chi2, Reduced-chi2
fwz.write('%s %.2f %.5f' % (ID0, ZZ, fitc[1]))
AA_tmp = np.zeros(len(age), dtype='float32')
ZZ_tmp = np.zeros(len(age), dtype='float32')
for aa in range(len(age)):
AA_tmp[aa] = out_tmp.params['A' + str(aa)].value
fwz.write(' %.5f' % (AA_tmp[aa]))
Av_tmp = out_tmp.params['Av'].value
fwz.write(' %.5f' % (Av_tmp))
if int(inputs['ZEVOL']) == 1:
for aa in range(len(age)):
ZZ_tmp[aa] = out_tmp.params['Z' + str(aa)].value
fwz.write(' %.5f' % (ZZ_tmp[aa]))
else:
aa = 0
fwz.write(' %.5f' % (ZZ_tmp[aa]))
fwz.write('\n')
if fitc[1] < chidef:
chidef = fitc[1]
out = out_tmp
#
# Best fit
#
keys = fit_report(out).split('\n')
for key in keys:
if key[4:7] == 'chi':
skey = key.split(' ')
csq = float(skey[14])
if key[4:7] == 'red':
skey = key.split(' ')
rcsq = float(skey[7])
fitc = [csq, rcsq] # Chi2, Reduced-chi2
#fitc = fit_report_chi(out) # Chi2, Reduced-chi2
ZZ = Zbest # This is really important/does affect lnprob/residual.
print('\n\n')
print('#####################################')
print('Zbest, chi are;', Zbest, chidef)
print('Params are;', fit_report(out))
print('#####################################')
print('\n\n')
fwz.close()
Av_tmp = out.params['Av'].value
AA_tmp = np.zeros(len(age), dtype='float32')
ZZ_tmp = np.zeros(len(age), dtype='float32')
fm_tmp, xm_tmp = fnc.tmp04_val(ID0, PA0, out, zprev, lib, tau0=tau0)
########################
# Check redshift
########################
zrecom = zprev
# Observed data.
con_cz = (NR < 10000) #& (sn>snlim)
fy_cz = fy[con_cz]
ey_cz = ey[con_cz]
x_cz = x[con_cz] # Observed range
NR_cz = NR[con_cz]
xm_s = xm_tmp / (1 + zprev) * (1 + zrecom)
fm_s = np.interp(x_cz, xm_s, fm_tmp)
if fzvis == 1:
plt.plot(x_cz / (1 + zprev) * (1. + zrecom),
fm_s,
'gray',
linestyle='--',
linewidth=0.5,
label='Default ($z=%.5f$)' %
(zprev)) # Model based on input z.
plt.plot(x_cz,
fy_cz,
'b',
linestyle='-',
linewidth=0.5,
label='Obs.') # Observation
plt.errorbar(x_cz,
fy_cz,
yerr=ey_cz,
color='b',
capsize=0,
linewidth=0.5) # Observation
if flag_m == 0:
dez = 0.5
else:
dez = 0.2
#
# For Eazy
#
'''
dprob = np.loadtxt(eaz_path + 'photz_' + str(int(ID0)) + '.pz', comments='#')
zprob = dprob[:,0]
cprob = dprob[:,1]
zz_prob = np.arange(0,13,delzz)
cprob_s = np.interp(zz_prob, zprob, cprob)
prior_s = 1/cprob_s
prior_s /= np.sum(prior_s)
'''
zz_prob = np.arange(0, 13, delzz)
prior_s = zz_prob * 0 + 1.
prior_s /= np.sum(prior_s)
try:
print('############################')
print('Start MCMC for redshift fit')
print('############################')
res_cz, fitc_cz = check_redshift(fy_cz, ey_cz, x_cz, fm_tmp,
xm_tmp / (1 + zprev), zprev, dez,
prior_s, NR_cz, zliml, zlimu,
delzz, nmc_cz, nwalk_cz)
z_cz = np.percentile(res_cz.flatchain['z'], [16, 50, 84])
scl_cz0 = np.percentile(res_cz.flatchain['Cz0'], [16, 50, 84])
scl_cz1 = np.percentile(res_cz.flatchain['Cz1'], [16, 50, 84])
zrecom = z_cz[1]
Czrec0 = scl_cz0[1]
Czrec1 = scl_cz1[1]
# Switch to peak redshift:
from scipy import stats
from scipy.stats import norm
# find minimum and maximum of xticks, so we know
# where we should compute theoretical distribution
ser = res_cz.flatchain['z']
xmin, xmax = zprev - 0.2, zprev + 0.2
lnspc = np.linspace(xmin, xmax, len(ser))
print('\n\n')
print(
'Recommended redshift, Cz0 and Cz1, %.5f %.5f %.5f, with chi2/nu=%.3f'
% (zrecom, Cz0 * Czrec0, Cz1 * Czrec1, fitc_cz[1]))
print('\n\n')
except:
print('z fit failed. No spectral data set?')
try:
ezl = float(inputs['EZL'])
ezu = float(inputs['EZU'])
print('Redshift error is taken from input file.')
if ezl < ezmin:
ezl = ezmin #0.03
if ezu < ezmin:
ezu = ezmin #0.03
except:
ezl = ezmin
ezu = ezmin
print('Redshift error is assumed to %.1f.' % (ezl))
z_cz = [zprev - ezl, zprev, zprev + ezu]
zrecom = z_cz[1]
scl_cz0 = [1., 1., 1.]
scl_cz1 = [1., 1., 1.]
Czrec0 = scl_cz0[1]
Czrec1 = scl_cz1[1]
'''
try:
# lets try the normal distribution first
m, s = stats.norm.fit(ser) # get mean and standard deviation
pdf_g = stats.norm.pdf(lnspc, m, s) # now get theoretical values in our interval
z_cz[:] = [m-s, m, m+s]
zrecom = z_cz[1]
f_fitgauss = 1
except:
print('Guassian fitting to z distribution failed.')
f_fitgauss=0
'''
f_fitgauss = 0
xm_s = xm_tmp / (1 + zprev) * (1 + zrecom)
fm_s = np.interp(x_cz, xm_s, fm_tmp)
whtl = 1 / np.square(ey_cz)
try:
wht3, ypoly = check_line_cz_man(fy_cz, x_cz, whtl, fm_s, zrecom,
LW0)
except:
wht3, ypoly = whtl, fy_cz
con_line = (wht3 == 0)
if fzvis == 1:
plt.plot(x_cz,
fm_s,
'r',
linestyle='-',
linewidth=0.5,
label='Updated model ($z=%.5f$)' %
(zrecom)) # Model based on recomended z.
plt.plot(x_cz[con_line],
fm_s[con_line],
color='orange',
marker='o',
linestyle='',
linewidth=3.)
# Plot lines for reference
for ll in range(len(LW)):
try:
conpoly = (x_cz /
(1. + zrecom) > 3000) & (x_cz /
(1. + zrecom) < 8000)
yline = np.max(ypoly[conpoly])
yy = np.arange(yline / 1.02, yline * 1.1)
xxpre = yy * 0 + LW[ll] * (1. + zprev)
xx = yy * 0 + LW[ll] * (1. + zrecom)
plt.plot(xxpre,
yy / 1.02,
linewidth=0.5,
linestyle='--',
color='gray')
plt.text(LW[ll] * (1. + zprev),
yline / 1.05,
'%s' % (LN[ll]),
fontsize=8,
color='gray')
plt.plot(xx,
yy,
linewidth=0.5,
linestyle='-',
color='orangered')
plt.text(LW[ll] * (1. + zrecom),
yline,
'%s' % (LN[ll]),
fontsize=8,
color='orangered')
except:
pass
plt.plot(xbb,
fybb,
'.r',
linestyle='',
linewidth=0,
zorder=4,
label='Obs.(BB)')
plt.plot(xm_tmp,
fm_tmp,
color='gray',
marker='.',
ms=0.5,
linestyle='',
linewidth=0.5,
zorder=4,
label='Model')
try:
xmin, xmax = np.min(x_cz) / 1.1, np.max(x_cz) * 1.1
except:
xmin, xmax = 2000, 10000
plt.xlim(xmin, xmax)
try:
plt.ylim(0, yline * 1.1)
except:
pass
plt.xlabel('Wavelength ($\mathrm{\AA}$)')
plt.ylabel('$F_\\nu$ (arb.)')
plt.legend(loc=0)
zzsigma = ((z_cz[2] - z_cz[0]) / 2.) / zprev
zsigma = np.abs(zprev - zrecom) / (zprev)
C0sigma = np.abs(Czrec0 - Cz0) / Cz0
eC0sigma = ((scl_cz0[2] - scl_cz0[0]) / 2.) / Cz0
C1sigma = np.abs(Czrec1 - Cz1) / Cz1
eC1sigma = ((scl_cz1[2] - scl_cz1[0]) / 2.) / Cz1
print('Input redshift is %.3f per cent agreement.' %
((1. - zsigma) * 100))
print('Error is %.3f per cent.' % (zzsigma * 100))
print('Input Cz0 is %.3f per cent agreement.' %
((1. - C0sigma) * 100))
print('Error is %.3f per cent.' % (eC0sigma * 100))
print('Input Cz1 is %.3f per cent agreement.' %
((1. - C1sigma) * 100))
print('Error is %.3f per cent.' % (eC1sigma * 100))
plt.show()
#
# Ask interactively;
#
flag_z = raw_input(
'Do you want to continue with original redshift, Cz0 and Cz1, %.5f %.5f %.5f? ([y]/n/m) '
% (zprev, Cz0, Cz1))
else:
flag_z = 'y'
#################################################
# Gor for mcmc phase
#################################################
if flag_z == 'y' or flag_z == '':
zrecom = zprev
Czrec0 = Cz0
Czrec1 = Cz1
#######################
# Added
#######################
if fzmc == 1:
out_keep = out
#sigz = 1.0 #3.0
fit_params.add('zmc',
value=zrecom,
min=zrecom - (z_cz[1] - z_cz[0]) * sigz,
max=zrecom + (z_cz[2] - z_cz[1]) * sigz)
#print(zrecom,zrecom-(z_cz[1]-z_cz[0])*sigz,zrecom+(z_cz[2]-z_cz[1])*sigz)
#####################
# Error parameter
#####################
try:
ferr = int(inputs['F_ERR'])
if ferr == 1:
fit_params.add('f', value=1e-2, min=0, max=1e2)
ndim += 1
except:
ferr = 0
pass
#####################
# Dust;
#####################
if f_dust:
Tdust = np.arange(DT0, DT1, dDT)
fit_params.add('TDUST',
value=len(Tdust) / 2.,
min=0,
max=len(Tdust) - 1)
#fit_params.add('TDUST', value=1, min=0, max=len(Tdust)-1)
fit_params.add('MDUST', value=1e6, min=0, max=1e10)
ndim += 2
# Append data;
dat_d = np.loadtxt(DIR_TMP + 'spec_dust_obs_' + ID0 +
'_PA' + PA0 + '.cat',
comments='#')
x_d = dat_d[:, 1]
fy_d = dat_d[:, 2]
ey_d = dat_d[:, 3]
fy = np.append(fy, fy_d)
x = np.append(x, x_d)
wht = np.append(wht, 1. / np.square(ey_d))
wht2 = check_line_man(fy, x, wht, fy, zprev, LW0)
# Then, minimize again.
out = minimize(
residual,
fit_params,
args=(fy, wht2, f_dust),
method=fit_name
) # It needs to define out with redshift constrain.
print(fit_report(out))
# Fix params to what we had before.
out.params['zmc'].value = zrecom
out.params['Av'].value = out_keep.params['Av'].value
for aa in range(len(age)):
out.params['A' +
str(aa)].value = out_keep.params['A' +
str(aa)].value
try:
out.params['Z' + str(aa)].value = out_keep.params[
'Z' + str(aa)].value
except:
out.params['Z0'].value = out_keep.params['Z0'].value
##############################
# Save fig of z-distribution.
##############################
try: # if spectrum;
fig = plt.figure(figsize=(6.5, 2.5))
fig.subplots_adjust(top=0.96,
bottom=0.16,
left=0.09,
right=0.99,
hspace=0.15,
wspace=0.25)
ax1 = fig.add_subplot(111)
#n, nbins, patches = ax1.hist(res_cz.flatchain['z'], bins=200, normed=False, color='gray',label='')
n, nbins, patches = ax1.hist(res_cz.flatchain['z'],
bins=200,
normed=True,
color='gray',
label='')
if f_fitgauss == 1:
ax1.plot(lnspc,
pdf_g,
label='Gaussian fit',
color='g',
linestyle='-') # plot it
ax1.set_xlim(m - s * 3, m + s * 3)
yy = np.arange(0, np.max(n), 1)
xx = yy * 0 + z_cz[1]
ax1.plot(
xx,
yy,
linestyle='-',
linewidth=1,
color='orangered',
label='$z=%.5f_{-%.5f}^{+%.5f}$\n$C_z0=%.3f$\n$C_z1=%.3f$'
% (z_cz[1], z_cz[1] - z_cz[0], z_cz[2] - z_cz[1], Czrec0,
Czrec1))
xx = yy * 0 + z_cz[0]
ax1.plot(xx,
yy,
linestyle='--',
linewidth=1,
color='orangered')
xx = yy * 0 + z_cz[2]
ax1.plot(xx,
yy,
linestyle='--',
linewidth=1,
color='orangered')
xx = yy * 0 + zprev
ax1.plot(xx, yy, linestyle='-', linewidth=1, color='royalblue')
ax1.set_xlabel('Redshift')
ax1.set_ylabel('$dn/dz$')
ax1.legend(loc=0)
plt.savefig('zprob_' + ID0 + '_PA' + PA0 + '.pdf', dpi=300)
plt.close()
except:
print('z-distribution figure is not generated.')
pass
##############################
print('\n\n')
print('###############################')
print('Input redshift is adopted.')
print('Starting long journey in MCMC.')
print('###############################')
print('\n\n')
#################
# Initialize mm.
#################
mm = 0
# add a noise parameter
# out.params.add('f', value=1, min=0.001, max=20)
wht2 = wht
################################
print('\nMinimizer Defined\n')
mini = Minimizer(lnprob,
out.params,
fcn_args=[fy, wht2, f_dust],
f_disp=f_disp,
f_move=f_move)
print('######################')
print('### Starting emcee ###')
print('######################')
import multiprocess
ncpu0 = int(multiprocess.cpu_count() / 2)
try:
ncpu = int(inputs['NCPU'])
if ncpu > ncpu0:
print('!!! NCPU is larger than No. of CPU. !!!')
#print('Now set to %d'%(ncpu0))
#ncpu = ncpu0
except:
ncpu = ncpu0
pass
print('No. of CPU is set to %d' % (ncpu))
start_mc = timeit.default_timer()
res = mini.emcee(burn=int(nmc / 2),
steps=nmc,
thin=10,
nwalkers=nwalk,
params=out.params,
is_weighted=True,
ntemps=ntemp,
workers=ncpu)
stop_mc = timeit.default_timer()
tcalc_mc = stop_mc - start_mc
print('###############################')
print('### MCMC part took %.1f sec ###' % (tcalc_mc))
print('###############################')
#----------- Save pckl file
#-------- store chain into a cpkl file
start_mc = timeit.default_timer()
import corner
burnin = int(nmc / 2)
savepath = './'
cpklname = 'chain_' + ID0 + '_PA' + PA0 + '_corner.cpkl'
savecpkl(
{
'chain': res.flatchain,
'burnin': burnin,
'nwalkers': nwalk,
'niter': nmc,
'ndim': ndim
}, savepath + cpklname) # Already burn in
stop_mc = timeit.default_timer()
tcalc_mc = stop_mc - start_mc
print('#################################')
print('### Saving chain took %.1f sec' % (tcalc_mc))
print('#################################')
Avmc = np.percentile(res.flatchain['Av'], [16, 50, 84])
#Zmc = np.percentile(res.flatchain['Z'], [16,50,84])
Avpar = np.zeros((1, 3), dtype='float32')
Avpar[0, :] = Avmc
out = res
####################
# Best parameters
####################
Amc = np.zeros((len(age), 3), dtype='float32')
Ab = np.zeros(len(age), dtype='float32')
Zmc = np.zeros((len(age), 3), dtype='float32')
Zb = np.zeros(len(age), dtype='float32')
NZbest = np.zeros(len(age), dtype='int')
f0 = fits.open(DIR_TMP + 'ms_' + ID0 + '_PA' + PA0 + '.fits')
sedpar = f0[1]
ms = np.zeros(len(age), dtype='float32')
for aa in range(len(age)):
Ab[aa] = out.params['A' + str(aa)].value
Amc[aa, :] = np.percentile(res.flatchain['A' + str(aa)],
[16, 50, 84])
try:
Zb[aa] = out.params['Z' + str(aa)].value
Zmc[aa, :] = np.percentile(res.flatchain['Z' + str(aa)],
[16, 50, 84])
except:
Zb[aa] = out.params['Z0'].value
Zmc[aa, :] = np.percentile(res.flatchain['Z0'],
[16, 50, 84])
NZbest[aa] = bfnc.Z2NZ(Zb[aa])
ms[aa] = sedpar.data['ML_' + str(NZbest[aa])][aa]
Avb = out.params['Av'].value
if f_dust:
Mdust_mc = np.zeros(3, dtype='float32')
Tdust_mc = np.zeros(3, dtype='float32')
Mdust_mc[:] = np.percentile(res.flatchain['MDUST'],
[16, 50, 84])
Tdust_mc[:] = np.percentile(res.flatchain['TDUST'],
[16, 50, 84])
print(Mdust_mc)
print(Tdust_mc)
####################
# MCMC corner plot.
####################
if mcmcplot:
fig1 = corner.corner(res.flatchain, labels=res.var_names, \
label_kwargs={'fontsize':16}, quantiles=[0.16, 0.84], show_titles=False, \
title_kwargs={"fontsize": 14}, truths=list(res.params.valuesdict().values()), \
plot_datapoints=False, plot_contours=True, no_fill_contours=True, \
plot_density=False, levels=[0.68, 0.95, 0.997], truth_color='gray', color='#4682b4')
fig1.savefig('SPEC_' + ID0 + '_PA' + PA0 + '_corner.pdf')
plt.close()
#########################
msmc0 = 0
for aa in range(len(age)):
msmc0 += res.flatchain['A' + str(aa)] * ms[aa]
msmc = np.percentile(msmc0, [16, 50, 84])
# Do analysis on MCMC results.
# Write to file.
stop = timeit.default_timer()
tcalc = stop - start
# Load writing package;
from .writing import Analyze
wrt = Analyze(inputs) # Set up for input
start_mc = timeit.default_timer()
wrt.get_param(res,
lib_all,
zrecom,
Czrec0,
Czrec1,
z_cz[:],
scl_cz0[:],
scl_cz1[:],
fitc[:],
tau0=tau0,
tcalc=tcalc)
stop_mc = timeit.default_timer()
tcalc_mc = stop_mc - start_mc
print('##############################################')
print('### Writing params tp file took %.1f sec ###' % (tcalc_mc))
print('##############################################')
return 0, zrecom, Czrec0, Czrec1
###################################################################
elif flag_z == 'm':
zrecom = float(
raw_input('What is your manual input for redshift? '))
Czrec0 = float(raw_input('What is your manual input for Cz0? '))
Czrec1 = float(raw_input('What is your manual input for Cz1? '))
print('\n\n')
print('Generate model templates with input redshift and Scale.')
print('\n\n')
return 1, zrecom, Czrec0, Czrec1
else:
print('\n\n')
print('Terminated because of redshift estimate.')
print('Generate model templates with recommended redshift.')
print('\n\n')
flag_gen = raw_input(
'Do you want to make templates with recommended redshift, Cz0, and Cz1 , %.5f %.5f %.5f? ([y]/n) '
% (zrecom, Czrec0, Czrec1))
if flag_gen == 'y' or flag_gen == '':
#return 1, zrecom, Cz0 * Czrec0, Cz1 * Czrec1
return 1, zrecom, Czrec0, Czrec1
else:
print('\n\n')
print('There is nothing to do.')
print('\n\n')
return 0, zprev, Czrec0, Czrec1
def __init__(self,
params=B2008,
MStar_bounds=['extrapolate', float('NaN')],
phiStar_bounds=['constant', float('NaN')],
alpha_bounds=['constant', float('NaN')],
extrap_args={},
extrap_var='z',
sedParams={},
wavelength=1500.,
**cosmo):
for (k, v) in params.iteritems():
params[k] = numpy.asarray(v)
self.params = params
self.MStar_bounds = MStar_bounds
self.phiStar_bounds = phiStar_bounds
self.alpha_bounds = alpha_bounds
self.extrap_args = extrap_args
self.extrap_var = extrap_var
self.sedParams = sedParams
self.wavelength = wavelength
self.cosmo = cosmo
self.zobs = params['z']
self.tobs = cd.age(self.zobs, **cosmo)[0]
self.MStar = params['MStar']
self.phiStar = params['phiStar']
self.alpha = params['alpha']
if extrap_var == 't':
self.xobs = self.tobs
self._iPhotFunc = \
lambda t1, mag: (self.iPhotonRateDensity_t(t1,
maglim=mag))
elif extrap_var == 'z':
self.xobs = self.zobs
MStar_bounds = MStar_bounds[::-1]
phiStar_bounds = phiStar_bounds[::-1]
alpha_bounds = alpha_bounds[::-1]
self._iPhotFunc = \
lambda z1, mag: (self.iPhotonRateDensity_z(z1,
maglim=mag))
self._MStarfunc = utils.Extrapolate1d(self.xobs,
self.MStar,
bounds_behavior=MStar_bounds,
**extrap_args)
print("M*:", )
print(self._MStarfunc.extrap_string())
self._phiStarfunc = utils.Extrapolate1d(self.xobs,
self.phiStar,
bounds_behavior=phiStar_bounds,
**extrap_args)
print("phi*:", )
print(self._phiStarfunc.extrap_string())
self._alphafunc = utils.Extrapolate1d(self.xobs,
self.alpha,
bounds_behavior=alpha_bounds,
**extrap_args)
print("alpha:", )
print(self._alphafunc.extrap_string())
self._SED = BrokenPowerlawSED(**sedParams)
self._rQL = self._SED.iPhotonRateRatio(wavelength)
for name, func in globals().items():
if not name.startswith('schechter'):
continue
def newfunc(z, _name=name, _func=func, **kwargs):
params = self.params_z(z)
M = params['MStar']
phi = params['phiStar']
alpha = params['alpha']
return _func(MStar=M, phiStar=phi, alpha=alpha, **kwargs)
newfunc.__name__ = func.__name__
newfunc.__doc__ = func.__doc__
self.__dict__[name] = newfunc
import matplotlib.pyplot as plt
from scipy.integrate import quad
from scipy.stats import truncnorm
import cosmolopy.distance as cd
import cosmolopy.constants as cc
nObj = 1000000
intercept = -8.
slope = 1.
scatter = 0.3
cosmo = {'omega_M_0' : 0.3, 'omega_lambda_0' : 0.7, 'h' : 0.7}
cosmo = cd.set_omega_k_0(cosmo)
zObs=2.0
ageUniv = cd.age(zObs, **cosmo)/cc.yr_s
ageUniv5 = cd.age(5.0, **cosmo)/cc.yr_s
msa = 10**np.random.uniform(low=6, high=np.log10(ageUniv), size=nObj) # yrs
tau = 10**np.random.uniform(low=7, high=10.5, size=nObj) # yrs
tau_exp = 10**np.random.uniform(low=7, high=10.5, size=nObj) # yrs
mass = 10**np.random.uniform(low=7, high=12, size=nObj) # solar masses
A = np.zeros(nObj)
for i in range(nObj):
integrand = lambda T: T*np.heaviside(tau[i]-T, 0) + tau[i]*np.exp((tau[i]-T)/tau_exp[i])*np.heaviside(T-tau[i], 1)
integral = quad(integrand, 0, msa[i])
def ionization_from_luminosity(z,
ratedensityfunc,
xHe=1.0,
rate_is_tfunc=False,
ratedensityfunc_args=(),
method='romberg',
**cosmo):
"""Integrate the ionization history given an ionizing luminosity
function, ignoring recombinations.
Parameters
----------
ratedensityfunc: callable
function giving comoving ionizing photon emission rate
density, or ionizing emissivity (photons s^-1 Mpc^-3) as a
function of redshift (or time).
rate_is_tfunc: boolean
Set to true if ratedensityfunc is a function of time rather than z.
Notes
-----
Ignores recombinations.
The ionization rate is computed as ratedensity / nn, where nn = nH
+ xHe * nHe. So if xHe is 1.0, we are assuming that helium becomes
singly ionized at proportionally the same rate as hydrogen. If xHe
is 2.0, we are assuming helium becomes fully ionizing at
proportionally the same rate as hydrogen.
The returened x is therefore the ionized fraction of hydrogen, and
the ionized fraction of helium is xHe * x.
"""
cosmo = cd.set_omega_k_0(cosmo)
rhoc, rho0, nHe, nH = cden.baryon_densities(**cosmo)
nn = (nH + xHe * nHe)
if rate_is_tfunc:
t = cd.age(z, **cosmo)[0]
def dx_dt(t1):
return numpy.nan_to_num(
ratedensityfunc(t1, *ratedensityfunc_args) / nn)
sorti = numpy.argsort(t)
x = numpy.empty(t.shape)
x[sorti] = cu.integrate_piecewise(dx_dt, t[sorti], method=method)
return x
else:
dt_dz = lambda z1: cd.lookback_integrand(z1, **cosmo)
def dx_dz(z1):
z1 = numpy.abs(z1)
return numpy.nan_to_num(
dt_dz(z1) * ratedensityfunc(z1, *ratedensityfunc_args) / nn)
sorti = numpy.argsort(-z)
x = numpy.empty(z.shape)
x[sorti] = cu.integrate_piecewise(dx_dz, -z[sorti], method=method)
return x
def plot_mz(ID0,
PA,
Z=np.arange(-1.2, 0.4249, 0.05),
age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0]):
#col = ['darkred', 'r', 'coral','orange','g','lightgreen', 'lightblue', 'b','indigo','violet','k']
import matplotlib.cm as cm
flim = 0.01
lsfrl = -1 # log SFR low limit
mmax = 500
Txmax = 4 # Max x value
lmmin = 10.3
nage = np.arange(0, len(age), 1)
fnc = Func(Z, nage) # Set up the number of Age/ZZ
bfnc = Basic(Z)
age = np.asarray(age)
################
# RF colors.
import os.path
home = os.path.expanduser('~')
#fil_path = '/Users/tmorishita/eazy-v1.01/PROG/FILT/'
fil_path = home + '/Dropbox/FILT/'
fil_u = fil_path + 'u.fil'
fil_b = fil_path + 'b.fil'
fil_v = fil_path + "v.fil"
fil_j = fil_path + "j.fil"
fil_k = fil_path + "k.fil"
fil_f125w = fil_path + "f125w.fil"
fil_f160w = fil_path + "f160w.fil"
fil_36 = fil_path + "3.6.fil"
fil_45 = fil_path + "4.5.fil"
du = np.loadtxt(fil_u, comments="#")
lu = du[:, 1]
fu = du[:, 2]
db = np.loadtxt(fil_b, comments="#")
lb = db[:, 1]
fb = db[:, 2]
dv = np.loadtxt(fil_v, comments="#")
lv = dv[:, 1]
fv = dv[:, 2]
dj = np.loadtxt(fil_j, comments="#")
lj = dj[:, 1]
fj = dj[:, 2]
c = 3.e18 # A/s
chimax = 1.
mag0 = 25.0
d = 10**(73.6 / 2.5) * 1e-18 # From [ergs/s/cm2/A] to [ergs/s/cm2/Hz]
#d = 10**(-73.6/2.5) # From [ergs/s/cm2/Hz] to [ergs/s/cm2/A]
#############
# Plot.
#############
fig = plt.figure(figsize=(6, 6))
fig.subplots_adjust(top=0.98,
bottom=0.08,
left=0.1,
right=0.99,
hspace=0.18,
wspace=0.25)
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
#ax3t = ax3.twiny()
#ax4t = ax4.twiny()
##################
# Fitting Results
##################
DIR_TMP = './templates/'
SNlim = 3 # avobe which SN line is shown.
###########################
# Open result file
###########################
file = 'summary_' + ID0 + '_PA' + PA + '.fits'
hdul = fits.open(file) # open a FITS file
zbes = hdul[0].header['z']
Asum = 0
A50 = np.arange(len(age), dtype='float32')
for aa in range(len(A50)):
A50[aa] = hdul[1].data['A' + str(aa)][1]
Asum += A50[aa]
# Cosmo;
DL = cd.luminosity_distance(zbes, **
cosmo) * Mpc_cm # Luminositydistance in cm
Cons = (4. * np.pi * DL**2 / (1. + zbes))
Tuni = cd.age(zbes, use_flat=True, **cosmo) # age at zobs.
Tuni0 = (Tuni / cc.Gyr_s - age[:])
delT = np.zeros(len(age), dtype='float32')
delTl = np.zeros(len(age), dtype='float32')
delTu = np.zeros(len(age), dtype='float32')
col = np.zeros((len(age), 4), dtype='float32')
for aa in range(len(age)):
col[aa, :] = cm.nipy_spectral_r((aa + 0.1) / (len(age)))
for aa in range(len(age)):
if aa == 0:
delTl[aa] = age[aa]
delTu[aa] = (age[aa + 1] - age[aa]) / 2.
delT[aa] = delTu[aa] + delTl[aa]
elif Tuni / cc.Gyr_s < age[aa]:
delTl[aa] = (age[aa] - age[aa - 1]) / 2.
delTu[aa] = 10.
delT[aa] = delTu[aa] + delTl[aa]
elif aa == len(age) - 1:
delTl[aa] = (age[aa] - age[aa - 1]) / 2.
delTu[aa] = Tuni / cc.Gyr_s - age[aa]
delT[aa] = delTu[aa] + delTl[aa]
else:
delTl[aa] = (age[aa] - age[aa - 1]) / 2.
delTu[aa] = (age[aa + 1] - age[aa]) / 2.
delT[aa] = delTu[aa] + delTl[aa]
delT[:] *= 1e9 # Gyr to yr
delTl[:] *= 1e9 # Gyr to yr
delTu[:] *= 1e9 # Gyr to yr
#print(age, delT, delTu, delTl)
##############################
# Load Pickle
##############################
samplepath = './'
pfile = 'chain_' + ID0 + '_PA' + PA + '_corner.cpkl'
niter = 0
data = loadcpkl(os.path.join(samplepath + '/' + pfile))
try:
#if 1>0:
ndim = data[
'ndim'] # By default, use ndim and burnin values contained in the cpkl file, if present.
burnin = data['burnin']
nmc = data['niter']
nwalk = data['nwalkers']
Nburn = burnin #* nwalk/10/2 # I think this takes 3/4 of samples
#if nmc>1000:
# Nburn = 500
samples = data['chain'][:]
except:
print(
' = > NO keys of ndim and burnin found in cpkl, use input keyword values'
)
return -1
######################
# Mass-to-Light ratio.
######################
# Wht do you want from MCMC sampler?
AM = np.zeros((len(age), mmax), dtype='float32') # Mass in each bin.
AC = np.zeros((len(age), mmax),
dtype='float32') # Cumulative mass in each bin.
ZM = np.zeros((len(age), mmax), dtype='float32') # Z.
ZC = np.zeros((len(age), mmax), dtype='float32') # Cumulative Z.
TC = np.zeros((len(age), mmax), dtype='float32') # Mass weighted T.
ZMM = np.zeros((len(age), mmax), dtype='float32') # Mass weighted Z.
SF = np.zeros((len(age), mmax), dtype='float32') # SFR
mm = 0
while mm < mmax:
mtmp = np.random.randint(len(samples)) # + Nburn
AAtmp = np.zeros(len(age), dtype='float32')
ZZtmp = np.zeros(len(age), dtype='float32')
mslist = np.zeros(len(age), dtype='float32')
Av_tmp = samples['Av'][mtmp]
f0 = fits.open(DIR_TMP + 'ms_' + ID0 + '_PA' + PA + '.fits')
sedpar = f0[1]
for aa in range(len(age)):
AAtmp[aa] = samples['A' + str(aa)][mtmp]
ZZtmp[aa] = samples['Z' + str(aa)][mtmp]
nZtmp = bfnc.Z2NZ(ZZtmp[aa])
mslist[aa] = sedpar.data['ML_' + str(nZtmp)][aa]
AM[aa, mm] = AAtmp[aa] * mslist[aa]
SF[aa, mm] = AAtmp[aa] * mslist[aa] / delT[aa]
ZM[aa, mm] = ZZtmp[aa] # AAtmp[aa] * mslist[aa]
ZMM[aa, mm] = (10**ZZtmp[aa]) * AAtmp[aa] * mslist[aa]
for aa in range(len(age)):
AC[aa, mm] = np.sum(AM[aa:, mm])
ZC[aa, mm] = np.log10(np.sum((ZMM)[aa:, mm]) / AC[aa, mm])
ACs = 0
for bb in range(aa, len(age), 1):
TC[aa, mm] += age[bb] * AAtmp[bb] * mslist[bb]
ACs += AAtmp[bb] * mslist[bb]
TC[aa, mm] /= ACs
mm += 1
#############
# Plot
#############
AMp = np.zeros((len(age), 3), dtype='float32')
ACp = np.zeros((len(age), 3), dtype='float32')
ZMp = np.zeros((len(age), 3), dtype='float32')
ZCp = np.zeros((len(age), 3), dtype='float32')
SFp = np.zeros((len(age), 3), dtype='float32')
for aa in range(len(age)):
AMp[aa, :] = np.percentile(AM[aa, :], [16, 50, 84])
ACp[aa, :] = np.percentile(AC[aa, :], [16, 50, 84])
ZMp[aa, :] = np.percentile(ZM[aa, :], [16, 50, 84])
ZCp[aa, :] = np.percentile(ZC[aa, :], [16, 50, 84])
SFp[aa, :] = np.percentile(SF[aa, :], [16, 50, 84])
###################
msize = np.zeros(len(age), dtype='float32')
for aa in range(len(age)):
if A50[aa] / Asum > flim: # if >1%
msize[aa] = 10 + 150 * A50[aa] / Asum
conA = (msize >= 0)
#
# M-SFR
#
#ax1.fill_between(age[conA], np.log10(SFp[:,0])[conA], np.log10(SFp[:,2])[conA], linestyle='-', color='k', alpha=0.3)
ax1.scatter(np.log10(ACp[:, 1])[conA],
np.log10(SFp[:, 1])[conA],
marker='o',
c=col[:],
s=msize[conA],
edgecolors='k',
zorder=2)
ax1.errorbar(np.log10(ACp[:, 1])[conA],
np.log10(SFp[:, 1])[conA],
linestyle='--',
color='k',
lw=1.,
marker='',
zorder=0,
alpha=1.)
lM = np.arange(9, 13, 0.1)
delSFR = np.zeros(len(age), dtype='float32')
delSFRl = np.zeros(len(age), dtype='float32')
delSFRu = np.zeros(len(age), dtype='float32')
for ii in range(len(Tuni0)):
lSFR = (0.84 - 0.026 * Tuni0[ii]) * (lM - 0.19) - (6.51 -
0.11 * Tuni0[ii])
ax1.fill_between(lM,
lSFR - 0.1,
lSFR + 0.1,
linestyle='None',
lw=0.5,
zorder=-5,
alpha=0.5,
color=col[ii]) # 0.19 is for Kroupa to Salpeter.
lSFRtmp = (0.84 - 0.026 * Tuni0[ii]) * np.log10(ACp[ii, 1]) - (
6.51 - 0.11 * Tuni0[ii])
delSFR[ii] = np.log10(SFp[ii, 1]) - lSFRtmp
delSFRl[ii] = np.log10(SFp[ii, 0]) - lSFRtmp
delSFRu[ii] = np.log10(SFp[ii, 2]) - lSFRtmp
#
# t - delta SRF relation (right top)
#
#ax2.plot(age[:][conA], delSFR[conA], marker='', c='k',zorder=1, lw=1, linestyle='-')
ax2.scatter(age[:][conA],
delSFR[conA],
marker='o',
c=col[:],
s=msize[conA],
edgecolors='k',
zorder=2)
ax2.errorbar(age[:][conA],
delSFR[:][conA],
linestyle='--',
fmt='--',
color='k',
lw=1.,
marker='',
zorder=0,
alpha=1.)
#
# Mass - Z relation (left bottom)
#
ax2label = ''
#ax2.fill_between(age[conA], np.log10(ACp[:,0])[conA], np.log10(ACp[:,2])[conA], linestyle='-', color='k', alpha=0.3)
ax3.errorbar(
np.log10(ACp[:, 1])[conA],
ZCp[:, 1][conA],
linestyle='--',
zorder=0,
color='k',
lw=1.,
label=ax2label,
alpha=1.
) #, xerr=[np.log10(ACp[:,1])[conA]-np.log10(ACp[:,0])[conA],np.log10(ACp[:,2])[conA]-np.log10(ACp[:,1])[conA]], yerr=[ZCp[:,1][conA]-ZCp[:,0][conA],ZCp[:,2][conA]-ZCp[:,1][conA]]
ax3.scatter(np.log10(ACp[:, 1])[conA],
ZCp[:, 1][conA],
marker='o',
c=col[:],
s=msize,
edgecolors='k',
zorder=2)
#
# Mass-Z from Gallazzi+05
#
lM = [
8.91, 9.11, 9.31, 9.51, 9.72, 9.91, 10.11, 10.31, 10.51, 10.72, 10.91,
11.11, 11.31, 11.51, 11.72, 11.91
]
lZ50 = [
-0.6, -0.61, -0.65, -0.61, -.52, -.41, -.23, -.11, -.01, .04, .07, .10,
.12, .13, .14, .15
]
lZ16 = [
-1.11, -1.07, -1.1, -1.03, -.97, -.90, -.8, -.65, -.41, -.24, -.14,
-.09, -.06, -.04, -.03, -.03
]
lZ84 = [
-0., -0., -0.05, -0.01, .05, .09, .14, .17, .20, .22, .24, .25, .26,
.28, .29, .30
]
lM = np.asarray(lM)
lZ50 = np.asarray(lZ50)
lZ16 = np.asarray(lZ16)
lZ84 = np.asarray(lZ84)
ax3.errorbar(lM,
lZ50,
marker='',
color='gray',
ms=15,
linestyle='-',
lw=1,
zorder=-2) #, yerr=[lZ50-lZ16, lZ84-lZ50]
ax3.fill_between(lM,
lZ16,
lZ84,
color='gray',
linestyle='None',
lw=1,
alpha=0.4,
zorder=-2)
#
# Fundamental Metal
#
bsfr = -0.32 # From Mannucci+10
#ax4.fill_between(age[conA], ZCp[:,0][conA], ZCp[:,2][conA], linestyle='-', color='k', alpha=0.3)
ax4.scatter((np.log10(ACp[:, 1]) + bsfr * np.log10(SFp[:, 1]))[conA],
ZCp[:, 1][conA],
marker='o',
c=col[:],
s=msize[conA],
edgecolors='k',
zorder=2)
ax4.errorbar((np.log10(ACp[:, 1]) + bsfr * np.log10(SFp[:, 1]))[conA],
ZCp[:, 1][conA],
linestyle='--',
color='k',
lw=1.,
zorder=0,
alpha=1.)
for iic in range(len(A50)):
if msize[iic] > 10:
lwe = 1.5
ax1.errorbar(np.log10(ACp[iic, 1]),
np.log10(SFp[iic, 1]),
xerr=[[np.log10(ACp[iic, 1]) - np.log10(ACp[iic, 0])],
[np.log10(ACp[iic, 2]) - np.log10(ACp[iic, 1])]
],
yerr=[[np.log10(SFp[iic, 1]) - np.log10(SFp[iic, 0])],
[np.log10(SFp[iic, 2]) - np.log10(SFp[iic, 1])]
],
linestyle='-',
color=col[iic],
lw=lwe,
marker='',
zorder=1)
ax2.errorbar(age[iic],
delSFR[iic],
xerr=[[delTl[iic] / 1e9], [delTu[iic] / 1e9]],
yerr=[[delSFR[iic] - delSFRl[iic]],
[delSFRu[iic] - delSFR[iic]]],
linestyle='-',
fmt='-',
color=col[iic],
lw=lwe,
marker='',
zorder=1)
ax3.errorbar(np.log10(ACp[iic, 1]),
ZCp[iic, 1],
xerr=[[np.log10(ACp[iic, 1]) - np.log10(ACp[iic, 0])],
[np.log10(ACp[iic, 2]) - np.log10(ACp[iic, 1])]
],
yerr=[[ZCp[iic, 1] - ZCp[iic, 0]],
[ZCp[iic, 2] - ZCp[iic, 1]]],
linestyle='-',
color=col[iic],
lw=lwe,
label=ax2label,
zorder=1)
xerl_ax4 = np.sqrt(
(np.log10(ACp[iic, 1]) - np.log10(ACp[iic, 0]))**2 + bsfr**2 *
(np.log10(SFp[iic, 1]) - np.log10(SFp[iic, 0]))**2)
xeru_ax4 = np.sqrt(
(np.log10(ACp[iic, 2]) - np.log10(ACp[iic, 1]))**2 + bsfr**2 *
(np.log10(SFp[iic, 2]) - np.log10(SFp[iic, 1]))**2)
ax4.errorbar(
(np.log10(ACp[iic, 1]) + bsfr * np.log10(SFp[iic, 1])),
ZCp[iic, 1],
xerr=[[xerl_ax4], [xeru_ax4]],
yerr=[[ZCp[iic, 1] - ZCp[iic, 0]],
[ZCp[iic, 2] - ZCp[iic, 1]]],
linestyle='-',
color=col[iic],
lw=lwe,
label=ax2label,
zorder=1)
#########################
# Title
#########################
#ax1.set_title('Each $t$-bin', fontsize=12)
#ax2.set_title('Net system', fontsize=12)
#ax3.set_title('Each $t$-bin', fontsize=12)
#ax4.set_title('Net system', fontsize=12)
#############
# Axis
#############
#ax1.set_xlabel('$t$ (Gyr)', fontsize=12)
ax1.set_ylabel('$\log \dot{M_*}/M_\odot$yr$^{-1}$', fontsize=12)
#ax1.set_ylabel('$\log M_*/M_\odot$', fontsize=12)
y3min, y3max = np.min(Z), np.max(Z)
lsfru = 2.8
if np.max(np.log10(SFp[:, 2])) > 2.8:
lsfru = np.max(np.log10(SFp[:, 2])) + 0.1
y2min, y2max = 9.5, 12.5
ax1.set_xlim(y2min, y2max)
ax1.set_ylim(lsfrl, lsfru)
#ax1.set_xscale('log')
ax1.set_xlabel('$\log M_*/M_\odot$', fontsize=12)
#ax1.xaxis.labelpad = -3
#ax1.yaxis.labelpad = -2
#ax2t.set_yticklabels(())
#ax2.set_xlabel('$t$ (Gyr)', fontsize=12)
#ax2.set_ylabel('$\log M_*/M_\odot$', fontsize=12)
ax2.set_xlabel('$t-t_\mathrm{obs.}$/Gyr', fontsize=12)
ax2.set_ylabel('$\Delta_\mathrm{SFR}$', fontsize=12)
#ax2.set_ylim(lsfrl, lsfru)
ax2.set_ylim(-4, 2.5)
ax2.set_xlim(0.008, 3.2)
ax2.set_xscale('log')
dely2 = 0.1
while (y2max - y2min) / dely2 > 7:
dely2 *= 2.
#y2ticks = np.arange(y2min, y2max, dely2)
#ax2.set_yticks(y2ticks)
#ax2.set_yticklabels(np.arange(y2min, y2max, 0.1), minor=False)
#ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax3.set_xlim(y2min, y2max)
ax3.set_ylim(y3min, y3max)
ax3.set_xlabel('$\log M_*/M_\odot$', fontsize=12)
ax3.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
#ax3.set_xscale('log')
#ax2.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
# For redshift
if zbes < 2:
zred = [zbes, 2, 3, 6]
#zredl = ['$z_\mathrm{obs.}$', 2, 3, 6]
zredl = ['$z_\mathrm{obs.}$', 2, 3, 6]
elif zbes < 2.5:
zred = [zbes, 2.5, 3, 6]
zredl = ['$z_\mathrm{obs.}$', 2.5, 3, 6]
elif zbes < 3.:
zred = [zbes, 3, 6]
zredl = ['$z_\mathrm{obs.}$', 3, 6]
elif zbes < 6:
zred = [zbes, 6]
zredl = ['$z_\mathrm{obs.}$', 6]
Tzz = np.zeros(len(zred), dtype='float32')
for zz in range(len(zred)):
Tzz[zz] = (Tuni - cd.age(zred[zz], use_flat=True, **cosmo)) / cc.Gyr_s
if Tzz[zz] < 0.01:
Tzz[zz] = 0.01
#ax3t.set_xscale('log')
#ax3t.set_xlim(0.008, Txmax)
ax4.set_xlabel('$\log M_*/M_\odot - 0.32 \log \dot{M_*}/M_\odot$yr$^{-1}$',
fontsize=12)
ax4.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
#ax4t.set_xscale('log')
#ax4t.set_xlim(0.008, Txmax)
ax4.set_xlim(9, 12.3)
ax4.set_ylim(y3min, y3max)
#ax4.set_xscale('log')
#ax4.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
ax3.yaxis.labelpad = -2
ax4.yaxis.labelpad = -2
####################
## Save
####################
ax1.legend(loc=1, fontsize=11)
ax2.legend(loc=3, fontsize=8)
plt.savefig('MZ_' + ID0 + '_PA' + PA + '_pcl.pdf')
def maketemp(inputs,
zbest,
Z=np.arange(-1.2, 0.45, 0.1),
age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0],
fneb=0,
DIR_TMP='./templates/'):
#
# inputs : Configuration file.
# zbest(float): Best redshift at this iteration. Templates are generated based on this reshift.
# Z (array) : Stellar phase metallicity in logZsun.
# age (array) : Age, in Gyr.
# fneb (int) : flag for adding nebular emissionself.
#
nage = np.arange(0, len(age), 1)
fnc = Func(Z, nage) # Set up the number of Age/ZZ
bfnc = Basic(Z)
ID = inputs['ID']
PA = inputs['PA']
try:
DIR_EXTR = inputs['DIR_EXTR']
if len(DIR_EXTR) == 0:
DIR_EXTR = False
except:
DIR_EXTR = False
DIR_FILT = inputs['DIR_FILT']
try:
CAT_BB_IND = inputs['CAT_BB_IND']
except:
CAT_BB_IND = False
try:
CAT_BB = inputs['CAT_BB']
except:
CAT_BB = False
SFILT = inputs['FILTER'] # filter band string.
SFILT = [x.strip() for x in SFILT.split(',')]
FWFILT = fil_fwhm(SFILT, DIR_FILT)
# If FIR data;
try:
DFILT = inputs['FIR_FILTER'] # filter band string.
DFILT = [x.strip() for x in DFILT.split(',')]
DFWFILT = fil_fwhm(DFILT, DIR_FILT)
CAT_BB_DUST = inputs['CAT_BB_DUST']
DT0 = float(inputs['TDUST_LOW'])
DT1 = float(inputs['TDUST_HIG'])
dDT = float(inputs['TDUST_DEL'])
f_dust = True
print('FIR is implemented.')
except:
print('No FIR is implemented.')
f_dust = False
pass
#
# Tau for MCMC parameter; not as fitting parameters.
#
tau0 = inputs['TAU0']
tau0 = [float(x.strip()) for x in tau0.split(',')]
print('############################')
print('Making templates at %.4f' % (zbest))
print('############################')
####################################################
# Get extracted spectra.
####################################################
#
# Get ascii data.
#
#ninp1 = 0
#ninp2 = 0
f_spec = False
try:
spec_files = inputs['SPEC_FILE'].replace('$ID', '%s' % (ID))
spec_files = [x.strip() for x in spec_files.split(',')]
ninp0 = np.zeros(len(spec_files), dtype='int')
for ff, spec_file in enumerate(spec_files):
try:
fd0 = np.loadtxt(DIR_EXTR + spec_file, comments='#')
lm0tmp = fd0[:, 0]
fobs0 = fd0[:, 1]
eobs0 = fd0[:, 2]
ninp0[ff] = len(lm0tmp) #[con_tmp])
except Exception:
print('File, %s, cannot be open.' % (spec_file))
pass
# Constructing arrays.
lm = np.zeros(np.sum(ninp0[:]), dtype='float32')
fobs = np.zeros(np.sum(ninp0[:]), dtype='float32')
eobs = np.zeros(np.sum(ninp0[:]), dtype='float32')
fgrs = np.zeros(np.sum(ninp0[:]), dtype='int') # FLAG for G102/G141.
for ff, spec_file in enumerate(spec_files):
try:
fd0 = np.loadtxt(DIR_EXTR + spec_file, comments='#')
lm0tmp = fd0[:, 0]
fobs0 = fd0[:, 1]
eobs0 = fd0[:, 2]
for ii1 in range(ninp0[ff]):
if ff == 0:
ii = ii1
else:
ii = ii1 + np.sum(ninp0[:ff])
fgrs[ii] = ff
lm[ii] = lm0tmp[ii1]
fobs[ii] = fobs0[ii1]
eobs[ii] = eobs0[ii1]
f_spec = True
except Exception:
pass
except:
print('No spec file is provided.')
pass
#############################
# Extracting BB photometry:
#############################
if CAT_BB:
fd0 = np.loadtxt(CAT_BB, comments='#')
try:
id0 = fd0[:, 0]
ii0 = np.argmin(np.abs(id0[:] - int(ID)))
if int(id0[ii0]) != int(ID):
return -1
fd = fd0[ii0, :]
except:
id0 = fd0[0]
if int(id0) != int(ID):
return -1
fd = fd0[:]
id = fd[0]
fbb = np.zeros(len(SFILT), dtype='float32')
ebb = np.zeros(len(SFILT), dtype='float32')
for ii in range(len(SFILT)):
fbb[ii] = fd[ii * 2 + 1]
ebb[ii] = fd[ii * 2 + 2]
elif CAT_BB_IND: # if individual photometric catalog; made in get_sdss.py
fd0 = fits.open(DIR_EXTR + CAT_BB_IND)
hd0 = fd0[1].header
bunit_bb = float(hd0['bunit'][:5])
lmbb0 = fd0[1].data['wavelength']
fbb0 = fd0[1].data['flux'] * bunit_bb
ebb0 = 1 / np.sqrt(fd0[1].data['inverse_variance']) * bunit_bb
unit = 'nu'
try:
unit = inputs['UNIT_SPEC']
except:
print('No param for UNIT_SPEC is found.')
print('BB flux unit is assumed to Fnu.')
pass
if unit == 'lambda':
print('#########################')
print('Changed BB from Flam to Fnu')
snbb0 = fbb0 / ebb0
fbb = flamtonu(lmbb0, fbb0)
ebb = fbb / snbb0
else:
snbb0 = fbb0 / ebb0
fbb = fbb0
ebb = ebb0
else:
fbb = np.zeros(len(SFILT), dtype='float32')
ebb = np.zeros(len(SFILT), dtype='float32')
for ii in range(len(SFILT)):
fbb[ii] = 0
ebb[ii] = -99 #1000
# Dust flux;
if f_dust:
fdd = np.loadtxt(CAT_BB_DUST, comments='#')
try:
id0 = fdd[:, 0]
ii0 = np.argmin(np.abs(id0[:] - int(ID)))
if int(id0[ii0]) != int(ID):
return -1
fd = fdd[ii0, :]
except:
id0 = fdd[0]
if int(id0) != int(ID):
return -1
fd = fdd[:]
id = fd[0]
fbb_d = np.zeros(len(DFILT), dtype='float32')
ebb_d = np.zeros(len(DFILT), dtype='float32')
for ii in range(len(DFILT)):
fbb_d[ii] = fd[ii * 2 + 1]
ebb_d[ii] = fd[ii * 2 + 2]
#############################
# Getting Morphology params.
#############################
Amp = 0
f_morp = False
if f_spec:
try:
if inputs['MORP'] == 'moffat' or inputs['MORP'] == 'gauss':
f_morp = True
try:
mor_file = inputs['MORP_FILE'].replace('$ID', '%s' % (ID))
fm = np.loadtxt(DIR_EXTR + mor_file, comments='#')
#Amp = fm[0]
#gamma = fm[1]
Amp = fm[2]
gamma = fm[4]
if inputs['MORP'] == 'moffat':
#alp = fm[2]
alp = fm[5]
else:
alp = 0
except Exception:
print('Error in reading morphology params.')
print('No morphology convolution.')
#return -1
pass
else:
print('MORP Keywords does not match.')
print('No morphology convolution.')
except:
pass
############################
# Template convolution;
############################
try:
sig_temp = float(inputs['SIG_TEMP'])
except:
sig_temp = 50.
print('Template resolution is unknown.')
print('Set to %.1f km/s.' % (sig_temp))
dellam = lm[1] - lm[0] # AA/pix
R_temp = c / (sig_temp * 1e3 * 1e10)
sig_temp_pix = np.median(lm) / R_temp / dellam # delta v in pixel;
#
sig_inst = 0 #65 #km/s for Manga
# If grism;
if f_morp:
print('Templates convolution (intrinsic morphology).')
if gamma > sig_temp_pix:
sig_conv = np.sqrt(gamma**2 - sig_temp_pix**2)
else:
sig_conv = 0
print('Template resolution is broader than Morphology.')
print('No convolution is applied to templates.')
xMof = np.arange(-5, 5.1, .1) # dimension must be even.
if inputs['MORP'] == 'moffat' and Amp > 0 and alp > 0:
LSF = moffat(xMof, Amp, 0, np.sqrt(gamma**2 - sig_temp_pix**2),
alp)
#print(np.sqrt(gamma**2-sig_temp_pix**2))
print('Template convolution with Moffat.')
#print('params are;',Amp, 0, gamma, alp)
elif inputs['MORP'] == 'gauss':
sigma = gamma
LSF = gauss(xMof, Amp, np.sqrt(sigma**2 - sig_temp_pix**2))
print('Template convolution with Gaussian.')
print('params is sigma;', sigma)
else:
print('Something is wrong.')
return -1
else: # For slit spectroscopy. To be updated...
print('Templates convolution (intrinsic velocity).')
f_disp = False
try:
vdisp = float(inputs['VDISP'])
dellam = lm[1] - lm[0] # AA/pix
#R_disp = c/(vdisp*1e3*1e10)
R_disp = c / (np.sqrt(vdisp**2 - sig_inst**2) * 1e3 * 1e10)
vdisp_pix = np.median(
lm) / R_disp / dellam # delta v in pixel;
print('Templates are convolved at %.2f km/s.' % (vdisp))
if vdisp_pix - sig_temp_pix > 0:
sig_conv = np.sqrt(vdisp_pix**2 - sig_temp_pix**2)
else:
sig_conv = 0
except:
vdisp = 0.
print('Templates are not convolved.')
sig_conv = 0 #np.sqrt(sig_temp_pix**2)
pass
xMof = np.arange(-5, 5.1, .1) # dimension must be even.
Amp = 1.
LSF = gauss(xMof, Amp, sig_conv)
else:
lm = []
####################################
# Start generating templates
####################################
#DIR_TMP = './templates/'
#DIR_TMP = inputs['DIR_TEMP']
f0 = fits.open(DIR_TMP + 'ms.fits')
mshdu = f0[1]
col00 = []
col01 = []
col02 = []
for zz in range(len(Z)):
for pp in range(len(tau0)):
f1 = fits.open(DIR_TMP + 'spec_all.fits')
spechdu = f1[1]
Zbest = Z[zz]
Na = len(age)
Nz = 1
param = np.zeros((Na, 6), dtype='float32')
param[:, 2] = 1e99
Ntmp = 1
chi2 = np.zeros(Ntmp) + 1e99
snorm = np.zeros(Ntmp)
agebest = np.zeros(Ntmp)
avbest = np.zeros(Ntmp)
age_univ = cd.age(zbest, use_flat=True, **cosmo)
if zz == 0 and pp == 0:
lm0 = spechdu.data['wavelength']
if fneb == 1:
spec0 = spechdu.data['efspec_' + str(zz) + '_0_' + str(pp)]
#logU = f1[0].header['logU']
else:
spec0 = spechdu.data['fspec_' + str(zz) + '_0_' + str(pp)]
lmbest = np.zeros((Ntmp, len(lm0)), dtype='float32')
fbest = np.zeros((Ntmp, len(lm0)), dtype='float32')
lmbestbb = np.zeros((Ntmp, len(SFILT)), dtype='float32')
fbestbb = np.zeros((Ntmp, len(SFILT)), dtype='float32')
A = np.zeros(Na, dtype='float32') + 1
spec_mul = np.zeros((Na, len(lm0)), dtype='float32')
spec_mul_nu = np.zeros((Na, len(lm0)), dtype='float32')
spec_mul_nu_conv = np.zeros((Na, len(lm0)), dtype='float64')
ftmpbb = np.zeros((Na, len(SFILT)), dtype='float32')
ltmpbb = np.zeros((Na, len(SFILT)), dtype='float32')
ftmp_nu_int = np.zeros((Na, len(lm)), dtype='float32')
spec_av_tmp = np.zeros((Na, len(lm)), dtype='float32')
ms = np.zeros(Na, dtype='float32')
Ls = np.zeros(Na, dtype='float32')
ms[:] = mshdu.data['ms_' + str(zz)][:] # [:] is necessary.
Ls[:] = mshdu.data['Ls_' + str(zz)][:]
Fuv = np.zeros(Na, dtype='float32')
for ss in range(Na):
wave = spechdu.data['wavelength']
if fneb == 1:
spec_mul[ss] = spechdu.data['efspec_' + str(zz) + '_' +
str(ss) + '_' + str(pp)]
else:
spec_mul[ss] = spechdu.data['fspec_' + str(zz) + '_' +
str(ss) + '_' + str(pp)]
###################
# IGM attenuation.
###################
spec_av_tmp = madau_igm_abs(wave, spec_mul[ss, :], zbest)
spec_mul_nu[ss, :] = flamtonu(wave, spec_av_tmp)
if len(lm) > 0:
try:
spec_mul_nu_conv[ss, :] = convolve(spec_mul_nu[ss],
LSF,
boundary='extend')
except:
spec_mul_nu_conv[ss, :] = spec_mul_nu[ss]
if zz == 0 and ss == 0:
print('Kernel is too small. No convolution.')
else:
spec_mul_nu_conv[ss, :] = spec_mul_nu[ss]
spec_sum = 0 * spec_mul[0] # This is dummy file.
DL = cd.luminosity_distance(
zbest, **cosmo) * Mpc_cm # Luminositydistance in cm
wavetmp = wave * (1. + zbest)
#spec_av = flamtonu(wavetmp, spec_sum) # Conversion from Flambda to Fnu.
#ftmp_int = data_int(lm, wavetmp, spec_av)
Lsun = 3.839 * 1e33 #erg s-1
stmp_common = 1e10 # 1 tmp is in 1e10Lsun
#ftmpbb[ss,:] *= Lsun/(4.*np.pi*DL**2/(1.+zbest))
#ftmpbb[ss,:] *= (1./Ls[ss])*stmp_common
spec_mul_nu_conv[ss, :] *= Lsun / (4. * np.pi * DL**2 /
(1. + zbest))
spec_mul_nu_conv[ss, :] *= (
1. /
Ls[ss]) * stmp_common # in unit of erg/s/Hz/cm2/ms[ss].
ms[ss] *= (
1. / Ls[ss]
) * stmp_common # M/L; 1 unit template has this mass in [Msolar].
if f_spec:
ftmp_nu_int[ss, :] = data_int(lm, wavetmp,
spec_mul_nu_conv[ss, :])
ltmpbb[ss, :], ftmpbb[ss, :] = filconv(SFILT, wavetmp,
spec_mul_nu_conv[ss, :],
DIR_FILT)
# UV magnitude;
#print('%s AA is used as UV reference.'%(xm_tmp[iiuv]))
#print(ms[ss], (Lsun/(4.*np.pi*DL**2/(1.+zbest))))
#print('m-M=',5*np.log10(DL/Mpc_cm*1e6/10))
##########################################
# Writing out the templates to fits table.
##########################################
if ss == 0 and pp == 0 and zz == 0:
# First file
nd1 = np.arange(0, len(lm), 1)
nd3 = np.arange(10000, 10000 + len(ltmpbb[ss, :]), 1)
nd_ap = np.append(nd1, nd3)
lm_ap = np.append(lm, ltmpbb[ss, :])
col1 = fits.Column(name='wavelength',
format='E',
unit='AA',
array=lm_ap)
col2 = fits.Column(name='colnum',
format='K',
unit='',
array=nd_ap)
col00 = [col1, col2]
# Second file
col3 = fits.Column(name='wavelength',
format='E',
unit='AA',
array=wavetmp)
nd = np.arange(0, len(wavetmp), 1)
col4 = fits.Column(name='colnum',
format='K',
unit='',
array=nd)
col01 = [col3, col4]
spec_ap = np.append(ftmp_nu_int[ss, :], ftmpbb[ss, :])
colspec = fits.Column(name='fspec_' + str(zz) + '_' + str(ss) +
'_' + str(pp),
format='E',
unit='Fnu',
disp='%s' % (age[ss]),
array=spec_ap)
col00.append(colspec)
colspec_all = fits.Column(name='fspec_' + str(zz) + '_' +
str(ss) + '_' + str(pp),
format='E',
unit='Fnu',
disp='%s' % (age[ss]),
array=spec_mul_nu_conv[ss, :])
col01.append(colspec_all)
#########################
# Summarize the ML
#########################
if pp == 0:
colms = fits.Column(name='ML_' + str(zz),
format='E',
unit='Msun/1e10Lsun',
array=ms)
col02.append(colms)
#########################
# Summarize the templates
#########################
coldefs_spec = fits.ColDefs(col00)
hdu = fits.BinTableHDU.from_columns(coldefs_spec)
hdu.writeto(DIR_TMP + 'spec_' + ID + '_PA' + PA + '.fits', overwrite=True)
coldefs_spec = fits.ColDefs(col01)
hdu2 = fits.BinTableHDU.from_columns(coldefs_spec)
hdu2.writeto(DIR_TMP + 'spec_all_' + ID + '_PA' + PA + '.fits',
overwrite=True)
coldefs_ms = fits.ColDefs(col02)
hdu3 = fits.BinTableHDU.from_columns(coldefs_ms)
hdu3.writeto(DIR_TMP + 'ms_' + ID + '_PA' + PA + '.fits', overwrite=True)
######################
# Add dust component;
######################
if f_dust:
Temp = np.arange(DT0, DT1, dDT)
lambda_d = np.arange(
1e4, 1e7,
1e3) # RF wavelength, in AA. #* (1.+zbest) # 1um to 1000um;
#lambda_d = np.arange(4000,1e7,1e3) # RF wavelength, in AA. #* (1.+zbest) # 1um to 1000um;
# c in AA/s.
kb = 1.380649e-23 # Boltzmann constant, in J/K
hp = 6.62607015e-34 # Planck constant, in J*s
# from Eq.3 of Bianchi 13
kabs0 = 4.0 # in cm2/g
beta_d = 2.08 #
lam0 = 250. * 1e4 # mu m to AA
kappa = kabs0 * (lam0 / lambda_d)**beta_d # cm2/g
kappa *= (1e8)**2 # AA2/g
for tt in range(len(Temp)):
if tt == 0:
# For full;
nd_d = np.arange(0, len(lambda_d), 1)
colspec_dw = fits.Column(name='wavelength',
format='E',
unit='AA',
array=lambda_d * (1. + zbest))
col_dw = fits.Column(name='colnum',
format='K',
unit='',
array=nd_d)
col03 = [colspec_dw, col_dw]
nu_d = c / lambda_d # 1/s = Hz
BT_nu = 2 * hp * nu_d[:]**3 / c**2 / (
np.exp(hp * nu_d[:] / (kb * Temp[tt])) - 1
) # J*s * (1/s)^3 / (AA/s)^2 / sr = J / AA^2 / sr = J/s/AA^2/Hz/sr.
# in side exp: J*s * (1/s) / (J/K * K) = 1;
# if optically thin;
#kappa = nu_d ** beta_d
fnu_d = 1.0 / (
4. * np.pi * DL**2 / (1. + zbest)
) * kappa * BT_nu # 1/cm2 * AA2/g * J/s/AA^2/Hz = J/s/cm^2/Hz/g
#fnu_d = 1.0 / (4.*np.pi*DL**2/(1.+zbest)) * BT_nu # 1/cm2 * AA2/g * J/s/AA^2/Hz = J/s/cm^2/Hz/g
fnu_d *= 1.989e+33 # J/s/cm^2/Hz/Msun; i.e. 1 flux is in 1Msun
fnu_d *= 1e7 # erg/s/cm^2/Hz/Msun.
#fnu_d *= 1e9 # Now 1 flux is in 1e9Msun
print('Somehow, crazy scale is required for FIR normalization...')
fnu_d *= 1e40
colspec_d = fits.Column(name='fspec_' + str(tt),
format='E',
unit='Fnu(erg/s/cm^2/Hz/Msun)',
disp='%.2f' % (Temp[tt]),
array=fnu_d)
col03.append(colspec_d)
#print('At z=%.2f, luminosity surface is %.2e cm^2'%(zbest,4.*np.pi*DL**2/(1.+zbest)))
# Convolution;
#ltmpbb_d, ftmpbb_d = filconv(DFILT,lambda_d*(1.+zbest),fnu_d,DIR_FILT)
ALLFILT = np.append(SFILT, DFILT)
ltmpbb_d, ftmpbb_d = filconv(ALLFILT, lambda_d * (1. + zbest),
fnu_d, DIR_FILT)
if False:
#plt.plot(nu_d/1e9/(1.+zbest),fnu_d)
#nubb_d = c / ltmpbb_d
#plt.plot(nubb_d/1e9, ftmpbb_d, 'x')
plt.plot(lambda_d / 1e4, fnu_d)
plt.plot(lambda_d * (1. + zbest) / 1e4, fnu_d)
plt.plot(ltmpbb_d / 1e4, ftmpbb_d, 'x')
plt.show()
if tt == 0:
# For conv;
col3 = fits.Column(name='wavelength',
format='E',
unit='AA',
array=ltmpbb_d)
nd_db = np.arange(0, len(ltmpbb_d), 1)
col4 = fits.Column(name='colnum',
format='K',
unit='',
array=nd_db)
col04 = [col3, col4]
colspec_db = fits.Column(name='fspec_' + str(tt),
format='E',
unit='Fnu',
disp='%.2f' % (Temp[tt]),
array=ftmpbb_d)
col04.append(colspec_db)
coldefs_d = fits.ColDefs(col03)
hdu4 = fits.BinTableHDU.from_columns(coldefs_d)
hdu4.writeto(DIR_TMP + 'spec_dust_all_' + ID + '_PA' + PA + '.fits',
overwrite=True)
coldefs_db = fits.ColDefs(col04)
hdu5 = fits.BinTableHDU.from_columns(coldefs_db)
hdu5.writeto(DIR_TMP + 'spec_dust_' + ID + '_PA' + PA + '.fits',
overwrite=True)
##########################################
# For observation.
# Write out for the Multi-component fitting.
##########################################
lamliml = 0.
lamlimu = 20000.
ebblim = 1e5
ncolbb = 10000
fw = open(DIR_TMP + 'spec_obs_' + ID + '_PA' + PA + '.cat', 'w')
fw.write('# BB data (>%d) in this file are not used in fitting.\n' %
(ncolbb))
for ii in range(len(lm)):
if fgrs[ii] == 0: # G102
if lm[ii] / (1. + zbest) > lamliml and lm[ii] / (1. +
zbest) < lamlimu:
fw.write('%d %.5f %.5e %.5e\n' %
(ii, lm[ii], fobs[ii], eobs[ii]))
else:
fw.write('%d %.5f 0 1000\n' % (ii, lm[ii]))
elif fgrs[ii] == 1: # G141
if lm[ii] / (1. + zbest) > lamliml and lm[ii] / (1. +
zbest) < lamlimu:
fw.write('%d %.5f %.5e %.5e\n' %
(ii + 1000, lm[ii], fobs[ii], eobs[ii]))
else:
fw.write('%d %.5f 0 1000\n' % (ii + 1000, lm[ii]))
for ii in range(len(ltmpbb[0, :])):
if ebb[ii] > ebblim:
fw.write('%d %.5f 0 1000\n' % (ii + ncolbb, ltmpbb[0, ii]))
else:
fw.write('%d %.5f %.5e %.5e\n' %
(ii + ncolbb, ltmpbb[0, ii], fbb[ii], ebb[ii]))
fw.close()
fw = open(DIR_TMP + 'spec_dust_obs_' + ID + '_PA' + PA + '.cat', 'w')
if f_dust:
nbblast = len(ltmpbb[0, :])
for ii in range(len(ebb_d[:])):
if ebb_d[ii] > ebblim:
fw.write('%d %.5f 0 1000\n' %
(ii + ncolbb + nbblast, ltmpbb_d[ii + nbblast]))
else:
fw.write('%d %.5f %.5e %.5e\n' %
(ii + ncolbb + nbblast, ltmpbb_d[ii + nbblast],
fbb_d[ii], ebb_d[ii]))
fw.close()
fw = open(DIR_TMP + 'bb_obs_' + ID + '_PA' + PA + '.cat', 'w')
for ii in range(len(ltmpbb[0, :])):
if ebb[ii] > ebblim:
fw.write('%d %.5f 0 1000 %.1f\n' %
(ii + ncolbb, ltmpbb[0, ii], FWFILT[ii] / 2.))
else:
fw.write('%d %.5f %.5e %.5e %.1f\n' %
(ii + ncolbb, ltmpbb[0, ii], fbb[ii], ebb[ii],
FWFILT[ii] / 2.))
fw.close()
fw = open(DIR_TMP + 'bb_dust_obs_' + ID + '_PA' + PA + '.cat', 'w')
if f_dust:
for ii in range(len(ebb_d[:])):
if ebb_d[ii] > ebblim:
fw.write('%d %.5f 0 1000 %.1f\n' %
(ii + ncolbb + nbblast, ltmpbb_d[ii + nbblast],
DFWFILT[ii] / 2.))
else:
fw.write('%d %.5f %.5e %.5e %.1f\n' %
(ii + ncolbb + nbblast, ltmpbb_d[ii + nbblast],
fbb_d[ii], ebb_d[ii], DFWFILT[ii] / 2.))
fw.close()
da = cd.angular_diameter_distance(z[i], **Cosmology)
#See equations 18-19 of David Hogg's arXiv:astro-ph/9905116v4
dl = cd.luminosity_distance(z[i], **Cosmology)
#Units are Mpc
dVc = cd.diff_comoving_volume(z[i], **Cosmology)
#The differential comoving volume element dV_c/dz/dSolidAngle.
#Dimensions are volume per unit redshift per unit solid angle.
#Units are Mpc**3 Steradians^-1.
#See David Hogg's arXiv:astro-ph/9905116v4, equation 28
tl = cd.lookback_time(z[i], **Cosmology)
#See equation 30 of David Hogg's arXiv:astro-ph/9905116v4. Units are s.
agetl = cd.age(z[i], **Cosmology)
#Age at z is lookback time at z'->Infinity minus lookback time at z.
tH = 3.09e17 / Cosmology['h']
ez = cd.e_z(z[i], **Cosmology)
#The unitless Hubble expansion rate at redshift z.
#In David Hogg's (arXiv:astro-ph/9905116v4) formalism, this is
#equivalent to E(z), defined in his eq. 14.
if PlotNumber == 1:
val[i] = dm / dh
xtitle = "redshift z"
ytitle = "Proper motion distance Dm/Dh"
elif PlotNumber == 2:
4, 5, 8, 10, 11, 12, 13, 14, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 31, 32, 34, 35, 36, 37, 39, 41, 42, 43, 44, 45, 47, 49, 50, 51, 53,
55, 56, 58, 61, 62, 63, 64, 65, 66, 68, 70, 71, 73, 75, 78, 79, 81, 83, 84,
88, 89, 90, 92, 93, 94, 95, 96, 98, 99
])
ID_ff_DE = np.array([3, 59])
ID_rf_DE = np.array([])
ID_fr_DE = np.array([
2, 7, 9, 15, 18, 33, 38, 40, 52, 69, 72, 74, 76, 77, 80, 82, 85, 86, 87,
91, 97, 100
])
test = [(4)]
cosmo = {'omega_M_0': 0.3, 'omega_lambda_0': 0.7, 'h': 0.7}
cosmo = cd.set_omega_k_0(cosmo)
ageUniv2 = cd.age(2.0, **cosmo) / cc.yr_s
ageUniv999 = cd.age(999.0, **cosmo) / cc.yr_s
xlin = np.linspace(1, 1e10, 100000)
for j in test:
i = (np.abs(ID - j)).argmin()
plt.figure(figsize=(4 * fsize, fsize))
plt.title('{} ID {}'.format(revision.replace('_', ' '), j))
plt.xlim(0, 1e10)
# plt.xlim(0.9*msa, 1.1*msa)
plt.ylim(0.0, 1.1)
sfr_in = 1 * (xlin - (ageUniv2 - msa_in[i])) * np.exp(
def __init__(self, params=B2008,
MStar_bounds = ['extrapolate', float('NaN')],
phiStar_bounds = ['constant', float('NaN')],
alpha_bounds = ['constant', float('NaN')],
extrap_args = {},
extrap_var = 'z',
sedParams = {},
wavelength = 1500.,
**cosmo):
for (k, v) in params.iteritems():
params[k] = numpy.asarray(v)
self.params = params
self.MStar_bounds = MStar_bounds
self.phiStar_bounds = phiStar_bounds
self.alpha_bounds = alpha_bounds
self.extrap_args = extrap_args
self.extrap_var = extrap_var
self.sedParams = sedParams
self.wavelength = wavelength
self.cosmo = cosmo
self.zobs = params['z']
self.tobs = cd.age(self.zobs, **cosmo)[0]
self.MStar = params['MStar']
self.phiStar = params['phiStar']
self.alpha = params['alpha']
if extrap_var == 't':
self.xobs = self.tobs
self._iPhotFunc = \
lambda t1, mag: (self.iPhotonRateDensity_t(t1,
maglim=mag))
elif extrap_var == 'z':
self.xobs = self.zobs
MStar_bounds = MStar_bounds[::-1]
phiStar_bounds = phiStar_bounds[::-1]
alpha_bounds = alpha_bounds[::-1]
self._iPhotFunc = \
lambda z1, mag: (self.iPhotonRateDensity_z(z1,
maglim=mag))
self._MStarfunc = utils.Extrapolate1d(self.xobs, self.MStar,
bounds_behavior=MStar_bounds,
**extrap_args
)
print "M*:",
print self._MStarfunc.extrap_string()
self._phiStarfunc = utils.Extrapolate1d(self.xobs, self.phiStar,
bounds_behavior=phiStar_bounds,
**extrap_args
)
print "phi*:",
print self._phiStarfunc.extrap_string()
self._alphafunc = utils.Extrapolate1d(self.xobs, self.alpha,
bounds_behavior=alpha_bounds,
**extrap_args
)
print "alpha:",
print self._alphafunc.extrap_string()
self._SED = BrokenPowerlawSED(**sedParams)
self._rQL = self._SED.iPhotonRateRatio(wavelength)
for name, func in globals().items():
if not name.startswith('schechter'):
continue
def newfunc(z, _name=name, _func=func, **kwargs):
params = self.params_z(z)
M = params['MStar']
phi = params['phiStar']
alpha = params['alpha']
return _func(MStar=M, phiStar=phi, alpha=alpha, **kwargs)
newfunc.__name__ = func.__name__
newfunc.__doc__ = func.__doc__
self.__dict__[name] = newfunc
def plotLFevo(hist=None,
params=B2008,
extrap_var='t',
#maglim = -21.07 - 2.5 * numpy.log10(0.2)):
maglim = -21. - 2.5 * numpy.log10(0.2),
z_max=20.0,
skipIon=True
):
"""Plot evolution of luminosity function params and total luminsity.
Schechter function at each redshift is integrated up to maglim to
find total luminsity.
"""
for (k, v) in params.iteritems():
params[k] = numpy.asarray(v)
if hist is None:
cosmo = cp.WMAP7_BAO_H0_mean(flat=True)
hist = LFHistory(params, extrap_var=extrap_var, **cosmo)
else:
params = hist.params
extrap_var = hist.extrap_var
cosmo = hist.cosmo
z = params['z']
t = cd.age(z, **cosmo)[0] / cc.yr_s
MStar = params['MStar']
phiStar = params['phiStar']
alpha = params['alpha']
if maglim is None:
ltot = schechterTotLM(MStar=MStar, phiStar=phiStar, alpha=alpha)
else:
ltot = schechterCumuLM(magnitudeAB=maglim,
MStar=MStar, phiStar=phiStar, alpha=alpha)
print hist._MStarfunc.extrap_string()
zPlot = numpy.arange(z.min()-0.1, z_max, 0.1)
tPlot = cd.age(zPlot, **cosmo)[0] / cc.yr_s
newparams = hist.params_z(zPlot)
MPlot = newparams['MStar']
phiPlot = newparams['phiStar']
alphaPlot = newparams['alpha']
if maglim is None:
ltotPlot = hist.schechterTotLM(zPlot)
else:
ltotPlot = hist.schechterCumuLM(zPlot, magnitudeAB=maglim)
# From Table 6 of 2008ApJ...686..230B
lB2008 =10.** numpy.array([26.18, 25.85, 25.72, 25.32, 25.14])
iPhot = hist.iPhotonRateDensity_z(zPlot, maglim=maglim)
# iPhotFunc = lambda t1: cc.yr_s * hist.iPhotonRateDensity_t(t1,
# maglim=maglim)
if not skipIon:
xH = hist.ionization(zPlot, maglim)
import pylab
pylab.figure(1)
pylab.gcf().set_label('LFion_vs_z')
if skipIon:
pylab.subplot(111)
else:
pylab.subplot(211)
pylab.plot(zPlot, iPhot)
if not skipIon:
pylab.subplot(212)
pylab.plot(zPlot, xH)
pylab.ylim(0.0, 1.5)
pylab.figure(2)
pylab.gcf().set_label('LFparams_vs_z')
pylab.subplot(311)
pylab.plot(z, MStar, '-')
pylab.plot(z, MStar, 'o')
pylab.plot(zPlot, MPlot, ':')
pylab.subplot(312)
pylab.plot(z, phiStar, '-')
pylab.plot(z, phiStar, 'o')
pylab.plot(zPlot, phiPlot, ':')
pylab.subplot(313)
pylab.plot(z, alpha, '-')
pylab.plot(z, alpha, 'o')
pylab.plot(zPlot, alphaPlot, ':')
pylab.figure(3)
pylab.gcf().set_label('LFparams_vs_t')
pylab.subplot(311)
pylab.plot(t, MStar, '-')
pylab.plot(t, MStar, 'o')
pylab.plot(tPlot, MPlot, ':')
pylab.subplot(312)
pylab.plot(t, phiStar, '-')
pylab.plot(t, phiStar, '.')
pylab.plot(tPlot, phiPlot, ':')
pylab.subplot(313)
pylab.plot(t, alpha, '-')
pylab.plot(t, alpha, 'o')
pylab.plot(tPlot, alphaPlot, ':')
pylab.figure(4)
pylab.gcf().set_label('LFlum_vs_z')
pylab.subplot(121)
pylab.plot(z, ltot, 'o')
pylab.plot(z, lB2008, 'x')
pylab.plot(zPlot, ltotPlot)
pylab.subplot(122)
pylab.plot(t, ltot, 'o')
pylab.plot(t, lB2008, 'x')
pylab.plot(tPlot, ltotPlot)
def integrate_ion_recomb(z,
ion_func,
clump_fact_func,
xHe=1.0,
temp_gas=1e4,
alpha_B=None,
bubble=True,
**cosmo):
"""Integrate IGM ionization and recombination given an ionization function.
Parameters:
z: array
The redshift values at which to calculate the ionized
fraction. This array should be in reverse numerical order. The
first redshift specified should be early enough that the
universe is still completely neutral.
ion_func:
A function giving the ratio of the total density of emitted
ionizing photons to the density hydrogen atoms (or hydrogen
plus helium, if you prefer) as a function of redshift.
temp_gas:
Gas temperature used to calculate the recombination coefficient
if alpha_b is not specified.
alpha_B:
Optional recombination coefficient in units of cm^3
s^-1. In alpha_B=None, it is calculated from temp_gas.
clump_fact_func: function
Function returning the clumping factor when given a redshift,
defined as <n_HII^2>/<n_HII>^2.
cosmo: dict
Dictionary specifying the cosmological parameters.
Notes:
We only track recombination of hydrogen, but if xHe > 0, then the
density is boosted by the addition of xHe * nHe. This is
eqiuvalent to assuming the the ionized fraction of helium is
always proportional to the ionized fraction of hydrogen. If
xHe=1.0, then helium is singly ionized in the same proportion as
hydrogen. If xHe=2.0, then helium is fully ionized in the same
proportion as hydrogen.
We assume, as is fairly standard, that the ionized
fraction is contained in fully ionized bubbles surrounded by a
fully neutral IGM. The output is therefore the volume filling
factor of ionized regions, not the ionized fraction of a
uniformly-ionized IGM.
I have also made the standard assumption that all ionized photons
are immediately absorbed, which allows the two differential
equations (one for ionization-recombination and one for
emission-photoionizaion) to be combined into a single ODE.
"""
# Determine recombination coefficient.
if alpha_B is None:
alpha_B_cm = recomb_rate_coeff_HG(temp_gas, 'H', 'B')
else:
alpha_B_cm = alpha_B
alpha_B = alpha_B_cm * cc.Gyr_s / (cc.Mpc_cm**3.)
print(
"Recombination rate alpha_B = %.4g (Mpc^3 Gyr^-1) = %.4g (cm^3 s^-1)" %
(alpha_B, alpha_B_cm))
# Normalize power spectrum.
if 'deltaSqr' not in cosmo:
cosmo['deltaSqr'] = cp.norm_power(**cosmo)
# Calculate useful densities.
rho_crit, rho_0, n_He_0, n_H_0 = cden.baryon_densities(**cosmo)
# Boost density to approximately account for helium.
nn = (n_H_0 + xHe * n_He_0)
# Function used in the integration.
# Units: (Mpc^3 Gyr^-1) * Mpc^-3 = Gyr^-1
coeff_rec_func = lambda z1: (clump_fact_func(z1) * alpha_B * nn *
(1. + z1)**3.)
# Generate a function that converts age of the universe to z.
red_func = cd.quick_redshift_age_function(zmax=1.1 * numpy.max(z),
zmin=-0.0,
dz=0.01,
**cosmo)
ref_func_Gyr = lambda t1: red_func(t1 * cc.Gyr_s)
# Convert specified redshifts to cosmic time (age of the universe).
t = cd.age(z, **cosmo)[0] / cc.Gyr_s
# Integrate to find u(z) = x(z) - w(z), where w is the ionization fraction
u = si.odeint(_udot,
y0=0.0,
t=t,
args=(coeff_rec_func, ref_func_Gyr, ion_func, bubble))
u = u.flatten()
w = ion_func(z)
x = u + w
#x[x > 1.0] = 1.0
return x, w, t
def integrate_ion_recomb_collapse(z,
coeff_ion,
temp_min=1e4,
passed_min_mass=False,
temp_gas=1e4,
alpha_B=None,
clump_fact_func=clumping_factor_BKP,
**cosmo):
"""IGM ionization state with recombinations from halo collapse
fraction. Integrates an ODE describing IGM ionization and
recombination rates.
z: array
The redshift values at which to calculate the ionized
fraction. This array should be in reverse numerical order. The
first redshift specified should be early enough that the
universe is still completely neutral.
coeff_ion:
The coefficient converting the collapse fraction to ionized
fraction, neglecting recombinations. Equivalent to the product
(f_star * f_esc_gamma * N_gamma) in the BKP paper.
temp_min:
See docs for ionization_from_collapse. Either the minimum virial
temperature or minimum mass of halos contributing to
reionization.
passed_temp_min:
See documentation for ionization_from_collapse.
temp_gas:
Gas temperature used to calculate the recombination coefficient
if alpha_b is not specified.
alpha_B:
Optional recombination coefficient in units of cm^3
s^-1. In alpha_B=None, it is calculated from temp_gas.
clump_fact_func: function
Function returning the clumping factor when given a redshift.
cosmo: dict
Dictionary specifying the cosmological parameters.
We assume, as is fairly standard, that the ionized
fraction is contained in fully ionized bubbles surrounded by a
fully neutral IGM. The output is therefore the volume filling
factor of ionized regions, not the ionized fraction of a
uniformly-ionized IGM.
I have also made the standard assumption that all ionized photons
are immediately absorbed, which allows the two differential
equations (one for ionization-recombination and one for
emission-photoionizaion) to be combined into a single ODE.
"""
# Determine recombination coefficient.
if alpha_B is None:
alpha_B_cm = recomb_rate_coeff_HG(temp_gas, 'H', 'B')
else:
alpha_B_cm = alpha_B
alpha_B = alpha_B_cm / (cc.Mpc_cm**3.)
print("Recombination rate alpha_B = %.4g (Mpc^3 s^-1) = %.4g (cm^3 s^-1)" %
(alpha_B, alpha_B_cm))
# Normalize power spectrum.
if 'deltaSqr' not in cosmo:
cosmo['deltaSqr'] = cp.norm_power(**cosmo)
# Calculate useful densities.
rho_crit, rho_0, n_He_0, n_H_0 = cden.baryon_densities(**cosmo)
# Function used in the integration.
# Units: (Mpc^3 s^-1) * Mpc^-3 = s^-1
coeff_rec_func = lambda z: (clump_fact_func(z)**2. * alpha_B * n_H_0 *
(1. + z)**3.)
# Generate a function that converts redshift to age of the universe.
redfunc = cd.quick_redshift_age_function(zmax=1.1 * numpy.max(z),
zmin=-0.0,
**cosmo)
# Function used in the integration.
ionfunc = quick_ion_col_function(coeff_ion,
temp_min,
passed_min_mass=passed_min_mass,
zmax=1.1 * numpy.max(z),
zmin=-0.0,
zstep=0.1,
**cosmo)
# Convert specified redshifts to cosmic time (age of the universe).
t = cd.age(z, **cosmo)
# Integrate to find u(z) = x(z) - w(z), where w is the ionization fraction
u = si.odeint(_udot, y0=0.0, t=t, args=(coeff_rec_func, redfunc, ionfunc))
u = u.flatten()
w = ionization_from_collapse(z,
coeff_ion,
temp_min,
passed_min_mass=passed_min_mass,
**cosmo)
x = u + w
x[x > 1.0] = 1.0
return x, w, t
def current_age(snapshot):
import cosmolopy.distance as cd
import cosmolopy.constants as cc
cosmo = snapshot.cosmo
return cd.age(**cosmo) / cc.Gyr_s # Gyr
da = cd.angular_diameter_distance(z[i],**Cosmology)
#See equations 18-19 of David Hogg's arXiv:astro-ph/9905116v4
dl = cd.luminosity_distance(z[i],**Cosmology)
#Units are Mpc
dVc = cd.diff_comoving_volume(z[i], **Cosmology)
#The differential comoving volume element dV_c/dz/dSolidAngle.
#Dimensions are volume per unit redshift per unit solid angle.
#Units are Mpc**3 Steradians^-1.
#See David Hogg's arXiv:astro-ph/9905116v4, equation 28
tl = cd.lookback_time(z[i],**Cosmology)
#See equation 30 of David Hogg's arXiv:astro-ph/9905116v4. Units are s.
agetl = cd.age(z[i],**Cosmology)
#Age at z is lookback time at z'->Infinity minus lookback time at z.
tH = 3.09e17/Cosmology['h']
ez = cd.e_z(z[i],**Cosmology)
#The unitless Hubble expansion rate at redshift z.
#In David Hogg's (arXiv:astro-ph/9905116v4) formalism, this is
#equivalent to E(z), defined in his eq. 14.
if PlotNumber == 1:
val[i] = dm/dh
xtitle = "redshift z"
ytitle = "Proper motion distance Dm/Dh"
elif PlotNumber == 2: