def abs_m(m, z, **cosmo):
""" Return the absolute magnitude
m: apparent magnitude
z: redshift
>>> abs_m(17.7, 0.1)
array([-20.61520457])
"""
m = np.atleast_1d(m)
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
lum_dist = cd.luminosity_distance(z, **cosmo_in)
absm = -25. + m - 5.0 * np.log10(lum_dist)
return absm
def Get_SdV(z,frq_r,L_line,L_units):
""" PURPOSE:
Computes the velocity-integrated line flux (SdV)
for a given source redshift, rest-frame line frequency,
and line luminosity.
INPUT:
z : source redshift
frq_r : rest-frame frequency [GHz]
L_line : line luminosity
L_units : 'prime': [L_line] = K km/s pc^2 or
'solar': = [L_line] = Lsolar
OUTPUT:
SdV : Line flux [Jy km/s]
HISTORY: Feb 2015 : created by [email protected]
"""
#Calculate luminosity distance in [Mpc]
d_L = cd.luminosity_distance(z, **cosmo)
#calculate observed frequency
frq_o = frq_r/(1.0+z)
if L_units == 'prime':
SdV = (L_line/3.25E7)*(frq_o**2)*((1.0+z)**3)/(d_L**2)
elif L_units == 'solar':
SdV = (((L_line/1.04E-3)*(1.+z))/(d_L*d_L))/frq_r
else:
print 'Wrong input'
return -1
return SdV
def modelTheo(z, omegam, omegal):
tab = []
cosmo = {'omega_M_0': omegam, 'omega_lambda_0': omegal, 'h': 0.70}
cosmo = cd.set_omega_k_0(cosmo)
for i in range(0, len(z)):
tab.append(cd.luminosity_distance(z[i], **cosmo) * 10**5)
return tab
def zdistance(self, clus_z, H0=100.0):
"""
Finds the angular diameter distance for an array of cluster center redshifts.
Instead, use angular distance file precalculated and upload.
"""
cosmo = {'omega_M_0': 0.3, 'omega_lambda_0': 0.7, 'h': H0 / 100.0}
cosmo = cd.set_omega_k_0(cosmo)
ang_d = cd.angular_diameter_distance(clus_z, **cosmo)
lum_d = cd.luminosity_distance(clus_z, **cosmo)
return ang_d, lum_d
def zdistance(self,clus_z,H0=100.0):
"""
Finds the angular diameter distance for an array of cluster center redshifts.
Instead, use angular distance file precalculated and upload.
"""
cosmo = {'omega_M_0':0.3,'omega_lambda_0':0.7,'h':H0/100.0}
cosmo = cd.set_omega_k_0(cosmo)
ang_d = cd.angular_diameter_distance(clus_z,**cosmo)
lum_d = cd.luminosity_distance(clus_z,**cosmo)
return ang_d,lum_d
def get_column(zin, z_r=6.0):
#
# Returns : HI column density in IGM.
#
delz = 0.1
z = np.arange(z_r, zin, delz)
try:
nH = np.zeros(len(z), dtype='float32')
except:
nH = 0
# From Cen & Haiman 2000
nH = 8.5e-5 * ((1. + z) / 8)**3 # in cm^-3
NH = 0
for zz in range(len(z)):
d1 = cd.luminosity_distance(z[zz] - delz, **cosmo)
d2 = cd.luminosity_distance(z[zz] + delz, **cosmo)
dx = (d2 - d1) * Mpc_cm
NH += nH[zz] * dx / (1. + z[zz])
return NH
def test_distances(threshold = 1e-3):
"""Compare distance measures with calculations from http://icosmo.org/"""
cosmo = cosmo_wmap_5()
print "Comparing distances with calculations from http://icosmo.org/"
# load external distance calculations
# z DA(z) DT(z) DL(z)
distance_file = os.path.dirname(os.path.abspath(__file__))
distance_file = os.path.join(distance_file, 'icosmo_testdata',
'distances.txt')
ic_dists = numpy.loadtxt(distance_file)
z = ic_dists[:,0]
cd_da = cd.angular_diameter_distance(z, **cosmo)
cd_dt = cd.light_travel_distance(z, **cosmo)
cd_dl = cd.luminosity_distance(z, **cosmo)
cd_dm = cd.comoving_distance_transverse(z, **cosmo)
cd_dists = numpy.vstack((cd_dm, cd_da, cd_dt, cd_dl))
labels = [ r'$D_M$', r'$D_A$', r'$D_T$', r'$D_L$']
threshold = [1e-7, 1e-7, 1e-3, 1e-7 ]
# print ic_dists[-1].transpose()
# print cd_dists[:,-1]
pylab.figure()
for i in range(len(labels)):
pylab.plot(ic_dists[:,0], ic_dists[:,i+1],
label=labels[i] +' IC', ls=':')
pylab.plot(z, cd_dists[i], label=labels[i] +' distance.py', ls='-')
pylab.legend(loc='best')
pylab.figure()
for i in range(len(labels)):
diff = (cd_dists[i] - ic_dists[:,i+1]) / ic_dists[:,i+1]
maxdiff = numpy.max(numpy.abs(diff[ic_dists[:,i+1] > 0]))
print "Maximum fraction difference in %s is %e." % (labels[i],
maxdiff)
assert(numpy.all(maxdiff < threshold[i]))
pylab.plot(ic_dists[:,0],
diff,
label=labels[i], ls='-')
#pylab.plot(z, err2, label=labels[1] + ' err.', ls=':')
#pylab.plot(z, err3, label=labels[2] + ' err.', ls=':')
#pylab.plot(z, err5, label=labels[3] + ' err.', ls=':')
#pylab.plot(z, err6, label=labels[0] + ' err.', ls=':')
pylab.legend(loc='best')
def test_distances(threshold = 1e-3):
"""Compare distance measures with calculations from http://icosmo.org/"""
cosmo = cosmo_wmap_5()
print("Comparing distances with calculations from http://icosmo.org/")
# load external distance calculations
# z DA(z) DT(z) DL(z)
distance_file = os.path.dirname(os.path.abspath(__file__))
distance_file = os.path.join(distance_file, 'icosmo_testdata',
'distances.txt')
ic_dists = numpy.loadtxt(distance_file)
z = ic_dists[:,0]
cd_da = cd.angular_diameter_distance(z, **cosmo)
cd_dt = cd.light_travel_distance(z, **cosmo)
cd_dl = cd.luminosity_distance(z, **cosmo)
cd_dm = cd.comoving_distance_transverse(z, **cosmo)
cd_dists = numpy.vstack((cd_dm, cd_da, cd_dt, cd_dl))
labels = [ r'$D_M$', r'$D_A$', r'$D_T$', r'$D_L$']
threshold = [1e-7, 1e-7, 1e-3, 1e-7 ]
# print ic_dists[-1].transpose()
# print cd_dists[:,-1]
pylab.figure()
for i in range(len(labels)):
pylab.plot(ic_dists[:,0], ic_dists[:,i+1],
label=labels[i] +' IC', ls=':')
pylab.plot(z, cd_dists[i], label=labels[i] +' distance.py', ls='-')
pylab.legend(loc='best')
pylab.figure()
for i in range(len(labels)):
diff = (cd_dists[i] - ic_dists[:,i+1]) / ic_dists[:,i+1]
maxdiff = numpy.max(numpy.abs(diff[ic_dists[:,i+1] > 0]))
print("Maximum fraction difference in %s is %e." % (labels[i],
maxdiff))
assert(numpy.all(maxdiff < threshold[i]))
pylab.plot(ic_dists[:,0],
diff,
label=labels[i], ls='-')
#pylab.plot(z, err2, label=labels[1] + ' err.', ls=':')
#pylab.plot(z, err3, label=labels[2] + ' err.', ls=':')
#pylab.plot(z, err5, label=labels[3] + ' err.', ls=':')
#pylab.plot(z, err6, label=labels[0] + ' err.', ls=':')
pylab.legend(loc='best')
def __init__(self, redshift, limitingObsMag=27):
self.cosmo = {'omega_M_0': 0.3, 'omega_lambda_0': 0.7, 'h': 0.7}
self.cosmo = distance.set_omega_k_0(self.cosmo)
distancePc = \
distance.luminosity_distance(redshift, **self.cosmo)*1e6
limitingAbsoluteMag = limitingObsMag - \
5.*np.log10(distancePc) + 5
self.magnitudes = np.linspace(-28, limitingAbsoluteMag, 10000)
self.dMag = self.magnitudes[1] - self.magnitudes[0]
self.redshift = redshift
self.getLuminosityStar()
self.getMagnitudeStar()
self.getLuminosityFunction()
def get_lumdist(self, z):
"""Calculates the luminosity distance to redshift z.
Uses :func:`cosmolopy.distance.luminosity_distance`,
which is based on David Hogg's `Distance measures in cosmology.
<https://arxiv.org/abs/astro-ph/9905116>`_
Args:
z (float): Redshift
Returns:
float: Luminosity distance in cm
"""
# luminosity distance in Mpc
dlum_mpc = cd.luminosity_distance(z, **self.cosmo)
return dlum_mpc * cc.Mpc_cm
def Get_Line_Luminosity(z, SdV, transition, *unit):
"""This function calculates the line luminosity for a given redshift (z), transition line
flux density (sdv, in units of Jy km/s). The output luminosity is in units of K km/s pc^2 or
Lsolar if 'Lsolar' keyword is set"""
d_L = cd.luminosity_distance(z, **cosmo) #[Mpc]
f=open('../../frequencies/frequencies.dat','r')
lines=f.readlines()
f.close()
trans=[]
frq_r=[]
for line in lines:
p=line.split()
if p != []:
trans.append(p[0])
frq_r.append(p[1])
frq_r=[float(x) for x in frq_r]
trans=np.array(trans)
indices=[i for i,x in enumerate(trans) if transition in x]
frq_r=[frq_r[i] for i in indices]
frq_r=np.array(frq_r)/1.E3 #[GHz]
frq_o=(frq_r/(1.+z)) #[GHz]
L_transition=3.25E7*SdV*(1./(frq_o*frq_o))*d_L*d_L/((1.0+z)**3.) #[K km/s pc^2]
if unit == 'Lsolar':
L_transition=3.18E4*((frq_r/100.)**3.)*(L_transition/1.E9) #[Lsolar]
#If SdV is undefined, then L_transition is undefined
indices=[i for i, item in enumerate(SdV) if item == -999]
if indices != []:
for j in indices:
L_transition[j]=-999
#If SdV is upper limit, then L_transition is upper limit
indices=[i for i, item in enumerate(SdV) if item == 99]
if indices != []:
for j in indices:
L_transition[j]=99
return L_transition
def distance_modulus(z, **cosmo):
"""Distance modulus mu = m-M.
The distance modulus is the difference between the apparent and
absolute magnitudes,
mu = 5 log(d/10 pc)
Usage
-----
>>> from cosmolopy import fidcosmo, magnitudes
>>> "mu(z=6) = %.4g" % magnitudes.distance_modulus(6.0, **fidcosmo)
'mu(z=6) = 48.86'
"""
dl = cd.luminosity_distance(z, **cosmo)
mu = 5 * numpy.log10(dl / (10e-6))
return mu
def distance_modulus(z, **cosmo):
"""Distance modulus mu = m-M.
The distance modulus is the difference between the apparent and
absolute magnitudes,
mu = 5 log(d/10 pc)
Usage
-----
>>> from cosmolopy import fidcosmo, magnitudes
>>> "mu(z=6) = %.4g" % magnitudes.distance_modulus(6.0, **fidcosmo)
'mu(z=6) = 48.86'
"""
dl = cd.luminosity_distance(z, **cosmo)
mu = 5 * numpy.log10(dl/(10e-6))
return mu
def test_figure3():
"""Plot Hogg fig. 3: The dimensionless luminosity distance DL/DH
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
DL / DH from 0--16
"""
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 = ['-', ':', '--']
dh = cd.hubble_distance_z(0, **cosmo)
dl = cd.luminosity_distance(z, **cosmo)
pylab.figure(figsize=(6,6))
for i in range(len(linestyle)):
pylab.plot(z, (dl/dh)[i], ls=linestyle[i])
pylab.xlim(0,5)
pylab.ylim(0,16)
pylab.xlabel("redshift z")
pylab.ylabel(r"luminosity distance $D_L/D_H$")
pylab.title("compare to " + inspect.stack()[0][3].replace('test_', '') +
" (astro-ph/9905116v4)")
def test_figure3():
"""Plot Hogg fig. 3: The dimensionless luminosity distance DL/DH
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
DL / DH from 0--16
"""
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 = ['-', ':', '--']
dh = cd.hubble_distance_z(0, **cosmo)
dl = cd.luminosity_distance(z, **cosmo)
pylab.figure(figsize=(6, 6))
for i in range(len(linestyle)):
pylab.plot(z, (dl / dh)[i], ls=linestyle[i])
pylab.xlim(0, 5)
pylab.ylim(0, 16)
pylab.xlabel("redshift z")
pylab.ylabel(r"luminosity distance $D_L/D_H$")
pylab.title("compare to " + inspect.stack()[0][3].replace('test_', '') +
" (astro-ph/9905116v4)")
ar = 0
gal_vdisp3d = np.zeros(100)
particle_vdisp3d = np.zeros(100)
caustic_mass = np.zeros(100)
for k in range(1):#HaloID.size): #loop over the halos
i = ar+k
#i = 0 #halo number to use
mem_flag = 1 #This is a flag to alerting to (1) if members > 0 and (0) if not. Affects number output.
HaloZ = Halo_Z[i]
#Get current galaxy info
print 'GETTING GALAXIES'
gal_haloid,gal_z,gal_umag,gal_gmag,gal_rmag,gal_imag,gal_zmag,gal_xpos,gal_ypos,gal_zpos,gal_vx,gal_vy,gal_vz = G.get_galsbig(HaloID[i],H0,HaloZ)
gal_mags = np.vstack((gal_umag,gal_gmag,gal_rmag,gal_imag,gal_zmag)).T
lumdist = cd.luminosity_distance(gal_z,**cosmo)
gal_abs_rmag = gal_rmag - 5*np.log10(lumdist*1e6/10.0)
'''
gal_haloid,gal_umag,gal_gmag,gal_rmag,gal_imag,gal_zmag,gal_xpos,gal_ypos,gal_zpos,gal_vx,gal_vy,gal_vz,gal_vdisp = G.get_galsbig2(HaloID[i],H0)
gal_mags = np.vstack((gal_umag,gal_gmag,gal_rmag,gal_imag,gal_zmag)).T
gal_abs_rmag = gal_rmag
'''
gal_p = np.array([gal_xpos,gal_ypos,gal_zpos])
gal_v = np.array([gal_vx,gal_vy,gal_vz])
gal_mem = np.zeros(gal_umag.size)+1
#organize the current halo position and velocity
Halo_P = np.array([Halo_PX[i],Halo_PY[i],Halo_PZ[i]]) #current halo position
Halo_V = np.array([Halo_VX[i],Halo_VY[i],Halo_VZ[i]]) #current halo velocity
HVD = HaloVD[i]
HVD500 = HaloVD500[i]
def find_horizon_range(m1, m2, network, asdfile, pwfile, approx=ls.IMRPhenomD):
fmin = 1.
fref = 1.
df = 1e-2
ra, dec, psi, iota = genfromtxt('../data/horizon_coord_' + pwfile + '.txt',
unpack=True)
psdinterp_dict = {}
minimum_freq = zeros(size(network))
maximum_freq = zeros(size(network))
for detector in range(0, size(network)):
input_freq, strain = loadtxt(asdfile[detector],
unpack=True,
usecols=[0, 1])
minimum_freq[detector] = maximum(min(input_freq), fmin)
maximum_freq[detector] = minimum(max(input_freq), 5000.)
psdinterp_dict[network[detector]] = interp1d(input_freq, strain**2)
#initial guess of horizon redshift and luminosity distance
z0 = 0.1
input_dist = cd.luminosity_distance(z0, **cosmo)
hplus_tilda, hcross_tilda, freqs = get_htildas((1. + z0) * m1,
(1. + z0) * m2,
input_dist,
iota=iota,
fmin=fmin,
fref=fref,
df=df,
approx=approx)
fsel = list()
psd_interp = list()
for detector in range(0, size(network)):
fsel.append(
logical_and(freqs > minimum_freq[detector],
freqs < maximum_freq[detector]))
psd_interp.append(psdinterp_dict[network[detector]](
freqs[fsel[detector]]))
input_snr = compute_horizonSNR(hplus_tilda, hcross_tilda, network, ra, dec,
psi, psd_interp, fsel, df)
input_redshift = z0
guess_snr = 0
njump = 0
#evaluate the horizon recursively
while abs(
guess_snr - snr_th
) > snr_th * 0.001 and njump < 10: #require the error within 0.1%
try:
guess_redshift, guess_dist = horizon_dist_eval(
input_dist, input_snr,
input_redshift) #horizon guess based on the old SNR
hplus_tilda, hcross_tilda, freqs = get_htildas(
(1. + guess_redshift) * m1, (1. + guess_redshift) * m2,
guess_dist,
iota=iota,
fmin=fmin,
fref=fref,
df=df,
approx=approx)
except:
njump = 10
print "Will try interpolation."
fsel = list()
psd_interp = list()
for detector in range(0, size(network)):
fsel.append(
logical_and(freqs > minimum_freq[detector],
freqs < maximum_freq[detector]))
psd_interp.append(psdinterp_dict[network[detector]](
freqs[fsel[detector]]))
guess_snr = compute_horizonSNR(hplus_tilda, hcross_tilda, network, ra,
dec, psi, psd_interp, fsel,
df) #calculate the new SNR
input_snr = guess_snr
input_redshift = guess_redshift
input_dist = guess_dist
njump += 1
horizon_redshift = guess_redshift
#at high redshift the recursive jumps lead to too big a jump for each step, and the recursive loop converge slowly.
#so I interpolate the z-SNR curve directly.
if njump >= 10:
print "Recursive search for the horizon failed. Interpolation instead."
try:
interp_z = linspace(0.001, 100, 200)
interp_snr = zeros(size(interp_z))
for i in range(0, size(interp_z)):
hplus_tilda, hcross_tilda, freqs = get_htildas(
(1. + interp_z[i]) * m1, (1. + interp_z[i]) * m2,
cd.luminosity_distance(interp_z[i], **cosmo),
iota=iota,
fmin=fmin,
fref=fref,
df=df,
approx=approx)
fsel = list()
psd_interp = list()
for detector in range(0, size(network)):
fsel.append(
logical_and(freqs > minimum_freq[detector],
freqs < maximum_freq[detector]))
psd_interp.append(psdinterp_dict[network[detector]](
freqs[fsel[detector]]))
interp_snr[i] = compute_horizonSNR(hplus_tilda, hcross_tilda,
network, ra, dec, psi,
psd_interp, fsel, df)
interpolate_snr = interp1d(interp_snr[::-1], interp_z[::-1])
horizon_redshift = interpolate_snr(snr_th)
except RuntimeError: #If the sources lie outside the given interpolating redshift the sources can not be observe, so I cut down the interpolation range.
print "some of the SNR at the interpolated redshifts cannot be calculated."
interpolate_snr = interp1d(interp_snr[::-1], interp_z[::-1])
horizon_redshift = interpolate_snr(snr_th)
except ValueError: #horizon outside the interpolated redshifts. Can potentially modify the interpolation range, but we basically can not observe the type of source or the source has to be catastrophically close.
print "Horizon further than z=100 or less than z=0.001. Need to modify the interpolated redshift range."
return
#sampled universal antenna power pattern for code sped up
w_sample, P_sample = genfromtxt('../data/pw_' + pwfile + '.txt',
unpack=True)
P = interp1d(w_sample, P_sample, bounds_error=False, fill_value=0.0)
n_zstep = 200
z, dz = linspace(horizon_redshift,
0,
n_zstep,
endpoint=False,
retstep=True)
dz = abs(dz)
unit_volume = zeros(size(z))
compensate_detect_frac = zeros(size(z))
for i in range(0, size(z)):
hplus_tilda, hcross_tilda, freqs = get_htildas(
(1. + z[i]) * m1, (1. + z[i]) * m2,
cd.luminosity_distance(z[i], **cosmo),
iota=iota,
fmin=fmin,
fref=fref,
df=df,
approx=approx)
for detector in range(0, size(network)):
fsel.append(
logical_and(freqs > minimum_freq[detector],
freqs < maximum_freq[detector]))
psd_interp.append(psdinterp_dict[network[detector]](
freqs[fsel[detector]]))
optsnr_z = compute_horizonSNR(hplus_tilda, hcross_tilda, network, ra,
dec, psi, psd_interp, fsel, df)
w = snr_th / optsnr_z
compensate_detect_frac[i] = P(w)
unit_volume[i] = (cd.comoving_volume(z[i] + dz / 2., **cosmo) -
cd.comoving_volume(z[i] - dz / 2., **cosmo)) / (
1. + z[i]) * P(w)
#Find out the redshift at which we detect 50%/90% of the sources at the redshift
z_reach50 = max(z[where(compensate_detect_frac >= 0.5)])
z_reach90 = max(z[where(compensate_detect_frac >= 0.1)])
vol_sum = sum(unit_volume)
#Find out the redshifts that 50%/90% of the sources lie within assuming constant-comoving-rate density
z50 = max(z[where(cumsum(unit_volume) >= 0.5 * vol_sum)])
z90 = max(z[where(cumsum(unit_volume) >= 0.1 * vol_sum)])
#Find out the redshifts that 50%/90% of the sources lie within assuming star formation rate
sfr_vol_sum = sum(unit_volume * sfr(z))
sfr_z50 = max(z[where(cumsum(unit_volume * sfr(z)) >= 0.5 * sfr_vol_sum)])
sfr_z90 = max(z[where(cumsum(unit_volume * sfr(z)) >= 0.1 * sfr_vol_sum)])
#average redshift
z_mean = sum(unit_volume * z) / vol_sum
sfr_z_mean = sum(unit_volume * sfr(z) * z) / sfr_vol_sum
return (3. * vol_sum / 4. / pi)**(
1. / 3.
), z_reach50, z_reach90, horizon_redshift, vol_sum / 1E9, z50, z90, sfr_z50, sfr_z90, z_mean, sfr_z_mean
def calc_pdf(density=1e-7,
L_nu=1e50,
sigma=1,
gamma=2.19,
logMu_range=[-10, 6],
N_Mu_bins=200,
z_limits=[0.04, 10.],
nzbins=120,
Lum_limits=[1e45, 1e54],
nLbins=120,
flux_to_mu=10763342917.859608):
"""
Parameter:
- density in 1/Mpc^3
- L_nu in erg/yr
- sigma in dex
- gamma
- flux_to_mu
Integration Parameters
- logMu_range = [-10,6] # expected range in log mu,
- N_Mu_bins = 200 # number of bins for log nu histogram
- z_limits = [0.04, 10.] # Redshift limits
- nzbins = 120 # number of z bins
- Lum_limits = [1e45,1e54] # Luminosity limits
- nLbins = 120 # number of logLuminosity bins
"""
# Conversion Factors
Mpc_to_cm = 3.086e+24
erg_to_GeV = 624.151
year2sec = 365 * 24 * 3600
cosmology = {'omega_M_0': 0.308, 'omega_lambda_0': 0.692, 'h': 0.678}
cosmology = set_omega_k_0(cosmology) # Flat universe
### Define the Redshift and Luminosity Evolution
redshift_evolution = lambda z: HopkinsBeacom2006StarFormationRate(z)
LF = lambda z, logL: redshift_evolution(z)*np.log(10)*10**logL * \
lognorm.pdf(10**logL, np.log(10)*sigma,
scale=L_nu*np.exp(-0.5*(np.log(10)*sigma)**2))
N_tot, int_norm = tot_num_src(redshift_evolution, cosmology, z_limits[-1],
density)
print "Total number of sources {:.0f} (All-Sky)".format(N_tot)
# Setup Arrays
logMu_array = np.linspace(logMu_range[0], logMu_range[1], N_Mu_bins)
Flux_from_fixed_z = []
zs = np.linspace(z_limits[0], z_limits[1], nzbins)
deltaz = (float(z_limits[1]) - float(z_limits[0])) / nzbins
Ls = np.linspace(np.log10(Lum_limits[0]), np.log10(Lum_limits[1]), nLbins)
deltaL = (np.log10(Lum_limits[1]) - np.log10(Lum_limits[0])) / nLbins
# Integration
t0 = time.time()
Count_array = np.zeros(N_Mu_bins)
muError = []
tot_bins = nLbins * nzbins
print('Starting Integration...Going to evaluate {} bins'.format(tot_bins))
N_sum = 0
Flux_from_fixed_z.append([])
print "-" * 20
# Loop over redshift bins
for z_count, z in enumerate(zs):
# Conversion Factor for given z
bz = calc_conversion_factor(z, gamma)
dlz = luminosity_distance(z, **cosmology)
tot_flux_from_z = 0.
# Loop over Luminosity bins
for l_count, lum in enumerate(Ls):
run_id = z_count * nLbins + l_count
if run_id % (tot_bins / 10) == 0.:
print "{}%".format(100 * run_id / tot_bins)
# Number of Sources in
dN = calc_dN(LF, lum, z, deltaL, deltaz, N_tot, int_norm,
cosmology)
N_sum += dN
#Flux to Source Strength
logmu = np.log10(flux_to_mu * erg_to_GeV * 10**lum / year2sec /
(4 * np.pi * (Mpc_to_cm * dlz)**2) * bz)
# Add dN to Histogram
if logmu < logMu_range[1] and logmu > logMu_range[0]:
tot_flux_from_z += dN * 10**logmu
idx = int((logmu - logMu_range[0]) * N_Mu_bins /
(logMu_range[1] - logMu_range[0]))
Count_array[idx] += dN
else:
muError.append(logmu)
Flux_from_fixed_z.append(tot_flux_from_z)
print "Number of Mu out of Range: {}".format(len(muError))
print "Num Sou {}".format(N_sum)
t1 = time.time()
print "-" * 20
print "\n Time needed for {}x{} bins: {}s".format(nzbins, nLbins,
int(t1 - t0))
return logMu_array, Count_array, zs, Flux_from_fixed_z
#print wavelength_micron_redshifted_cut[0:50]
#print flux_mjy_redshifted_cut[0:50]
#------Unit conversion------------(micron-->m-->Hz-->reverse; mJy-->Jy-->W*m-2*Hz-1-->reverse-->*4pi*luminosity_distance)
wavelength_sed_aless = []
flux_sed_aless = []
for m in range(0, len(wavelength_micron_redshifted_cut_aless)):
wavelength_sed_aless.append(
scipy.constants.c /
(wavelength_micron_redshifted_cut_aless[m] * 1e-6)) #now in Hz
#flux_sed.append(1e-26*flux_mjy_redshifted_cut[m]*1e-3*4.0*math.pi*pow(1e6*3.0857e16*cosmo.luminosity_distance(z_want[i]).value, 2.0)) #now in W/Hz
flux_sed_aless.append(
1e-26 * flux_mjy_redshifted_cut_aless[m] * 1e-3 * 4.0 * math.pi *
pow(
1e6 * 3.0857e16 * cd.luminosity_distance(
z_want_aless[i], **cosmo), 2.0)) #now in W/Hz
wavelength_sed_aless = wavelength_sed_aless[::
-1] #reverse so get increasing in Hz
flux_sed_aless = flux_sed_aless[::-1]
#print wavelength_sed[0:50]
#print flux_sed[0:50]
L_area_aless = np.trapz(flux_sed_aless, wavelength_sed_aless)
L_area_solar_aless = L_area_aless / 3.828e26 #in unit of solar luminosity
L_area_solar_aless = np.log10(
L_area_solar_aless) #make it log to plot in corner diagram
#print L_area_solar
def updatelumdist(self):
import cosmolopy.distance as cd
self.dL28 = 3.08567758e-4 * cd.luminosity_distance(self.z, **cosmology)
val = numpy.zeros(len(z))
for i in xrange(len(z)):
#Hubble Distance
dh = cd.hubble_distance_z(z[i], **Cosmology) * cd.e_z(z[i], **Cosmology)
#In David Hogg's (arXiv:astro-ph/9905116v4) formalism, this is equivalent to D_H / E(z) = c / (H_0 E(z)) [see his eq. 14], which
#appears in the definitions of many other distance measures.
dm = cd.comoving_distance_transverse(z[i], **Cosmology)
#See equation 16 of David Hogg's arXiv:astro-ph/9905116v4
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']
def getF(z,L): DL = luminosity_distance(z, **cosmo) # In MPc DL = DL * (10**6. * pc) * 10**2. # in cm return L / (4 * math.pi * DL**2.) # ergs/s/cm2
Lmax = Lstart * Lmult**(j+1)
# Compute comoving volume in Mpc-3
dV = ( comoving_volume(zmax,**cosmo) - comoving_volume(zmin,**cosmo) ) * (Tel_Area / 41253.) # Mpc3
# Get Phi in Mpc-3
alpha,logLStar,logPhiStar = LF_Data.retrieve_LF(z,line)
LStar,PhiStar = 10.**(logLStar),10.**(logPhiStar)
n = scipy.integrate.quad(lambda L: schechterL(L,PhiStar, alpha, LStar),Lmin, Lmax)[0]
# Number = number density * volume
N = n * dV
# Compute flux
DL = luminosity_distance(z, **cosmo) # In MPc
DL = DL * (10**6. * pc) * 10**2. # in cm
F = Lmin / (4 * math.pi * DL**2.) # ergs/s/cm2
Dist.append([zmin,zmax,math.log10(Lmin),math.log10(Lmax), int(N), F])
# Plot L vs. z
x = [elem[0] + 0.5 * (elem[1] - elem[0]) for elem in Dist] # av z
y = [elem[2] + 0.5 * (elem[3] - elem[2]) for elem in Dist] # av log10
z = [elem[4] for elem in Dist]
#DensityPlot(x,y,z)
# Make zLF distributions
total = np.sum(z)
_zLF = []
def int_ensamble(name, dir1, dir3, dir4, fo, fi=0, pdf=1, fits_f=0, m_t=""):
names = name.split("-")
dir3 = dir3 #+"/"+names[1]+"/"+names[2]
dir_map = dir3 + "/" + names[1] + "/" + names[2] + m_t
DIRS = dir3.split("/")
DRT = ""
for DR in DIRS:
DRT = DRT + DR + "/"
call = "mkdir -p " + DRT
sycall(call)
DIRS = dir_map.split("/")
DRT = ""
for DR in DIRS:
DRT = DRT + DR + "/"
call = "mkdir -p " + DRT
#sycall(call)
call = "mkdir -p " + dir3 + '/Plots'
sycall(call)
speed_of_light = 299792.458
#dir1=dir1+"/"+names[1]+"/stadi/"+names[2]+m_t
dir1 = dir1 + "/" + names[1] + "/" + names[2] + m_t
dir2 = dir1
file1 = dir1 + "/coeffs_auto_ssp." + name + ".int.out"
file2 = dir2 + "/auto_ssp." + name + ".int.out"
pdl_cube = []
pdl_cube_e = []
ages = []
cont = 0
f1 = open(file1, "r")
for line in f1:
if not "#" in line:
line = line.replace("\n", "")
data = line.split(" ")
data = filter(None, data)
if (cont % 4) == 0:
ages.extend([np.log10(float_(data[1])) + 9.0])
pdl_cube.extend([float_(data[3])])
pdl_cube_e.extend([float_(data[8])])
cont = cont + 1
f1.close()
f2 = open(file2, "r")
for line in f2:
if not "#" in line:
line = line.replace("\n", "")
data = line.split(",")
data = filter(None, data)
pdl_flux = float_(data[13])
pdl_flux_e = float_(data[14])
pdl_Av = float_(data[5])
pdl_Av_e = float_(data[6])
redshift = float_(data[7])
f2.close()
ages = np.array(ages)
ages = sorted(ages, reverse=True)
#np.sor
#sys.exit()
pdl_cube = np.array(pdl_cube)
pdl_cube_e = np.array(pdl_cube_e)
# f=open(dir4+"/BASE.gsd01","r")
f = open(dir4 + "/BASE.bc17_salp_Agelin_Metlin_330", "r")
yunk = f.readline()
age_t = []
met_t = []
cor_t = []
for line in f:
if not "#" in line:
data = line.split(" ")
data = filter(None, data)
age_t.extend([float_(data[1])])
met_t.extend([float_(data[2])])
cor_t.extend([float_(data[4])])
n_t = len(age_t)
age_t = np.array(age_t)
met_t = np.array(met_t)
cor_t = np.array(cor_t)
age_t = np.around(age_t / 1e9, decimals=4)
met_t = np.around(met_t, decimals=4)
f.close()
cosmo = {'omega_M_0': 0.27, 'omega_lambda_0': 0.73, 'h': 0.71}
cosmo = cd.set_omega_k_0(cosmo)
DL1 = cd.luminosity_distance(redshift, **cosmo)
#print DL, redshift
ratio = 3.08567758e24
modz = 5.0 * np.log10(DL1) + 25.0
DL = DL1 * ratio
DA = DL1 / (1 + redshift)**2.0 * 1e6 * np.pi / 180. / 3600.
L = 4.0 * np.pi * (DL**2.0) #/(1+$redshift);
Factor = (L * 1e-16) / 3.826e33
filed = file1
f2 = open(filed, "r")
n = 0
n_ini = 0
ML = np.zeros(156)
a_age = []
a_met = []
n_age = 0
n_met = 0
AGE = np.zeros(156)
MET = np.zeros(156)
COR = np.zeros(156)
for line in f2:
if n_ini < 156:
if not "#" in line:
data = line.split(" ")
data = filter(None, data)
n = int(data[0])
AGE[n] = float_(data[1])
MET[n] = float_(data[2])
ML[n] = float_(data[5])
diff_age = 1
for i in range(0, n):
if AGE[n] == AGE[i]:
diff_age = 0
if diff_age == 1:
a_age.extend([AGE[n]])
n_age = n_age + 1
diff_met = 1
for i in range(0, n):
if MET[n] == MET[i]:
diff_met = 0
if diff_met == 1:
a_met.extend([MET[n]])
n_met = n_met + 1
n_ini = n_ini + 1
for jt in range(0, n_t):
if age_t[jt] == 0.02:
age_t[jt] = 0.0199
if AGE[n] == age_t[jt]:
if MET[n] == met_t[jt]:
COR[n] = cor_t[jt]
f2.close()
n = n + 1
MassT = 0
LighT = 0
massN = 0
massN_e = 0
lightN = 0
lightN_e = 0
#mas1=0
#mas2=0
#mas3=0
#mas4=0
mass = np.zeros([n_age])
mass_e = np.zeros([n_age])
light = np.zeros([n_age])
light_e = np.zeros([n_age])
#ages=np.zeros([n_age])
sfrt = np.zeros([n_age])
sfdt = np.zeros([n_age])
nz = len(pdl_cube)
temp_a = 0
mass_age = 0
# pdl_cube[np.isnan(pdl_cube)]=1
#sys.exit()
#for i in range(nt-1, 155, -1):
# label=hdr['FILE_'+str(i)]
# time=label.replace('_NORM_age.fits.gz','')
# time=float_(time.replace('map.CS.'+name+'_',''))
# ages[38-i+156]=np.log10(time)+9
#print np.log10(time)+9, 38-i+156
#temp=pdl_cube[i,:,:]
#temp=temp*10.0**(ML[i-156])*pdl_flux*Factor
#temp_a=temp+temp_a
# MassT=MassT+np.sum(temp)
# mas1=np.sum(temp[n1])+mas1
# mas2=np.sum(temp[n2][n2a])+mas2
# mas3=np.sum(temp[n3][n3a])+mas3
# mas4=np.sum(temp[n4][n4a])+mas4
# mass[38-i+156,0]=np.log10(mas1)
# mass[38-i+156,1]=np.log10(mas2)
# mass[38-i+156,2]=np.log10(mas3)
# mass[38-i+156,3]=np.log10(mas4)
f2 = open(dir3 + "/" + name + m_t + "_Ensemble_int.csv", "w")
f2.write(
"# LOG_AGE N_MASSR_1 N_MASSR_2 N_MASSR_3 N_MASSR_4 LOG_MASSR_1 LOG_MASSR_2 LOG_MASSR_3 LOG_MASSR_4 \n"
)
fL2 = open(dir3 + "/" + name + m_t + "_Ensemble_int_L.csv", "w")
fL2.write(
"# LOG_AGE N_LIGHTR_1 N_LIGHTR_2 N_LIGHTR_3 N_LIGHTR_4 LOG_LIGHTR_1 LOG_LIGHTR_2 LOG_LIGHTR_3 LOG_LIGHTR_4 \n"
)
mass_age_t = np.zeros([n_age])
mass_age_t_2 = np.zeros([n_age])
mass_age_t_e = np.zeros([n_age])
light_age_t = np.zeros([n_age])
light_age_t_2 = np.zeros([n_age])
light_age_t_e = np.zeros([n_age])
for i in range(0, n_age):
age_now = a_age[i]
pdl_age = 0
pdl_age_2 = 0
pdl_age_e = 0
pdl_ageL = 0
pdl_age_2L = 0
pdl_age_eL = 0
for j in range(0, n):
if age_now == AGE[j]:
#if AGE[j] <= 2:
pdl_age = pdl_age + pdl_cube[j] * 10.0**(
ML[j]) * pdl_flux * Factor * 10.0**(0.4 * pdl_Av
) #*0.25/np.pi#/1.47
pdl_age_e = pdl_age_e + (
(pdl_cube_e[j] / pdl_cube[j])**2.0 +
(pdl_flux_e / pdl_flux)**2.0 +
(np.log(10.0) * 0.4 * pdl_Av_e)**2.0
) * (pdl_cube[j] * 10.0**(ML[j]) * pdl_flux * Factor * 10.0**
(0.4 * pdl_Av))**2.0
pdl_age_2 = pdl_age_2 + pdl_cube[j] * 10.0**(
ML[j]) * pdl_flux * Factor * 10.0**(0.4 * pdl_Av) / COR[j]
pdl_age_2L = pdl_age_2L + pdl_cube[
j] * pdl_flux * Factor * 10.0**(0.4 * pdl_Av) / COR[j]
pdl_ageL = pdl_ageL + pdl_cube[j] * pdl_flux * Factor * 10.0**(
0.4 * pdl_Av)
pdl_age_eL = pdl_age_eL + (
(pdl_cube_e[j] / pdl_cube[j])**2.0 +
(pdl_flux_e / pdl_flux)**2.0 +
(np.log(10.0) * 0.4 * pdl_Av_e)**2.0) * (
pdl_cube[j] * pdl_flux * Factor * 10.0**
(0.4 * pdl_Av))**2.0
#print age_now
if np.isfinite(pdl_age) == False:
pdl_age = 0
if np.isfinite(pdl_age_2) == False:
pdl_age_2 = 0
if np.isfinite(pdl_age_e) == False:
pdl_age_e = 0
if np.isfinite(pdl_ageL) == False:
pdl_ageL = 0
if np.isfinite(pdl_age_2L) == False:
pdl_age_2L = 0
if np.isfinite(pdl_age_eL) == False:
pdl_age_eL = 0
# pdl_age_e[np.isnan(pdl_age_e)]=0
for k in range(0, n_age):
if np.log10(age_now) + 9 == ages[k]:
#print ages[k],np.log10(age_now)+9,age_now
mass_age_t[k] = pdl_age
mass_age_t_2[k] = pdl_age_2
mass_age_t_e[k] = pdl_age_e
light_age_t[k] = pdl_ageL
light_age_t_2[k] = pdl_age_2L
light_age_t_e[k] = pdl_age_eL
#print mass_age_t
mass_temp_total = 0
light_temp_total = 0
#sys.exit()
for i in range(0, n_age):
if i == 0:
age_s = 10.0**((ages[i] + ages[i + 1]) / 2.0)
age_i = 0.0
elif i == n_age - 1:
age_i = 10.0**((ages[i] + ages[i - 1]) / 2.0)
age_s = 2.0 * 10.0**(ages[i]) - age_i
else:
age_i = 10.0**((ages[i] + ages[i - 1]) / 2.0)
age_s = 10.0**((ages[i] + ages[i + 1]) / 2.0)
Dt_age = np.abs(age_s - age_i)
sfdt[i] = Dt_age / 1e6
temp = mass_age_t[i]
temp_2 = mass_age_t_2[i]
temp_e = mass_age_t_e[i]
tempL = light_age_t[i]
temp_2L = light_age_t_2[i]
temp_eL = light_age_t_e[i]
#temp[np.where(np.isfinite(temp) == False)]=0
#temp_e[np.where(np.isfinite(temp_e) == False)]=1
#temp_2[np.where(np.isfinite(temp_2) == False)]=0
if i == 0:
if fits_f == 1:
MASS_map_cube = np.zeros([n_age])
MGH_map_cube = np.zeros([n_age])
SFH_map_cube = np.zeros([n_age])
LIGHT_map_cube = np.zeros([n_age])
LGH_map_cube = np.zeros([n_age])
temp1 = temp
temp1L = tempL
else:
temp1 = temp1 + temp
temp1L = temp1L + tempL
if fits_f == 1:
MASS_map_cube[i] = temp
MGH_map_cube[i] = temp1
SFH_map_cube[i] = temp_2 / Dt_age
LIGHT_map_cube[i] = tempL
LGH_map_cube[i] = temp1L
#if pdf==1:
#map_plot(temp1,ages[i],pdl_rad,dir=dir_map+"/",pdf=1,title=name,form='pdf',fname=name+'_smap_'+str(i),minval=lovalue,maxval=upvalue)
MassT = MassT + np.sum(temp)
LighT = LighT + np.sum(tempL)
# print temp[ind[ii]]
# print len(temp[ind[ii]]),np.amax(inda[ii])
Dt_mass = temp
Dt_mass_e = temp_e
Dt_mass_2 = temp_2
Dt_light = tempL
Dt_light_e = temp_eL
Dt_light_2 = temp_2L
massN = Dt_mass + massN
massN_e = Dt_mass_e + massN_e
lightN = Dt_light + lightN
lightN_e = Dt_light_e + lightN_e
#mas2=np.sum(temp[n2][n2a])+mas2
#mas3=np.sum(temp[n3][n3a])+mas3
#mas4=np.sum(temp[n4][n4a])+mas4
# print temp[ind[ii]][inda[ii]]
mass[i] = np.log10(massN)
mass_e[i] = massN_e
light[i] = np.log10(lightN)
light_e[i] = lightN_e
sfrt[
i] = Dt_mass_2 / Dt_age #/massN[ii]#/(np.pi*(rx[ii+1]**2.0-rx[ii]**2.0)*rad**2.0)#/(float_(len(temp[ind[ii]][inda[ii]]))*(0.5*DA)**2.0)
#mass[i,1]=np.log10(mas2)
#mass[i,2]=np.log10(mas3)
#mass[i,3]=np.log10(mas4)
# print mass[i],massN
mass_temp_total = np.log10(np.sum(10**mass[n_age - 1]))
light_temp_total = np.log10(np.sum(10**light[n_age - 1]))
MassT = np.log10(MassT)
MassT = mass_temp_total
LighT = np.log10(LighT)
LighT = light_temp_total
if fits_f == 1:
#if ptt.exists() == True:
h1 = pyf.PrimaryHDU(MASS_map_cube) #.header
h2 = pyf.PrimaryHDU(SFH_map_cube) #.header
h3 = pyf.PrimaryHDU(MGH_map_cube)
h4 = pyf.PrimaryHDU(LIGHT_map_cube)
h5 = pyf.PrimaryHDU(LGH_map_cube)
h = h1.header
h["NAXIS"] = 2
h["NAXIS1"] = n_age
h["NAXIS2"] = 1
hlist = pyf.HDUList([h1])
hlist.update_extend()
wfits_ext(dir_map + "/" + "MASS_maps_" + name + ".fits", hlist)
#if ptt.exists() == True:
h = h2.header
h["NAXIS"] = 2
h["NAXIS1"] = n_age
h["NAXIS2"] = 1
hlist = pyf.HDUList([h2])
hlist.update_extend()
wfits_ext(dir_map + "/" + "SFH_maps_" + name + ".fits", hlist)
#if ptt.exists() == True:
h = h3.header
h["NAXIS"] = 2
h["NAXIS1"] = n_age
h["NAXIS2"] = 1
hlist = pyf.HDUList([h3])
hlist.update_extend()
wfits_ext(dir_map + "/" + "MGH_maps_" + name + ".fits", hlist)
h = h4.header
h["NAXIS"] = 2
h["NAXIS1"] = n_age
h["NAXIS2"] = 1
hlist = pyf.HDUList([h4])
hlist.update_extend()
wfits_ext(dir_map + "/" + "LIGHT_maps_" + name + ".fits", hlist)
h = h5.header
h["NAXIS"] = 2
h["NAXIS1"] = n_age
h["NAXIS2"] = 1
hlist = pyf.HDUList([h5])
hlist.update_extend()
wfits_ext(dir_map + "/" + "LGH_maps_" + name + ".fits", hlist)
#print MassT,name
#print Ha,(L*1e-16)
#sys.exit(0)
mass_n = 10**(10**(mass - mass[n_age - 1]))
mass_n_e = np.sqrt((10**(mass - mass[n_age - 1]))**2.0 *
((mass_e / 10**(2.0 * mass)) +
(mass_e[n_age - 1] / 10**(2.0 * mass[n_age - 1]))))
light_n = 10**(10**(light - light[n_age - 1]))
light_n_e = np.sqrt((10**(light - light[n_age - 1]))**2.0 *
((light_e / 10**(2.0 * light)) +
(light_e[n_age - 1] / 10**(2.0 * light[n_age - 1]))))
#print mass_n
#mass_n=10**(mass-mass[nt-156-1,:])
#mass_n=(mass-mass[nt-156-1,:])
for i in range(0, n_age):
#print ages[i],a_age[i],"test_ages"
line = ''
line = line + str(ages[i])
line = line + ';' + str(mass_n[i])
line = line + ';' + str(mass[i])
line = line + ';' + str(sfrt[i])
line = line + ';' + str(mass_n_e[i])
line = line + ';' + str(sfdt[i])
#print line
line = line + ' \n'
f2.write(line)
lineL = ''
lineL = lineL + str(ages[i])
lineL = lineL + ';' + str(light_n[i])
lineL = lineL + ';' + str(light[i])
lineL = lineL + ';' + str(sfrt[i])
lineL = lineL + ';' + str(light_n_e[i])
lineL = lineL + ';' + str(sfdt[i])
lineL = lineL + ' \n'
fL2.write(lineL)
# print mass_n.shape,ages.shape
if not pdf == 0:
dev = dir3 + '/Plots/' + name + m_t + "_int_Relative_Mass2.pdf"
#if pdf == 1:
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(6, 5.5))
ax.set_xlabel("$log_{10}(time/yr)$", fontsize=14)
ax.set_ylabel("$M(t)/M_{0}$", fontsize=14)
#MassT=10.32
ax.set_title(name + ' $\log M_{tot}=' + ('%7.2f' % MassT) + '$',
fontsize=15)
ax.set_xlim(8.6, 10.1)
#ax.set_ylim(0,12)
ax.set_ylim(func_plot(np.log10(1.78), ftype=1),
func_plot(np.log10(12), ftype=1))
plt.plot(
ages, func_plot(np.log10(mass_n), ftype=1)
) #,label='$'+('%6.1f' % rx[ii])+'R_e<R<'+('%6.1f' % rx[ii+1])+'R_e$'
plt.legend(loc=3)
plt.plot(np.arange(0, 20, .1),
np.ones(200) * func_plot(0.95, ftype=1),
'--',
color='black')
plt.plot(np.arange(0, 20, .1),
np.ones(200) * func_plot(0.50, ftype=1),
'--',
color='green')
fig.canvas.draw()
labels = [item.get_text() for item in ax.get_yticklabels()]
for i in range(0, len(labels)):
labels[i] = labels[i].replace(u'\u2212', '-')
for i in range(0, len(labels)):
if labels[i] != u'':
if float_(labels[i]) == 0:
labels[i] = u'%3.2f' % 10**(0)
else:
labels[i] = u'%3.2f' % 10**(float_(labels[i]))
ax.set_yticklabels(labels)
if pdf == 1:
fig.tight_layout()
plt.savefig(dev) #,dpi = 1000)
else:
plt.show()
plt.close()
f2.close()
fL2.close()
fo.write(name + " " + str(MassT) + " " + str(redshift) + " \n")
def ensamble(name,
dir1,
dir3,
dir4,
fo,
fi=0,
fii=0,
pdf=1,
rx=[0, 0.5, 1.0, 1.5],
fits_f=0):
rad = 1.0
names = name.split("-")
dir3 = dir3 #+"/"+names[1]+"/"+names[2]
dir_map = dir3 + "/" + names[1] + "/" + names[2]
DIRS = dir3.split("/")
DRT = ""
for DR in DIRS:
DRT = DRT + DR + "/"
call = "mkdir -p " + DRT
sycall(call)
DIRS = dir_map.split("/")
DRT = ""
for DR in DIRS:
DRT = DRT + DR + "/"
call = "mkdir -p " + DRT
sycall(call)
speed_of_light = 299792.458
dir1 = dir1 + "/" + names[1] + "/" + names[2]
# dir2=dir2+"/"+names[1]+"-"+names[2]
file = dir1 + "/" + name + ".SFH.cube.fits.gz"
file2 = dir1 + "/" + name + ".p_e.pdl_r.fits"
#file2=dir2+"/"+name+".photo.r_Lc_rad.fits"
#file3=dir1+"/"+'mask.'+name+'.V.fits.gz'
file3 = dir1 + "/" + 'DMASK.' + name + '.fits.gz'
[pdl_cube, hdr] = gdata(file, 0, header=True)
[pdl_rad, hdr2] = gdata(file2, 0, header=True)
[pdl_mask, hdr3] = gdata(file3, 0, header=True)
pdl_mask = 1.0 - pdl_mask
if np.sum(pdl_mask) == 0:
pdl_mask[:, :] = 1.0
ind = []
inda = []
nr = len(rx)
for ii in range(0, nr - 1):
nt = np.where(pdl_rad < rx[ii + 1] * rad)
nta = np.where(pdl_rad[nt] >= rx[ii] * rad)
ind.extend([nt])
inda.extend([nta])
# n2=np.where(pdl_rad< r2*rad)
# n2a=np.where(pdl_rad[n2]>= r1*rad)
# n3=np.where(pdl_rad< r3*rad)
# n3a=np.where(pdl_rad[n3]>= r2*rad)
# n4=np.where(pdl_rad< r4*rad)
# n4a=np.where(pdl_rad[n4]>= r3*rad)
SN_file = "norm_SN_" + name + ".CS.fits.gz"
if ptt.exists(dir1 + "/" + SN_file) == True:
[pdl_SN, hdr000] = gdata(dir1 + "/" + SN_file, 0, header=True)
Ha_file = "map.CS." + name + "_flux_6562.fits.gz"
if ptt.exists(dir1 + "/" + Ha_file) == True:
[pdl_ha, hdr001] = gdata(dir1 + "/" + Ha_file, 0, header=True)
pdl_ha = pdl_ha * pdl_mask #[0,:,:]#-z_r*speed_of_light
pdl_ha[np.isnan(pdl_ha)] = 0
Av_file = "map.CS." + name + "_Av_ssp.fits.gz"
[pdl_Av, hdr002] = gdata(dir1 + "/" + Av_file, 0, header=True)
Av_file_e = "map.CS." + name + "_e_Av_ssp.fits.gz"
if ptt.exists(dir1 + "/" + Av_file_e) == True:
[pdl_Av_e, hdr002e] = gdata(dir1 + "/" + Av_file_e, 0, header=True)
pdl_Av_e[np.isnan(pdl_Av_e)] = 0
else:
pdl_Av_e = np.zeros(Av_file.shape)
nt = hdr['NAXIS3'] - 5 #5#4#n_met
flux_file = "map.CS." + name + "_flux_ssp.fits.gz"
[pdl_flux, hdr0] = gdata(dir1 + "/" + flux_file, 0, header=True)
flux_file_e = "map.CS." + name + "_e_flux_ssp.fits.gz"
if ptt.exists(dir1 + "/" + flux_file_e) == True:
[pdl_flux_e, hdr0e] = gdata(dir1 + "/" + flux_file_e, 0, header=True)
pdl_flux_e[np.isnan(pdl_flux_e)] = 0
else:
pdl_flux_e = np.zeros(pdl_flux.shape)
mass_file = "map.CS." + name + "_Mass_dust_cor_ssp.fits.gz" #dust_cor_
[pdl_mass, hdr00] = gdata(dir1 + "/" + mass_file, 0, header=True)
pdl_mass[np.isnan(pdl_mass)] = 1
MassT2 = np.log10(np.sum(10.0**pdl_mass))
#print MassT2, name
f = open(dir4 + "/BASE.gsd01", "r")
#f=open(dir4+"/BASE.bc17_salp_Agelin_Metlin_330","r")
yunk = f.readline()
age_t = []
met_t = []
cor_t = []
for line in f:
if not "#" in line:
data = line.split(" ")
data = filter(None, data)
age_t.extend([float_(data[1])])
met_t.extend([float_(data[2])])
cor_t.extend([float_(data[4])])
n_t = len(age_t)
age_t = np.array(age_t)
met_t = np.array(met_t)
cor_t = np.array(cor_t)
age_t = np.around(age_t / 1e9, decimals=4)
met_t = np.around(met_t, decimals=4)
f.close()
a_redshift = []
filet = "auto_ssp.CS." + name + ".rss.out"
f = open(dir1 + "/" + filet, "r")
for line in f:
if not "#" in line:
data = line.split(",")
data = filter(None, data)
#print data
a_redshift.extend([float_(data[7])])
f.close()
a_redshift = np.array(a_redshift)
redshift = np.median(a_redshift)
cosmo = {'omega_M_0': 0.27, 'omega_lambda_0': 0.73, 'h': 0.71}
cosmo = cd.set_omega_k_0(cosmo)
DL1 = cd.luminosity_distance(redshift, **cosmo)
#print DL, redshift
ratio = 3.08567758e24
modz = 5.0 * np.log10(DL1) + 25.0
DL = DL1 * ratio
DA = DL1 / (1 + redshift)**2.0 * 1e6 * np.pi / 180. / 3600.
L = 4.0 * np.pi * (DL**2.0) #/(1+$redshift);
Factor = (L * 1e-16) / 3.826e33
filed = "coeffs_auto_ssp.CS." + name + ".rss.out"
f2 = open(dir1 + "/" + filed, "r")
n = 0
n_ini = 0
n_ssp = 156 #330
ML = np.zeros(n_ssp)
a_age = []
a_met = []
n_age = 0
n_met = 0
AGE = np.zeros(n_ssp)
MET = np.zeros(n_ssp)
COR = np.zeros(n_ssp)
for line in f2:
if n_ini < n_ssp:
if not "#" in line:
data = line.split(" ")
data = filter(None, data)
n = int(data[0])
AGE[n] = float_(data[1])
MET[n] = float_(data[2])
ML[n] = float_(data[5])
diff_age = 1
for i in range(0, n):
if AGE[n] == AGE[i]:
diff_age = 0
if diff_age == 1:
a_age.extend([AGE[n]])
n_age = n_age + 1
diff_met = 1
for i in range(0, n):
if MET[n] == MET[i]:
diff_met = 0
if diff_met == 1:
a_met.extend([MET[n]])
n_met = n_met + 1
n_ini = n_ini + 1
for jt in range(0, n_t):
if age_t[jt] == 0.02:
age_t[jt] = 0.0199
if AGE[n] == age_t[jt]:
if MET[n] == met_t[jt]:
COR[n] = cor_t[jt]
f2.close()
n = n + 1
MassT = 0
LighT = 0
massN = np.zeros(nr)
massN_e = np.zeros(nr)
lightN = np.zeros(nr)
lightN_e = np.zeros(nr)
#mas1=0
#mas2=0
#mas3=0
#mas4=0
mass = np.zeros([n_age, nr])
mass_e = np.zeros([n_age, nr])
light = np.zeros([n_age, nr])
light_e = np.zeros([n_age, nr])
ages = np.zeros([n_age])
sfrt = np.zeros([n_age, nr])
sfdt = np.zeros([n_age])
[nz, nx, ny] = pdl_cube.shape
temp_a = np.zeros([nx, ny])
mass_age = np.zeros([nx, ny])
pdl_cube[np.isnan(pdl_cube)] = 1
pdl_cube_e = np.zeros([n, nx, ny])
#print name
for i in range(0, n):
norm_file = dir1 + "/" + "map.CS." + name + "_eNORM_" + str(
i) + "_ssp.fits.gz"
if ptt.exists(norm_file) == True:
pdl_cube_e[i, :, :] = gdata(norm_file)
else:
pdl_cube_e[i, :, :] = np.zeros([nx, ny])
pdl_cube_e[np.isnan(pdl_cube_e)] = 0
#print AGE
#sys.exit()
for i in range(nt - 1, n_ssp - 1, -1):
label = hdr['FILE_' + str(i)]
# print label,n_age,n_met,n_age-i+n_ssp-1,-i+n_ssp,i
time = label.replace('_NORM_age.fits.gz', '')
time = float_(time.replace('map.CS.' + name + '_', ''))
ages[n_age - i + n_ssp - 1] = np.log10(time) + 9
#print np.log10(time)+9, 38-i+156
#temp=pdl_cube[i,:,:]
#temp=temp*10.0**(ML[i-156])*pdl_flux*Factor
#temp_a=temp+temp_a
# MassT=MassT+np.sum(temp)
# mas1=np.sum(temp[n1])+mas1
# mas2=np.sum(temp[n2][n2a])+mas2
# mas3=np.sum(temp[n3][n3a])+mas3
# mas4=np.sum(temp[n4][n4a])+mas4
# mass[38-i+156,0]=np.log10(mas1)
# mass[38-i+156,1]=np.log10(mas2)
# mass[38-i+156,2]=np.log10(mas3)
# mass[38-i+156,3]=np.log10(mas4)
f2 = open(dir3 + "/" + name + "_Ensemble.csv", "w")
f2.write(
"# LOG_AGE N_MASSR_1 N_MASSR_2 N_MASSR_3 N_MASSR_4 LOG_MASSR_1 LOG_MASSR_2 LOG_MASSR_3 LOG_MASSR_4 \n"
)
fL2 = open(dir3 + "/" + name + "_Ensemble_L.csv", "w")
fL2.write(
"# LOG_AGE N_LIGHTR_1 N_LIGHTR_2 N_LIGHTR_3 N_LIGHTR_4 LOG_LIGHTR_1 LOG_LIGHTR_2 LOG_LIGHTR_3 LOG_LIGHTR_4 \n"
)
mass_age_t = np.zeros([n_age, nx, ny])
mass_age_t_2 = np.zeros([n_age, nx, ny])
mass_age_t_e = np.zeros([n_age, nx, ny])
light_age_t = np.zeros([n_age, nx, ny])
light_age_t_2 = np.zeros([n_age, nx, ny])
light_age_t_e = np.zeros([n_age, nx, ny])
for i in range(0, n_age):
age_now = a_age[i]
pdl_age = np.zeros([nx, ny])
pdl_age_2 = np.zeros([nx, ny])
pdl_age_e = np.zeros([nx, ny])
pdl_ageL = np.zeros([nx, ny])
pdl_age_2L = np.zeros([nx, ny])
pdl_age_eL = np.zeros([nx, ny])
for j in range(0, n):
if age_now == AGE[j]:
#if AGE[j] <= 2:
pdl_age = pdl_age + pdl_cube[j, :, :] * 10.0**(
ML[j]) * pdl_flux * Factor * 10.0**(
0.4 * pdl_Av) * pdl_mask #*0.25/np.pi#/1.47
pdl_age_e = pdl_age_e + (
(pdl_cube_e[j, :, :] / pdl_cube[j, :, :])**2.0 +
(pdl_flux_e / pdl_flux)**2.0 +
(np.log(10.0) * 0.4 * pdl_Av_e)**2.0) * (
pdl_cube[j, :, :] * 10.0**(ML[j]) * pdl_flux * Factor *
pdl_mask * 10.0**(0.4 * pdl_Av))**2.0
pdl_age_2 = pdl_age_2 + pdl_cube[j, :, :] * 10.0**(
ML[j]) * pdl_flux * Factor * pdl_mask * 10.0**(
0.4 * pdl_Av) / COR[j]
pdl_age_2L = pdl_age_2L + pdl_cube[
j, :, :] * pdl_flux * Factor * pdl_mask * 10.0**(
0.4 * pdl_Av) / COR[j]
pdl_ageL = pdl_ageL + pdl_cube[
j, :, :] * pdl_flux * Factor * 10.0**(0.4 *
pdl_Av) * pdl_mask
pdl_age_eL = pdl_age_eL + (
(pdl_cube_e[j, :, :] / pdl_cube[j, :, :])**2.0 +
(pdl_flux_e / pdl_flux)**2.0 +
(np.log(10.0) * 0.4 * pdl_Av_e)**2.0
) * (pdl_cube[j, :, :] * pdl_flux * Factor * pdl_mask * 10.0**
(0.4 * pdl_Av))**2.0
#else:
# pdl_age=pdl_age+pdl_cube[j,:,:]*10.0**(ML[j])*pdl_flux*Factor*pdl_mask*COR[j]
pdl_age[np.where(np.isfinite(pdl_age) == False)] = 0
pdl_age_2[np.where(np.isfinite(pdl_age_2) == False)] = 0
pdl_age_e[np.where(np.isfinite(pdl_age_e) == False)] = 0
pdl_ageL[np.where(np.isfinite(pdl_ageL) == False)] = 0
pdl_age_2L[np.where(np.isfinite(pdl_age_2L) == False)] = 0
pdl_age_eL[np.where(np.isfinite(pdl_age_eL) == False)] = 0
#pdl_age_e[np.isnan(pdl_age_e)]=0
for k in range(0, n_age):
if np.log10(age_now) + 9 == ages[k]:
mass_age_t[k, :, :] = pdl_age
mass_age_t_2[k, :, :] = pdl_age_2
mass_age_t_e[k, :, :] = pdl_age_e
light_age_t[k, :, :] = pdl_ageL
light_age_t_2[k, :, :] = pdl_age_2L
light_age_t_e[k, :, :] = pdl_age_eL
temp5 = np.sum(mass_age_t, axis=0) + 0.01
# temp6=np.log10(np.sum(mass_age_t,axis=0)+1.0)#QUITAR
# wfits(dir3+"/"+name+"mass_tot.fits",temp6,hdr001)#QUITAR
upvalue = math.ceil(np.log10(np.amax(temp5)) / .05) * .05
if np.isinf(upvalue):
upvalue = 8
if upvalue - 1 <= 6.5:
lovalue = math.ceil(np.log10(np.amin(temp5)) / .05) * .05
if upvalue - 2 > lovalue:
lovalue = upvalue - 2
else:
lovalue = 6.5
mass_temp_total = 0
light_temp_total = 0
for i in range(0, n_age):
if i == 0:
age_s = 10.0**((ages[i] + ages[i + 1]) / 2.0)
age_i = 0.0
elif i == n_age - 1:
age_i = 10.0**((ages[i] + ages[i - 1]) / 2.0)
age_s = 2.0 * 10.0**(ages[i]) - age_i
else:
age_i = 10.0**((ages[i] + ages[i - 1]) / 2.0)
age_s = 10.0**((ages[i] + ages[i + 1]) / 2.0)
Dt_age = np.abs(age_s - age_i)
sfdt[i] = Dt_age / 1e6
temp = mass_age_t[i, :, :]
temp_2 = mass_age_t_2[i, :, :]
temp_e = mass_age_t_e[i, :, :]
tempL = light_age_t[i, :, :]
temp_2L = light_age_t_2[i, :, :]
temp_eL = light_age_t_e[i, :, :]
#temp[np.where(np.isfinite(temp) == False)]=0
#temp_e[np.where(np.isfinite(temp_e) == False)]=1
#temp_2[np.where(np.isfinite(temp_2) == False)]=0
if i == 0:
if fits_f == 1:
[nx, ny] = temp.shape
MASS_map_cube = np.zeros([n_age, nx, ny])
MGH_map_cube = np.zeros([n_age, nx, ny])
SFH_map_cube = np.zeros([n_age, nx, ny])
LIGHT_map_cube = np.zeros([n_age, nx, ny])
LGH_map_cube = np.zeros([n_age, nx, ny])
temp1 = temp
temp1L = tempL
else:
temp1 = temp1 + temp
temp1L = temp1L + tempL
if fits_f == 1:
MASS_map_cube[i, :, :] = temp
MGH_map_cube[i, :, :] = temp1
SFH_map_cube[i, :, :] = temp_2 / Dt_age
LIGHT_map_cube[i, :, :] = tempL
LGH_map_cube[i, :, :] = temp1L
#if pdf==1:
#map_plot(temp1,ages[i],pdl_rad,dir=dir_map+"/",pdf=1,title=name,form='pdf',fname=name+'_smap_'+str(i),minval=lovalue,maxval=upvalue)
MassT = MassT + np.sum(temp)
LighT = LighT + np.sum(tempL)
for ii in range(0, nr - 1):
# print ind[ii],inda[ii],ii
# print temp[ind[ii]]
# print len(temp[ind[ii]]),np.amax(inda[ii])
Dt_mass = np.sum(temp[ind[ii]][inda[ii]])
Dt_mass_e = np.sum(temp_e[ind[ii]][inda[ii]])
Dt_mass_2 = np.sum(temp_2[ind[ii]][inda[ii]])
Dt_light = np.sum(tempL[ind[ii]][inda[ii]])
Dt_light_e = np.sum(temp_eL[ind[ii]][inda[ii]])
Dt_light_2 = np.sum(temp_2L[ind[ii]][inda[ii]])
massN[ii] = Dt_mass + massN[ii]
massN_e[ii] = Dt_mass_e + massN_e[ii]
lightN[ii] = Dt_light + lightN[ii]
lightN_e[ii] = Dt_light_e + lightN_e[ii]
#mas2=np.sum(temp[n2][n2a])+mas2
#mas3=np.sum(temp[n3][n3a])+mas3
#mas4=np.sum(temp[n4][n4a])+mas4
# print temp[ind[ii]][inda[ii]]
mass[i, ii] = np.log10(massN[ii])
mass_e[i, ii] = massN_e[ii]
light[i, ii] = np.log10(lightN[ii])
light_e[i, ii] = lightN_e[ii]
sfrt[
i,
ii] = Dt_mass_2 / Dt_age #/massN[ii]#/(np.pi*(rx[ii+1]**2.0-rx[ii]**2.0)*rad**2.0)#/(float_(len(temp[ind[ii]][inda[ii]]))*(0.5*DA)**2.0)
#mass[i,1]=np.log10(mas2)
#mass[i,2]=np.log10(mas3)
#mass[i,3]=np.log10(mas4)
mass_temp_total = np.log10(np.sum(10**mass[nt - n_ssp - 1, :]))
light_temp_total = np.log10(np.sum(10**light[nt - n_ssp - 1, :]))
MassT = np.log10(MassT)
MassT = mass_temp_total
LighT = np.log10(LighT)
LighT = light_temp_total
if fits_f == 1:
#if ptt.exists() == True:
h1 = pyf.PrimaryHDU(MASS_map_cube) #.header
h2 = pyf.PrimaryHDU(SFH_map_cube) #.header
h3 = pyf.PrimaryHDU(MGH_map_cube)
h4 = pyf.PrimaryHDU(LIGHT_map_cube)
h5 = pyf.PrimaryHDU(LGH_map_cube)
h = h1.header
h["NAXIS"] = 3
h["NAXIS3"] = n_age
h["NAXIS1"] = nx
h["NAXIS2"] = ny
hlist = pyf.HDUList([h1])
hlist.update_extend()
wfits_ext(dir_map + "/" + "MASS_maps_" + name + ".fits", hlist)
#if ptt.exists() == True:
h = h2.header
h["NAXIS"] = 3
h["NAXIS3"] = n_age
h["NAXIS1"] = nx
h["NAXIS2"] = ny
hlist = pyf.HDUList([h2])
hlist.update_extend()
wfits_ext(dir_map + "/" + "SFH_maps_" + name + ".fits", hlist)
#if ptt.exists() == True:
h = h3.header
h["NAXIS"] = 3
h["NAXIS3"] = n_age
h["NAXIS1"] = nx
h["NAXIS2"] = ny
hlist = pyf.HDUList([h3])
hlist.update_extend()
wfits_ext(dir_map + "/" + "MGH_maps_" + name + ".fits", hlist)
h = h4.header
h["NAXIS"] = 3
h["NAXIS3"] = n_age
h["NAXIS1"] = nx
h["NAXIS2"] = ny
hlist = pyf.HDUList([h4])
hlist.update_extend()
wfits_ext(dir_map + "/" + "LIGHT_maps_" + name + ".fits", hlist)
h = h5.header
h["NAXIS"] = 3
h["NAXIS3"] = n_age
h["NAXIS1"] = nx
h["NAXIS2"] = ny
hlist = pyf.HDUList([h5])
hlist.update_extend()
wfits_ext(dir_map + "/" + "LGH_maps_" + name + ".fits", hlist)
#print MassT,name
if ptt.exists(dir1 + "/" + SN_file) == True:
SN = np.zeros(nr - 1)
sn_l = ''
for ii in range(0, nr - 1):
SN[ii] = np.average(pdl_SN[ind[ii]][inda[ii]])
sn_l = sn_l + ' , ' + str(SN[ii])
if fi != 0:
fi.write(name + sn_l + ' \n')
if ptt.exists(dir1 + "/" + Ha_file) == True:
Ha = np.zeros(nr - 1)
ha_l = ''
for ii in range(0, nr - 1):
Ha[ii] = np.sum(pdl_ha[ind[ii]][inda[ii]] * 10.0**
(0.4 * pdl_Av[ind[ii]][inda[ii]])) * (L * 1e-16)
ha_l = ha_l + ' , ' + str(Ha[ii])
if fii != 0:
fii.write(name + ha_l + ' \n')
else:
ha_l = ''
for ii in range(0, nr - 1):
ha_l = ha_l + ' , ' + str(-100)
if fii != 0:
fii.write(name + ha_l + ' \n')
#print Ha,(L*1e-16)
#sys.exit(0)
mass_n = 10**(10**(mass - mass[nt - n_ssp - 1, :]))
mass_n_e = np.sqrt(
(10**(mass - mass[nt - n_ssp - 1, :]))**2.0 *
((mass_e / 10**(2.0 * mass)) +
(mass_e[nt - n_ssp - 1, :] / 10**(2.0 * mass[nt - n_ssp - 1, :]))))
light_n = 10**(10**(light - light[nt - n_ssp - 1, :]))
light_n_e = np.sqrt(
(10**(light - light[nt - n_ssp - 1, :]))**2.0 *
((light_e / 10**(2.0 * light)) +
(light_e[nt - n_ssp - 1, :] / 10**(2.0 * light[nt - n_ssp - 1, :]))))
#mass_n=10**(mass-mass[nt-156-1,:])
#mass_n=(mass-mass[nt-156-1,:])
for i in range(0, n_age):
#print ages[i],a_age[i],"test_ages"
line = ''
line = line + str(ages[i])
for ii in range(0, nr - 1):
line = line + ';' + str(mass_n[i, ii])
for ii in range(0, nr - 1):
line = line + ';' + str(mass[i, ii])
for ii in range(0, nr - 1):
line = line + ';' + str(sfrt[i, ii])
for ii in range(0, nr - 1):
line = line + ';' + str(mass_n_e[i, ii])
line = line + ';' + str(sfdt[i])
line = line + ' \n'
f2.write(line)
lineL = ''
lineL = lineL + str(ages[i])
for ii in range(0, nr - 1):
lineL = lineL + ';' + str(light_n[i, ii])
for ii in range(0, nr - 1):
lineL = lineL + ';' + str(light[i, ii])
for ii in range(0, nr - 1):
lineL = lineL + ';' + str(sfrt[i, ii])
for ii in range(0, nr - 1):
lineL = lineL + ';' + str(light_n_e[i, ii])
lineL = lineL + ';' + str(sfdt[i])
lineL = lineL + ' \n'
fL2.write(lineL)
#if not pdf == 0:
#dev=dir3+"/"+name+"_Relative_Mass.pdf"
##if pdf == 1:
# #matplotlib.use('Agg')
#import matplotlib.pyplot as plt
##plt.axis([8, 10.5, 0, 10])
#plt.xlabel("$log_{10}(time/yr)$",fontsize=14)
#plt.ylabel("$10^{M(t)/M_{0}}$",fontsize=14)
#plt.title(name+' $\log M_{tot}='+('%7.2f' % MassT)+'$',fontsize=15)
##plt.semilogx('log')
#for ii in range(0, nr-1):
# plt.plot(ages,mass_n[:,ii],label='$'+('%6.1f' % rx[ii])+'R_e<R<'+('%6.1f' % rx[ii+1])+'R_e$')
##plt.plot(ages,mass_n[:,1],label='$'+('%6.1f' % r1)+'<R<'+('%6.1f' % r2)+'R_e$')
##plt.plot(ages,mass_n[:,2],label='$'+('%6.1f' % r2)+'<R<'+('%6.1f' % r3)+'R_e$')
##plt.plot(ages,mass_n[:,3],label='$'+('%6.1f' % r3)+'<R<'+('%6.1f' % r4)+'R_e$')
#plt.legend(loc=3)
#if pdf == 1:
# plt.savefig(dev,dpi = 1000)
#else:
# plt.show()
#plt.close()
if not pdf == 0:
dev = dir3 + '/' + name + "_Relative_Mass2.pdf"
#if pdf == 1:
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(6, 5.5))
ax.set_xlabel("$log_{10}(time/yr)$", fontsize=14)
ax.set_ylabel("$M(t)/M_{0}$", fontsize=14)
#MassT=10.32
ax.set_title(name + ' $\log M_{tot}=' + ('%7.2f' % MassT) + '$',
fontsize=15)
ax.set_xlim(8.6, 10.1)
#ax.set_ylim(0,12)
ax.set_ylim(func_plot(np.log10(1.78), ftype=1),
func_plot(np.log10(12), ftype=1))
for ii in range(0, nr - 1):
plt.plot(ages,
func_plot(np.log10(mass_n[:, ii]), ftype=1),
label='$' + ('%6.1f' % rx[ii]) + 'R_e<R<' +
('%6.1f' % rx[ii + 1]) + 'R_e$')
plt.legend(loc=3)
plt.plot(np.arange(0, 20, .1),
np.ones(200) * func_plot(0.95, ftype=1),
'--',
color='black')
plt.plot(np.arange(0, 20, .1),
np.ones(200) * func_plot(0.50, ftype=1),
'--',
color='green')
fig.canvas.draw()
labels = [item.get_text() for item in ax.get_yticklabels()]
for i in range(0, len(labels)):
labels[i] = labels[i].replace(u'\u2212', '-')
for i in range(0, len(labels)):
if labels[i] != u'':
if float_(labels[i]) == 0:
labels[i] = u'%3.2f' % 10**(0)
else:
labels[i] = u'%3.2f' % 10**(float_(labels[i]))
ax.set_yticklabels(labels)
if pdf == 1:
fig.tight_layout()
plt.savefig(dev) #,dpi = 1000)
else:
plt.show()
plt.close()
f2.close()
fL2.close()
fo.write(name + " " + str(MassT) + " " + str(redshift) + " ")
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_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 DensityPlot(x,y,z):
# Create a density plot
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate
x,y,z = np.array(x),np.array(y),np.array(z)
for i in range(0,len(x)):
print x[i],y[i],z[i]
# Set up a regular grid of interpolation points
xi, yi = np.linspace(x.min(), x.max(), 100), np.linspace(y.min(), y.max(), 100)
#xi, yi = np.meshgrid(xi, yi)
# Interpolate
#rbf = scipy.interpolate.Rbf(x, y, z, function = 'linear')
densityfit = scipy.interpolate.interp2d(x, y, z, kind = 'linear')
zi = densityfit(xi, yi)
fig = plt.figure()
ax = fig.add_subplot(111)
# Plot labels
for i in range(0,len(z)):
ax.annotate(
str(z[i]),
xy = (x[i],y[i])
, xytext = (0, 0),
textcoords = 'offset points', ha = 'center', va = 'center', color='white',size=13, fontname='Times New Roman')
# set your ticks manually
#ax.xaxis.set_ticks([0.0,0.1,0.2,0.3,0.4,0.5])
ax.xaxis.set_ticks([0.5,0.75,1.0,1.25,1.50])
plt.xlabel('redshift',fontname='Times New Roman',size=16)
plt.ylabel('log L [erg/s]', fontname='Times New Roman',size=16)
plt.imshow(zi, vmin=z.min(), vmax=z.max(), origin='lower',
extent=[0.5, 1.5, 38, 45.], aspect='auto',cmap = 'jet')
#plt.scatter(x, y, c=z)
matplotlib.rcParams.update({'font.size': 13, 'font': 'Times New Roman'})
ax.grid(color='#B0B0B0',linestyle='-',linewidth=1.)
_F = [10**(-18.),10**(-17.),10**(-16.),10**(-15.),10**(-14.)]
for F in _F:
_z = np.linspace(0.001,1.5,100)
_L = []
for z in _z:
DL = luminosity_distance(z, **cosmo) # In MPc
DL = DL * (10**6. * pc) * 10**2. # in cm
L = math.log10(4 * math.pi * DL**2. * F)
_L.append(L)
plt.plot(_z,_L,'w--')
#plt.xlim(0.0,0.5)
plt.xlim(0.5,1.5)
plt.ylim(38,44)
plt.savefig('LvsZ.png')
def DensityPlot(x, y, z):
# Create a density plot
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate
x, y, z = np.array(x), np.array(y), np.array(z)
for i in range(0, len(x)):
print x[i], y[i], z[i]
# Set up a regular grid of interpolation points
xi, yi = np.linspace(x.min(), x.max(),
100), np.linspace(y.min(), y.max(), 100)
#xi, yi = np.meshgrid(xi, yi)
# Interpolate
#rbf = scipy.interpolate.Rbf(x, y, z, function = 'linear')
densityfit = scipy.interpolate.interp2d(x, y, z, kind='linear')
zi = densityfit(xi, yi)
fig = plt.figure()
ax = fig.add_subplot(111)
# Plot labels
for i in range(0, len(z)):
ax.annotate(str(z[i]),
xy=(x[i], y[i]),
xytext=(0, 0),
textcoords='offset points',
ha='center',
va='center',
color='white',
size=13,
fontname='Times New Roman')
# set your ticks manually
#ax.xaxis.set_ticks([0.0,0.1,0.2,0.3,0.4,0.5])
ax.xaxis.set_ticks([0.5, 0.75, 1.0, 1.25, 1.50])
plt.xlabel('redshift', fontname='Times New Roman', size=16)
plt.ylabel('log L [erg/s]', fontname='Times New Roman', size=16)
plt.imshow(zi,
vmin=z.min(),
vmax=z.max(),
origin='lower',
extent=[0.5, 1.5, 38, 45.],
aspect='auto',
cmap='jet')
#plt.scatter(x, y, c=z)
matplotlib.rcParams.update({'font.size': 13, 'font': 'Times New Roman'})
ax.grid(color='#B0B0B0', linestyle='-', linewidth=1.)
_F = [10**(-18.), 10**(-17.), 10**(-16.), 10**(-15.), 10**(-14.)]
for F in _F:
_z = np.linspace(0.001, 1.5, 100)
_L = []
for z in _z:
DL = luminosity_distance(z, **cosmo) # In MPc
DL = DL * (10**6. * pc) * 10**2. # in cm
L = math.log10(4 * math.pi * DL**2. * F)
_L.append(L)
plt.plot(_z, _L, 'w--')
#plt.xlim(0.0,0.5)
plt.xlim(0.5, 1.5)
plt.ylim(38, 44)
plt.savefig('LvsZ.png')
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()
# Compute comoving volume in Mpc-3
dV = (comoving_volume(zmax, **cosmo) -
comoving_volume(zmin, **cosmo)) * (Tel_Area / 41253.) # Mpc3
# Get Phi in Mpc-3
alpha, logLStar, logPhiStar = LF_Data.retrieve_LF(z, line)
LStar, PhiStar = 10.**(logLStar), 10.**(logPhiStar)
n = scipy.integrate.quad(
lambda L: schechterL(L, PhiStar, alpha, LStar), Lmin, Lmax)[0]
# Number = number density * volume
N = n * dV
# Compute flux
DL = luminosity_distance(z, **cosmo) # In MPc
DL = DL * (10**6. * pc) * 10**2. # in cm
F = Lmin / (4 * math.pi * DL**2.) # ergs/s/cm2
Dist.append(
[zmin, zmax,
math.log10(Lmin),
math.log10(Lmax),
int(N), F])
# Plot L vs. z
x = [elem[0] + 0.5 * (elem[1] - elem[0]) for elem in Dist] # av z
y = [elem[2] + 0.5 * (elem[3] - elem[2]) for elem in Dist] # av log10
z = [elem[4] for elem in Dist]
#DensityPlot(x,y,z)
def findzfromDL(z, DL):
return DL - cd.luminosity_distance(z, **cosmo)
def getF(z, L):
DL = luminosity_distance(z, **cosmo) # In MPc
DL = DL * (10**6. * pc) * 10**2. # in cm
return L / (4 * math.pi * DL**2.) # ergs/s/cm2
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 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')
val = numpy.zeros(len(z))
for i in xrange(len(z)):
#Hubble Distance
dh = cd.hubble_distance_z(z[i],**Cosmology)*cd.e_z(z[i],**Cosmology)
#In David Hogg's (arXiv:astro-ph/9905116v4) formalism, this is equivalent to D_H / E(z) = c / (H_0 E(z)) [see his eq. 14], which
#appears in the definitions of many other distance measures.
dm = cd.comoving_distance_transverse(z[i],**Cosmology)
#See equation 16 of David Hogg's arXiv:astro-ph/9905116v4
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']