def ymodel(data_nus, z, dictkey_arrays, dict_modelfluxes, *par):
"""Constructs the total model from parameter values.
## inputs: data_nus, z, dictkey_arrays, dict_modelfluxes, *par
## output:
- total model
- bands
- galaxy_flux (to be used by ln_prior)
## dependencies:
This fct is used
(1)in ln_likelihood, this same script.
(2)in scriptPLOTandWRITE.
"""
STARBURSTFdict, BBBFdict, GALAXYFdict, TORUSFdict, _, _, _, _, _ = dict_modelfluxes
gal_do, irlum_dict, nh_dict, BBebv_dict, _ = dictkey_arrays
# Call MCMC-parameter values
tau, agelog, nh, irlum, SB, BB, GA, TO, BBebv, GAebv = par[0:10]
age = 10**agelog
# Pick dictionary key-values, nearest to the MCMC- parameter values
irlum_dct = model.pick_STARBURST_template(irlum, irlum_dict)
nh_dct = model.pick_TORUS_template(nh, nh_dict)
ebvbbb_dct = model.pick_BBB_template(BBebv, BBebv_dict)
gal_do.nearest_par2dict(tau, age, GAebv)
tau_dct, age_dct, ebvg_dct = gal_do.t, gal_do.a, gal_do.e
# Call fluxes from dictionary using keys-values
try:
bands, gal_Fnu = GALAXYFdict[tau_dct, age_dct, ebvg_dct]
bands, sb_Fnu = STARBURSTFdict[irlum_dct]
bands, bbb_Fnu = BBBFdict[ebvbbb_dct]
bands, tor_Fnu = TORUSFdict[nh_dct]
except ValueError:
print 'Error: Dictionary does not contain some values'
# Renormalize to have similar amplitudes. Keep these fixed!
sb_Fnu_norm = sb_Fnu.squeeze() / 1e20
bbb_Fnu_norm = bbb_Fnu.squeeze() / 1e60
gal_Fnu_norm = gal_Fnu.squeeze() / 1e18
tor_Fnu_norm = tor_Fnu.squeeze() / 1e-40
# Total SED sum
#--------------------------------------------------------------------
lum = 10**(SB)* sb_Fnu_norm + 10**(BB)*bbb_Fnu_norm \
+ 10**(GA)*gal_Fnu_norm +(10**TO) *tor_Fnu_norm
#--------------------------------------------------------------------
lum = lum.reshape((np.size(lum), ))
return lum, bands, 10**(GA) * gal_Fnu_norm
def ymodel(data_nus, z, dictkey_arrays, dict_modelfluxes, *par):
"""Constructs the total model from parameter values.
## inputs: data_nus, z, dictkey_arrays, dict_modelfluxes, *par
## output:
- total model
- bands
- galaxy_flux (to be used by ln_prior)
## dependencies:
This fct is used
(1)in ln_likelihood, this same script.
(2)in scriptPLOTandWRITE.
"""
STARBURSTFdict, BBBFdict, GALAXYFdict, TORUSFdict, _, _, _, _, _ = dict_modelfluxes
gal_do, irlum_dict, nh_dict, BBebv_dict, _ = dictkey_arrays
# Call MCMC-parameter values
tau, agelog, nh, irlum, SB, BB, GA, TO, BBebv, GAebv = par[0:10]
age = 10 ** agelog
# Pick dictionary key-values, nearest to the MCMC- parameter values
irlum_dct = model.pick_STARBURST_template(irlum, irlum_dict)
nh_dct = model.pick_TORUS_template(nh, nh_dict)
ebvbbb_dct = model.pick_BBB_template(BBebv, BBebv_dict)
gal_do.nearest_par2dict(tau, age, GAebv)
tau_dct, age_dct, ebvg_dct = gal_do.t, gal_do.a, gal_do.e
# Call fluxes from dictionary using keys-values
try:
bands, gal_Fnu = GALAXYFdict[tau_dct, age_dct, ebvg_dct]
bands, sb_Fnu = STARBURSTFdict[irlum_dct]
bands, bbb_Fnu = BBBFdict[ebvbbb_dct]
bands, tor_Fnu = TORUSFdict[nh_dct]
except ValueError:
print "Error: Dictionary does not contain some values"
# Renormalize to have similar amplitudes. Keep these fixed!
sb_Fnu_norm = sb_Fnu.squeeze() / 1e20
bbb_Fnu_norm = bbb_Fnu.squeeze() / 1e60
gal_Fnu_norm = gal_Fnu.squeeze() / 1e18
tor_Fnu_norm = tor_Fnu.squeeze() / 1e-40
# Total SED sum
# --------------------------------------------------------------------
lum = 10 ** (SB) * sb_Fnu_norm + 10 ** (BB) * bbb_Fnu_norm + 10 ** (GA) * gal_Fnu_norm + (10 ** TO) * tor_Fnu_norm
# --------------------------------------------------------------------
lum = lum.reshape((np.size(lum),))
return lum, bands, 10 ** (GA) * gal_Fnu_norm
def MOCKdata (z, dict_modelfiles, dict_modelfluxes, parameters): all_tau, all_age, all_nh, all_irlum, filename_0_galaxy, filename_0_starburst, filename_0_torus = dict_modelsfiles STARBURSTFdict , BBBFdict, GALAXYFdict, TORUSFdict, EBVbbb_array, EBVgal_array = dict_modelfluxes tau, age, nh, irlum, SB ,BB, GA,TO, BBebv, GAebv = parameters SB_filename = model.pick_STARBURST_template(irlum, filename_0_starburst, all_irlum) GA_filename = model.pick_GALAXY_template(tau, age, filename_0_galaxy, all_tau, all_age) TOR_filename = model.pick_TORUS_template(nh, all_nh, filename_0_torus) BB_filename = model.pick_BBB_template() EBV_bbb_0 = model.pick_EBV_grid(EBVbbb_array, BBebv) EBV_bbb = ( str(int(EBV_bbb_0)) if float(EBV_bbb_0).is_integer() else str(EBV_bbb_0)) EBV_gal_0 = model.pick_EBV_grid(EBVgal_array,GAebv) EBV_gal = ( str(int(EBV_gal_0)) if float(EBV_gal_0).is_integer() else str(EBV_gal_0)) if (GA_filename, EBV_gal) in GALAXYFdict: bands, gal_Fnu = GALAXYFdict[GA_filename, EBV_gal] else: print 'Error: Dictionary does not contain key of ', GA_filename, EBV_gal, ' or the E(B-V) grid or the DICTIONARIES_AGNfitter file does not match when the one used in PARAMETERSPACE_AGNfitter/ymodel.py' if SB_filename in STARBURSTFdict: bands, sb_Fnu= STARBURSTFdict[SB_filename] else: print 'Error: Dictionary does not contain key'+SB_filename+'. all STARBURST files or the E(B-V) grid.' if (BB_filename, EBV_bbb) in BBBFdict: bands, bbb_Fnu = BBBFdict[BB_filename, EBV_bbb] else: print'Error: Dictionary does not contain key: '+ BB_filename, EBV_bbb +'or the E(B-V) grid of the DICTIONARIES_AGNfitter file does not match when the one used in PARAMETERSPACE_AGNfitter/ymodel.py' if TOR_filename in TORUSFdict: bands, tor_Fnu= TORUSFdict[TOR_filename] else: print 'Error: Dictionary does not contain TORUS file:'+TOR_filename NormalizationFLux = 1e-28 # This is the flux to which the models are normalized to have comparable NORM factors sb_Fnu_norm = sb_Fnu / 1e18 bbb_Fnu_norm = bbb_Fnu / 1e58 gal_Fnu_norm = gal_Fnu/ 1e12 tor_Fnu_norm = tor_Fnu/ 1e-42 # Sum components # ---------------------------------------------------------------------------------------------------------------------------------------------------------# lum = 10**(SB)* sb_Fnu_norm + 10**(BB)*bbb_Fnu_norm + (10**GA)*gal_Fnu_norm + 10**TO *tor_Fnu_norm #----------------------------------------------------------------------------------------------------------------------------------------------------------- mockfluxes_array = lum.reshape((np.size(lum),)) return bands, mockfluxes_array
def fluxes_arrays(data_nus, catalog, sourceline, dict_modelsfiles, filterdict, chain, Nrealizations, path, dict_modelfluxes, mock_input):
"""
This function constructs the luminosities arrays for many realizations from the parameter values
## inputs:
- v: frequency
- catalog file name
- sourcelines
## output:
- dictionary P with all parameter characteristics
"""
#LIST OF OUTPUT
SBFnu_list = []
BBFnu_list = []
GAFnu_list= []
TOFnu_list = []
TOTALFnu_list = []
BBFnu_deredd_list = []
filtered_modelpoints_list = []
STARBURSTFdict , BBBFdict, GALAXYFdict, TORUSFdict, EBVbbb_array, EBVgal_array = dict_modelfluxes
all_tau, all_age, all_nh, all_irlum, filename_0_galaxy, filename_0_starburst, filename_0_torus = dict_modelsfiles
nsample, npar = chain.shape
source = NAME(catalog, sourceline)
#CALL PARAMETERS FROM INPUT
itau, iage, inh, iirlum, iSB ,iBB, iGA ,iTO, iBBebv, iGAebv= mock_input[sourceline] #calling parameter
#CALL PARAMETERS OF OUTPUT
tau, agelog, nh, irlum, SB ,BB, GA,TO, BBebv0, GAebv0= [ chain[:,i] for i in range(npar)] #calling parameters
z = REDSHIFT(catalog, sourceline)
age = 10**agelog
agelog = np.log10(age)
iagelog = np.log10(iage)
#LEFT PLOT
for inp in range(1):
SB_filename = path + model.pick_STARBURST_template(iirlum, filename_0_starburst, all_irlum)
GA_filename = path + model.pick_GALAXY_template(itau, iage, filename_0_galaxy, all_tau, all_age)
TO_filename = path + model.pick_TORUS_template(inh, all_nh, filename_0_torus)
BB_filename = path + model.pick_BBB_template()
all_model_nus = np.arange(12, 16, 0.001)#np.log10(dicts.stack_all_model_nus(filename_0_galaxy, filename_0_starburst, filename_0_torus, z, path ))
gal_nu, gal_nored_Fnu = model.GALAXY_read_4plotting( GA_filename, all_model_nus)
gal_nu, gal_Fnu_red = model.GALAXY_nf2( gal_nu, gal_nored_Fnu, iGAebv )
all_gal_nus, all_gal_Fnus =gal_nu, gal_Fnu_red
sb_nu0, sb_Fnu0 = model.STARBURST_read_4plotting(SB_filename, all_model_nus)
all_sb_nus, all_sb_Fnus = sb_nu0, sb_Fnu0
bbb_nu, bbb_nored_Fnu = model.BBB_read_4plotting(BB_filename, all_model_nus)
all_bbb_nus, all_bbb_Fnus = model.BBB_nf2(bbb_nu, bbb_nored_Fnu, iBBebv, z )
all_bbb_nus, all_bbb_Fnus_deredd =all_bbb_nus, bbb_nored_Fnu
tor_nu0, tor_Fnu0 = model.TORUS_read_4plotting(TO_filename, z, all_model_nus)
all_tor_nus, all_tor_Fnus = tor_nu0, tor_Fnu0
par_input = itau, iagelog, inh, iirlum, iSB ,iBB, iGA ,iTO, iBBebv, iGAebv
ifiltered_modelpoints = parspace.ymodel(data_nus, z, dict_modelsfiles, dict_modelfluxes, *par_input)
if len(all_gal_nus)==len(all_sb_nus) and len(all_sb_nus)==len(all_bbb_nus) and len(all_tor_nus)==len(all_bbb_nus) :
nu= all_gal_nus
all_sb_Fnus_norm = all_sb_Fnus /1e20
all_bbb_Fnus_norm = all_bbb_Fnus / 1e60
all_gal_Fnus_norm = all_gal_Fnus/ 1e18
all_tor_Fnus_norm = all_tor_Fnus/ 1e-40
all_bbb_Fnus_deredd_norm = all_bbb_Fnus_deredd / 1e60
iSBFnu = all_sb_Fnus_norm *10**float(iSB)
iBBFnu = all_bbb_Fnus_norm * 10**float(iBB)
iGAFnu = all_gal_Fnus_norm * 10**float(iGA)
iTOFnu = all_tor_Fnus_norm * 10**float(iTO)# /(1+z)
iBBFnu_deredd = all_bbb_Fnus_deredd_norm* 10**float(iBB)
iTOTALFnu = iSBFnu + iBBFnu + iGAFnu + iTOFnu
#RIGHT PLOT
for gi in range(Nrealizations): #LOOP for a 100th part of the realizations
g= gi*(nsample/Nrealizations)
BBebv1 = model.pick_EBV_grid(EBVbbb_array, BBebv0[g])
BBebv2 = ( str(int(BBebv1)) if float(BBebv1).is_integer() else str(BBebv1))
GAebv1 = model.pick_EBV_grid(EBVgal_array,GAebv0[g])
GAebv2 = ( str(int(GAebv1)) if float(GAebv1).is_integer() else str(GAebv1))
SB_filename = path + model.pick_STARBURST_template(irlum[g], filename_0_starburst, all_irlum)
GA_filename = path + model.pick_GALAXY_template(tau[g], age[g], filename_0_galaxy, all_tau, all_age)
TO_filename = path + model.pick_TORUS_template(nh[g], all_nh, filename_0_torus)
BB_filename = path + model.pick_BBB_template()
all_model_nus = np.arange(12, 16, 0.001)#np.log10(dicts.stack_all_model_nus(filename_0_galaxy, filename_0_starburst, filename_0_torus, z, path ))
gal_nu, gal_nored_Fnu = model.GALAXY_read_4plotting( GA_filename, all_model_nus)
gal_nu, gal_Fnu_red = model.GALAXY_nf2( gal_nu, gal_nored_Fnu, float(GAebv2))
all_gal_nus, all_gal_Fnus =gal_nu, gal_Fnu_red
sb_nu0, sb_Fnu0 = model.STARBURST_read_4plotting(SB_filename, all_model_nus)
all_sb_nus, all_sb_Fnus = sb_nu0, sb_Fnu0
bbb_nu, bbb_nored_Fnu = model.BBB_read_4plotting(BB_filename, all_model_nus)
bbb_nu0, bbb_Fnu_red = model.BBB_nf2(bbb_nu, bbb_nored_Fnu, float(BBebv2), z )
all_bbb_nus, all_bbb_Fnus = bbb_nu0, bbb_Fnu_red
all_bbb_nus, all_bbb_Fnus_deredd = bbb_nu0, bbb_nored_Fnu
tor_nu0, tor_Fnu0 = model.TORUS_read_4plotting(TO_filename, z, all_model_nus)
all_tor_nus, all_tor_Fnus = tor_nu0, tor_Fnu0
par1 = tau[g], agelog[g], nh[g], irlum[g], SB[g] ,BB[g], GA[g] ,TO[g], float(BBebv2), float(GAebv2)
filtered_modelpoints = parspace.ymodel(data_nus, z, dict_modelsfiles, dict_modelfluxes, *par1)
if len(all_gal_nus)==len(all_sb_nus) and len(all_sb_nus)==len(all_bbb_nus) and len(all_tor_nus)==len(all_bbb_nus) :
nu= all_gal_nus
all_sb_Fnus_norm = all_sb_Fnus /1e20
all_bbb_Fnus_norm = all_bbb_Fnus / 1e60
all_gal_Fnus_norm = all_gal_Fnus/ 1e18
all_tor_Fnus_norm = all_tor_Fnus/ 1e-40
all_bbb_Fnus_deredd_norm = all_bbb_Fnus_deredd / 1e60
SBFnu = all_sb_Fnus_norm *10**float(SB[g])
BBFnu = all_bbb_Fnus_norm * 10**float(BB[g])
GAFnu = all_gal_Fnus_norm * 10**float(GA[g])
TOFnu = all_tor_Fnus_norm * 10**float(TO[g]) #/(1+z)
BBFnu_deredd = all_bbb_Fnus_deredd_norm* 10**float(BB[g])
TOTALFnu = SBFnu + BBFnu + GAFnu + TOFnu
SBFnu_list.append(SBFnu)
BBFnu_list.append(BBFnu)
GAFnu_list.append(GAFnu)
TOFnu_list.append(TOFnu)
TOTALFnu_list.append(TOTALFnu)
BBFnu_deredd_list.append(BBFnu_deredd)
filtered_modelpoints_list.append(filtered_modelpoints)
else:
print 'Error:'
print 'The frequencies in the MODELdict_plot dictionaries are not equal for all models and could not be added.'
print 'Check that the dictionary_plot.py stacks all frequencies (bands+galaxy+bbb+sb+torus) properly.'
SBFnu_array = np.array(SBFnu_list)
BBFnu_array = np.array(BBFnu_list)
GAFnu_array = np.array(GAFnu_list)
TOFnu_array = np.array(TOFnu_list)
TOTALFnu_array = np.array(TOTALFnu_list)
BBFnu_array_deredd = np.array(BBFnu_deredd_list)
filtered_modelpoints = np.array(filtered_modelpoints_list)
FLUXES4plotting = (SBFnu_array, BBFnu_array, GAFnu_array, TOFnu_array, TOTALFnu_array,BBFnu_array_deredd, iSBFnu, iBBFnu, iGAFnu, iTOFnu, iTOTALFnu,iBBFnu_deredd)
return all_model_nus, FLUXES4plotting, filtered_modelpoints, ifiltered_modelpoints
def fluxes(self, data):
"""
This is the main function of the class.
"""
self.chain_obj.props()
SBFnu_list = []
BBFnu_list = []
GAFnu_list= []
TOFnu_list = []
TOTALFnu_list = []
BBFnu_deredd_list = []
if self.output_type == 'plot':
filtered_modelpoints_list = []
gal_do, irlum_dict, nh_dict, BBebv_dict,_ = data.dictkey_arrays
# Take the last 4 dictionaries, which are for plotting. (the first 4 were at bands)
_,_,_,_,STARBURSTFdict , BBBFdict, GALAXYFdict, TORUSFdict,_= data.dict_modelfluxes
nsample, npar = self.chain_obj.flatchain.shape
source = data.name
if self.output_type == 'plot':
tau, agelog, nh, irlum, SB ,BB, GA,TO, BBebv, GAebv= self.chain_obj.flatchain[np.random.choice(nsample, (self.out['realizations2plot'])),:].T
elif self.output_type == 'int_lums':
tau, agelog, nh, irlum, SB ,BB, GA,TO, BBebv, GAebv= self.chain_obj.flatchain[np.random.choice(nsample, (self.out['realizations2int'])),:].T
elif self.output_type == 'best_fit':
tau, agelog, nh, irlum, SB ,BB, GA,TO, BBebv, GAebv= self.best_fit_pars
age = 10**agelog
self.all_nus_rest = np.arange(11.5, 16, 0.001)
for g in range(len(tau)):
# Pick dictionary key-values, nearest to the MCMC- parameter values
irlum_dct = model.pick_STARBURST_template(irlum[g], irlum_dict)
nh_dct = model.pick_TORUS_template(nh[g], nh_dict)
ebvbbb_dct = model.pick_BBB_template(BBebv[g], BBebv_dict)
gal_do.nearest_par2dict(tau[g], age[g], GAebv[g])
tau_dct, age_dct, ebvg_dct=gal_do.t, gal_do.a,gal_do.e
#Produce model fluxes at all_nus_rest for plotting, through interpolation
all_gal_nus, gal_Fnus = GALAXYFdict[tau_dct, age_dct,ebvg_dct]
GAinterp = scipy.interpolate.interp1d(all_gal_nus, gal_Fnus, bounds_error=False, fill_value=0.)
all_gal_Fnus = GAinterp(self.all_nus_rest)
all_sb_nus, sb_Fnus= STARBURSTFdict[irlum_dct]
SBinterp = scipy.interpolate.interp1d(all_sb_nus, sb_Fnus, bounds_error=False, fill_value=0.)
all_sb_Fnus = SBinterp(self.all_nus_rest)
all_bbb_nus, bbb_Fnus = BBBFdict[ebvbbb_dct]
BBinterp = scipy.interpolate.interp1d(all_bbb_nus, bbb_Fnus, bounds_error=False, fill_value=0.)
all_bbb_Fnus = BBinterp(self.all_nus_rest)
all_bbb_nus, bbb_Fnus_deredd = BBBFdict['0.0']
BBderedinterp = scipy.interpolate.interp1d(all_bbb_nus, bbb_Fnus_deredd, bounds_error=False, fill_value=0.)
all_bbb_Fnus_deredd = BBderedinterp(self.all_nus_rest)
all_tor_nus, tor_Fnus= TORUSFdict[nh_dct]
TOinterp = scipy.interpolate.interp1d(all_tor_nus, np.log10(tor_Fnus), bounds_error=False, fill_value=0.)
all_tor_Fnus = 10**(TOinterp(self.all_nus_rest))
if self.output_type == 'plot':
par2 = tau[g], agelog[g], nh[g], irlum[g], SB[g] ,BB[g], GA[g] ,TO[g], BBebv[g], GAebv[g]
filtered_modelpoints, _, _ = parspace.ymodel(data.nus,data.z, data.dictkey_arrays, data.dict_modelfluxes, *par2)
#Using the costumized normalization
SBFnu = (all_sb_Fnus /1e20) *10**float(SB[g])
BBFnu = (all_bbb_Fnus /1e60) * 10**float(BB[g])
GAFnu = (all_gal_Fnus/ 1e18) * 10**float(GA[g])
TOFnu = (all_tor_Fnus/ 1e-40) * 10**float(TO[g])
BBFnu_deredd = (all_bbb_Fnus_deredd /1e60) * 10**float(BB[g])
TOTALFnu = SBFnu + BBFnu + GAFnu + TOFnu
#Append to the list for all realizations
SBFnu_list.append(SBFnu)
BBFnu_list.append(BBFnu)
GAFnu_list.append(GAFnu)
TOFnu_list.append(TOFnu)
TOTALFnu_list.append(TOTALFnu)
BBFnu_deredd_list.append(BBFnu_deredd)
#Only if SED plotting: do the same with the modelled flux values at each data point
if self.output_type == 'plot':
filtered_modelpoints_list.append(filtered_modelpoints)
#Convert lists into Numpy arrays
SBFnu_array = np.array(SBFnu_list)
BBFnu_array = np.array(BBFnu_list)
GAFnu_array = np.array(GAFnu_list)
TOFnu_array = np.array(TOFnu_list)
TOTALFnu_array = np.array(TOTALFnu_list)
BBFnu_array_deredd = np.array(BBFnu_deredd_list)
#Put them all together to transport
FLUXES4plotting = (SBFnu_array, BBFnu_array, GAFnu_array, TOFnu_array, TOTALFnu_array,BBFnu_array_deredd)
#Convert Fluxes to nuLnu
self.nuLnus4plotting = self.FLUXES2nuLnu_4plotting(self.all_nus_rest, FLUXES4plotting, data.z)
#Only if SED plotting:
if self.output_type == 'plot':
filtered_modelpoints = np.array(filtered_modelpoints_list)
distance= model.z2Dlum(data.z)
lumfactor = (4. * math.pi * distance**2.)
self.filtered_modelpoints_nuLnu = (filtered_modelpoints *lumfactor* 10**(data.nus))
#Only if calculating integrated luminosities:
elif self.output_type == 'int_lums':
self.int_lums= np.log10(self.integrated_luminosities(self.out ,self.all_nus_rest, self.nuLnus4plotting))
def fluxes(self, data):
"""
This is the main function of the class.
"""
self.chain_obj.props()
SBFnu_list = []
BBFnu_list = []
GAFnu_list = []
TOFnu_list = []
TOTALFnu_list = []
BBFnu_deredd_list = []
if self.output_type == 'plot':
filtered_modelpoints_list = []
gal_do, irlum_dict, nh_dict, BBebv_dict, _ = data.dictkey_arrays
# Take the last 4 dictionaries, which are for plotting. (the first 4 were at bands)
_, _, _, _, STARBURSTFdict, BBBFdict, GALAXYFdict, TORUSFdict, _ = data.dict_modelfluxes
nsample, npar = self.chain_obj.flatchain.shape
source = data.name
if self.output_type == 'plot':
tau, agelog, nh, irlum, SB, BB, GA, TO, BBebv, GAebv = self.chain_obj.flatchain[
np.random.choice(nsample,
(self.out['realizations2plot'])), :].T
elif self.output_type == 'int_lums':
tau, agelog, nh, irlum, SB, BB, GA, TO, BBebv, GAebv = self.chain_obj.flatchain[
np.random.choice(nsample, (self.out['realizations2int'])), :].T
elif self.output_type == 'best_fit':
tau, agelog, nh, irlum, SB, BB, GA, TO, BBebv, GAebv = self.best_fit_pars
age = 10**agelog
self.all_nus_rest = np.arange(11.5, 16, 0.001)
for g in range(len(tau)):
# Pick dictionary key-values, nearest to the MCMC- parameter values
irlum_dct = model.pick_STARBURST_template(irlum[g], irlum_dict)
nh_dct = model.pick_TORUS_template(nh[g], nh_dict)
ebvbbb_dct = model.pick_BBB_template(BBebv[g], BBebv_dict)
gal_do.nearest_par2dict(tau[g], age[g], GAebv[g])
tau_dct, age_dct, ebvg_dct = gal_do.t, gal_do.a, gal_do.e
#Produce model fluxes at all_nus_rest for plotting, through interpolation
all_gal_nus, gal_Fnus = GALAXYFdict[tau_dct, age_dct, ebvg_dct]
GAinterp = scipy.interpolate.interp1d(all_gal_nus,
gal_Fnus,
bounds_error=False,
fill_value=0.)
all_gal_Fnus = GAinterp(self.all_nus_rest)
all_sb_nus, sb_Fnus = STARBURSTFdict[irlum_dct]
SBinterp = scipy.interpolate.interp1d(all_sb_nus,
sb_Fnus,
bounds_error=False,
fill_value=0.)
all_sb_Fnus = SBinterp(self.all_nus_rest)
all_bbb_nus, bbb_Fnus = BBBFdict[ebvbbb_dct]
BBinterp = scipy.interpolate.interp1d(all_bbb_nus,
bbb_Fnus,
bounds_error=False,
fill_value=0.)
all_bbb_Fnus = BBinterp(self.all_nus_rest)
all_bbb_nus, bbb_Fnus_deredd = BBBFdict['0.0']
BBderedinterp = scipy.interpolate.interp1d(all_bbb_nus,
bbb_Fnus_deredd,
bounds_error=False,
fill_value=0.)
all_bbb_Fnus_deredd = BBderedinterp(self.all_nus_rest)
all_tor_nus, tor_Fnus = TORUSFdict[nh_dct]
TOinterp = scipy.interpolate.interp1d(all_tor_nus,
np.log10(tor_Fnus),
bounds_error=False,
fill_value=0.)
all_tor_Fnus = 10**(TOinterp(self.all_nus_rest))
if self.output_type == 'plot':
par2 = tau[g], agelog[g], nh[g], irlum[g], SB[g], BB[g], GA[
g], TO[g], BBebv[g], GAebv[g]
filtered_modelpoints, _, _ = parspace.ymodel(
data.nus, data.z, data.dictkey_arrays,
data.dict_modelfluxes, *par2)
#Using the costumized normalization
SBFnu = (all_sb_Fnus / 1e20) * 10**float(SB[g])
BBFnu = (all_bbb_Fnus / 1e60) * 10**float(BB[g])
GAFnu = (all_gal_Fnus / 1e18) * 10**float(GA[g])
TOFnu = (all_tor_Fnus / 1e-40) * 10**float(TO[g])
BBFnu_deredd = (all_bbb_Fnus_deredd / 1e60) * 10**float(BB[g])
TOTALFnu = SBFnu + BBFnu + GAFnu + TOFnu
#Append to the list for all realizations
SBFnu_list.append(SBFnu)
BBFnu_list.append(BBFnu)
GAFnu_list.append(GAFnu)
TOFnu_list.append(TOFnu)
TOTALFnu_list.append(TOTALFnu)
BBFnu_deredd_list.append(BBFnu_deredd)
#Only if SED plotting: do the same with the modelled flux values at each data point
if self.output_type == 'plot':
filtered_modelpoints_list.append(filtered_modelpoints)
#Convert lists into Numpy arrays
SBFnu_array = np.array(SBFnu_list)
BBFnu_array = np.array(BBFnu_list)
GAFnu_array = np.array(GAFnu_list)
TOFnu_array = np.array(TOFnu_list)
TOTALFnu_array = np.array(TOTALFnu_list)
BBFnu_array_deredd = np.array(BBFnu_deredd_list)
#Put them all together to transport
FLUXES4plotting = (SBFnu_array, BBFnu_array, GAFnu_array, TOFnu_array,
TOTALFnu_array, BBFnu_array_deredd)
#Convert Fluxes to nuLnu
self.nuLnus4plotting = self.FLUXES2nuLnu_4plotting(
self.all_nus_rest, FLUXES4plotting, data.z)
#Only if SED plotting:
if self.output_type == 'plot':
filtered_modelpoints = np.array(filtered_modelpoints_list)
distance = model.z2Dlum(data.z)
lumfactor = (4. * math.pi * distance**2.)
self.filtered_modelpoints_nuLnu = (filtered_modelpoints *
lumfactor * 10**(data.nus))
#Only if calculating integrated luminosities:
elif self.output_type == 'int_lums':
self.int_lums = np.log10(
self.integrated_luminosities(self.out, self.all_nus_rest,
self.nuLnus4plotting))
