def cloudy_spectrum(ax):
from prospect.models import model_setup
param_file = '/Users/joel/code/python/prospector_alpha/parameter_files/brownseds_np/brownseds_np_params.py'
run_params = model_setup.get_run_params(param_file=param_file)
sps = model_setup.load_sps(**run_params)
model = model_setup.load_model(**run_params)
model.params['dust2'] = np.array(0.0)
obs = model_setup.load_obs(**run_params)
spec,_,_ = model.mean_model(model.initial_theta, obs, sps=sps)
model.params['add_neb_emission'] = np.array(False)
model.params['add_neb_continuum'] = np.array(False)
spec_neboff,_,_ = model.mean_model(model.initial_theta, obs, sps=sps)
spec_neb = (spec-spec_neboff)*3e18/sps.wavelengths**2
in_plot = (sps.wavelengths/1e4 > 6) & (sps.wavelengths/1e4 < 30)
spec_neb_smooth = smooth_spectrum(sps.wavelengths[in_plot], spec_neb[in_plot], 3500)
spec_neb_smooth *= 1e5 / spec_neb_smooth.max()
'''
neb_color = '0.7'
alpha = 0.7
ax.fill_between(sps.wavelengths[in_plot]/1e4, np.zeros_like(spec_neb_smooth), spec_neb_smooth,
color=neb_color,
alpha=alpha)
### label H2 + [ArII] (second is in cloudy but very small)
ax.fill_between([9.55,9.85],[0,0],[1,1],color=neb_color,alpha=alpha)
ax.fill_between([6.8,7.1],[0,0],[1,1],color=neb_color,alpha=alpha)
'''
lines = ['[ArII]',r'H$_2$','[SIV]','[NeIII]','[SIII]']
lam = [6.95,9.7,10.45,15.5,18.7]
# removed because they're too weak, just distracting
#lines = ['[ArIII]','[NeII]']
#lam = [9.0,12.8]
for ii in xrange(len(lines)):
ax.text(lam[ii]*1.008,0.14,lines[ii],
ha='left',fontsize=9.5)
for ii in xrange(len(lam)): ax.plot([lam[ii],lam[ii]],[0,1e5],linestyle='--',lw=1.5,color='k')
import numpy as np
import matplotlib.pyplot as plt
import spot_utils as pread
from prospect.io.read_results import results_from
from prospect.models import model_setup
paramfile = 'ad_params.py'
clargs = {'param_file': paramfile}
run_params = model_setup.get_run_params(argv=paramfile, **clargs)
print(run_params)
obs = model_setup.load_obs(**run_params)
sps = model_setup.load_sps(**run_params)
model = model_setup.load_model(**run_params)
wspec = sps.csp.wavelengths # *restframe* spectral wavelengths
a = 1.0 + model.params.get('zred', 0.6) # cosmological redshifting
wphot = np.array([f.wave_effective for f in obs['filters']])
# In [21]
# grab results, powell results, and our corresponding models
#res, pr, mod = results_from("{}_mcmc.h5".format(outroot))
res, pr, mod = results_from("demo_galphot_1508433060_mcmc.h5")
# In [22]
# To see how our MCMC samples look, we can examine a few traces
choice = np.random.choice
tracefig = pread.param_evol(res,
figsize=(20, 10),
chains=choice(128, size=10, replace=False))
# load sps model (default)
sps = model_setup.load_sps(**run_params)
print(sps)
# In [6]
# load noise model (none)
spec_noise, phot_noise = model_setup.load_gp(**run_params)
# In [7]
# demo model
model = model_setup.load_model(**run_params)
# In [8]
# demo data (generated from the script)
# note: the phottable=photfile line is an addiiton
obs = model_setup.load_obs(phottable=photfile, **run_params)
#obs = model_setup.load_obs(**run_params)
#print('Mock S/N={}'.format(obs['mock_snr']))
#if run_params['add_noise']:
# print('Noise realization added to mock photometry')
#else:
# print('No noise added to mock photometry')
# In [9]
from prospect.likelihood import lnlike_spec, lnlike_phot, write_log
# In [10]
def lnprobfn(theta):
"""Given a parameter vector, a dictionary of observational data
and a model object, return the ln of the posterior.
def compute_specmags(runname=None, outfolder=None):
'''
step 1: load observed spectra, model spectra, photometry
step 2: normalize model spectra to observed spectra (at red end, + blue if using u-band)
step 3: meld them together
step 4: calculate u, g, r mags from spectra
step 5: plot spectral u, g, r versus photometric u, g, r
dump into pickle file, call perform_wavelength_cal() to apply
future steps: don't use super low-res spectra
'''
print 'THIS FUNCTION IS BROKEN, UNTIL YOU LET THREED_DUTILS USE LUMDIST'
from bsfh import model_setup
if runname == None:
runname = 'brownseds'
#### load up prospector results
filebase, parm_basename, ancilname=prosp_dutils.generate_basenames(runname)
outname = os.getenv('APPS')+'/prospector_alpha/plots/'+runname+'/pcomp/sfrcomp.png'
alldata = prospector_io.load_alldata(runname=runname)
sps = prosp_dutils.setup_sps(custom_filter_key=None)
optphot = np.zeros(shape=(3,len(alldata)))
obsphot = np.zeros(shape=(3,len(alldata)))
for ii,dat in enumerate(alldata):
#### load up model spec
z = dat['residuals']['phot']['z']
mod_spec = dat['bfit']['spec']
mod_wav = sps.wavelengths
#### load up observed spec
# arrives in maggies * Hz
# change to maggies
spec_dict = prospector_io.load_spectra(dat['objname'])
opt_idx = spec_dict['source'] == 1
obs_wav = spec_dict['obs_lam'][opt_idx]
obs_spec = spec_dict['flux'][opt_idx] / (3e18 / obs_wav)
#### find adjoining sections in model
minwav = np.min(obs_wav)
maxwav = np.max(obs_wav)
lamlim = (2800,7500)
lower_join = (mod_wav > lamlim[0]) & (mod_wav < minwav)
upper_join = (mod_wav < lamlim[1]) & (mod_wav > maxwav)
#### normalize and combine
# take 300 angstrom slices on either side
dellam = 300
low_obs = obs_wav < minwav+dellam
low_mod = (mod_wav < minwav + dellam) & (mod_wav > minwav)
up_obs = obs_wav > maxwav-dellam
up_mod = (mod_wav > maxwav - dellam) & (mod_wav < maxwav)
# avoid known emission lines: [OII], [NII], Halpha, [SII]
elines = np.array([3727,6549,6563,6583,6717,6731])
lambuff = 22
for line in elines:
low_obs[(obs_wav > line - lambuff) & (obs_wav < line + lambuff)] = False
low_mod[(mod_wav > line - lambuff) & (mod_wav < line + lambuff)] = False
up_obs[(obs_wav > line - lambuff) & (obs_wav < line + lambuff)] = False
up_mod[(mod_wav > line - lambuff) & (mod_wav < line + lambuff)] = False
# calculate mean
lownorm = np.mean(obs_spec[low_obs]) / np.mean(mod_spec[low_mod])
upnorm = np.mean(obs_spec[up_obs]) / np.mean(mod_spec[up_mod])
# combine
comblam = np.concatenate((mod_wav[lower_join],obs_wav,mod_wav[upper_join]))
combspec = np.concatenate((mod_spec[lower_join]*lownorm,obs_spec,mod_spec[upper_join]*upnorm))
# plot conjoined spectra
if True:
# observed spectra
plt.plot(obs_wav,np.log10(obs_spec),color='black')
#plt.plot(obs_wav[up_obs],np.log10(obs_spec[up_obs]),color='purple')
#plt.plot(mod_wav[up_mod],np.log10(mod_spec[up_mod]),color='green')
# post-break model
plt.plot(mod_wav[lower_join],np.log10(mod_spec[lower_join]*lownorm),color='red')
plt.plot(mod_wav[upper_join],np.log10(mod_spec[upper_join]*upnorm),color='red')
plt.plot(mod_wav[lower_join],np.log10(mod_spec[lower_join]),color='grey')
plt.plot(mod_wav[upper_join],np.log10(mod_spec[upper_join]),color='grey')
# limits, save
plt.xlim(2800,7200)
plt.savefig('/Users/joel/code/python/prospector_alpha/plots/'+runname+'/pcomp/specnorm/'+dat['objname']+'.png',dpi=100)
plt.close()
#### convert combspec from maggies to Lsun/Hz
pc2cm = 3.08568E18
dfactor = (1+z) / ( 4.0 * np.pi * (dat['residuals']['phot']['lumdist']*1e6*pc2cm)**2)
combspec *= 3631*1e-23 / constants.L_sun.cgs.value / dfactor # to Jy, to erg/s/cm^2/Hz, to Lsun/cm^2/Hz, to Lsun/Hz
#### integrate spectra, save mags
optphot[0,ii],_ = prosp_dutils.integrate_mag(comblam,combspec,'SDSS Camera u',alt_file='/Users/joel/code/fsps/data/allfilters.dat',z=z)
optphot[1,ii],_ = prosp_dutils.integrate_mag(comblam,combspec,'SDSS Camera g',alt_file='/Users/joel/code/fsps/data/allfilters.dat',z=z)
optphot[2,ii],_ = prosp_dutils.integrate_mag(comblam,combspec,'SDSS Camera r',alt_file='/Users/joel/code/fsps/data/allfilters.dat',z=z)
optphot[:,ii] = 10**(-0.4*optphot[:,ii])
#### save observed mags
run_params = model_setup.get_run_params(param_file=parm_basename[ii])
obs = model_setup.load_obs(**run_params)
obsphot[0,ii] = obs['maggies'][obs['filters'] == 'SDSS_u']
obsphot[1,ii] = obs['maggies'][obs['filters'] == 'SDSS_g']
obsphot[2,ii] = obs['maggies'][obs['filters'] == 'SDSS_r']
##### plot
kwargs = {'color':'0.5','alpha':0.8,'histtype':'bar','lw':2,'normed':1,'range':(0.5,2.0)}
nbins = 80
x = obsphot / optphot
t**s = [r'(photometric flux / spectral flux) [u-band]',
r'(photometric flux / spectral flux) [g-band]',
r'(photometric flux / spectral flux) [r-band]']
#### ALL GALAXIES
fig, axes = plt.subplots(1, 3, figsize = (18.75,6))
for ii, ax in enumerate(axes):
num, b, p = ax.hist(x[ii,:],nbins,**kwargs)
save_xlim = ax.get_xlim()
b = (b[:-1] + b[1:])/2.
ax.set_ylabel('N')
ax.set_xlabel(t**s[ii])
ax.set_xlim(save_xlim)
ax.xaxis.set_major_locator(MaxNLocator(5))
plt.savefig('/Users/joel/code/python/prospector_alpha/plots/'+runname+'/pcomp/spectral_integration.png',dpi=dpi)
plt.close()
out = {'obs_phot':obsphot,'spec_phot':optphot}
prospector_io.save_spec_cal(out,runname=runname)
def compare_sed():
### custom parameter file
### where mass is a six-parameter thing
param_file = 'brownseds_np_params.py'
run_params = model_setup.get_run_params(param_file=param_file)
sps = model_setup.load_sps(**run_params)
model = model_setup.load_model(**run_params)
obs = model_setup.load_obs(**run_params)
thetas = model.initial_theta
### create star formation history and metallicity history
mformed = 1e10
m_constant = return_declining_sfh(model,mformed, tau=1e12)
met = return_zt(model)
#met = np.ones_like(met)
thetas[model.theta_labels().index('logzsol')] = 0.0
### simple
i1, i2 = model.theta_index['mass']
thetas[i1:i2] = m_constant
nsamp = 21
met_comp = np.linspace(-1,0,nsamp)
spec, phot = [], []
for m in met_comp:
thetas[model.theta_labels().index('logzsol')] = m
specx, photx, x = model.mean_model(thetas, obs, sps=sps)
spec.append(specx)
phot.append(photx)
### complex
for i in xrange(met.shape[0]):
# zero out all masses
thetas[i1:i2] = np.zeros_like(m_constant)
# fill in masses and metallicities
thetas[model.theta_labels().index('logzsol')] = np.log10(met[i])
thetas[i1+i] = m_constant[i]
# generate spectrum, add to existing spectrum
sps.ssp.params.dirtiness = 1
specx, photx, x = model.mean_model(thetas, obs, sps=sps)
if i == 0:
spec_agez, phot_agez = specx, photx
else:
spec_agez += specx
phot_agez += photx
### plot
fig, ax = plt.subplots(1,1, figsize=(8, 7))
cmap = get_cmap(nsamp)
for i in xrange(0,nsamp,4): ax.plot(sps.wavelengths/1e4, np.log10(spec[i] / spec_agez),color=cmap(i), label=met_comp[i])
ax.axhline(0, linestyle='--', color='0.1')
ax.legend(loc=4,prop={'size':20},title=r'log(Z/Z$_{\odot}$) [fixed]')
ax.set_xlim(0.1,10)
ax.set_xscale('log',nonposx='clip',subsx=(2,5))
ax.xaxis.set_minor_formatter(minorFormatter)
ax.xaxis.set_major_formatter(majorFormatter)
ax.set_ylabel(r'log(f$_{\mathrm{Z}_{\mathrm{fixed}}}$ / f$_{\mathrm{Z(t)}}$)')
ax.set_xlabel('wavelength [microns]')
ax.set_ylim(-0.4,0.4)
plt.tight_layout()
plt.show()
print 1/0
mpl.rc('text', usetex=True)
mpl.rcParams['text.latex.preamble']=[r"\usepackage{bm}"]
mpl.rcParams.update({'font.size': 30})
sfhsize = 40
#### parameter plot style
colors = ['blue', 'black', 'red']
alpha = 0.8
lw = 3
#### define model
param_file='/Users/joel/code/python/prospector_alpha/parameter_files/brownseds_np/brownseds_np_params.py'
run_params = model_setup.get_run_params(param_file=param_file)
sps = model_setup.load_sps(**run_params)
model = model_setup.load_model(**run_params)
obs = model_setup.load_obs(**run_params)
#### output locations
out1 = '/Users/joel/code/python/prospector_alpha/plots/brownseds_np/pcomp/model_diagram1.png'
out2 = '/Users/joel/code/python/prospector_alpha/plots/brownseds_np/pcomp/model_diagram2.png'
#### set initial theta
# 'logmass','sfr_fraction_1', sfr_fraction_2,
# sfr_fraction_3, sfr_fraction_4, 'sfr_fraction_5',
# dust2', 'logzsol', 'dust_index',
# 'dust1', 'duste_qpah', 'duste_gamma',
# 'duste_umin'
labels = model.theta_labels()
itheta = np.array([10, 0.02, 0.1,
0.15, 0.2, 0.25,