def read_results(runs):
results, models = [], []
for run in runs:
res, pr, mod = bread.read_pickles(run, model_file=run.replace('_mcmc','_model'))
results.append(res)
models.append(mod)
return results, models
'dust2': ["Cassisi et al. (2008)"]}
real_cal="results/ggc_ngc1851_1432787257_mcmc"
real_uncal="results/ggc_ngc1851_uncal_tightprior_1433448554_mcmc"
real_cal = "results/real_specphot_otherjitter.NGC1851.calTrue_5699610_1441238007_mcmc"
real_uncal = "results/real_specphot_otherjitter.NGC1851.calFalse_5699625_1441238175_mcmc"
real_cal = "results/real_specphot_mtrim_llnoise_v2.NGC1851.calTrue_5912384_1445132251_mcmc"
real_uncal = "results/real_specphot_mtrim_llnoise_v2.NGC1851.calFalse_5912391_1445132211_mcmc"
real_phot = "results/real_photonly.NGC1851.calTrue_1441258499_mcmc"
resfiles = [real_cal, real_uncal]#, real_phot]
clr = ['green', 'orange', 'red']
results = [bread.read_pickles(rfile, model_file=rfile.replace('mcmc','model'))[0]
for rfile in resfiles]
obsdat = results[0]['obs']
showpars = np.array(['mass', 'tage', 'zmet', 'dust2'])
parlims = np.array([[0.4, 4],
[5, 11],
[-1.3, -0.75],
[0, 0.5]])
parlims = np.array(4 * [[None, None]])
npar = len(showpars)
fig = pl.figure()
gs = gridspec.GridSpec(npar, npar)
for i, p1 in enumerate(showpars):
dax = pl.subplot(gs[i,i])
if __name__ == "__main__":
bcolor='green'
if len(sys.argv) > 1:
resfile=sys.argv[1]
try:
bcolor = sys.argv[2]
except:
pass
else:
resfile = 'results/ggc_mock.u0.t1.0_z0.0_a0.5_1426268715_mcmc'
model_file = resfile.replace('_mcmc','_model')
pcolor='orange'
nsample = 10
res, pr, mod = bread.read_pickles(resfile, model_file=model_file)
obsdat = res['obs']
# Get samples and component spectra
fsamples = np.random.uniform(0,1,nsample)
thetas, start, samples = theta_samples(res, samples=fsamples, start=0.75, thin=1)
fwave, _, _, fspecvecs = comp_samples_fullspec(thetas, mod, obsdat,
sps=sps, gp=None)
mwave, mospec, mounc, specvecs = comp_samples(thetas, mod, obsdat,
sps=sps, gp=gp)
pwave, mosed, mosed_unc, pvecs = comp_samples_phot(thetas, mod, obsdat, sps=sps)
# Get truth spectrum and sed
# THERE IS NO TRUTH
# Get calibration vector
results, models = read_results(runs)
noiseless_run = 'results/ggc_mock_specphot_linear.u0.t12.0_z0.0_a0.5_5313542_1432675345_mcmc'
bcolor = 'maroon'
if as_hist:
hfig, haxes = pl.subplots(2,2)
for j, (res, mod) in enumerate(zip(results, models)):
samples, pord = hist_samples(res, mod, pnames, thin=thin,
start=sfraction)
for i, (ax, name) in enumerate(zip(haxes.flatten(), pnames)):
ax.hist(samples[:,i], bins=kwargs.get("bins", 50),
histtype="stepfilled",
color=kwargs.get("color", bcolor),
alpha=kwargs.get("alpha",0.3))
res, pr, mod = bread.read_pickles(noiseless_run,
model_file=noiseless_run.replace('_mcmc','_model'))
samples, pord = hist_samples(res, mod, pnames, thin=thin,
start=sfraction)
truths = [res['obs']['mock_params'][k] for k in pord]
for i, (ax, name) in enumerate(zip(haxes.flatten(), pnames)):
ax.hist(samples[:,i], bins=kwargs.get("bins", 50),
histtype="stepfilled",
color=bcolor,
alpha=kwargs.get("alpha",0.3))
ax.axvline(truths[i], color=kwargs.get('truth_color','k'),
label='Mock Truth')
ax.set_xlabel(name, fontsize=8)
ax.set_yticklabels([])
pl.setp(ax.get_xticklabels(), fontsize=8)
hfig.show()
resfile = sys.argv[1]
try:
basecolor = sys.argv[2]
except:
pass
try:
suptitle = sys.argv[3]
except:
pass
else:
resfile = 'results/ggc_mock_photonly.c0.t9.0_z0.0_a0.5_1430274922_mcmc'
model_file = resfile.replace('_mcmc', '_model')
pointcolor = 'orange'
nsample = 10
res, pr, mod = bread.read_pickles(resfile, model_file=model_file)
obsdat = res['obs']
# Get samples and component spectra
fsamples = np.random.uniform(0, 1, nsample)
thetas, start, samples = theta_samples(res,
samples=fsamples,
start=0.75,
thin=1)
fwave, mospec, mounc, specvecs = comp_samples_fullspec(thetas,
mod,
obsdat,
sps=sps,
gp=None)
pwave, mosed, mosed_unc, pvecs = comp_samples_phot(thetas,
mod,
if len(sys.argv) > 1:
resfile=sys.argv[1]
else:
resfile = 'results/ggc_mock.u0.t1.0_z0.0_a0.5_1426268715_mcmc'
model_file = resfile.replace('_mcmc','_model')
showpars_phys = ['mass', 'tage', 'zmet', 'dust2', 'zred', 'sigma_smooth']
showpars_cal = ['poly_coeffs_1', 'poly_coeffs_2', 'spec_norm', 'low_level_amplitude',
'gp_length', 'gp_amplitude', 'gp_jitter', 'gp_jitter_add',
'phot_jitter']
showpars_pc = ['mass', 'tage', 'dust2', 'zmet', 'gp_length'
'spec_norm', 'poly_coeffs_1', 'poly_coeffs_2']
result, pr, model = bread.read_pickles(resfile, model_file=model_file)
ns = result['chain'].shape[0] * result['chain'].shape[1]
start = int(result['chain'].shape[1]*0.75)
start = 0
thin = 20
#sys.exit()
try:
efig = bread.param_evol(result, showpars=showpars_phys+showpars_cal)
efig.savefig(resfile.replace('_mcmc','.pevol.pdf'))
pl.close(efig)
except(IndexError):
print('not enough parameters to show in pevol plot')
try:
tfig = bread.subtriangle(result, showpars=showpars_phys,
start=start, thin=thin)
# rdir+'b192-g242.020.nocal_1405677518.sampler01']
#res = [rdir+'b192-g242.225.cal_1407376313.sampler01']
#res = [rdir+'b192-g242.225.cal_1407608570.sampler01']
#res = [rdir+'b192-g242.225.cal_1409443437.sampler01']
res = [#rdir+'b192-g242.225.cal_1407608570.sampler01']
#rdir+'b192-g242.225.cal_1409443437.sampler01']
#rdir+'b192-g242.225.cal_1409477803.sampler01',
#rdir+'b192-g242.225.cal_1409534549.sampler01',
rdir+'b192-g242.225.cal_1412345250.sampler01']
inlog = True
name = ['B192 cal.', 'B192 no cal.']
for i,r in enumerate(res):
sf, mf = r+'_mcmc', r+'_model'
result, pr, model = diagnostics.read_pickles(sf, model_file = mf)
result['model'] = model
#best = np.argmin([p.fun for p in powell_guesses])
#result['optimizer_results'] = pr[best]
of = result['run_params']['outfile'].split('/')[-1]
of = of.replace('.','_')
ns = result['chain'].shape[0] * result['chain'].shape[1]
sample = [int(s * ns) for s in samples]
gfig = gp_figure(result, samples=sample, inlog=inlog)
gfig.axes[0].set_yscale('log')
gfig.savefig('gp_figure.png')
sfig = spec_figure(result, samples=sample,
linewidth = 0.5, xlim = (3650, 7300),
if len(sys.argv) > 1:
specphot = sys.argv[1]
try:
bcolor = sys.argv[2]
except:
pass
try:
suptitle = sys.argv[3]
except:
suptitle = ''
else:
specphot = 'results/ggc_mock_specphot_linear.u0.t9.0_z0.0_a0.5_5280432_1431898211_mcmc'
resfiles = [specphot]
clr = [bcolor]
results = [
bread.read_pickles(rfile, model_file=rfile.replace('mcmc', 'model'))[0]
for rfile in resfiles
]
obsdat = results[0]['obs']
showpars = np.array([
'dust2', 'gp_jitter', 'gp_length', 'gp_amplitude', 'spec_norm',
'poly_coeffs_1', 'poly_coeffs_2'
])
parlims = np.array([[0.0, 1.5], [None, None], [100, 1000], [0, 0.5],
[-1, 1], [-10, 10], [-10, 10]])
npar = len(showpars)
fig = pl.figure(figsize=(10, 8))
gs = gridspec.GridSpec(npar, npar)
for i, p1 in enumerate(showpars):
sps.params['logzsol'] = 0.0
else:
# we are using MILES with better optical resolution
sps.params['sigma_smooth'] = 0.0
halpha = (w > 6556) & (w < 6573)
sps.params['zmet'] = 4
sps.params['logzsol'] = 0.0
ns = 40
Cha = np.zeros(ns)
gtage = np.random.uniform(7.0, 14.0, ns)
gtau = np.random.uniform(0.1, 10.0, ns)
mcmc_filename = os.getenv('APPS')+'/prospector_alpha/results/dtau_intmet/dtau_intmet_0181_15268_1424608305_mcmc'
model_filename = os.getenv('APPS')+'/prospector_alpha/results/dtau_intmet/dtau_intmet_0181_15268_1424608305_model'
sample_results, powell_results, model = read_results.read_pickles(mcmc_filename, model_file=model_filename,inmod=None)
# set redshift
sample_results['model'].params['zred'] = np.atleast_1d(0.00)
#params = sample_results['model'].params
#print 1/0
#for kk,nn in params.iteritems():
# if kk in sps.params.all_params:
# if kk == 'zmet':
# vv = np.abs(nn - (np.arange( len(sps.zlegend))+1)).argmin()+1
# else:
# vv = nn.copy()
# sps.params[kk] = vv
#get the spectrum with neb for a variety of neb parameters, compute line luminosity