import time, sys, os
import numpy as np
np.errstate(invalid='ignore')
from prospect.models import model_setup
from prospect.io import write_results
from prospect.io import read_results as pr
from prospect import fitting
from prospect.likelihood import lnlike_spec, lnlike_phot, write_log, chi_spec, chi_phot
# --------------
# Read command line arguments
# --------------
sargv = sys.argv
argdict = {'restart_from': '', 'niter': 1024}
clargs = model_setup.parse_args(sargv, argdict=argdict)
# ----------
# Result object and Globals
# ----------
result, global_obs, global_model = pr.results_from(clargs["restart_from"])
is_emcee = (len(result["chain"].shape) == 3) & (result["chain"].shape[0] > 1)
assert is_emcee, "Result file does not have a chain of the proper shape."
# SPS Model instance (with libraries check)
sps = pr.get_sps(result)
run_params = result["run_params"]
run_params.update(clargs)
# Noise model (this should be doable via read_results)
from prospect.models.model_setup import import_module_from_string
def post_processing(out_file, param_file, full_h5file=True, out_incl=False, **kwargs):
"""
Driver. Loads output, runs post-processing routine.
"""
# Dense, complex, terrible bookkeeping on my part here based on my naming system for prospector output files
obj = ''
field = ''
base = ''
count = 0
slash = 0
for i in kwargs['outname']:
if i == '/':
slash += 1
elif i == '_':
count += 1
elif out_incl:
if slash == 2 and count == 1:
obj += i
elif count == 2:
field += i
elif count == 3:
base += i
elif count == 4:
break
elif full_h5file:
if slash == 13 and count == 1:
obj += i
elif count == 2:
field += i
elif count == 3:
base += i
elif count == 4:
break
elif count == 0:
obj += i
elif count == 1:
field += i
elif count == 2:
base += i
elif count == 3 and not outy:
break
print(obj, field)
full_base = obj + '_' + field + '_' + base
img_base = 'img/' + full_base # /home/jonathan/img' +
print(full_base, img_base)
pkl = 'out.pkl'
res, pr, mod = bread.results_from(out_file)
print('bread')
# create flatchain, run post-processing!
res['flatchain'] = prosp_dutils.chop_chain(res['chain'], **res['run_params'])
res['flatprob'] = prosp_dutils.chop_chain(res['lnprobability'], **res['run_params'])
extra_output = calc_extra_quantities(res, **kwargs)
print('extra calculated')
print(base)
# choose correct folder where .h5 file is stored based on param file name
if base == 'corr':
folder = 'pkl_ecorr/' # 'pkl_ncorr/'
elif base == 'fico':
folder = 'pkl_efico/' # 'pkl_nfico/'
elif base == 'masstest':
folder = 'pkl_masstest/'
elif base == 'tt':
folder = 'pkl_tt/'
else:
folder = 'pkl_simsfh/'
'''
if base == 'thirty':
folder = 'etpkls/'
elif base == 'nth':
folder = 'ntpkls/'
elif base == 'fixedmet':
folder = 'pkls/'
elif base == 'otherbins':
folder = 'opkls/'
elif base == 'noelg':
folder = 'nmpkls/'
elif base == 'nother':
folder = 'nopkls/'
elif base == 'vary':
folder = 'ecorr_pkl/'
# folder = 'evar_pkl/'
elif base == 'noneb' or 'evarnoneb':
folder = 'nonebpkls/'
elif base == 'efifty2':
folder = 'efifty2_pkls/'
elif base == 'evar2':
folder = 'evar2_pkls/'
elif base == 'masstest':
folder = 'pkl_masstest'
'''
# folder = 'evar2_pkls/' # 'efifty2_pkls/'
# pkl extra output!
extra = folder + full_base + '_extra_' + pkl # full_base + '_extra_' + pkl
print(extra)
with open(extra, 'wb') as newfile: # 'wb' because binary format
pickle.dump(extra_output, newfile, pickle.HIGHEST_PROTOCOL)
print('extra pickled')
# PRINT TRACE SHOWING HOW ITERATIONS PROGRESS FOR EACH PARAMETER
# I edited param_evol to also store lnprob, but this is a silly and long-obsolete way of doing this
tracefig = bread.param_evol(res) # prints tracefig
plt.title(full_base) # BUCKET just added
# plt.savefig(img_base + '_tracefig.png', bbox_inches='tight')
# plt.show()
# FIND WALKER, ITERATION THAT GIVE MAX PROBABILITY
# a result of the silly, long-obsolete way I'm grabbing lnprob above
prob = res['lnprobability'][..., 0:]
print('max', prob.max())
row = prob.argmax() / len(prob[0])
col = prob.argmax() - row * len(prob[0])
walker, iteration = row, col
print(walker, iteration)
# PRINT CORNERFIG CONTOURS/HISTOGRAMS FOR EACH PARAMETER
bread.subtriangle(res, start=-1000, thin=5, show_titles=True)
plt.title(full_base) # BUCKET just added
# plt.savefig(img_base + '_cornerfig.png', bbox_inches='tight')
# plt.show()
# For FAST: truths=[mass, age, tau, dust2] (for 1824: [9.78, 0.25, -1., 0.00])
# We need the correct sps object to generate models
sargv = sys.argv
argdict = {'param_file': param_file}
clargs = model_setup.parse_args(sargv, argdict=argdict)
run_params = model_setup.get_run_params(argv=sargv, **clargs)
sps = model_setup.load_sps(**run_params)
print('sps')
# GET MODELED SPECTRA AND PHOTOMETRY
# These have the same shape as the obs['spectrum'] and obs['maggies'] arrays.
spec, phot, mfrac = mod.mean_model(res['chain'][walker, iteration, :], obs=res['obs'], sps=sps)
print('spec')
# PLOT SPECTRUM
wave = [f.wave_effective for f in res['obs']['filters']]
wave = np.asarray(wave)
# CHANGING OBSERVED TO REST FRAME WAVELENGTH
# grabbing data from catalogs
if field == 'cdfs':
datname = '/home/jonathan/cdfs/cdfs.v1.6.11.cat'
zname = '/home/jonathan/cdfs/cdfs.v1.6.9.awk.zout'
elif field == 'cosmos':
datname = '/home/jonathan/cosmos/cosmos.v1.3.8.cat' # main catalog
zname = '/home/jonathan/cosmos/cosmos.v1.3.6.awk.zout' # redshift catalog
elif field == 'uds':
datname = '/home/jonathan/uds/uds.v1.5.10.cat'
zname = '/home/jonathan/uds/uds.v1.5.8.awk.zout'
elif field == 'sim': # hacking for now
datname = '/home/jonathan/cosmos/cosmos.v1.3.8.cat' # main catalog
zname = '/home/jonathan/cosmos/cosmos.v1.3.6.awk.zout' # redshift catalog
# photometry catalog
with open(datname, 'r') as f:
hdr = f.readline().split()
dtype = np.dtype([(hdr[1], 'S20')] + [(n, np.float) for n in hdr[2:]])
dat = np.loadtxt(datname, comments='#', delimiter=' ', dtype=dtype)
# redshift catalog
with open(zname, 'r') as fz:
hdr_z = fz.readline().split()
dtype_z = np.dtype([(hdr_z[1], 'S20')] + [(n, np.float) for n in hdr_z[2:]])
zout = np.loadtxt(zname, comments='#', delimiter=' ', dtype=dtype_z)
# if z_spec exists, use it; else use best-fit z_phot
idx = dat['id'] == obj # array filled with: False for all dat['id'] != obj, True for dat['id'] == obj
zred = zout['z_spec'][idx][0]
if zred == -99:
zred = zout['z_peak'][idx][0]
print('redshift', zred)
# convert stored wavelengths to rest frame wavelengths
wave_rest = []
for j in range(len(wave)):
wave_rest.append(wave[j]/(1 + zred)) # 1 + z = l_obs / l_emit --> l_emit = l_obs / (1 + z)
wave_rest = np.asarray(wave_rest)
# OUTPUT SED results to pkls
full_base = folder + full_base
write_res = full_base + '_res_' + pkl # results
with open(write_res, 'wb') as newfile: # 'wb' because binary format
pickle.dump(res, newfile, pickle.HIGHEST_PROTOCOL) # res includes res['obs']['maggies'], ['maggies_unc'], etc.
write_sed = full_base + '_sed_' + pkl # model sed
with open(write_sed, 'wb') as newfile: # 'wb' because binary format
pickle.dump(phot, newfile, pickle.HIGHEST_PROTOCOL)
write_restwave = full_base + '_restwave_' + pkl # rest frame wavelengths
with open(write_restwave, 'wb') as newfile: # 'wb' because binary format
pickle.dump(wave_rest, newfile, pickle.HIGHEST_PROTOCOL)
write_spec = full_base + '_spec_' + pkl # spectrum
with open(write_spec, 'wb') as newfile: # 'wb' because binary format
pickle.dump(spec, newfile, pickle.HIGHEST_PROTOCOL)
write_sps = full_base + '_spswave_' + pkl # wavelengths that go with spectrum
with open(write_sps, 'wb') as newfile: # 'wb' because binary format
pickle.dump(sps.wavelengths, newfile, pickle.HIGHEST_PROTOCOL)
# OUTPUT chi_sq results to pkls
chi_sq = ((res['obs']['maggies'] - phot) / res['obs']['maggies_unc']) ** 2
write_chisq = full_base + '_chisq_' + pkl
with open(write_chisq, 'wb') as newfile: # 'wb' because binary format
pickle.dump(chi_sq, newfile, pickle.HIGHEST_PROTOCOL)
write_justchi = full_base + '_justchi_' + pkl
with open(write_justchi, 'wb') as newfile:
pickle.dump((res['obs']['maggies'] - phot) / res['obs']['maggies_unc'], newfile, pickle.HIGHEST_PROTOCOL)
# PLOT chi_sq
plt.plot(wave_rest, chi_sq, 'o', color='b')
plt.title(str(obj) + r' $\chi^2$')
plt.xlabel('Rest frame wavelength [angstroms]')
plt.ylabel(r'$\chi^2$')
# plt.savefig(img_base + '_chisq.png', bbox_inches='tight')
# plt.show()
# HOW CONVERGED IS THE CODE?? LET'S FIND OUT! --> kl test
parnames = np.array(res['model'].theta_labels())
fig, kl_ax = plt.subplots(1, 1, figsize=(7, 7))
for l in xrange(parnames.shape[0]):
kl_ax.plot(res['kl_iteration'], np.log10(res['kl_divergence'][:, l]), 'o', label=parnames[l], lw=1.5,
linestyle='-', alpha=0.6)
# OUTPUT kl test to pkls
write_klit = full_base + '_klit_' + pkl
with open(write_klit, 'wb') as newfile:
pickle.dump(res['kl_iteration'], newfile, pickle.HIGHEST_PROTOCOL)
write_kldvg = full_base + '_kldvg_' + pkl
with open(write_kldvg, 'wb') as newfile:
pickle.dump(res['kl_divergence'], newfile, pickle.HIGHEST_PROTOCOL)
kl_ax.set_ylabel('log(KL divergence)')
kl_ax.set_xlabel('iteration')
# kl_ax.set_xlim(0, nsteps*1.1)
kl_div_lim = res['run_params'].get('convergence_kl_threshold', 0.018)
kl_ax.axhline(np.log10(kl_div_lim), linestyle='--', color='red', lw=2, zorder=1)
kl_ax.legend(prop={'size': 5}, ncol=2, numpoints=1, markerscale=0.7)
plt.title(str(obj) + ' kl')
def ems(param_file, out_file, objname='21442', field='cdfs', enames=None):
res, pr, model = bread.results_from(out_file)
# get lnprob, based on bread.param_evol()
chain = res['chain'][..., 0:, :]
lnprob = res['lnprobability'][..., 0:]
# deal with single chain (i.e. nested sampling) results
if len(chain.shape) == 2:
lnprob = lnprob[None, ...]
# tracefig, prob = bread.param_evol(res) # store probability
# plt.show()
print('max', lnprob.max())
row = lnprob.argmax() / len(lnprob[0])
col = lnprob.argmax() - row * len(lnprob[0])
walker, iteration = row, col
print(walker, iteration)
# Get emission lines!
# We need the correct sps object to generate models
sargv = sys.argv
argdict = {'param_file': param_file}
clargs = model_setup.parse_args(sargv, argdict=argdict)
run_params = model_setup.get_run_params(argv=sargv, **clargs)
sps = model_setup.load_sps(**run_params)
print('sps')
# spec, mags, sm = model.mean_model(res['chain'][walker, iteration, :], obs=res['obs'], sps=sps) # spec [maggies/Hz]
print('mean model')
w = sps.wavelengths
### save redshift, lumdist
z = model.params.get('zred', np.array(0.0))
lumdist = model.params.get('lumdist', np.array(0.0))
nebinspec = model.params.get('nebemlineinspec', True)
model.params['zred'] = np.array(0.0)
if lumdist:
model.params['lumdist'] = np.array(1e-5)
if nebinspec == False:
model.params['nebemlineinspec'] = True
### if we want restframe optical photometry, generate fake obs file
### else generate NO obs file (don't do extra filter convolutions if not necessary)
obs = {'filters': [], 'wavelength': None}
### calculate SED. comes out as maggies per Hz, @ 10pc
spec, mags, sm = model.mean_model(res['chain'][walker, iteration, :],
obs=obs,
sps=sps) # maggies/Hz at 10pc
w = sps.wavelengths
### reset model
model.params['zred'] = z
if lumdist:
model.params['lumdist'] = lumdist
if nebinspec == False:
model.params['nebemlineinspec'] = False
spec *= dfactor_10pc / constants.L_sun.cgs.value * to_ergs # spec * cm^2 * (s/erg) * erg/maggies = s*cm^2 / Hz
# note erg = [g cm^2 / s^2]
# according to measure_restframe_properties(), above line converts to Lsun / Hz
to_flam = 3e18 / w**2 # for f_nu in erg s^-1 Hz^-1 cm^-2: to_flam [(Ang / s^-1) * (1 / Ang^2)]
spec_flam = spec * to_flam # erg cm^-2 s^-1 ang^-1
smooth_spec = smooth_spectrum(w, spec_flam, 250.0, minlam=3e3, maxlam=7e3)
### load fsps emission line list
loc = os.getenv('SPS_HOME') + '/data/emlines_info.dat'
dat = np.loadtxt(loc,
delimiter=',',
dtype={
'names': ('lam', 'name'),
'formats': ('f16', 'S40')
})
print('env')
print(type(enames))
### define emission lines
# legacy code compatible
if type(enames) == bool:
lines = np.array(
['Hdelta', 'Hbeta', '[OIII]1', '[OIII]2', 'Halpha', '[NII]'])
fsps_name = np.array([
'H delta 4102', 'H beta 4861', '[OIII]4960', '[OIII]5007',
'H alpha 6563', '[NII]6585'
])
else:
lines = enames
fsps_name = enames
print(lines, enames) # None, None
# lines = np.array(['Hdelta', 'Hbeta', '[OIII]1', '[OIII]2', 'Halpha', '[NII]'])
# fsps_name = np.array(['H delta 4102', 'H beta 4861', '[OIII]4960', '[OIII]5007', 'H alpha 6563', '[NII]6585'])
lines = np.array([
'[OII]1', '[OII]2', 'Hdelta', 'Hbeta', '[OIII]1', '[OIII]2', 'Halpha',
'[NII]'
])
fsps_name = np.array([
'[OII]3726', '[OII]3729', 'H delta 4102', 'H beta 4861', '[OIII]4960',
'[OIII]5007', 'H alpha 6563', '[NII]6585'
])
##### measure emission line flux + EQW
out = {}
# print(1, sps.emline_wavelengths) # big long array, 900 to 6*1e6
for jj in xrange(len(lines)):
# if we don't do nebular emission, zero this out
if not hasattr(sps, 'get_nebline_luminosity'):
print('hi')
out[lines[jj]] = {'flux': 0.0, 'eqw': 0.0}
continue
### calculate luminosity (in Lsun)
idx = fsps_name[jj] == dat['name']
# L_so = 3.84*10**33 erg/s
print(sps.params['mass'].sum())
eflux = float(sps.get_nebline_luminosity[idx] *
sps.params['mass'].sum()) # L_sun
elam = float(sps.emline_wavelengths[idx]) # Angstroms!
print(sps.get_nebline_luminosity[idx])
print(eflux, 'e luminosity') # typically 10**40 to 10**42
print(elam, 'elam') # Angstroms
# simple continuum estimation
tidx = np.abs(
sps.wavelengths - elam
) < 100 # True for sps.wavelengths within 100 Angstroms of elam wavelengths
eqw = eflux / np.median(
smooth_spec[tidx]
) # (erg/s) / (erg cm^-2 s^-1 Ang^-1) = Ang * cm**2
eflux *= 3.84 * 10**33 # erg/s (Lum, not flux)
out[lines[jj]] = {'flux': eflux, 'eqw': eqw}
return out, fsps_name
def post_processing(out_file, param_file, out_incl=False, full_h5file=False): # , **kwargs):
"""
Driver. Loads output, runs post-processing routine.
"""
obj = ''
field = ''
base = ''
count = 0
slash = 0
for i in out_file:
if i == '/':
slash += 1
elif i == '_':
count += 1
elif out_incl:
if slash == 1 and count == 1:
obj += i
elif count == 2:
field += i
elif count == 3:
base += i
elif count == 4:
break
elif full_h5file:
if slash == 13 and count == 1: # slash=12
obj += i
elif count == 2:
field += i
elif count == 3:
base += i
elif count == 4:
break
elif count == 0:
obj += i
elif count == 1:
field += i
elif count == 2:
base += i
elif count == 3 and not outy:
break
print(field)
git = '/home/jonathan/.conda/envs/snowflakes/lib/python2.7/site-packages/prospector/git/'
full_base = 'pkl_tfn/' + obj + '_' + field + '_' + base # 'pkl_efastnoem/' # 'pkl_efico/'
pkl = 'out.pkl'
res, pr, mod = bread.results_from(out_file)
print('bread')
# create flatchain, run post-processing
res['flatchain'] = prosp_dutils.chop_chain(res['chain'], **res['run_params'])
res['flatprob'] = prosp_dutils.chop_chain(res['lnprobability'], **res['run_params'])
'''
extra_output = calc_extra_quantities(res) # , **kwargs)
print('extra calculated')
extra = full_base + '_extra_' + pkl
with open(extra, 'wb') as newfile: # 'wb' because binary format
pickle.dump(extra_output, newfile, pickle.HIGHEST_PROTOCOL)
print('extra pickled')
'''
prob = res['lnprobability'][:, 0:]
# PRINT TRACE SHOWING HOW ITERATIONS CONVERGE FOR EACH PARAMETER
# tracefig, prob = bread.param_evol(res) # print tracefig, store probability
# plt.title(full_base) # BUCKET just added
# plt.savefig('img/' + full_base + '_tracefig.png', bbox_inches='tight')
# plt.show()
# FIND WALKER, ITERATION THAT GIVE MAX PROBABILITY
print('max', prob.max())
row = prob.argmax() / len(prob[0])
col = prob.argmax() - row * len(prob[0])
walker, iteration = row, col
print(walker, iteration)
# PRINT CORNERFIG CONTOURS/HISTOGRAMS FOR EACH PARAMETER
'''
bread.subtriangle(res, start=0, thin=5, show_titles=True)
plt.title(full_base) # BUCKET just added
plt.savefig('img/' + full_base + '_cornerfig.png', bbox_inches='tight')
# plt.show()
'''
# For FAST: truths=[mass, age, tau, dust2] (for 1824: [9.78, 0.25, -1., 0.00])
# We need the correct sps object to generate models
sargv = sys.argv
argdict = {'param_file': param_file}
clargs = model_setup.parse_args(sargv, argdict=argdict)
run_params = model_setup.get_run_params(argv=sargv, **clargs)
sps = model_setup.load_sps(**run_params)
print('sps')
# GET MODELED SPECTRA AND PHOTOMETRY
# These have the same shape as the obs['spectrum'] and obs['maggies'] arrays.
spec, phot, mfrac = mod.mean_model(res['chain'][walker, iteration, :], obs=res['obs'], sps=sps)
print('spec')
# PLOT SPECTRUM
wave = [f.wave_effective for f in res['obs']['filters']]
wave = np.asarray(wave)
# CHANGING OBSERVED TO REST FRAME WAVELENGTH
if field == 'cdfs':
datname = '/home/jonathan/cdfs/cdfs.v1.6.11.cat'
zname = '/home/jonathan/cdfs/cdfs.v1.6.9.awk.zout'
elif field == 'cosmos':
datname = '/home/jonathan/cosmos/cosmos.v1.3.8.cat' # main catalog
zname = '/home/jonathan/cosmos/cosmos.v1.3.6.awk.zout' # redshift catalog
elif field == 'uds':
datname = '/home/jonathan/uds/uds.v1.5.10.cat'
zname = '/home/jonathan/uds/uds.v1.5.8.awk.zout'
with open(datname, 'r') as f:
hdr = f.readline().split()
dtype = np.dtype([(hdr[1], 'S20')] + [(n, np.float) for n in hdr[2:]])
dat = np.loadtxt(datname, comments='#', delimiter=' ', dtype=dtype)
with open(zname, 'r') as fz:
hdr_z = fz.readline().split()
dtype_z = np.dtype([(hdr_z[1], 'S20')] + [(n, np.float) for n in hdr_z[2:]])
zout = np.loadtxt(zname, comments='#', delimiter=' ', dtype=dtype_z)
idx = dat['id'] == obj # array filled: False when dat['id'] != obj, True when dat['id'] == obj
print(obj)
zred = zout['z_spec'][idx][0] # z = z_spec
if zred == -99:
zred = zout['z_peak'][idx][0] # if z_spec does not exist, z = z_phot
print('redshift', zred)
wave_rest = [] # REST FRAME WAVELENGTH
for j in range(len(wave)):
wave_rest.append(wave[j] / (1 + zred)) # 1 + z = l_obs / l_emit --> l_emit = l_obs / (1 + z)
wave_rest = np.asarray(wave_rest)
# OUTPUT SED results to files
write_res = full_base + '_res_' + pkl # results
with open(write_res, 'wb') as newfile: # 'wb' because binary format
pickle.dump(res, newfile, pickle.HIGHEST_PROTOCOL) # res includes res['obs']['maggies'] and ...['maggies_unc']
write_sed = full_base + '_sed_' + pkl # model sed
with open(write_sed, 'wb') as newfile: # 'wb' because binary format
pickle.dump(phot, newfile, pickle.HIGHEST_PROTOCOL)
write_restwave = full_base + '_restwave_' + pkl # rest frame wavelengths
with open(write_restwave, 'wb') as newfile: # 'wb' because binary format
pickle.dump(wave_rest, newfile, pickle.HIGHEST_PROTOCOL)
write_spec = full_base + '_spec_' + pkl # spectrum
with open(write_spec, 'wb') as newfile: # 'wb' because binary format
pickle.dump(spec, newfile, pickle.HIGHEST_PROTOCOL)
write_sps = full_base + '_spswave_' + pkl # wavelengths that go with spectrum
with open(write_sps, 'wb') as newfile: # 'wb' because binary format
try:
wlengths = sps.wavelengths
except AttributeError:
wlengths = sps.csp.wavelengths
pickle.dump(wlengths, newfile, pickle.HIGHEST_PROTOCOL)
# pickle.dump(sps.wavelengths, newfile, pickle.HIGHEST_PROTOCOL)
# OUTPUT CHI_SQ results to files
chi_sq = ((res['obs']['maggies'] - phot) / res['obs']['maggies_unc']) ** 2
write_chisq = full_base + '_chisq_' + pkl
with open(write_chisq, 'wb') as newfile: # 'wb' because binary format
pickle.dump(chi_sq, newfile, pickle.HIGHEST_PROTOCOL)
write_justchi = full_base + '_justchi_' + pkl
with open(write_justchi, 'wb') as newfile:
pickle.dump((res['obs']['maggies'] - phot) / res['obs']['maggies_unc'], newfile, pickle.HIGHEST_PROTOCOL)
# PLOT CHISQ
'''
plt.plot(wave_rest, chi_sq, 'o', color='b')
plt.title(str(obj) + r' $\chi^2$')
plt.xlabel('Rest frame wavelength [angstroms]')
plt.ylabel(r'$\chi^2$')
plt.savefig('img/' + full_base + '_chisq.png', bbox_inches='tight')
# plt.show()
'''
# HOW CONVERGED IS THE CODE?? LET'S FIND OUT!
'''
parnames = np.array(res['model'].theta_labels())
fig, kl_ax = plt.subplots(1, 1, figsize=(7, 7))
for l in xrange(parnames.shape[0]):
kl_ax.plot(res['kl_iteration'], np.log10(res['kl_divergence'][:, l]), 'o', label=parnames[l], lw=1.5,
linestyle='-', alpha=0.6)
'''
write_klit = full_base + '_klit_' + pkl
with open(write_klit, 'wb') as newfile:
pickle.dump(res['kl_iteration'], newfile, pickle.HIGHEST_PROTOCOL)
write_kldvg = full_base + '_kldvg_' + pkl
with open(write_kldvg, 'wb') as newfile:
pickle.dump(res['kl_divergence'], newfile, pickle.HIGHEST_PROTOCOL)
'''
import numpy as np
np.errstate(invalid='ignore')
from prospect.models import model_setup
from prospect.io import write_results
from prospect.io import read_results as pr
from prospect import fitting
from prospect.likelihood import lnlike_spec, lnlike_phot, write_log, chi_spec, chi_phot
# --------------
# Read command line arguments
# --------------
sargv = sys.argv
argdict = {'restart_from': '', 'niter': 1024}
clargs = model_setup.parse_args(sargv, argdict=argdict)
# ----------
# Result object and Globals
# ----------
result, global_obs, global_model = pr.results_from(clargs["restart_from"])
is_emcee = (len(result["chain"].shape) == 3) & (result["chain"].shape[0] > 1)
assert is_emcee, "Result file does not have a chain of the proper shape."
# SPS Model instance (with libraries check)
sps = pr.get_sps(result)
run_params = result["run_params"]
run_params.update(clargs)
# Noise model (this should be doable via read_results)
from prospect.models.model_setup import import_module_from_string
def sed(objname, field, res, mod, walker, iteration, param_file, **kwargs):
# PRINT MODEL SED FOR GALAXY
# We need the correct sps object to generate models
sargv = sys.argv
argdict = {'param_file': param_file}
clargs = model_setup.parse_args(sargv, argdict=argdict)
run_params = model_setup.get_run_params(argv=sargv, **clargs)
sps = model_setup.load_sps(**run_params)
# GET MODELED SPECTRA AND PHOTOMETRY
# These have the same shape as the obs['spectrum'] and obs['maggies'] arrays.
spec, phot, mfrac = mod.mean_model(res['chain'][walker, iteration, :], obs=res['obs'], sps=sps)
mean = ((res['obs']['maggies']-phot)/res['obs']['maggies']).mean()
print(mean, 'mean') # print normalized mean difference between model and observations
# PLOT SPECTRUM
wave = [f.wave_effective for f in res['obs']['filters']]
wave = np.asarray(wave)
print('len', len(sps.wavelengths), len(spec))
''' #
plt.plot(sps.wavelengths, spec)
plt.xlabel('Wavelength [angstroms]')
plt.title(str(objname) + ' spec')
plt.show()
# ''' #
''' #
# HOW CONVERGED IS THE CODE?? LET'S FIND OUT!
parnames = np.array(res['model'].theta_labels())
fig, kl_ax = plt.subplots(1, 1, figsize=(7, 7))
for i in xrange(parnames.shape[0]):
kl_ax.plot(res['kl_iteration'], np.log10(res['kl_divergence'][:, i]),
'o', label=parnames[i], lw=1.5, linestyle='-', alpha=0.6)
kl_ax.set_ylabel('log(KL divergence)')
kl_ax.set_xlabel('iteration')
# kl_ax.set_xlim(0, nsteps*1.1)
kl_div_lim = res['run_params'].get('convergence_kl_threshold', 0.018)
kl_ax.axhline(np.log10(kl_div_lim), linestyle='--', color='red', lw=2, zorder=1)
kl_ax.legend(prop={'size': 5}, ncol=2, numpoints=1, markerscale=0.7)
plt.title(str(objname) + ' kl')
plt.show()
# ''' #
# field = kwargs['field']
# CHANGING OBSERVED TO REST FRAME WAVELENGTH
if field == 'cdfs':
datname = '/home/jonathan/cdfs/cdfs.v1.6.11.cat'
zname = '/home/jonathan/cdfs/cdfs.v1.6.9.awk.zout'
elif field == 'cosmos':
datname = '/home/jonathan/cosmos/cosmos.v1.3.8.cat' # main catalog
zname = '/home/jonathan/cosmos/cosmos.v1.3.6.awk.zout' # redshift catalog
elif field == 'uds':
datname = '/home/jonathan/uds/uds.v1.5.10.cat'
zname = '/home/jonathan/uds/uds.v1.5.8.zout'
with open(datname, 'r') as f:
hdr = f.readline().split()
dtype = np.dtype([(hdr[1], 'S20')] + [(n, np.float) for n in hdr[2:]])
dat = np.loadtxt(datname, comments='#', delimiter=' ', dtype=dtype)
with open(zname, 'r') as fz:
hdr_z = fz.readline().split()
dtype_z = np.dtype([(hdr_z[1], 'S20')] + [(n, np.float) for n in hdr_z[2:]])
zout = np.loadtxt(zname, comments='#', delimiter=' ', dtype=dtype_z)
idx = dat['id'] == objname # array filled: False when dat['id'] != objname, True when dat['id'] == objname
zred = zout['z_spec'][idx][0] # z = z_spec
if zred == -99:
zred = zout['z_peak'][idx][0] # if z_spec does not exist, z = z_phot
print(zred)
wave_rest = [] # REST FRAME WAVELENGTH
for i in range(len(wave)):
wave_rest.append(wave[i]/(1 + zred)) # 1 + z = l_obs / l_emit --> l_emit = l_obs / (1 + z)
# PLOT MODEL SED BEST FIT, INPUT PHOT
yerr = res['obs']['maggies_unc']
plt.subplot(111, xscale="log", yscale="log")
plt.errorbar(wave_rest, res['obs']['maggies'], yerr=yerr, marker='o', linestyle='', color='purple',
label='Observed photometry')
plt.plot(wave_rest, phot, 'D', label='Model', color='b', markerfacecolor='None', markersize=10,
markeredgewidth=1.25, markeredgecolor='k') # label='Model at {},{}'.format(walker, iteration)
plt.legend(loc="best", fontsize=20)
plt.title(str(field) + '-' + str(objname) + ' SED')
plt.plot(sps.wavelengths, spec, color='k', alpha=0.5)
plt.xlabel(r'Wavelength (Rest) [$\AA$]')
plt.ylabel('Maggies')
plt.xlim(10**3, 2.5*10**4)
plt.ylim(10**-5, 4*10**3)
plt.show()
# ''' #
# PLOT CHI_SQ BESTFIT
chi_sq = ((res['obs']['maggies'] - phot) / res['obs']['maggies_unc']) ** 2
plt.plot(wave_rest, chi_sq, 'o', color='b')
plt.title(str(objname) + r' $\chi^2$')
plt.xlabel('Rest frame wavelength [angstroms]')
plt.ylabel(r'$\chi^2$')
plt.show()