def read_results(filename):
res, obs, mod = reader.results_from(path_res + filename)
# update data table
res['run_params']['data_table'] = path_wdir + 'data/halo7d_with_phot.fits'
mod = reader.get_model(res)
# update filters
filternames = [str(ii) for ii in obs['filters']]
obs['filters'] = load_filters(filternames, directory=filter_folder)
# load sps
sps = reader.get_sps(res)
return (res, obs, mod, sps)
def get_results(cluster, ID, binNum, results_type='dynesty', version=''):
# convenience function
infile = '{}/h5/{}_ID_{}_bin_{}{}.h5'.format(cluster, cluster, ID, binNum,
version)
result, obs, _ = reader.results_from(infile, dangerous=True)
model = reader.get_model(result)
sps = reader.get_sps(result)
# t_hr = result['sampling_duration']/3600
# print('The sampling took {:.2f} hours'.format(t_hr))
return result, obs, model, sps
# --------------
# 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
param_file = (result['run_params'].get('param_file',
''), result.get("paramfile_text", ''))
path, filename = os.path.split(param_file[0])
modname = filename.replace('.py', '')
user_module = import_module_from_string(param_file[1], modname)
spec_noise, phot_noise = user_module.load_gp(**run_params)
# -----------------
# LnP function as global
# ------------------
N = res['lnprobability'].shape[0] * res['lnprobability'].shape[1] # all values
print('N is', N)
imaxmult = np.argpartition(res['lnprobability'], -N, axis=None)[-N:]
csz = res["chain"].shape
i, j = np.unravel_index(imax, res['lnprobability'].shape)
theta_max = res['chain'][i, j, :].copy()
flatchain = res["chain"].reshape(csz[0] * csz[1], csz[2])
max_percentile = np.zeros(model.ndim)
for i in range(model.ndim):
max_percentile[i] = stats.percentileofscore(flatchain[:, i], theta_max[i])
print('max percentile', max_percentile[i])
sps = reader.get_sps(res)
# generate fake obs to get full resolution spectra
fake_obs = obs.copy()
fake_obs['spectrum'] = None
fake_obs['wavelength'] = None
spec, phot, x = model.predict(theta_max, obs=obs, sps=sps)
full_spec = model.predict(theta_max, obs=fake_obs, sps=sps)[0]
wave_eff = [f.wave_effective for f in res['obs']['filters']]
print('Maximum posterior probability parameters: ')
file = open(args.path + "/Analysis/bestfit_params.txt", "w")
for i in range(len(res['theta_labels'])):
print('now reading files')
galaxy_num = "{:03d}".format(galaxy)
infile = prosp_dir + '/galaxy_' + str(int(galaxy)) + '_pagboff.h5'
globfiles = glob.glob(infile)
for prosp_output in glob.glob(infile):
print(prosp_output)
res, obs, mod = pread.results_from(prosp_output)
pdfile = pd_dir + '/grid_physical_properties.305_galaxy' + str(
int(galaxy)) + '.npz'
pd_data = np.load(pdfile)
int_mass = np.log10(np.sum(pd_data['grid_star_mass']) / 1.989e33)
print('sps and model')
#sps = build_sps()
sps = pread.get_sps(res)
#mod = build_model()
thetas = mod.theta_labels()
thetas_50 = []
thetas_16 = []
thetas_84 = []
print('quantiles for all thetas')
for theta in thetas:
idx = thetas.index(theta)
chain = [item[idx] for item in res['chain']]
quan = quantile(chain, [.16, .5, .84])
thetas_50.append(quan[1])
thetas_16.append(quan[0])
thetas_84.append(quan[2])
import numpy as np
import matplotlib.pyplot as plt
import prospect.io.read_results as reader
results_type = 'dynesty' # 'emcee' or 'dynesty'
verbose = True
plots = True
# get the results and observation dictionaries
result, obs, _ = reader.results_from('demo_params_{}.h5'.format(results_type),
dangerous=True)
# also get the sps and model objects. This works if using a parameter file with
# `build_*` methods
sps = reader.get_sps(result)
model = reader.get_model(result)
# print the contents of the `results` dictionary
if verbose:
print(result.keys())
# STEP 1: invetigate the parameter traces
if plots:
if results_type == 'emcee':
chosen = np.random.choice(result['run_params']['nwalkers'],
size=10,
replace=False)
tracefig = reader.traceplot(result, figsize=(16, 9), chains=chosen)
else:
tracefig = reader.traceplot(result, figsize=(16, 9))
textFile.close()
# Calculate Prospector attenuation curves
# Take bestfit parameters from dust fit, use only
# non-dust related parameters with nodust model
# to give dust-free spectrum
if attBool:
nodust_path = '/scratch/ntf229/RT_fit/projects/' + galaxies[
i] + '/maxLevel13/wavelengths601/numPhotons1e9/niter4096/inc0/nodust/' + phot[
k] + '/walkers512/Prospector_files/fit.h5'
dust_path = '/scratch/ntf229/RT_fit/projects/' + galaxies[
i] + '/maxLevel13/wavelengths601/numPhotons1e9/niter4096/inc0/dust/dustFraction0.2/maxTemp8000/' + phot[
k] + '/walkers512/Prospector_files/fit.h5'
nodust_res, nodust_obs, nodust_model = reader.results_from(
nodust_path)
nodust_sps = reader.get_sps(nodust_res)
dust_res, dust_obs, dust_model = reader.results_from(dust_path)
dust_sps = reader.get_sps(dust_res)
# generate fake obs to get full resolution spectra
nodust_fake_obs = nodust_obs.copy()
nodust_fake_obs['spectrum'] = None
nodust_fake_obs['wavelength'] = None
nodust_theta_max = [
estMasses[i, k], estLogzsol[i, k], estAges[i, k], estTaus[i, k]
] # non-dust related bestfit parameters from dust fit
nodust_full_spec = nodust_model.predict(nodust_theta_max,
obs=nodust_fake_obs,
sps=nodust_sps)[0]
dust_fake_obs = dust_obs.copy()
# --------------
# 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
param_file = (result['run_params'].get('param_file', ''),
result.get("paramfile_text", ''))
path, filename = os.path.split(param_file[0])
modname = filename.replace('.py', '')
user_module = import_module_from_string(param_file[1], modname)
spec_noise, phot_noise = user_module.load_gp(**run_params)
# -----------------
# LnP function as global
# ------------------
lw=1.,
label='True Attenuation')
#plt.xscale('log')
plt.xlim([900, 7000])
plt.xlabel('$\lambda$ [$\AA$]', fontsize=23)
plt.ylim([0, 5])
plt.ylabel('A$_{\lambda}$ / A$_{V}$', fontsize=23)
plt.legend(loc='best', fontsize=15)
plt.title('Galaxy ' + str(gal) + ': KC Attenuation', fontsize=23)
#plt.savefig('/Users/sidneylower/Documents/prosp_final_plots/attenuation_curve'+str(gal)+'.png', dpi=300)
# In[9]:
sps_dir = pread.get_sps(res_dir)
thetas_dir, theta_best_dir = get_best(res_dir)
dust2_idx_dir = [i for i, s in enumerate(thetas_dir) if 'dust2' in s]
dust2_dir = theta_best_dir[dust2_idx_dir[0]]
dustindex_idx_dir = [i for i, s in enumerate(thetas_dir) if 'dust_index' in s]
dust_index_dir = theta_best_dir[dustindex_idx_dir[0]]
# In[10]:
sps_dust1 = pread.get_sps(res_dust1)
thetas_dust1, theta_best_dust1 = get_best(res_dust1)
dust2_idx_dust1 = [i for i, s in enumerate(thetas_dust1) if 'dust2' in s]
dust2_dust1 = theta_best_dust1[dust2_idx_dust1[0]]
dustindex_idx_dust1 = [
i for i, s in enumerate(thetas_dust1) if 'dust_index' in s
]
