def show_walkers(self, savefile=None, **kwargs):
"""
Plot the fitted parameters walkers.
Return the figure.
Options
-------
savefile : [string or None]
Give a directory to save the figure.
Default is None (not saved).
showpars : [list(string) or None]
List of the parameters to show.
If None, plot every fitted parameters (listed in 'self.theta_labels').
Defaults is None.
start : [int]
Integer giving the iteration number from which to start plotting.
Default is 0.
chains : [np.array or slice]
If results are from an ensemble sampler, setting 'chain' to an integer
array of walker indices will cause only those walkers to be used in
generating the plot.
Useful for to keep the plot from getting too cluttered.
Default is slice(None, None, None).
truths : [list(float) or None]
List of truth values for the chosen parameters to add them on the plot.
Default is None.
**plot_kwargs:
Extra keywords are passed to the 'matplotlib.axes._subplots.AxesSubplot.plot()' method.
Returns
-------
matplotlib.Figure
"""
import prospect.io.read_results as reader
_data = self._get_plot_data_()
_fig = reader.traceplot(_data, **kwargs)
if savefile is not None:
_fig.savefig(savefile)
return _fig
# 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))
# STEP 2: create corner plots
imax = np.argmax(result['lnprobability'])
if results_type == 'emcee':
i, j = np.unravel_index(imax, result['lnprobability'].shape)
theta_max = result['chain'][i, j, :].copy()
thin = 5
else:
theta_max = result['chain'][imax, :]
thin = 1
if verbose:
def viewer(file_name):
import time, sys, os
import h5py
import numpy as np
import scipy
from matplotlib.pyplot import *
import matplotlib.pyplot as plt
import fsps
import sedpy
import prospect
import emcee
import astropy
import math
from func import *
import prospect.io.read_results as reader
results_type = "emcee" # | "dynesty"
# grab results (dictionary), the obs dictionary, and our corresponding models
# When using parameter files set `dangerous=True`
tem_name = file_name[:-8] + '{}.h5'
#result, obs, _ = reader.results_from(tem_name.format(results_type), dangerous=False)
result, obs, _ = reader.results_from(file_name, dangerous=False)
#The following commented lines reconstruct the model and sps object,
# if a parameter file continaing the `build_*` methods was saved along with the results
#model = reader.get_model(result)
#sps = reader.get_sps(result)
# let's look at what's stored in the `result` dictionary
print(result.keys())
if results_type == "emcee":
chosen = np.random.choice(result["run_params"]["nwalkers"],
size=10,
replace=False)
tracefig = reader.traceplot(result, figsize=(20, 10), chains=chosen)
else:
tracefig = reader.traceplot(result, figsize=(20, 10))
# maximum a posteriori (of the locations visited by the MCMC sampler)
imax = np.argmax(result['lnprobability'])
if results_type == "emcee":
i, j = np.unravel_index(imax, result['lnprobability'].shape)
theta_max = result['chain'][i, j, :].copy()
thin = 5
else:
theta_max = result["chain"][imax, :]
thin = 1
print('MAP value: {}'.format(theta_max))
cornerfig = reader.subcorner(result,
start=0,
thin=thin,
fig=subplots(15, 15, figsize=(27, 27))[0])
# --- Draw final fitting curve ---
run_params = result['run_params']
print(run_params)
model = build_model(**run_params)
print(model)
sps = build_sps(**run_params)
a = 1.0 + model.params.get('zred', 0.0) # cosmological redshifting
# photometric effective wavelengths
wphot = obs["phot_wave"]
# spectroscopic wavelengths
if obs["wavelength"] is None:
# *restframe* spectral wavelengths, since obs["wavelength"] is None
wspec = sps.wavelengths
wspec *= a #redshift them
else:
wspec = obs["wavelength"]
# Get enssential data to calculate sfr
z_fraction = theta_max[5:10]
total_mass = theta_max[0]
agebins = model.params['agebins']
x = np.zeros(6)
for i in range(6):
if i == 5:
x[i] = (agebins[i][0] + agebins[i][1]) / 2
else:
x[i] = (agebins[i][0] + agebins[i + 1][0]) / 2
sfr = prospect.models.transforms.zfrac_to_sfr(total_mass, z_fraction,
agebins)
# Calculate the 16% and 84% value from emcee results
z_fraction1 = []
z_fraction2 = []
z_fraction3 = []
z_fraction4 = []
z_fraction5 = []
for i in range(30):
for j in range(3000):
z_fraction1.append(result['chain'][i][j][5])
z_fraction2.append(result['chain'][i][j][6])
z_fraction3.append(result['chain'][i][j][7])
z_fraction4.append(result['chain'][i][j][8])
z_fraction5.append(result['chain'][i][j][9])
zerr_1 = [np.percentile(z_fraction1, 16), np.percentile(z_fraction1, 84)]
zerr_2 = [np.percentile(z_fraction2, 16), np.percentile(z_fraction2, 84)]
zerr_3 = [np.percentile(z_fraction3, 16), np.percentile(z_fraction3, 84)]
zerr_4 = [np.percentile(z_fraction4, 16), np.percentile(z_fraction4, 84)]
zerr_5 = [np.percentile(z_fraction5, 16), np.percentile(z_fraction5, 84)]
# Build upper and lower z_fraction array
z_max = np.zeros(5)
z_min = np.zeros(5)
for i in range(5):
z_min[i] = eval('zerr_{}[0]'.format(i + 1))
z_max[i] = eval('zerr_{}[1]'.format(i + 1))
#print(z_min)
#print(z_max)
# Build new sfr
sfr_max = prospect.models.transforms.zfrac_to_sfr(total_mass, z_max,
agebins)
sfr_min = prospect.models.transforms.zfrac_to_sfr(total_mass, z_min,
agebins)
# randomly chosen parameters from chain
randint = np.random.randint
if results_type == "emcee":
nwalkers, niter = run_params['nwalkers'], run_params['niter']
# Draw 100 random plots from mcmc
figure(figsize=(16, 16))
ax = plt.subplot(211)
for i in range(100):
# Get data from any random chain
theta = result['chain'][randint(nwalkers), randint(niter)]
mspec_t, mphot_t, mextra = model.mean_model(theta, obs, sps=sps)
# unit conversion to erg/cm^2/s
mspec = (mspec_t * 1e-23) * 3e18 / wspec
mphot = (mphot_t * 1e-23) * 3e18 / wphot
# plot them out
loglog(wspec, mspec, lw=0.7, color='grey', alpha=0.3)
else:
theta = result["chain"][randint(len(result["chain"]))]
# now do it again for best fit model
# sps = reader.get_sps(result) # this works if using parameter files
mspec_map_t, mphot_map_t, _ = model.mean_model(theta_max, obs, sps=sps)
# units conversion to erg/cm^2/s
mspec_map = (mspec_map_t * 1e-23) * 3e18 / wspec
mphot_map = (mphot_map_t * 1e-23) * 3e18 / wphot
ob = (obs['maggies'] * 1e-23) * 3e18 / wphot
ob_unc = (obs['maggies_unc'] * 1e-23) * 3e18 / wphot
# calculate reduced chi^2
chi = np.float128(0)
for i in range(len(wphot)):
var = ob_unc[i]
chi += np.float128((mphot_map[i] - ob[i])**2) / var**2
# Make plot of best fit model
loglog(wspec,
mspec_map,
label='Model spectrum (MAP)',
lw=0.7,
color='green',
alpha=0.7)
errorbar(wphot,
mphot_map,
label='Model photometry (MAP)',
marker='s',
markersize=10,
alpha=0.8,
ls='',
lw=3,
markerfacecolor='none',
markeredgecolor='green',
markeredgewidth=3)
errorbar(wphot,
ob,
yerr=ob_unc,
label='Observed photometry',
ecolor='red',
marker='o',
markersize=10,
ls='',
lw=3,
alpha=0.8,
markerfacecolor='none',
markeredgecolor='red',
markeredgewidth=3)
text(0,
1,
'Chi-squared/N-phot =' + str(chi / len(wphot)),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
# Setup bound
xmin, xmax = np.min(wphot) * 0.6, np.max(wphot) / 0.8
temp = np.interp(np.linspace(xmin, xmax, 10000), wphot, mphot_map)
ymin, ymax = temp.min() * 0.8, temp.max() * 10
xlabel('Wavelength [A]', fontsize=20)
ylabel('Flux Density [erg/cm^2/s]', fontsize=20)
xlim([xmin, xmax])
ylim([ymin, ymax])
legend(loc='lower right', fontsize=15)
tight_layout()
# plt.show()
# plot sfr on the same canvas
from mpl_toolkits.axes_grid.inset_locator import inset_axes
in_axes = inset_axes(
ax,
width="25%", # width = 30% of parent_bbox
height=2, # height : 1 inch
loc='upper center')
in_axes.scatter(10**(x - 9), sfr)
#plt.fill_between(10**(x-9), sfr_min, sfr_max)
xlabel('Age [Gyr]', fontsize=10)
ylabel('SFR [M_sun/yr]', fontsize=10)
#plt.xscale('log')
plt.yscale('log')
# plot residue as subplot
figure(figsize=(16, 8))
plt.subplot(212, sharex=ax)
import pylab
residue = abs(mphot_map - ob)
scatter(wphot, abs(mphot_map - ob), label='Residue', lw=2, alpha=0.8)
plt.xscale('log')
plt.yscale('log')
xmin, xmax = np.min(wphot) * 0.8, np.max(wphot) / 0.8
temp = np.interp(np.linspace(xmin, xmax, 10000), wphot, residue)
ymin, ymax = temp.min() * 0.1, temp.max() / 0.8
xlabel('Wavelength [A]', fontsize=20)
ylabel('Flux Change [erg/cm^2/s]', fontsize=20)
xlim([xmin, xmax])
ylim([ymin, ymax])
legend(loc='best', fontsize=15)
tight_layout()
return
def main():
vers = (np.__version__, scipy.__version__, h5py.__version__,
fsps.__version__, prospect.__version__)
print(
"Numpy: {}\nScipy: {}\nH5PY: {}\nFSPS: {}\nProspect: {}".format(*vers))
# And we will store some meta-parameters that control the input arguments to this method:
run_params = {}
run_params["snr"] = 10.0
run_params["ldist"] = 10.0
# Build the obs dictionary using the meta-parameters
obs = build_obs(**run_params)
# Look at the contents of the obs dictionary
print("Obs Dictionary Keys:\n\n{}\n".format(obs.keys()))
print("--------\nFilter objects:\n")
print(obs["filters"])
# --- Plot the Data ----
"""
# This is why we stored these...
wphot = obs["phot_wave"]
# establish bounds
xmin, xmax = np.min(wphot)*0.8, np.max(wphot)/0.8
ymin, ymax = obs["maggies"].min()*0.8, obs["maggies"].max()/0.4
fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(111)
# plot all the data
ax.plot(wphot, obs['maggies'],
label='All observed photometry',
marker='o', markersize=12, alpha=0.8, ls='', lw=3,
color='slateblue')
# overplot only the data we intend to fit
mask = obs["phot_mask"]
ax.errorbar(wphot[mask], obs['maggies'][mask],
yerr=obs['maggies_unc'][mask],
label='Photometry to fit',
marker='o', markersize=8, alpha=0.8, ls='', lw=3,
ecolor='tomato', markerfacecolor='none', markeredgecolor='tomato',
markeredgewidth=3)
# plot Filters
for f in obs['filters']:
w, t = f.wavelength.copy(), f.transmission.copy()
t = t / t.max()
t = 10**(0.2*(np.log10(ymax/ymin)))*t * ymin
ax.loglog(w, t, lw=3, color='gray', alpha=0.7)
# prettify
ax.set_xlabel('Wavelength [A]')
ax.set_ylabel('Flux Density [maggies]')
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
ax.set_xscale("log")
ax.set_yscale("log")
ax.legend(loc='best', fontsize=20)
fig.tight_layout()
plt.show()
"""
# ----------------------------
from prospect.models import priors
mass_param = {
"name": "mass",
# The mass parameter here is a scalar, so it has N=1
"N": 1,
# We will be fitting for the mass, so it is a free parameter
"isfree": True,
# This is the initial value. For fixed parameters this is the
# value that will always be used.
"init": 1e8,
# This sets the prior probability for the parameter
"prior": priors.LogUniform(mini=1e6, maxi=1e12),
# this sets the initial dispersion to use when generating
# clouds of emcee "walkers". It is not required, but can be very helpful.
"init_disp": 1e6,
# this sets the minimum dispersion to use when generating
#clouds of emcee "walkers". It is not required, but can be useful if
# burn-in rounds leave the walker distribution too narrow for some reason.
"disp_floor": 1e6,
# This is not required, but can be helpful
"units": "solar masses formed",
}
from prospect.models.templates import TemplateLibrary
# Look at all the prepackaged parameter sets
TemplateLibrary.show_contents()
# Check a specific model.
# This shows the defaults and params for the model.
# parametric_sfh in this case (delayed-tau)
# This is the one we will use
TemplateLibrary.describe("parametric_sfh")
run_params["object_redshift"] = 0.0
run_params["fixed_metallicity"] = None
run_params["add_duste"] = True
model = build_model(**run_params)
print(model)
print("\nInitial free parameter vector theta:\n {}\n".format(model.theta))
print("Initial parameter dictionary:\n{}".format(model.params))
run_params["zcontinuous"] = 1
sps = build_sps(**run_params)
# Generate the model SED at the initial value of theta
theta = model.theta.copy()
initial_spec, initial_phot, initial_mfrac = model.sed(theta,
obs=obs,
sps=sps)
#title_text = ','.join(["{}={}".format(p, model.params[p][0])
# for p in model.free_params])
a = 1.0 + model.params.get('zred', 0.0) # cosmological redshifting
# photometric effective wavelengths
wphot = obs["phot_wave"]
# spectroscopic wavelengths
if obs["wavelength"] is None:
# *restframe* spectral wavelengths, since obs["wavelength"] is None
wspec = sps.wavelengths
wspec *= a #redshift them
else:
wspec = obs["wavelength"]
# establish bounds
xmin, xmax = np.min(wphot) * 0.8, np.max(wphot) / 0.8
temp = np.interp(np.linspace(xmin, xmax, 10000), wspec, initial_spec)
ymin, ymax = temp.min() * 0.8, temp.max() / 0.4
"""
fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(111)
# plot model + data
ax.loglog(wspec, initial_spec, label='Model spectrum',
lw=0.7, color='navy', alpha=0.7)
ax.errorbar(wphot, initial_phot, label='Model photometry',
marker='s',markersize=10, alpha=0.8, ls='', lw=3,
markerfacecolor='none', markeredgecolor='blue',
markeredgewidth=3)
ax.errorbar(wphot, obs['maggies'], yerr=obs['maggies_unc'],
label='Observed photometry',
marker='o', markersize=10, alpha=0.8, ls='', lw=3,
ecolor='red', markerfacecolor='none', markeredgecolor='red',
markeredgewidth=3)
ax.set_title(title_text)
# plot Filters
for f in obs['filters']:
w, t = f.wavelength.copy(), f.transmission.copy()
t = t / t.max()
t = 10**(0.2*(np.log10(ymax/ymin)))*t * ymin
ax.loglog(w, t, lw=3, color='gray', alpha=0.7)
# prettify
ax.set_xlabel('Wavelength [A]')
ax.set_ylabel('Flux Density [maggies]')
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
ax.legend(loc='best', fontsize=20)
fig.tight_layout()
plt.show()
"""
verbose = True
run_params["verbose"] = verbose
# Here we will run all our building functions
obs = build_obs(**run_params)
sps = build_sps(**run_params)
model = build_model(**run_params)
# For fsps based sources it is useful to
# know which stellar isochrone and spectral library
# we are using
print(sps.ssp.libraries)
# --- start minimization ----
run_params["dynesty"] = False
run_params["emcee"] = False
run_params["optimize"] = True
run_params["min_method"] = 'lm'
# We'll start minimization from "nmin" separate places,
# the first based on the current values of each parameter and the
# rest drawn from the prior. Starting from these extra draws
# can guard against local minima, or problems caused by
# starting at the edge of a prior (e.g. dust2=0.0)
run_params["nmin"] = 2
output = fit_model(obs, model, sps, lnprobfn=lnprobfn, **run_params)
print("Done optmization in {}s".format(output["optimization"][1]))
print(model.theta)
(results, topt) = output["optimization"]
# Find which of the minimizations gave the best result,
# and use the parameter vector for that minimization
ind_best = np.argmin([r.cost for r in results])
print(ind_best)
theta_best = results[ind_best].x.copy()
print(theta_best)
# generate model
prediction = model.mean_model(theta_best, obs=obs, sps=sps)
pspec, pphot, pfrac = prediction
"""
fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(111)
# plot Data, best fit model, and old models
ax.loglog(wspec, initial_spec, label='Old model spectrum',
lw=0.7, color='gray', alpha=0.5)
ax.errorbar(wphot, initial_phot, label='Old model Photometry',
marker='s', markersize=10, alpha=0.6, ls='', lw=3,
markerfacecolor='none', markeredgecolor='gray',
markeredgewidth=3)
ax.loglog(wspec, pspec, label='Model spectrum',
lw=0.7, color='slateblue', alpha=0.7)
ax.errorbar(wphot, pphot, label='Model photometry',
marker='s', markersize=10, alpha=0.8, ls='', lw=3,
markerfacecolor='none', markeredgecolor='slateblue',
markeredgewidth=3)
ax.errorbar(wphot, obs['maggies'], yerr=obs['maggies_unc'],
label='Observed photometry',
marker='o', markersize=10, alpha=0.8, ls='', lw=3,
ecolor='tomato', markerfacecolor='none', markeredgecolor='tomato',
markeredgewidth=3)
# plot filter transmission curves
for f in obs['filters']:
w, t = f.wavelength.copy(), f.transmission.copy()
t = t / t.max()
t = 10**(0.2*(np.log10(ymax/ymin)))*t * ymin
ax.loglog(w, t, lw=3, color='gray', alpha=0.7)
# Prettify
ax.set_xlabel('Wavelength [A]')
ax.set_ylabel('Flux Density [maggies]')
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
ax.legend(loc='best', fontsize=20)
fig.tight_layout()
plt.show()
"""
# Set this to False if you don't want to do another optimization
# before emcee sampling (but note that the "optimization" entry
# in the output dictionary will be (None, 0.) in this case)
# If set to true then another round of optmization will be performed
# before sampling begins and the "optmization" entry of the output
# will be populated.
run_params["optimize"] = False
run_params["emcee"] = True
run_params["dynesty"] = False
# Number of emcee walkers
run_params["nwalkers"] = 128
# Number of iterations of the MCMC sampling
run_params["niter"] = 512
# Number of iterations in each round of burn-in
# After each round, the walkers are reinitialized based on the
# locations of the highest probablity half of the walkers.
run_params["nburn"] = [16, 32, 64]
print("Now running with Emcee.")
output = fit_model(obs, model, sps, lnprobfn=lnprobfn, **run_params)
print('done emcee in {0}s'.format(output["sampling"][1]))
hfile = "demo_emcee_mcmc.h5"
writer.write_hdf5(hfile,
run_params,
model,
obs,
output["sampling"][0],
output["optimization"][0],
tsample=output["sampling"][1],
toptimize=output["optimization"][1])
print('Finished')
# -------------------
print("Now running with Dynesty.")
run_params["dynesty"] = True
run_params["optmization"] = False
run_params["emcee"] = False
run_params["nested_method"] = "rwalk"
run_params["nlive_init"] = 400
run_params["nlive_batch"] = 200
run_params["nested_dlogz_init"] = 0.05
run_params["nested_posterior_thresh"] = 0.05
run_params["nested_maxcall"] = int(1e7)
output = fit_model(obs, model, sps, lnprobfn=lnprobfn, **run_params)
print('done dynesty in {0}s'.format(output["sampling"][1]))
hfile = "demo_dynesty_mcmc.h5"
writer.write_hdf5(hfile,
run_params,
model,
obs,
output["sampling"][0],
output["optimization"][0],
tsample=output["sampling"][1],
toptimize=output["optimization"][1])
print('Finished')
# -------------------------
# Visualizing results
results_type = "dynesty" # "emcee" | "dynesty"
# grab results (dictionary), the obs dictionary, and our corresponding models
# When using parameter files set `dangerous=True`
result, obs, _ = reader.results_from(
"demo_{}_mcmc.h5".format(results_type), dangerous=False)
#The following commented lines reconstruct the model and sps object,
# if a parameter file continaing the `build_*` methods was saved along with the results
#model = reader.get_model(result)
#sps = reader.get_sps(result)
# let's look at what's stored in the `result` dictionary
print(result.keys())
if results_type == "emcee":
chosen = np.random.choice(result["run_params"]["nwalkers"],
size=10,
replace=False)
tracefig = reader.traceplot(result, figsize=(20, 10), chains=chosen)
else:
tracefig = reader.traceplot(result, figsize=(20, 10))
# maximum a posteriori (of the locations visited by the MCMC sampler)
imax = np.argmax(result['lnprobability'])
if results_type == "emcee":
i, j = np.unravel_index(imax, result['lnprobability'].shape)
theta_max = result['chain'][i, j, :].copy()
thin = 5
else:
theta_max = result["chain"][imax, :]
thin = 1
print('Optimization value: {}'.format(theta_best))
print('MAP value: {}'.format(theta_max))
cornerfig = reader.subcorner(result,
start=0,
thin=thin,
truths=theta_best,
fig=plt.subplots(5, 5, figsize=(8, 8))[0])
plt.show()
return None
# let's look at what's stored in the `res` dictionary
print('-------')
print('Stored info:')
print(res.keys())
print('')
if 'mock' in outroot:
theta_input = np.squeeze(
np.array([obs["mock_params"][p] for p in res['theta_labels']]))
print(theta_input)
#Parameter Traces
chosen = np.random.choice(run_params["nwalkers"], size=10, replace=False)
else:
theta_input = None
chosen = np.random.choice(run_params["nwalkers"], size=10, replace=False)
tracefig = traceplot(res, figsize=(20, 10), chains=chosen)
savefig(outroot + '_parameter_traces.png')
# Conerplot
# Maximum Likelihood (of the locations visited by the MCMC sampler)
imax = np.argmax(res['lnprobability'])
i, j = np.unravel_index(imax, res['lnprobability'].shape)
theta_max = res['chain'][i, j, :].copy()
n_param = len(theta_max)
size = int(n_param * n_param) + 2
print('Max Likelihood value: {}'.format(theta_max))
cornerfig = subcorner(res,
start=0,
thin=5,
def run_prospector(name_z):
import time, sys, os
import h5py
import numpy as np
import scipy
from matplotlib.pyplot import *
#%matplotlib inline
import fsps
import sedpy
import prospect
import emcee
import astropy
import math
from prospect.fitting import fit_model
from prospect.fitting import lnprobfn
from func import *
import matplotlib.pyplot as plt
# --- Output files ---
name = name_z[0]
z = name_z[1]
path_input = '/home/kk/work/data/red_magic/' + name
path = '/home/kk/work/result/red_magic/' + name[:-4]
'''try:
os.mkdir(path)
except:
pass
fe = open(path+'/para.txt','w')'''
# --- Set up the essential parameter ---
data_list = [
'sdss_u', 'sdss_g', 'sdss_r', 'sdss_i', 'galex_fuv', 'galex_nuv',
'pan-starrs_ps1_i', 'pan-starrs_ps1_r', 'pan-starrs_ps1_g',
'pan-starrs_ps1_y', 'pan-starrs_ps1_z', 'sdss_z', 'ukidss_j',
'ukidss_h', 'ukidss_k', 'wise_w1', 'spitzer_irac_3.6', '_=3.6um',
'spitzer_irac_4.5', '_=4.5um', 'wise_w2', 'spitzer_irac_5.8',
'spitzer_irac_8.0', 'wise_w3', 'wise_w4', 'spitzer_mips_24', '2mass_h',
'2mass_ks', '2mass_j', 'gaia_gaia2_gbp', 'gaia_gaia2_g',
'gaia_gaia2_grp', 'gaia_g', 'ukidss_y', 'vista_j', 'vista_h',
'vista_ks'
]
run_params = {}
run_params["snr"] = 10.0
run_params["object_redshift"] = z
run_params["fixed_metallicity"] = None
run_params["add_duste"] = True
# --- Start build up the model ---
fit_data = load_data(path_input, data_list)
obs = build_obs(fit_data, **run_params)
sps = build_sps(**run_params)
model = build_model(**run_params)
#fe.write(str(obs))
#print(fit_data)
# --- Draw initial data plot ---
wphot = obs["phot_wave"]
# establish bounds
xmin, xmax = np.min(wphot) * 0.8, np.max(wphot) / 0.8
ymin, ymax = obs["maggies"].min() * 0.8, obs["maggies"].max() / 0.4
figure(figsize=(16, 8))
# plot all the data
plot(wphot,
obs['maggies'],
label='All observed photometry',
marker='o',
markersize=12,
alpha=0.8,
ls='',
lw=3,
color='slateblue')
# overplot only the data we intend to fit
mask = obs["phot_mask"]
errorbar(wphot[mask],
obs['maggies'][mask],
yerr=obs['maggies_unc'][mask],
label='Photometry to fit',
marker='o',
markersize=8,
alpha=0.8,
ls='',
lw=3,
ecolor='tomato',
markerfacecolor='none',
markeredgecolor='tomato',
markeredgewidth=3)
# prettify
xlabel('Wavelength [A]')
ylabel('Flux Density [maggies]')
xlim([xmin, xmax])
ylim([ymin, ymax])
xscale("log")
yscale("log")
legend(loc='best', fontsize=20)
#tight_layout()
#plt.savefig(path+'/input.png')
# run minimization and emcee with error handling
condition = False
while condition is False:
try:
# --- start minimization ----
run_params["dynesty"] = False
run_params["emcee"] = False
run_params["optimize"] = True
run_params["min_method"] = 'lm'
run_params["nmin"] = 5
output = fit_model(obs,
model,
sps,
lnprobfn=lnprobfn,
**run_params)
print("Done optmization in {}s".format(output["optimization"][1]))
# save theta_best for later use
(results, topt) = output["optimization"]
ind_best = np.argmin([r.cost for r in results])
theta_best = results[ind_best].x.copy()
# --- start emcee ---
run_params["optimize"] = False
run_params["emcee"] = True
run_params["dynesty"] = False
run_params["nwalkers"] = 30
run_params["niter"] = 8000
run_params["nburn"] = [300, 800, 1600]
output = fit_model(obs,
model,
sps,
lnprobfn=lnprobfn,
**run_params)
print('done emcee in {0}s'.format(output["sampling"][1]))
condition = True
# sort out different errors and apply method to proceed
except AssertionError as e:
# error: sfr cannot be negative
if str(e) == 'sfr cannot be negative.':
obs = build_obs(fit_data, **run_params)
sps = build_sps(**run_params)
model = build_model(**run_params)
# error: number of residules are less than variables
except ValueError as e:
if str(
e
) == "Method 'lm' doesn't work when the number of residuals is less than the number of variables.":
print(name)
return
# error: residule are not finite in the initial point
elif str(e) == "Residuals are not finite in the initial point.":
print(name)
return
# write final results to file
from prospect.io import write_results as writer
hfile = path + '_emcee.h5'
print(hfile)
writer.write_hdf5(hfile,
run_params,
model,
obs,
output["sampling"][0],
output["optimization"][0],
tsample=output["sampling"][1],
toptimize=output["optimization"][1])
#fe.write(str(obs))
print('Finished')
##############################################################################
import prospect.io.read_results as reader
results_type = "emcee" # | "dynesty"
# grab results (dictionary), the obs dictionary, and our corresponding models
# When using parameter files set `dangerous=True`
#tem_name = hfile[:-8]+'{}.h5'
#result, obs, _ = reader.results_from(tem_name.format(results_type), dangerous=False)
result, obs, _ = reader.results_from(hfile, dangerous=False)
#The following commented lines reconstruct the model and sps object, # if a parameter file continaing the `build_*` methods was saved along with the results
#model = reader.get_model(result)
#sps = reader.get_sps(result)
# let's look at what's stored in the `result` dictionary
#print(result.keys())
# Make plot of data and model
if results_type == "emcee":
chosen = np.random.choice(result["run_params"]["nwalkers"],
size=10,
replace=False)
tracefig = reader.traceplot(result, figsize=(20, 10), chains=chosen)
else:
tracefig = reader.traceplot(result, figsize=(20, 10))
#plt.savefig(path+'/trace.png')
# maximum a posteriori (of the locations visited by the MCMC sampler)
imax = np.argmax(result['lnprobability'])
if results_type == "emcee":
i, j = np.unravel_index(imax, result['lnprobability'].shape)
theta_max = result['chain'][i, j, :].copy()
thin = 5
else:
theta_max = result["chain"][imax, :]
thin = 1
#f.Write('Optimization value: {}'.format(theta_best))
#fe.write(str('MAP value: {}'.format(theta_max)))
#fe.close()
cornerfig = reader.subcorner(result,
start=0,
thin=thin,
truths=theta_best,
fig=subplots(15, 15, figsize=(27, 27))[0])
#plt.savefig(path+'/corner.png')
# --- Draw final fitting curve ---
a = 1.0 + model.params.get('zred', 0.0) # cosmological redshifting
# photometric effective wavelengths
wphot = obs["phot_wave"]
# spectroscopic wavelengths
if obs["wavelength"] is None:
# *restframe* spectral wavelengths, since obs["wavelength"] is None
wspec = sps.wavelengths
wspec *= a #redshift them
else:
wspec = obs["wavelength"]
# Get enssential data to calculate sfr
z_fraction = theta_max[5:10]
total_mass = theta_max[0]
agebins = model.params['agebins']
x = np.zeros(6)
for i in range(6):
if i == 5:
x[i] = (agebins[i][0] + agebins[i][1]) / 2
else:
x[i] = (agebins[i][0] + agebins[i + 1][0]) / 2
sfr = prospect.models.transforms.zfrac_to_sfr(total_mass, z_fraction,
agebins)
# Calculate the 16% and 84% value from emcee results
z_fraction1 = []
z_fraction2 = []
z_fraction3 = []
z_fraction4 = []
z_fraction5 = []
for i in range(30):
for j in range(3000):
z_fraction1.append(result['chain'][i][j][5])
z_fraction2.append(result['chain'][i][j][6])
z_fraction3.append(result['chain'][i][j][7])
z_fraction4.append(result['chain'][i][j][8])
z_fraction5.append(result['chain'][i][j][9])
zerr_1 = [np.percentile(z_fraction1, 16), np.percentile(z_fraction1, 84)]
zerr_2 = [np.percentile(z_fraction2, 16), np.percentile(z_fraction2, 84)]
zerr_3 = [np.percentile(z_fraction3, 16), np.percentile(z_fraction3, 84)]
zerr_4 = [np.percentile(z_fraction4, 16), np.percentile(z_fraction4, 84)]
zerr_5 = [np.percentile(z_fraction5, 16), np.percentile(z_fraction5, 84)]
# Build upper and lower z_fraction array
z_max = np.zeros(5)
z_min = np.zeros(5)
for i in range(5):
z_min[i] = eval('zerr_{}[0]'.format(i + 1))
z_max[i] = eval('zerr_{}[1]'.format(i + 1))
#print(z_min)
#print(z_max)
# Build new sfr
sfr_max = prospect.models.transforms.zfrac_to_sfr(total_mass, z_max,
agebins)
sfr_min = prospect.models.transforms.zfrac_to_sfr(total_mass, z_min,
agebins)
# randomly chosen parameters from chain
randint = np.random.randint
if results_type == "emcee":
nwalkers, niter = run_params['nwalkers'], run_params['niter']
# Draw 100 random plots from mcmc
figure(figsize=(16, 16))
ax = plt.subplot(211)
for i in range(100):
# Get data from any random chain
theta = result['chain'][randint(nwalkers), randint(niter)]
mspec_t, mphot_t, mextra = model.mean_model(theta, obs, sps=sps)
# unit conversion to erg/cm^2/s
mspec = (mspec_t * 1e-23) * 3e18 / wspec
mphot = (mphot_t * 1e-23) * 3e18 / wphot
# plot them out
loglog(wspec, mspec, lw=0.7, color='grey', alpha=0.3)
else:
theta = result["chain"][randint(len(result["chain"]))]
# now do it again for best fit model
# sps = reader.get_sps(result) # this works if using parameter files
mspec_map_t, mphot_map_t, _ = model.mean_model(theta_max, obs, sps=sps)
# units conversion to erg/cm^2/s
mspec_map = (mspec_map_t * 1e-23) * 3e18 / wspec
mphot_map = (mphot_map_t * 1e-23) * 3e18 / wphot
ob = (obs['maggies'] * 1e-23) * 3e18 / wphot
ob_unc = (obs['maggies_unc'] * 1e-23) * 3e18 / wphot
# calculate reduced chi^2
chi = 0
for i in range(len(wphot)):
var = ob_unc[i]
chi += (mphot_map[i] - ob[i])**2 / var**2
# Make plot of best fit model
loglog(wspec,
mspec_map,
label='Model spectrum (MAP)',
lw=0.7,
color='green',
alpha=0.7)
errorbar(wphot,
mphot_map,
label='Model photometry (MAP)',
marker='s',
markersize=10,
alpha=0.8,
ls='',
lw=3,
markerfacecolor='none',
markeredgecolor='green',
markeredgewidth=3)
errorbar(wphot,
ob,
yerr=ob_unc,
label='Observed photometry',
ecolor='red',
marker='o',
markersize=10,
ls='',
lw=3,
alpha=0.8,
markerfacecolor='none',
markeredgecolor='red',
markeredgewidth=3)
text(0,
1,
'Chi-squared/Nphot =' + str(chi / len(wphot)),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
# Setup bound
xmin, xmax = np.min(wphot) * 0.6, np.max(wphot) / 0.8
temp = np.interp(np.linspace(xmin, xmax, 10000), wphot, mphot_map)
ymin, ymax = temp.min() * 0.8, temp.max() * 10
xlabel('Wavelength [A]', fontsize=20)
ylabel('Flux Density [erg/cm^2/s]', fontsize=20)
xlim([xmin, xmax])
ylim([ymin, ymax])
legend(loc='lower right', fontsize=15)
tight_layout()
# plt.show()
# plot sfr on the same canvas
from mpl_toolkits.axes_grid.inset_locator import inset_axes
in_axes = inset_axes(
ax,
width="25%", # width = 30% of parent_bbox
height=2, # height : 1 inch
loc='upper center')
in_axes.scatter(10**(x - 9), sfr)
#plt.fill_between(10**(x-9), sfr_min, sfr_max)
xlabel('Age [Gyr]', fontsize=10)
ylabel('SFR [M_sun/yr]', fontsize=10)
#plt.xscale('log')
plt.yscale('log')
plt.savefig(path + '_fitting.png')
# plot residue as subplot
figure(figsize=(16, 8))
plt.subplot(212, sharex=ax)
import pylab
residue = abs(mphot_map - ob)
scatter(wphot, abs(mphot_map - ob), label='Residue', lw=2, alpha=0.8)
plt.xscale('log')
plt.yscale('log')
xmin, xmax = np.min(wphot) * 0.8, np.max(wphot) / 0.8
temp = np.interp(np.linspace(xmin, xmax, 10000), wphot, residue)
ymin, ymax = temp.min() * 0.1, temp.max() / 0.8
xlabel('Wavelength [A]', fontsize=20)
ylabel('Flux Change [erg/cm^2/s]', fontsize=20)
xlim([xmin, xmax])
ylim([ymin, ymax])
legend(loc='best', fontsize=15)
tight_layout()