forked from JohannesBuchner/PyMultiNest
/
multinest_marginals.py
155 lines (128 loc) · 4.55 KB
/
multinest_marginals.py
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#!/usr/bin/env python
from __future__ import absolute_import, unicode_literals, print_function
__doc__ = """
Script that does default visualizations (marginal plots, 1-d and 2-d).
Author: Johannes Buchner (C) 2013
"""
import numpy
from numpy import exp, log
import matplotlib.pyplot as plt
import sys, os
import json
import pymultinest
if len(sys.argv) != 2:
sys.stderr.write("""SYNOPSIS: %s <output-root>
output-root: Where the output of a MultiNest run has been written to.
Example: chains/1-
%s""" % (sys.argv[0], __doc__))
sys.exit(1)
prefix = sys.argv[1]
print('model "%s"' % prefix)
if not os.path.exists(prefix + 'params.json'):
sys.stderr.write("""Expected the file %sparams.json with the parameter names.
For example, for a three-dimensional problem:
["Redshift $z$", "my parameter 2", "A"]
%s""" % (sys.argv[1], __doc__))
sys.exit(2)
parameters = json.load(open(prefix + 'params.json'))
n_params = len(parameters)
a = pymultinest.Analyzer(n_params = n_params, outputfiles_basename = prefix)
s = a.get_stats()
json.dump(s, open(prefix + 'stats.json', 'w'), indent=4)
print(' marginal likelihood:')
print(' ln Z = %.1f +- %.1f' % (s['global evidence'], s['global evidence error']))
print(' parameters:')
for p, m in zip(parameters, s['marginals']):
lo, hi = m['1sigma']
med = m['median']
sigma = (hi - lo) / 2
if sigma == 0:
i = 3
else:
i = max(0, int(-numpy.floor(numpy.log10(sigma))) + 1)
fmt = '%%.%df' % i
fmts = '\t'.join([' %-15s' + fmt + " +- " + fmt])
print(fmts % (p, med, sigma))
print('creating marginal plot ...')
p = pymultinest.PlotMarginal(a)
values = a.get_equal_weighted_posterior()
assert n_params == len(s['marginals'])
modes = s['modes']
dim2 = os.environ.get('D', '1' if n_params > 20 else '2') == '2'
nbins = 100 if n_params < 3 else 20
if dim2:
plt.figure(figsize=(5*n_params, 5*n_params))
for i in range(n_params):
plt.subplot(n_params, n_params, i + 1)
plt.xlabel(parameters[i])
m = s['marginals'][i]
plt.xlim(m['5sigma'])
oldax = plt.gca()
x,w,patches = oldax.hist(values[:,i], bins=nbins, edgecolor='grey', color='grey', histtype='stepfilled', alpha=0.2)
oldax.set_ylim(0, x.max())
newax = plt.gcf().add_axes(oldax.get_position(), sharex=oldax, frameon=False)
p.plot_marginal(i, ls='-', color='blue', linewidth=3)
newax.set_ylim(0, 1)
ylim = newax.get_ylim()
y = ylim[0] + 0.05*(ylim[1] - ylim[0])
center = m['median']
low1, high1 = m['1sigma']
#print(center, low1, high1)
newax.errorbar(x=center, y=y,
xerr=numpy.transpose([[center - low1, high1 - center]]),
color='blue', linewidth=2, marker='s')
oldax.set_yticks([])
#newax.set_yticks([])
newax.set_ylabel("Probability")
ylim = oldax.get_ylim()
newax.set_xlim(m['5sigma'])
oldax.set_xlim(m['5sigma'])
#plt.close()
for j in range(i):
plt.subplot(n_params, n_params, n_params * (j + 1) + i + 1)
p.plot_conditional(i, j, bins=20, cmap = plt.cm.gray_r)
for m in modes:
plt.errorbar(x=m['mean'][i], y=m['mean'][j], xerr=m['sigma'][i], yerr=m['sigma'][j])
plt.xlabel(parameters[i])
plt.ylabel(parameters[j])
#plt.savefig('cond_%s_%s.pdf' % (params[i], params[j]), bbox_tight=True)
#plt.close()
plt.savefig(prefix + 'marg.pdf')
plt.savefig(prefix + 'marg.png')
plt.close()
else:
from matplotlib.backends.backend_pdf import PdfPages
sys.stderr.write('1dimensional only. Set the D environment variable \n')
sys.stderr.write('to D=2 to force 2d marginal plots.\n')
pp = PdfPages(prefix + 'marg1d.pdf')
for i in range(n_params):
plt.figure(figsize=(3, 3))
plt.xlabel(parameters[i])
plt.locator_params(nbins=5)
m = s['marginals'][i]
iqr = m['q99%'] - m['q01%']
xlim = m['q01%'] - 0.3 * iqr, m['q99%'] + 0.3 * iqr
#xlim = m['5sigma']
plt.xlim(xlim)
oldax = plt.gca()
x,w,patches = oldax.hist(values[:,i], bins=numpy.linspace(xlim[0], xlim[1], 20), edgecolor='grey', color='grey', histtype='stepfilled', alpha=0.2)
oldax.set_ylim(0, x.max())
newax = plt.gcf().add_axes(oldax.get_position(), sharex=oldax, frameon=False)
p.plot_marginal(i, ls='-', color='blue', linewidth=3)
newax.set_ylim(0, 1)
ylim = newax.get_ylim()
y = ylim[0] + 0.05*(ylim[1] - ylim[0])
center = m['median']
low1, high1 = m['1sigma']
#print center, low1, high1
newax.errorbar(x=center, y=y,
xerr=numpy.transpose([[center - low1, high1 - center]]),
color='blue', linewidth=2, marker='s')
oldax.set_yticks([])
newax.set_ylabel("Probability")
ylim = oldax.get_ylim()
newax.set_xlim(xlim)
oldax.set_xlim(xlim)
plt.savefig(pp, format='pdf', bbox_inches='tight')
plt.close()
pp.close()