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visualize.py
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visualize.py
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"""
Created by Shane Bussmann
A number of visualization tools are included here to aid the user in evaluating
the:
convergence of lnprob
the posterior PDFs
the evolution of the PDFs for each parameter of the model
the covariance matrix for the posterior PDFs
the best-fit model
a number of randomly drawn realizations from the posterior PDFs
"""
from __future__ import print_function
import os
from astropy.io import fits
import visualutil
#import sys
#cwd = os.getcwd()
#sys.path.append(cwd)
#import config
import yaml
configloc = 'config.yaml'
configfile = open(configloc)
config = yaml.load(configfile)
def convergence(bestfitloc='posteriorpdf.fits'):
"""
Plot the convergence profile. I.e., Max(lnprob) - lnprob as a function of
iteration number.
"""
import numpy
import matplotlib.pyplot as plt
from pylab import savefig
print("Reading burnin results from {0:s}".format(bestfitloc))
pdf = fits.getdata(bestfitloc)
keyname = 'lnprob'
lnprob = pdf[keyname]
lnprob = numpy.array(lnprob)
lnprob = lnprob.max() - lnprob
lnprob = numpy.abs(lnprob)
plt.clf()
plt.plot(lnprob, ',', alpha=0.5)
plt.xlabel('iteration')
plt.ylabel('max(lnprob) - lnprob')
tmpcwd = os.getcwd()
startindx = tmpcwd.find('ModelFits') + 10
endindx = tmpcwd.find('uvfit') + 7
objname = tmpcwd[startindx:endindx]
plt.title(objname)
plt.semilogy()
outfile = 'convergence'
savefig(outfile)
def posteriorPDF(bestfitloc='posteriorpdf.fits'):
"""
Plot the posterior PDF of each parameter of the model.
"""
# read posterior PDF
print("Reading output from emcee")
fitresults = fits.getdata(bestfitloc)
tag = 'posterior'
visualutil.plotPDF(fitresults, tag, Ngood=5000, axes='auto')
def evolvePDF(bestfitloc='posteriorpdf.fits', stepsize=50000):
"""
Plot the evolution of the PDF of each parameter of the model.
"""
import setuputil
# Get upper and lower limits on the parameters to set the plot limits
paramData = setuputil.loadParams(config)
p_u = paramData['p_u']
p_l = paramData['p_l']
limits = [p_l, p_u]
# read posterior PDF
fitresults = fits.getdata(bestfitloc)
nresults = len(fitresults)
print("Output from emcee has = " + str(nresults) + " iterations.")
start = 0
for iresult in range(0, nresults, stepsize):
strstep = str(stepsize)
nchar = len(str(nresults))
striresult = str(iresult).zfill(nchar)
tag = 'evolution' + strstep + '.' + striresult + '.'
trimresults = fitresults[start:start + stepsize]
start += stepsize
visualutil.plotPDF(trimresults, tag, limits=limits, Ngood=1000,
axes='initial')
def covariance(bestfitloc='posteriorpdf.fits'):
"""
Plot the covariance matrix for the parameters of the model.
"""
import matplotlib.pyplot as plt
import numpy
from pylab import savefig
import modifypdf
from astropy.table import Table
from matplotlib import rc
# plotting parameters
rc('font',**{'family':'sans-serif', 'sans-serif':['Arial Narrow'],
'size':'6'})
posteriorpdf = Table.read(bestfitloc)
posteriorpdf = posteriorpdf[-5000:]
# remove columns where the values are not changing
posteriorpdfclean = modifypdf.cleanColumns(posteriorpdf)
posteriorpdfgood = modifypdf.prune(posteriorpdfclean)
headers = posteriorpdf.colnames
ncol = len(headers)
k = 0
xsize = ncol * 2
ysize = ncol * 1.5
fig = plt.figure(figsize=(xsize, ysize))
plt.subplots_adjust(left=0.020, bottom=0.02, right=0.99, top=0.97,
wspace=0.5, hspace=0.5)
#for i in numpy.arange(ncol):
# ax = plt.subplot(ncol, ncol, i + 1)
# namex = 'mu_aper'
# namey = headers[i]
# datax = mupdfgood[namex]
# datay = posteriorpdfgood[namey]
# if namex == 'lnprob':
# datax = datax.max() - datax
# if namey == 'lnprob':
# datay = datay.max() - datay
# lnprob = posteriorpdfgood['lnprob'].max() - posteriorpdfgood['lnprob']
# plt.hexbin(datax, datay, C = lnprob)
# plt.xlabel(namex)
# plt.ylabel(namey)
for i in numpy.arange(ncol):
for j in numpy.arange(ncol - i - 1) + i + 1:
plotspot = ncol * i + j
ax = plt.subplot(ncol, ncol, plotspot)
namex = headers[i]
namey = headers[j]
#datax = posteriorpdforig[namex]
#datay = posteriorpdforig[namey]
#lnprob = posteriorpdforig['lnprob']
#plt.hexbin(datax, datay, C = lnprob, color='black')
datax = posteriorpdfgood[namex]
datay = posteriorpdfgood[namey]
if namex == 'lnprob':
datax = datax.max() - datax
if namey == 'lnprob':
datay = datay.max() - datay
lnprob = posteriorpdfgood['lnprob'].max() - posteriorpdfgood['lnprob']
plt.hexbin(datax, datay, C = lnprob)
plt.xlabel(namex)
plt.ylabel(namey)
print(i, j, plotspot, namex, namey)
k += 1
#plt.suptitle(iau_address, x=0.5, y=0.987, fontsize='xx-large')
savefig('covariance.pdf')
plt.clf()
def printFitParam(fitresult, fitKeys, mag=False):
""" Print parameters for this model
mag: bool
if True, print magnification factors as well
"""
if mag is False:
fitresult = fitresult[:-4]
fitKeys = fitKeys[:-4]
print("Found the following parameters for this fit:")
for k, v in zip(fitKeys, fitresult):
print("%s : %.4f" %(k,v))
def bestFit(bestfitloc='posteriorpdf.fits', showOptical=False, cleanup=True,
interactive=True, plotonly=False):
"""
Read posterior PDF and identify best-fit parameters. Plot the best-fit
model and compare to the data. Also plot the residuals obtained after
subtracting the best-fit model from the data and compare to the data.
Optionally plot the best available optical image and compare to the data.
"""
# read the posterior PDFs
print("Found posterior PDF file: {:s}".format(bestfitloc))
fitresults = fits.getdata(bestfitloc)
from astropy.table import Table
fitKeys = Table.read(bestfitloc).keys()
# identify best-fit model
minchi2 = fitresults['lnprob'].max()
index = fitresults['lnprob'] == minchi2
bestfit = fitresults[index][0]
tag = 'bestfit'
printFitParam(bestfit, fitKeys)
visualutil.plotFit(config, bestfit, tag=tag, cleanup=cleanup,
showOptical=showOptical, interactive=interactive,
plotonly=plotonly)
def goodFits(bestfitloc='posteriorpdf.fits', Nfits=12, Ngood=5000,
cleanup=True, interactive=True, showOptical=False):
"""
Read posterior PDF and draw Nfits realizations from the final Ngood models
at random. Plot the model from each realization and compare to the data.
Also plot the residuals obtained after subtracting the model from the data
and compare to the data. By default: Nfits = 12, Ngood=5000.
"""
import modifypdf
import numpy
# read the posterior PDFs
print("Found posterior PDF file: {:s}".format(bestfitloc))
fitresults = fits.getdata(bestfitloc)
fitresults = fitresults[-Ngood:]
fitresults = modifypdf.prune(fitresults)
# get keys
from astropy.table import Table
fitKeys = Table.read(bestfitloc).keys()
# select the random realizations model
Nunprune = len(fitresults)
realids = numpy.floor(numpy.random.uniform(0, Nunprune, Nfits))
for ifit in range(Nfits):
realid = numpy.int(realids[ifit])
fitresult = fitresults[realid]
tag = 'goodfit' + str(realid).zfill(4)
printFitParam(fitresult, fitKeys)
visualutil.plotFit(config, fitresult, tag=tag, showOptical=showOptical,
cleanup=cleanup, interactive=interactive)