parfile = opts.parfile
else:
print >> sys.stderr, "No par file specified!"
sys.exit(0)
# get time/date for file creation
now = datetime.datetime.now()
# list of amplitude spectral densities, upper limits and spin-down ratios
# for the IFOs
asds = []
sdlist = []
# read data from par file
try:
par = pppu.psr_par(parfile)
except:
print >> sys.stderr, "Par file %s could not be opened!" % parfile
sys.exit(0)
pname = par['PSRJ']
if not pname:
print >> sys.stderr, "No PSRJ value in par file %s" % parfile
pname = par['PSR']
if not pname:
print >> sys.stderr, "No PSR value in par file %s" % parfile
sys.exit(0)
# check if heterodyned data exists for this pulsar and each detector
if not jointonly:
resultsnode.set_parfile(pfile)
resultsnode.set_outpath(outdir)
if mcmcdirs:
for mcmcdir in mcmcdirs:
if not os.path.isdir(mcmcdir):
print("MCMC directory %s not found!" % mcmcdir, file=sys.stderr)
sys.exit(1)
resultsnode.set_domcmc()
resultsnode.set_mcmcdir(mcmcdirs)
elif nesteddirs:
resultsnode.set_donested()
# get pulsar name from par file
psr = pppu.psr_par(pfile)
name = None
try:
name = psr['PSRJ']
except:
try:
name = psr['PSR']
except:
try:
name = psr['NAME']
except:
print("Cannot get pulsar name from par file" % mcmcdir, file=sys.stderr)
sys.exit(1)
# if name is one of the standard hardware injection names set hwinj to true
if not hwinj and 'PULSAR' in name:
resultsnode.set_parfile(pfile)
resultsnode.set_outpath(outdir)
if mcmcdirs:
for mcmcdir in mcmcdirs:
if not os.path.isdir(mcmcdir):
print >> sys.stderr, "MCMC directory %s not found!" % mcmcdir
sys.exit(1)
resultsnode.set_domcmc()
resultsnode.set_mcmcdir(mcmcdirs)
elif nesteddirs:
resultsnode.set_donested()
# get pulsar name from par file
psr = pppu.psr_par(pfile)
name = None
try:
name = psr['PSRJ']
except:
try:
name = psr['PSR']
except:
try:
name = psr['NAME']
except:
print >> sys.stderr, "Cannot get pulsar name from par file" % mcmcdir
sys.exit(1)
# if name is one of the standard hardware injection names set hwinj to true
if not hwinj and 'PULSAR' in name:
\usepackage{amsmath}
% The rotate command for deluxetable does not work in emulateapj, so use a
% landscape mode instead
\usepackage{lscape}
\\begin{document}
""")
# perform sorting
names = []
freqs = []
ras = []
decs = []
for i, parfile in enumerate(param_files):
try:
par = pppu.psr_par(parfile)
except:
print >> sys.stderr, "Par file %s could not be opened!" % parfile
# remove from list and move on to next pulsar
param_files.remove(i)
continue
if par['PSRJ']:
names.append(par['PSRJ'])
else:
names.append(None)
if par['F0']:
freqs.append(par['F0'])
else:
freqs.append(None)
def pulsarBayesPostProc(
outdir,
data,
upperlimit,
histbins,
#nested sampling options
priorfile,
parfile,
ns_Nlive,
# analysis data
Bkdata=None,
ifos=None,
# pulsar information
corfile=None #,
#Turn on 2D kdes
#twodkdeplots=False,
#Turn on R convergence tests
#RconvergenceTests=False
):
# check data is input
if data is None:
raise RuntimeError('You must specify an input data file')
# check output directory is input
if outdir is None:
raise RuntimeError("You must specify an output directory.")
# create output dir if it doesn't exist
if not os.path.isdir(outdir):
os.makedirs(outdir)
# list of posteriors for each ifo
poslist = []
# loop over ifos and generate posteriors
for idx, ifo in enumerate(ifos):
# nested sampling data files for IFO idx
idat = data[idx]
# if input format is xml
votfile = None
if '.xml' in data[0]:
peparser = bppu.PEOutputParser('xml')
commonResultsObj = peparser.parse(idat[0])
thefile = open(idat[0], 'r')
votfile = thefile.read()
else: # input as standard nested sampling input
peparser = bppu.PEOutputParser('ns')
commonResultsObj = peparser.parse(idat,
Nlive=ns_Nlive,
xflag=False)
# Create an instance of the posterior class using the posterior values
# loaded from the file.
pos = bppu.Posterior( commonResultsObj, SimInspiralTableEntry=None, \
votfile=votfile )
# append to list
poslist.append(pos)
# read in par file if given
if parfile is not None:
par = pppu.psr_par(parfile)
# convert phi0' and psi' to phi0 and psi
if 'phi0prime' in pos.names and 'psiprime' in pos.names:
phi0samps, psisamps = pppu.phipsiconvert( pos['phi0prime'].samples, \
pos['psiprime'].samples )
# append phi0 and psi to posterior
phi0_pos = bppu.OneDPosterior('phi0', phi0samps)
pos.append(phi0_pos)
psi_pos = bppu.OneDPosterior('psi', phi0samps)
pos.append(psi_pos)
# get parameters to plot from prior file and those with "Errors" in the par
# file
plotpars = []
pri = pppu.psr_prior(priorfile)
for pars in pppu.float_keys:
# prior file values have preference over par file values with errors in the
# nested sampling code
pars_err = pars + '_ERR'
if pri[pars] is not None:
plotpars.append(pars)
elif par[pars_err] is not None:
plotpars.append(pars)
# if h0 exists plot the pdf
if 'H0' in plotpars:
# set bounds of h0
bounds = [0, float("inf")]
plotFig, ulvals = pppu.plot_posterior_hist(poslist, 'H0'.lower(), ifos,
bounds, histbins,
upperlimit)
# output plots for each IFO
for i, ifo in enumerate(ifos):
figname = 'H0'.lower() + '_' + ifo + '.png'
oneDplotPath = os.path.join(outdir, figname)
plotFig[i].savefig(oneDplotPath)
# if the heterodyned data files have been included create time series plots
# of |B_k| and a amplitude spectral density spectrogram of the data
if Bkdata is not None:
Bkfigs, ASDfigs = pppu.plot_Bks_ASDs(Bkdata, ifos)
# output plots
for i, ifo in enumerate(ifos):
figname = 'Bk' + '_' + ifo + '.png'
Bkplotpath = os.path.join(outdir, figname)
Bkfigs[i].savefig(Bkplotpath)
figname = 'ASD' + '_' + ifo + '.png'
ASDplotpath = os.path.join(outdir, figname)
ASDfigs[i].savefig(ASDplotpath)
def pulsarBayesPostProc( outdir, data,
upperlimit, histbins,
#nested sampling options
priorfile, parfile, ns_Nlive,
# analysis data
Bkdata=None, ifos=None,
# pulsar information
corfile=None #,
#Turn on 2D kdes
#twodkdeplots=False,
#Turn on R convergence tests
#RconvergenceTests=False
):
# check data is input
if data is None:
raise RuntimeError('You must specify an input data file')
# check output directory is input
if outdir is None:
raise RuntimeError("You must specify an output directory.")
# create output dir if it doesn't exist
if not os.path.isdir(outdir):
os.makedirs(outdir)
# list of posteriors for each ifo
poslist = []
# loop over ifos and generate posteriors
for idx, ifo in enumerate(ifos):
# nested sampling data files for IFO idx
idat = data[idx]
# if input format is xml
votfile = None
if '.xml' in data[0]:
peparser = bppu.PEOutputParser('xml')
commonResultsObj = peparser.parse(idat[0])
thefile = open(idat[0],'r')
votfile = thefile.read()
else: # input as standard nested sampling input
peparser = bppu.PEOutputParser('ns')
commonResultsObj = peparser.parse(idat, Nlive=ns_Nlive, xflag=False)
# Create an instance of the posterior class using the posterior values
# loaded from the file.
pos = bppu.Posterior( commonResultsObj, SimInspiralTableEntry=None, \
votfile=votfile )
# append to list
poslist.append(pos)
# read in par file if given
if parfile is not None:
par = pppu.psr_par(parfile)
# convert phi0' and psi' to phi0 and psi
if 'phi0prime' in pos.names and 'psiprime' in pos.names:
phi0samps, psisamps = pppu.phipsiconvert( pos['phi0prime'].samples, \
pos['psiprime'].samples )
# append phi0 and psi to posterior
phi0_pos = bppu.OneDPosterior('phi0', phi0samps)
pos.append(phi0_pos)
psi_pos = bppu.OneDPosterior('psi', phi0samps)
pos.append(psi_pos)
# get parameters to plot from prior file and those with "Errors" in the par
# file
plotpars = []
pri = pppu.psr_prior(priorfile)
for pars in pppu.float_keys:
# prior file values have preference over par file values with errors in the
# nested sampling code
pars_err = pars+'_ERR'
if pri[pars] is not None:
plotpars.append(pars)
elif par[pars_err] is not None:
plotpars.append(pars)
# if h0 exists plot the pdf
if 'H0' in plotpars:
# set bounds of h0
bounds = [0, float("inf")]
plotFig, ulvals = pppu.plot_posterior_hist( poslist, 'H0'.lower(), ifos,
bounds, histbins, upperlimit )
# output plots for each IFO
for i, ifo in enumerate(ifos):
figname = 'H0'.lower()+'_'+ifo+'.png'
oneDplotPath = os.path.join(outdir, figname)
plotFig[i].savefig(oneDplotPath)
# if the heterodyned data files have been included create time series plots
# of |B_k| and a amplitude spectral density spectrogram of the data
if Bkdata is not None:
Bkfigs, ASDfigs = pppu.plot_Bks_ASDs( Bkdata, ifos )
# output plots
for i, ifo in enumerate(ifos):
figname = 'Bk'+'_'+ifo+'.png'
Bkplotpath = os.path.join(outdir, figname)
Bkfigs[i].savefig(Bkplotpath)
figname = 'ASD'+'_'+ifo+'.png'
ASDplotpath = os.path.join(outdir, figname)
ASDfigs[i].savefig(ASDplotpath)