cbcBayesPostProc.py File Reference

Prototypes
def	cbcBayesPostProc.email_notify (address, path)
def	cbcBayesPostProc.pickle_to_file (obj, fname)
	Pickle/serialize 'obj' into 'fname'. More...
def	cbcBayesPostProc.oneD_dict_to_file (dict, fname)
def	cbcBayesPostProc.multipleFileCB (opt, opt_str, value, parser)
def	cbcBayesPostProc.dict2html (d, parent=None)
def	cbcBayesPostProc.extract_hdf5_metadata (h5grp, parent=None)
def	cbcBayesPostProc.cbcBayesPostProc (outdir, data, oneDMenus, twoDGreedyMenu, GreedyRes, confidence_levels, twoDplots, injfile=None, eventnum=None, trigfile=None, trignum=None, skyres=None, dievidence=False, boxing=64, difactor=1.0, ellevidence=False, bayesfactornoise=None, bayesfactorcoherent=None, snrfactor=None, ns_flag=False, ns_Nlive=None, ss_flag=False, ss_spin_flag=False, li_flag=False, deltaLogP=None, fixedBurnins=None, nDownsample=None, oldMassConvention=False, fm_flag=False, inj_spin_frame='OrbitalL', noacf=False, twodkdeplots=False, RconvergenceTests=False, savepdfs=True, covarianceMatrices=None, meanVectors=None, header=None, psd_files=None, greedy=True ## If true will use greedy bin for 1-d credible regions. Otherwise use 2-steps KDE)
	This is a demonstration script for using the functionality/data structures contained in lalinference.bayespputils . More...

Go to the source code of this file.

Namespaces
namespace	cbcBayesPostProc

Variables
int	cbcBayesPostProc.fig_width_pt = 246
float	cbcBayesPostProc.inches_per_pt = 1.0/72.27
tuple	cbcBayesPostProc.golden_mean = (2.236-1.0)/2.0
int	cbcBayesPostProc.fig_width = fig_width_pt*inches_per_pt
int	cbcBayesPostProc.fig_height = fig_width*golden_mean
list	cbcBayesPostProc.fig_size = [fig_width,fig_height]
string	cbcBayesPostProc.USAGE
	cbcBayesPostProc.parser = OptionParser(USAGE)
	cbcBayesPostProc.dest
	cbcBayesPostProc.help
	cbcBayesPostProc.metavar
	cbcBayesPostProc.action
	cbcBayesPostProc.callback
	cbcBayesPostProc.multipleFileCB
	cbcBayesPostProc.default
	cbcBayesPostProc.None
	cbcBayesPostProc.type
	cbcBayesPostProc.False
	cbcBayesPostProc.True
	cbcBayesPostProc.opts
	cbcBayesPostProc.args
list	cbcBayesPostProc.datafiles = []
list	cbcBayesPostProc.fixedBurnins = [int(opts.fixedBurnin[0]) for df in datafiles]
dictionary	cbcBayesPostProc.oneDMenus = {'Masses':None,'SourceFrame':None,'Timing':None,'Extrinsic':None,'Spins':None,'StrongField':None,'Others':None}
list	cbcBayesPostProc.ifos_menu = ['h1','l1','v1']
list	cbcBayesPostProc.twoDGreedyMenu = []
	cbcBayesPostProc.greedyBinSizes = bppu.greedyBinSizes
	cbcBayesPostProc.deltaLogP = opts.deltaLogL
	cbcBayesPostProc.confidenceLevels = bppu.confidenceLevels
list	cbcBayesPostProc.twoDplots = twoDGreedyMenu
	cbcBayesPostProc.injfile
	cbcBayesPostProc.eventnum
	cbcBayesPostProc.trigfile
	cbcBayesPostProc.trignum
	cbcBayesPostProc.skyres
	cbcBayesPostProc.dievidence
	cbcBayesPostProc.boxing
	cbcBayesPostProc.difactor
	cbcBayesPostProc.ellevidence
	cbcBayesPostProc.bayesfactornoise
	cbcBayesPostProc.bsn
	cbcBayesPostProc.bayesfactorcoherent
	cbcBayesPostProc.snrfactor
	cbcBayesPostProc.ns_flag
	cbcBayesPostProc.ns
	cbcBayesPostProc.ns_Nlive
	cbcBayesPostProc.ss_flag
	cbcBayesPostProc.ss
	cbcBayesPostProc.ss_spin_flag
	cbcBayesPostProc.li_flag
	cbcBayesPostProc.lalinfmcmc
	cbcBayesPostProc.nDownsample
	cbcBayesPostProc.downsample
	cbcBayesPostProc.oldMassConvention
	cbcBayesPostProc.fm_flag
	cbcBayesPostProc.inj_spin_frame
	cbcBayesPostProc.noacf
	cbcBayesPostProc.twodkdeplots
	cbcBayesPostProc.RconvergenceTests
	cbcBayesPostProc.savepdfs
	cbcBayesPostProc.covarianceMatrices
	cbcBayesPostProc.meanVectors
	cbcBayesPostProc.header
	cbcBayesPostProc.psd_files
	cbcBayesPostProc.greedy