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LALInference 4.1.9.1-1865d10
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Functions | Variables
cbcBayesPostProc Namespace Reference

Functions

def email_notify (address, path)
 
def pickle_to_file (obj, fname)
 Pickle/serialize 'obj' into 'fname'. More...
 
def oneD_dict_to_file (dict, fname)
 
def multipleFileCB (opt, opt_str, value, parser)
 
def dict2html (d, parent=None)
 
def extract_hdf5_metadata (h5grp, parent=None)
 
def 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...
 

Variables

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

Function Documentation

◆ email_notify()

def cbcBayesPostProc.email_notify (   address,
  path 
)

Definition at line 84 of file cbcBayesPostProc.py.

◆ pickle_to_file()

def cbcBayesPostProc.pickle_to_file (   obj,
  fname 
)

Pickle/serialize 'obj' into 'fname'.

Definition at line 104 of file cbcBayesPostProc.py.

◆ oneD_dict_to_file()

def cbcBayesPostProc.oneD_dict_to_file (   dict,
  fname 
)

Definition at line 109 of file cbcBayesPostProc.py.

◆ multipleFileCB()

def cbcBayesPostProc.multipleFileCB (   opt,
  opt_str,
  value,
  parser 
)

Definition at line 114 of file cbcBayesPostProc.py.

◆ dict2html()

def cbcBayesPostProc.dict2html (   d,
  parent = None 
)

Definition at line 141 of file cbcBayesPostProc.py.

◆ extract_hdf5_metadata()

def cbcBayesPostProc.extract_hdf5_metadata (   h5grp,
  parent = None 
)

Definition at line 153 of file cbcBayesPostProc.py.

◆ cbcBayesPostProc()

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 .

It will produce a webpage from a file containing posterior samples generated by the parameter estimation codes with 1D/2D plots and stats from the marginal posteriors for each parameter/set of parameters.

Definition at line 170 of file cbcBayesPostProc.py.

Variable Documentation

◆ fig_width_pt

int cbcBayesPostProc.fig_width_pt = 246

Definition at line 49 of file cbcBayesPostProc.py.

◆ inches_per_pt

float cbcBayesPostProc.inches_per_pt = 1.0/72.27

Definition at line 50 of file cbcBayesPostProc.py.

◆ golden_mean

tuple cbcBayesPostProc.golden_mean = (2.236-1.0)/2.0

Definition at line 51 of file cbcBayesPostProc.py.

◆ fig_width

int cbcBayesPostProc.fig_width = fig_width_pt*inches_per_pt

Definition at line 52 of file cbcBayesPostProc.py.

◆ fig_height

int cbcBayesPostProc.fig_height = fig_width*golden_mean

Definition at line 53 of file cbcBayesPostProc.py.

◆ fig_size

list cbcBayesPostProc.fig_size = [fig_width,fig_height]

Definition at line 54 of file cbcBayesPostProc.py.

◆ USAGE

string cbcBayesPostProc.USAGE
Initial value:
1= '''%prog [options] datafile.dat [datafile2.dat ...]
2Generate a web page displaying results of parameter estimation based on the contents
3of one or more datafiles containing samples from one of the bayesian algorithms (MCMC, nested sampling).
4Options specify which extra statistics to compute and allow specification of additional information.
5'''

Definition at line 1084 of file cbcBayesPostProc.py.

◆ parser

cbcBayesPostProc.parser = OptionParser(USAGE)

Definition at line 1093 of file cbcBayesPostProc.py.

◆ dest

cbcBayesPostProc.dest

Definition at line 1094 of file cbcBayesPostProc.py.

◆ help

cbcBayesPostProc.help

Definition at line 1094 of file cbcBayesPostProc.py.

◆ metavar

cbcBayesPostProc.metavar

Definition at line 1094 of file cbcBayesPostProc.py.

◆ action

cbcBayesPostProc.action

Definition at line 1095 of file cbcBayesPostProc.py.

◆ callback

cbcBayesPostProc.callback

Definition at line 1095 of file cbcBayesPostProc.py.

◆ multipleFileCB

cbcBayesPostProc.multipleFileCB

Definition at line 1095 of file cbcBayesPostProc.py.

◆ default

cbcBayesPostProc.default

Definition at line 1097 of file cbcBayesPostProc.py.

◆ None

cbcBayesPostProc.None

Definition at line 1100 of file cbcBayesPostProc.py.

◆ type

cbcBayesPostProc.type

Definition at line 1100 of file cbcBayesPostProc.py.

◆ False

cbcBayesPostProc.False

Definition at line 1105 of file cbcBayesPostProc.py.

◆ True

cbcBayesPostProc.True

Definition at line 1136 of file cbcBayesPostProc.py.

◆ opts

cbcBayesPostProc.opts

Definition at line 1144 of file cbcBayesPostProc.py.

◆ args

cbcBayesPostProc.args

Definition at line 1144 of file cbcBayesPostProc.py.

◆ datafiles

list cbcBayesPostProc.datafiles = []

Definition at line 1146 of file cbcBayesPostProc.py.

◆ fixedBurnins

list cbcBayesPostProc.fixedBurnins = [int(opts.fixedBurnin[0]) for df in datafiles]

Definition at line 1155 of file cbcBayesPostProc.py.

◆ oneDMenus

dictionary cbcBayesPostProc.oneDMenus = {'Masses':None,'SourceFrame':None,'Timing':None,'Extrinsic':None,'Spins':None,'StrongField':None,'Others':None}

Definition at line 1163 of file cbcBayesPostProc.py.

◆ ifos_menu

list cbcBayesPostProc.ifos_menu = ['h1','l1','v1']

Definition at line 1176 of file cbcBayesPostProc.py.

◆ twoDGreedyMenu

list cbcBayesPostProc.twoDGreedyMenu = []

Definition at line 1181 of file cbcBayesPostProc.py.

◆ greedyBinSizes

cbcBayesPostProc.greedyBinSizes = bppu.greedyBinSizes

Definition at line 1269 of file cbcBayesPostProc.py.

◆ deltaLogP

cbcBayesPostProc.deltaLogP = opts.deltaLogL

Definition at line 1280 of file cbcBayesPostProc.py.

◆ confidenceLevels

cbcBayesPostProc.confidenceLevels = bppu.confidenceLevels

Definition at line 1284 of file cbcBayesPostProc.py.

◆ twoDplots

list cbcBayesPostProc.twoDplots = twoDGreedyMenu

Definition at line 1287 of file cbcBayesPostProc.py.

◆ injfile

cbcBayesPostProc.injfile

Definition at line 1292 of file cbcBayesPostProc.py.

◆ eventnum

cbcBayesPostProc.eventnum

Definition at line 1292 of file cbcBayesPostProc.py.

◆ trigfile

cbcBayesPostProc.trigfile

Definition at line 1293 of file cbcBayesPostProc.py.

◆ trignum

cbcBayesPostProc.trignum

Definition at line 1293 of file cbcBayesPostProc.py.

◆ skyres

cbcBayesPostProc.skyres

Definition at line 1294 of file cbcBayesPostProc.py.

◆ dievidence

cbcBayesPostProc.dievidence

Definition at line 1296 of file cbcBayesPostProc.py.

◆ boxing

cbcBayesPostProc.boxing

Definition at line 1296 of file cbcBayesPostProc.py.

◆ difactor

cbcBayesPostProc.difactor

Definition at line 1296 of file cbcBayesPostProc.py.

◆ ellevidence

cbcBayesPostProc.ellevidence

Definition at line 1298 of file cbcBayesPostProc.py.

◆ bayesfactornoise

cbcBayesPostProc.bayesfactornoise

Definition at line 1300 of file cbcBayesPostProc.py.

◆ bsn

cbcBayesPostProc.bsn

Definition at line 1300 of file cbcBayesPostProc.py.

◆ bayesfactorcoherent

cbcBayesPostProc.bayesfactorcoherent

Definition at line 1300 of file cbcBayesPostProc.py.

◆ snrfactor

cbcBayesPostProc.snrfactor

Definition at line 1302 of file cbcBayesPostProc.py.

◆ ns_flag

cbcBayesPostProc.ns_flag

Definition at line 1304 of file cbcBayesPostProc.py.

◆ ns

cbcBayesPostProc.ns

Definition at line 1304 of file cbcBayesPostProc.py.

◆ ns_Nlive

cbcBayesPostProc.ns_Nlive

Definition at line 1304 of file cbcBayesPostProc.py.

◆ ss_flag

cbcBayesPostProc.ss_flag

Definition at line 1306 of file cbcBayesPostProc.py.

◆ ss

cbcBayesPostProc.ss

Definition at line 1306 of file cbcBayesPostProc.py.

◆ ss_spin_flag

cbcBayesPostProc.ss_spin_flag

Definition at line 1306 of file cbcBayesPostProc.py.

◆ li_flag

cbcBayesPostProc.li_flag

Definition at line 1308 of file cbcBayesPostProc.py.

◆ lalinfmcmc

cbcBayesPostProc.lalinfmcmc

Definition at line 1308 of file cbcBayesPostProc.py.

◆ nDownsample

cbcBayesPostProc.nDownsample

Definition at line 1308 of file cbcBayesPostProc.py.

◆ downsample

cbcBayesPostProc.downsample

Definition at line 1308 of file cbcBayesPostProc.py.

◆ oldMassConvention

cbcBayesPostProc.oldMassConvention

Definition at line 1308 of file cbcBayesPostProc.py.

◆ fm_flag

cbcBayesPostProc.fm_flag

Definition at line 1310 of file cbcBayesPostProc.py.

◆ inj_spin_frame

cbcBayesPostProc.inj_spin_frame

Definition at line 1312 of file cbcBayesPostProc.py.

◆ noacf

cbcBayesPostProc.noacf

Definition at line 1314 of file cbcBayesPostProc.py.

◆ twodkdeplots

cbcBayesPostProc.twodkdeplots

Definition at line 1316 of file cbcBayesPostProc.py.

◆ RconvergenceTests

cbcBayesPostProc.RconvergenceTests

Definition at line 1318 of file cbcBayesPostProc.py.

◆ savepdfs

cbcBayesPostProc.savepdfs

Definition at line 1320 of file cbcBayesPostProc.py.

◆ covarianceMatrices

cbcBayesPostProc.covarianceMatrices

Definition at line 1322 of file cbcBayesPostProc.py.

◆ meanVectors

cbcBayesPostProc.meanVectors

Definition at line 1324 of file cbcBayesPostProc.py.

◆ header

cbcBayesPostProc.header

Definition at line 1326 of file cbcBayesPostProc.py.

◆ psd_files

cbcBayesPostProc.psd_files

Definition at line 1328 of file cbcBayesPostProc.py.

◆ greedy

cbcBayesPostProc.greedy

Definition at line 1329 of file cbcBayesPostProc.py.