cbcBayesCompPos.py File Reference

Prototypes
def	cbcBayesCompPos.open_url (url, username, password)
def	cbcBayesCompPos.all_pairs (L)
def	cbcBayesCompPos.open_url_curl (url, args=[])
def	cbcBayesCompPos.test_and_switch_param (common_output_table_header, test, switch)
def	cbcBayesCompPos.compare_plots_one_param_pdf (list_of_pos_by_name, param, analyticPDF=None)
	Plots a gaussian kernel density estimate for a set of Posteriors onto the same axis. More...
def	cbcBayesCompPos.compare_plots_one_param_line_hist (list_of_pos_by_name, param, cl, color_by_name, cl_lines_flag=True, legend='right', analyticPDF=None)
	Plots a gaussian kernel density estimate for a set of Posteriors onto the same axis. More...
def	cbcBayesCompPos.compute_ks_pvalue_matrix (list_of_pos_by_name, param)
	Returns a matrix of ks p-value tests between pairs of posteriors on the 1D marginalized distributions for param. More...
def	cbcBayesCompPos.compare_plots_one_param_line_hist_cum (list_of_pos_by_name, param, cl, color_by_name, cl_lines_flag=True, analyticCDF=None, legend='auto')
	Plots a gaussian kernel density estimate for a set of Posteriors onto the same axis. More...
def	cbcBayesCompPos.compare_bayes (outdir, names_and_pos_folders, injection_path, eventnum, username, password, reload_flag, clf, ldg_flag, contour_figsize=(4.5, 4.5), contour_dpi=250, contour_figposition=[0.15, 0.15, 0.5, 0.75], fail_on_file_err=True, covarianceMatrices=None, meanVectors=None, Npixels2D=50)
def	cbcBayesCompPos.output_confidence_levels_tex (clevels, outpath)
	Outputs a LaTeX table of parameter and run medians and confidence levels. More...
def	cbcBayesCompPos.output_confidence_levels_dat (clevels, outpath)
	Outputs a LaTeX table of parameter and run medians and confidence levels. More...
def	cbcBayesCompPos.output_confidence_uncertainty (cluncertainty, outpath)

Go to the source code of this file.

Namespaces
namespace	cbcBayesCompPos

Variables
list	cbcBayesCompPos.oneDMenu = ['mtotal','m1','m2','mchirp','mc','chirpmass','distance','distMPC','dist','iota','psi','eta','q','asym_massratio','spin1','spin2','a1','a2','phi1','theta1','phi2','theta2','costilt1','costilt2','costhetas','cosbeta','phi_orb', 'lambdat', 'dlambdat', 'lambda1', 'lambda2', 'lam_tilde', 'dlam_tilde','theta_jn','a1z','a2z'] + bppu.spininducedquadParams + bppu.snrParams + bppu.spinParams + bppu.cosmoParam + bppu.calParams + bppu.tigerParams + bppu.lorentzInvarianceViolationParams + bppu.qnmtestParams
list	cbcBayesCompPos.twoDGreedyMenu = [['mc','eta'],['mchirp','eta'],['chirpmass','eta'],['mc','q'],['mchirp','q'],['chirpmass','q'],['mc','asym_massratio'],['mchirp','asym_massratio'],['chirpmass','asym_massratio'],['m1','m2'],['mtotal','eta'],['distance','iota'],['dist','iota'],['dist','m1'],['ra','dec'],['dist','cos(iota)'],['phi_orb','iota'],['theta_jn','dist'],['spin1','spin2'],['spin1','mchirp'],['spin1','m1'],['a1','a2'],['a1','mchirp'],['a1','m1'],['tilt1','tilt2'],['tilt1','mchirp'],['tilt1','m1'],['a1z','a2z']]
dictionary	cbcBayesCompPos.paramNameLatexMap
list	cbcBayesCompPos.clTableParams = ['mchirp', 'mc', 'chirpmass', 'eta', 'q', 'm1', 'm2', 'distance', 'distMPC', 'dist', 'cos(iota)', 'iota', 'theta_jn', 'psi', 'ra', 'dec', 'time', 'phase', 'a1', 'a2', 'costilt1', 'costilt2','dchiMinus2','dchiMinus1','dchi0','dchi1','dchi2','dchi3','dchi3S','dchi3NS','dchi4','dchi4S','dchi4NS','dchi5','dchi5S','dchi5NS','dchi5l','dchi5lS','dchi5lNS','dchi6','dchi6S','dchi6NS','dchi6l','dchi7','dchi7S','dchi7NS','dbeta2','dbeta3','dsigma2','dsigma3','dsigma4','dbeta2','dbeta3', 'log10lambda_eff','log10lambda_a','log10livamp','lambda_eff','lambda_a','liv_amp','dquadmons','dchikappaS','dchikappaA','domega220', 'dtau220', 'domega210', 'dtau210', 'domega330', 'dtau330', 'domega440', 'dtau440', 'domega550', 'dtau550', 'db1', 'db2', 'db3', 'db4', 'dc1', 'dc2', 'dc4', 'dcl']
dictionary	cbcBayesCompPos.greedyBinSizes = {'mc':0.001,'m1':0.1,'m2':0.1,'mass1':0.1,'mass2':0.1,'mtotal':0.1,'eta':0.001,'q':0.001,'asym_massratio':0.001,'iota':0.05,'time':1e-4,'distance':5.0,'dist':1.0,'mchirp':0.01,'chirpmass':0.01,'a1':0.02,'a2':0.02,'phi1':0.05,'phi2':0.05,'theta1':0.05,'theta2':0.05,'ra':0.05,'dec':0.005,'psi':0.1,'cos(iota)':0.01, 'cos(tilt1)':0.01, 'cos(tilt2)':0.01, 'tilt1':0.05, 'tilt2':0.05, 'cos(thetas)':0.01, 'cos(beta)':0.01,'phi_orb':0.2,'inclination':0.05,'theta_jn':0.05,'spin1':0.02,'spin2':0.02}
list	cbcBayesCompPos.OneDconfidenceLevels = [0.9]
list	cbcBayesCompPos.TwoDconfidenceLevels = OneDconfidenceLevels
list	cbcBayesCompPos.twoDplots = [['m1','m2'],['mass1','mass2'],['RA','dec'],['ra','dec'],['cos(thetas)','cos(beta)'],['distance','iota'],['dist','iota'],['dist','cosiota'],['distance','cosiota'],['psi','iota'],['psi','distance'],['psi','phi0'],['dist','cos(iota)'],['phi_orb','iota'],['distance','inclination'],['dist','inclination'],['theta_jn','dist'],['spin1','spin2'],['spin1','mchirp'],['spin1','m1'],['a1','a2'],['a1','mchirp'],['a1','m1'],['tilt1','tilt2'],['tilt1','mchirp'],['tilt1','m1']]
list	cbcBayesCompPos.allowed_params = ['mtotal','m1','m2','mchirp','mc','chirpmass','q','asym_massratio','distance','distMPC','dist','iota','psi','eta','ra','dec','a1','a2','spin1','spin2','phi1','theta1','phi2','theta2','cos(iota)','cos(tilt1)','cos(tilt2)','tilt1','tilt2','cos(thetas)','cos(beta)','phi_orb','inclination', 'logl', 'lambdat', 'dlambdat', 'lambda1', 'lambda2', 'lam_tilde', 'dlam_tilde','theta_jn','a1z','a2z','dquadmons','dquadmona','dquadmon1','dquadmon2']+bppu.snrParams + bppu.spinParams + bppu.cosmoParam + bppu.calParams + bppu.tigerParams
	cbcBayesCompPos.parser = OptionParser()
	cbcBayesCompPos.dest
	cbcBayesCompPos.help
	cbcBayesCompPos.metavar
	cbcBayesCompPos.action
	cbcBayesCompPos.default
	cbcBayesCompPos.True
	cbcBayesCompPos.False
	cbcBayesCompPos.None
	cbcBayesCompPos.type
	cbcBayesCompPos.opts
	cbcBayesCompPos.args
	cbcBayesCompPos.outpath = os.getcwd()
list	cbcBayesCompPos.names = []
	cbcBayesCompPos.pos_list
	cbcBayesCompPos.greedy2savepaths
	cbcBayesCompPos.oned_data
	cbcBayesCompPos.confidence_uncertainty
	cbcBayesCompPos.confidence_levels
	cbcBayesCompPos.max_logls
	cbcBayesCompPos.dics
	cbcBayesCompPos.ldg_flag
	cbcBayesCompPos.contour_figsize
	cbcBayesCompPos.contour_dpi
	cbcBayesCompPos.contour_figposition
	cbcBayesCompPos.fail_on_file_err
	cbcBayesCompPos.readFileErr
	cbcBayesCompPos.covarianceMatrices
	cbcBayesCompPos.meanVectors
	cbcBayesCompPos.Npixels2D
	cbcBayesCompPos.compare_page = bppu.htmlPage('Compare PDFs (single event)',css=bppu.__default_css_string)
	Print Confidence Levels######. More...
	cbcBayesCompPos.param_section = compare_page.add_section('Meta')
string	cbcBayesCompPos.param_section_write = '<div><p>This comparison was created from the following analyses</p>'
	cbcBayesCompPos.save_paths
	cbcBayesCompPos.cl_table_str
	cbcBayesCompPos.ks_table_str
	cbcBayesCompPos.cl_uncer_str
bool	cbcBayesCompPos.clf_toggle = False
	cbcBayesCompPos.head
	cbcBayesCompPos.plotfile
	cbcBayesCompPos.temp
	cbcBayesCompPos.param_name = param_name.split('.')[0]
	cbcBayesCompPos.compare_page_footer = compare_page.add_section('')
	cbcBayesCompPos.cc_args = copy.deepcopy(sys.argv)
	cbcBayesCompPos.user_idx = cc_args.index('-u')
	cbcBayesCompPos.pass_idx = cc_args.index('-x')
string	cbcBayesCompPos.cc_args_str = ''
	cbcBayesCompPos.resultspage = open(os.path.join(outpath,'index.html'),'w')