lalinference.bayespputils Namespace Reference

Data Structures
class	PosteriorOneDPDF
	A data structure representing one parameter in a chain of posterior samples. More...
class	Posterior
	Data structure for a table of posterior samples . More...
class	BurstPosterior
	Data structure for a table of posterior samples . More...
class	KDTree
	A kD-tree. More...
class	KDTreeVolume
	A kD-tree suitable for splitting parameter spaces and counting hypervolumes. More...
class	KDSkeleton
	object to store the structure of a kd tree More...
class	PosteriorSample
	A single parameter sample object, suitable for inclusion in a kD-tree. More...
class	AnalyticLikelihood
	Return analytic likelihood values. More...
class	htmlChunk
	A base class for representing web content using ElementTree . More...
class	htmlPage
	A concrete class for generating an XHTML(1) document. More...
class	htmlSection
	Represents a block of html fitting within a htmlPage. More...
class	htmlCollapseSection
	Represents a block of html fitting within a htmlPage. More...
class	RALocator
	RA tick locations with some intelligence. More...
class	DecLocator
	Dec tick locations with some intelligence. More...
class	RAFormatter
class	DecFormatter
class	ACLError
class	PEOutputParser
	A parser for the output of Bayesian parameter estimation codes. More...
class	VOT2HTML

Functions
def	get_end (siminspiral)
def	replace_column (table, old, new)
	Workaround for missing astropy.table.Table.replace_column method, which was added in Astropy 1.1. More...
def	as_array (table)
	Workaround for missing astropy.table.Table.as_array method, which was added in Astropy 1.0. More...
def	det_end_time (ifo_prefix, inj)
def	get_prior (name)
def	plot_label (param)
	A lookup table for plot labels. More...
def	skyArea (bounds)
	functions used in 2stage kdtree More...
def	random_split (items, fraction)
def	addSample (tree, coordinates)
def	kdtree_bin_sky_volume (posterior, confidence_levels)
def	kdtree_bin_sky_area (posterior, confidence_levels, samples_per_bin=10)
	takes samples and applies a KDTree to them to return confidence levels returns confidence_intervals - dictionary of user_provided_CL:calculated_area b - ordered list of KD leaves injInfo - if injection values provided then returns [Bounds_of_inj_kd_leaf ,number_samples_in_box, weight_of_box,injection_CL ,injection_CL_area] Not quite sure that the repeated samples case is fixed, posibility of infinite loop. More...
def	kdtree_bin (posterior, coord_names, confidence_levels, initial_boundingbox=None, samples_per_bin=10)
	takes samples and applies a KDTree to them to return confidence levels returns confidence_intervals - dictionary of user_provided_CL:calculated_volume b - ordered list of KD leaves initial_boundingbox - list of lists [upperleft_coords,lowerright_coords] injInfo - if injection values provided then returns [Bounds_of_inj_kd_leaf ,number_samples_in_box, weight_of_box,injection_CL ,injection_CL_volume] Not quite sure that the repeated samples case is fixed, posibility of infinite loop. More...
def	kdtree_bin2Step (posterior, coord_names, confidence_levels, initial_boundingbox=None, samples_per_bin=10, injCoords=None, alternate=False, fraction=0.5, skyCoords=False)
	input: posterior class instance, list of confidence levels, optional choice of inital parameter space, samples per box in kdtree note initial_boundingbox is [[lowerbound of each param][upper bound of each param]], if not specified will just take limits of samples fraction is proportion of samples used for making the tree structure. More...
def	greedy_bin_two_param (posterior, greedy2Params, confidence_levels)
	Determine the 2-parameter Bayesian Confidence Intervals using a greedy binning algorithm. More...
def	pol2cart (long, lat)
	Utility function to convert longitude,latitude on a unit sphere to cartesian co-ordinates. More...
def	sph2cart (r, theta, phi)
	Utiltiy function to convert r,theta,phi to cartesian co-ordinates. More...
def	cart2sph (x, y, z)
	Utility function to convert cartesian coords to r,theta,phi. More...
def	plot_sky_map (hpmap, outdir, inj=None, nest=True)
	Plots a sky map from a healpix map, optionally including an injected position. More...
def	skymap_confidence_areas (hpmap, cls)
	Returns the area (in square degrees) for each confidence level with a greedy binning algorithm for the given healpix map. More...
def	skymap_inj_pvalue (hpmap, inj, nest=True)
	Returns the greedy p-value estimate for the given injection. More...
def	mc2ms (mc, eta)
	Utility function for converting mchirp,eta to component masses. More...
def	q2ms (mc, q)
	Utility function for converting mchirp,q to component masses. More...
def	q2eta (q)
	Utility function for converting q to eta. More...
def	mc2q (mc, eta)
	Utility function for converting mchirp,eta to new mass ratio q (m2/m1). More...
def	ang_dist (long1, lat1, long2, lat2)
	Find the angular separation of (long1,lat1) and (long2,lat2), which are specified in radians. More...
def	array_dot (vec1, vec2)
	Calculate dot products between vectors in rows of numpy arrays. More...
def	array_ang_sep (vec1, vec2)
	Find angles between vectors in rows of numpy arrays. More...
def	array_polar_ang (vec)
	Find polar angles of vectors in rows of a numpy array. More...
def	rotation_matrix (angle, direction)
	Compute general rotation matrices for a given angles and direction vectors. More...
def	ROTATEZ (angle, vx, vy, vz)
def	ROTATEY (angle, vx, vy, vz)
def	orbital_momentum (fref, m1, m2, inclination)
	Calculate orbital angular momentum vector. More...
def	orbital_momentum_mag (fref, m1, m2, eta)
def	component_momentum (m, a, theta, phi)
	Calculate BH angular momentum vector. More...
def	symm_tidal_params (lambda1, lambda2, q)
	Calculate best tidal parameters [Eqs. More...
def	spin_angles (fref, mc, eta, incl, a1, theta1, phi1, a2=None, theta2=None, phi2=None)
	Calculate physical spin angles. More...
def	chi_precessing (m1, a1, tilt1, m2, a2, tilt2)
	Calculate the magnitude of the effective precessing spin following convention from Phys. More...
def	calculate_redshift (distance, h=0.6790, om=0.3065, ol=0.6935, w0=-1.0)
	Calculate the redshift from the luminosity distance measurement using the Cosmology Calculator provided in LAL. More...
def	source_mass (mass, redshift)
	Calculate source mass parameter for mass m as: m_source = m / (1.0 + z) For a parameter m. More...
def	integrand_distance (redshift, nonGR_alpha)
	Following functions added for testing Lorentz violations. More...
def	DistanceMeasure (redshift, nonGR_alpha)
	D_alpha = ((1+z)^(1-alpha))/H_0 * D_alpha # from eq.15 of arxiv 1110.2720 D_alpha calculated from integrand in above function. More...
def	lambda_a (redshift, nonGR_alpha, lambda_eff, distance)
	Converting from the effective wavelength-like parameter to lambda_A: lambda_A = lambda_{eff}*(D_alpha/D_L)^(1/(2-alpha))*(1/(1+z)^((1-alpha)/(2-alpha))) More...
def	amplitudeMeasure (redshift, nonGR_alpha, lambda_eff, distance)
	Converting to Lorentz violating parameter "A" in dispersion relation from lambda_A: A = (lambda_A/h)^(alpha-2) # eqn. More...
def	physical2radiationFrame (theta_jn, phi_jl, tilt1, tilt2, phi12, a1, a2, m1, m2, fref, phiref)
	changes for testing Lorentz violations made till here More...
def	plot_one_param_pdf_kde (fig, onedpos)
def	plot_one_param_pdf (posterior, plot1DParams, analyticPDF=None, analyticCDF=None, plotkde=False)
	Plots a 1D histogram and (gaussian) kernel density estimate of the distribution of posterior samples for a given parameter. More...
def	getRAString (radians, accuracy='auto')
def	getDecString (radians, accuracy='auto')
def	plot_corner (posterior, levels, parnames=None)
	Make a corner plot using the triangle module (See http://github.com/dfm/corner.py) More...
def	plot_two_param_kde_greedy_levels (posteriors_by_name, plot2DkdeParams, levels, colors_by_name, line_styles=__default_line_styles, figsize=(4, 3), dpi=250, figposition=[0.2, 0.2, 0.48, 0.75], legend='right', hatches_by_name=None, Npixels=50)
	Plots a 2D kernel density estimate of the 2-parameter marginal posterior. More...
def	plot_two_param_kde (posterior, plot2DkdeParams)
	Plots a 2D kernel density estimate of the 2-parameter marginal posterior. More...
def	get_inj_by_time (injections, time)
	Filter injections to find the injection with end time given by time +/- 0.1s. More...
def	histogram2D (posterior, greedy2Params, confidence_levels)
	Returns a 2D histogram and edges for the two parameters passed in greedy2Params, plus the actual discrete confidence levels imposed by the finite number of samples. More...
def	plot_two_param_greedy_bins_contourf (posteriors_by_name, greedy2Params, confidence_levels, colors_by_name, figsize=(7, 6), dpi=120, figposition=[0.3, 0.3, 0.5, 0.5], legend='right', hatches_by_name=None)
def	plot_two_param_greedy_bins_hist (posterior, greedy2Params, confidence_levels)
	Histograms of the ranked pixels produced by the 2-parameter greedy binning algorithm colured by their confidence level. More...
def	greedy_bin_one_param (posterior, greedy1Param, confidence_levels)
	Determine the 1-parameter Bayesian Confidence Interval using a greedy binning algorithm. More...
def	contigious_interval_one_param (posterior, contInt1Params, confidence_levels)
	Calculates the smallest contigious 1-parameter confidence interval for a set of given confidence levels. More...
def	autocorrelation (series)
	Returns an estimate of the autocorrelation function of a given series. More...
def	autocorrelation_length_estimate (series, acf=None, M=5, K=2)
	Attempts to find a self-consistent estimate of the autocorrelation length of a given series. More...
def	effectiveSampleSize (samples, Nskip=1)
	Compute the effective sample size, calculating the ACL using only the second half of the samples to avoid ACL overestimation due to chains equilibrating after adaptation. More...
def	readCoincXML (xml_file, trignum)
def	find_ndownsample (samples, nDownsample)
	Given a list of files, threshold value, and a desired number of outputs posterior samples, return the skip number to achieve the desired number of posterior samples. More...
def	parse_converge_output_section (fo)
def	vo_nest2pos (nsresource, Nlive=None)
	Parse a VO Table RESOURCE containing nested sampling output and return a VOTable TABLE element with posterior samples in it. More...
def	confidence_interval_uncertainty (cl, cl_bounds, posteriors)
	Returns a tuple (relative_change, fractional_uncertainty, percentile_uncertainty) giving the uncertainty in confidence intervals from multiple posteriors. More...
def	plot_waveform (pos=None, siminspiral=None, event=0, path=None, ifos=['H1', 'L1', 'V1'])
def	plot_psd (psd_files, outpath=None, f_min=30.)
def	cred_interval (x, cl=.9, lower=True)
	Return location of lower or upper confidence levels Args: x: List of samples. More...
def	spline_angle_xform (delta_psi)
	Returns the angle in degrees corresponding to the spline calibration parameters delta_psi. More...
def	plot_spline_pos (logf, ys, nf=100, level=0.9, color='k', label=None, xform=None)
	Plot calibration posterior estimates for a spline model in log space. More...
def	plot_calibration_pos (pos, level=.9, outpath=None)
def	plot_burst_waveform (pos=None, simburst=None, event=0, path=None, ifos=['H1', 'L1', 'V1'])
def	make_1d_table (html, legend, label, pos, pars, noacf, GreedyRes, onepdfdir, sampsdir, savepdfs, greedy, analyticLikelihood, nDownsample)

Variables
	hostname_short = socket.gethostbyaddr(socket.gethostname())[0].split('.',1)[1]
	calParams = calAmpParams + calPhaseParams
list	massParams = ['m1','m2','chirpmass','mchirp','mc','eta','q','massratio','asym_massratio','mtotal','mf','mf_evol','mf_nonevol']
list	spinParamsPrec = ['a1','a2','phi1','theta1','phi2','theta2','costilt1','costilt2','costheta_jn','cosbeta','tilt1','tilt1_isco','tilt2','tilt2_isco','phi_jl','theta_jn','phi12','phi12_isco','af','af_evol','af_nonevol','afz','afz_evol','afz_nonevol']
list	spinParamsAli = ['spin1','spin2','a1z','a2z']
list	spinParamsEff = ['chi','effectivespin','chi_eff','chi_tot','chi_p']
list	spinParams = spinParamsPrec+spinParamsEff+spinParamsAli
list	cosmoParam = ['m1_source','m2_source','mtotal_source','mc_source','redshift','mf_source','mf_source_evol','mf_source_nonevol','m1_source_maxldist','m2_source_maxldist','mtotal_source_maxldist','mc_source_maxldist','redshift_maxldist','mf_source_maxldist','mf_source_maxldist_evol','mf_source_maxldist_nonevol']
list	ppEParams = ['ppEalpha','ppElowera','ppEupperA','ppEbeta','ppElowerb','ppEupperB','alphaPPE','aPPE','betaPPE','bPPE']
list	tigerParams = ['dchi%i'%(i) for i in range(8)] + ['dchi%il'%(i) for i in [5,6] ] + ['dxi%d'%(i+1) for i in range(6)] + ['dalpha%i'%(i+1) for i in range(5)] + ['dbeta%i'%(i+1) for i in range(3)] + ['dsigma%i'%(i+1) for i in range(4)] + ['dipolecoeff'] + ['dchiminus%i'%(i) for i in [1,2]] + ['dchiMinus%i'%(i) for i in [1,2]] + ['db1','db2','db3','db4','dc1','dc2','dc4','dcl']
list	qnmtestParams = ['domega220','dtau220','domega210','dtau210','domega330','dtau330','domega440','dtau440','domega550','dtau550']
list	bransDickeParams = ['omegaBD','ScalarCharge1','ScalarCharge2']
list	massiveGravitonParams = ['lambdaG']
list	lorentzInvarianceViolationParams = ['log10lambda_a','lambda_a','log10lambda_eff','lambda_eff','log10livamp','liv_amp']
list	tidalParams = ['lambda1','lambda2','lam_tilde','dlam_tilde','lambdat','dlambdat','lambdas','bluni']
list	fourPiecePolyParams = ['logp1','gamma1','gamma2','gamma3']
list	spectralParams = ['sdgamma0','sdgamma1','sdgamma2','sdgamma3']
list	energyParams = ['e_rad', 'e_rad_evol', 'e_rad_nonevol', 'l_peak', 'l_peak_evol', 'l_peak_nonevol', 'e_rad_maxldist', 'e_rad_maxldist_evol', 'e_rad_maxldist_nonevol']
list	spininducedquadParams = ['dquadmon1', 'dquadmon2', 'dquadmona', 'dquadmona']
tuple	strongFieldParams
list	distParams = ['distance','distMPC','dist','distance_maxl']
list	incParams = ['iota','inclination','cosiota']
list	polParams = ['psi','polarisation','polarization']
list	skyParams = ['ra','rightascension','declination','dec']
list	phaseParams = ['phase', 'phi0','phase_maxl']
list	timeParams = ['time','time_mean']
list	endTimeParams = ['l1_end_time','h1_end_time','v1_end_time']
list	statsParams = ['logprior','logl','deltalogl','deltaloglh1','deltalogll1','deltaloglv1','deltaloglh2','deltaloglg1']
list	calibParams = ['calpha_l1','calpha_h1','calpha_v1','calamp_l1','calamp_h1','calamp_v1']
list	confidenceLevels = [0.67,0.9,0.95,0.99]
	Greedy bin sizes for cbcBPP and confidence leves used for the greedy bin intervals. More...
dictionary	greedyBinSizes = {'mc':0.025,'m1':0.1,'m2':0.1,'mass1':0.1,'mass2':0.1,'mtotal':0.1,'mc_source':0.025,'m1_source':0.1,'m2_source':0.1,'mtotal_source':0.1,'mc_source_maxldist':0.025,'m1_source_maxldist':0.1,'m2_source_maxldist':0.1,'mtotal_source_maxldist':0.1,'eta':0.001,'q':0.01,'asym_massratio':0.01,'iota':0.01,'cosiota':0.02,'time':1e-4,'time_mean':1e-4,'distance':1.0,'dist':1.0,'distance_maxl':1.0,'redshift':0.01,'redshift_maxldist':0.01,'mchirp':0.025,'chirpmass':0.025,'spin1':0.04,'spin2':0.04,'a1z':0.04,'a2z':0.04,'a1':0.02,'a2':0.02,'phi1':0.05,'phi2':0.05,'theta1':0.05,'theta2':0.05,'ra':0.05,'dec':0.05,'chi':0.05,'chi_eff':0.05,'chi_tot':0.05,'chi_p':0.05,'costilt1':0.02,'costilt2':0.02,'thatas':0.05,'costheta_jn':0.02,'beta':0.05,'omega':0.05,'cosbeta':0.02,'ppealpha':1.0,'ppebeta':1.0,'ppelowera':0.01,'ppelowerb':0.01,'ppeuppera':0.01,'ppeupperb':0.01,'polarisation':0.04,'rightascension':0.05,'declination':0.05,'massratio':0.001,'inclination':0.01,'phase':0.05,'tilt1':0.05,'tilt2':0.05,'phi_jl':0.05,'theta_jn':0.05,'phi12':0.05,'flow':1.0,'phase_maxl':0.05,'calamp_l1':0.01,'calamp_h1':0.01,'calamp_v1':0.01,'calpha_h1':0.01,'calpha_l1':0.01,'calpha_v1':0.01,'logdistance':0.1,'psi':0.1,'costheta_jn':0.1,'mf':0.1,'mf_evol':0.1,'mf_nonevol':0.1,'mf_source':0.1,'mf_source_evol':0.1,'mf_source_nonevol':0.1,'mf_source_maxldist':0.1,'mf_source_maxldist_evol':0.1,'mf_source_maxldist_nonevol':0.1,'af':0.02,'af_evol':0.02,'af_nonevol':0.02,'afz':0.02,'afz_evol':0.01,'afz_nonevol':0.01,'e_rad':0.1,'e_rad_evol':0.1,'e_rad_nonevol':0.1,'e_rad_maxldist':0.1,'e_rad_maxldist_evol':0.1,'e_rad_maxldist_nonevol':0.1,'l_peak':0.1,'l_peak_evol':0.1,'l_peak_nonevol':0.1}
	cred_level = lambda cl, x: np.sort(x, axis=0)[int(cl*len(x))]
	unrho
	when called by kdtree.operate will be used to calculate the density of each bin (sky area) More...
	tabname
	intable
	tableElementName

Function Documentation

get_end()

def lalinference.bayespputils.get_end

(

siminspiral

)

Definition at line 102 of file bayespputils.py.

replace_column()

def lalinference.bayespputils.replace_column	(		table,
			old,
			new
	)

Workaround for missing astropy.table.Table.replace_column method, which was added in Astropy 1.1.

FIXME: remove this function when LALSuite depends on Astropy >= 1.1.

Definition at line 109 of file bayespputils.py.

as_array()

def lalinference.bayespputils.as_array

(

table

)

Workaround for missing astropy.table.Table.as_array method, which was added in Astropy 1.0.

FIXME: remove this function when LALSuite depends on Astropy >= 1.0.

Definition at line 118 of file bayespputils.py.

det_end_time()

def lalinference.bayespputils.det_end_time	(		ifo_prefix,
			inj
	)

Definition at line 199 of file bayespputils.py.

get_prior()

def lalinference.bayespputils.get_prior

(

name

)

Definition at line 307 of file bayespputils.py.

plot_label()

def lalinference.bayespputils.plot_label

(

param

)

A lookup table for plot labels.

Definition at line 427 of file bayespputils.py.

skyArea()

def lalinference.bayespputils.skyArea

(

bounds

)

functions used in 2stage kdtree

Definition at line 3147 of file bayespputils.py.

random_split()

def lalinference.bayespputils.random_split	(		items,
			fraction
	)

Definition at line 3150 of file bayespputils.py.

addSample()

def lalinference.bayespputils.addSample	(		tree,
			coordinates
	)

Definition at line 3155 of file bayespputils.py.

kdtree_bin_sky_volume()

def lalinference.bayespputils.kdtree_bin_sky_volume	(		posterior,
			confidence_levels
	)

Definition at line 3168 of file bayespputils.py.

kdtree_bin_sky_area()

def lalinference.bayespputils.kdtree_bin_sky_area	(		posterior,
			confidence_levels,
			samples_per_bin = 10
	)

takes samples and applies a KDTree to them to return confidence levels returns confidence_intervals - dictionary of user_provided_CL:calculated_area b - ordered list of KD leaves injInfo - if injection values provided then returns [Bounds_of_inj_kd_leaf ,number_samples_in_box, weight_of_box,injection_CL ,injection_CL_area] Not quite sure that the repeated samples case is fixed, posibility of infinite loop.

Definition at line 3230 of file bayespputils.py.

kdtree_bin()

def lalinference.bayespputils.kdtree_bin	(		posterior,
			coord_names,
			confidence_levels,
			initial_boundingbox = None,
			samples_per_bin = 10
	)

takes samples and applies a KDTree to them to return confidence levels returns confidence_intervals - dictionary of user_provided_CL:calculated_volume b - ordered list of KD leaves initial_boundingbox - list of lists [upperleft_coords,lowerright_coords] injInfo - if injection values provided then returns [Bounds_of_inj_kd_leaf ,number_samples_in_box, weight_of_box,injection_CL ,injection_CL_volume] Not quite sure that the repeated samples case is fixed, posibility of infinite loop.

Definition at line 3337 of file bayespputils.py.

kdtree_bin2Step()

def lalinference.bayespputils.kdtree_bin2Step	(		posterior,
			coord_names,
			confidence_levels,
			initial_boundingbox = None,
			samples_per_bin = 10,
			injCoords = None,
			alternate = False,
			fraction = 0.5,
			skyCoords = False
	)

input: posterior class instance, list of confidence levels, optional choice of inital parameter space, samples per box in kdtree note initial_boundingbox is [[lowerbound of each param][upper bound of each param]], if not specified will just take limits of samples fraction is proportion of samples used for making the tree structure.

returns: confidence_intervals, sorted list of kd objects, initial_boundingbox, injInfo where injInfo is [bounding box injection is found within, samples in said box, weighting of box (in case of repeated samples),inj_confidence, inj_confidence_area]

Definition at line 3460 of file bayespputils.py.

greedy_bin_two_param()

def lalinference.bayespputils.greedy_bin_two_param	(		posterior,
			greedy2Params,
			confidence_levels
	)

Determine the 2-parameter Bayesian Confidence Intervals using a greedy binning algorithm.

Parameters

posterior	an instance of the Posterior class.
greedy2Params	a dict - {param1Name:param1binSize,param2Name:param2binSize} .
confidence_levels	A list of floats of the required confidence intervals [(0-1)].

Definition at line 3577 of file bayespputils.py.

pol2cart()

def lalinference.bayespputils.pol2cart	(		long,
			lat
	)

Utility function to convert longitude,latitude on a unit sphere to cartesian co-ordinates.

Definition at line 3663 of file bayespputils.py.

sph2cart()

def lalinference.bayespputils.sph2cart	(		r,
			theta,
			phi
	)

Utiltiy function to convert r,theta,phi to cartesian co-ordinates.

Definition at line 3674 of file bayespputils.py.

cart2sph()

def lalinference.bayespputils.cart2sph	(		x,
			y,
			z
	)

Utility function to convert cartesian coords to r,theta,phi.

Definition at line 3684 of file bayespputils.py.

plot_sky_map()

def lalinference.bayespputils.plot_sky_map	(		hpmap,
			outdir,
			inj = None,
			nest = True
	)

Plots a sky map from a healpix map, optionally including an injected position.

This is a temporary map to display before ligo.skymap utility is used to generated a smoother one.

:param hpmap: An array representing a healpix map (in nested ordering if nest = True).

:param outdir: The output directory.

:param inj: If not None, then [ra, dec] of the injection associated with the posterior map.

:param nest: Flag indicating the pixel ordering in healpix.

Definition at line 3708 of file bayespputils.py.

skymap_confidence_areas()

def lalinference.bayespputils.skymap_confidence_areas	(		hpmap,
			cls
	)

Returns the area (in square degrees) for each confidence level with a greedy binning algorithm for the given healpix map.

Definition at line 3726 of file bayespputils.py.

skymap_inj_pvalue()

def lalinference.bayespputils.skymap_inj_pvalue	(		hpmap,
			inj,
			nest = True
	)

Returns the greedy p-value estimate for the given injection.

Definition at line 3746 of file bayespputils.py.

mc2ms()

def lalinference.bayespputils.mc2ms	(		mc,
			eta
	)

Utility function for converting mchirp,eta to component masses.

The masses are defined so that m1>m2. The rvalue is a tuple (m1,m2).

Definition at line 3762 of file bayespputils.py.

q2ms()

def lalinference.bayespputils.q2ms	(		mc,
			q
	)

Utility function for converting mchirp,q to component masses.

The masses are defined so that m1>m2. The rvalue is a tuple (m1,m2).

Definition at line 3778 of file bayespputils.py.

q2eta()

def lalinference.bayespputils.q2eta

(

q

)

Utility function for converting q to eta.

The rvalue is eta.

Definition at line 3790 of file bayespputils.py.

mc2q()

def lalinference.bayespputils.mc2q	(		mc,
			eta
	)

Utility function for converting mchirp,eta to new mass ratio q (m2/m1).

Definition at line 3799 of file bayespputils.py.

ang_dist()

def lalinference.bayespputils.ang_dist	(		long1,
			lat1,
			long2,
			lat2
	)

Find the angular separation of (long1,lat1) and (long2,lat2), which are specified in radians.

Definition at line 3810 of file bayespputils.py.

array_dot()

def lalinference.bayespputils.array_dot	(		vec1,
			vec2
	)

Calculate dot products between vectors in rows of numpy arrays.

Definition at line 3826 of file bayespputils.py.

array_ang_sep()

def lalinference.bayespputils.array_ang_sep	(		vec1,
			vec2
	)

Find angles between vectors in rows of numpy arrays.

Definition at line 3838 of file bayespputils.py.

array_polar_ang()

def lalinference.bayespputils.array_polar_ang

(

vec

)

Find polar angles of vectors in rows of a numpy array.

Definition at line 3848 of file bayespputils.py.

rotation_matrix()

def lalinference.bayespputils.rotation_matrix	(		angle,
			direction
	)

Compute general rotation matrices for a given angles and direction vectors.

Definition at line 3861 of file bayespputils.py.

ROTATEZ()

def lalinference.bayespputils.ROTATEZ	(		angle,
			vx,
			vy,
			vz
	)

Definition at line 3883 of file bayespputils.py.

ROTATEY()

def lalinference.bayespputils.ROTATEY	(		angle,
			vx,
			vy,
			vz
	)

Definition at line 3889 of file bayespputils.py.

orbital_momentum()

def lalinference.bayespputils.orbital_momentum	(		fref,
			m1,
			m2,
			inclination
	)

Calculate orbital angular momentum vector.

Note: The units of Lmag are different than what used in lalsimulation. Mc must be called in units of Msun here.

Note that if one wants to build J=L+S1+S2 with L returned by this function, S1 and S2 must not get the Msun^2 factor.

Definition at line 3903 of file bayespputils.py.

orbital_momentum_mag()

def lalinference.bayespputils.orbital_momentum_mag	(		fref,
			m1,
			m2,
			eta
	)

Definition at line 3910 of file bayespputils.py.

component_momentum()

def lalinference.bayespputils.component_momentum	(		m,
			a,
			theta,
			phi
	)

Calculate BH angular momentum vector.

Definition at line 3921 of file bayespputils.py.

symm_tidal_params()

def lalinference.bayespputils.symm_tidal_params	(		lambda1,
			lambda2,
			q
	)

Calculate best tidal parameters [Eqs.

(5) and (6) in Wade et al. PRD 89, 103012 (2014)] Requires q <= 1

Definition at line 3931 of file bayespputils.py.

spin_angles()

def lalinference.bayespputils.spin_angles	(		fref,
			mc,
			eta,
			incl,
			a1,
			theta1,
			phi1,
			a2 = None,
			theta2 = None,
			phi2 = None
	)

Calculate physical spin angles.

Definition at line 3950 of file bayespputils.py.

chi_precessing()

def lalinference.bayespputils.chi_precessing	(		m1,
			a1,
			tilt1,
			m2,
			a2,
			tilt2
	)

Calculate the magnitude of the effective precessing spin following convention from Phys.

Rev. D 91, 024043 – arXiv:1408.1810 note: the paper uses naming convention where m1 < m2 (and similar for associated spin parameters) and q > 1

Definition at line 3975 of file bayespputils.py.

calculate_redshift()

def lalinference.bayespputils.calculate_redshift	(		distance,
			h = 0.6790,
			om = 0.3065,
			ol = 0.6935,
			w0 = -1.0
	)

Calculate the redshift from the luminosity distance measurement using the Cosmology Calculator provided in LAL.

By default assuming cosmological parameters from arXiv:1502.01589 - 'Planck 2015 results. XIII. Cosmological parameters' Using parameters from table 4, column 'TT+lowP+lensing+ext' This corresponds to Omega_M = 0.3065, Omega_Lambda = 0.6935, H_0 = 67.90 km s^-1 Mpc^-1 Returns an array of redshifts

Definition at line 3993 of file bayespputils.py.

source_mass()

def lalinference.bayespputils.source_mass	(		mass,
			redshift
	)

Calculate source mass parameter for mass m as: m_source = m / (1.0 + z) For a parameter m.

Definition at line 4009 of file bayespputils.py.

integrand_distance()

def lalinference.bayespputils.integrand_distance	(		redshift,
			nonGR_alpha
	)

Following functions added for testing Lorentz violations.

Calculate D_alpha integral; multiplicative factor put later D_alpha = integral{ ((1+z')^(alpha-2))/sqrt(Omega_m*(1+z')^3 +Omega_lambda) dz'} # eq.15 of arxiv 1110.2720

Definition at line 4017 of file bayespputils.py.

DistanceMeasure()

def lalinference.bayespputils.DistanceMeasure	(		redshift,
			nonGR_alpha
	)

D_alpha = ((1+z)^(1-alpha))/H_0 * D_alpha # from eq.15 of arxiv 1110.2720 D_alpha calculated from integrand in above function.

Definition at line 4028 of file bayespputils.py.

lambda_a()

def lalinference.bayespputils.lambda_a	(		redshift,
			nonGR_alpha,
			lambda_eff,
			distance
	)

Converting from the effective wavelength-like parameter to lambda_A: lambda_A = lambda_{eff}*(D_alpha/D_L)^(1/(2-alpha))*(1/(1+z)^((1-alpha)/(2-alpha)))

Definition at line 4041 of file bayespputils.py.

amplitudeMeasure()

def lalinference.bayespputils.amplitudeMeasure	(		redshift,
			nonGR_alpha,
			lambda_eff,
			distance
	)

Converting to Lorentz violating parameter "A" in dispersion relation from lambda_A: A = (lambda_A/h)^(alpha-2) # eqn.

13 of arxiv 1110.2720

Definition at line 4051 of file bayespputils.py.

physical2radiationFrame()

def lalinference.bayespputils.physical2radiationFrame	(		theta_jn,
			phi_jl,
			tilt1,
			tilt2,
			phi12,
			a1,
			a2,
			m1,
			m2,
			fref,
			phiref
	)

changes for testing Lorentz violations made till here

Wrapper function for SimInspiralTransformPrecessingNewInitialConditions(). Vectorizes function for use in append_mapping() methods of the posterior class.

Definition at line 4062 of file bayespputils.py.

plot_one_param_pdf_kde()

def lalinference.bayespputils.plot_one_param_pdf_kde	(		fig,
			onedpos
	)

Definition at line 4145 of file bayespputils.py.

plot_one_param_pdf()

def lalinference.bayespputils.plot_one_param_pdf	(		posterior,
			plot1DParams,
			analyticPDF = None,
			analyticCDF = None,
			plotkde = False
	)

Plots a 1D histogram and (gaussian) kernel density estimate of the distribution of posterior samples for a given parameter.

Parameters

posterior	an instance of the Posterior class.
plot1DParams	a dict; {paramName:Nbins}
analyticPDF	an analytic probability distribution function describing the distribution.
analyticCDF	an analytic cumulative distribution function describing the distribution.
plotkde	Use KDE to smooth plot (default: False)

Definition at line 4177 of file bayespputils.py.

getRAString()

def lalinference.bayespputils.getRAString	(		radians,
			accuracy = 'auto'
	)

Definition at line 4344 of file bayespputils.py.

getDecString()

def lalinference.bayespputils.getDecString	(		radians,
			accuracy = 'auto'
	)

Definition at line 4363 of file bayespputils.py.

plot_corner()

def lalinference.bayespputils.plot_corner	(		posterior,
			levels,
			parnames = None
	)

Make a corner plot using the triangle module (See http://github.com/dfm/corner.py)

Parameters

posterior	The Posterior object
levels	a list of confidence levels
parnames	list of parameters to include

Definition at line 4404 of file bayespputils.py.

plot_two_param_kde_greedy_levels()

def lalinference.bayespputils.plot_two_param_kde_greedy_levels	(		posteriors_by_name,
			plot2DkdeParams,
			levels,
			colors_by_name,
			line_styles = __default_line_styles,
			figsize = (4,3),
			dpi = 250,
			figposition = [0.2,0.2,0.48,0.75],
			legend = 'right',
			hatches_by_name = None,
			Npixels = 50
	)

Plots a 2D kernel density estimate of the 2-parameter marginal posterior.

Parameters

posteriors_by_name	dictionary of Posterior objects, indexed by name
plot2DkdeParams	a dict {param1Name:Nparam1Bins,param2Name:Nparam2Bins}
levels	list of credible levels
colors_by_name	dict of colors, indexed by name
line_styles	list of line styles for the credible levels
figsize	figsize to pass to matplotlib
dpi	dpi to pass to matplotlib
figposition	figposition to pass to matplotlib
legend	position of legend
hatches_by_name	dict of hatch styles indexed by name
Npixels	Number of pixels in each axis of the 2D grid

Definition at line 4449 of file bayespputils.py.

plot_two_param_kde()

def lalinference.bayespputils.plot_two_param_kde	(		posterior,
			plot2DkdeParams
	)

Plots a 2D kernel density estimate of the 2-parameter marginal posterior.

Parameters

posterior	an instance of the Posterior class.
plot2DkdeParams	a dict {param1Name:Nparam1Bins,param2Name:Nparam2Bins}

Definition at line 4653 of file bayespputils.py.

get_inj_by_time()

def lalinference.bayespputils.get_inj_by_time	(		injections,
			time
	)

Filter injections to find the injection with end time given by time +/- 0.1s.

Definition at line 4725 of file bayespputils.py.

histogram2D()

def lalinference.bayespputils.histogram2D	(		posterior,
			greedy2Params,
			confidence_levels
	)

Returns a 2D histogram and edges for the two parameters passed in greedy2Params, plus the actual discrete confidence levels imposed by the finite number of samples.

H,xedges,yedges,Hlasts = histogram2D(posterior,greedy2Params,confidence_levels)

Parameters

posterior	Posterior instance
greedy2Params	a dict ;{param1Name:param1binSize,param2Name:param2binSize}
confidence_levels	a list of the required confidence levels to plot on the contour map.

Definition at line 4738 of file bayespputils.py.

plot_two_param_greedy_bins_contourf()

def lalinference.bayespputils.plot_two_param_greedy_bins_contourf	(		posteriors_by_name,
			greedy2Params,
			confidence_levels,
			colors_by_name,
			figsize = (7,6),
			dpi = 120,
			figposition = [0.3,0.3,0.5,0.5],
			legend = 'right',
			hatches_by_name = None
	)

Parameters

posteriors_by_name	A dictionary of posterior objects indexed by name
greedy2Params	a dict ;{param1Name:param1binSize,param2Name:param2binSize}
confidence_levels	a list of the required confidence levels to plot on the contour map.
colors_by_name	dict of colors, indexed by name
figsize	figsize to pass to matplotlib
dpi	dpi to pass to matplotlib
figposition	figposition to pass to matplotlib
legend	Legend position to pass to matplotlib
hatches_by_name	dict of hatch styles, indexed by name

Definition at line 4784 of file bayespputils.py.

plot_two_param_greedy_bins_hist()

def lalinference.bayespputils.plot_two_param_greedy_bins_hist	(		posterior,
			greedy2Params,
			confidence_levels
	)

Histograms of the ranked pixels produced by the 2-parameter greedy binning algorithm colured by their confidence level.

Parameters

posterior	an instance of the Posterior class.
greedy2Params	a dict ;{param1Name:param1binSize,param2Name:param2binSize}
confidence_levels	list of confidence levels to show

Definition at line 4836 of file bayespputils.py.

greedy_bin_one_param()

def lalinference.bayespputils.greedy_bin_one_param	(		posterior,
			greedy1Param,
			confidence_levels
	)

Determine the 1-parameter Bayesian Confidence Interval using a greedy binning algorithm.

Parameters

posterior	an instance of the posterior class.
greedy1Param	a dict; {paramName:paramBinSize}.
confidence_levels	A list of floats of the required confidence intervals [(0-1)].

Definition at line 4993 of file bayespputils.py.

contigious_interval_one_param()

def lalinference.bayespputils.contigious_interval_one_param	(		posterior,
			contInt1Params,
			confidence_levels
	)

Calculates the smallest contigious 1-parameter confidence interval for a set of given confidence levels.

Parameters

posterior	an instance of the Posterior class.
contInt1Params	a dict {paramName:paramBinSize}.
confidence_levels	Required confidence intervals.

Definition at line 5059 of file bayespputils.py.

autocorrelation()

def lalinference.bayespputils.autocorrelation

(

series

)

Returns an estimate of the autocorrelation function of a given series.

Returns only the positive-lag portion of the ACF, normalized so that the zero-th element is 1.

Definition at line 5170 of file bayespputils.py.

autocorrelation_length_estimate()

def lalinference.bayespputils.autocorrelation_length_estimate	(		series,
			acf = None,
			M = 5,
			K = 2
	)

Attempts to find a self-consistent estimate of the autocorrelation length of a given series.

If C(tau) is the autocorrelation function (normalized so C(0) = 1, for example from the autocorrelation procedure in this module), then the autocorrelation length is the smallest s such that

1 + 2*C(1) + 2*C(2) + ... + 2*C(M*s) < s

In words: the autocorrelation length is the shortest length so that the sum of the autocorrelation function is smaller than that length over a window of M times that length.

The maximum window length is restricted to be len(series)/K as a safety precaution against relying on data near the extreme of the lags in the ACF, where there is a lot of noise. Note that this implies that the series must be at least M*K*s samples long in order to get a reliable estimate of the ACL.

If no such s can be found, raises ACLError; in this case it is likely that the series is too short relative to its true autocorrelation length to obtain a consistent ACL estimate.

Definition at line 5205 of file bayespputils.py.

effectiveSampleSize()

def lalinference.bayespputils.effectiveSampleSize	(		samples,
			Nskip = 1
	)

Compute the effective sample size, calculating the ACL using only the second half of the samples to avoid ACL overestimation due to chains equilibrating after adaptation.

Definition at line 5235 of file bayespputils.py.

readCoincXML()

def lalinference.bayespputils.readCoincXML	(		xml_file,
			trignum
	)

Definition at line 5247 of file bayespputils.py.

find_ndownsample()

def lalinference.bayespputils.find_ndownsample	(		samples,
			nDownsample
	)

Given a list of files, threshold value, and a desired number of outputs posterior samples, return the skip number to achieve the desired number of posterior samples.

Definition at line 5275 of file bayespputils.py.

parse_converge_output_section()

def lalinference.bayespputils.parse_converge_output_section

(

fo

)

Definition at line 6014 of file bayespputils.py.

vo_nest2pos()

def lalinference.bayespputils.vo_nest2pos	(		nsresource,
			Nlive = None
	)

Parse a VO Table RESOURCE containing nested sampling output and return a VOTable TABLE element with posterior samples in it.

This can be added to an existing tree by the user. Nlive will be read from the nsresource, unless specified

Definition at line 6051 of file bayespputils.py.

confidence_interval_uncertainty()

def lalinference.bayespputils.confidence_interval_uncertainty	(		cl,
			cl_bounds,
			posteriors
	)

Returns a tuple (relative_change, fractional_uncertainty, percentile_uncertainty) giving the uncertainty in confidence intervals from multiple posteriors.

The uncertainty in the confidence intervals is the difference in length between the widest interval, formed from the smallest to largest values among all the cl_bounds, and the narrowest interval, formed from the largest-small and smallest-large values among all the cl_bounds. Note that neither the smallest nor the largest confidence intervals necessarily correspond to one of the cl_bounds.

The relative change relates the confidence interval uncertainty to the expected value of the parameter, the fractional uncertainty relates this length to the length of the confidence level from the combined posteriors, and the percentile uncertainty gives the change in percentile over the combined posterior between the smallest and largest confidence intervals.

Parameters

cl	The confidence level (between 0 and 1).
cl_bounds	A list of (low, high) pairs giving the confidence interval associated with each posterior.
posteriors	A list of PosteriorOneDPDF objects giving the posteriors.

Definition at line 6192 of file bayespputils.py.

plot_waveform()

def lalinference.bayespputils.plot_waveform	(		pos = None,
			siminspiral = None,
			event = 0,
			path = None,
			ifos = ['H1','L1','V1']
	)

Definition at line 6240 of file bayespputils.py.

plot_psd()

def lalinference.bayespputils.plot_psd	(		psd_files,
			outpath = None,
			f_min = 30.
	)

Definition at line 6660 of file bayespputils.py.

cred_interval()

def lalinference.bayespputils.cred_interval	(		x,
			cl = .9,
			lower = True
	)

Return location of lower or upper confidence levels Args: x: List of samples.

cl: Confidence level to return the bound of. lower: If True, return the lower bound, otherwise return the upper bound.

Definition at line 6715 of file bayespputils.py.

spline_angle_xform()

def lalinference.bayespputils.spline_angle_xform

(

delta_psi

)

Returns the angle in degrees corresponding to the spline calibration parameters delta_psi.

Definition at line 6725 of file bayespputils.py.

plot_spline_pos()

def lalinference.bayespputils.plot_spline_pos	(		logf,
			ys,
			nf = 100,
			level = 0.9,
			color = 'k',
			label = None,
			xform = None
	)

Plot calibration posterior estimates for a spline model in log space.

Args: logf: The (log) location of spline control points. ys: List of posterior draws of function at control points logf nx: Number of points to evaluate spline at for plotting. level: Credible level to fill in. color: Color to plot with. label: Label for plot. xform: Function to transform the spline into plotted values.

Definition at line 6740 of file bayespputils.py.

plot_calibration_pos()

def lalinference.bayespputils.plot_calibration_pos	(		pos,
			level = .9,
			outpath = None
	)

Definition at line 6772 of file bayespputils.py.

plot_burst_waveform()

def lalinference.bayespputils.plot_burst_waveform	(		pos = None,
			simburst = None,
			event = 0,
			path = None,
			ifos = ['H1','L1','V1']
	)

Definition at line 6832 of file bayespputils.py.

make_1d_table()

def lalinference.bayespputils.make_1d_table	(		html,
			legend,
			label,
			pos,
			pars,
			noacf,
			GreedyRes,
			onepdfdir,
			sampsdir,
			savepdfs,
			greedy,
			analyticLikelihood,
			nDownsample
	)

Definition at line 7275 of file bayespputils.py.

Variable Documentation

hostname_short

string lalinference.bayespputils.hostname_short = socket.gethostbyaddr(socket.gethostname())[0].split('.',1)[1]

Definition at line 82 of file bayespputils.py.

calParams

lalinference.bayespputils.calParams = calAmpParams + calPhaseParams

Definition at line 134 of file bayespputils.py.

massParams

list lalinference.bayespputils.massParams = ['m1','m2','chirpmass','mchirp','mc','eta','q','massratio','asym_massratio','mtotal','mf','mf_evol','mf_nonevol']

Definition at line 136 of file bayespputils.py.

spinParamsPrec

list lalinference.bayespputils.spinParamsPrec = ['a1','a2','phi1','theta1','phi2','theta2','costilt1','costilt2','costheta_jn','cosbeta','tilt1','tilt1_isco','tilt2','tilt2_isco','phi_jl','theta_jn','phi12','phi12_isco','af','af_evol','af_nonevol','afz','afz_evol','afz_nonevol']

Definition at line 138 of file bayespputils.py.

spinParamsAli

list lalinference.bayespputils.spinParamsAli = ['spin1','spin2','a1z','a2z']

Definition at line 139 of file bayespputils.py.

spinParamsEff

list lalinference.bayespputils.spinParamsEff = ['chi','effectivespin','chi_eff','chi_tot','chi_p']

Definition at line 140 of file bayespputils.py.

spinParams

list lalinference.bayespputils.spinParams = spinParamsPrec+spinParamsEff+spinParamsAli

Definition at line 141 of file bayespputils.py.

cosmoParam

list lalinference.bayespputils.cosmoParam = ['m1_source','m2_source','mtotal_source','mc_source','redshift','mf_source','mf_source_evol','mf_source_nonevol','m1_source_maxldist','m2_source_maxldist','mtotal_source_maxldist','mc_source_maxldist','redshift_maxldist','mf_source_maxldist','mf_source_maxldist_evol','mf_source_maxldist_nonevol']

Definition at line 143 of file bayespputils.py.

ppEParams

list lalinference.bayespputils.ppEParams = ['ppEalpha','ppElowera','ppEupperA','ppEbeta','ppElowerb','ppEupperB','alphaPPE','aPPE','betaPPE','bPPE']

Definition at line 145 of file bayespputils.py.

tigerParams

list lalinference.bayespputils.tigerParams = ['dchi%i'%(i) for i in range(8)] + ['dchi%il'%(i) for i in [5,6] ] + ['dxi%d'%(i+1) for i in range(6)] + ['dalpha%i'%(i+1) for i in range(5)] + ['dbeta%i'%(i+1) for i in range(3)] + ['dsigma%i'%(i+1) for i in range(4)] + ['dipolecoeff'] + ['dchiminus%i'%(i) for i in [1,2]] + ['dchiMinus%i'%(i) for i in [1,2]] + ['db1','db2','db3','db4','dc1','dc2','dc4','dcl']

Definition at line 146 of file bayespputils.py.

qnmtestParams

list lalinference.bayespputils.qnmtestParams = ['domega220','dtau220','domega210','dtau210','domega330','dtau330','domega440','dtau440','domega550','dtau550']

Definition at line 147 of file bayespputils.py.

bransDickeParams

list lalinference.bayespputils.bransDickeParams = ['omegaBD','ScalarCharge1','ScalarCharge2']

Definition at line 148 of file bayespputils.py.

massiveGravitonParams

list lalinference.bayespputils.massiveGravitonParams = ['lambdaG']

Definition at line 149 of file bayespputils.py.

lorentzInvarianceViolationParams

list lalinference.bayespputils.lorentzInvarianceViolationParams = ['log10lambda_a','lambda_a','log10lambda_eff','lambda_eff','log10livamp','liv_amp']

Definition at line 150 of file bayespputils.py.

tidalParams

list lalinference.bayespputils.tidalParams = ['lambda1','lambda2','lam_tilde','dlam_tilde','lambdat','dlambdat','lambdas','bluni']

Definition at line 151 of file bayespputils.py.

fourPiecePolyParams

list lalinference.bayespputils.fourPiecePolyParams = ['logp1','gamma1','gamma2','gamma3']

Definition at line 152 of file bayespputils.py.

spectralParams

list lalinference.bayespputils.spectralParams = ['sdgamma0','sdgamma1','sdgamma2','sdgamma3']

Definition at line 153 of file bayespputils.py.

energyParams

list lalinference.bayespputils.energyParams = ['e_rad', 'e_rad_evol', 'e_rad_nonevol', 'l_peak', 'l_peak_evol', 'l_peak_nonevol', 'e_rad_maxldist', 'e_rad_maxldist_evol', 'e_rad_maxldist_nonevol']

Definition at line 154 of file bayespputils.py.

spininducedquadParams

list lalinference.bayespputils.spininducedquadParams = ['dquadmon1', 'dquadmon2', 'dquadmona', 'dquadmona']

Definition at line 155 of file bayespputils.py.

strongFieldParams

tuple lalinference.bayespputils.strongFieldParams

Initial value:

=  (
    ppEParams + tigerParams + bransDickeParams + massiveGravitonParams
    + tidalParams + fourPiecePolyParams + spectralParams + energyParams
    + lorentzInvarianceViolationParams + spininducedquadParams + qnmtestParams
)

Definition at line 156 of file bayespputils.py.

distParams

list lalinference.bayespputils.distParams = ['distance','distMPC','dist','distance_maxl']

Definition at line 163 of file bayespputils.py.

incParams

list lalinference.bayespputils.incParams = ['iota','inclination','cosiota']

Definition at line 164 of file bayespputils.py.

polParams

list lalinference.bayespputils.polParams = ['psi','polarisation','polarization']

Definition at line 165 of file bayespputils.py.

skyParams

list lalinference.bayespputils.skyParams = ['ra','rightascension','declination','dec']

Definition at line 166 of file bayespputils.py.

phaseParams

list lalinference.bayespputils.phaseParams = ['phase', 'phi0','phase_maxl']

Definition at line 167 of file bayespputils.py.

timeParams

list lalinference.bayespputils.timeParams = ['time','time_mean']

Definition at line 169 of file bayespputils.py.

endTimeParams

list lalinference.bayespputils.endTimeParams = ['l1_end_time','h1_end_time','v1_end_time']

Definition at line 170 of file bayespputils.py.

statsParams

list lalinference.bayespputils.statsParams = ['logprior','logl','deltalogl','deltaloglh1','deltalogll1','deltaloglv1','deltaloglh2','deltaloglg1']

Definition at line 172 of file bayespputils.py.

calibParams

list lalinference.bayespputils.calibParams = ['calpha_l1','calpha_h1','calpha_v1','calamp_l1','calamp_h1','calamp_v1']

Definition at line 173 of file bayespputils.py.

confidenceLevels

list lalinference.bayespputils.confidenceLevels = [0.67,0.9,0.95,0.99]

Greedy bin sizes for cbcBPP and confidence leves used for the greedy bin intervals.

Definition at line 176 of file bayespputils.py.

greedyBinSizes

dictionary lalinference.bayespputils.greedyBinSizes = {'mc':0.025,'m1':0.1,'m2':0.1,'mass1':0.1,'mass2':0.1,'mtotal':0.1,'mc_source':0.025,'m1_source':0.1,'m2_source':0.1,'mtotal_source':0.1,'mc_source_maxldist':0.025,'m1_source_maxldist':0.1,'m2_source_maxldist':0.1,'mtotal_source_maxldist':0.1,'eta':0.001,'q':0.01,'asym_massratio':0.01,'iota':0.01,'cosiota':0.02,'time':1e-4,'time_mean':1e-4,'distance':1.0,'dist':1.0,'distance_maxl':1.0,'redshift':0.01,'redshift_maxldist':0.01,'mchirp':0.025,'chirpmass':0.025,'spin1':0.04,'spin2':0.04,'a1z':0.04,'a2z':0.04,'a1':0.02,'a2':0.02,'phi1':0.05,'phi2':0.05,'theta1':0.05,'theta2':0.05,'ra':0.05,'dec':0.05,'chi':0.05,'chi_eff':0.05,'chi_tot':0.05,'chi_p':0.05,'costilt1':0.02,'costilt2':0.02,'thatas':0.05,'costheta_jn':0.02,'beta':0.05,'omega':0.05,'cosbeta':0.02,'ppealpha':1.0,'ppebeta':1.0,'ppelowera':0.01,'ppelowerb':0.01,'ppeuppera':0.01,'ppeupperb':0.01,'polarisation':0.04,'rightascension':0.05,'declination':0.05,'massratio':0.001,'inclination':0.01,'phase':0.05,'tilt1':0.05,'tilt2':0.05,'phi_jl':0.05,'theta_jn':0.05,'phi12':0.05,'flow':1.0,'phase_maxl':0.05,'calamp_l1':0.01,'calamp_h1':0.01,'calamp_v1':0.01,'calpha_h1':0.01,'calpha_l1':0.01,'calpha_v1':0.01,'logdistance':0.1,'psi':0.1,'costheta_jn':0.1,'mf':0.1,'mf_evol':0.1,'mf_nonevol':0.1,'mf_source':0.1,'mf_source_evol':0.1,'mf_source_nonevol':0.1,'mf_source_maxldist':0.1,'mf_source_maxldist_evol':0.1,'mf_source_maxldist_nonevol':0.1,'af':0.02,'af_evol':0.02,'af_nonevol':0.02,'afz':0.02,'afz_evol':0.01,'afz_nonevol':0.01,'e_rad':0.1,'e_rad_evol':0.1,'e_rad_nonevol':0.1,'e_rad_maxldist':0.1,'e_rad_maxldist_evol':0.1,'e_rad_maxldist_nonevol':0.1,'l_peak':0.1,'l_peak_evol':0.1,'l_peak_nonevol':0.1}

Definition at line 178 of file bayespputils.py.

cred_level

lalinference.bayespputils.cred_level = lambda cl, x: np.sort(x, axis=0)[int(cl*len(x))]

Definition at line 6707 of file bayespputils.py.

unrho

lalinference.bayespputils.unrho

when called by kdtree.operate will be used to calculate the density of each bin (sky area)

when called by kdtree.operate will be used to calculate the density of each bin

Definition at line 3176 of file bayespputils.py.

tabname

lalinference.bayespputils.tabname

Definition at line 6859 of file bayespputils.py.

intable

lalinference.bayespputils.intable

Definition at line 6860 of file bayespputils.py.

tableElementName

lalinference.bayespputils.tableElementName

Definition at line 6861 of file bayespputils.py.