bayespputils.py File Reference

Prototypes
def	lalinference.bayespputils.get_end (siminspiral)
def	lalinference.bayespputils.replace_column (table, old, new)
	Workaround for missing astropy.table.Table.replace_column method, which was added in Astropy 1.1. More...
def	lalinference.bayespputils.as_array (table)
	Workaround for missing astropy.table.Table.as_array method, which was added in Astropy 1.0. More...
def	lalinference.bayespputils.det_end_time (ifo_prefix, inj)
def	lalinference.bayespputils.get_prior (name)
def	lalinference.bayespputils.plot_label (param)
	A lookup table for plot labels. More...
def	lalinference.bayespputils.skyArea (bounds)
	functions used in 2stage kdtree More...
def	lalinference.bayespputils.random_split (items, fraction)
def	lalinference.bayespputils.addSample (tree, coordinates)
def	lalinference.bayespputils.kdtree_bin_sky_volume (posterior, confidence_levels)
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. More...
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. More...
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. More...
def	lalinference.bayespputils.greedy_bin_two_param (posterior, greedy2Params, confidence_levels)
	Determine the 2-parameter Bayesian Confidence Intervals using a greedy binning algorithm. More...
def	lalinference.bayespputils.pol2cart (long, lat)
	Utility function to convert longitude,latitude on a unit sphere to cartesian co-ordinates. More...
def	lalinference.bayespputils.sph2cart (r, theta, phi)
	Utiltiy function to convert r,theta,phi to cartesian co-ordinates. More...
def	lalinference.bayespputils.cart2sph (x, y, z)
	Utility function to convert cartesian coords to r,theta,phi. More...
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. More...
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. More...
def	lalinference.bayespputils.skymap_inj_pvalue (hpmap, inj, nest=True)
	Returns the greedy p-value estimate for the given injection. More...
def	lalinference.bayespputils.mc2ms (mc, eta)
	Utility function for converting mchirp,eta to component masses. More...
def	lalinference.bayespputils.q2ms (mc, q)
	Utility function for converting mchirp,q to component masses. More...
def	lalinference.bayespputils.q2eta (q)
	Utility function for converting q to eta. More...
def	lalinference.bayespputils.mc2q (mc, eta)
	Utility function for converting mchirp,eta to new mass ratio q (m2/m1). More...
def	lalinference.bayespputils.ang_dist (long1, lat1, long2, lat2)
	Find the angular separation of (long1,lat1) and (long2,lat2), which are specified in radians. More...
def	lalinference.bayespputils.array_dot (vec1, vec2)
	Calculate dot products between vectors in rows of numpy arrays. More...
def	lalinference.bayespputils.array_ang_sep (vec1, vec2)
	Find angles between vectors in rows of numpy arrays. More...
def	lalinference.bayespputils.array_polar_ang (vec)
	Find polar angles of vectors in rows of a numpy array. More...
def	lalinference.bayespputils.rotation_matrix (angle, direction)
	Compute general rotation matrices for a given angles and direction vectors. More...
def	lalinference.bayespputils.ROTATEZ (angle, vx, vy, vz)
def	lalinference.bayespputils.ROTATEY (angle, vx, vy, vz)
def	lalinference.bayespputils.orbital_momentum (fref, m1, m2, inclination)
	Calculate orbital angular momentum vector. More...
def	lalinference.bayespputils.orbital_momentum_mag (fref, m1, m2, eta)
def	lalinference.bayespputils.component_momentum (m, a, theta, phi)
	Calculate BH angular momentum vector. More...
def	lalinference.bayespputils.symm_tidal_params (lambda1, lambda2, q)
	Calculate best tidal parameters [Eqs. More...
def	lalinference.bayespputils.spin_angles (fref, mc, eta, incl, a1, theta1, phi1, a2=None, theta2=None, phi2=None)
	Calculate physical spin angles. More...
def	lalinference.bayespputils.chi_precessing (m1, a1, tilt1, m2, a2, tilt2)
	Calculate the magnitude of the effective precessing spin following convention from Phys. More...
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. More...
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. More...
def	lalinference.bayespputils.integrand_distance (redshift, nonGR_alpha)
	Following functions added for testing Lorentz violations. More...
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. More...
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))) More...
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. More...
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 More...
def	lalinference.bayespputils.plot_one_param_pdf_kde (fig, onedpos)
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. More...
def	lalinference.bayespputils.getRAString (radians, accuracy='auto')
def	lalinference.bayespputils.getDecString (radians, accuracy='auto')
def	lalinference.bayespputils.plot_corner (posterior, levels, parnames=None)
	Make a corner plot using the triangle module (See http://github.com/dfm/corner.py) More...
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. More...
def	lalinference.bayespputils.plot_two_param_kde (posterior, plot2DkdeParams)
	Plots a 2D kernel density estimate of the 2-parameter marginal posterior. More...
def	lalinference.bayespputils.get_inj_by_time (injections, time)
	Filter injections to find the injection with end time given by time +/- 0.1s. More...
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. More...
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)
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. More...
def	lalinference.bayespputils.greedy_bin_one_param (posterior, greedy1Param, confidence_levels)
	Determine the 1-parameter Bayesian Confidence Interval using a greedy binning algorithm. More...
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. More...
def	lalinference.bayespputils.autocorrelation (series)
	Returns an estimate of the autocorrelation function of a given series. More...
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. More...
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. More...
def	lalinference.bayespputils.readCoincXML (xml_file, trignum)
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. More...
def	lalinference.bayespputils.parse_converge_output_section (fo)
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. More...
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. More...
def	lalinference.bayespputils.plot_waveform (pos=None, siminspiral=None, event=0, path=None, ifos=['H1', 'L1', 'V1'])
def	lalinference.bayespputils.plot_psd (psd_files, outpath=None, f_min=30.)
def	lalinference.bayespputils.cred_interval (x, cl=.9, lower=True)
	Return location of lower or upper confidence levels Args: x: List of samples. More...
def	lalinference.bayespputils.spline_angle_xform (delta_psi)
	Returns the angle in degrees corresponding to the spline calibration parameters delta_psi. More...
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. More...
def	lalinference.bayespputils.plot_calibration_pos (pos, level=.9, outpath=None)
def	lalinference.bayespputils.plot_burst_waveform (pos=None, simburst=None, event=0, path=None, ifos=['H1', 'L1', 'V1'])
def	lalinference.bayespputils.make_1d_table (html, legend, label, pos, pars, noacf, GreedyRes, onepdfdir, sampsdir, savepdfs, greedy, analyticLikelihood, nDownsample)

Go to the source code of this file.

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

Namespaces
namespace	lalinference
namespace	lalinference.bayespputils

Variables
	lalinference.bayespputils.hostname_short = socket.gethostbyaddr(socket.gethostname())[0].split('.',1)[1]
list	lalinference.bayespputils.logParams = ['logl','loglh1','loglh2','logll1','loglv1','deltalogl','deltaloglh1','deltalogll1','deltaloglv1','logw','logprior','logpost','nulllogl','chain_log_evidence','chain_delta_log_evidence','chain_log_noise_evidence','chain_log_bayes_factor']
list	lalinference.bayespputils.relativePhaseParams = [ a+b+'_relative_phase' for a,b in combinations(['h1','l1','v1'],2)]
list	lalinference.bayespputils.snrParams = ['snr','optimal_snr','matched_filter_snr','coherence'] + ['%s_optimal_snr'%(i) for i in ['h1','l1','v1']] + ['%s_cplx_snr_amp'%(i) for i in ['h1','l1','v1']] + ['%s_cplx_snr_arg'%(i) for i in ['h1', 'l1', 'v1']] + relativePhaseParams
list	lalinference.bayespputils.calAmpParams = ['calamp_%s'%(ifo) for ifo in ['h1','l1','v1']]
list	lalinference.bayespputils.calPhaseParams = ['calpha_%s'%(ifo) for ifo in ['h1','l1','v1']]
list	lalinference.bayespputils.calParams = calAmpParams + calPhaseParams
list	lalinference.bayespputils.massParams = ['m1','m2','chirpmass','mchirp','mc','eta','q','massratio','asym_massratio','mtotal','mf','mf_evol','mf_nonevol']
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']
list	lalinference.bayespputils.spinParamsAli = ['spin1','spin2','a1z','a2z']
list	lalinference.bayespputils.spinParamsEff = ['chi','effectivespin','chi_eff','chi_tot','chi_p']
list	lalinference.bayespputils.spinParams = spinParamsPrec+spinParamsEff+spinParamsAli
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']
list	lalinference.bayespputils.ppEParams = ['ppEalpha','ppElowera','ppEupperA','ppEbeta','ppElowerb','ppEupperB','alphaPPE','aPPE','betaPPE','bPPE']
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']
list	lalinference.bayespputils.qnmtestParams = ['domega220','dtau220','domega210','dtau210','domega330','dtau330','domega440','dtau440','domega550','dtau550']
list	lalinference.bayespputils.bransDickeParams = ['omegaBD','ScalarCharge1','ScalarCharge2']
list	lalinference.bayespputils.massiveGravitonParams = ['lambdaG']
list	lalinference.bayespputils.lorentzInvarianceViolationParams = ['log10lambda_a','lambda_a','log10lambda_eff','lambda_eff','log10livamp','liv_amp']
list	lalinference.bayespputils.tidalParams = ['lambda1','lambda2','lam_tilde','dlam_tilde','lambdat','dlambdat','lambdas','bluni']
list	lalinference.bayespputils.fourPiecePolyParams = ['logp1','gamma1','gamma2','gamma3']
list	lalinference.bayespputils.spectralParams = ['sdgamma0','sdgamma1','sdgamma2','sdgamma3']
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']
list	lalinference.bayespputils.spininducedquadParams = ['dquadmon1', 'dquadmon2', 'dquadmona', 'dquadmona']
tuple	lalinference.bayespputils.strongFieldParams
list	lalinference.bayespputils.distParams = ['distance','distMPC','dist','distance_maxl']
list	lalinference.bayespputils.incParams = ['iota','inclination','cosiota']
list	lalinference.bayespputils.polParams = ['psi','polarisation','polarization']
list	lalinference.bayespputils.skyParams = ['ra','rightascension','declination','dec']
list	lalinference.bayespputils.phaseParams = ['phase', 'phi0','phase_maxl']
list	lalinference.bayespputils.timeParams = ['time','time_mean']
list	lalinference.bayespputils.endTimeParams = ['l1_end_time','h1_end_time','v1_end_time']
list	lalinference.bayespputils.statsParams = ['logprior','logl','deltalogl','deltaloglh1','deltalogll1','deltaloglv1','deltaloglh2','deltaloglg1']
list	lalinference.bayespputils.calibParams = ['calpha_l1','calpha_h1','calpha_v1','calamp_l1','calamp_h1','calamp_v1']
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. More...
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}
string	lalinference.bayespputils.xmlns = 'http://www.ivoa.net/xml/VOTable/v1.1'
	lalinference.bayespputils.cred_level = lambda cl, x: np.sort(x, axis=0)[int(cl*len(x))]
	lalinference.bayespputils.unrho
	when called by kdtree.operate will be used to calculate the density of each bin (sky area) More...
	lalinference.bayespputils.tabname
	lalinference.bayespputils.intable
	lalinference.bayespputils.tableElementName