
margLikelihoodMonteCarlo(VTs,
lambs,
mu,
mcerrs=None)
This function marginalizes the loudest event likelihood over unknown
Monte Carlo errors, assumed to be independent between each
experiment. 
source code



margLikelihood(VTs,
lambs,
mu,
calerr=0,
mcerrs=None)
This function marginalizes the loudest event likelihood over unknown
Monte Carlo and calibration errors. 
source code



integral_element(mu,
pdf)
Returns an array of elements of the integrand dP = p(mu) dmu for a
density p(mu) defined at sample values mu ; samples need not be
equally spaced. 
source code



normalize_pdf(mu,
pofmu)
Takes a function pofmu defined at rate sample values mu and
normalizes it to be a suitable pdf. 
source code



compute_upper_limit(mu_in,
post,
alpha=0.9)
Returns the upper limit mu_high of confidence level alpha for a
posterior distribution post on the given parameter mu. 
source code



compute_lower_limit(mu_in,
post,
alpha=0.9)
Returns the lower limit mu_low of confidence level alpha for a
posterior distribution post on the given parameter mu. 
source code



confidence_interval_min_width(mu,
post,
alpha=0.9)
Returns the minimalwidth confidence interval [mu_low, mu_high] of
confidence level alpha for a posterior distribution post on the
parameter mu. 
source code



hpd_coverage(mu,
pdf,
thresh)
Integrates a pdf over mu taking only bins where the mean over the bin
is above a given threshold This gives the coverage of the HPD
interval for the given threshold. 
source code



hpd_threshold(mu_in,
post,
alpha,
tol)
For a PDF post over samples mu_in, find a density threshold such that
the region having higher density has coverage of at least alpha, and
less than alpha plus a given tolerance. 
source code



hpd_credible_interval(mu_in,
post,
alpha=0.9,
tolerance=1e3)
Returns the minimum and maximum rate values of the HPD (Highest
Posterior Density) credible interval for a posterior post defined at
the sample values mu_in. 
source code



integrate_efficiency(dbins,
eff,
err=0,
logbins=False) 
source code



compute_efficiency(f_dist,
m_dist,
dbins)
Compute the efficiency as a function of distance for the given sets
of found and missed injection distances. 
source code



mean_efficiency_volume(found,
missed,
dbins) 
source code



volume_montecarlo(found,
missed,
distribution_param,
distribution,
limits_param,
max_param=None,
min_param=None)
Compute the sensitive volume and standard error using a direct Monte Carlo integral 
source code



filter_injections_by_mass(injs,
mbins,
bin_num,
bin_type,
bin_num2=None)
For a given set of injections (sim_inspiral rows), return the subset
of injections that fall within the given mass range. 
source code



compute_volume_vs_mass(found,
missed,
mass_bins,
bin_type,
dbins=None,
distribution_param=None,
distribution=None,
limits_param=None,
max_param=None,
min_param=None)
Compute the average luminosity an experiment was sensitive to given
the sets of found and missed injections and assuming luminosity is
uniformly distributed in space. 
source code



log_volume_derivative_fit(x,
vols)
Performs a linear least squares to log(vols) as a function of x. 
source code



get_loudest_event(connection,
coinc_table="coinc_inspiral",
datatype="exclude_play") 
source code

