


decisive_dist(h_dist,
l_dist,
v_dist,
mchirp,
weight_dist,
ifos) 
source code





get_livetime(connection,
veto_cat,
on_ifos,
datatype) 
source code



inj_dist_range(dist_bounds,
dist_scale="linear",
step=4.0) 
source code



successful_injections(connection,
tag,
on_ifos,
veto_cat,
dist_type="distance",
weight_dist=False,
verbose=False)
My attempt to get a list of the simulations that actually made it
into some level of coincident time 
source code



found_injections(connection,
tag,
on_ifos,
dist_type="distance",
weight_dist=False,
verbose=False) 
source code



binomial_confidence(K,
N,
eff_bin_edges,
confidence)
Calculate the optimal Bayesian credible interval for p(effk,n)
Posterior generated with binomial p(keff,n) and a uniform p(eff) is
the beta function: Beta(effk+1,nk+1) where n is the number of
injected signals and k is the number of found signals. 
source code



detection_efficiency(successful_inj,
found_inj,
found_fars,
far_list,
r,
confidence)
This function determines the peak efficiency for a given bin and
associated 'highest density' confidence interval. 
source code



rescale_dist(on_ifos,
dist_type,
weight_dist,
phys_dist=None,
param_dist=None) 
source code



eff_vs_dist(measured_eff,
prob_dc_d)
This function creates a weighted average efficiency as a function of
distance by computing eff_wavg(D) = \sum_dc eff_mode(dc)p(dcd). 
source code



volume_efficiency(measured_eff,
V_shell,
prob_dc_d)
This function creates a weighted average efficiency within a given
volume by computing eff_wavg(D) = \sum_dc eff_mode(dc)p(dcD). 
source code

