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| decisive_dist(h_dist,
l_dist,
v_dist,
mchirp,
weight_dist,
ifos) |
source code
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| get_livetime(connection,
veto_cat,
on_ifos,
datatype) |
source code
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| inj_dist_range(dist_bounds,
dist_scale="linear",
step=4.0) |
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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
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| found_injections(connection,
tag,
on_ifos,
dist_type="distance",
weight_dist=False,
verbose=False) |
source code
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binomial_confidence(K,
N,
eff_bin_edges,
confidence)
Calculate the optimal Bayesian credible interval for p(eff|k,n)
Posterior generated with binomial p(k|eff,n) and a uniform p(eff) is
the beta function: Beta(eff|k+1,n-k+1) where n is the number of
injected signals and k is the number of found signals. |
source code
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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
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| rescale_dist(on_ifos,
dist_type,
weight_dist,
phys_dist=None,
param_dist=None) |
source code
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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(dc|d). |
source code
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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(dc|D). |
source code
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