p_astro_gstlal module

p_astro_gstlal.compute_p_astro(event_ln_likelihood_ratio, event_mass1, event_mass2, snr, far, rankingstatpdf, livetime=14394240.0, mean_values_dict={'counts_BBH': 9.26042350393, 'counts_BNS': 2.11050326523, 'counts_MassGap': 2.40800240248, 'counts_NSBH': 1.56679410666, 'counts_Terrestrial': 3923}, activation_counts=None)

Task to compute p_astro by source category.

Parameters

event_ln_likelihood_ratiofloat

Event’s log likelihood ratio value

event_mass1float

Event’s heavier component mass value

event_mass2float

Event’s lighter component mass value

snr: float

Event’s snr value

far: float

Event’s far value

livetime: float

Livetime consistent with estimate of Poisson counts. Eg. If counts are from O1 and O2, livetime is O1-O2 livetime in seconds.

mean_values_dictdictionary

dictionary of source specific FGMC Poisson counts

activation_countsdictionary

(optional) dictionary of activation counts keyed on bin number and source category

Returns

p_astrosstr

JSON dump of the p_astro by source category

Example

>>> import json
>>> p_astros = json.loads(compute_p_astro(files))
>>> p_astros
{'BNS': 0.999, 'BBH': 0.0, 'NSBH': 0.0, 'Terrestrial': 0.001}

p_astro_gstlal.compute_p_astro_approx(snr, far, mass1, mass2, livetime, mean_values_dict)

Task to compute p_astro by source category.

Parameters

snrfloat

event’s SNR

farfloat

event’s cfar

mass1float

event’s mass1

mass2float

event’s mass2

livetime: float

Livetime consistent with estimate of Poisson counts. Eg. If counts are from O1 and O2, livetime is O1-O2 livetime in seconds.

mean_values_dictdictionary

dictionary of source specific FGMC Poisson counts

Returns

p_astrosstr

JSON dump of the p_astro by source category

Example

>>> import json
>>> p_astros = json.loads(compute_p_astro(files))
>>> p_astros
{'BNS': 0.999, 'BBH': 0.0, 'NSBH': 0.0, 'Terrestrial': 0.001}

p_astro_gstlal.evaluate_p_astro_from_bayesfac(astro_bayesfac, mean_values_dict, mass1, mass2, activation_counts=None)

Evaluates p_astro for a new event using Bayes factor, masses, and number of astrophysical categories. Invoked with every new GraceDB entry.

Parameters

astro_bayesfacfloat

astrophysical Bayes factor

mean_values_dict: dictionary

mean values of Poisson counts

mass1float

event mass1

mass2float

event mass2

activation_counts: dictionary

array of activation counts keyed on source type and bin number

Returns

p_astrodictionary

p_astro for all source categories

p_astro_gstlal.make_weights_from_hardcuts(mass1, mass2)

Construct binary weights from component masses based on cuts in component mass space that define astrophysical source categories. To be used for MBTA, PyCBC and SPIIR.

Parameters

mass1float

heavier component mass of the event

mass2float

lighter component mass of the event

Returns

a_bns, a_bbh, a_nshb, a_mgfloats

binary weights (i.e, 1 or 0)

p_astro_gstlal.make_weights_from_histograms(mass1, mass2, activation_counts=None)

Construct binary weights from bin number provided by GstLAL, and a weights matrix pre-constructed and stored in a file, to be read from ???. The weights are keyed on template parameters of Gstlal’s template bank. If that doesn’t work, construct binary weights.

Parameters

mass1float

heavier component mass of the event

mass2float

lighter component mass of the event

spin1zfloat

z component spin of heavier mass

spin2zfloat

z component spin of lighter mass

activation_countshdf5 object/ dictionary

dictionary of activation counts keyed on bin number and source category

Returns

a_hat_bns, a_hat_bbh, a_hat_nsbh, a_hat_mgfloats

mass-based template weights

p_astro_gstlal.p_astro_update(category, event_bayesfac_dict, mean_values_dict)

Compute p_astro for a new event using mean values of Poisson expected counts constructed from all the previous events. Invoked with every new GraceDB entry.

Parameters

categorystring

source category

event_bayesfac_dictdictionary

event Bayes factors

mean_values_dictdictionary

mean values of Poisson counts

Returns

p_astrofloat

p_astro by source category