def __init__(self, rankingstat, nsamples = 2**24, verbose = False):
#
# bailout used by .from_xml() class method to get an
# uninitialized instance
#
if rankingstat is None:
return
#
# initialize binnings
#
self.noise_lr_lnpdf = rate.BinnedLnPDF(rate.NDBins((rate.ATanBins(-10., 30., 3000),)))
self.signal_lr_lnpdf = rate.BinnedLnPDF(rate.NDBins((rate.ATanBins(-10., 30., 3000),)))
self.candidates_lr_lnpdf = rate.BinnedLnPDF(rate.NDBins((rate.ATanBins(-10., 30., 3000),)))
#
# obtain analyzed segments that will be used to obtain livetime
#
self.segments = segmentsUtils.vote(rankingstat.denominator.triggerrates.segmentlistdict().values(),rankingstat.min_instruments)
#
# run importance-weighted random sampling to populate binnings.
#
self.signal_lr_lnpdf.array, self.noise_lr_lnpdf.array = binned_log_likelihood_ratio_rates_from_samples(
self.signal_lr_lnpdf,
self.noise_lr_lnpdf,
rankingstat.ln_lr_samples(rankingstat.denominator.random_params(), rankingstat),
nsamples = nsamples)
if verbose:
print("done computing ranking statistic PDFs", file=sys.stderr)
#
# apply density estimation kernels to counts
#
self.density_estimate(self.noise_lr_lnpdf, "noise model")
self.density_estimate(self.signal_lr_lnpdf, "signal model")
#
# set the total sample count in the noise and signal
# ranking statistic histogram equal to the total expected
# count of the respective events from the experiment. This
# information is required so that when adding ranking
# statistic PDFs in our .__iadd__() method they are
# combined with the correct relative weights, so that
# .__iadd__() has the effect of marginalizing the
# distribution over the experients being combined.
#
self.noise_lr_lnpdf.array /= self.noise_lr_lnpdf.array.sum()
self.noise_lr_lnpdf.normalize()
self.signal_lr_lnpdf.array /= self.signal_lr_lnpdf.array.sum()
self.signal_lr_lnpdf.normalize()
def __init__(self, instruments):
self.densities = {}
for instrument in instruments:
self.densities["%s_snr2_chi2" % instrument] = rate.BinnedLnPDF(rate.NDBins((rate.ATanLogarithmicBins(10, 1e7, 801), rate.ATanLogarithmicBins(.1, 1e4, 801))))
for pair in itertools.combinations(sorted(instruments), 2):
dt = 0.005 + snglcoinc.light_travel_time(*pair) # seconds
self.densities["%s_%s_dt" % pair] = rate.BinnedLnPDF(rate.NDBins((rate.ATanBins(-dt, +dt, 801),)))
self.densities["%s_%s_dA" % pair] = rate.BinnedLnPDF(rate.NDBins((rate.ATanBins(-0.5, +0.5, 801),)))
self.densities["%s_%s_df" % pair] = rate.BinnedLnPDF(rate.NDBins((rate.ATanBins(-0.2, +0.2, 501),)))
# only non-negative rss timing residual bins will be used
# but we want a binning that's linear at the origin so
# instead of inventing a new one we just use atan bins that
# are symmetric about 0
self.densities["instrumentgroup,rss_timing_residual"] = rate.BinnedLnPDF(rate.NDBins((InstrumentBins(names = instruments), rate.ATanBins(-0.02, +0.02, 1001))))
def __init__(self, instruments):
self.densities = {}
for pair in intertools.combinations(sorted(instruments), 2):
# FIXME: hard-coded for directional search
#dt = 0.02 + snglcoinc.light_travel_time(*pair)
dt = 0.02
self.densities["%s_%s_dt" % pair] = rate.BinnedLnDPF(rate.NDBins((rate.ATanBins(-dt, +dt, 12001), rate.LinearBins(0.0, 2 * math.pi, 61))))
self.densities["%s_%s_dband" % pair] = rate.BinnedLnDPF(rate.NDBins((rate.LinearBins(-2.0, +2.0, 12001), rate.LinearBins(0.0, 2 * math.pi, 61))))
self.densities["%s_%s_ddur" % pair] = rate.BinnedLnDPF(rate.NDBins((rate.LinearBins(-2.0, +2.0, 12001), rate.LinearBins(0.0, 2 * math.pi, 61))))
self.densities["%s_%s_df" % pair] = rate.BinnedLnDPF(rate.NDBins((rate.LinearBins(-2.0, +2.0, 12001), rate.LinearBins(0.0, 2 * math.pi, 61))))
self.densities["%s_%s_dh" % pair] = rate.BinnedLnDPF(rate.NDBins((rate.LinearBins(-2.0, +2.0, 12001), rate.LinearBins(0.0, 2 * math.pi, 61))))
def __init__(self,
rankingstat,
signal_noise_pdfs=None,
nsamples=2**24,
nthreads=8,
verbose=False):
#
# bailout out used by .from_xml() class method to get an
# uninitialized instance
#
if rankingstat is None:
return
#
# initialize binnings
#
self.noise_lr_lnpdf = rate.BinnedLnPDF(
rate.NDBins((rate.ATanBins(0., 110., 6000), )))
self.signal_lr_lnpdf = rate.BinnedLnPDF(
rate.NDBins((rate.ATanBins(0., 110., 6000), )))
self.zero_lag_lr_lnpdf = rate.BinnedLnPDF(
rate.NDBins((rate.ATanBins(0., 110., 6000), )))
self.segments = segmentsUtils.vote(rankingstat.segmentlists.values(),
rankingstat.min_instruments)
if rankingstat.template_ids is None:
raise ValueError(
"cannot be initialized from a RankingStat that is not for a specific set of templates"
)
self.template_ids = rankingstat.template_ids
#
# bailout used by codes that want all-zeros histograms
#
if not nsamples:
return
#
# run importance-weighted random sampling to populate
# binnings.
#
if signal_noise_pdfs is None:
signal_noise_pdfs = rankingstat
nthreads = int(nthreads)
assert nthreads >= 1
threads = []
for i in range(nthreads):
assert nsamples // nthreads >= 1
q = multiprocessing.SimpleQueue()
p = multiprocessing.Process(
target=lambda: self.
binned_log_likelihood_ratio_rates_from_samples_wrapper(
q,
self.signal_lr_lnpdf,
self.noise_lr_lnpdf,
rankingstat.ln_lr_samples(
rankingstat.denominator.random_params(),
signal_noise_pdfs),
nsamples=nsamples // nthreads))
p.start()
threads.append((p, q))
nsamples -= nsamples // nthreads
nthreads -= 1
# sleep a bit to help random number seeds change
time.sleep(1.5)
while threads:
p, q = threads.pop(0)
signal_counts, noise_counts = q.get()
self.signal_lr_lnpdf.array += signal_counts
self.noise_lr_lnpdf.array += noise_counts
p.join()
if p.exitcode:
raise Exception("sampling thread failed")
if verbose:
print("done computing ranking statistic PDFs", file=sys.stderr)
#
# apply density estimation kernels to counts
#
self.density_estimate(self.noise_lr_lnpdf, "noise model")
self.density_estimate(self.signal_lr_lnpdf, "signal model")
#
# set the total sample count in the noise and signal
# ranking statistic histogram equal to the total expected
# count of the respective events from the experiment. this
# information is required so that when adding ranking
# statistic PDFs in our .__iadd__() method they are
# combined with the correct relative weights, so that
# .__iadd__() has the effect of marginalizing the
# distribution over the experiments being combined.
#
self.noise_lr_lnpdf.array *= sum(
rankingstat.denominator.candidate_count_model().values(
)) / self.noise_lr_lnpdf.array.sum()
self.noise_lr_lnpdf.normalize()
self.signal_lr_lnpdf.array *= rankingstat.numerator.candidate_count_model(
) / self.signal_lr_lnpdf.array.sum()
self.signal_lr_lnpdf.normalize()