diff --git a/gstlal-inspiral/python/far.py b/gstlal-inspiral/python/far.py
index 50e48760ffaf7605a7b5febf743c3f7332212cb1..a4621a322f297d4ac6a76179a8f0cf864b058293 100644
--- a/gstlal-inspiral/python/far.py
+++ b/gstlal-inspiral/python/far.py
@@ -215,10 +215,10 @@ class RankingStat(snglcoinc.LnLikelihoodRatioMixin):
return self(
segments = segs,
snrs = dict((event.ifo, event.snr) for event in events),
+ chi2s_over_snr2s = dict((event.ifo, event.chisq / event.snr**2.) for event in events),
phase = dict((event.ifo, event.coa_phase) for event in events),
dt = dict((event.ifo, float(event.end - ref_end) + offsetvector[event.ifo] - ref_offset) for event in events),
- template_id = template_id,
- **dict(("%s_snr_chi" % event.ifo, (event.snr, event.chisq / event.snr**2.)) for event in events)
+ template_id = template_id
)
except (ValueError, AssertionError) as e:
raise type(e)("%s: event IDs %s, offsets %s" % (str(e), ", ".join(sorted(str(event.event_id) for event in events)), str(offsetvector)))
diff --git a/gstlal-inspiral/python/stats/inspiral_lr.py b/gstlal-inspiral/python/stats/inspiral_lr.py
index fadd5181360c4706c8a8667e7d4ecea1e164a052..16653154c323e1ac86fb2c88bdeba90416362b1a 100644
--- a/gstlal-inspiral/python/stats/inspiral_lr.py
+++ b/gstlal-inspiral/python/stats/inspiral_lr.py
@@ -273,7 +273,7 @@ class LnSignalDensity(LnLRDensity):
self.InspiralExtrinsics = inspiral_extrinsics.InspiralExtrinsics(self.min_instruments)
- def __call__(self, segments, snrs, phase, dt, template_id, **kwargs):
+ def __call__(self, segments, snrs, chi2s_over_snr2s, phase, dt, template_id):
assert frozenset(segments) == self.instruments
if len(snrs) < self.min_instruments:
return float("-inf")
@@ -328,7 +328,7 @@ class LnSignalDensity(LnLRDensity):
# evalute the (snr, \chi^2 | snr) PDFs (same for all
# instruments)
interp = self.interps["snr_chi"]
- return lnP + sum(interp(*value) for name, value in kwargs.items() if name.endswith("_snr_chi"))
+ return lnP + sum(interp(snrs[instrument], chi2_over_snr2) for instrument, chi2_over_snr2 in chi2s_over_snr2s.items())
def __iadd__(self, other):
super(LnSignalDensity, self).__iadd__(other)
@@ -525,7 +525,7 @@ class DatalessLnSignalDensity(LnSignalDensity):
# so we're ready to go!
self.add_signal_model()
- def __call__(self, segments, snrs, phase, dt, template_id, **kwargs):
+ def __call__(self, segments, snrs, chi2s_over_snr2s, phase, dt, template_id):
# evaluate P(t) \propto number of templates
lnP = math.log(len(self.template_ids))
@@ -553,7 +553,7 @@ class DatalessLnSignalDensity(LnSignalDensity):
# evalute the (snr, \chi^2 | snr) PDFs (same for all
# instruments)
interp = self.interps["snr_chi"]
- return lnP + sum(interp(*value) for name, value in kwargs.items() if name.endswith("_snr_chi"))
+ return lnP + sum(interp(snrs[instrument], chi2_over_snr2) for instrument, chi2_over_snr2 in chi2s_over_snr2s.items())
def __iadd__(self, other):
raise NotImplementedError
@@ -663,7 +663,7 @@ class LnNoiseDensity(LnLRDensity):
def segmentlists(self):
return self.triggerrates.segmentlistdict()
- def __call__(self, segments, snrs, phase, dt, template_id, **kwargs):
+ def __call__(self, segments, snrs, chi2s_over_snr2s, phase, dt, template_id):
assert frozenset(segments) == self.instruments
if len(snrs) < self.min_instruments:
return float("-inf")
@@ -700,7 +700,7 @@ class LnNoiseDensity(LnLRDensity):
# evaluate the rest
interps = self.interps
- return lnP + sum(interps[name](*value) for name, value in kwargs.items() if name in interps)
+ return lnP + sum(interps["%s_snr_chi" % instrument](snrs[instrument], chi2_over_snr2) for instrument, chi2_over_snr2 in chi2s_over_snr2s.items())
def __iadd__(self, other):
super(LnNoiseDensity, self).__iadd__(other)
@@ -803,9 +803,6 @@ class LnNoiseDensity(LnLRDensity):
"""
snr_slope = 0.8 / len(self.instruments)**3
- # evaluate dt and dphi parameters
- # NOTE: uniform and normalized so that the log should be zero, but there is no point in doing that
- #lnP_dt_dphi = 0
snrchi2gens = dict((instrument, iter(self.densities["%s_snr_chi" % instrument].bins.randcoord(ns = (snr_slope, 1.), domain = (slice(self.snr_min, None), slice(None, None)))).next) for instrument in self.instruments)
t_and_rate_gen = iter(self.triggerrates.random_uniform()).next
t_offsets_gen = dict((instruments, self.coinc_rates.plausible_toas(instruments).next) for instruments in self.coinc_rates.all_instrument_combos)
@@ -831,35 +828,38 @@ class LnNoiseDensity(LnLRDensity):
continue
# to pick instruments, we first pick an integer k
# between m = min_instruments and n =
- # len(instruments) inclusively, then choose than
+ # len(instruments) inclusively, then choose that
# many unique names from among the available
# instruments. the probability of the outcome is
#
# = P(k) * P(selection | k)
# = 1 / (n - m + 1) * 1 / nCk
#
- # where nCk = number of k choices possible from a
- # collection of n things.
+ # where nCk = number of k choices without
+ # replacement from a collection of n things.
k = random_randint(self.min_instruments, len(instruments))
lnP_instruments = -math.log((len(instruments) - self.min_instruments + 1) * nCk(len(instruments), k))
instruments = frozenset(random_sample(instruments, k))
seq = sum((snrchi2gens[instrument]() for instrument in instruments), ())
- kwargs = dict(("%s_snr_chi" % instrument, value) for instrument, value in zip(instruments, seq[0::2]))
- # FIXME: waveform duration hard-coded to 10 s,
- # generalize
- kwargs["segments"] = dict.fromkeys(self.instruments, segment(t - 10.0, t))
- kwargs["snrs"] = dict((instrument, kwargs["%s_snr_chi" % instrument][0]) for instrument in instruments)
- # FIXME: add dt to segments? not self-consistent
- # if we don't, but doing so screws up the test that
- # was done to check which instruments are on and
- # off at "t"
- kwargs["dt"] = t_offsets_gen[instruments]()
- kwargs["phase"] = dict((instrument, random_uniform(0., twopi)) for instrument in instruments)
- # FIXME random_params needs to be given a meaningful
- # template_id, but for now it is not used in the
- # likelihood-ratio assignment so we don't care
- kwargs["template_id"] = random.choice(template_ids)
+ kwargs = {
+ # FIXME: waveform duration hard-coded to
+ # 10 s, generalize
+ "segments": dict.fromkeys(self.instruments, segment(t - 10.0, t)),
+ "snrs": dict((instrument, value[0]) for instrument, value in zip(instruments, seq[0::2])),
+ "chi2s_over_snr2s": dict((instrument, value[1]) for instrument, value in zip(instruments, seq[0::2])),
+ "phase": dict((instrument, random_uniform(0., twopi)) for instrument in instruments),
+ # FIXME: add dt to segments? not
+ # self-consistent if we don't, but doing so
+ # screws up the test that was done to check
+ # which instruments are on and off at "t"
+ "dt": t_offsets_gen[instruments](),
+ # FIXME random_params needs to be given a
+ # meaningful template_id, but for now it is
+ # not used in the likelihood-ratio
+ # assignment so we don't care
+ "template_id": random.choice(template_ids)
+ }
# NOTE: I think the result of this sum is, in
# fact, correctly normalized, but nothing requires
# it to be (only that it be correct up to an
@@ -914,7 +914,7 @@ class DatalessLnNoiseDensity(LnNoiseDensity):
}
- def __call__(self, segments, snrs, phase, dt, template_id, **kwargs):
+ def __call__(self, segments, snrs, chi2s_over_snr2s, phase, dt, template_id):
# assume all instruments are on, 1 trigger per second per
# template
triggers_per_second_per_template = dict.fromkeys(segments, 1.)
@@ -933,9 +933,10 @@ class DatalessLnNoiseDensity(LnNoiseDensity):
# P(instruments | t, noise)
lnP += self.coinc_rates.lnP_instruments(**triggers_per_second_per_template)[frozenset(snrs)]
- # evaluate the SNR, \chi^2 factors
+ # evalute the (snr, \chi^2 | snr) PDFs (same for all
+ # instruments)
interp = self.interps["snr_chi"]
- return lnP + sum(interp(*value) for name, value in kwargs.items() if name.endswith("_snr_chi"))
+ return lnP + sum(interp(snrs[instrument], chi2_over_snr2) for instrument, chi2_over_snr2 in chi2s_over_snr2s.items())
def random_params(self):
# won't work