From 4820ecc0d86c618fbc25b52136d7c946419353b0 Mon Sep 17 00:00:00 2001
From: Colm Talbot <colm.talbot@ligo.org>
Date: Wed, 28 Apr 2021 14:51:29 +0000
Subject: [PATCH] Make building distance marginalization lookup table faster
---
bilby/gw/likelihood.py | 35 ++++++++++++++++++++---------------
1 file changed, 20 insertions(+), 15 deletions(-)
diff --git a/bilby/gw/likelihood.py b/bilby/gw/likelihood.py
index e0558c04d..9e704fd62 100644
--- a/bilby/gw/likelihood.py
+++ b/bilby/gw/likelihood.py
@@ -912,24 +912,29 @@ class GravitationalWaveTransient(Likelihood):
def _create_lookup_table(self):
""" Make the lookup table """
+ from tqdm.auto import tqdm
logger.info('Building lookup table for distance marginalisation.')
self._dist_margd_loglikelihood_array = np.zeros((400, 800))
- for ii, optimal_snr_squared_ref in enumerate(self._optimal_snr_squared_ref_array):
- optimal_snr_squared_array = (
- optimal_snr_squared_ref * self._ref_dist ** 2. /
- self._distance_array ** 2)
- for jj, d_inner_h_ref in enumerate(self._d_inner_h_ref_array):
- d_inner_h_array = (
- d_inner_h_ref * self._ref_dist / self._distance_array)
- if self.phase_marginalization:
- d_inner_h_array =\
- self._bessel_function_interped(abs(d_inner_h_array))
- self._dist_margd_loglikelihood_array[ii][jj] = \
- logsumexp(d_inner_h_array - optimal_snr_squared_array / 2,
- b=self.distance_prior_array * self._delta_distance)
- log_norm = logsumexp(0. / self._distance_array,
- b=self.distance_prior_array * self._delta_distance)
+ scaling = self._ref_dist / self._distance_array
+ d_inner_h_array_full = np.outer(self._d_inner_h_ref_array, scaling)
+ h_inner_h_array_full = np.outer(self._optimal_snr_squared_ref_array, scaling ** 2)
+ if self.phase_marginalization:
+ d_inner_h_array_full = self._bessel_function_interped(abs(
+ d_inner_h_array_full
+ ))
+ prior_term = self.distance_prior_array * self._delta_distance
+ for ii, optimal_snr_squared_array in tqdm(
+ enumerate(h_inner_h_array_full), total=len(self._optimal_snr_squared_ref_array)
+ ):
+ for jj, d_inner_h_array in enumerate(d_inner_h_array_full):
+ self._dist_margd_loglikelihood_array[ii][jj] = logsumexp(
+ d_inner_h_array - optimal_snr_squared_array / 2,
+ b=prior_term
+ )
+ log_norm = logsumexp(
+ 0 / self._distance_array, b=self.distance_prior_array * self._delta_distance
+ )
self._dist_margd_loglikelihood_array -= log_norm
self.cache_lookup_table()
--
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