From 716edbdf7e525e65bff25da08b163b57ff1ec3d3 Mon Sep 17 00:00:00 2001
From: Sylvia Biscoveanu <sylvia.biscoveanu@ligo.org>
Date: Mon, 8 Jun 2020 13:00:50 -0500
Subject: [PATCH] Make time reconstruction more efficient
---
bilby/gw/likelihood.py | 25 +++++++++++++++----------
1 file changed, 15 insertions(+), 10 deletions(-)
diff --git a/bilby/gw/likelihood.py b/bilby/gw/likelihood.py
index 25d0c37ee..0b6ea7d51 100644
--- a/bilby/gw/likelihood.py
+++ b/bilby/gw/likelihood.py
@@ -399,20 +399,32 @@ class GravitationalWaveTransient(Likelihood):
signal_polarizations = \
self.waveform_generator.frequency_domain_strain(self.parameters)
+ times = create_time_series(
+ sampling_frequency=16384,
+ starting_time=self.parameters['geocent_time'] - self.waveform_generator.start_time,
+ duration=self.waveform_generator.duration)
+ times = times % self.waveform_generator.duration
+ times += self.waveform_generator.start_time
+
+ prior = self.priors["geocent_time"]
+ in_prior = (times >= prior.minimum) & (times < prior.maximum)
+ times = times[in_prior]
+
n_time_steps = int(self.waveform_generator.duration * 16384)
- d_inner_h = np.zeros(n_time_steps, dtype=np.complex)
+ d_inner_h = np.zeros(len(times), dtype=np.complex)
psd = np.ones(n_time_steps)
signal_long = np.zeros(n_time_steps, dtype=np.complex)
data = np.zeros(n_time_steps, dtype=np.complex)
h_inner_h = np.zeros(1)
for ifo in self.interferometers:
ifo_length = len(ifo.frequency_domain_strain)
+ mask = ifo.frequency_mask
signal = ifo.get_detector_response(
signal_polarizations, self.parameters)
signal_long[:ifo_length] = signal
data[:ifo_length] = np.conj(ifo.frequency_domain_strain)
- psd[:ifo_length] = ifo.power_spectral_density_array
- d_inner_h += np.fft.fft(signal_long * data / psd)
+ psd[:ifo_length][mask] = ifo.power_spectral_density_array[mask]
+ d_inner_h += np.fft.fft(signal_long * data / psd)[in_prior]
h_inner_h += ifo.optimal_snr_squared(signal=signal).real
if self.distance_marginalization:
@@ -424,13 +436,6 @@ class GravitationalWaveTransient(Likelihood):
else:
time_log_like = (d_inner_h.real - h_inner_h.real / 2)
- times = create_time_series(
- sampling_frequency=16384,
- starting_time=self.parameters['geocent_time'] - self.waveform_generator.start_time,
- duration=self.waveform_generator.duration)
- times = times % self.waveform_generator.duration
- times += self.waveform_generator.start_time
-
time_prior_array = self.priors['geocent_time'].prob(times)
time_post = (
np.exp(time_log_like - max(time_log_like)) * time_prior_array)
--
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