Burst

This branch has a sine Gaussian signal model and a supernova signal model with example python scripts.

examples/other_examples/sine_gaussian_example.py

+#!/bin/python
+"""
+Tutorial to demonstrate running parameter estimation on a sine gaussian injected signal.
+
+"""
+from __future__ import division, print_function
+import tupak
+import numpy as np
+
+# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
+time_duration = 4.
+sampling_frequency = 2048.
+
+# Specify the output directory and the name of the simulation.
+outdir = 'outdir'
+label = 'sine_gaussian'
+tupak.utils.setup_logger(outdir=outdir, label=label)
+
+# Set up a random seed for result reproducibility.  This is optional!
+np.random.seed(170801)
+
+# We are going to inject a sine gaussian waveform.  We first establish a dictionary of parameters that
+# includes all of the different waveform parameters
+injection_parameters = dict(hrss = 1e-22, Q = 5.0, frequency = 200.0, ra = 1.375, dec = -1.2108, 
+                             geocent_time = 1126259642.413, psi= 2.659)
+
+# Create the waveform_generator using a sine Gaussian source function
+waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=time_duration,
+                                                                sampling_frequency=sampling_frequency,
+                                                                frequency_domain_source_model=tupak.source.sinegaussian,
+                                                                parameters=injection_parameters)
+hf_signal = waveform_generator.frequency_domain_strain()
+
+# Set up interferometers.  In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
+# and Virgo (V1)).  These default to their design sensitivity
+IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
+    name, injection_polarizations=hf_signal, injection_parameters=injection_parameters, time_duration=time_duration,
+    sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
+
+# Set up prior, which is a dictionary
+priors = dict()
+# By default we will sample all terms in the signal models.  However, this will take a long time for the calculation,
+# so for this example we will set almost all of the priors to be equall to their injected values.  This implies the
+# prior is a delta function at the true, injected value.  In reality, the sampler implementation is smart enough to
+# not sample any parameter that has a delta-function prior.
+for key in ['psi', 'ra', 'dec', 'geocent_time']:
+    priors[key] = injection_parameters[key]
+
+# The above list does *not* include frequency and Q, which means those are the parameters
+# that will be included in the sampler.  If we do nothing, then the default priors get used.
+#priors['Q'] = tupak.prior.create_default_prior(name='Q')
+#priors['frequency'] = tupak.prior.create_default_prior(name='frequency')
+priors['Q'] = tupak.prior.Uniform(2, 50, 'Q')
+priors['frequency'] = tupak.prior.Uniform(30, 1000, 'frequency')
+priors['hrss'] = tupak.prior.Uniform(1e-23, 1e-21, 'hrss')
+
+# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
+likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+
+# Run sampler.  In this case we're going to use the `dynesty` sampler
+result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
+                                   injection_parameters=injection_parameters, outdir=outdir, label=label)
+
+# make some plots of the outputs
+result.plot_corner()
+print(result)
+
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