From 4fd1d371bbbea9fa20e57fcf910966186c29f224 Mon Sep 17 00:00:00 2001
From: plasky <paul.lasky@monash.edu>
Date: Fri, 18 May 2018 11:40:37 +1000
Subject: [PATCH] removed two examples
---
examples/injection_examples/injection.py | 63 -------------------
.../time_domain_source_model.py | 48 --------------
2 files changed, 111 deletions(-)
delete mode 100644 examples/injection_examples/injection.py
delete mode 100644 examples/injection_examples/time_domain_source_model.py
diff --git a/examples/injection_examples/injection.py b/examples/injection_examples/injection.py
deleted file mode 100644
index 6dad16b4f..000000000
--- a/examples/injection_examples/injection.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""
-Tutorial to show signal injection using new features of detector.py
-"""
-from __future__ import division, print_function
-import numpy as np
-import tupak
-import logging
-
-
-def main():
-
- tupak.utils.setup_logger()
-
- outdir = 'outdir'
- label = 'injection'
-
- # Create the waveform generator
- waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=4, sampling_frequency=2048,
- frequency_domain_source_model=tupak.source.lal_binary_black_hole,
- parameters={'reference_frequency': 50.0,
- 'waveform_approximant': 'IMRPhenomPv2'})
-
- # Define the prior
- # Merger time is some time in 2018, shame LIGO will never see it...
- time_of_event = np.random.uniform(1198800018, 1230336018)
- prior = dict()
- prior['luminosity_distance'] = tupak.prior.PowerLaw(alpha=2, minimum=100, maximum=5000, name='luminosity_distance')
- prior['geocent_time'] = tupak.prior.Uniform(time_of_event - 0.01, time_of_event + 0.01, name='geocent_time')
- prior['mass_1'] = tupak.prior.Gaussian(mu=40, sigma=5, name='mass_1')
- prior['mass_2'] = tupak.prior.Gaussian(mu=40, sigma=5, name='mass_2')
- tupak.prior.fill_priors(prior, waveform_generator)
-
- # Create signal injection
- injection_parameters = {name: prior[name].sample() for name in prior}
- if injection_parameters['mass_1'] < injection_parameters['mass_2']:
- injection_parameters['mass_1'], injection_parameters['mass_2'] =\
- injection_parameters['mass_2'], injection_parameters['mass_1']
- logging.info("Injection parameters:\n{}".format("\n".join(["{}: {}".format(key, injection_parameters[key])
- for key in injection_parameters])))
- for parameter in injection_parameters:
- waveform_generator.parameters[parameter] = injection_parameters[parameter]
- injection_polarizations = waveform_generator.frequency_domain_strain()
-
- # Create interferometers and inject signal
- interferometers = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
- name, injection_polarizations=injection_polarizations, injection_parameters=injection_parameters,
- sampling_frequency=2048, time_duration=4, outdir=outdir) for name in ['H1', 'L1', 'V1']]
-
- # Define a likelihood
- likelihood = tupak.likelihood.MarginalizedLikelihood(interferometers, waveform_generator, prior=prior,
- distance_marginalization=True, phase_marginalization=True)
-
- # Run the sampler
- result = tupak.sampler.run_sampler(
- likelihood, prior, label=label, sampler='dynesty', npoints=500, resume=False, outdir=outdir, use_ratio=True,
- injection_parameters=injection_parameters)
- truth = [injection_parameters[parameter] for parameter in result.search_parameter_keys]
- result.plot_corner(truth=truth)
- print(result)
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/injection_examples/time_domain_source_model.py b/examples/injection_examples/time_domain_source_model.py
deleted file mode 100644
index b9446703e..000000000
--- a/examples/injection_examples/time_domain_source_model.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import tupak
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-def frequency_domain_sine_gaussian(f, A, f0, tau, phi0, geocent_time, ra, dec, psi):
- arg = -(np.pi * tau * (f-f0))**2 + 1j * phi0
- plus = np.sqrt(np.pi) * A * tau * np.exp(arg) / 2.
- cross = plus * np.exp(1j*np.pi/2)
- return {'plus': plus, 'cross': cross}
-
-
-def time_domain_sine_gaussian(t, A, t0, f0, tau, phi0, geocent_time, ra, dec, psi):
- arg = -(-(t-t0)/tau)**2
- plus = A * np.exp(arg) *np.cos(2*np.pi*f0*t + phi0)
- cross = plus * np.exp(1j*np.pi/2)
- return {'plus': plus, 'cross': cross}
-
-
-
-parameters = dict()
-parameters['A'] = 10000
-parameters['f0'] = 5
-parameters['t0'] = 10
-parameters['tau'] = 3
-parameters['geocent_time'] = 0
-parameters['phi0'] = 0
-parameters['ra'] = 0
-parameters['dec'] = 0
-parameters['psi'] = 0
-
-wg = tupak.waveform_generator.WaveformGenerator(time_duration=2000, sampling_frequency=1000,
- time_domain_source_model=time_domain_sine_gaussian,
- parameters=parameters)
-wg.parameters = parameters
-plt.plot(wg.frequency_array, wg.frequency_domain_strain()['plus'])
-plt.xlim(4, 6)
-plt.show()
-plt.plot(wg.frequency_array, wg.frequency_domain_strain()['cross'])
-plt.xlim(4, 6)
-plt.show()
-plt.plot(wg.time_array, wg.time_domain_strain()['plus'])
-plt.xlim(0, 20)
-plt.show()
-plt.plot(wg.time_array, wg.time_domain_strain()['cross'])
-plt.xlim(0, 20)
-plt.show()
--
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