Moritz Huebner: Created an example as to how you would implement a custom source

tutorials/custom_source_tutorial.py

+import numpy as np
+import pylab as plt
+
+import dynesty.plotting as dyplot
+import corner
+import peyote
+
+peyote.setup_logger()
+
+time_duration = 1.
+sampling_frequency = 4096.
+
+
+def delta_function_frequency_domain_strain(frequency_array, amplitude, peak_time, phase, ra, dec, geocent_time, psi):
+    ht = {'plus': amplitude * np.sin(2 * np.pi * peak_time * frequency_array + phase) + 0j,
+          'cross': amplitude * np.cos(2 * np.pi * peak_time * frequency_array + phase) + 0j}
+    return ht
+
+
+def gaussian_frequency_domain_strain(frequency_array, amplitude, sigma, ra, dec, geocent_time, psi):
+    ht = {'plus': amplitude*np.exp(-frequency_array**2 * sigma**2/2 + 0j),
+          'cross': amplitude*np.exp(-frequency_array**2 * sigma**2/2) + 0j}
+    return ht
+
+
+simulation_parameters = dict(amplitude=10, sigma=0.1,
+                             ra=1.375,
+                             dec=-1.2108,
+                             geocent_time=1126259642.413,
+                             psi=2.659)
+
+# Create the waveform_generator using a LAL BinaryBlackHole source function
+waveform_generator = peyote.waveform_generator.WaveformGenerator('BBH', sampling_frequency, time_duration,
+                                                                 gaussian_frequency_domain_strain)
+waveform_generator.set_values(simulation_parameters)
+hf_signal = waveform_generator.frequency_domain_strain()
+
+# Simulate the data in H1
+H1 = peyote.detector.H1
+H1_hf_noise, frequencies = H1.power_spectral_density.get_noise_realisation(sampling_frequency, time_duration)
+H1.set_data(sampling_frequency, time_duration, frequency_domain_strain=H1_hf_noise)
+H1.inject_signal(waveform_generator)
+H1.set_spectral_densities()
+
+# Simulate the data in L1
+L1 = peyote.detector.L1
+L1_hf_noise, frequencies = L1.power_spectral_density.get_noise_realisation(sampling_frequency, time_duration)
+L1.set_data(sampling_frequency, time_duration, frequency_domain_strain=L1_hf_noise)
+L1.inject_signal(waveform_generator)
+L1.set_spectral_densities()
+
+IFOs = [H1, L1]
+
+# Plot the noise and signal
+fig, ax = plt.subplots()
+plt.loglog(frequencies, np.abs(H1_hf_noise), lw=1.5, label='H1 noise+signal')
+plt.loglog(frequencies, np.abs(L1_hf_noise), lw=1.5, label='L1 noise+signal')
+plt.loglog(frequencies, np.abs(hf_signal['plus']), lw=0.8, label='signal')
+plt.xlim(10, 1000)
+plt.legend()
+plt.xlabel(r'frequency')
+plt.ylabel(r'strain')
+fig.savefig('data')
+
+likelihood = peyote.likelihood.Likelihood(IFOs, waveform_generator)
+
+prior = simulation_parameters.copy()
+prior['amplitude'] = peyote.parameter.Parameter('amplitude', prior=peyote.prior.Uniform(lower=9, upper=11),
+                                                latex_label='$A$')
+prior['sigma'] = peyote.parameter.Parameter('sigma', prior=peyote.prior.Uniform(lower=0.05, upper=0.2),
+                                                latex_label='$f$')
+
+result = peyote.run_sampler(likelihood, prior, sampler='nestle', verbose=True)
+
+truths = [simulation_parameters[x] for x in result.search_parameter_keys]
+fig = corner.corner(result.samples, truths=truths, labels=result.search_parameter_keys)
+fig.savefig('corner')
+
+fig, axes = dyplot.traceplot(result['sampler_output'])
+fig.savefig('trace')