Adds capability to run PE on eccentric waveforms

examples/injection_examples/eccentric_GW150914_inspiral.py

+#!/bin/python
+"""
+Tutorial to demonstrate running parameter estimation on a reduced parameter space for an injected eccentric binary 
+black hole signal with masses & distnace similar to GW150914.
+
+This uses the same binary parameters that were used to make Figures 1, 2 & 5 in Lower et al. (2018) -> arXiv:1806.05350.
+
+For a more comprehensive look at what goes on in each step, refer to the "basic_tutorial.py" example.
+"""
+from __future__ import division, print_function
+
+import numpy as np
+
+import tupak
+
+import matplotlib.pyplot as plt
+
+duration = 64.
+sampling_frequency = 2048.
+
+outdir = 'outdir'
+label = 'eccentric_GW140914'
+tupak.core.utils.setup_logger(outdir=outdir, label=label)
+
+# Set up a random seed for result reproducibility.
+np.random.seed(150914)
+
+injection_parameters = dict(mass_1=35., mass_2=30., eccentricity=0.1, luminosity_distance=440.,
+                            iota=0.4, psi=0.1, phase=1.2, geocent_time=1180002601.0, ra=45, dec=5.73)
+
+waveform_arguments = dict(waveform_approximant='EccentricFD', reference_frequency=10., minimum_frequency=10.)
+
+# Create the waveform_generator using the LAL eccentric black hole no spins source function
+waveform_generator = tupak.gw.WaveformGenerator(
+    duration=duration, sampling_frequency=sampling_frequency,
+    frequency_domain_source_model=tupak.gw.source.lal_eccentric_binary_black_hole_no_spins,
+    parameters=injection_parameters, waveform_arguments=waveform_arguments)
+
+hf_signal = waveform_generator.frequency_domain_strain()
+
+# Setting up three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)) at their design sensitivities.
+# The maximum frequency is set just prior to the point at which the waveform model terminates. This is to avoid any biases 
+# introduced from using a sharply terminating waveform model.
+minimum_frequency = 10.0
+maximum_frequency = 133.0
+
+def get_interferometer(name, injection_polarizations, injection_parameters, duration, sampling_frequency,
+                       minimum_frequency, maximum_frequency, outdir):
+    """
+    Sets up the interferometers & injects the signal into them
+    """
+    start_time = injection_parameters['geocent_time'] + 2 - duration
+    
+    ifo = tupak.gw.detector.get_empty_interferometer(name)
+    if name == 'V1':
+        ifo.power_spectral_density.set_from_power_spectral_density_file('AdV_psd.txt')
+    else:
+        ifo.power_spectral_density.set_from_power_spectral_density_file('aLIGO_ZERO_DET_high_P_psd.txt')
+    
+    ifo.set_strain_data_from_power_spectral_density(sampling_frequency=sampling_frequency, 
+            duration=duration, start_time=start_time)
+    
+    injection_polarizations = ifo.inject_signal(parameters=injection_parameters,
+                              injection_polarizations=injection_polarizations,
+                              waveform_generator=waveform_generator)
+
+    signal = ifo.get_detector_response(injection_polarizations, injection_parameters)
+
+    ifo.minimum_frequency = minimum_frequency
+    ifo.maximum_frequency = maximum_frequency
+    
+    ifo.plot_data(signal=signal, outdir=outdir, label=label)
+    ifo.save_data(outdir, label=label)
+    
+    return ifo
+
+# IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(name, injection_polarizations=hf_signal, 
+#     injection_parameters=injection_parameters, duration=duration,
+#     sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
+
+name = ['H1', 'L1', 'V1']
+IFOs = []
+
+for i in range(0,3):
+    IFOs.append(get_interferometer(name[i], injection_polarizations=hf_signal, injection_parameters=injection_parameters,
+                                   duration=duration, sampling_frequency=sampling_frequency, 
+                                   minimum_frequency=minimum_frequency, maximum_frequency=maximum_frequency, 
+                                   outdir=outdir))
+
+# Now we set up the priors on each of the binary parameters.
+priors = dict()
+priors["mass_1"] = tupak.core.prior.Uniform(name='mass_1', minimum=5, maximum=60)
+priors["mass_2"] = tupak.core.prior.Uniform(name='mass_2', minimum=5, maximum=60)
+priors["eccentricity"] = tupak.core.prior.PowerLaw(name='eccentricity', latex_label='$e$', alpha=-1, minimum=1e-4, maximum=0.4)
+priors["luminosity_distance"] =  tupak.gw.prior.UniformComovingVolume(name='luminosity_distance', minimum=1e2, maximum=2e3)
+priors["dec"] =  tupak.core.prior.Cosine(name='dec')
+priors["ra"] =  tupak.core.prior.Uniform(name='ra', minimum=0, maximum=2 * np.pi)
+priors["iota"] =  tupak.core.prior.Sine(name='iota')
+priors["psi"] =  tupak.core.prior.Uniform(name='psi', minimum=0, maximum=2 * np.pi)
+priors["phase"] =  tupak.core.prior.Uniform(name='phase', minimum=0, maximum=2 * np.pi)
+priors["geocent_time"] = tupak.core.prior.Uniform(1180002600.9, 1180002601.1, name='geocent_time')
+
+# Initialising the likelihood function.
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,
+                                              time_marginalization=False, phase_marginalization=False,
+                                              distance_marginalization=False, prior=priors)
+
+# Now we run sampler (PyMultiNest in our case).
+result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='pymultinest', npoints=1000,
+                           injection_parameters=injection_parameters, outdir=outdir, label=label)
+
+# And finally we make some plots of the output posteriors.
+result.plot_corner()

tupak/.version

+0.2.1: (UNCLEAN) a85bcc8 2018-07-31 23:36:23 -0500

tupak/gw/source.py

@@ -108,6 +108,86 @@ def lal_binary_black_hole(
 
     return {'plus': h_plus, 'cross': h_cross}
 
+def lal_eccentric_binary_black_hole_no_spins(
+        frequency_array, mass_1, mass_2, eccentricity, luminosity_distance, iota, phase, ra, dec, 
+        geocent_time, psi, **kwargs):        
+    """ Eccentric binary black hole waveform model using lalsimulation (EccentricFD)
+
+    Parameters
+    ----------
+    frequency_array: array_like
+        The frequencies at which we want to calculate the strain
+    mass_1: float
+        The mass of the heavier object in solar masses
+    mass_2: float
+        The mass of the lighter object in solar masses
+    eccentricity: float
+        The orbital eccentricity of the system
+    luminosity_distance: float
+        The luminosity distance in megaparsec
+    iota: float
+        Orbital inclination
+    phase: float
+        The phase at coalescence
+    ra: float
+        The right ascension of the binary
+    dec: float
+        The declination of the object
+    geocent_time: float
+        The time at coalescence
+    psi: float
+        Orbital polarisation
+    kwargs: dict
+        Optional keyword arguments
+
+    Returns
+    -------
+    dict: A dictionary with the plus and cross polarisation strain modes
+    """
+    
+    waveform_kwargs = dict(waveform_approximant='EccentricFD', reference_frequency=10.0,
+                           minimum_frequency=10.0)
+    waveform_kwargs.update(kwargs)
+    waveform_approximant = waveform_kwargs['waveform_approximant']
+    reference_frequency = waveform_kwargs['reference_frequency']
+    minimum_frequency = waveform_kwargs['minimum_frequency']
+
+    if mass_2 > mass_1:
+        return None
+
+    luminosity_distance = luminosity_distance * 1e6 * utils.parsec
+    mass_1 = mass_1 * utils.solar_mass
+    mass_2 = mass_2 * utils.solar_mass
+    
+    spin_1x = 0.0
+    spin_1y = 0.0
+    spin_1z = 0.0 
+    spin_2x = 0.0
+    spin_2y = 0.0
+    spin_2z = 0.0
+    
+    longitude_ascending_nodes = 0.0
+    mean_per_ano = 0.0
+
+    waveform_dictionary = None
+
+    approximant = lalsim.GetApproximantFromString(waveform_approximant)
+
+    maximum_frequency = frequency_array[-1]
+    delta_frequency = frequency_array[1] - frequency_array[0]
+
+    hplus, hcross = lalsim.SimInspiralChooseFDWaveform(
+        mass_1, mass_2, spin_1x, spin_1y, spin_1z, spin_2x, spin_2y,
+        spin_2z, luminosity_distance, iota, phase,
+        longitude_ascending_nodes, eccentricity, mean_per_ano, delta_frequency,
+        minimum_frequency, maximum_frequency, reference_frequency,
+        waveform_dictionary, approximant)
+
+    h_plus = hplus.data.data
+    h_cross = hcross.data.data
+
+    return {'plus': h_plus, 'cross': h_cross}
+
 
 def sinegaussian(frequency_array, hrss, Q, frequency, ra, dec, geocent_time, psi):
     tau = Q / (np.sqrt(2.0)*np.pi*frequency)