Merge remote-tracking branch 'origin/master'

peyote/likelihood.py

@@ -5,6 +5,8 @@ class Likelihood:
     def __init__(self, interferometers, source):
         self.interferometers = interferometers
         self.source = source
+        self.noise_log_likelihood = 0
+        self.set_noise_log_likelihood()
 
     def get_interferometer_signal(self, waveform_polarizations, interferometer):
         h = []
@@ -12,7 +14,6 @@ class Likelihood:
             det_response = interferometer.antenna_response(
                 self.source.ra, self.source.dec,
                 self.source.geocent_time, self.source.psi, mode)
-
             h.append(waveform_polarizations[mode] * det_response)
         signal = np.sum(h, axis=0)
 
@@ -38,6 +39,16 @@ class Likelihood:
                                                            / interferometer.power_spectral_density_array)
         return log_l.real
 
+    def log_likelihood_ratio(self):
+        return self.log_likelihood() - self.noise_log_likelihood
+
+    def set_noise_log_likelihood(self):
+        log_l = 0
+        for interferometer in self.interferometers:
+            log_l -= 4. / self.source.time_duration * np.sum(abs(interferometer.data)**2
+                                                             / interferometer.power_spectral_density_array)
+        self.noise_log_likelihood = log_l.real
+
 
 class LikelihoodB(Likelihood):
 

tutorials/Injection.py

@@ -24,7 +24,11 @@ for IFO in IFOs:
     IFO.inject_signal(source)
 
 
-likelihood = peyote.likelihood.LikelihoodB(source=source, interferometers=IFOs)
+likelihood = peyote.likelihood.Likelihood(source=source, interferometers=IFOs)
+
+print(likelihood.noise_log_likelihood)
+print(likelihood.log_likelihood())
+print(likelihood.log_likelihood_ratio())
 
 prior = source.copy()
 prior.mass_1 = peyote.parameter.Parameter('mass_1', prior=peyote.prior.Uniform(lower=35, upper=37),
@@ -32,8 +36,8 @@ prior.mass_1 = peyote.parameter.Parameter('mass_1', prior=peyote.prior.Uniform(l
 prior.mass_2 = peyote.parameter.Parameter('mass_2', prior=peyote.prior.Uniform(lower=28, upper=30),
                                           latex_label='$m_2$')
 
-result = peyote.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=100, print_progress=True)
+# result = peyote.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=100, print_progress=True)
 
-truths = [source.__dict__[x] for x in result.search_parameter_keys]
-fig = corner.corner(result.samples, labels=result.labels, truths=truths)
-fig.savefig('Injection Test')
+# truths = [source.__dict__[x] for x in result.search_parameter_keys]
+# fig = corner.corner(result.samples, labels=result.labels, truths=truths)
+# fig.savefig('Injection Test')