Moritz Huebner: Renamed likelihood -> Likelihood

peyote/likelihood.py

 import numpy as np
 
 
-class likelihood:
+class Likelihood:
     def __init__(self, interferometers, source):
         self.interferometers = interferometers
         self.source = source
@@ -36,10 +36,10 @@ class likelihood:
         return log_l.real
 
 
-class likelihood_b(likelihood):
+class likelihood_b(Likelihood):
 
     def __init__(self, interferometers, source):
-        likelihood.__init__(self, interferometers, source)
+        Likelihood.__init__(self, interferometers, source)
 
         for interferometer in self.interferometers:
             interferometer.whiten_data()

peyote/sampler.py

@@ -47,7 +47,7 @@ class Sampler:
 
     Parameters
     ----------
-    likelihood: peyote.likelihood.likelihood
+    likelihood: peyote.likelihood.Likelihood
         A  object with a log_l method
     prior: dict
         The prior to be used in the search. Elements can either be floats

tests.py

@@ -45,7 +45,7 @@ class Test(unittest.TestCase):
         self.assertAlmostEqual(all(self.msd['hf_signal_and_noise'] - hf_signal_and_noise_saved), 0.00000000, 5)
 
     def test_recover_luminosity_distance(self):
-        likelihood = peyote.likelihood.likelihood(
+        likelihood = peyote.likelihood.Likelihood(
             [self.msd['IFO']], self.msd['source'])
 
         prior = self.msd['simulation_parameters'].copy()

tutorials/BasicTutorial.py

@@ -66,7 +66,7 @@ IFOs = [H1, L1]
 # plt.show()
 
 
-likelihood = peyote.likelihood.likelihood(IFOs, source)
+likelihood = peyote.likelihood.Likelihood(IFOs, source)
 
 prior = simulation_parameters.copy()
 prior['mass_1'] = peyote.parameter.Parameter(

tutorials/GW150914.py

@@ -59,7 +59,7 @@ L1.set_data(sampling_frequency, time_duration,
 IFOs = [H1, L1]
 
 source = peyote.source.BinaryBlackHole('BBH', sampling_frequency, time_duration)
-likelihood = peyote.likelihood.likelihood(IFOs, source)
+likelihood = peyote.likelihood.Likelihood(IFOs, source)
 
 prior = dict(
     spin_1 = [0, 0, 0],